103 research outputs found
Macrostate Data Clustering
We develop an effective nonhierarchical data clustering method using an
analogy to the dynamic coarse graining of a stochastic system. Analyzing the
eigensystem of an interitem transition matrix identifies fuzzy clusters
corresponding to the metastable macroscopic states (macrostates) of a diffusive
system. A "minimum uncertainty criterion" determines the linear transformation
from eigenvectors to cluster-defining window functions. Eigenspectrum gap and
cluster certainty conditions identify the proper number of clusters. The
physically motivated fuzzy representation and associated uncertainty analysis
distinguishes macrostate clustering from spectral partitioning methods.
Macrostate data clustering solves a variety of test cases that challenge other
methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral
graph theory, dynamic eigenvectors, machine learning, macrostates,
classificatio
Generative modeling of the enteric nervous system employing point pattern analysis and graph construction
We describe a generative network model of the architecture of the enteric
nervous system (ENS) in the colon employing data from images of human and mouse
tissue samples obtained through confocal microscopy. Our models combine spatial
point pattern analysis with graph generation to characterize the spatial and
topological properties of the ganglia (clusters of neurons and glial cells),
the inter-ganglionic connections, and the neuronal organization within the
ganglia. We employ a hybrid hardcore-Strauss process for spatial patterns and a
planar random graph generation for constructing the spatially embedded network.
We show that our generative model may be helpful in both basic and
translational studies, and it is sufficiently expressive to model the ENS
architecture of individuals who vary in age and health status. Increased
understanding of the ENS connectome will enable the use of neuromodulation
strategies in treatment and clarify anatomic diagnostic criteria for people
with bowel motility disorders.Comment: 17 pages, 5 figure
Advanced Multilevel Node Separator Algorithms
A node separator of a graph is a subset S of the nodes such that removing S
and its incident edges divides the graph into two disconnected components of
about equal size. In this work, we introduce novel algorithms to find small
node separators in large graphs. With focus on solution quality, we introduce
novel flow-based local search algorithms which are integrated in a multilevel
framework. In addition, we transfer techniques successfully used in the graph
partitioning field. This includes the usage of edge ratings tailored to our
problem to guide the graph coarsening algorithm as well as highly localized
local search and iterated multilevel cycles to improve solution quality even
further. Experiments indicate that flow-based local search algorithms on its
own in a multilevel framework are already highly competitive in terms of
separator quality. Adding additional local search algorithms further improves
solution quality. Our strongest configuration almost always outperforms
competing systems while on average computing 10% and 62% smaller separators
than Metis and Scotch, respectively
Mesh decomposition and communication procedures for finite element applications on the Connection Machine CM-5 system
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
iSAM2 : incremental smoothing and mapping using the Bayes tree
Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419.We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of
existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique
tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the
square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three
insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in
terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple
editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm
for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental
variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of
iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent
mapping algorithms in both quality and efficiency.M. Kaess, H. Johannsson and J. Leonard were partially supported
by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by
NSF, award number 0713162, “RI: Inference in Large-Scale
Graphical Models”. V. Ila has been partially supported by the
Spanish MICINN under the Programa Nacional de Movilidad
de Recursos Humanos de Investigación
Psychiatric services in primary care settings: a survey of general practitioners in Thailand
BACKGROUND: General Practitioners (GPs) in Thailand play an important role in treating psychiatric disorders since there is a shortage of psychiatrists in the country. Our aim was to examine GP's perception of psychiatric problems, drug treatment and service problems encountered in primary care settings. METHODS: We distributed 1,193 postal questionnaires inquiring about psychiatric practices and service problems to doctors in primary care settings throughout Thailand. RESULTS: Four hundred and thirty-four questionnaires (36.4%) were returned. Sixty-seven of the respondents (15.4%) who had taken further special training in various fields were excluded from the analysis, giving a total of 367 GPs in this study. Fifty-six per cent of respondents were males and they had worked for 4.6 years on average (median = 3 years). 65.6% (SD = 19.3) of the total patients examined had physical problems, 10.7% (SD = 7.9) had psychiatric problems and 23.9% (SD = 16.0) had both problems. The most common psychiatric diagnoses were anxiety disorders (37.5%), alcohol and drugs abuse (28.1%), and depressive disorders (29.2%). Commonly prescribed psychotropic drugs were anxiolytics and antidepressants. The psychotropic drugs most frequently prescribed were diazepam among anti-anxiety drugs, amitriptyline among antidepressant drugs, and haloperidol among antipsychotic drugs. CONCLUSION: Most drugs available through primary care were the same as what existed 3 decades ago. There should be adequate supply of new and appropriate psychotropic drugs in primary care. Case-finding instruments for common mental disorders might be helpful for GPs whose quality of practice was limited by large numbers of patients. However, the service delivery system should be modified in order to maintain successful care for a large number of psychiatric patients
Origin of Co-Expression Patterns in E.coli and S.cerevisiae Emerging from Reverse Engineering Algorithms
BACKGROUND: The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative gene-gene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of "physical" networks of interactions among genes or gene products. RESULTS: This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E. coli and S. cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor-binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, co-localization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E. coli. CONCLUSION: The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E. coli to S. cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies
Ensemble approach for generalized network dismantling
Finding a set of nodes in a network, whose removal fragments the network
below some target size at minimal cost is called network dismantling problem
and it belongs to the NP-hard computational class. In this paper, we explore
the (generalized) network dismantling problem by exploring the spectral
approximation with the variant of the power-iteration method. In particular, we
explore the network dismantling solution landscape by creating the ensemble of
possible solutions from different initial conditions and a different number of
iterations of the spectral approximation.Comment: 11 Pages, 4 Figures, 4 Table
Prevalence of Depression in a Large Urban South Indian Population — The Chennai Urban Rural Epidemiology Study (Cures – 70)
BACKGROUND: In India there are very few population based data on prevalence of depression. The aim of the study was to determine the prevalence of depression in an urban south Indian population. METHODS AND FINDINGS: Subjects were recruited from the Chennai Urban Rural Epidemiology Study (CURES), involving 26,001 subjects randomly recruited from 46 of the 155 corporation wards of Chennai (formerly Madras) city in South India. 25,455 subjects participated in this study (response rate 97.9%). Depression was assessed using a self-reported and previously validated instrument, the Patient Health Questionnaire (PHQ) - 12. Age adjustment was made according to the 2001 census of India. The overall prevalence of depression was 15.1% (age-adjusted, 15.9%) and was higher in females (females 16.3% vs. males 13.9%, p<0.0001). The odds ratio (OR) for depression in female subjects was 1.20 [Confidence Intervals (CI): 1.12-1.28, p<0.001] compared to male subjects. Depressed mood was the most common symptom (30.8%), followed by tiredness (30.0%) while more severe symptoms such as suicidal thoughts (12.4%) and speech and motor retardation (12.4%) were less common. There was an increasing trend in the prevalence of depression with age among both female (p<0.001) and male subjects (p<0.001). The prevalence of depression was higher in the low income group (19.3%) compared to the higher income group (5.9%, p<0.001). Prevalence of depression was also higher among divorced (26.5%) and widowed (20%) compared to currently married subjects (15.4%, p<0.001). CONCLUSIONS: This is the largest population-based study from India to report on prevalence of depression and shows that among urban south Indians, the prevalence of depression was 15.1%. Age, female gender and lower socio-economic status are some of the factors associated with depression in this population
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