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An Improved Unbiased Particle Filter

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arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. We assume that, for numerical reasons, one has to time-discretize the diffusion process which typically leads to filtering that is subject to discretization bias. The approach in [16] establishes that when only having access to the time discretized diffusion it is possible to remove the discretization bias with an estimator of finite variance. We improve on the method in [16] by introducing a modified estimator based on the recent work of [17]. We show that this new estimator is unbiased and has finite variance. Moreover, we conjecture and verify in numerical simulations that substantial gains are obtained. That is, for a given mean square error (MSE) and a particular class of multi-dimensional diffusion, the cost to achieve the said MSE falls. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

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IFAC-PapersOnLine 2023年第2期56卷 9056-9061页

作者： Vittorio Giammarino James Queeney Lucas C. Carstensen Michael E. Hasselmo Ioannis Ch Paschalidis Division of Systems Engineering Boston University Boston MA 02215 USA Center for Systems Neuroscience Department of Psychological and Brain Sciences and Graduate Program for Neuroscience Boston University Boston MA 02215 USA College of Engineering and Hariri Institute for Computing & Computational Science & Engineering Boston University Boston MA 02215 USA

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.

关键词： Learning for Control Reinforcement Learning Deep Learning RL with demonstrations Imitation Learning Bio-inspired Control

Interannual variability and climatic sensitivity of global wildfire activity

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Advances in Climate Change Research 2021年第5期12卷 686-695页

作者： Rongyun TANG Jiafu MAO Mingzhou JIN Anping CHEN Yan YU Xiaoying SHI Yulong ZHANG Forrest M.HOFFMAN Min XU Yaoping WANG Institute for a Secure and Sustainable Environment University of TennesseeKnoxville37996USA Environmental Sciences Division and Climate Change Science Institute Oak Ridge National LaboratoryOak Ridge37830USA Department of Biology and Graduate Degree Program in Ecology Colorado State UniversityFort Collins80523USA Atmospheric and Oceanic Sciences Program Princeton UniversityPrinceton08544USA Computational Sciences and Engineering Division and Climate Change Science Institute Oak Ridge National LaboratoryOak Ridge37830USA

Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources;2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7%–99.7% of the global wildfire IAV estimated by both the satellite observations and ELM simulations;3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs;and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.

关键词： Wildfire Burned area Climatic sensitivity E3SM land model Global fire emission database

Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

Multilevel Particle Filters for Partially Observed McKean-Vlasov Stochastic Differential Equations

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arXiv 2024年

作者： Awadelkarim, Elsiddig Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

In this paper we consider the filtering problem associated to partially observed McKean-Vlasov stochastic differential equations (SDEs). The model consists of data that are observed at regular and discrete times and the objective is to compute the conditional expectation of (functionals) of the solutions of the SDE at the current time. This problem, even the ordinary SDE case is challenging and requires numerical approximations. Based upon the ideas in [3, 12] we develop a new particle filter (PF) and multilevel particle filter (MLPF) to approximate the afore-mentioned expectations. We prove under assumptions that, for ϵ > 0, to obtain a mean square error of O(ϵ2) the PF has a cost per-observation time of O(ϵ−5) and the MLPF costs O(ϵ−4) (best case) or O(ϵ−4 log(ϵ)2) (worst case). Our theoretical results are supported by numerical experiments. © 2024, CC BY.

关键词： Mean square error

Global variations in critical drought thresholds that impact vegetation

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National Science Review 2023年第5期10卷 180-193页

作者： Xiangyi Li Shilong Piao Chris Huntingford Josep Pe?uelas Hui Yang Hao Xu Anping Chen Pierre Friedlingstein Trevor F.Keenan Stephen Sitch Xuhui Wang Jakob Zscheischler Miguel D.Mahecha Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking University Key Laboratory of Alpine Ecology and Biodiversity Institute of Tibetan Plateau ResearchCenter for Excellence in Tibetan Earth ScienceChinese Academy of Sciences UK Centre for Ecology and Hydrology CSIC Global Ecology Unit CREAF-CSIC-UAB CREAF Cerdanyola de Vallès Department of Biogeochemical Integration Max Planck Institute for Biogeochemistry Department of Biology Colorado State University Graduate Degree Program in Ecology Colorado State University Department of Mathematics College of Engineering Mathematics and Physical SciencesUniversity of Exeter Laboratoire de Météorologie Dynamique Institut Pierre-Simon LaplaceCNRS-ENS-UPMC-XDépartement de GeósciencesEcole Normale Superieure Earth and Environmental Sciences Area Lawrence Berkeley National Laboratory Department of Environmental Science Policy&ManagementUniversity of California Berkeley Department of Geography College of Life and Environmental SciencesUniversity of Exeter Department of Computational Hydrosystems Helmholtz Centre for Environmental Research—Ufz Remote Sensing Centre for Earth System Research Leipzig University

Identifying the thresholds of drought that,if crossed,suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and *** present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using four remotely sensed vegetation proxies spanning *** thresholds identified represent critical inflection points for changing vegetation responses from highly resistant to highly vulnerable in response to drought stress,and as a warning signal for substantial vegetation *** thresholds varied geographically,with much lower percentiles of soil-moisture anomalies in vegetated areas covered by more forests,corresponding to a comparably stronger capacity to mitigate soil water deficit stress in forested ***,those lower thresholds are detected in more humid ***-of-the-art land models,however,overestimated thresholds of soil moisture(*** drought impacts),especially in more humid areas with higher forest covers and arid areas with few forest *** on climate model projections,we predict that the risk of vegetation damage will increase by the end of the twenty-first century in some hotspots like East Asia,Europe,Amazon,southern Australia and eastern and southern *** data-based results will inform projections on future drought impacts on terrestrial ecosystems and provide an effective tool for drought management.

关键词： drought threshold inflection points vegetation response soil moisture drought impacts

Fixation dynamics on hypergraphs

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arXiv 2023年

作者： Liu, Ruodan Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Sciences and Engineering Program State University of New York at Buffalo BuffaloNY United States

Hypergraphs have been a useful tool for analyzing population dynamics such as opinion formation and the public goods game occurring in overlapping groups of individuals. In the present study, we propose and analyze evolutionary dynamics on hypergraphs, in which each node takes one of the two types of different but constant fitness values. For the corresponding dynamics on conventional networks, under the birth-death process and uniform initial conditions, most networks are known to be amplifiers of natural selection;amplifiers by definition enhance the difference in the strength of the two competing types in terms of the probability that the mutant type fixates in the population. In contrast, we provide strong computational evidence that a majority of hypergraphs are suppressors of selection under the same conditions by combining theoretical and numerical analyses. We also show that this suppressing effect is not explained by one-mode projection, which is a standard method for expressing hypergraph data as a conventional network. Our results suggest that the modeling framework for structured populations in addition to the specific network structure is an important determinant of evolutionary dynamics, paving a way to studying fixation dynamics on higher-order networks including hypergraphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

How Media Competition Fuels the Spread of Misinformation

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arXiv 2024年

作者： Amini, Arash Bayiz, Yigit Ege Lee, Eun-Ju Somer-Topcu, Zeynep Marculescu, Radu Topcu, Ufuk The University of Texas at Austin Oden Institute for Computational Engineering and Sciences United States The University of Texas at Austin Department of Electrical and Computer Engineering United States Seoul National University Department of Communication & Interdisciplinary Program in Artificial Intelligence Korea Republic of The University of Texas at Austin Department of Government United States

Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain in audience engagement, it may damage the reputation of these sources, resulting in a loss of audience. To understand the rationale behind sharing misinformation, we model the competition as a zero-sum sequential game, where each news source influences individuals based on its credibility—how trustworthy the public perceives it—and the individual’s opinion and susceptibility. In this game, news sources can decide whether to share factual information to enhance their credibility or disseminate misinformation for greater immediate attention at the cost of losing credibility. We employ the quantal response equilibrium concept, which accounts for the bounded rationality of human decision-making, allowing for imperfect or probabilistic choices. Our analysis shows that the resulting equilibria for this game reproduce the credibility-bias distribution observed in real-world news sources, with hyper-partisan sources more likely to spread misinformation than centrist ones. It further illustrates that disseminating misinformation can polarize the public. Notably, our model reveals that when one player increases misinformation dissemination, the other player is likely to follow, exacerbating the spread of misinformation. We conclude by discussing potential strategies to mitigate the spread of fake news and promote a more factual and reliable information landscape. Copyright © 2024, The Authors. All rights reserved.

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