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Generating realistic assemblages with a joint species distribution model

METHODS IN ECOLOGY AND EVOLUTION 2015年第4期6卷 465-473页

作者： Harris, David J. Ctr Populat Biol Davis CA 95616 USA

Species distribution models (SDMs) represent important analytical and predictive tools for ecologists. Until now, these models have either assumed (i) that species' occurrence probabilities are uncorrelated or (ii) that species respond linearly to preselected environmental variables. These two assumptions currently prevent ecologists from modelling assemblages with realistic co-occurrence and species richness properties. This paper introduces a stochastic feedforward neural network, called mistnet', which makes neither assumption. Thus, unlike most SDMs, mistnet can account for non-independent co-occurrence patterns driven by unobserved environmental heterogeneity. And unlike several recently proposed joint SDMs, the model can also learn nonlinear functions relating species' occurrence probabilities to environmental predictors. Mistnet makes more accurate predictions about the North American bird communities found along Breeding Bird Survey transects than several alternative methods tested. In particular, typical assemblages held out of sample for validation were each tens of thousands of times more likely under the mistnet model than under independent combinations of single-species predictions. Apart from improved accuracy, mistnet shows two other important benefits for ecological research and management. First: by analysing co-occurrence data, mistnet can identify unmeasured - and perhaps unanticipated - environmental variables that drive species turnover. For example, the model identified a strong grassland/forest gradient, even though only temperature and precipitation were given as model inputs. Second: mistnet is able to take advantage of outside information to guide its predictions towards more realistic assemblages. For example, mistnet automatically adjusts its expectations to include more forest-associated species in response to a stray observation of a forest-dwelling warbler.

关键词： birds breeding bird survey neural network probabilistic graphical model species assemblages species distribution model

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Bayesian accounts of covert selective attention: A tutorial review

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ATTENTION PERCEPTION & PSYCHOPHYSICS 2015年第4期77卷 1013-1032页

作者： Vincent, Benjamin T. Univ Dundee Sch Psychol Dundee Scotland

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

关键词： Covert attention Signal detection theory Bayesian Optimal observer probabilistic graphical model

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A Predictive Chronological model of Multiple Clinical Observations 3

A Predictive Chronological Model of Multiple Clinical Observ...

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International Conference on Healthcare Informatics (ICHI)

作者： Goodwin, Travis Harabagiu, Sanda M. Univ Texas Dallas Human Language Technol Res Inst Dallas TX 75080 USA

ISBN: (纸本)9781467395489

The expanding clinical information provided by the advent of electronic medical records offers an exciting opportunity to substantially improve the quality of health care. By examining the clinical observations (such as diagnoses, risk factors, and medications) mentioned in longitudinal EMRs, we can use patients' medical chronologies to automatically predict the progression of their pathologies. In this paper, we present a novel probabilistic model which jointly learns how to (1) group patients based on the similarities between their clinical observations as well as how to (2) predict the way a new patient's clinical observations might evolve in the future. We show that our model can be used to not only track how a patient's clinical findings might change over time, but to also identify which patients are due for preventative visits. In addition, our model has the potential to improve the quality of over-all patient care in practice by predicting the most likely set of clinical observations at an arbitrary point in the future.

关键词： Adaptation models Diseases Heart Hidden Markov models Medical diagnostic imaging Predictive models longitudinal medical record predictive model probabilistic graphical model temporal representation Prediction models Hidden Markov models probabilistic model maladjustment Heart Quality of Health Care Medical Adaptation models diagnostic imaging Electronic Health Records

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Probablistic-based Framework For Medical CT Images Segmentation 7

Probablistic-based Framework For Medical CT Images Segmentat...

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IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Mohamed, Alaa Salah Ei-Din Salem, Mohammed A. M. Hegazy, Doaa Shedeed, Howida A. Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo Egypt

ISBN: (纸本)9781509019496

Liver segmentation is a difficult process due to wide variability of livers shapes and sizes between patients and the intensity similarity between the liver and other organs. Liver segmentation from abdominal Computed Tomography (CT) images is very useful in many diagnostic and surgical processes. It is the essential step in many clinical applications. Medical decisions are rarely taken without the use of imaging technology such as CT, Magnetic Resonance Imaging (MRI), or Ultrasound Imaging (US). In this paper, an automated probabilistic-based framework for liver segmentation from abdominal CT images is presented. The framework consists of four stages;thresholding stage, superpixels construction stage, Bayesian network construction stage and region merging stage. We train and validate our model using 20 clinical volumes. We use the MICCAI dataset (Medical Image Computing and Computer Assisted Intervention for Liver Segmentation). MICCAI dataset is used in more than 90 researches.

关键词： Liver segmentation Computed Tomography Super pixels probabilistic graphical model Bayesian Network Region Merging

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modeling Truth Existence in Truth Discovery 15

Modeling Truth Existence in Truth Discovery

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21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhi, Shi Zhao, Bo Tong, Wenzhu Gao, Jing Yu, Dian Ji, Heng Han, Jiawei Univ Illinois Urbana IL 61801 USA LinkedIn Mountain View CA USA SUNY Buffalo Buffalo NY USA Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9781450336642

When integrating information from multiple sources, it is common to encounter conflicting answers to the same question. Truth discovery is to infer the most accurate and complete integrated answers from conflicting sources. In some cases, there exist questions for which the true answers are excluded from the candidate answers provided by all sources. Without any prior knowledge, these questions, named no-truth questions, are difficult to be distinguished from the questions that have true answers, named has truth questions. In particular, these no-truth questions degrade the precision of the answer integration system. We address such a challenge by introducing source quality, which is made up of three fine-grained measures: silent rate, false spoken rate and true spoken rate. By incorporating these three measures, we propose a probabilistic graphical model, which simultaneously infers truth as well as source quality without any a priori training involving ground truth answers. Moreover, since inferring this graphical model requires parameter tuning of the prior of truth, we propose an initialization scheme based upon a quantity named truth existence score, which synthesizes two indicators, namely, participation rate and consistency rate. Compared with existing methods, our method can effectively filter out no-truth questions, which results in more accurate source quality estimation. Consequently, our method provides more accurate and complete answers to both has-truth and no-truth questions. Experiments on three real-world datasets illustrate the notable advantage of our method over existing state-of-the-art truth discovery methods.

关键词： information integration system knowledge base truth existence knowledge graph truth finding probabilistic graphical model truth discovery source quality information extraction

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Latent Forests to model Genetical Data for the Purpose of Multilocus Genome-Wide Association Studies. Which Clustering Should Be Chosen? 8th

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8th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)

作者： Phan, Duc-Thanh Leray, Philippe Sinoquet, Christine Univ Nantes POLYTECH UMR CNRS 6241 LINA F-44306 Nantes France Univ Nantes Fac Sci UMR CNRS 6241 LINA 2 Rue Houssiniere F-44322 Nantes France

ISBN: (纸本)9783319277073;9783319277066

The aim of genetic association studies, and in particular genome-wide association studies (GWASs), is to unravel the genetics of complex diseases. In this domain, machine learning offers an attractive alternative to classical statistical approaches. The seminal works of Mourad et al. [1] have led to the proposal of a novel class of probabilistic graphical models, the forest of latent trees (FLTM). The design of this model was motivated by the necessity to model genetical data at the genome scale, prior to a multilocus GWAS. A multilocus GWAS fully exploits information about the complex dependences existing within genetical data, to help detect the loci associated with the studied pathology. The FLTM framework also allows data dimension reduction. The FLTM model is a hierarchical Bayesian network with latent variables. Central to the FLTM construction is the recursive clustering of variables, in a bottom up subsuming process. This article focuses on the analysis of the impact of the choice of the clustering method used in the FLTM learning algorithm, in a GWAS context. We rely on a real GWAS data set describing 41400 variables for each of 3004 controls and 2005 cases affected by Crohn's disease, and compare the impact of three clustering methods. We compare statistics about data dimension reduction as well as trends concerning the ability to split or group putative causal SNPs in agreement with the underlying biological reality. To assess the risk of missing significant association results due to subsumption, we also compare the clustering methods through the corresponding FLTMbased GWASs. In the GWAS context and in this framework, the choice of the clustering method does not influence the satisfying performance of the GWAS.

关键词： Genome-wide association study Multilocus association study Linkage disequilibrium probabilistic graphical model Latent variable Data dimension reduction Bayesian network

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A Novel Approach For Global Abnormal Event Detection in Multi-camera Surveillance System

A Novel Approach For Global Abnormal Event Detection in Mult...

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IEEE International Conference on Information and Automation 2015

作者： Guo, Huiwen Wu, Xinyu Wang, Huan Chen, Lixin Ou, Yongsheng Feng, Wei Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Beijing 100864 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synerg Syst Beijing 100864 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467391047

In this paper, a novel global abnormal event detection algorithm is proposed for multiple disjoint synchronous camera network. We treat detecting unusual global events as discovering context-incoherent patterns through learning temporal dependencies between distributed local activities observed within and across camera views. Trajectories are firstly extracted using mean-shift approach in each camera view. As local activities are learned by applying clustering algorithm for trajectories, we model global event patterns using a probabilistic graphical model with different nodes representing entry/exit regions from different views and the directed links between nodes encoding their temporal dependencies. A novel two-stage structure learning algorithm is formulated to learn globally optimized temporal dependencies. Modified Dynamic Time Warping is used to learning the links in unobservable regions in camera network. Then, Monte Carlo(MC) algorithm is used to refine the structure and produce a final dependency structure. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a research institute.

关键词： Global Abnormal Event Detection Multi-camera Temporal Dependency probabilistic graphical model Dynamic Time Warping

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Continuous visual speech recognition for audio speech enhancement 40

Continuous visual speech recognition for audio speech enhanc...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Benhaim, Eric Sahbi, Hichem Vittey, Guillaume Telecom ParisTech CNRS-LTCI 46 rue Barrault Paris France Parrot S.A. 174 quai de Jemmapes Paris France

ISBN: (纸本)9781467369978

We introduce in this paper a novel non-blind speech enhancement procedure based on visual speech recognition (VSR). The latter is based on a generative process that analyzes sequences of talking faces and classifies them into visual speech units known as visemes. We use an effective graphical model able to segment and label a given sequence of talking faces into a sequence of visemes. Our model captures unary potential as well as pairwise interaction;the former models visual appearance of speech units while the latter models their interactions using boundary and visual language model activations. Experiments conducted on a standard challenging dataset, show that when feeding the results of VSR to the speech enhancement procedure, it clearly outperforms baseline blind methods as well as related work. © 2015 IEEE.

关键词： belief propagation model-based speech enhancement probabilistic graphical model Visual speech recognition

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Multi-task sparse structure learning with Gaussian copula models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf André R. Gonçalves Fernando J. Von Zuben Arindam Banerjee Google MPI for Intelligent Systems School of Electrical and Computer Engineering University of Campinas São Paulo Brazil Computer Science Department University of Minnesota Twin Cities Minneapolis

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of tasks relationship. In particular, we consider a joint estimation problem of the tasks relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship revealed by structure learning is founded on recent advances in Gaussian graphical models endowed with sparse estimators of the precision (inverse covariance) matrix. An extension to include exible Gaussian copula models that relaxes the Gaussian marginal assumption is also proposed. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark data sets for regression and classification. We also consider the problem of combining Earth System model (ESM) outputs for better projections of future climate, with focus on projections of temperature by combining ESMs in South and North America, and show that the proposed model outperforms several existing methods for the problem.

关键词： Gaussian copula multi-task learning probabilistic graphical model sparse modeling structure learning

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Continuous visual speech recognition for audio speech enhancement

Continuous visual speech recognition for audio speech enhanc...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： E. Benhaim H. Sahbi G. Vittey LTCI Telecom ParisTech Paris France

ISBN: (纸本)9781467369985

关键词： Visual speech recognition belief propagation model-based speech enhancement probabilistic graphical model Speech enhancement Belief propagation Speech recognition Vibratory stress relieving probabilistic model Visualization Speaking Audio

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