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作者： Kang, Qi Yao, Siya Zhou, Mengchu Zhang, Kai Abusorrah, Abdullah Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering Faculty of Engineering and Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21481 Saudi Arabia Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions. © 2014 IEEE.

关键词： Image classification

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Optical and photoluminescence modulation in monolayer molybdenum sulfide thin films via electrochemical deposition: Exploring the influence of deposition voltage and time

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Results in Physics 2024年 61卷

作者： Ajayeoba, Yetunde A. Adewinbi, Saheed A. Akinrinola, Olusola Akande, Akinlolu Pillai, Suresh C Awodugba, Ayodeji O. Department of Physics Faculty of Basic and Applied Sciences College of Science Engineering and Technology Osun State University P.M.B 4494 Osun State Osogbo Nigeria Department of Pure and Applied Physics Ladoke Akintola University of Technology Ogbomosho P.M.B 4000 Oyo State Ogbomosho Nigeria Nanotechnology and Bio-Engineering Research Group Department of Environmental Science Faculty of Science Atlantic Technological University ATU Sligo Ash Lane Sligo F91 YW50 Ireland Instituto de Fisica Facultad de Fisica Pontificia Universidad Catolica de Chile Santiago 7820436 Chile Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) Atlantic Technological University Ash Lane Ballytivnan Sligo F91 YW50 Ireland

Monolayer Molybdenum Disulfide (MoS2) exhibits a direct bandgap characterized by strong visible photoluminescence, high on/off ratio, high optical transparency, low dissipation rate, and light absorption in a wide ene... 详细信息

Monolayer Molybdenum Disulfide (MoS₂) exhibits a direct bandgap characterized by strong visible photoluminescence, high on/off ratio, high optical transparency, low dissipation rate, and light absorption in a wide energy spectrum. The influence of different deposition voltages and durations on the optical and photoluminescent characteristics was investigated using Raman spectroscopy, UV–visible spectrophotometry, and photoluminescence spectroscopy. All samples were analyzed for optical properties to confirm their suitability for optoelectronics. The energy band gap and Urbach energy (band tail width) of all films were 1.87 to 2.52 eV and 0.31 to 0.49 eV, respectively. The findings reveal a correlation between enhanced absorption properties and a decrease in the bandgap of the semiconducting film layers, implying the potential utility of the deposited films as efficient solar absorbers. Furthermore, the improved transmittance observed within the visible wavelength range suggests their applicability as effective window layers in thin-film photodiodes. It was established that varying the voltage and time of deposition alone can enhance the optoelectronic performance of the material in any device. © 2024 The Author(s)

关键词： 2D-MoS2 Deposition potential Deposition time Electrodeposition Optical properties Photoluminescence

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UNPREDICTABILITY IN CONGESTION GAMES: MEMORY LOSS CAN PREVENT CHAOS

arXiv

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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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EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Xu Chen, Xianda Zhu, Meixin Cai, Pinlong Zhou, Jianshan Chu, Xiaowen Data Science and Analytics Thrust Information Hub Guangzhou China Intelligent Transportation Thrust Systems Hub Guangzhou China Sensing and Computation for Ubiquitous Internet of Things Guangdong Provincial Key Lab of Integrated Communication China Southeast University School of Transportation Nanjing211189 China Shanghai Artificial Intelligence Laboratory Shanghai China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control State Key Laboratory of Intelligent Transportation Systems China Beihang University School of Transportation Science and Engineering Beijing100191 China

Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as they cannot fully capture the complexity inherent in car-following processes, or may struggle in unseen scenarios due to their reliance on confined driving skills present in training data. It is worth noting that each car-following model possesses its own strengths and weaknesses depending on specific driving scenarios. Therefore, we propose EnsembleFollower, a closed-loop hierarchical planning framework for achieving human-like autonomous car-following. The EnsembleFollower framework involves a high-level Reinforcement Learning-based agent responsible for judiciously managing multiple low-level models (such as intelligent Driver Model and Gipps model) according to the current state, either by selecting an appropriate car-following model to perform an action or by allocating different weights across all primitive models. Moreover, we propose a jerk-constrained kinematic model for more convincing microscopic traffic simulations. We evaluate the proposed method based on real-world driving data from the HighD dataset. The experimental results illustrate that EnsembleFollower yields an improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse driving conditions by leveraging the strengths of various car-following models. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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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

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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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