Dataset Viewer
problem
stringlengths 933
42.4k
| solution
stringclasses 16
values | dataset
stringclasses 3
values | split
stringclasses 1
value |
|---|---|---|---|
Classify the node 'TrIAs: Trainable Information Assistants for Cooperative Problem Solving Software agents are intended to perform certain tasks on behalf of their users. In many cases, however, the agent's competence is not sufficient to produce the desired outcome. This paper presents an approach to cooperative problem solving in which an information agent and its user try to support each other in the achievement of a particular goal. As a side effect the user can extend the agent's capabilities in a programming-by-demonstration dialog, thus enabling it to autonomously perform similar tasks in the future. 1 Introduction Software agents are intended to autonomously perform certain tasks on behalf of their users. In many cases, however, the agent's competence might not be sufficient to produce the desired outcome. Instead of simply giving up and leaving the whole task to the user, a much better alternative would be to precisely identify what the cause of the current problem is, communicate it to another agent who can be expected to be able (and willing) to help, and use th...' into one of the following categories:
Agents; ML (Machine Learning); IR (Information Retrieval); DB (Databases); HCI (Human-Computer Interaction); AI (Artificial Intelligence).
Refer to neighbour nodes:
<|endoftext|>0: Programming by Demonstration for Information Agents this article we will refer to the user in the female form, while the agent will be referred to using male forms.<|endoftext|>
|
Agents
|
citeseer
|
train
|
Classify the node 'Title: Ciprofibrate therapy improves endothelial function and reduces postprandial lipemia and oxidative stress in type 2 diabetes mellitus.
Abstract: BACKGROUND: Exaggerated postprandial lipemia (PPL) is a factor in atherogenesis, involving endothelial dysfunction and enhanced oxidative stress. We examined the effect of ciprofibrate therapy on these parameters in type 2 diabetes mellitus. METHODS AND RESULTS: Twenty patients entered a 3-month, double-blind, placebo-controlled study. Each subject was studied fasting and after a fatty meal, at baseline, and after 3 months of treatment. Glucose and lipid profiles were measured over an 8-hour postprandial period. Endothelial function (flow-mediated endothelium-dependent vasodilatation [FMD]) and oxidative stress (electron paramagnetic resonance spectroscopy) were measured after fasting and 4 hours postprandially. At baseline, both groups exhibited similar PPL and deterioration in endothelial function. After ciprofibrate, fasting and postprandial FMD values were significantly higher (from 3.8+/-1. 8% and 1.8+/-1.3% to 4.8+/-1.1% and 3.4+/-1.1%; P<0.05). This was mirrored by a fall in fasting and postprandial triglycerides (3. 1+/-2.1 and 6.6+/-4.1 mmol/L to 1.5+/-0.8 and 2.8+/-1.3 mmol/L, P<0. 05). Fasting and postprandial HDL cholesterol was also elevated (0. 9+/-0.1 and 0.8+/-0.1 mmol/L and 1.2+/-0.2 and 1.2+/-0.1 mmol/L, P<0. 05). There were no changes in total or LDL cholesterol. Fasting and postprandial triglyceride enrichment of all lipoproteins was attenuated, with cholesterol depletion of VLDL and enrichment of HDL. There were similar postprandial increases in oxidative stress in both groups at baseline, which was significantly attenuated by ciprofibrate (0.3+/-0.6 versus 1.5+/-1.1 U, P<0.05). CONCLUSIONS: This study demonstrates that fibrate therapy improves fasting and postprandial endothelial function in type 2 diabetes. Attenuation of PPL and the associated oxidative stress, with increased HDL cholesterol levels, may be important.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: PPAR gamma agonist normalizes glomerular filtration rate, tissue levels of homocysteine, and attenuates endothelial-myocyte uncoupling in alloxan induced diabetic mice.
Abstract: BACKGROUND: Homocysteine (Hcy) is an independent cardiovascular risk factor; however, in diabetes, the role of tissue Hcy leading to cardiac dysfunction is unclear. AIMS: To determine whether tissue Hcy caused endothelial-myocyte uncoupling and ventricular dysfunction in diabetes. METHODS: Diabetes was created in C57BL/6J male mice by injecting 65 mg/kg alloxan. To reverse diabetic complications, ciglitazone (CZ) was administered in the drinking water. Plasma glucose, Hcy, left ventricular (LV) tissue levels of Hcy and nitric oxide (NO) were measured. Glomerular filtration rate (GFR) was measured by inulin-FITC. Endothelial-myocyte coupling was measured in cardiac rings. In vivo diastolic relaxation and LV diameters were measured by a Millar catheter in LV and by M-mode echocardiography, respectively. RESULTS: Plasma glucose, GFR and LV tissue Hcy were increased in diabetic mice and were normalized after CZ treatment; whereas, elevated plasma Hcy level remained unchanged with or without CZ treatment. NO levels in the LV were found inversely related to tissue Hcy levels. Attenuated endothelial-myocyte function in diabetic mice was ameliorated by CZ treatment. Cardiac relaxation, the ratio of LV wall thickness to LV diameter was decreased in diabetes, and normalized after CZ treatment. CONCLUSION: CZ normalized LV tissue levels of Hcy and ameliorated endothelial-myocyte coupling; therefore, specifically suggest the association of LV tissue Hcy levels with impair endothelial-myocyte function in diabetes.<|endoftext|>
|
Diabetes Mellitus Type 2
|
pubmed
|
train
|
Classify the node ' Brain-Structured Networks That Perceive and Learn. : This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for the need for, and usefulness of, appropriate successively larger brain-like structures; and examines parallel-hierarchical Recognition Cone models of perception from this perspective, as examples of such structures. The anatomy, physiology, behavior, and development of the visual system are briefly summarized to motivate the architecture of brain-structured networks for perceptual recognition. Results are presented from simulations of carefully pre-designed Recognition Cone structures that perceive objects (e.g., houses) in digitized photographs. A framework for perceptual learning is introduced, including mechanisms for generation-discovery (feedback-guided growth of new links and nodes, subject to brain-like constraints (e.g., local receptive fields, global convergence-divergence). The information processing transforms discovered through generation are fine-tuned by feedback-guided reweight-ing of links. Some preliminary results are presented of brain-structured networks that learn to recognize simple objects (e.g., letters of the alphabet, cups, apples, bananas) through feedback-guided generation and reweighting. These show large improvements over networks that either lack brain-like structure or/and learn by reweighting of links alone.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Experiments with the cascade-correlation algorithm. Microcomputer Applications, : Technical Report # 91-16 July 1991; Revised August 1991<|endoftext|>
<|endoftext|>1: Some Biases For Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns. : This paper introduces and explores some representational biases for efficient learning of spatial, temporal, or spatio-temporal patterns in connectionist networks (CN) massively parallel networks of simple computing elements. It examines learning mechanisms that constructively build up network structures that encode information from environmental stimuli at successively higher resolutions as needed for the tasks (e.g., perceptual recognition) that the network has to perform. Some simple examples are presented to illustrate the the basic structures and processes used in such networks to ensure the parsimony of learned representations by guiding the system to focus its efforts at the minimal adequate resolution. Several extensions of the basic algorithm for efficient learning using multi-resolution representations of spatial, temporal, or spatio-temporal patterns are discussed.<|endoftext|>
<|endoftext|>2: Perceptual Development and Learning: From Behavioral, Neurophysiological and Morphological Evidence to Computational Models. : An intelligent system has to be capable of adapting to a constantly changing environment. It therefore, ought to be capable of learning from its perceptual interactions with its surroundings. This requires a certain amount of plasticity in its structure. Any attempt to model the perceptual capabilities of a living system or, for that matter, to construct a synthetic system of comparable abilities, must therefore, account for such plasticity through a variety of developmental and learning mechanisms. This paper examines some results from neuroanatomical, morphological, as well as behavioral studies of the development of visual perception; integrates them into a computational framework; and suggests several interesting experiments with computational models that can yield insights into the development of visual perception. In order to understand the development of information processing structures in the brain, one needs knowledge of changes it undergoes from birth to maturity in the context of a normal environment. However, knowledge of its development in aberrant settings is also extremely useful, because it reveals the extent to which the development is a function of environmental experience (as opposed to genetically determined pre-wiring). Accordingly, we consider development of the visual system under both normal and restricted rearing conditions. The role of experience in the early development of the sensory systems in general, and the visual system in particular, has been widely studied through a variety of experiments involving carefully controlled manipulation of the environment presented to an animal. Extensive reviews of such results can be found in (Mitchell, 1984; Movshon, 1981; Hirsch, 1986; Boothe, 1986; Singer, 1986). Some examples of manipulation of visual experience are total pattern deprivation (e.g., dark rearing), selective deprivation of a certain class of patterns (e.g., vertical lines), monocular deprivation in animals with binocular vision, etc. Extensive studies involving behavioral deficits resulting from total visual pattern deprivation indicate that the deficits arise primarily as a result of impairment of visual information processing in the brain. The results of these experiments suggest specific developmental or learning mechanisms that may be operating at various stages of development, and at different levels in the system. We will discuss some of these hhhhhhhhhhhhhhh This is a working draft. All comments, especially constructive criticism and suggestions for improvement, will be appreciated. I am indebted to Prof. James Dannemiller for introducing me to some of the literature in infant development; to Prof. Leonard Uhr for his helpful comments on an initial draft of the paper; and to numerous researchers whose experimental work has provided the basis for the model outlined in this paper. This research was partially supported by grants from the National Science Foundation and the University of Wisconsin Graduate School.<|endoftext|>
<|endoftext|>3: Coordination and Control Structures and Processes: Possibilities for Connectionist Networks. : The absence of powerful control structures and processes that synchronize, coordinate, switch between, choose among, regulate, direct, modulate interactions between, and combine distinct yet interdependent modules of large connectionist networks (CN) is probably one of the most important reasons why such networks have not yet succeeded at handling difficult tasks (e.g. complex object recognition and description, complex problem-solving, planning). In this paper we examine how CN built from large numbers of relatively simple neuron-like units can be given the ability to handle problems that in typical multi-computer networks and artificial intelligence programs along with all other types of programs are always handled using extremely elaborate and precisely worked out central control (coordination, synchronization, switching, etc.). We point out the several mechanisms for central control of this un-brain-like sort that CN already have built into them albeit in hidden, often overlooked, ways. We examine the kinds of control mechanisms found in computers, programs, fetal development, cellular function and the immune system, evolution, social organizations, and especially brains, that might be of use in CN. Particularly intriguing suggestions are found in the pacemakers, oscillators, and other local sources of the brain's complex partial synchronies; the diffuse, global effects of slow electrical waves and neurohormones; the developmental program that guides fetal development; communication and coordination within and among living cells; the working of the immune system; the evolutionary processes that operate on large populations of organisms; and the great variety of partially competing partially cooperating controls found in small groups, organizations, and larger societies. All these systems are rich in control but typically control that emerges from complex interactions of many local and diffuse sources. We explore how several different kinds of plausible control mechanisms might be incorporated into CN, and assess their potential benefits with respect to their cost.<|endoftext|>
|
Neural Networks
|
cora
|
train
|
Classify the node 'Title: Elevated plasma interleukin-18 is a marker of insulin-resistance in type 2 diabetic and non-diabetic humans.
Abstract: Elevated plasma IL-18 is present in several conditions sharing insulin-resistance as common trait, but the association with insulin-resistance per se is not established. Plasma/serum IL-6, IL-18, TNF-alpha, soluble TNF receptor II (sTNFR2), and C-reactive protein (CRP) were measured in 97 patients with type 2 diabetes (DM) and 84 non-diabetic controls (CON). The association with insulin-resistance-estimated using the homeostasis model assessment (HOMA-IR)-was analyzed using multivariate linear and logistic regression. Compared to CON, DM demonstrated higher plasma levels of IL-18 (P = 0.001), IL-6 (P < 0.001), sTNFR2 (P = 0.005), and CRP (P < 0.001), while TNF-alpha was lower (P = 0.017). Plasma IL-18 increased across HOMA-IR quartiles in both DM and CON, both with and without adjustment for confounders (all P < 0.05). In contrast, neither IL-6, TNF-alpha, sTNFR2, nor CRP was associated with HOMA-IR in CON when adjusting for confounders. Accordingly, 50% increase of IL-18 corresponded to a marked increase of HOMA-IR in both DM and CON (DM: 26%, P = 0.014; CON: 25%, P = 0.003) after adjustment for confounders. Our results show that plasma IL-18 was associated with HOMA-IR independent of obesity and type 2 diabetes.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: Aberrant activation profile of cytokines and mitogen-activated protein kinases in type 2 diabetic patients with nephropathy.
Abstract: Cytokine-induced inflammation is involved in the pathogenesis of type 2 diabetes mellitus (DM). We investigated plasma concentrations and ex vivo production of cytokines and chemokines, and intracellular signalling molecules, mitogen-activated protein kinases (MAPK) in T helper (Th) cells and monocytes in 94 type 2 diabetic patients with or without nephropathy and 20 healthy controls. Plasma concentrations of inflammatory cytokines tumour necrosis factor (TNF)-alpha, interleukin (IL)-6, IL-18 and chemokine CCL2 in patients with diabetic nephropathy (DN) were significantly higher than control subjects, while IL-10, CXCL8, CXCL9, CXCL10 and adiponectin concentrations of DN were significantly higher than patients without diabetic nephropathy (NDN) and control subjects (all P < 0.05). Plasma concentrations of TNF-alpha, IL-6, IL-10, IL-18, CCL2, CXCL8, CXCL9, CXCL10 and adiponectin exhibited significant positive correlation with urine albumin : creatinine ratio in DN patients. The percentage increases of ex vivo production of IL-6, CXCL8, CXCL10, CCL2 and CCL5 upon TNF-alpha activation were significantly higher in both NDN and DN patients than controls (all P < 0.05). The percentage increases in IL-18-induced phosphorylation of extracellular signal-regulated kinase (ERK) in Th cells of NDN and DN were significantly higher than controls (P < 0.05), while the percentage increase in TNF-alpha-induced phosphorylation of p38 MAPK in monocytes and IL-18-induced phosphorylation of p38 MAPK in Th cells and monocytes were significantly higher in NDN patients than controls. These results confirmed that the aberrant production of inflammatory cytokines and chemokines and differential activation of MAPK in different leucocytes are the underlying immunopathological mechanisms of type 2 DM patients with DN.<|endoftext|>
|
Diabetes Mellitus Type 2
|
pubmed
|
train
|
Classify the node ' Bias, variance, and error correcting output codes for local learners. : This paper focuses on a bias variance decomposition analysis of a local learning algorithm, the nearest neighbor classifier, that has been extended with error correcting output codes. This extended algorithm often considerably reduces the 0-1 (i.e., classification) error in comparison with nearest neighbor (Ricci & Aha, 1997). The analysis presented here reveals that this performance improvement is obtained by drastically reducing bias at the cost of increasing variance. We also show that, even in classification problems with few classes (m5), extending the codeword length beyond the limit that assures column separation yields an error reduction. This error reduction is not only in the variance, which is due to the voting mechanism used for error-correcting output codes, but also in the bias.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Bias plus variance decomposition for zero-one loss functions. : We present a bias-variance decomposition of expected misclassification rate, the most commonly used loss function in supervised classification learning. The bias-variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was offered for the more commonly used zero-one (misclassification) loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). Their decomposition suffers from some major shortcomings though (e.g., potentially negative variance), which our decomposition avoids. We show that, in practice, the naive frequency-based estimation of the decomposition terms is by itself biased and show how to correct for this bias. We illustrate the decomposition on various algorithms and datasets from the UCI repository.<|endoftext|>
<|endoftext|>1: Instance-based learning algorithms. : <|endoftext|>
<|endoftext|>2: Improving the performance of radial basis function networks by learning center locations. : <|endoftext|>
<|endoftext|>3: Error-correcting output codes: A general method for improving multiclass inductive learning programs. : Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying large collections of training examples of the form hx i ; f(x i )i. Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system. This paper compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multi-class problems.<|endoftext|>
|
Theory
|
cora
|
train
|
Classify the node 'Title: HLA and insulin gene associations with IDDM.
Abstract: The HLA DR genotype frequencies in insulin-dependent diabetes mellitus (IDDM) patients and the frequencies of DR alleles transmitted from affected parent to affected child both indicate that the DR3-associated predisposition is more "recessive" and the DR4-associated predisposition more "dominant" in inheritance after allowing for the DR3/DR4 synergistic effect. B locus distributions on patient haplotypes indicate that only subsets of both DR3 and DR4 are predisposing. Heterogeneity is detected for both the DR3 and DR4 predisposing haplotypes based on DR genotypic class. With appropriate use of the family structure of the data a control population of "unaffected" alleles can be defined. Application of this method confirms the predisposing effect associated with the class 1 allele of the polymorphic region 5' to the insulin gene.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: Evidence of a non-MHC susceptibility locus in type I diabetes linked to HLA on chromosome 6.
Abstract: Linkage studies have led to the identification of several chromosome regions that may contain susceptibility loci to type I diabetes (IDDM), in addition to the HLA and INS loci. These include two on chromosome 6q, denoted IDDM5 and IDDM8, that are not linked to HLA. In a previous study, we noticed that the evidence for linkage to IDDM susceptibility around the HLA locus extended over a total distance of 100 cM, which suggested to us that another susceptibility locus could reside near HLA. We developed a statistical method to test this hypothesis in a panel of 523 multiplex families from France, the United States, and Denmark (a total of 667 affected sib pairs, 536 with both parents genotyped), and here present evidence (P = .00003) of a susceptibility locus for IDDM located 32 cM from HLA in males but not linked to HLA in females and distinct from IDDM5 and IDDM8. A new statistical method to test for the presence of a second susceptibility locus linked to a known first susceptibility locus (here HLA) is presented. In addition, we analyzed our current family panel with markers for IDDM5 and IDDM8 on chromosome 6 and found suggestions of linkage for both of these loci (P = .002 and .004, respectively, on the complete family panel). When cumulated with previously published results, with overlapping families removed, the affected-sib-pair tests had a significance of P = .0001 for IDDM5 and P = .00004 for IDDM8.<|endoftext|>
<|endoftext|>1: Title: T cell receptor haplotypes in families of patients with insulin-dependent diabetes mellitus.
Abstract: The frequencies of Bgl 11 and BamH1 restriction fragment length polymorphisms (RFLP) of C beta, V beta 8, V beta 11 and V beta 7.2 have been defined in a healthy Australian population. Linkage disequilibrium between alleles of the T cell receptor (TCR) V beta 8 and V beta 11 gene segments has been confirmed. We have also confirmed the lack of linkage disequilibrium between either of these loci and alleles at C beta or V beta 7.2. Using RFLPs at V beta 11 and V beta 8 loci TCR beta haplotypes have been identified in five families in which the probands have insulin-dependent diabetes mellitus (IDDM). An extremely rare haplotype, marked by the higher molecular weight BamH1 allele (H, H) at each of V beta 11 and V beta 8, was found in the DR4+ DR3- probands of two families (P = 0.004). In three families in which the probands had DR3, the more common TCR haplotype LH (V beta 11, V beta 8) was found. Taken together, these data confirm that linkage disequilibrium does exist in the TCR beta locus, at least in some regions, and suggest that detailed analysis of the relationship between TCR V beta haplotypes and HLA is warranted since these RFLPs may be markers for important allelic V gene sequence variations.<|endoftext|>
<|endoftext|>2: Title: Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).
Abstract: A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5' flanking polymorphism [5'FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5'FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.<|endoftext|>
<|endoftext|>3: Title: The aggregation of the 5' insulin gene polymorphism in insulin dependent (type I) diabetes mellitus families.
Abstract: Population studies have suggested an increased frequency of small DNA insertions (class I alleles) 5' to the insulin gene in insulin dependent (type I) diabetes mellitus (IDDM). The present study examined this relationship within families. Forty-one families with at least one diabetic offspring were studied. Analysis of the insulin gene polymorphism was performed by digestion of DNA with Bg1I, SstI, RsaI, or PvuII and hybridisation with an insulin gene probe or polymorphic region specific probes. An increased frequency of class I alleles was found among the parents of diabetics (p = 0.02), as well as a trend towards increased frequency of parents homozygous for class I alleles and matings of two homozygous subjects. This increased homozygosity for class I alleles was present in non-diabetic sibs as well (p = 0.01). These results show that ascertainment through an offspring with IDDM selects for families with high frequencies of homozygosity for the class I allele and thus suggests that the insulin gene polymorphism is indeed providing part of the genetic predisposition to IDDM. When the major portion of genetic predisposition is provided by other genes (estimates are that HLA accounts for 30 to 70% in IDDM), identification of additional susceptibility genes becomes difficult. Even when formal linkage analysis is uninformative, our studies indicate that analysis for aggregation of specific alleles within families is a useful approach to this problem.<|endoftext|>
<|endoftext|>4: Title: Genetic analysis of type 1 diabetes using whole genome approaches.
Abstract: Whole genome linkage analysis of type 1 diabetes using affected sib pair families and semi-automated genotyping and data capture procedures has shown how type 1 diabetes is inherited. A major proportion of clustering of the disease in families can be accounted for by sharing of alleles at susceptibility loci in the major histocompatibility complex on chromosome 6 (IDDM1) and at a minimum of 11 other loci on nine chromosomes. Primary etiological components of IDDM1, the HLA-DQB1 and -DRB1 class II immune response genes, and of IDDM2, the minisatellite repeat sequence in the 5' regulatory region of the insulin gene on chromosome 11p15, have been identified. Identification of the other loci will involve linkage disequilibrium mapping and sequencing of candidate genes in regions of linkage.<|endoftext|>
<|endoftext|>5: Title: The role of HLA class II genes in insulin-dependent diabetes mellitus: molecular analysis of 180 Caucasian, multiplex families.
Abstract: We report here our analysis of HLA class II alleles in 180 Caucasian nuclear families with at least two children with insulin-dependent diabetes mellitus (IDDM). DRB1, DQA1, DQB1, and DPB1 genotypes were determined with PCR/sequence-specific oligonucleotide probe typing methods. The data allowed unambiguous determination of four-locus haplotypes in all but three of the families. Consistent with other studies, our data indicate an increase in DR3/DR4, DR3/DR3, and DR4/DR4 genotypes in patients compared to controls. In addition, we found an increase in DR1/DR4, DR1/DR3, and DR4/DR8 genotypes. While the frequency of DQB1*0302 on DR4 haplotypes is dramatically increased in DR3/DR4 patients, DR4 haplotypes in DR1/DR4 patients exhibit frequencies of DQB1*0302 and DQB1*0301 more closely resembling those in control populations. The protective effect of DR2 is evident in this data set and is limited to the common DRB1*1501-DQB1*0602 haplotype. Most DR2+ patients carry the less common DR2 haplotype DRB1*1601-DQB1*0502, which is not decreased in patients relative to controls. DPB1 also appears to play a role in disease susceptibility. DPB1*0301 is increased in patients (P < .001) and may contribute to the disease risk of a number of different DR-DQ haplotypes. DPB1*0101, found almost exclusively on DR3 haplotypes in patients, is slightly increased, and maternal transmissions of DRB1*0301-DPB1*0101 haplotypes to affected children occur twice as frequently as do paternal transmissions. Transmissions of DR3 haplotypes carrying other DPB1 alleles occur at approximately equal maternal and paternal frequencies. The complex, multigenic nature of HLA class II-associated IDDM susceptibility is evident from these data.<|endoftext|>
<|endoftext|>6: Title: T-cell receptor genes and insulin-dependent diabetes mellitus (IDDM): no evidence for linkage from affected sib pairs.
Abstract: Several investigators have reported an association between insulin-dependent diabetes mellitus (IDDM) and an RFLP detected with a probe for the constant region of the beta chain (C beta) of the human T-cell receptor (TCR). A likely hypothesis is that the closely linked TCR variable (V beta) region genes contribute to IDDM susceptibility and that the association with the TCR C beta locus reflects this contribution, via linkage disequilibrium between V beta and C beta. The products of the beta-chain genes might be expected to be involved in the etiology of IDDM because of the autoimmune aspects of IDDM, the known involvement of HLA, and the necessity for TCR and HLA molecules to interact in an immune response. In order to investigate the hypothesis, we tested for linkage between IDDM and V genes encoded at either the TCR beta locus on chromosome 7 or the TCR alpha locus on chromosome 14, using 36 families with multiple affected sibs. No excess sharing of haplotypes defined by V alpha or V beta gene RFLPs was observed in affected sib pairs from IDDM families. We also studied unrelated IDDM patients (N = 73) and controls (N = 45) with the C beta RFLP but were unable to confirm the reported association even when the sample was stratified by HLA-DR type. Our results are incompatible with close linkage, in the majority of families, between either the TCR alpha or TCR beta locus and a gene making a major contribution to susceptibility to IDDM.<|endoftext|>
<|endoftext|>7: Title: Testing parental imprinting in insulin-dependent diabetes mellitus by the marker-association-segregation-chi 2 method.
Abstract: Among patients with insulin-dependent diabetes mellitus (IDDM), an excess of DR3 and DR4 alleles is classically described when compared with the general population. In addition, an excess of maternal DR3 and paternal DR4 alleles among patients (DR3DR4) is observed. In order to explain these observations, two alternative hypotheses can be tested: maternal effect and parental imprinting. Maternal effect has been tested and not rejected on a sample of 416 caucasians affected with IDDM. Under this hypothesis, the children of a DR3 mother are expected to have an earlier exposure and, hence, an earlier age at onset. However, we did not observe such a difference in age at onset in this data set. Using the marker-association-segregation-chi 2 method, we have tested four hypotheses with different parental effects of two susceptibility alleles, alpha 0 and beta 0, at two different closely linked loci. Under the hypothesis that best fitted the data, the probability of being affected depended on the parental inheritance of the susceptibility alleles, suggesting parental imprinting (i.e., differential role of maternal and paternal allele), without evidence for a cis-trans effect. We conclude that parental imprinting on a specific allelic combination may explain the observations on the HLA genotypes of the patients and their relatives.<|endoftext|>
|
Diabetes Mellitus Type 1
|
pubmed
|
train
|
Classify the node 'LEARNING FOR DECISION MAKING: The FRD Approach and a Comparative Study Machine Learning and Inference Laboratory: This paper concerns the issue of what is the best form for learning, representing and using knowledge for decision making. The proposed answer is that such knowledge should be learned and represented in a declarative form. When needed for decision making, it should be efficiently transferred to a procedural form that is tailored to the specific decision making situation. Such an approach combines advantages of the declarative representation, which facilitates learning and incremental knowledge modification, and the procedural representation, which facilitates the use of knowledge for decision making. This approach also allows one to determine decision structures that may avoid attributes that unavailable or difficult to measure in any given situation. Experimental investigations of the system, FRD-1, have demonstrated that decision structures obtained via the declarative route often have not only higher predictive accuracy but are also are simpler than those learned directly from facts.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: The Estimation of Probabilities in Attribute Selection Measures for Decision Structure Induction in Proceeding of the European Summer School on Machine Learning, : In this paper we analyze two well-known measures for attribute selection in decision tree induction, informativity and gini index. In particular, we are interested in the influence of different methods for estimating probabilities on these two measures. The results of experiments show that different measures, which are obtained by different probability estimation methods, determine the preferential order of attributes in a given node. Therefore, they determine the structure of a constructed decision tree. This feature can be very beneficial, especially in real-world applications where several different trees are often required.<|endoftext|>
<|endoftext|>1: R.S. and Imam, I.F. On Learning Decision Structures. : A decision structure is an acyclic graph that specifies an order of tests to be applied to an object (or a situation) to arrive at a decision about that object. and serves as a simple and powerful tool for organizing a decision process. This paper proposes a methodology for learning decision structures that are oriented toward specific decision making situations. The methodology consists of two phases: 1determining and storing declarative rules describing the decision process, 2deriving online a decision structure from the rules. The first step is performed by an expert or by an AQ-based inductive learning program that learns decision rules from examples of decisions (AQ15 or AQ17). The second step transforms the decision rules to a decision structure that is most suitable for the given decision making situation. The system, AQDT-2, implementing the second step, has been applied to a problem in construction engineering. In the experiments, AQDT-2 outperformed all other programs applied to the same problem in terms of the accuracy and the simplicity of the generated decision structures. Key words: machine learning, inductive learning, decision structures, decision rules, attribute selection.<|endoftext|>
<|endoftext|>2: An empirical comparison of selection measures for decision-tree induction. : Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990.<|endoftext|>
|
Rule Learning
|
cora
|
train
|
Classify the node ' How to dynamically merge markov decision processes. : We are frequently called upon to perform multiple tasks that compete for our attention and resource. Often we know the optimal solution to each task in isolation; in this paper, we describe how this knowledge can be exploited to efficiently find good solutions for doing the tasks in parallel. We formulate this problem as that of dynamically merging multiple Markov decision processes (MDPs) into a composite MDP, and present a new theoretically-sound dynamic programming algorithm for finding an optimal policy for the composite MDP. We analyze various aspects of our algorithm and Every day, we are faced with the problem of doing multiple tasks in parallel, each of which competes for our attention and resource. If we are running a job shop, we must decide which machines to allocate to which jobs, and in what order, so that no jobs miss their deadlines. If we are a mail delivery robot, we must find the intended recipients of the mail while simultaneously avoiding fixed obstacles (such as walls) and mobile obstacles (such as people), and still manage to keep ourselves sufficiently charged up. Frequently we know how to perform each task in isolation; this paper considers how we can take the information we have about the individual tasks and combine it to efficiently find an optimal solution for doing the entire set of tasks in parallel. More importantly, we describe a theoretically-sound algorithm for doing this merging dynamically; new tasks (such as a new job arrival at a job shop) can be assimilated online into the solution being found for the ongoing set of simultaneous tasks. illustrate its use on a simple merging problem.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Learning to Act using Real- Time Dynamic Programming. : fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526).<|endoftext|>
<|endoftext|>1: High-Performance Job-Shop Scheduling With A Time-Delay TD() Network. : Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-T D() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches significantly outperform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.<|endoftext|>
|
Reinforcement Learning
|
cora
|
train
|
Classify the node ' Dynamically adjusting concepts to accommodate changing contexts. : In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Efficient feature selection in conceptual clustering. : Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms.<|endoftext|>
<|endoftext|>1: Context-sensitive feature selection for lazy learners. : <|endoftext|>
|
Case Based
|
cora
|
train
|
Classify the node 'Analyzing Web Robots and Their Impact on Caching Understanding the nature and the characteristics of Web robots is an essential step to analyze their impact on caching. Using a multi-layer hierarchical workload model, this paper presents a characterization of the workload generated by autonomous agents and robots. This characterization focuses on the statistical properties of the arrival process and on the robot behavior graph model. A set of criteria is proposed for identifying robots in real logs. We then identify and characterize robots from real logs applying a multi-layered approach. Using a stack distance based analytical model for the interaction between robots and Web site caching, we assess the impact of robots' requests on Web caches. Our analyses point out that robots cause a significant increase in the miss ratio of a server-side cache. Robots have a referencing pattern that completely disrupts locality assumptions. These results indicate not only the need for a better understanding of the behavior of robots, but also the need of Web caching policies that treat robots' requests differently than human generated requests.' into one of the following categories:
Agents; ML (Machine Learning); IR (Information Retrieval); DB (Databases); HCI (Human-Computer Interaction); AI (Artificial Intelligence).
Refer to neighbour nodes:
<|endoftext|>0: Aliasing on the World Wide Web: Prevalence and Performance Implications Aliasing occurs in Web transactions when requests containing different URLs elicit replies containing identical data payloads. Aliasing can cause cache misses, and there is reason to suspect that offthe -shelf Web authoring tools might increase aliasing on the Web. Existing research literature, however, says little about the prevalence of aliasing in user-initiated transactions or its impact on endto -end performance in large multi-level cache hierarchies.<|endoftext|>
<|endoftext|>1: WWW Robots and Search Engines The Web robots are programs that automatically traverse through networks. Currently, their most visible and familiar application is to provide indices for search engines, such as Lycos and Alta Vista, and semiautomatically maintained topic references or subject directories. In this article, we survey the state-of-art of the Web robots, and the search engines that utilize the results of robot searches. We also present notions about robot ethics and distributed Web robots.<|endoftext|>
|
IR (Information Retrieval)
|
citeseer
|
train
|
Classify the node 'Title: Mutations in the small heterodimer partner gene are associated with mild obesity in Japanese subjects.
Abstract: Mutations in several genes encoding transcription factors of the hepatocyte nuclear factor (HNF) cascade are associated with maturity-onset diabetes of the young (MODY), a monogenic form of early-onset diabetes mellitus. The ability of the orphan nuclear receptor small heterodimer partner (SHP, NR0B2) to modulate the transcriptional activity of MODY1 protein, the nuclear receptor HNF-4alpha, suggested SHP as a candidate MODY gene. We screened 173 unrelated Japanese subjects with early-onset diabetes for mutations in this gene and found five different mutations (H53fsdel10, L98fsdel9insAC, R34X, A195S, and R213C) in 6 subjects as well as one apparent polymorphism (R216H), all present in the heterozygous state. Interestingly, all of the subjects with the mutations were mildly or moderately obese at onset of diabetes, and analysis of the lineages of these individuals indicated that the SHP mutations were associated with obesity rather than with diabetes. Therefore, an additional group of 101 unrelated nondiabetic subjects with early-onset obesity was screened for mutations in the SHP gene. Two of the previously observed mutations (R34X and A195S) and two additional mutations (R57W and G189E) were identified in 6 subjects, whereas no mutations were identified in 116 young nondiabetic lean controls (P = 0.0094). Functional studies of the mutant proteins show that the mutations result in the loss of SHP activity. These results suggest that genetic variation in the SHP gene contributes to increased body weight and reveal a pathway leading to this common metabolic disorder in Japanese.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: Mutations in the hepatocyte nuclear factor-1alpha gene in maturity-onset diabetes of the young (MODY3)
Abstract: The disease non-insulin-dependent (type 2) diabetes mellitus (NIDDM) is characterized by abnormally high blood glucose resulting from a relative deficiency of insulin. It affects about 2% of the world's population and treatment of diabetes and its complications are an increasing health-care burden. Genetic factors are important in the aetiology of NIDDM, and linkage studies are starting to localize some of the genes that influence the development of this disorder. Maturity-onset diabetes of the young (MODY), a single-gene disorder responsible for 2-5% of NIDDM, is characterized by autosomal dominant inheritance and an age of onset of 25 years or younger. MODY genes have been localized to chromosomes 7, 12 and 20 (refs 5, 7, 8) and clinical studies indicate that mutations in these genes are associated with abnormal patterns of glucose-stimulated insulin secretion. The gene on chromosome 7 (MODY2) encodes the glycolytic enzyme glucokinases which plays a key role in generating the metabolic signal for insulin secretion and in integrating hepatic glucose uptake. Here we show that subjects with the MODY3-form of NIDDM have mutations in the gene encoding hepatocyte nuclear factor-1alpha (HNF-1alpha, which is encoded by the gene TCF1). HNF-1alpha is a transcription factor that helps in the tissue-specific regulation of the expression of several liver genes and also functions as a weak transactivator of the rat insulin-I gene.<|endoftext|>
<|endoftext|>1: Title: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.
Abstract: The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.<|endoftext|>
<|endoftext|>2: Title: Altered insulin secretory responses to glucose in subjects with a mutation in the MODY1 gene on chromosome 20.
Abstract: This study was undertaken to test the hypothesis that the diabetes susceptibility gene on chromosome 20q12 responsible for maturity-onset diabetes of the young (MODY) in a large kindred, the RW family, results in characteristic alterations in the dose-response relationships between plasma glucose concentration and insulin secretion rate (ISR) that differentiate this form of MODY from MODY in subjects with glucokinase mutations. Ten marker-positive subjects and six matched nondiabetic marker-negative subjects from the RW family received graded intravenous glucose infusions on two occasions separated by a 42-h continuous intravenous glucose infusion designed to prime the beta-cell to secrete more insulin in response to glucose. ISR was derived by deconvolution of peripheral C-peptide levels. Basal glucose and insulin levels were similar in marker-negative and marker-positive groups (5.3 +/- 0.2 vs. 5.0 +/- 0.2 mmol/l, P > 0.2, and 86.1 +/- 3.9 vs. 63.7 +/- 12.1 pmol/l, P > 0.1, respectively). However, the marker-positive subjects had defective insulin secretory responses to an increase in plasma glucose concentrations. Thus, as the glucose concentration was raised above 7 mmol/l, the slope of the curve relating glucose and ISR was significantly blunted in the marker-positive subjects (13 +/- 4 vs. 68 +/- 8 pmol.min-1.mmol-1 x 1, P < 0.0001).(ABSTRACT TRUNCATED AT 250 WORDS)<|endoftext|>
<|endoftext|>3: Title: Mutations in the hepatocyte nuclear factor-4alpha gene in maturity-onset diabetes of the young (MODY1)
Abstract: The disease maturity-onset diabetes of the young (MODY) is a genetically heterogeneous monogenic form of non-insulin-dependent (type 2) diabetes mellitus (NIDDM), characterized by early onset, usually before 25 years of age and often in adolescence or childhood, and by autosomal dominant inheritance. It has been estimated that 2-5% of patients with NIDDM may have this form of diabetes mellitus. Clinical studies have shown that prediabetic MODY subjects have normal insulin sensitivity but suffer from a defect in glucose-stimulated insulin secretion, suggesting that pancreatic beta-cell dysfunction rather than insulin resistance is the primary defect in this disorder. Linkage studies have localized the genes that are mutated in MODY on human chromosomes 20 (MODY1), 7 (MODY2) and 12 (MODY3), with MODY2 and MODY3 being allelic with the genes encoding glucokinase, a key regulator of insulin secretion, and hepatocyte nuclear factor-1alpha (HNF-1alpha), a transcription factor involved in tissue-specific regulation of liver genes but also expressed in pancreatic islets, insulinoma cells and other tissues. Here we show that MODY1 is the gene encoding HNF-4alpha (gene symbol, TCF14), a member of the steroid/thyroid hormone receptor superfamily and an upstream regulator of HNF-1alpha expression.<|endoftext|>
<|endoftext|>4: Title: Maturity-onset diabetes of the young due to a mutation in the hepatocyte nuclear factor-4 alpha binding site in the promoter of the hepatocyte nuclear factor-1 alpha gene.
Abstract: Recent studies have shown that mutations in the transcription factor hepatocyte nuclear factor (HNF)-1 alpha are the cause of one form of maturity-onset diabetes of the young (MODY3). These studies have identified mutations in the mRNA and protein coding regions of this gene that result in the synthesis of an abnormal mRNA or protein. Here, we report an Italian family in which an A-->C substitution at nucleotide-58 of the promoter region of the HNF-1 alpha gene cosegregates with MODY. This mutation is located in a highly conserved region of the promoter and disrupts the binding site for the transcription factor HNF-4 alpha, mutations in the gene encoding HNF-4 alpha being another cause of MODY (MODY1). This result demonstrates that decreased levels of HNF-1 alpha per se can cause MODY. Moreover, it indicates that both the promoter and coding regions of the HNF-1 alpha gene should be screened for mutations in subjects thought to have MODY because of mutations in this gene.<|endoftext|>
<|endoftext|>5: Title: Liver and kidney function in Japanese patients with maturity-onset diabetes of the young.
Abstract: OBJECTIVE: Heterozygous mutations in the transcription factors hepatocyte nuclear factor (HNF)-1 alpha, HNF-1 beta, and HNF-4 alpha are associated with maturity-onset diabetes of the young (MODY) and are believed to cause this form of diabetes by impairing pancreatic beta-cell function. The HNFs also play a central role in the tissue-specific regulation of gene expression in liver and kidney, suggesting that patients with MODY due to a mutation in HNF-1 alpha, HNF-1 beta, or HNF-4 alpha may exhibit abnormal liver or kidney function. Here, we have examined liver and kidney function in a series of Japanese patients with HNF-4 alpha/MODY1, HNF-1 alpha/MODY3, and HNF-1 beta/MODY5 diabetes. RESEARCH DESIGN AND METHODS: Clinical and biochemical data were obtained from Japanese subjects with HNF-1 alpha, HNF-1 beta, and HNF-4 alpha diabetes. The clinical data included information on BMI, age at diagnosis, current treatment, and the presence and nature of any complications. The biochemical studies examined liver and kidney function and included measures of alanine and aspartate aminotransferase, gamma-glutamyl transpeptidase, blood urea nitrogen, creatinine, uric acid, total and HDL cholesterol, triglycerides, and 17 serum proteins. RESULTS: The present age and duration of diabetes were similar in patients with HNF-1 alpha, HNF-1 beta, or HNF-4 alpha diabetes, as was the age at diagnosis of diabetes in the youngest generation. All subjects were lean. Of the subjects with HNF-1 alpha and HNF-4 alpha diabetes, 50% were treated with insulin, as were all three subjects with HNF-1 beta diabetes. Retinopathy was present in patients with each form of diabetes. None of the subjects with HNF-4 alpha diabetes had evidence of nephropathy, whereas 36% of the patients with HNF-1 alpha diabetes and 100% of those with HNF-1 beta diabetes showed diminished kidney function. The three subjects with HNF-1 beta diabetes also had abnormally high serum creatinine, uric acid, and blood urea nitrogen levels, which are consistent with impaired kidney function, and one of seven subjects with HNF-1 alpha diabetes had a mild elevation in creatinine and blood urea nitrogen levels. These values were within the normal range in the three patients with HNF-4 alpha diabetes. Although the HNFs play a role in regulating the expression of the genes for most, if not all, serum proteins, there was no decrease in the levels of any of the 17 serum proteins examined, and most were within or slightly above the normal range. Lipoprotein(a) [Lp(a)] levels were elevated in the three patients with HNF-4 alpha diabetes and in one patient with HNF-1 beta diabetes, and in a second patient with HNF-1 beta diabetes, Lp(a) was at the upper limit of normal. CONCLUSIONS: The results indicate that as in white patients, MODY resulting from mutations in the HNF-1 alpha, HNF-1 beta, and HNF-4 alpha genes in Japanese patients may be a severe disease similar to classic type 2 diabetes. In addition, they suggest that patients with HNF-1 beta diabetes may be characterized by diminished kidney function and perhaps abnormal liver function. Further studies are needed to determine whether tests of liver and kidney function will be useful in the diagnosis and subclassification of MODY.<|endoftext|>
<|endoftext|>6: Title: Altered insulin secretory responses to glucose in diabetic and nondiabetic subjects with mutations in the diabetes susceptibility gene MODY3 on chromosome 12.
Abstract: One form of maturity-onset diabetes of the young (MODY) results from mutations in a gene, designated MODY3, located on chromosome 12 in band q24. The present study was undertaken to define the interactions between glucose and insulin secretion rate (ISR) in subjects with mutations in MODY3. Of the 13 MODY3 subjects, six subjects with normal fasting glucose and glycosylated hemoglobin and seven overtly diabetic subjects were studied as were six nondiabetic control subjects. Each subject received graded intravenous glucose infusions on two occasions separated by a 42-h continuous intravenous glucose infusion designed to prime the beta-cell to secrete more insulin in response to glucose. ISRs were derived by deconvolution of peripheral C-peptide levels. Basal glucose levels were higher and insulin levels were lower in MODY3 subjects with diabetes compared with nondiabetic subjects or with normal healthy control subjects. In response to the graded glucose infusion, ISRs were significantly lower in the diabetic subjects over a broad range of glucose concentrations. ISRs in the nondiabetic MODY3 subjects were not significantly different from those of the control subjects at plasma glucose levels <8 mmol/l. As glucose rose above this level, however, the increase in insulin secretion in these subjects was significantly reduced. Administration of glucose by intravenous infusion for 42 h resulted in a significant increase in the amount of insulin secreted over the 5-9 mmol/l glucose concentration range in the control subjects and nondiabetic MODY3 subjects (by 38 and 35%, respectively), but no significant change was observed in the diabetic MODY3 subjects. In conclusion, in nondiabetic MODY3 subjects insulin secretion demonstrates a diminished ability to respond when blood glucose exceeds 8 mmol/l. The priming effect of glucose on insulin secretion is preserved. Thus, beta-cell dysfunction is present before the onset of overt hyperglycemia in this form of MODY. The defect in insulin secretion in the nondiabetic MODY3 subjects differs from that reported previously in nondiabetic MODY1 or mildly diabetic MODY2 subjects.<|endoftext|>
<|endoftext|>7: Title: Phenotypic characteristics of early-onset autosomal-dominant type 2 diabetes unlinked to known maturity-onset diabetes of the young (MODY) genes.
Abstract: OBJECTIVE: To investigate whether there are forms of early-onset autosomal-dominant type 2 diabetes that are distinct from typical maturity-onset diabetes of the young (MODY) and to characterize their phenotypic characteristics. RESEARCH DESIGN AND METHODS: The study included 220 affected subjects from 29 families in which early-onset type 2 diabetes occurred in multiple generations and was not linked to known MODY genes (MODY gene-negative families). All individuals underwent an oral glucose tolerance test and other clinical measurements aimed at investigating the underlying metabolic defect and the presence of diabetic complications. For comparison, 79 affected carriers of MODY3 (hepatocyte nuclear factor [HNF]-1 alpha) mutations were similarly examined. RESULTS: Subjects from MODY gene-negative pedigrees were diagnosed with diabetes at an older age (36 +/- 17 vs. 21 +/- 10 years, P = 0.0001) and were more frequently obese (52 vs. 18%, P = 0.0001) than MODY3 individuals. MODY gene-negative patients who were insulin treated required more exogenous insulin than did MODY3 subjects (0.7 +/- 0.4 vs. 0.45 +/- 0.2 U.kg-1.day-1, P = 0.04), despite similar C-peptide levels. Among subjects not treated with insulin, MODY gene-negative subjects had significantly higher serum insulin levels, both fasting (16.5 +/- 15 vs. 6.5 +/- 5 microU/ml, P = 0.027) and 2 h after a glucose load (53 +/- 44 vs. 11 +/- 10, P = 0.002). They also had higher serum triglycerides (P = 0.02), higher cholesterol levels (P = 0.02), more hypertension (P = 0.0001), and more nephropathy (P = 0.001). Differences persisted when families were matched for age at diagnosis. CONCLUSIONS: Our findings indicate the existence of forms of early-onset autosomal-dominant type 2 diabetes that are distinct from MODY and are frequently characterized by insulin resistance, similar to later-onset type 2 diabetes. Because of the Mendelian pattern of inheritance, the goal of identifying the genes involved in these forms of diabetes appears to be particularly feasible.<|endoftext|>
<|endoftext|>8: Title: Maturity-onset diabetes of the young (MODY).
Abstract: This review summarized aspects of the widening scope, phenotypic expression, natural history, recognition, pathogeneses, and heterogenous nature of maturity-onset diabetes of the young (MODY), an autosomal dominant inherited subtype of NIDDM, which can be recognized at a young age. There are differences in metabolic, hormonal, and vascular abnormalities in different ethnic groups and even among Caucasian pedigrees. In MODY patients with low insulin responses, there is a delayed and decreased insulin and C-peptide secretory response to glucose from childhood or adolescence, even before glucose intolerance appears; it may represent the basic genetic defect. The nondiabetic siblings have had normal insulin responses for decades. The fasting hyperglycemia of some MODY has been treated successfully with sulfonylureas for more than 30 years. In a few, after years or decades of diabetes, the insulin and C-peptide responses to glucose are so low that they may resemble those of early Type I diabetes. The rate of progression of the insulin secretory defect over time does distinguish between these two types of diabetes. In contrast are patients from families who have very high insulin responses to glucose despite glucose intolerance and fasting hyperglycemia similar to those seen in patients with low insulin responses. In many of these patients, there is in vivo and in vitro evidence of insulin resistance. Whatever its mechanism, the compensatory insulin responses to nutrients must be insufficient to maintain normal carbohydrate tolerance. This suggests that diabetes occurs only in those patients who have an additional islet cell defect, i.e., insufficient beta cell reserve and secretory capacity. In a few MODY pedigrees with high insulin responses to glucose and lack of evidence of insulin resistance, an insulin is secreted which is a structurally abnormal, mutant insulin molecule that is biologically ineffective. No associations have been found between specific HLA antigens and MODY in Caucasian, black, and Asian pedigrees. Linkage studies of the insulin gene, the insulin receptor gene, the erythrocyte/Hep G2 glucose transporter locus, and the apolipoprotein B locus have shown no association with MODY. Vascular disease may be as prevalent as in conventional NIDDM. Because of autosomal dominant transmission and penetrance at a young age, MODY is a good model for further investigations of etiologic and pathogenetic factors in NIDDM, including the use of genetic linkage strategies to identify diabetogenic genes.<|endoftext|>
|
Diabetes Mellitus Type 2
|
pubmed
|
train
|
Classify the node 'Title: Birth weight and non-insulin dependent diabetes: thrifty genotype, thrifty phenotype, or surviving small baby genotype?
Abstract: OBJECTIVE: To determine the prevalence of diabetes in relation to birth weight in Pima Indians. DESIGN: Follow up study of infants born during 1940-72 who had undergone a glucose tolerance test at ages 20-39 years. SETTING: Gila River Indian community, Arizona. SUBJECTS: 1179 American Indians. MAIN OUTCOME MEASURE: Prevalence of non-insulin dependent diabetes mellitus (plasma glucose concentration > or = 11.1 mmol/l two hours after ingestion of carbohydrate). RESULTS: The prevalence was greatest in those with the lowest and highest birth weights. The age adjusted prevalences for birth weights < 2500 g, 2500-4499 g, and > or = 4500 g were 30%, 17%, and 32%, respectively. When age, sex, body mass index, maternal diabetes during pregnancy, and birth year were controlled for, subjects with birth weights < 2500 g had a higher rate than those with weights 2500-4499 g (odds ratio 3.81; 95% confidence interval 1.70 to 8.52). The risk for subsequent diabetes among higher birthweight infants (> or = 4500 g) was associated with maternal diabetes during pregnancy. Most diabetes, however, occurred in subjects with intermediate birth weights (2500-4500 g). CONCLUSIONS: The relation of the prevalence of diabetes to birth weight in the Pima Indians is U shaped and is related to parental diabetes. Low birth weight is associated with non-insulin dependent diabetes. Given the high mortality of low birthweight infants selective survival in infancy of those genetically predisposed to insulin resistance and diabetes provides an explanation for the observed relation between low birth weight and diabetes and the high prevalence of diabetes in many populations.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: Impaired glucose tolerance as a disorder of insulin action. Longitudinal and cross-sectional studies in Pima Indians.
Abstract: Impaired glucose tolerance often presages the development of non-insulin-dependent diabetes mellitus. We have studied insulin action and secretion in 24 Pima Indians before and after the development of impaired glucose tolerance and in 254 other subjects representing the whole spectrum of glucose tolerance, including subjects with overt non-insulin-dependent diabetes. The transition from normal to impaired glucose tolerance was associated with a decrease in glucose uptake during hyperinsulinemia, from 0.018 to 0.016 mmol per minute (from 3.3 to 2.8 mg per kilogram of fat-free body mass per minute) (P less than 0.0003). Mean plasma insulin concentrations increased during an oral glucose-tolerance test, from 1200 to 1770 pmol per liter (from 167 to 247 microU per milliliter). In 151 subjects with normal glucose tolerance, the insulin concentration measured during an oral glucose-tolerance test correlated with the plasma glucose concentration (r = 0.48, P less than or equal to 0.0001). This relation was used to predict an insulin concentration of 1550 pmol per liter (216 microU per milliliter) in subjects with impaired glucose tolerance (actual value, 1590 pmol per liter [222 microU per milliliter]; P not significant), suggesting that these subjects had normal secretion of insulin. In contrast, plasma insulin concentrations in the diabetics decreased as glucose concentrations increased (r = -0.75, P less than or equal to 0.0001), suggesting deficient secretion of insulin. This relative insulin deficiency first appears at the lower end of the second (diabetic) mode seen in population frequency distributions of plasma glucose concentrations. Our data show that impaired glucose tolerance in our study population is primarily due to impaired insulin action. In patients with non-insulin-dependent diabetes mellitus, by contrast, impaired insulin action and insulin secretory failure are both present.<|endoftext|>
<|endoftext|>1: Title: Fetal origins of adult disease: epidemiology and mechanisms.
Abstract: The past 10 years have provided unequivocal evidence that there are associations between birth size measures and future development of adult diseases, such as type 2 diabetes and coronary artery disease. Despite initial concern that bias or residual confounding in the analyses had produced these rather bizarre associations, the findings have now been reproduced in different cohorts by independent investigators from many parts of the world. The challenge for the next decade must be to discover the cellular and molecular mechanisms giving rise to these associations. If this aim is accomplished, it might be possible to devise strategies to reduce the impact of these disabling, chronic, and expensive diseases. The purpose of this review is to describe some of the relevant, important, and more recent epidemiological studies, and also to discuss potential mechanisms underpinning the associations.<|endoftext|>
<|endoftext|>2: Title: Fetal growth and impaired glucose tolerance in men and women.
Abstract: A follow-up study was carried out to determine whether reduced fetal growth is associated with the development of impaired glucose tolerance in men and women aged 50 years. Standard oral glucose tolerance tests were carried out on 140 men and 126 women born in Preston (Lancashire, UK) between 1935 and 1943, whose size at birth had been measured in detail. Those subjects found to have impaired glucose tolerance or non-insulin-dependent diabetes mellitus had lower birthweight, a smaller head circumference and were thinner at birth. They also had a higher ratio of placental weight to birthweight. The prevalence of impaired glucose tolerance or diabetes fell from 27% in subjects who weighed 2.50 kg (5.5 pounds) or less at birth to 6% in those who weighed more than 3.41 kg (7.5 pounds) (p < 0.002 after adjusting for body mass index). Plasma glucose concentrations taken at 2-h in the glucose tolerance test fell progressively as birthweight increased (p < 0.004), as did 2-h plasma insulin concentrations (p < 0.001). The trends with birthweight were independent of duration of gestation and must therefore be related to reduced rates of fetal growth. These findings confirm the association between impaired glucose tolerance in adult life and low birthweight previously reported in Hertfordshire (UK), and demonstrate it in women as well as men. It is suggested that the association reflects the long-term effects of reduced growth of the endocrine pancreas and other tissues in utero. This may be a consequence of maternal undernutrition.<|endoftext|>
<|endoftext|>3: Title: Type 2 diabetes in grandparents and birth weight in offspring and grandchildren in the ALSPAC study.
Abstract: OBJECTIVE: To examine the association between a history of type 2 diabetes and birth weight of offspring and grandchildren. DESIGN: Prospective observational study. Diabetic status, as reported by mothers (F1 generation) was collected on grandparents (F0) of babies (F2) born to mothers (F1) who participated in a study of maternal and child health. Associations between risk of grandparental diabetes and birth weight in mothers (F1) and grandchildren (F2) were analysed using linear and logistic regression. SETTING: Avon: comprising of the city of Bristol and surrounding areas. PARTICIPANTS: 12 076 singleton babies (F2), their parents (F1) and maternal and paternal grandparents (F0). RESULTS: Women (F1) who had no parents with type 2 diabetes had lower birth weights than women with one or two diabetic parents, after controlling for the age of both parents. There was a U shaped association between maternal birth weight and grandmaternal diabetes, but no evidence of an association with grandpaternal diabetes. The grandchildren of maternal grandparents with type 2 diabetes were more likely to be in the top tertile of birth weight than grandchildren of non-diabetics. There was evidence for an inverted U shaped association between birth weight of grandchildren and diabetes in paternal grandmothers. CONCLUSIONS: This is the first study to show intergenerational associations between type 2 diabetes in one generation and birth weight in the subsequent two generations. While the study has limitations mainly because of missing data, the findings nevertheless provide some support for the role of developmental intrauterine effects and genetically determined insulin resistance in impaired insulin mediated growth in the fetus.<|endoftext|>
<|endoftext|>4: Title: Fetal and infant growth and impaired glucose tolerance at age 64.
Abstract: OBJECTIVE: To discover whether reduced fetal and infant growth is associated with non-insulin dependent diabetes and impaired glucose tolerance in adult life. DESIGN: Follow up study of men born during 1920-30 whose birth weights and weights at 1 year were known. SETTING: Hertfordshire, England. SUBJECTS: 468 men born in east Hertfordshire and still living there. MAIN OUTCOME MEASURES: Fasting plasma glucose, insulin, proinsulin, and 32-33 split pro-insulin concentrations and plasma glucose and insulin concentrations 30 and 120 minutes after a 75 g glucose drink. RESULTS: 93 men had impaired glucose tolerance or hitherto undiagnosed diabetes. They had had a lower mean birth weight and a lower weight at 1 year. The proportion of men with impaired glucose tolerance fell progressively from 26% (6/23) among those who had weighted 18 lb (8.16 kg) or less at 1 year to 13% (3/24) among those who had weighed 27 lb (12.25 kg) or more. Corresponding figures for diabetes were 17% (4/23) and nil (0/24). Plasma glucose concentrations at 30 and 120 minutes fell with increasing birth weight and weight at 1 year. Plasma 32-33 split proinsulin concentration fell with increasing weight at 1 year. All these trends were significant and independent of current body mass. Blood pressure was inversely related to birth weight and strongly related to plasma glucose and 32-33 split proinsulin concentrations. CONCLUSIONS: Reduced growth in early life is strongly linked with impaired glucose tolerance and non-insulin dependent diabetes. Reduced early growth is also related to a raised plasma concentration of 32-33 split proinsulin, which is interpreted as a sign of beta cell dysfunction. Reduced intrauterine growth is linked with high blood pressure, which may explain the association between hypertension and impaired glucose tolerance.<|endoftext|>
<|endoftext|>5: Title: Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth.
Abstract: Two follow-up studies were carried out to determine whether lower birthweight is related to the occurrence of syndrome X-Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia. The first study included 407 men born in Hertfordshire, England between 1920 and 1930 whose weights at birth and at 1 year of age had been recorded by health visitors. The second study included 266 men and women born in Preston, UK, between 1935 and 1943 whose size at birth had been measured in detail. The prevalence of syndrome X fell progressively in both men and women, from those who had the lowest to those who had the highest birthweights. Of 64-year-old men whose birthweights were 2.95 kg (6.5 pounds) or less, 22% had syndrome X. Their risk of developing syndrome X was more than 10 times greater than that of men whose birthweights were more than 4.31 kg (9.5 pounds). The association between syndrome X and low birthweight was independent of duration of gestation and of possible confounding variables including cigarette smoking, alcohol consumption and social class currently or at birth. In addition to low birthweight, subjects with syndrome X had small head circumference and low ponderal index at birth, and low weight and below-average dental eruption at 1 year of age. It is concluded that Type 2 diabetes and hypertension have a common origin in sub-optimal development in utero, and that syndrome X should perhaps be re-named "the small-baby syndrome".<|endoftext|>
<|endoftext|>6: Title: Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus. Prospective studies of Pima Indians.
Abstract: BACKGROUND: The relative roles of obesity, insulin resistance, insulin secretory dysfunction, and excess hepatic glucose production in the development of non-insulin-dependent diabetes mellitus (NIDDM) are controversial. We conducted a prospective study to determine which of these factors predicted the development of the disease in a group of Pima Indians. METHODS: A body-composition assessment, oral and intravenous glucose-tolerance tests, and a hyperinsulinemic--euglycemic clamp study were performed in 200 non-diabetic Pima Indians (87 women and 113 men; mean [+/- SD] age, 26 +/- 6 years). The subjects were followed yearly thereafter for an average of 5.3 years. RESULTS: Diabetes developed in 38 subjects during follow-up. Obesity, insulin resistance (independent of obesity), and low acute plasma insulin response to intravenous glucose (with the degree of obesity and insulin resistance taken into account) were predictors of NIDDM: The six-year cumulative incidence of NIDDM was 39 percent in persons with values below the median for both insulin action and acute insulin response, 27 percent in those with values below the median for insulin action but above that for acute insulin response, 13 percent in those with values above the median for insulin action and below that for acute insulin response, and 0 in those with values originally above the median for both characteristics. CONCLUSIONS: Insulin resistance is a major risk factor for the development of NIDDM: A low acute insulin response to glucose is an additional but weaker risk factor.<|endoftext|>
<|endoftext|>7: Title: Congenital susceptibility to NIDDM. Role of intrauterine environment.
Abstract: Non-insulin-dependent diabetes mellitus (NIDDM) during pregnancy in Pima Indian women results in offspring who have a higher prevalence of NIDDM (45%) at age 20-24 yr than in offspring of nondiabetic women (1.4%) or offspring of prediabetic women (8.6%), i.e., women who developed diabetes only after the pregnancy. These differences persist after taking into account paternal diabetes, age at onset of diabetes in the parents, and the offspring's weight relative to height. The findings suggest that the intrauterine environment is an important determinant of the development of diabetes and that its effect is in addition to effects of genetic factors.<|endoftext|>
<|endoftext|>8: Title: Relation of size at birth to non-insulin dependent diabetes and insulin concentrations in men aged 50-60 years.
Abstract: OBJECTIVE: To establish whether the relation between size at birth and non-insulin dependent diabetes is mediated through impaired beta cell function or insulin resistance. DESIGN: Cohort study. SETTING: Uppsala, Sweden. SUBJECTS: 1333 men whose birth records were traced from a cohort of 2322 men born during 1920-4 and resident in Uppsala in 1970. MAIN OUTCOME MEASURES: Intravenous glucose tolerance test at age 50 years and non-insulin dependent diabetes at age 60 years. RESULTS: There was a weak inverse correlation (r=-0.07, P=0.03) between ponderal index at birth and 60 minute insulin concentrations in the intravenous glucose tolerance test at age 50 years. This association was stronger (r=-0.19, P=0.001) in the highest third of the distribution of body mass index than in the other two thirds (P=0.01 for the interaction between ponderal index and the body mass index). Prevalence of diabetes at age 60 years was 8% in men whose birth weight was less than 3250 g compared with 5% in men with birth weight 3250 g or more (P=0.08; 95% confidence interval for difference -0.3% to 6.8%). There was a stronger association between diabetes and ponderal index: prevalence of diabetes was 12% in the lowest fifth of ponderal index compared with 4% in the other four fifths (P=0.001; 3.0% to 12.6%). CONCLUSION: These results confirm that reduced fetal growth is associated with increased risk of diabetes and suggest a specific association with thinness at birth. This relation seems to be mediated through insulin resistance rather than through impaired beta cell function and to depend on an interaction with obesity in adult life.<|endoftext|>
<|endoftext|>9: Title: Role of glucose and insulin resistance in development of type 2 diabetes mellitus: results of a 25-year follow-up study.
Abstract: Type 2 diabetes mellitus is characterised by resistance of peripheral tissues to insulin and a relative deficiency of insulin secretion. To find out which is the earliest or primary determinant of disease, we used a minimum model of glucose disposal and insulin secretion based on intravenous glucose tolerance tests to estimate insulin sensitivity (SI), glucose effectiveness (ie, insulin-independent glucose removal rate, SG), and first-phase and second-phase beta-cell responsiveness in normoglycaemic offspring of couples who both had type 2 diabetes. 155 subjects from 86 families were followed-up for 6-25 years. More than 10 years before the development of diabetes, subjects who developed the disease had lower values of both SI (mean 3.2 [SD 2.4] vs 8.1 [6.7] 10(-3) I min-1 pmol-1 insulin; p < 0.0001) and SG (1.6 [0.9] vs 2.3 [1.2] 10(-2) min-1, p < 0.0001) than did those who remained normoglycaemic). For the subjects with both SI and SG below the group median, the cumulative incidence of type 2 diabetes during the 25 years was 76% (95% confidence interval 54-99). By contrast, no subject with both SI and SG above the median developed the disease. Subjects with low SI/high SG or high SI/low SG had intermediate risks. Insulin secretion, especially first phase, tended to be increased rather than decreased in this prediabetic phase and was appropriate for the level of insulin resistance. The development of type 2 diabetes is preceded by and predicted by defects in both insulin-dependent and insulin-independent glucose uptake; the defects are detectable when the patients are normoglycaemic and in most cases more than a decade before diagnosis of disease.<|endoftext|>
|
Diabetes Mellitus Type 2
|
pubmed
|
train
|
Classify the node 'Automated Derivation of Complex Agent Architectures from Analysis Specifications Multiagent systems have been touted as a way to meet the need for distributed software systems that must operate in dynamic and complex environments. However, in order for multiagent systems to be effective, they must be reliable and robust. Engineering multiagent systems is a non-trivial task, providing ample opportunity for even experts to make mistakes. Formal transformation systems can provide automated support for synthesizing multiagent systems, which can greatly improve their correctness and reliability. This paper describes a semi-automated transformation system that generates an agents internal architecture from the analysis specification for the MaSE methodology. 1.' into one of the following categories:
Agents; ML (Machine Learning); IR (Information Retrieval); DB (Databases); HCI (Human-Computer Interaction); AI (Artificial Intelligence).
Refer to neighbour nodes:
<|endoftext|>0: Multiagent Systems Engineering: A Methodology And Language for Designing Agent Systems This paper overviews MaSE and provides a high-level introduction to one critical component used within MaSE, the Agent Modeling Language. Details on the Agent Definition Language and detailed agent design are left for a future paper.<|endoftext|>
<|endoftext|>1: Multiagent Systems Engineering: A Methodology For Analysis And Design Of Multiagent Systems ................................................................................................................................................. IX I. INTRODUCTION ........................................................................................................................................... 1 1.1 Background................................................................................................................................. 2 1.2 Problem....................................................................................................................................... 3 1.3 Goal ............................................................................................................................................ 4 1.4 Assumptions ............................................................................................................................... 4 1.5 Areas of Collaboration.............................................................................<|endoftext|>
|
Agents
|
citeseer
|
train
|
Classify the node 'Title: Comparative trial between insulin glargine and NPH insulin in children and adolescents with type 1 diabetes mellitus.
Abstract: The objective of this study was to compare the efficacy and safety of insulin glargine, a long-acting insulin analog, with NPH insulin in children and adolescents with type 1 diabetes mellitus (T1DM). In a multicenter, open-label, randomized, 6-month study, 349 patients with TIDM, aged 5-16 years, received insulin glargine once daily or NPH insulin either once or twice daily, based on their prior treatment regimen. Although there was no significant difference between the NPH insulin and insulin glargine treatment groups with respect to baseline to endpoint change in HbA1c levels, fasting blood glucose (FBG) levels decreased significantly more in the insulin glargine group (-1.29 mmol/l) than in the NPH insulin group (-0.68 mmol/L, p = 0.02). The percentage of symptomatic hypoglycemic events was similar between groups; however, fewer patients in the insulin glargine group reported severe hypoglycemia (23% vs 29%) and severe nocturnal hypoglycemia (13% vs 18%), although these differences were not statistically significant (p = 0.22 and p = 0.19, respectively). Fewer serious adverse events occurred in the insulin glargine group than in the NPH insulin group (p < 0.02). A once-daily subcutaneous dose of insulin glargine provides effective glycemic control and is well tolerated in children and adolescents with T1DM.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: Diabetes screening, diagnosis, and therapy in pediatric patients with type 2 diabetes.
Abstract: The dramatic rise in the incidence and prevalence of type 2 diabetes mellitus in the pediatric and adolescent populations has been associated with the ongoing epidemic of overweight, obesity, insulin resistance, and metabolic syndrome seen in these age groups. Although the majority of pediatric patients diagnosed with diabetes are still classified as having type 1 diabetes, almost 50% of patients with diabetes in the pediatric age range (under 18 years) may have type 2 diabetes. Screening of high-risk patients for diabetes and prediabetes is important. Prompt diagnosis and accurate diabetes classification facilitate appropriate and timely treatment and may reduce the risk for complications. This is especially important in children because lifestyle interventions may be successful and the lifelong risk for complications is greatest. Treatment usually begins with dietary modification, weight loss, and a structured program of physical exercise. Oral antidiabetic agents are added when lifestyle intervention alone fails to maintain glycemic control. Given the natural history of type 2 diabetes, most if not all patients will eventually require insulin therapy. In those requiring insulin, improved glycemic control and reduced frequency of hypoglycemia can be achieved with insulin analogs. It is common to add insulin therapy to existing oral therapy only when oral agents no longer provide adequate glycemic control.<|endoftext|>
<|endoftext|>1: Title: Institution of basal-bolus therapy at diagnosis for children with type 1 diabetes mellitus.
Abstract: OBJECTIVE: We studied whether the institution of basal-bolus therapy immediately after diagnosis improved glycemic control in the first year after diagnosis for children with newly diagnosed type 1 diabetes mellitus. METHODS: We reviewed the charts of 459 children > or =6 years of age who were diagnosed as having type 1 diabetes between July 1, 2002, and June 30, 2006 (212 treated with basal-bolus therapy and 247 treated with a more-conventional neutral protamine Hagedorn regimen). We abstracted data obtained at diagnosis and at quarterly clinic visits and compared groups by using repeated-measures, mixed-linear model analysis. We also reviewed the records of 198 children with preexisting type 1 diabetes mellitus of >1-year duration who changed from the neutral protamine Hagedorn regimen to a basal-bolus regimen during the review period. RESULTS: Glargine-treated subjects with newly diagnosed diabetes had lower hemoglobin A1c levels at 3, 6, 9, and 12 months after diagnosis than did neutral protamine Hagedorn-treated subjects (average hemoglobin A1c levels of 7.05% with glargine and 7.63% with neutral protamine Hagedorn, estimated across months 3, 6, 9, and 12, according to repeated-measures models adjusted for age at diagnosis and baseline hemoglobin A1c levels; treatment difference: 0.58%). Children with long-standing diabetes had no clinically important changes in their hemoglobin A1c levels in the first year after changing regimens. CONCLUSION: The institution of basal-bolus therapy with insulin glargine at the time of diagnosis of type 1 diabetes was associated with improved glycemic control, in comparison with more-conventional neutral protamine Hagedorn regimens, during the first year after diagnosis.<|endoftext|>
|
Diabetes Mellitus Type 1
|
pubmed
|
train
|
Classify the node ' Incremental reduced error pruning. : This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Transferring and retraining learned information filters. : Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct transfer" of learned filters in this setting|a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization.<|endoftext|>
<|endoftext|>1: More Efficient Windowing: Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that separate-and-conquer rule learning algorithms are more appropriate for windowing than divide-and-conquer algorithms, because they learn rules independently and are less susceptible to changes in class distributions. In particular, we will present a new windowing algorithm that achieves additional gains in efficiency by exploiting this property of separate-and-conquer algorithms. While the presented algorithm is only suitable for redundant, noise-free data sets, we will also briefly discuss the problem of noisy data in windowing and present some preliminary ideas how it might be solved with an extension of the algorithm introduced in this paper.<|endoftext|>
|
Rule Learning
|
cora
|
train
|
Classify the node 'The CyberShoe: A Wireless Multisensor Interface for a Dancer's Feet : As a bridge between our interest in Wearable Computer systems and new performance interfaces for digital music, we have built a highly instrumented pair of sneakers for interactive dance. These shoes each measure 16 different, continuous parameters expressed by each foot and are able to transmit them wirelessly to a base station placed well over 30 meters away, updating all values up to 60 times per second. This paper describes our system, illustrates its performance, and outlines a few musical mappings that we have created for demonstrations in computer-augmented dance. ____________________________________ Electronic sensors have been incorporated into footwear for several different applications over the last several years. Employing force-sensing resistor arrays or pixelated capacitive sensing, insoles with very dense pressure sampling have been developed for research at the laboratories of footwear manufacturers and pediatric treatment facilities (Cavanaugh, et. al., 1992). Alth...' into one of the following categories:
Agents; ML (Machine Learning); IR (Information Retrieval); DB (Databases); HCI (Human-Computer Interaction); AI (Artificial Intelligence).
Refer to neighbour nodes:
<|endoftext|>0: Interactive Music for Instrumented Dancing Shoes We have designed and built a pair of sneakers that each sense 16 different tactile and free-gesture parameters. These include continuous pressure at 3 points in the forward sole, dynamic pressure at the heel, bidirectional bend of the sole, height above instrumented portions of the floor, 3-axis orientation about the Earth's magnetic field, 2-axis gravitational tilt and low-G acceleration, 3-axis shock, angular rate about the vertical, and translational position via a sonar transponder. Both shoes transfer these parameters to a base station across an RF link at 50 Hz State updates. As they are powered by a local battery, there are no tethers or wires running off the shoe. A PC monitors the data streaming off both shoes and translates it into real-time interactive music. The shoe design is introduced, and the interactive music mappings that we have developed for dance performances are discussed. 1) Introduction A trained dancer is capable of expressing highly dexterous control...<|endoftext|>
|
HCI (Human-Computer Interaction)
|
citeseer
|
train
|
Classify the node ' Profile-driven instruction level parallel scheduling with application to super blocks. : Code scheduling to exploit instruction level parallelism (ILP) is a critical problem in compiler optimization research, in light of the increased use of long-instruction-word machines. Unfortunately, optimum scheduling is com-putationally intractable, and one must resort to carefully crafted heuristics in practice. If the scope of application of a scheduling heuristic is limited to basic blocks, considerable performance loss may be incurred at block boundaries. To overcome this obstacle, basic blocks can be coalesced across branches to form larger regions such as super blocks. In the literature, these regions are typically scheduled using algorithms that are either oblivious to profile information (under the assumption that the process of forming the region has fully utilized the profile information), or use the profile information as an addendum to classical scheduling techniques. We believe that even for the simple case of linear code regions such as super blocks, additional performance improvement can be gained by utilizing the profile information in scheduling as well. We propose a general paradigm for converting any profile-insensitive list sched-uler to a profile-sensitive scheduler. Our technique is developed via a theoretical analysis of a simplified abstract model of the general problem of profile-driven scheduling over any acyclic code region, yielding a scoring measure for ranking branch instructions. The ranking digests the profile information and has the useful property that scheduling with respect to rank is provably good for minimizing the expected completion time of the region, within the limits of the abstraction. While the ranking scheme is computation-ally intractable in the most general case, it is practicable for super blocks and suggests the heuristic that we present in this paper for profile-driven scheduling of super blocks. Experiments show that our heuristic offers substantial performance improvement over prior methods on a range of integer benchmarks and several machine models.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Speculative hedge: Regulating compile-time speculation against profile variations. : Path-oriented scheduling methods, such as trace scheduling and hyperblock scheduling, use speculation to extract instruction-level parallelism from control-intensive programs. These methods predict important execution paths in the current scheduling scope using execution profiling or frequency estimation. Aggressive speculation is then applied to the important execution paths, possibly at the cost of degraded performance along other paths. Therefore, the speed of the output code can be sensitive to the compiler's ability to accurately predict the important execution paths. Prior work in this area has utilized the speculative yield function by Fisher, coupled with dependence height, to distribute instruction priority among execution paths in the scheduling scope. While this technique provides more stability of performance by paying attention to the needs of all paths, it does not directly address the problem of mismatch between compile-time prediction and run-time behavior. The work presented in this paper extends the speculative yield and dependence height heuristic to explicitly minimize the penalty suffered by other paths when instructions are speculated along a path. Since the execution time of a path is determined by the number of cycles spent between a path's entrance and exit in the scheduling scope, the heuristic attempts to eliminate unnecessary speculation that delays any path's exit. Such control of speculation makes the performance much less sensitive to the actual path taken at run time. The proposed method has a strong emphasis on achieving minimal delay to all exits. Thus the name, speculative hedge, is used. This paper presents the speculative hedge heuristic, and shows how it controls over-speculation in a superblock/hyperblock scheduler. The stability of out Copyright 1996 IEEE. Published in the Proceedings of the 29th Annual International Symposium on Microarchitecture, De-cember 2-4, 1996, Paris, France. Personal use of this material is permitted. However, permission to reprint/republish this material for resale or redistribution purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966<|endoftext|>
|
Rule Learning
|
cora
|
train
|
Classify the node ' Automatic Parameter Selection by Minimizing Estimated Error. : We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Irrelevant features and the subset selection problem. : We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.<|endoftext|>
<|endoftext|>1: Feature subset selection as search with probabilistic estimates. : Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique.<|endoftext|>
<|endoftext|>2: Learning symbolic rules using artificial neural networks. : A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neural networks is to extract symbolic rules from trained networks. In this paper we describe and investigate an approach for extracting rules from networks that uses (1) the NofM extraction algorithm, and (2) the network training method of soft weight-sharing. Previously, the NofM algorithm had been successfully applied only to knowledge-based neural networks. Our experiments demonstrate that our extracted rules generalize better than rules learned using the C4.5 system. In addition to being accurate, our extracted rules are also reasonably comprehensible.<|endoftext|>
<|endoftext|>3: A study of cross-validation and bootstrap for accuracy estimation and model selection. : We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds.<|endoftext|>
<|endoftext|>4: `A case study in machine learning\', : This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for generating decision trees and prediction rules from cases in the CONFMAN database. The results show that simple patterns and rules are often not only more understandable, but also more reliable than complex rules. Simple decision trees are able to improve the chances of correctly predicting the outcome of a conflict management attempt. This suggests that mediation is more repetitive than conflicts per se, where such results have not been achieved so far.<|endoftext|>
<|endoftext|>5: The Power of Decision Tables, : We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time.<|endoftext|>
|
Theory
|
cora
|
train
|
Classify the node 'Close Encounters: Supporting Mobile Collaboration through Interchange of User Profiles . This paper introduces the notion of profile-based cooperation as a way to support awareness and informal communication between mobile users during chance encounters. We describe the design of Proem, a wearable system for profile-based cooperation that enables users to publish and exchange personal profile information during physical encounters. The Proem system is used to initiate contact between individuals by identifying mutual interests or common friends. In contrast to most previous research that concentrates on collaboration in well-defined and closed user groups, Proem supports informal communication between individuals who have never met before and who don't know each other. We illustrate the benefits of profile-based cooperation by describing several usage scenarios for the Proem system. 1 Introduction During the course of a day we encounter and meet a large number of people, some of whom we know personally and some of whom we never met before. In everyday languag...' into one of the following categories:
Agents; ML (Machine Learning); IR (Information Retrieval); DB (Databases); HCI (Human-Computer Interaction); AI (Artificial Intelligence).
Refer to neighbour nodes:
<|endoftext|>0: Disseminating Trust Information in Wearable Communities : This paper describes a framework for managing and distributing trust information in a community of mobile and wearable computer users. Trust information in the form of reputations are used to aid users during their social interactions with the rest of the community. Keywords: Wearable computing, social networks, social interaction, trust. Introduction In our modern world, the use of communication technologies like phone, fax and email has become commonplace. Despite this fact, most social interactions between individuals still occur when we meet people face-to-face. Many of our daily interactions are actually the result of a chance encounter, i.e. a situation in which we meet someone unexpectedly, for example in a hallway or an elevator. In most cases, the majority of the people we encounter every day we don't know and have never met before; however, some are familiar. Any encounter with another person, friend or stranger, is a chance for striking up a conversation and for exchang...<|endoftext|>
<|endoftext|>1: Providing an Embedded Software Environment for Wireless PDAs . The use of wireless Pdas is foreseen to outrun the one of Pcs in the near future. However, for this to actually happen, adequate software environments must be devised in order to allow the execution of various types of applications. This paper introduces the base features of such an environment, which is a customizable Jvm-based middleware. In particular, the middleware platform embeds services for appropriate resource management and for supporting novel Pda-oriented applications. 1 Introduction The use of wireless Personal Digital Assistant (Pda) devices is foreseen to outrun the one of Pcs in the near future. However, for this to actually happen, there is still the need to devise adequate software and hardware platforms. The use of Pdas should be as convenient as the one of Pcs and in particular must not overly restrict the applications that are supported. Considering the ongoing effort towards providing convenient hardware platforms in industry, this paper focuses on design issu...<|endoftext|>
<|endoftext|>2: SIDE Surfer: a Spontaneous Information Discovery and Exchange System Development of wireless communications enables the rise of networking applications in embedded systems. Web interactions, which are the most spread, are nowadays available on wireless PDAs. Moreover, we can observe a development of ubiquitous computing. Based on this concept, many works aim to consider user's context as part of the parameters of the applications. The context notion can include the user's location, his social activity . . . Taking part from emerging technologies enabling short range and direct wireless communications (which allow to define a proximity context), the aim of our study is to design a new kind of application, extending the Web paradigm: spontaneous and proximate Web interactions.<|endoftext|>
<|endoftext|>3: When Cyborgs Meet: Building Communities of Cooperating Wearable Agents This paper ... Keywords Wearable computing, personal agents, ... 1 INTRODUCTION Our modern world/society is characterized by an ever increasing ubiquity/pervasiveness of electronic communication technologies like phone and email. Despite this fact, most human interactions still occur when we physically meet other people. Every day, we encounter a large number of people - friends, colleagues and strangers alike. At places like coffee shops, grocery stores, and offices we interact with people to trade news, tell stories, gossip or exchange goods and services. Often we use these situations 1 to pursue our own goals. For example, we purchase items, coordinate schedules, or make other arrangements when we meet other people. Wearable computers provide a chance to augment such human every-day interactions and to advance cooperation (Why? Features of wearables: always on, always active, senses environment, proactive -- ability to support user during every-day life, ability to act as use...<|endoftext|>
|
HCI (Human-Computer Interaction)
|
citeseer
|
train
|
Classify the node ' Learning to achieve goals. : Temporal difference methods solve the temporal credit assignment problem for reinforcement learning. An important subproblem of general reinforcement learning is learning to achieve dynamic goals. Although existing temporal difference methods, such as Q learning, can be applied to this problem, they do not take advantage of its special structure. This paper presents the DG-learning algorithm, which learns efficiently to achieve dynamically changing goals and exhibits good knowledge transfer between goals. In addition, this paper shows how traditional relaxation techniques can be applied to the problem. Finally, experimental results are given that demonstrate the superiority of DG learning over Q learning in a moderately large, synthetic, non-deterministic domain.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to: In many learning tasks, data-query is neither free nor of constant cost. Often the cost of a query depends on the distance from the current location in state space to the desired query point. This is easiest to visualize in robotics environments where a robot must physically move to a location in order to learn something there. The cost of this learning is the time and effort it takes to reach the new location. Furthermore, this cost is characterized by a distance relationship: When the robot moves as directly as possible from a source state to a destination state, the states through which it passes are closer (i.e., cheaper to reach) than is the destination state. Distance relationships hold in many real-world non-robotics tasks also | any environment where states are not immediately accessible. Optimizing the performance of a chemical plant, for example, requires the adjustment of analog controls which have a continuum of intermediate states. Querying possibly optimal regions of state space in these environments is inadvisable if the path to the query point intersects a region of known volatility. In discrete environments with small numbers of states, it's possible to keep track of precisely where and to what degree learning has already been done sufficiently and where it still needs to be done. It is also possible to keep best known estimates of the distances from each state to each other (see Kaelbling, 1993). Kael-bling's DG-learning algorithm is based on Floyd's all-pairs shortest-path algorithm (Aho, Hopcroft, & Ull-man 1983) and is just slightly different from that used here. These "all-goals" algorithms (after Kaelbling) can provide a highly satisfying representation of the distance/benefit tradeoff. where E x is the exploration value of state x (the potential benefit of exploring state x), D xy is the distance to state y, and A xy is the action to take in state x to move most cheaply to state y. This information can be learned incrementally and completely : That is, it can be guaranteed that if a path from any state x to any state y is deducible from the state transitions seen so far, then (1) the algorithm will have a non-null entry for S xy (i.e., the algorithm will know a path from x to y), and (2) The current value for D xy will be the best deducible value from all data seen so far. With this information, decisions about which areas to explore next can be based on not just the amount to be gained from such exploration but also on the cost of reaching each area together with the benefit of incidental exploration done on the way. Though optimal exploration is NP-hard (i.e., it's at least as difficult as TSP) good approximations are easily computable. One such good approximation is to take the action at each state that leads in the direction of greatest accumulated exploration benefit:<|endoftext|>
<|endoftext|>1: Dynamic Programming and Markov Processes. : The problem of maximizing the expected total discounted reward in a completely observable Markovian environment, i.e., a Markov decision process (mdp), models a particular class of sequential decision problems. Algorithms have been developed for making optimal decisions in mdps given either an mdp specification or the opportunity to interact with the mdp over time. Recently, other sequential decision-making problems have been studied prompting the development of new algorithms and analyses. We describe a new generalized model that subsumes mdps as well as many of the recent variations. We prove some basic results concerning this model and develop generalizations of value iteration, policy iteration, model-based reinforcement-learning, and Q-learning that can be used to make optimal decisions in the generalized model under various assumptions. Applications of the theory to particular models are described, including risk-averse mdps, exploration-sensitive mdps, sarsa, Q-learning with spreading, two-player games, and approximate max picking via sampling. Central to the results are the contraction property of the value operator and a stochastic-approximation theorem that reduces asynchronous convergence to synchronous convergence.<|endoftext|>
<|endoftext|>2: Learning to predict by the methods of temporal differences. : This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.<|endoftext|>
<|endoftext|>3: Integrated Architectures for Learning, Planning and Reacting Based on Approximating Dynamic Programming, : This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We show that Dyna-Q architectures are easy to adapt for use in changing environments.<|endoftext|>
<|endoftext|>4: Learning to Act using Real- Time Dynamic Programming. : fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526).<|endoftext|>
<|endoftext|>5: Transfer of Learning by Composing Solutions of Elemental Sequential Tasks, : Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm.<|endoftext|>
|
Reinforcement Learning
|
cora
|
train
|
Classify the node ' An empirical comparison of selection measures for decision-tree induction. : Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Data Analysis Using Simulated Breeding and Inductive Learning Methods, : Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data. This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other methods.<|endoftext|>
<|endoftext|>1: LEARNING FOR DECISION MAKING: The FRD Approach and a Comparative Study Machine Learning and Inference Laboratory: This paper concerns the issue of what is the best form for learning, representing and using knowledge for decision making. The proposed answer is that such knowledge should be learned and represented in a declarative form. When needed for decision making, it should be efficiently transferred to a procedural form that is tailored to the specific decision making situation. Such an approach combines advantages of the declarative representation, which facilitates learning and incremental knowledge modification, and the procedural representation, which facilitates the use of knowledge for decision making. This approach also allows one to determine decision structures that may avoid attributes that unavailable or difficult to measure in any given situation. Experimental investigations of the system, FRD-1, have demonstrated that decision structures obtained via the declarative route often have not only higher predictive accuracy but are also are simpler than those learned directly from facts.<|endoftext|>
<|endoftext|>2: Geometric comparison of classifications and rule sets. : We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions. The system produces a correlation matrix that indicates the degree of similarity between each pair of classes. The technique can be applied to classifications generated by different algorithms, with different numbers of classes and different attribute sets. Experimental results from a case study in a medical domain are included.<|endoftext|>
<|endoftext|>3: On Pruning and Averaging Decision Trees, : Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. We perform an empirical comparison of pruning with the approach of averaging over decision trees. For this comparison we use a computa-tionally efficient method of averaging, namely averaging over the extended fanned set of a tree. Since there are a wide range of approaches to pruning, we compare tree averaging with a traditional pruning approach, along with an optimal pruning approach.<|endoftext|>
<|endoftext|>4: Fossil: A robust relational learner". : The research reported in this paper describes Fossil, an ILP system that uses a search heuristic based on statistical correlation. Several interesting properties of this heuristic are discussed, and a it is shown how it naturally can be extended with a simple, but powerful stopping criterion that is independent of the number of training examples. Instead, Fossil's stopping criterion depends on a search heuristic that estimates the utility of literals on a uniform scale. After a comparison with Foil and mFoil in the KRK domain and on the mesh data, we outline some ideas how Fossil can be adopted for top-down pruning and present some preliminary results.<|endoftext|>
<|endoftext|>5: Induction of one-level decision trees. : In recent years, researchers have made considerable progress on the worst-case analysis of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an average-case analysis of a simple algorithm that induces one-level decision trees for concepts defined by a single relevant attribute. Given knowledge about the number of training instances, the number of irrelevant attributes, the amount of class and attribute noise, and the class and attribute distributions, we derive the expected classification accuracy over the entire instance space. We then examine the predictions of this analysis for different settings of these domain parameters, comparing them to exper imental results to check our reasoning.<|endoftext|>
<|endoftext|>6: Multivariate Decision Trees: COINS Technical Report 92-82 December 1992 Abstract Multivariate decision trees overcome a representational limitation of univariate decision trees: univariate decision trees are restricted to splits of the instance space that are orthogonal to the feature's axis. This paper discusses the following issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present some new and review some well-known methods for forming multivariate decision trees. The methods are compared across a variety of learning tasks to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are more effective than others. In addition, the experiments confirm that allowing multivariate tests improves the accuracy of the resulting decision tree over univariate trees.<|endoftext|>
<|endoftext|>7: Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection. : COINS Technical Report 92-30 February 1992 Abstract The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation language it uses to express a generalization of the examples. Supervised learning algorithms differ in their underlying model class and in how they search for a good generalization. Given this characterization, it is not surprising that some algorithms find better generalizations for some, but not all tasks. Therefore, in order to find the best generalization for each task, an automated learning system must search for the appropriate model class in addition to searching for the best generalization within the chosen class. This thesis proposal investigates the issues involved in automating the selection of the appropriate model class. The presented approach has two facets. Firstly, the approach combines different model classes in the form of a model combination decision tree, which allows the best representation to be found for each subconcept of the learning task. Secondly, which model class is the most appropriate is determined dynamically using a set of heuristic rules. Explicit in each rule are the conditions in which a particular model class is appropriate and if it is not, what should be done next. In addition to describing the approach, this proposal describes how the approach will be evaluated in order to demonstrate that it is both an efficient and effective method for automatic model selection.<|endoftext|>
<|endoftext|>8: Incremental Reduced Error Pruning: This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of the algorithm cannot be recommended for domains which require a very specific concept description.<|endoftext|>
<|endoftext|>9: Decision tree induction: How effective is the greedy heuristic? In Proc. : Most existing decision tree systems use a greedy approach to induce trees | locally optimal splits are induced at every node of the tree. Although the greedy approach is suboptimal, it is believed to produce reasonably good trees. In the current work, we attempt to verify this belief. We quantify the goodness of greedy tree induction empirically, using the popular decision tree algorithms, C4.5 and CART. We induce decision trees on thousands of synthetic data sets and compare them to the corresponding optimal trees, which in turn are found using a novel map coloring idea. We measure the effect on greedy induction of variables such as the underlying concept complexity, training set size, noise and dimensionality. Our experiments show, among other things, that the expected classification cost of a greedily induced tree is consistently very close to that of the optimal tree.<|endoftext|>
<|endoftext|>10: A Theory of Learning Classification Rules. : <|endoftext|>
<|endoftext|>11: Fast Bounded Smooth Regression with Lazy Neural Trees: We propose the lazy neural tree (LNT) as the appropriate architecture for the realization of smooth regression systems. The LNT is a hybrid of a decision tree and a neural network. From the neural network it inherits smoothness of the generated function, incremental adaptability, and conceptual simplicity. From the decision tree it inherits the topology and initial parameter setting as well as a very efficient sequential implementation that out-performs traditional neural network simulations by the order of magnitudes. The enormous speed is achieved by lazy evaluation. A further speed-up can be obtained by the application of a window-ing scheme if the region of interesting results is restricted.<|endoftext|>
<|endoftext|>12: An investigation of noise-tolerant relational concept learning algorithms. : We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach experimentally.<|endoftext|>
<|endoftext|>13: Stochastic Inductive Logic Programming. : Concept learning can be viewed as search of the space of concept descriptions. The hypothesis language determines the search space. In standard inductive learning algorithms, the structure of the search space is determined by generalization/specialization operators. Algorithms perform locally optimal search by using a hill-climbing and/or a beam-search strategy. To overcome this limitation, concept learning can be viewed as stochastic search of the space of concept descriptions. The proposed stochastic search method is based on simulated annealing which is known as a successful means for solving combinatorial optimization problems. The stochastic search method, implemented in a rule learning system ATRIS, is based on a compact and efficient representation of the problem and the appropriate operators for structuring the search space. Furthermore, by heuristic pruning of the search space, the method enables also handling of imperfect data. The paper introduces the stochastic search method, describes the ATRIS learning algorithm and gives results of the experiments.<|endoftext|>
<|endoftext|>14: Committees of decision trees. : Many intelligent systems are designed to sift through a mass of evidence and arrive at a decision. Certain pieces of evidence may be given more weight than others, and this may affect the final decision significantly. When than one intelligent agent is available to make a decision, we can form a committee of experts. By combining the different opinions of these experts, the committee approach can sometimes outperform any individual expert. In this paper, we show how to exploit randomized learning algorithms in order to develop committees of experts. By using the majority vote of these experts to make decisions, we are able to improve the performance of the original learning algorithm. More precisely, we have developed a randomized decision tree induction algorithm, which generates different decision trees every time it is run. Each tree represents a different expert decision-maker. We combine these trees using a majority voting scheme in order to overcome small errors that appear in individual trees. We have tested our idea with several real data sets, and found that accuracy consistently improved when compared to the decision made by a single expert. We have developed some analytical results that explain why this effect occurs. Our experiments also show that the majority voting technique outperforms at least some alternative strategies for exploiting randomization.<|endoftext|>
<|endoftext|>15: Top-down pruning in relational learn-ing. : Pruning is an effective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the field of Inductive Logic Programming. However, it has been shown that these methods can be very inefficient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these clauses. We introduce a new method which searches for good theories in a top-down fashion to get a better starting point for the pruning algorithm. Experiments show that this approach can significantly lower the complexity of the task as well as increase predictive accuracy.<|endoftext|>
<|endoftext|>16: A comparison of pruning methods for relational concept learning. : Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integrate pre- and post-pruning in order to achieve both accuracy and efficiency. This is verified with test series in a chess position classification task.<|endoftext|>
<|endoftext|>17: Learning Classification Trees: Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 (Quinlan et al., 1987) and Breiman et al.'s CART (Breiman et al., 1984) show the full Bayesian algorithm can produce Publication: This paper is a final draft submitted for publication to the Statistics and Computing journal; a version with some minor changes appeared in Volume 2, 1992, pages 63-73. more accurate predictions than versions of these other approaches, though pay a computational price.<|endoftext|>
<|endoftext|>18: Finding new rules for incomplete theories: Explicit biases for induction with contextual information. : addressed in KBANN (which translates a theory into a neural-net, refines it using backpropagation, and then retranslates the result back into rules) by adding extra hidden units and connections to the initial network; however, this would require predetermining the num In this paper, we have presented constructive induction techniques recently added to the EITHER theory refinement system. Intermediate concept utilization employs existing rules in the theory to derive higher-level features for use in induction. Intermediate concept creation employs inverse resolution to introduce new intermediate concepts in order to fill gaps in a theory than span multiple levels. These revisions allow EITHER to make use of imperfect domain theories in the ways typical of previous work in both constructive induction and theory refinement. As a result, EITHER is able to handle a wider range of theory imperfections than any other existing theory refinement system.<|endoftext|>
<|endoftext|>19: R.S. and Imam, I.F. On Learning Decision Structures. : A decision structure is an acyclic graph that specifies an order of tests to be applied to an object (or a situation) to arrive at a decision about that object. and serves as a simple and powerful tool for organizing a decision process. This paper proposes a methodology for learning decision structures that are oriented toward specific decision making situations. The methodology consists of two phases: 1determining and storing declarative rules describing the decision process, 2deriving online a decision structure from the rules. The first step is performed by an expert or by an AQ-based inductive learning program that learns decision rules from examples of decisions (AQ15 or AQ17). The second step transforms the decision rules to a decision structure that is most suitable for the given decision making situation. The system, AQDT-2, implementing the second step, has been applied to a problem in construction engineering. In the experiments, AQDT-2 outperformed all other programs applied to the same problem in terms of the accuracy and the simplicity of the generated decision structures. Key words: machine learning, inductive learning, decision structures, decision rules, attribute selection.<|endoftext|>
<|endoftext|>20: "New roles for machine learning in design," : Research on machine learning in design has concentrated on the use and development of techniques that can solve simple well-defined problems. Invariably, this effort, while important at the early stages of the development of the field, cannot scale up to address real design problems since all existing techniques are based on simplifying assumptions that do not hold for real design. In particular they do not address the dependence on context and multiple, often conflicting, interests that are constitutive of design. This paper analyzes the present situation and criticizes a number of prevailing views. Subsequently, the paper offers an alternative approach whose goal is to advance the use of machine learning in design practice. The approach is partially integrated into a modeling system called n-dim. The use of machine learning in n-dim is presented and open research issues are outlined.<|endoftext|>
<|endoftext|>21: Learning Decision Trees from Decision Rules:: A method and initial results from a comparative study ABSTRACT A standard approach to determining decision trees is to learn them from examples. A disadvantage of this approach is that once a decision tree is learned, it is difficult to modify it to suit different decision making situations. Such problems arise, for example, when an attribute assigned to some node cannot be measured, or there is a significant change in the costs of measuring attributes or in the frequency distribution of events from different decision classes. An attractive approach to resolving this problem is to learn and store knowledge in the form of decision rules, and to generate from them, whenever needed, a decision tree that is most suitable in a given situation. An additional advantage of such an approach is that it facilitates building compact decision trees , which can be much simpler than the logically equivalent conventional decision trees (by compact trees are meant decision trees that may contain branches assigned a set of values , and nodes assigned derived attributes, i.e., attributes that are logical or mathematical functions of the original ones). The paper describes an efficient method, AQDT-1, that takes decision rules generated by an AQ-type learning system (AQ15 or AQ17), and builds from them a decision tree optimizing a given optimality criterion. The method can work in two modes: the standard mode , which produces conventional decision trees, and compact mode, which produces compact decision trees. The preliminary experiments with AQDT-1 have shown that the decision trees generated by it from decision rules (conventional and compact) have outperformed those generated from examples by the well-known C4.5 program both in terms of their simplicity and their predictive accuracy.<|endoftext|>
<|endoftext|>22: The Estimation of Probabilities in Attribute Selection Measures for Decision Structure Induction in Proceeding of the European Summer School on Machine Learning, : In this paper we analyze two well-known measures for attribute selection in decision tree induction, informativity and gini index. In particular, we are interested in the influence of different methods for estimating probabilities on these two measures. The results of experiments show that different measures, which are obtained by different probability estimation methods, determine the preferential order of attributes in a given node. Therefore, they determine the structure of a constructed decision tree. This feature can be very beneficial, especially in real-world applications where several different trees are often required.<|endoftext|>
<|endoftext|>23: Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents:: This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agents ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CI-based learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an architecture for a CI-based learning agent and gives an empirical comparison of a CI and SI for a set of six abstract domains involving DNF-type (disjunctive normal form) descriptions.<|endoftext|>
<|endoftext|>24: "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping," : The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping.<|endoftext|>
<|endoftext|>25: Pessimistic Decision Tree Pruning Based on Tree Size. : In this work we develop a new criteria to perform pessimistic decision tree pruning. Our method is theoretically sound and is based on theoretical concepts such as uniform convergence and the Vapnik-Chervonenkis dimension. We show that our criteria is very well motivated, from the theory side, and performs very well in practice. The accuracy of the new criteria is comparable to that of the current method used in C4.5.<|endoftext|>
|
Theory
|
cora
|
train
|
Classify the node ' (1997) MCMC Convergence Diagnostic via the Central Limit Theorem. : Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simulation based strategy for statistical inference. The application fields related to these methods, as well as theoretical convergence properties, have been intensively studied in the recent literature. However, many improvements are still expected to provide workable and theoretically well-grounded solutions to the problem of monitoring the convergence of actual outputs from MCMC algorithms (i.e. the convergence assessment problem). In this paper, we introduce and discuss a methodology based on the Central Limit Theorem for Markov chains to assess convergence of MCMC algorithms. Instead of searching for approximate stationarity, we primarily intend to control the precision of estimates of the invariant probability measure, or of integrals of functions with respect to this measure, through confidence regions based on normal approximation. The first proposed control method tests the normality hypothesis for normalized averages of functions of the Markov chain over independent parallel chains. This normality control provides good guarantees that the whole state space has been explored, even in multimodal situations. It can lead to automated stopping rules. A second tool connected with the normality control is based on graphical monitoring of the stabilization of the variance after n iterations near the limiting variance appearing in the CLT. Both methods require no knowledge of the sampler driving the chain. In this paper, we mainly focus on finite state Markov chains, since this setting allows us to derive consistent estimates of both the limiting variance and the variance after n iterations. Heuristic procedures based on Berry-Esseen bounds are investigated. An extension to the continuous case is also proposed. Numerical simulations illustrating the performance of these methods are given for several examples: a finite chain with multimodal invariant probability, a finite state random walk for which the theoretical rate of convergence to stationarity is known, and a continuous state chain with multimodal invariant probability issued from a Gibbs sampler.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Diagnosing convergence of Markov chain Monte Carlo algorithms. : We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are provided for particular classes of problems.<|endoftext|>
<|endoftext|>1: D.M. (1998) Convergence controls for MCMC algorithms, with applications to hidden Markov chains. : In complex models like hidden Markov chains, the convergence of the MCMC algorithms used to approximate the posterior distribution and the Bayes estimates of the parameters of interest must be controlled in a robust manner. We propose in this paper a series of on-line controls, which rely on classical non-parametric tests, to evaluate independence from the start-up distribution, stability of the Markov chain, and asymptotic normality. These tests lead to graphical control spreadsheets which are presented in the set-up of normal mixture hidden Markov chains to compare the full Gibbs sampler with an aggregated Gibbs sampler based on the forward-backward formulae.<|endoftext|>
<|endoftext|>2: Discretization of continuous Markov chains and MCMC convergence assessment: We show in this paper that continuous state space Markov chains can be rigorously discretized into finite Markov chains. The idea is to subsample the continuous chain at renewal times related to small sets which control the discretization. Once a finite Markov chain is derived from the MCMC output, general convergence properties on finite state spaces can be exploited for convergence assessment in several directions. Our choice is based on a divergence criterion derived from Kemeny and Snell (1960), which is first evaluated on parallel chains with a stopping time, and then implemented, more efficiently, on two parallel chains only, using Birkhoff's pointwise ergodic theorem for stopping rules. The performance of this criterion is illustrated on three standard examples.<|endoftext|>
|
Probabilistic Methods
|
cora
|
train
|
Classify the node 'Title: Demographic and clinical correlates of metabolic syndrome in Native African type-2 diabetic patients.
Abstract: OBJECTIVES: To describe the metabolic syndrome and its demographic and clinical correlates in native African type-2 diabetic patients. METHODS: Cross-sectional analysis of 254 type-2 diabetic indigenous Nigerians consecutively recruited in a teaching hospital. The main outcome measure was metabolic syndrome. Variables of interest included family history/duration of diabetes mellitus and hypertension, gender, socioeconomic class, occupation and place of domicile (urban or rural). Intergroup comparisons were made with Chi-squared tests or t-tests. RESULTS: Patients were aged 35-80 years (mean: 52.0 +/- 11.7 years) and made of 154 (60.6%) males and 100 (39.4%) females. Full-blown metabolic syndrome was noted in 52 patients (20.5%). Metabolic syndrome, as defined by the WHO, was noted in 150 patients (59.1%). About 72.4% of patients were dyslipidemic, 54.3% were hypertensive, 42.5% were obese, 44.9% were microalbuminuric and 32.3% were hyperuricemic. Ischemic heart disease (myocardial infarction) occurred in only 2.4% of patients. Concurrent hypertension and dyslipidemia; obesity and dyslipidemia; and hypertension and obesity occurred in 44.4%, 42.5% and 33.1% of type-2 diabetics, respectively. Compared to the diabetics without metabolic syndrome, those with the syndrome had a significantly higher proportion of patients with a family history of hypertension and diabetes (44% versus 25%; p = 0.003); among the upper/middle socioeconomic class: 52.0% versus 30.8% (p = 0.001); and among the urban dwelling: 68.0% versus 49.0% (p = 0.004). Metabolic syndrome was inversely proportional to the physical activity of an individual (chi2 = 21.69, df = 5, p = 0.001). Blood pressure was significantly higher among patients with metabolic syndrome than those without it (140.6 +/- 22.9/85.2 +/- 12.9 mmHg versus 126.9 +/- 15.4 mmHg; P < 0.01). CONCLUSIONS: The development of metabolic syndrome in African type-2 diabetic patients is influenced by demographic and clinical factors. Vigilant dietary habit and physical exercise may reduce the chance of metabolic syndrome in urban Nigerian type-2 diabetics.' into one of the following categories:
Diabetes Mellitus, Experimental; Diabetes Mellitus Type 1; Diabetes Mellitus Type 2.
Refer to neighbour nodes:
<|endoftext|>0: Title: What does the presence of hypertension portend in the Nigerian with non insulin dependent diabetes mellitus.
Abstract: 132 Nigerians with Non Insulin -dependent diabetes mellitus (NIDDM) were divided into two groups (NIDDM) patients with hypertension and those without) and their clinico-laboratory parameters were studied and analyzed. Their mean age (SD) was 59.5+/-9 years. Body mass index (BMI) was 25.2+/-3.5 kg/m2 and the duration of DM was 6.9+/-6 years. The prevalence of hypertension was 55(41.6%) No significant difference were observed in the age, sex ratio and BMI of both groups but the duration of DM showed a statistical difference between the two groups. However, laboratory parameters such as fasting blood glucose, serum urea, creatinine clearance and degree of proteinuria all showed statistically significant difference between the hypertensive and normotensive groups. Also the hypertensive diabetic group were observed to have more end organ damage i.e peripheral neuropathy, diabetic retinopathy and diabetic nephropathy than the normotensive diabetics. We conclude that, hypertension in NIDDM patients has prognostic implications and so more aggressive efforts be made in detecting and controlling hypertension in DM patients.<|endoftext|>
<|endoftext|>1: Title: Dyslipidaemia in African Americans, Hispanics and whites with type 2 diabetes mellitus and hypertension.
Abstract: AIM: To study the pattern of dyslipidaemia in African American, Hispanic, and White patients with type 2 diabetes mellitus and/or hypertension. METHODS: The data were collected retrospectively on 6450 patients followed in the Harris County Hospital District Community Clinics. The information collected from review of the charts included each patient's age, sex, race, body mass index (b.m.i.), duration of type 2 diabetes mellitus and hypertension, medications, fasting plasma glucose, haemoglobin A1c, and fasting lipid profile. Mean lipid and haemoglobin A1c levels in the three ethnic groups were compared. The risk of abnormal cholesterol and triglyceride levels was assessed with logistic regression analysis. RESULTS: The results show that in patients with type 2 diabetes mellitus after correcting for age, sex and b.m.i., African Americans have the lowest serum triglyceride concentrations and Whites have the highest values. This trend holds true even in patients with hypertension and in patients with both hypertension and type 2 diabetes mellitus. The risk of having abnormal triglycerides is 74% lower in African Americans, and 42% lower in Hispanics than Whites based on logistic regression model. Despite better glycaemic control, Whites have a greater increase in serum triglyceride concentrations than Hispanics and African Americans. CONCLUSIONS: Although African Americans are known to be at higher risk for cardiovascular complications than Whites or Hispanics, they appear to have lower triglyceride concentrations than Whites or Hispanics in the presence of type 2 diabetes mellitus. This suggests that an increased prevalence of other adverse factors must contribute to their heightened cardiovascular risk.<|endoftext|>
<|endoftext|>2: Title: Banting lecture 1988. Role of insulin resistance in human disease.
Abstract: Resistance to insulin-stimulated glucose uptake is present in the majority of patients with impaired glucose tolerance (IGT) or non-insulin-dependent diabetes mellitus (NIDDM) and in approximately 25% of nonobese individuals with normal oral glucose tolerance. In these conditions, deterioration of glucose tolerance can only be prevented if the beta-cell is able to increase its insulin secretory response and maintain a state of chronic hyperinsulinemia. When this goal cannot be achieved, gross decompensation of glucose homeostasis occurs. The relationship between insulin resistance, plasma insulin level, and glucose intolerance is mediated to a significant degree by changes in ambient plasma free-fatty acid (FFA) concentration. Patients with NIDDM are also resistant to insulin suppression of plasma FFA concentration, but plasma FFA concentrations can be reduced by relatively small increments in insulin concentration. Consequently, elevations of circulating plasma FFA concentration can be prevented if large amounts of insulin can be secreted. If hyperinsulinemia cannot be maintained, plasma FFA concentration will not be suppressed normally, and the resulting increase in plasma FFA concentration will lead to increased hepatic glucose production. Because these events take place in individuals who are quite resistant to insulin-stimulated glucose uptake, it is apparent that even small increases in hepatic glucose production are likely to lead to significant fasting hyperglycemia under these conditions. Although hyperinsulinemia may prevent frank decompensation of glucose homeostasis in insulin-resistant individuals, this compensatory response of the endocrine pancreas is not without its price. Patients with hypertension, treated or untreated, are insulin resistant, hyperglycemic, and hyperinsulinemic. In addition, a direct relationship between plasma insulin concentration and blood pressure has been noted. Hypertension can also be produced in normal rats when they are fed a fructose-enriched diet, an intervention that also leads to the development of insulin resistance and hyperinsulinemia. The development of hypertension in normal rats by an experimental manipulation known to induce insulin resistance and hyperinsulinemia provides further support for the view that the relationship between the three variables may be a causal one.(ABSTRACT TRUNCATED AT 400 WORDS)<|endoftext|>
<|endoftext|>3: Title: WHO and ATPIII proposals for the definition of the metabolic syndrome in patients with Type 2 diabetes.
Abstract: AIMS: Different criteria have been proposed by the World Health Organization (WHO) and by the Third Report of the National Cholesterol Education Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATPIII) for the diagnosis of the metabolic syndrome. Its identification is of particular importance for coronary risk assessment. METHODS: The prevalence of the metabolic syndrome was determined according to the two different proposals in 1569 consecutive subjects with Type 2 diabetes. RESULTS: By the WHO proposal, 81% of cases (95% confidence interval, 79-83) were labelled as metabolic syndrome. Microalbuminuria had the highest specificity (99%) and visceral obesity the highest sensitivity (93%). Seventy-eight per cent of patients (95% CI, 76-80) fulfilled the ATPIII criteria for metabolic syndrome, low HDL-cholesterol having the highest specificity (95%), elevated blood pressure having the highest sensitivity. According to both proposals, 1113 patients were positive; 183 were concordantly negative, indicative of a fairly good agreement (k statistics, 0.464). Subjects only positive for the WHO proposal were more frequently males, had a lower BMI and a higher arterial pressure. Only subjects identified by the ATPIII proposal had a significantly higher prevalence of previously detected coronary heart disease. CONCLUSIONS: Minimum criteria for the metabolic syndrome are met in most patients with Type 2 diabetes. Correct identification of the syndrome is important for an integrated approach to reduce the high costs and the associated disabilities. The ATPIII proposal more clearly identifies the burden of coronary heart disease associated with the metabolic syndrome.<|endoftext|>
<|endoftext|>4: Title: The role of TNFalpha and TNF receptors in obesity and insulin resistance.
Abstract: Insulin resistance, a smaller than expected response to a given dose of insulin, is associated with many common diseases including, ageing, polycystic ovarian disease, syndrome X, cancer, infections, trauma and, most significantly, obesity and type 2 diabetes mellitus. The biochemical basis of insulin resistance in type 2 diabetes has been the subject of many studies. Earlier studies have indicated that quantitative regulation of the insulin sensitive glucose transporters (Glut-4) and insulin receptors themselves may contribute to this disorder, however, these two factors are probably inadequate to explain the extent of insulin resistance. This point also became apparent by the development of only mild hyperinsulinaemia in mice with a targeted mutation in the Glut-4 gene. Studies on postreceptor defects in type 2 diabetes has recently focused on the intrinsic catalytic activity of the insulin receptor and downstream signalling events. A reduction in tyrosine phosphorylation of both the insulin receptor (IR) and the insulin receptor substrate-1 (IRS-1) has been noted in both animal and human type 2 diabetes. Importantly, this appears to occur in all of the major insulin-sensitive tissues, namely the muscle, fat and liver. It is now clear that decreased signalling capacity of the insulin receptor is an important component of this disease. I will review some of the potential mechanisms underlying this deficiency.<|endoftext|>
<|endoftext|>5: Title: Patterns of glucose and lipid abnormalities in black NIDDM subjects.
Abstract: OBJECTIVE: We had previously shown two variants among black non-insulin-dependent diabetic (NIDDM) subjects in a normoglycemic remission: one with insulin resistance and the other with normal insulin sensitivity. This study examined whether these two variants exist in the ordinary hyperglycemic black NIDDM population. RESEARCH DESIGN AND METHODS: Fifty-two black NIDDM subjects were assessed for insulin-stimulated glucose disposal (euglycemic clamp), glycemic control (fasting plasma glucose and HbA1c), and fasting lipid profiles. RESULTS: The distribution of glucose disposal in 30 black NIDDM subjects (body mass index; BMI less than 30 kg/m2) was bimodal, which indicated two populations. Eighteen of 30 subjects (BMI 26.4 +/- 0.5 kg/m2) had insulin resistance (glucose disposal 3.21 +/- 0.24 mg.kg-1.min-1). Twelve of 30 subjects (BMI 24.83 +/- 1.1 kg/m2) had normal insulin sensitivity (glucose disposal 7.19 +/- 0.46 mg.kg-1.min-1). Twenty-one of the remaining 22 subjects (BMI 33.4 +/- 0.7 kg/m2) were insulin resistant (glucose disposal 2.88 +/- 0.21 mg.kg-1.min-1). Fasting serum triglyceride levels were lowest in the insulin-sensitive population (0.91 +/- 0.07 mM) and different from the insulin-resistant population, BMI less than 30 and greater than 30 kg/m2, (1.20 +/- 0.10 mM, P less than 0.05 and 1.42 +/- 0.17 mM, P less than 0.025, respectively). Fasting serum low-density lipoprotein cholesterol levels were not significantly different among the groups, although it did increase with insulin resistance and increasing obesity. Total serum cholesterol levels and glycemic control were similar for all three groups. Serum high-density lipoprotein cholesterol levels were higher in women compared with men. CONCLUSIONS: In the hyperglycemic black NIDDM population, two variants exist: one with insulin resistance and one with normal insulin sensitivity. This insulin-sensitive variant represents 40% of subjects with a BMI less than 30 kg/m2. Moreover, the insulin-sensitive group has a lower risk profile for cardiovascular disease.<|endoftext|>
<|endoftext|>6: Title: Possible link between a low prevalence of cardiovascular disease and mild dyslipidaemia: a study in Japanese patients with type 2 diabetes.
Abstract: In 98 Japanese patients with Type 2 diabetes mellitus, serum total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL-C), free fatty acid (FFA), and apolipoproteins (apo) A-I, A-II, B, C-II, C-III, and E were determined. The data were compared with those in 47 normolipidaemic normal controls. The total cholesterol value of the diabetic patients was also compared to that of a general population (n = 2227). The diabetic patients were separated into those with cardiovascular disease (n = 20) and without it (n = 78) and a comparison of clinical characteristics and dyslipidaemia was also performed. The diabetic patients had slightly but significantly higher FFA, LDL-C, apo B, C-II, C-III, E, and B/A-I, and lower apo A-I and A-II compared to the normal controls. The total cholesterol level of the diabetic patients (5.17 +/- 0.96 mmol-1) was not significantly higher than that of the general population (5.12 +/- 0.91 mmol-1). By multivariate stepwise discriminant analyses, only total cholesterol significantly discriminated the patients with and without cardiovascular disease. In Japanese patients with Type 2 diabetes, a diabetic population with a very low prevalence of cardiovascular disease, high total cholesterol is a risk factor for developing cardiovascular disease. Nevertheless, a markedly low prevalence of cardiovascular disease in Japanese with Type 2 diabetes compared to Caucasian counterparts may partly be due to the mildness of dyslipidaemia.<|endoftext|>
<|endoftext|>7: Title: Dyslipidaemia in diabetes mellitus. Review of the main lipoprotein abnormalities and their consequences on the development of atherogenesis.
Abstract: Lipid abnormalities in diabetic patients are likely to play an important role in the development of atherogenesis. These lipid disorders include not only quantitative but also qualitative abnormalities of lipoproteins which are potentially atherogenic. Both types are present in non-insulin-dependent diabetes (NIDDM) and poorly controlled insulin-dependent diabetes (IDDM), whereas only qualitative abnormalities are observed in well- and moderately well-controlled IDDM. The main quantitative abnormalities are increased triglyceride levels related to elevated VLDL and IDL and decreased HDL-cholesterol levels due to a drop in the HDL2 subfraction. The increase of triglyceride-rich lipoproteins in plasma is related to higher VLDL production by the liver and a decrease in their clearance. Metabolic abnormalities of triglyceride-rich lipoproteins are more pronounced in the postprandial period. The decrease in HDL-cholesterol is related to increased HDL catabolism. Qualitative abnormalities include changes in lipoprotein size (large VLDL, small LDL), increase of triglyceride content of LDL and HDL, glycation of apolipoproteins, and increased susceptibility of LDL to oxidation. These qualitative abnormalities impair the normal metabolism of lipoproteins and could thus promote atherogenesis. The physiopathology of lipid disorders in diabetes mellitus is multifactorial and still imperfectly known. However, such factors as hyperglycaemia and insulin resistance (in NIDDM) are likely to play an important role.<|endoftext|>
<|endoftext|>8: Title: Metabolic syndrome disorders in urban black Zimbabweans with type 2 Diabetes mellitus.
Abstract: OBJECTIVE: The main aim of the study was to determine the prevalence of metabolic syndrome disorders and their interrelations in black Zimbabwean type 2 diabetic patients. STUDY DESIGN: Prospective cross sectional study. SETTING: Outpatient diabetic clinics at Harare and Parirenyatwa tertiary hospitals. MAIN OUTCOME MEASURES: We recruited 109 adult diabetic subjects attending a tertiary hospital Diabetic Clinic. Anthropometric and metabolic parameters were measured by standard methods. Eighty percent of the patients were hypertensive, 32% dyslipidaemic, 32% obese, 50% hyperinsulinaemic, 61% had poor glycaemic control and 43% of the participants had the metabolic syndrome. The means of BMI and triglycerides were significantly different in hyperinsulinaemic versus non-hyperinsulinaemic patients (p < 0.001 and 0.041 respectively), and diastolic blood pressure was significantly raised in the obese group (p = 0.043). The following significant associations were observed, hyperinsulinaemia with the metabolic syndrome (odds ratio = 3.9, p < 0.001) as well with obesity (odds ratio = 4.8, p < 0.001), however, only a weak association was observed between hypertension and hyperinsulinaemia (odds ratio = 2.5, p = 0.064). Patients exhibiting three metabolic disorders (dyslipidaemia, hypertension and obesity) were five times more likely to be hyperinsulinaemic (p = 0.025) and hypertensive patients were almost three times more likely to be hyperinsulinaemic. CONCLUSION: In comparison to their counterparts from certain ethnic groups, this urban diabetic population is also burdened with a variety of metabolic disorders which are risk factors for coronary artery disease. In this population, hyperinsulinaemia has a relatively weak association with hypertension and the relationship between obesity versus diastolic blood pressure as well as hypertriglyceridaemia versus serum insulin levels requires further investigation.<|endoftext|>
<|endoftext|>9: Title: Metabolic syndrome as a predictor of all-cause and cardiovascular mortality in type 2 diabetes: the Casale Monferrato Study.
Abstract: OBJECTIVE: The aim of this study was to assess in an 11-year survival follow-up of a population-based cohort of type 2 diabetes the predictive role of World Health Organization-defined metabolic syndrome, independent of conventional cardiovascular risk factors. RESEARCH DESIGN AND METHODS: During the follow-up (1991-2001), 1,565 patients were regularly examined with centralized measurements of HbA(1c). The independent role of the metabolic syndrome as a predictor of all-cause and cardiovascular mortality was assessed with multivariate Cox proportional hazards modeling. RESULTS: At baseline, the prevalence of the metabolic syndrome was 75.6% (95% CI 73.6-77.9). Results are based on 685 deaths (520 with the metabolic syndrome and 165 without it) in 10,890.2 person-years of observations. With respect to subjects without the metabolic syndrome, those with the metabolic syndrome had a similar hazard ratio (HR) of cardiovascular mortality after adjustment for age, sex, smoking, total cholesterol level, and coronary heart disease. In contrast, relative to subjects with diabetes only, the HR of subjects with only one component of the syndrome was 2.92 (1.16-7.33), independent of other risk factors. CONCLUSIONS: We found that 1) the prevalence of the metabolic syndrome in a population-based cohort of type 2 diabetes is high (75.6%); 2) the metabolic syndrome is not a predictor of 11-year all-cause and cardiovascular mortality; and 3) more than twofold higher cardiovascular risk, independent of conventional risk factors, is evident in diabetic subjects with only one component of the syndrome compared with those with diabetes only. Categorizing type 2 diabetic subjects as having or not having the metabolic syndrome does not provide further prediction compared with the knowledge of its single components.<|endoftext|>
<|endoftext|>10: Title: Metabolic syndrome in subjects with type-2 diabetes mellitus.
Abstract: BACKGROUND: Each component of metabolic syndrome (MS) conveys increased cardiovascular disease risk, but as a combination they become much more powerful. Vigorous early management of the syndrome may have a significant impact on the prevention of both diabetes and cardiovascular disease. AIM: This study aims to determine the frequency of occurrence of MS and its relation to cardiovascular events among patients with type-2 diabetic mellitus. METHODS: The study group consisted of 218 type-2 diabetic patients. These were screened for hypertension, hyperlipidemia, obesity, microalbuminuria, and cardiovascular events. RESULTS: There were 128 (58.7%) males and 90 (41.3%) females. The mean age was 53.4 +/- 6.3 years and a mean body mass index (BMI) of 25.5 +/- 5.4 (males-23.4 +/- 4.2; females-26.2 +/- 5.7). MS was present in 55 (25.2%) of the study population. Systemic hypertension was the most common component of MS seen in 84 (38.5%) patients. The mean serum total cholesterol was 168.6 +/- 25.8 mg% (men 153 +/- 23; women 169 +/- 19; p > 0.05). Eight female and 12 male patients had serum total cholesterol > or = 200 mg%. Dyslipidemia occurs more commonly in males than females. Obesity was more common in female patients than in males. Out of 128 male type-2 patients with diabetes seen, 111 (86.7%) were without microalbuminuria. The corresponding figure among the females was 90% (81 out of 90 patients). CONCLUSIONS: The study demonstrated that MS was present in 25.2% of the study population. The syndrome and its different components were positively associated with a higher risk of stroke, peripheral vascular disease, and occurrence of microalbuminuria, p < 0.001. Ischemic heart disease occurs rarely in the population. A long-term, targeted, intensive intervention involving multiple cardiovascular risk factors is recommended to reduce the risk of both cardiovascular and microvascular events among patients with type-2 diabetic mellitus.<|endoftext|>
<|endoftext|>11: Title: Physical activity, metabolic factors, and the incidence of coronary heart disease and type 2 diabetes.
Abstract: OBJECTIVE: To examine the role of nonfasting serum insulin level and components of the insulin resistance syndrome in the relationship between physical activity and the incidence of coronary heart disease and type 2 diabetes. METHODS: Prospective study of 5159 men aged 40 to 59 years with no history of coronary heart disease, type 2 diabetes, or stroke drawn from general practices in 18 British towns. During an average follow-up period of 16.8 years, there were 616 cases of major coronary heart disease events (fatal and nonfatal) and 196 incident cases of type 2 diabetes. RESULTS: After adjustment for potential confounders (lifestyle characteristics and preexisting disease), physical activity was inversely related to coronary heart disease rates, with the lowest rates in the men undertaking moderate physical activity and with no further benefit thereafter. For type 2 diabetes, risk decreased progressively with increasing levels of physical activity. Physical activity was associated with serum insulin level and with factors associated with insulin, ie, heart rate, hyperuricemia, diastolic blood pressure, and high-density lipoprotein cholesterol level, and with gamma-glutamyltransferase level, a possible marker of hepatic insulin resistance. Adjustment for insulin and associated factors made little difference to the relationship between physical activity and risk of coronary heart disease. By contrast, these factors together with gamma-glutamyltransferase level appear to explain a large proportion of the reduction in risk of type 2 diabetes associated with physical activity. CONCLUSIONS: The relationship between physical activity and type 2 diabetes appears to be mediated by serum true insulin level and components of the insulin resistance syndrome. However, these factors do not appear to explain the inverse relationship between physical activity and coronary heart disease.<|endoftext|>
|
Diabetes Mellitus Type 2
|
pubmed
|
train
|
Classify the node ' A benchmark for classifier learning. : Although many algorithms for learning from examples have been developed and many comparisons have been reported, there is no generally accepted benchmark for classifier learning. The existence of a standard benchmark would greatly assist such comparisons. Sixteen dimensions are proposed to describe classification tasks. Based on these, thirteen real-world and synthetic datasets are chosen by a set covering method from the UCI Repository of machine learning databases to form such a benchmark.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Constructing conjunctive tests for decision trees. : This paper discusses an approach of constructing new attributes based on decision trees and production rules. It can improve the concepts learned in the form of decision trees by simplifying them and improving their predictive accuracy. In addition, this approach can distinguish relevant primitive attributes from irrelevant primitive attributes.<|endoftext|>
<|endoftext|>1: "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping," : The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping.<|endoftext|>
<|endoftext|>2: Proben1: A set of neural network benchmark problems and benchmarking rules. : Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets represent realistic problems which could be called diagnosis tasks and all but one consist of real world data. The datasets are all presented in the same simple format, using an attribute representation that can directly be used for neural network training. Along with the datasets, Proben1 defines a set of rules for how to conduct and how to document neural network benchmarking. The purpose of the problem and rule collection is to give researchers easy access to data for the evaluation of their algorithms and networks and to make direct comparison of the published results feasible. This report describes the datasets and the benchmarking rules. It also gives some basic performance measures indicating the difficulty of the various problems. These measures can be used as baselines for comparison.<|endoftext|>
<|endoftext|>3: Instance-based learning algorithms. : <|endoftext|>
|
Rule Learning
|
cora
|
train
|
Classify the node 'Face Detection Using Mixtures of Linear Subspaces We present two methods using mixtures of linear subspaces for face detection in gray level images. One method uses a mixture of factor analyzers to concurrently perform clustering and, within each cluster, perform local dimensionality reduction. The parameters of the mixture model are estimated using an EM algorithm. A face is detected if the probability of an input sample is above a predefined threshold. The other mixture of subspaces method uses Kohonen’s self-organizing map for clustering and Fisher Linear Discriminant to find the optimal projection for pattern classification, and a Gaussian distribution to model the class-conditional density function of the projected samples for each class. The parameters of the class-conditional density functions are maximum likelihood estimates and the decision rule is also based on maximum likelihood. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions are used as the training set to capture the variations of human faces. Our methods have been tested on three sets of 225 images which contain 871 faces. Experimental results on the first two datasets show that our methods perform as well as the best methods in the literature, yet have fewer false detects. 1' into one of the following categories:
Agents; ML (Machine Learning); IR (Information Retrieval); DB (Databases); HCI (Human-Computer Interaction); AI (Artificial Intelligence).
Refer to neighbour nodes:
<|endoftext|>0: Rotation Invariant Neural Network-Based Face Detection In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; the first is a "router" network which processes each input window to determine its orientation and then uses this information to prepare the window for one or more "detector" networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces which are rotated out of the image plane, such as profiles and semi-profiles. This work was partially supported by grants from Hewlett-Packard Corporation, Siemens Corporate Research, Inc., the Department of the Army, Army Research Office under grant number DAAH04-94-G-0006, and by the Office of Naval Research under grant number...<|endoftext|>
|
ML (Machine Learning)
|
citeseer
|
train
|
Classify the node 'Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment: The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurrence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals. : A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of "social states" individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly.<|endoftext|>
<|endoftext|>1: Job Shop Scheduling with Genetic Algorithms, : In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition" is presented. The main advantage of this single chromosome representation is in analogy to the permutation scheme of the traveling salesman problem (TSP) that it cannot produce illegal sets of operation sequences (infeasible symbolic solutions). As a consequence of the representation scheme a new crossover operator preserving the initial scheme structure of permutations with repetition will be sketched. Its behavior is similar to the well known Order-Crossover for simple permutation schemes. Actually the GOX operator for permutations with repetition arises from a Generalisation of OX. Computational experiments show, that GOX passes the information from a couple of parent solutions efficiently to offspring solutions. Together, the new representation and GOX support the cooperative aspect of the genetic search for scheduling problems strongly.<|endoftext|>
|
Genetic Algorithms
|
cora
|
train
|
Classify the node ' The GP-Music System: Interactive Genetic Programming for Music Composition, : Technical Report CSRP-98-13 Abstract In this paper we present the GP-Music System, an interactive system which allows users to evolve short musical sequences using interactive genetic programming, and its extensions aimed at making the system fully automated. The basic GP-system works by using a genetic programming algorithm, a small set of functions for creating musical sequences, and a user interface which allows the user to rate individual sequences. With this user interactive technique it was possible to generate pleasant tunes over runs of 20 individuals over 10 generations. As the user is the bottleneck in interactive systems, the system takes rating data from a users run and uses it to train a neural network based automatic rater, or auto rater, which can replace the user in bigger runs. Using this auto rater we were able to make runs of up to 50 generations with 500 individuals per generation. The best of run pieces generated by the auto raters were pleasant but were not, in general, as nice as those generated in user interactive runs.' into one of the following categories:
Rule Learning; Neural Networks; Case Based; Genetic Algorithms; Theory; Reinforcement Learning; Probabilistic Methods.
Refer to neighbour nodes:
<|endoftext|>0: B.E., "Automated Fitness Raters for the GP-Music System," : <|endoftext|>
<|endoftext|>1: Induction and recapitulation of deep musical structure. : We describe recent extensions to our framework for the automatic generation of music-making programs. We have previously used genetic programming techniques to produce music-making programs that satisfy user-provided critical criteria. In this paper we describe new work on the use of connectionist techniques to automatically induce musical structure from a corpus. We show how the resulting neural networks can be used as critics that drive our genetic programming system. We argue that this framework can potentially support the induction and recapitulation of deep structural features of music. We present some initial results produced using neural and hybrid symbolic/neural critics, and we discuss directions for future work.<|endoftext|>
|
Genetic Algorithms
|
cora
|
train
|
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 6