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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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AN ADAPTIVE DECISION METHOD USING STRUCTURE FEATURE ANALYSIS ON DYNAMIC FAULT PROPAGATION model 12

AN ADAPTIVE DECISION METHOD USING STRUCTURE FEATURE ANALYSIS...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Dong, Chun-Ling Zhang, Qin Zhao, Yue Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Beijing 100083 Peoples R China Shandong Normal Univ Dept GIS Jinan 250014 Peoples R China

ISBN: (纸本)9781479902606

Effective online maintenance decisions for the troubleshooting of complex systems can avoid fault progressions and reduce potential losses. Inspired by the inhomogeneous topological nature of complex networks, in this study we intend to explore the pivotal node with high failure pervasion ability, so as to formulate an adaptive decision-making method. Dynamic Uncertain Causality Graph is introduced as the fault propagation model for evolving causalities. In order to evaluate the inherent topological structure feature of failure mode, the between centrality and non-symmetrical entropy are incorporated in the fault spreading risk measurement of nodes. Benefiting from solutions of time-varying structure decomposition and causality reduction on fault propagation model, the decision-making algorithm based on local causality structure achieves globally high efficiency and scalability. Verification experiments using generator faults of a nuclear power plant indicate the feasibility of this method in large-scale industrial applications.

关键词： probabilistic graphical model adaptive decision-making causality representation uncertain inference structure feature analysis causal influence assessment

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PySigma: Towards Enhanced Grand Unification for the Sigma Cognitive Architecture 14th

PySigma: Towards Enhanced Grand Unification for the Sigma Co...

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14th International Conference on Artificial General Intelligence (AGI)

作者： Zhou, Jincheng Ustun, Volkan Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA Univ Southern Calif Inst Creat Technol Los Angeles CA 90007 USA

ISBN: (纸本)9783030937577;9783030937584

The Sigma cognitive architecture is the beginning of an integrated computational model of intelligent behavior aimed at the grand goal of artificial general intelligence (AGI). However, whereas it has been proven to be capable of modeling a wide range of intelligent behaviors, the existing implementation of Sigma has suffered from several significant limitations. The most prominent one is the inadequate support for inference and learning on continuous variables. In this article, we propose solutions for this limitation that should together enhance Sigma's level of grand unification;that is, its ability to span both traditional cognitive capabilities and key non-cognitive capabilities central to general intelligence, bridging the gap between symbolic, probabilistic, and neural processing. The resulting design changes converge on a more capable version of the architecture called PySigma. We demonstrate such capabilities of PySigma in neural probabilistic processing via deep generative models, specifically variational autoencoders, as a concrete example.

关键词： Sigma Cognitive architecture probabilistic graphical model Message passing algorithm Approximate inference Deep generative model

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probabilistic DESIGN MIMING

PROBABILISTIC DESIGN MIMING

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ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference (DETC)

作者： Mukherjee, Arpan Zhang, Yunbo Rai, Rahul Univ Buffalo SUNY DART Lab Buffalo NY 14260 USA

ISBN: (纸本)9780791846407

Design miming is a process through which a person gesticulates (move hands) to describe a real-world physical object. The core idea of this paper is to present an efficient 3D shape synthesis system and interface that can identify and display the most relevant objects based on the movements of hands. In order to implement such a system three key things are needed. First, a moderately sized 3D shape repository from where the system can find good shape matches. Second, it requires a computational tool that can convert the gestures of a user to provide input to a display interface. Third, it requires the design of an interface where synthesized 3D shapeshnodels could be displayed. Large databases of 3D shapes exist in online shape repositories such as Google 3D warehouse and Turbosquid, which we reuse. We focus on the second and third components of the system. Specifically, we develop a probabilistic graphical model based 3D shape synthesis approach for structure recovery from a highly noisy 3D scan of a user's hand movements. Such a system should be real time and it imposes a computational method to have reduced time and memory complexity. We term this technique probabilistic Design Miming (PDM) and show the utility of this technique in multiple usage scenarios.

关键词： 3D Shape Retrieval Gesticulation probabilistic graphical model Bayesian Inference

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Distributed Frequency Offsets Estimation 19

Distributed Frequency Offsets Estimation

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19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Du, Jian Ma, Shaodan Yang, Guanghua Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Macau Dept Elect & Comp Engn Macau Peoples R China Jinan Univ Inst Phys Internet Zhuhai Campus Zhuhai Peoples R China

ISBN: (纸本)9781538635124

In this paper, we provide a distributed frequency offset estimation algorithm based on a variant of belief propagation (BP). Each agent in the network pre-compensates its carrier frequency individually so that there is no frequency offset from the desired carrier frequency between each pair of transceiver. The pre-compensated offset for each agent is computed in a distributed fashion in order to be adaptive to the distributed network. The updating procedure of the variant of BP is designed in a broadcasting fashion to reduce communication burden. It is rigorously proved that the proposed algorithm is convergence guaranteed. Simulations show that this method achieves almost the optimal frequency compensation accuracy with an error approaching the Cramer-Rao lower bound.

关键词： frequency offset probabilistic graphical model belief propagation

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Monte Carlo Query Processing of Uncertain Multidimensional Array Data

Monte Carlo Query Processing of Uncertain Multidimensional A...

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IEEE 27th International Conference on Data Engineering (ICDE 2011)

作者： Ge, Tingjian Grabiner, David Zdonik, Stan Univ Kentucky Dept Comp Sci Lexington KY 40506 USA Brown Univ Dept Comp Sci Providence RI USA

ISBN: (纸本)9781424489589

Array database systems are architected for scientific and engineering applications. In these applications, the value of a cell is often imprecise and uncertain. There are at least two reasons that a Monte Carlo query processing algorithm is usually required for such uncertain data. Firstly, a probabilistic graphical model must often be used to model correlation, which requires a Monte Carlo inference algorithm for the operations in our database. Secondly, mathematical operators required by science and engineering domains are much more complex than those of SQL. State-of-the-art query processing uses Monte Carlo approximation. We give an example of using Markov Random Fields combined with an array's chunking or tiling mechanism to model correlated data. We then propose solutions for two of the most challenging problems in this framework, namely the expensive array join operation, and the determination and optimization of stopping conditions of Monte Carlo query processing. Finally, we perform an extensive empirical study on a real world application.

关键词： Arrays Markov processes Markov random fields Monte Carlo approximation Monte Carlo inference algorithm Monte Carlo methods Monte Carlo methods Monte Carlo query processing algorithm Query processing Semantics Temperature distribution Temperature sensors approximation theory array chunking mechanism array database systems array tiling mechanism database management systems mathematical operators mathematical operators probabilistic graphical model probability query processing uncertain multidimensional array data

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modeling Global Body Configurations in American Sign Language 21

Modeling Global Body Configurations in American Sign Languag...

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Interspeech Conference

作者： Wilkins, Nicholas Galbraith, Max Cordes Nwogu, Ifeoma Rochester Inst Technol Rochester NY 14623 USA Sign Speak Rochester NY USA

ISBN: (纸本)9781713820697

In this paper we consider the problem of computationally representing American Sign Language (ASL) phonetics. We specifically present a computational model inspired by the sequential phonological ASL representation, known as the Movement-Hold (MH) model. Our computational model is capable of not only capturing ASL phonetics, but also has generative abilities. We present a probabilistic graphical model (PGM) which explicitly models holds and implicitly models movement in the MH model. For evaluation, we introduce a novel data corpus, ASLing, and compare our PGM to other models (GMM, LDA, and VAE) and show its superior performance. Finally, we demonstrate our model's interpretability by computing various phonetic properties of ASL through the inspection of our learned model.

关键词： Sign Language ASL Phonetics probabilistic graphical model Language model

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Proposing the deep dynamic Bayesian network as a future computer based medical system 29

Proposing the deep dynamic Bayesian network as a future comp...

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29th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

作者： Carbery, Caoimhe M. Marshall, Adele H. Woods, Roger Queens Univ Belfast Ctr Stat Sci & Operat Res CenSSOR Belfast BT7 1NN Antrim North Ireland Queens Univ Belfast Inst Elect Commun & Informat Technol ECIT Belfast BT7 1NN Antrim North Ireland

ISBN: (纸本)9781467390361

The development of new learning models has been of great importance throughout recent years;with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

关键词： probabilistic graphical model deep learning dynamic Bayesian network medical systems

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Analysis and Research models of the Estimation of Distribution Algorithms

Analysis and Research Models of the Estimation of Distributi...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Bi, Lihong Zhang, Shumin Shijiangzhuang Inst Railway Technol Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781457715846

The Estimation of Distribution Algorithms (EDAs) is a novel class of evolutionary algorithms which is motivated by the idea of building probabilistic graphical model of promising solutions to represent linkage information between variables in chromosome. Through learning of and sampling from probabilistic graphical model, new population is generated and optimization procedure is repeated until the stopping criteria are met. In this paper, the mechanism of the Estimation of Distribution Algorithms is analyzed. Currently existing EDAs are surveyed and categorized according to the probabilistic model they used, then the strengths and weakness and the future perspective of EDAs are concluded.

关键词： Estimation of Distribution Algorithms Genetic Algorithms probabilistic graphical model

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Generalization of graph network inferences in higher-order graphical models

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Journal of Applied and Computational Topology 2024年第5期8卷 1231-1256页

作者： Fei, Yicheng Pitkow, Xaq Department of Physics and Astronomy Rice University 6100 Main St Houston 77005 TX United States Neuroscience Institute Carnegie Mellon University Pittsburgh 15213 PA United States Department of Machine Learning Carnegie Mellon University Pittsburgh 15213 PA United States Department of Electrical and Computer Engineering Rice University 6100 Main St Houston 77005 TX United States Department of Neuroscience Baylor College of Medicine One Baylor Plaza Houston 77030 TX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine One Baylor Plaza Houston 77030 TX United States

probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we define the Recurrent Factor Graph Neural Network (RF-GNN) to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method outperforms Belief Propagation (BP). Moreover, we test the RF-GNN on a real-world Low-Density Parity-Check dataset as a benchmark along with other baseline models including BP variants and other GNN methods. Overall we find that RF-GNNs outperform other methods under high noise levels. © 2023, The Author(s).

关键词： 60 62 65 68 Graph neural network probabilistic graphical model probabilistic inference Third-order

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