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Comparison of Different Hypotheses Regarding the Spread of Alzheimer's Disease Using Markov Random Fields and Multimodal Imaging

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JOURNAL OF ALZHEIMERS DISEASE 2018年第3期65卷 731-746页

作者： Dyrba, Martin Grothe, Michel J. Mohammadi, Abdolreza Binder, Harald Kirste, Thomas Teipel, Stefan J. German Ctr Neurodegenerat Dis DZNE Site Rostock Greifswald Rostock Germany Tilburg Univ Dept Methodol & Stat Tilburg Netherlands Johannes Gutenberg Univ Mainz Univ Med Ctr Inst Med Biostat Epidemiol & Informat Mainz Germany Univ Rostock Mobile Multimedia Informat Syst Grp MMIS Rostock Germany Univ Med Ctr Rostock Clin Psychosomat & Psychotherapeut Med Rostock Germany

Alzheimer's disease (AD) is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. Markov random fields are a probabilistic graphical modeling approach that represent the interaction between individual random variables by an undirected graph. We propose the novel application of this approach to study the interregional associations and dependencies between multimodal imaging markers of AD pathology and to compare different hypotheses regarding the spread of the disease. We retrieved multimodal imaging data from 577 subjects enrolled in the Alzheimer's Disease Neuroimaging Initiative. Mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for the six principle nodes of the default mode network-a functional network of brain regions that appears to be preferentially targeted by AD. Multimodal Markov random field models were developed for three different hypotheses regarding the spread of the disease: the "intraregional evolution model", the "trans-neuronal spread" hypothesis, and the "wear-and-tear" hypothesis. The model likelihood to reflect the given data was evaluated using tenfold cross-validation with 1,000 repetitions. The most likely graph structure contained the posterior cingulate cortex as main hub region with edges to various other regions, in accordance with the "wear-and-tear" hypothesis of disease vulnerability. probabilistic graphical models facilitate the analysis of interactions between several variables in a network model and therefore afford great potential to complement traditional multiple regression analyses in multimodal neuroimaging research.

关键词： Alzheimer's disease AV45-PET FDG-PET Markov random field mild cognitive impairment multimodal imaging probabilistic graphical model

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Overlapping community detection and temporal analysis on Q&A sites

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WEB INTELLIGENCE 2017年第2期15卷 115-142页

作者： Meng, Zide Gandon, Fabien Zucker, Catherine Faron INRIA Sophia Antipolis Mediterranee Sophia Antipolis France Univ Nice Sophia Antipolis CNRS I3S Nice France

In many social networks, people interact based on their relationship network. Community detection algorithms are then useful to reveal the sub-structures of a network. Identifying these users' communities can help us assist their life-cycle. However, in certain kinds of online communities such as question-and-answer (Q&A) sites or forums, people interact based on common topics of interest, rather than an explicit relationship network. Therefore, many traditional community detection techniques do not apply directly. Discovering those topics of interest is critical to identify users' communities. Besides, users' activities on certain topics of interest are evolving with time and it is therefore very important to extract their temporal dynamics. In this paper, we first propose Topic Trees Distributions (TTD), an efficient approach for extracting topics from Q&A sites in order to detect overlapping communities. We then extend TTD to propose Temporal Topic Expertise Activity (TTEA), a graphical probabilistic model to extract both topics-based expertise and temporal information. We evaluated and compared our models with state-of-the-art approaches on a dataset extracted from the popular Q&A site StackOverflow.

关键词： Overlapping community detection topic model temporal analysis probabilistic graphical model community question answering

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Preface: Special Issue on Advanced Methodologies for Bayesian Networks

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NEW GENERATION COMPUTING 2017年第1期35卷 1-4页

作者： Ueno, Maomi Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

The second AMBN was held in Yokohama, Japan on November 16-18, 2015, co-sponsored by the Japanese Society Artificial Intelligence (JSAI) and the National Institute of Advanced Industrial Science and Technology (AIST).The AMBN workshop had over 100 participants and many lively and interesting discussions over the course of its duration. This special issue of New Generation Computing (NGC) has come out from these extensive activities of the researchers in BNs *** the workshop, six excellent papers in the conference were recommended to submit to this special issue with the requirement at least 30 extension from the Post Proceeding of AMBN2015 (LNAI, Springer) version. Each paper was reviewed by one meta-reviewer and two reviewers. As a result, all the six papers were accepted for publication in this journal.

关键词： Artificial intelligence Machine learning probabilistic graphical model Bayesian networks

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Multi-Matrices Factorization with Application to Missing Sensor Data Imputation

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SENSORS 2013年第11期13卷 15172-15186页

作者： Huang, Xiao-Yu Li, Wubin Chen, Kang Xiang, Xian-Hong Pan, Rong Li, Lei Cai, Wen-Xue Sun Yat Sen Univ Software Inst Guangzhou 510275 Guangdong Peoples R China S China Univ Technol Sch Econ & Commerce Guangzhou 510006 Guangdong Peoples R China Umea Univ Dept Comp Sci SE-90187 Umea Sweden Acad Guangdong Telecom Co Ltd Guangzhou 510630 Guangdong Peoples R China Sun Yat Sen Univ Affiliated Hosp 1 Dept Intervent Radiol Guangzhou 510080 Guangdong Peoples R China

We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the time, T-1, T-2, . . . , T-t, where the entry, R-i,R-j, is the aggregate value of the data collected in the ith area at T-j. We propose to approximate R by seeking a family of d-by-n probabilistic spatial feature matrices, U-(1), U-(2), . . . , U-(t), and a probabilistic temporal feature matrix, V epsilon R-dxt, where R-j approximate to U-(j)(T) T-j. We also present a solution algorithm to the proposed model. We evaluate MMF with synthetic data and a real-world sensor dataset extensively. Experimental results demonstrate that our approach outperforms the state-of-the-art comparison algorithms.

关键词： matrix factorization sensor data probabilistic graphical model missing estimation

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Reconstruction of digit images from human brain fMRI activity through connectivity informed Bayesian networks

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JOURNAL OF NEUROSCIENCE METHODS 2016年第0期257卷 159-167页

作者： Yargholi, Elahe' Hossein-Zadeh, Gholam-Ali Univ Tehran Univ Coll Engn Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran Inst Res Fundamental Sci IPM Sch Cognit Sci Tehran Iran

Background: Newly emerged developments in decoding of stimulus images from fMRI measurements have shown promising results. Decoding-classification has been the main concern of decoding studies, whereas the matter of reconstruction (decoding) of stimulus images from fMRI data, especially natural images, lacks adequate examination and it requires plenty of efforts to improve. New method: The present study employs Bayesian networks for decoding-reconstruction which is a novel application of this tool. Moreover, as a novel approach, we exploit the brain connectivity information in decoding-reconstruction procedure through Bayesian networks. Results: The proposed method was applied to reconstruct 100 images of digits 6 and 9 from the fMRI measurements obtained when showing some handwritten images of 6 and 9 to the subject. The information of only 10 brain voxels were exploited and an average (standard deviation) city-block distance error of 0.1071(0.0134) was obtained for all stimuli's reconstruction. In comparison with current common methods: The results reveal that Bayesian networks are successful in decoding-reconstruction of handwritten digits and inclusion of brain connectivity information makes them perform even more efficiently and improves decoding-reconstruction as well (reducing average error by almost 5%). Conclusion: In the task of decoding-reconstruction, the models including brain connectivity appear significantly superior to other existing models. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Brain decoding Connectivity informed decoding Object representation in the brain Visual cortex probabilistic graphical model Bayesian network

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Hardware Accelerator for probabilistic Inference in 65-nm CMOS

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2016年第3期24卷 837-845页

作者： Khan, Osama U. Wentzloff, David D. Univ Calif Berkeley Dept Elect Engn Berkeley CA 94720 USA Univ Michigan Dept Elect Engn Ann Arbor MI 48109 USA

A hardware accelerator is presented to compute the probabilistic inference for a Bayesian network (BN) in distributed sensing applications. For energy efficiency, the accelerator is operated at a near-threshold voltage of 0.5 V, while achieving a maximum clock frequency of 33 MHz. Clique-tree message passing algorithm is leveraged to compute the probabilistic inference. The theoretical maximum size of a factor that the proposed hardware accelerator can handle is 2((8x20)=160) entries, which is sufficient for handling massive BNs, such as PATHFINDER, MUNIN, and so on (>1000 nodes). A Logical Alarm Reduction Mechanism (ALARM) BN is used to benchmark the performance of the accelerator. The accelerator consumes 76 nJ to execute the ALARM network using a clique-tree message-passing algorithm, while the same algorithm executed on an ultralow-power microcontroller consumes 20 mJ.

关键词： Bayesian network (BN) clique-tree embedded machine learning hardware accelerator intelligent sensor node message passing probabilistic graphical model probabilistic inference

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modeling multivariate extreme value distributions via Markov trees

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SCANDINAVIAN JOURNAL OF STATISTICS 2024年第2期51卷 760-800页

作者： Hu, Shuang Peng, Zuoxiang Segers, Johan Chongqing Univ Posts & Telecommun Sch Sci Key Lab Intelligent Anal & Decis Complex Syst Chongqing 400065 Peoples R China Catholic Univ Louvain LIDAM ISBA Louvain La Neuve Belgium Southwest Univ Sch Math & Stat Chongqing Peoples R China

Multivariate extreme value distributions are a common choice for modeling multivariate extremes. In high dimensions, however, the construction of flexible and parsimonious models is challenging. We propose to combine bivariate max-stable distributions into a Markov random field with respect to a tree. Although in general not max-stable itself, this Markov tree is attracted by a multivariate max-stable distribution. The latter serves as a tree-based approximation to an unknown max-stable distribution with the given bivariate distributions as margins. Given data, we learn an appropriate tree structure by Prim's algorithm with estimated pairwise upper tail dependence coefficients as edge weights. The distributions of pairs of connected variables can be fitted in various ways. The resulting tree-structured max-stable distribution allows for inference on rare event probabilities, as illustrated on river discharge data from the upper Danube basin.

关键词： Kendall's tau Markov tree multivariate extreme value distribution Prim's algorithm probabilistic graphical model rare event tail dependence

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Bayesian networks and knowledge structures in cognitive assessment: Remarks on basic comparable aspects

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JOURNAL OF MATHEMATICAL PSYCHOLOGY 2024年 123卷

作者： Burigana, Luigi Univ Padua Padua Italy

Two theories of current interest and of mathematical and computational substance concerning knowledge assessment in education are discussed. These are the theory of knowledge structures and the theory of Bayesian networks as specifically related to educational assessment. In four separate sections, the two theories are compared by considering the sets of variables involved in their models, the set-theoretical and relational constructs defined on those variables, the probabilistic assumptions and properties, and the problems addressed by the theories in constructing their models. For the comparison, a common-base system of symbols and terms is adopted, which overcomes the peculiarities of expression in the corresponding streams of literature. This system gives us a better recognition of the similarities and differences between the two paradigms, and a precise appreciation of their arguments and abilities.

关键词： Knowledge assessment Knowledge structure Bayesian network probabilistic graphical model probabilistic inference

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Look Over Here: Attention-Directing Composition of Manga Elements

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ACM TRANSACTIONS ON GRAPHICS 2014年第4期33卷 p1-11页

作者： Cao, Ying Lau, Rynson W. H. Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Picture subjects and text balloons are basic elements in comics, working together to propel the story forward. Japanese comics artists often leverage a carefully designed composition of subjects and balloons (generally referred to as panel elements) to provide a continuous and fluid reading experience. However, such a composition is hard to produce for people without the required experience and knowledge. In this paper, we propose an approach for novices to synthesize a composition of panel elements that can effectively guide the reader's attention to convey the story. Our primary contribution is a probabilistic graphical model that describes the relationships among the artist's guiding path, the panel elements, and the viewer attention, which can be effectively learned from a small set of existing manga pages. We show that the proposed approach can measurably improve the readability, visual appeal, and communication of the story of the resulting pages, as compared to an existing method. We also demonstrate that the proposed approach enables novice users to create higher-quality compositions with less time, compared with commercially available programs.

关键词： manga composition probabilistic graphical model

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On quasi Polya thinning operator

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BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS 2023年第4期37卷 643-666页

作者： Peyhardi, Jean Univ Montpellier CNRS IMAG Montpellier France

Thinning operation is a stochastic operation that shrinks a random count variable into a smaller one. This kind of random operation has been intensively studied during the seventies to characterize some count distributions, such as the Poisson distribution using the binomial thinning operator (also named binomial damage model). Then, the closure under thinning operator has been studied in order to define some classes of integer valued autoregressive (INAR) models for count time series. These two properties will be studied in this paper for the new class of quasi Polya thinning operators. Classical results concerning the binomial thinning operator are recovered as a special case. The quasi Polya thinning operator is related to the new class of quasi Polya splitting distributions, defined for multivariate count data. The probabilistic graphical model (PGM) of these multivariate distributions is characterized. Finally a general class of integer valued autoregressive models is introduced, including the usual cases of Poisson marginal or generalized Poisson as a special cases and the generalized negative binomial as a new case.

关键词： Quasi Polya distribution binomial thinning operator probabilistic graphical model

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