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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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Optimizing Wireless Sensor Networks Based on Collaborative Beamforming

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Procedia computer science 2020年 174卷 561-571页

作者： Lei Shi Zhehao Li Xu Ding Juan Xu Zengwei Lv School of Computer Science and Information Engineering HeFei University of Technology Hefei 230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei 230009 China

With the expansion of the scale of the Internet of Things, the transmission distance and speed of wireless networks need to be improved. Due to the limitation of power and energy of sensor nodes, data transmission is basically carried out by multi-hop relay. Collaborative beamforming is a technology that uses multiple sensor nodes to form a virtual antenna array to transmit high-gain and directional beams, which greatly enhances the transmission range. Compared with the muilt-hop network, collaborative beamforming can reduce the numbers of hop or even transmit data to the target node at one time, thus reducing the time required for routing and forwarding. This means that collaborative beamforming can transmit data more quickly and improve data throughput. In this paper, a brief introduction of cooperative beamforming is given. Then we propose an algorithm based on Collaborative beamforming. Simulation results show that the Collaborative beamforming wireless sensor networks can achieve better throughput compared with multi-hop networks.

关键词： Collaborative Beamforming Wireless Sensor Network Throughput Set Cover

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A generalized sub-pixel mapping algorithm for auto-stereoscopic displays using slanted optical plates 2

A generalized sub-pixel mapping algorithm for auto-stereosco...

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2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Kim, Hyun-Woo Cho, Myungjin Kang, Min-Koo Kim, Sung-Kyu Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Iizuka city Japan Hankyong National University Department of Electrical Electronic and Control Engineering Anseong-si Korea Republic of Imaging Media Research Center Korea Institute of Science and Technology Seoul Korea Republic of

ISBN: (纸本)9781728149851

Auto-stereoscopic displays using slanted optical plates have inherent subpixel rasterization compared to the normal 2D displays, and mappings between subpixel positions and multi-view indices even vary according to the number of views and angles of slanted optical plates. In this paper, we derive a simple but generalized formula for subpixel mappings from a naïve ray-tracing technique on RGB stripe type panels. To verify the proposed algorithm, our proto-type auto-stereoscopic display using parallax barriers with a slanted angle was used and examined in the experiment. The proposed algorithm is expected to leverage converting multi-view 3D inputs to various types of auto-stereoscopic contents in real time. © 2020 IEEE.

关键词： Stereo image processing

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Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Enhanced Subspace Distribution Matching for Fast Visual Doma...

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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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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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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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Cubic singularities in binary linear electromechanical oscillators

arXiv

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arXiv 2023年

作者： Zhou, Xin Jing, Hui Ren, Xingjing Zhang, Jianqi Huang, Ran Li, Zhipeng Sun, Xiaopeng Wu, Xuezhong Qiu, Cheng-Wei Nori, Franco Xiao, Dingbang College of Intelligence Science and Technology NUDT Changsha410073 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Innovation Academy of Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan430071 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Wako-shi Saitama351-0198 Japan Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore

Singularities arise in diverse disciplines and play a key role in both exploring fundamental laws of physics and making highly-sensitive sensors [1–14]. Higher-order (≥ 3) singularities, with further improved performance [15–19], however, usually require exquisite tuning of multiple (≥ 3) coupled degrees of freedom [15–17] or nonlinear control [18, 19], thus severely limiting their applications in practice. Here we propose theoretically and confirm using mechanics experiments that, cubic singularities can be realized in a coupled binary system without any nonlinearity, only by observing the phase tomography of the driven response. By steering the cubic phase-tomographic singularities in an electrostatically-tunable micromechanical system, enhanced cubic-root response to frequency perturbation and voltage-controlled nonreciprocity are demonstrated. Our work opens up a new phase-tomographic method for interacted-system research, and sheds new light on building and engineering advanced singular devices with simple and well-controllable elements, with a wide range of applications including precision metrology, portable nonreciprocal devices, and on-chip mechanical computing. © 2023, CC BY-SA.

关键词： Degrees of freedom (mechanics)

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Biodegradation of Benzo[A]Pyrene By a Marine Chlorella Vulgaris Lh-1 with Heterotrophic Ability

SSRN

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SSRN 2023年

作者： Chen, Qingguo Li, Zhenzhen Li, Yijing Liu, Mei Wu, Yingqi Chen, Zhi Zhu, Baikang Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control Zhejiang Ocean University Zhoushan China National & local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology Zhejiang Ocean University Zhoushan316022 China School of Marine Science & Technology Zhejiang Ocean University Zhoushan China Department of Building Civil and Environmental Engineering Faculty of Engineering & Computer Sciences Concordia University MontrealQCH3G1M8 Canada

In this study, a microalga, Chlorella vulgaris LH-1, with heterotrophic ability to degrade benzo[a]pyrene (BaP) was explored. The effect of BaP concentration on microalga growth was investigated, and the possible biodegradation mechanism of BaP was proposed. Results showed that 5 mg/L had a significant inhibitory effect on its growth. During heterotrophic cultivation, 20 mg/L BaP inhibited its growth significantly. The expression of functional enzyme genes of LH-1 such as phenol 2-monooxygenase activity, protocatechuate 3,4-dioxygenase activity, catechol 1,2-dioxygenase activity, styrene degradation, and benzoate degradation were upregulated in the process of BaP degradation. The degradation of BaP by this microalga under mixotrophic condition goes through the degradation pathway of phthalic acid, whereas it goes through the degradation pathway of benzoic acid under heterotrophic condition. © 2023, The Authors. All rights reserved.

关键词： Biodegradation

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Adaptive AGV fleet management in a dynamically changing production environment

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Procedia Manufacturing 2021年 54卷 148-153页

作者： Júlia Bergmann Dávid Gyulai József Váncza EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Budapest Hungary Doctoral School of Informatics Eötvos Lóránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

In the era of smart manufacturing, autonomous mobile robots have become affordable for numerous companies, although the fleet management remains a challenging problem. A novel approach is proposed, supporting the solution of vehicle assignment problem. The method relies on adaptive workstation clustering that considers not only complex environment layout, but also the main characteristics of the material flow. The technique combines network analytical and optimization tools with a greedy algorithm of refinement. The implementation is presented, and the impact of clustering techniques on selected performance metrics are analyzed within a series of experiments, taken from an industrial case study.

关键词： internal logistics AGV network modularity MIQP

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Towards practical and massively parallel quantum computing emulation for quantum chemistry

arXiv

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arXiv 2023年

作者： Shang, Honghui Fan, Yi Shen, Li Guo, Chu Liu, Jie Duan, Xiaohui Li, Fang Li, Zhenyu Institute of Computing Technology Chinese Academy of Sciences Beijing China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Software Shandong University Jinan China National Research Center of Parallel Computer Engineering and Technology Beijing China

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein-ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOPS is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry. © 2023, CC BY.

关键词： Computational chemistry

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