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arXiv 2022年

作者： Bielawski, Jakub Chotibut, Thiparat Falniowski, Fryderyk Misiurewicz, Michal Piliouras, Georgios Department of Mathematics Cracow University of Economics Rakowicka 27 Kraków31-510 Poland Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Mathematical Sciences Indiana University-Purdue University Indianapolis 402 N. Blackford Street IndianapolisIN46202 United States Engineering Systems and Design Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore

We study the dynamics of simple congestion games with two resources where a continuum of agents behaves according to a version of Experience-Weighted Attraction (EWA) algorithm. The dynamics is characterized by two parameters: the (population) intensity of choice a > 0 capturing the economic rationality of the total population of agents and a discount factor σ ∈ [0, 1] capturing a type of memory loss where past outcomes matter exponentially less than the recent ones. Finally, our system adds a third parameter b ∈ (0, 1), which captures the asymmetry of the cost functions of the two resources. It is the proportion of the agents using the first resource at Nash equilibrium, with b = 1/2 capturing a symmetric network. Within this simple framework, we show a plethora of bifurcation phenomena where behavioral dynamics destabilize from global convergence to equilibrium, to limit cycles or even (formally proven) chaos as a function of the parameters a, b and σ. Specifically, we show that for any discount factor σ the system will be destabilized for a sufficiently large intensity of choice a. Although for discount factor σ = 0 almost always (i.e., b 6= 1/2) the system will become chaotic, as σ increases the chaotic regime will give place to the attracting periodic orbit of period 2. Therefore, memory loss can simplify game dynamics and make the system predictable. We complement our theoretical analysis with simulations and several bifurcation diagrams that showcase the unyielding complexity of the population dynamics (e.g., attracting periodic orbits of different lengths) even in the simplest possible potential games. © 2022, CC BY.

关键词： Dynamics

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Two-stage distributionally robust optimization for joint system design and maintenance scheduling in high-consequence systems

A I I E Transactions

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A I I E Transactions 2024年第8期56卷

作者： Hanxiao Zhang Yan-Fu Li Min Xie Chen Zhang a Department of Systems Engineering City University of Hong Kong Hong Kong Chinab Centre for Intelligent Multidimensional Data Analysis Hong Kong Science Park Hong Kong China c Department of Industrial Engineering Tsinghua University Beijing China a Department of Systems Engineering City University of Hong Kong Hong Kong Chinad City University of Hong Kong Shenzhen Research Institute Shenzhen China

The failures of high-consequence systems can cause serious harm to humans, including loss of human health, life security, finance, and even social chaos. To protect high-consequence systems, both optimal system design and maintenance activities contribute to improving system reliability and social safety. The existing works generally optimize these two problems sequentially and assume that the degradation process of components is precisely known. However, sequential optimization often results in significant losses due to redundancies, and such a presumption usually cannot be guaranteed in practice, due to limited historical data or a lack of expert knowledge, referred to as epistemic uncertainty. To fill this gap, in this article, we consider an integrated optimization of system design and maintenance scheduling for multi-state high-consequence systems in which the component’s degradation is known with limited distributional information. To address this issue, we utilize the framework of distributionally robust optimization to provide a risk-averse decision to decision-makers even under the worst realizations of random parameters, and develop a two-stage integer distributionally robust model with moment-based ambiguity set to determine the system design and maintenance scheduling simultaneously. The proposed model can be converted to a tractable approximation as an integer linear stochastic programming problem. In order to solve large-scale problems, we develop a sample-based adaptive large neighborhood search algorithm to find the optimal system designs. In the numerical experiments, we present a case study on feedwater heating systems in nuclear power plants and demonstrate that an integrated optimization consideration creates significant benefits in profitability. We also present the out-of-sample performance of the distributionally robust design to avoid extreme risk.

关键词： Reliability optimization condition-based maintenance preventive maintenance uncertainty stochastic programming distributionally robust optimization

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A Survey for Large Language Models in Biomedicine

arXiv

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Retrospective for the dynamic sensorium competition for predicting large-scale mouse primary visual cortex activity from videos 24

Retrospective for the dynamic sensorium competition for pred...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Polina Turishcheva Paul G. Fahey Michaela Vystrčilová Laura Hansel Rachel Froebe Kayla Ponder Yongrong Qiu Konstantin F. Willeke Mohammad Bashiri Ruslan Baikulov Yu Zhu Lei Ma Shan Yu Tiejun Huang Bryan M. Li Wolf De Wulf Nina Kudryashova Matthias H. Hennig Nathalie L. Rochefort Arno Onken Eric Wang Zhiwei Ding Andreas S. Tolias Fabian H. Sinz Alexander S. Ecker Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanford CA Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanford CA Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and International Max Planck Research School for Intelligent Systems Tübingen Germany and Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China and Beijing Academy of Artificial Intelligence China Beijing Academy of Artificial Intelligence China Institute of Automation Chinese Academy of Sciences China The Alan Turing Institute UK and School of Informatics University of Edinburgh UK School of Informatics University of Edinburgh UK Centre for Discovery Brain Sciences University of Edinburgh UK and Simons Initiative for the Developing Brain University of Edinburgh UK Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanf

ISBN: (纸本)9798331314385

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***.

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Digital Holography and 3D Imaging 2023: introduction to the joint feature issue in Applied Optics and Journal of the Optical Society of America A

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Applied optics 2024年第7期63卷 DH1-DH1页

作者： Blanche P.-A. Cheng C.-J. Ferraro P. Zhang Y. Wang Z.J. Formerly at the College of Optical Sciences The University of Arizonahttps://***/03m2x1q45 Tucson Arizona USA Institute of Electro-Optical Engineering National Taiwan Normal University Taipei 11677 Taiwan Institute of Applied Sciences & Intelligent Systems ISASI-CNR 80078 Pozzuoli Italy Yunnan Provincial Key Laboratory of Modern Information Optics Faculty of Science Kunming University of Science and Technology Kunming Yunnan 650500 China Los Alamos National Laboratory MS H846 P-4 Los Alamos New Mexico 87545 USA

The Optica Topical Meeting on Digital Holography and 3D Imaging (DH) was held 14-17 August 2023 in Boston, Massachusetts. The meeting was organized co-jointly with the Optica Imaging Congress. Feature issues based on the DH meeting series have been released by Applied Optics (AO) since 2007. Since 2017, AO and the Journal of the Optical Society of America A (JOSA A) have presented a feature issue in each journal. This feature issues includes 17 papers in AO and 9 in JOSA A. Together they cover a large range of topics, reflecting the rapidly expanding techniques and applications of digital holography and 3D imaging. The upcoming DH Conference (DH 2024) will be held from 3 to 6 June in Paestum, Italy.

关键词： Digital holography Holographic displays Holographic optical elements Holographic techniques Imaging systems Three dimensional imaging

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Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty

Robust Beamforming for IRS-assisted Wireless Communications ...

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IEEE Conference on Wireless Communications and Networking

作者： Yongchang Deng Yuze Zou Shimin Gong Bin Lyu Dinh Thai Hoang Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the receiver's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization (AO) method, we firstly propose a heuristic scheme to decompose the IRS's phase shift optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.

关键词： Wireless communication Uncertainty Array signal processing Simulation Transmitting antennas Channel estimation Receivers

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Distributed Optimal Control for Traffic Networks with Fog Computing

引用

China Communications 2019年第10期16卷 202-213页

作者： Yijie Wang Lei Wang Saeed Amir Qing-Guo Wang Department of Operations Research at the University of Texas at Austin AustinTexas76019 USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191 China Faculty of Engineering and the Built Environment Institute for Intelligent SystemsUniversity of JohannesburgJohannesburg 2006South Africa

This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.

关键词： fog computing traffic network distributed optimization distributed control

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Enhancement of thermal conductivity and mechanical properties of silicone rubber with oriented fillers connected by covalent bonds

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Composites Part A: Applied science and Manufacturing 2025年 198卷

作者： Liang Zhang Jianhui Qiu Eiichi Sakai Huixia Feng Hong Wu Hiroyuki Yamaguchi Yasunori Chonan Mitsuyoshi Nomura Department of Mechanical Engineering Faculty of Systems Science and Technology Akita Prefectural University Akita 015-0055 Japan College of Petrochemical Technology Lanzhou University of Technology Lanzhou 730050 China The State Key Laboratory of Polymer Materials Engineering Sichuan Provincial Engineering Laboratory of Plastic/Rubber Complex Processing Technology Polymer Research Institute of Sichuan University Chengdu 610065 China Department of Intelligent Mechatronics Faculty of Systems Science and Technology Akita Prefectural University Akita 015-0055 Japan Department of Machine Intelligence and Systems Engineering Akita Prefectural University Akita 015-0055 Japan

High-performance thermal interface materials (TIMs) are pivotal for extending the lifespan and ensuring the reliability of electronic devices, due to their excellent thermal conductivity (TC), flexibility, and electrical insulation properties. This study presents an in-depth analysis of the spatial distribution effects of hybrid fillers and proposes an innovative binary-filler synergistic strategy. By combining the unique characteristics of one-dimensional carbon nanotubes (CNTs) and two-dimensional boron nitride (BN), a vertically aligned silicone rubber/boron nitride/carbon nanotubes (SR/KBN/CNTs) flexible TIM was successfully designed and fabricated using a simple molding process. Surface-modified BN was covalently bonded with CNTs, collaboratively forming a continuous thermally conductive network aligned along the heat flow direction within the SR matrix. This structure significantly reduces interfacial thermal resistance and phonon scattering, thereby effectively enhancing the through-plane TC (K ⊥ ). Specifically, when 50 wt% of the composite filler KBN30/CNTs (KBN: CNTs, 30:1 w/w) was incorporated into the SR matrix, the resulting composite achieved a K ⊥ of 3.57 Wm −1 K −1 , an increase of 1983% compared to pure SR. Moreover, the composite exhibits a fast thermal response, excellent electrical insulation, and robust thermal stability. This combination of properties highlights its great potential for application in the thermal management of future electronic devices.

关键词： Thermal insulation

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A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

arXiv

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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Machine-Learning Methods for Detecting Tuberculosis in Ziehl-Neelsen Stained Slides: A Systematic Literature Review

SSRN

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SSRN 2023年

作者： Tamura, Gabriel Llano, Gonzalo Aristizábal, Andrés Valencia, Juan Sua, Luz Fernanda Fernandez, Liliana Department of Computing and Intelligent Systems Universidad Icesi Valle del Cauca Cali Colombia Center of Artificial Intelligence and Data Science Universidad Icesi Valle del Cauca Cali Colombia Universidad Icesi Valle del Cauca Cali Colombia Faculty of Health Sciences Universidad Icesi Valle del Cauca Cali Colombia Department of Pathology and Laboratory Medicine Fundación Valle del Lili Valle del Cauca Cali Colombia Department of Internal Medicine Pulmonology Service Fundación Valle del Lili Valle del Cauca Cali Colombia

In recent years, different Machine Learning (ML) methods applied to medical image analysis have been increasingly used for the detection of Mycobacterium Tuberculosis (MT) in Ziehl-Neelsen stained human samples, since manual detection and counting of bacteria is a tedious, costly, error-prone and slow task. This paper presents the results of a Systematic Literature Review (SLR) that comprehensively surveyed and analyzed the current stateof-the-art ML methods and approaches that have been used for TB detection in the period of 2017-2022, as well as their achieved metrics and the characteristics of the datasets on which they were applied. The primary goal of this⋆ This work was supported by Tecnoquímicas Corp. in partnertship with Fundación Valle del Lili and Universidad Icesi with the aim to strenghten the research and development of technology in health in the Valle del Cauca region. © 2023, The Authors. All rights reserved.

关键词： Computer vision

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