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2021 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2021

作者： Thomas, C. Watson, Z. Kim, M. Baidya, A. Lamsal, M. Chowdhury, M.H. Basnet, M. Poudel, K.N. East Central University Department of Mathematics and Computer Science AdaOK United States Middle Tennessee State University Computational Science Program MurfreesboroTN United States University of Memphis Department of Electrical and Computer Engineering MemphisTN United States Embry-Riddle Aeronautical University Department of Mathematics and Data Science FL United States

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APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN BIOLOGY WITH FLEXIBLE PRIORS ON EDGE STATES

arXiv

引用

arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

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Distributions and statistical power of optimal signal-detection methods in finite cases

arXiv

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arXiv 2017年

作者： Zhang, Hong Jin, Jiashun Wu, Zheyang Worcester Polytechnic Institute Carnegie Mellon University Department of Mathematical Sciences Program of Bioinformatics and Computational Biology Program of Data Science 100 Institute Road Worcester01609 Morocco

In big data analysis for detecting rare and weak signals among n features, some grouping-test methods such as Higher Criticism test (HC), Berk-Jones test (B-J), and φ-divergence test share the similar asymptotical optimality when n → ∞. However, in practical data analysis n is frequently small and moderately large at most. In order to properly apply these optimal tests and wisely choose them for practical studies, it is important to know how to get the p-values and statistical power of them. To address this problem in an even broader context, this paper provides analytical solutions for a general family of goodness-of-fit (GOF) tests, which covers these optimal tests. For any given i.i.d. and continuous distributions of the input test statistics of the n features, both p-value and statistical power of such a GOF test can be calculated. By calculation we compared the finite-sample performances of asymptotically optimal tests under the normal mixture alternative. Results show that HC is the best choice when signals are rare, while B-J is more robust over various signal patterns. In the application to a real genome-wide association study, results illustrate that the p-value calculation works well, and the optimal tests have potentials for detecting novel disease genes with weak genetic effects. The calculations have been implemented in an R package SetTest and published on the CRAN. Copyright © 2017, The Authors. All rights reserved.

关键词： Information analysis

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Yale at "Discharge Me!": Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information 23

Yale at "Discharge Me!": Evaluating Constrained Generation o...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Socrates, Vimig Huang, Thomas Ai, Xuguang Fereydooni, Soraya Chen, Qingyu Taylor, R. Andrew Chartash, David Department of Biomedical Informatics and Data Science Yale University School of Medicine United States Department of Emergency Medicine Yale School of Medicine United States Program of Computational Biology and Bioinformatics Yale University United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

ISBN: (纸本)9798891761308

In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: "Discharge Me!". We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.. ©2024 Association for computational Linguistics.

关键词： Decoding

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Between Local and Global Strategy Updating in Public Goods Game

SSRN

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SSRN 2022年

作者： Wang, Chaoqian Huang, Chaochao Program for Computational Social Science Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States International Institute of Finance School of Management University of Science and Technology of China Hefei230026 China

In the spatial public goods game (PGG), agents interact with local neighbors. In a well-mixed population, agents globally interact with the whole population. In this work, we explore the middle ground between the two from the perspective of strategy imitation. First, we assume each agent can select the reference agent for imitation by global interactions with a probability and otherwise local interactions. The results show that local interactions can maintain cooperation better under a strong dilemma, because cooperators can aggregate to defend against the invasion of defection. For a weak dilemma, both local and global interactions are beneficial to cooperation, because the dilemma is weak;however, the combination of the two is harmful to cooperation. Secondly, we consider different possibilities for cooperative and defective agents to select global references. The results show that when global interactions are frequently accessed by defectors but hardly accessed by cooperators, the system is most unfavorable for cooperation: defectors near the cooperation aggregation are hard to affect by cooperators around, while cooperators interact locally and are invaded by defectors around. However, when global interactions are moderately accessed by defectors and hardly accessed by cooperators, the system is most favorable for the cooperation domain: defectors near the cooperation aggregation have chances to imitate the strategy of cooperation, defectors far away from the aggregation also have chances to imitate the strategy of aggregated cooperators. Meanwhile, the aggregated cooperators hardly imitate defection and can avoid being invaded remotely. The present work unveils the evolutionary behavior of cooperation in the middle of structured and well-mixed pairwise imitation dynamics. © 2022, The Authors. All rights reserved.

关键词： Behavioral research

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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

引用

Physical Review Letters 2018年第14期120卷 143001-143001页

作者： Linfeng Zhang Jiequn Han Han Wang Roberto Car Weinan E Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA and Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing 100871 People’s Republic of China

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

关键词： Classical statistical mechanics Density functional theory First-principles calculations Interatomic & molecular potentials Learning Neuronal networks Potential energy surfaces Solid-state chemistry Thermal properties Van der Waals interaction Molecular dynamics

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MONGE-AMPÈRE FLOW FOR GENERATIVE MODELING

arXiv

引用

arXiv 2018年

作者： Zhang, Linfeng Weinan, E. Wang, Lei Program in Applied and Computational Mathematics Princeton University United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University United States Center for Data Science Peking University Beijing Institute of Big Data Research Beijing100871 China Institute of Physics Chinese Academy of Sciences Beijing100190 China

We present a deep generative model, named Monge-Ampère flow, which builds on continuous-time gradient flow arising from the Monge-Ampère equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Ampère flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Ampère equation, optimal transport, and fluid dynamics into reversible flow-based generative models. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical mechanics

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M3D: Dual-Stream Selective State Spaces and Depth-Driven Framework for High-Fidelity Single-View 3D Reconstruction

arXiv

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arXiv 2024年

作者： Zhang, Luoxi Shrestha, Pragyan Zhou, Yu Xie, Chun Kitahara, Itaru Doctoral Program in Empowerment Informatics University of Tsukuba Japan Center for Computational Science University of Tsukuba Japan Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University China

The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant difficulties in balancing the extraction of global and local features, particularly in diverse and complex environments, leading to insufficient reconstruction precision and quality. We propose M3D, a novel single-view 3D reconstruction framework, to tackle these challenges. This framework adopts a dual-stream feature extraction strategy based on Selective State Spaces to effectively balance the extraction of global and local features, thereby improving scene comprehension and representation precision. Additionally, a parallel branch extracts depth information, effectively integrating visual and geometric features to enhance reconstruction quality and preserve intricate details. Experimental results indicate that the fusion of multi-scale features with depth information via the dual-branch feature extraction significantly boosts geometric consistency and fidelity, achieving state-of-the-art reconstruction performance. Our code and dataset are publicly available at this URL. © 2024, CC BY-NC-ND.

关键词： 3D reconstruction

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Deterministic guarantees for Burer–Monteiro factorizations of smooth semidefinite programs

arXiv

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arXiv 2018年

作者： Boumal, Nicolas Voroninski, Vladislav Bandeira, Afonso S. Mathematics Department Program in Applied and Computational Mathematics Princeton University Department of Mathematics Center for Data Science Courant Institute of Mathematical Sciences New York University

We consider semidefinite programs (SDPs) with equality constraints. The variable to be optimized is a positive semidefinite matrix X of size n. Following the Burer–Monteiro approach, we optimize a factor Y of size n× p instead, such that X = YY. This ensures positive semidefiniteness at no cost and can reduce the dimension of the problem if p is small, but results in a non-convex optimization problem with a quadratic cost function and quadratic equality constraints in Y. In this paper, we show that if the set of constraints on Y regularly defines a smooth manifold, then, despite non-convexity, first- and second-order necessary optimality conditions are also sufficient, provided p is large enough. For smaller values of p, we show a similar result holds for almost all (linear) cost functions. Under those conditions, a global optimum Y maps to a global optimum X = YY of the SDP. We deduce old and new consequences for SDP relaxations of the generalized eigenvector problem, the trust-region subproblem and quadratic optimization over several spheres, as well as for the Max-Cut and Orthogonal-Cut SDPs which are common relaxations in stochastic block modeling and synchronization of rotations. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

End-to-end symmetry preserving inter-atomic potential energy...

引用

32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

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