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arXiv 2024年

作者： Al-Sharman, Mohammad Edes, Luc Sun, Bert Jayakumar, Vishal Daoud, Mohamed A. Rayside, Derek Melek, William University of Waterloo WaterlooONN2L 3G1 Canada David R. Cheriton School of Computer Science Watonomous University of Waterloo ON Canada The Faculty of Mathematics The Department of Combinatorics and Optimization Watonomous University of Waterloo ON Canada The Department of Mechanical and Mechatronics Engineering Watonomous University of Waterloo ON Canada

Autonomous driving at unsignalized intersections is still considered a challenging application for machine learning due to the complications associated with handling complex multiagent scenarios characterized by a high degree of uncertainty. Automating the decision-making process at these safety-critical environments involves comprehending multiple levels of abstractions associated with learning robust driving behaviors to enable the vehicle to navigate efficiently. In this survey, we aim at exploring the state-of-the-art techniques implemented for decision-making applications, with a focus on algorithms that combine Reinforcement Learning (RL) and deep learning for learning traversing policies at unsignalized intersections. The reviewed schemes vary in the proposed driving scenario, in the assumptions made for the used intersection model, in the tackled challenges, and in the learning algorithms that are used. We have presented comparisons for these techniques to highlight their limitations and strengths. Based on our in-depth investigation, it can be discerned that a robust decision-making scheme for navigating real-world unsignalized intersection has yet to be developed. Along with our analysis and discussion, we recommend potential research directions encouraging the interested players to tackle the highlighted challenges. By adhering to our recommendations, decision-making architectures that are both non-overcautious and safe, yet feasible, can be trained and validated in real-world unsignalized intersections environments. © 2024, CC BY-NC-ND.

关键词： Deep reinforcement learning

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Rotatable Antennas for Integrated Sensing and Communications

arXiv

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arXiv 2025年

作者： Zhou, Chao You, Changsheng Zheng, Beixiong Shao, Xiaodan Zhang, Rui Shenzhen518055 China School of Microelectronics South China University of Technology Guangzhou511442 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this letter, we propose to deploy rotatable antennas (RAs) at the base station (BS) to enhance both communication and sensing (C&S) performances, by exploiting a new spatial degree-of-freedom (DoF) offered by array rotation. Specifically, we formulate a multi-objective optimization problem to simultaneously maximize the sum-rate of multiple communication users and minimize the Cramér-Rao bound (CRB) for target angle estimation, by jointly optimizing the transmit beamforming vectors and the array rotation angle at the BS. To solve this problem, we first equivalently decompose it into two subproblems, corresponding to an inner problem for beamforming optimization and an outer problem for array rotation optimization. Although these two subproblems are non-convex, we obtain their high-quality solutions by applying the block coordinate descent (BCD) technique and one-dimensional exhaustive search, respectively. Moreover, we show that for the communication-only case, RAs provide an additional rotation gain to improve communication performance;while for the sensing-only case, the equivalent spatial aperture can be enlarged by RAs for achieving higher sensing accuracy. Finally, numerical results are presented to showcase the performance gains of RAs over fixed-rotation antennas in integrated sensing and communications (ISAC). Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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Deep learning and random light structuring ensure robust free-space communications

arXiv

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arXiv 2024年

作者： Li, Xiaofei Wang, Yu Liu, Xin Ma, Yuan Cai, Yangjian Ponomarenko, Sergey A. Liu, Xianlong Shandong Provincial Engineering and Technical Center of Light Manipulation Shandong Provincial Key Laboratory of Optics and Photonic Devices School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 2X4 Canada School of Information Science and Engineering Shandong Normal University Jinan250014 China Department of Physics and Atmospheric Science Dalhousie University HalifaxNSB3H 4R2 Canada

Having shown early promise, free-space optical communications (FSO) face formidable challenges in the age of information explosion. The ever-growing demand for greater channel communication capacity is one of the challenges. The inter-channel crosstalk, which severely degrades the quality of transmitted information, creates another roadblock in the way of efficient FSO implementation. Here we advance theoretically and realize experimentally a potentially high-capacity FSO protocol that enables high-fidelity transfer of an image, or set of images through a complex environment. In our protocol, we complement random light structuring at the transmitter with a deep learning image classification platform at the receiver. Multiplexing novel, independent, mutually orthogonal degrees of freedom available to structured random light can potentially significantly boost the channel communication capacity of our protocol without introducing any deleterious crosstalk. Specifically, we show how one can multiplex the degrees of freedom associated with the source coherence radius and a spatial position of a beamlet within an array of structured random beams to greatly enhance the capacity of our communication link. The superb resilience of structured random light to environmental noise, as well as extreme efficiency of deep learning networks at classifying images guarantees high-fidelity image transfer within the framework of our protocol. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical communication

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Unidirectional Chiral Emission via Twisted Bi-layer Metasurfaces

arXiv

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arXiv 2024年

作者： Gromyko, Dmitrii An, Shu Gorelik, Sergey Xu, Jiahui Lim, Li Jun Lee, Henry Yit Loong Tjiptoharsono, Febiana Tan, Zhi-Kuang Qiu, Cheng-Wei Dong, Zhaogang Wu, Lin 8 Somapah Road 487372 Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore 2 Fusionopolis Way Innovis #08-03 Singapore138634 Singapore 31 Biopolis Way #01-02 Nanos 138669 Singapore Department of Chemistry National University of Singapore 3 Science Drive 3 117543 Singapore Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore 1 Fusionopolis Way #16-16 Connexis 138632 Singapore

Controlling and channelling light emissions from unpolarized quantum dots into specific directions with chiral polarization remains a key challenge in modern photonics. Stacked metasurface designs offer a potential compact solution for chirality and directionality engineering. However, experimental observations of directional chiral radiation from resonant metasurfaces with quantum emitters remain obscure. In this paper, we present experimental observations of unidirectional chiral emission from a twisted bi-layer metasurface via multidimensional control, including twist angle, interlayer distance, and lateral displacement between the top and bottom layers, as enabled by doublet alignment lithography (DAL). First, maintaining alignment, the metasurface demonstrates a resonant intrinsic optical chirality with near-unity circular dichroism of 0.94 and reflectance difference of 74%, where a high circular dichroism greater than 0.9 persists across a wide range of angles from -11 to 11 degrees. Second, engineered lateral displacement induces a unidirectional chiral resonance, resulting in unidirectional chiral emission from the quantum dots deposited onto the metasurface. Our bilayer metasurfaces offer a universal compact platform for efficient radiation manipulation over a wide angular range, promising potential applications in miniaturized lasers, grating couplers, and chiral nanoantennas. Copyright © 2024, The Authors. All rights reserved.

关键词： Perovskite

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R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm

arXiv

引用

arXiv 2024年

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Bois, Naomi Du Smith, Alice E. Coyle, Damien Intelligent Systems Research Center School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Industrial and Systems Engineering Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Choosing an appropriate optimization algorithm is essential to achieving success in optimization challenges. Here we present a new evolutionary algorithm structure that utilizes a reinforcement learning-based agent aimed at addressing these issues. The agent employs a double deep q-network to choose a specific evolutionary operator based on feedback it receives from the environment during optimization. The algorithm’s structure contains five single-objective evolutionary algorithm operators. This single-objective structure is transformed into a multi-objective one using the R2 indicator. This indicator serves two purposes within our structure: first, it renders the algorithm multi-objective, and second, provides a means to evaluate each algorithm’s performance in each generation to facilitate constructing the reinforcement learning-based reward function. The proposed R2-reinforcement learning multi-objective evolutionary algorithm (R2-RLMOEA) is compared with six other multiobjective algorithms that are based on R2 indicators. These six algorithms include the operators used in R2-RLMOEA as well as an R2 indicator-based algorithm that randomly selects operators during optimization. We benchmark performance using the CEC09 functions, with performance measured by inverted generational distance and spacing. The R2-RLMOEA algorithm outperforms all other algorithms with strong statistical significance (p © 2024, CC BY.

关键词： Reinforcement learning

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Inverse Design with Dynamic Mode Decomposition

arXiv

引用

arXiv 2025年

作者： Zhu, Yunpeng Cheng, Liangliang Jing, Anping Huo, Hanyu Lang, Ziqiang Zhang, Bo Kutz, J. Nathan School of Engineering and Material Science Queen Mary University of London LondonE1 4NS United Kingdom ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands School of Mechanical Engineering Ningxia University Yinchuan750021 China School of Chemistry and Material Science University of Science and Technology of China Hefei230026 China School of Electrical and Electronic Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Department of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA United States

We introduce a computationally efficient method for the automation of inverse design in science and engineering. Based on simple least-square regression, the underlying dynamic mode decomposition algorithm can be used to construct a low-rank subspace spanning multiple experiments in parameter space. The proposed inverse design dynamic mode composition (ID-DMD) algorithm leverages the computed low-dimensional subspace to enable fast digital design and optimization on laptop-level computing, including the potential to prescribe the dynamics themselves. Moreover, the method is robust to noise, physically interpretable, and can provide uncertainty quantification metrics. The architecture can also efficiently scale to large-scale design problems using randomized algorithms in the ID-DMD. The simplicity of the method and its implementation are highly attractive in practice, and the ID-DMD has been demonstrated to be an order of magnitude more accurate than competing methods while simultaneously being 3-5 orders faster on challenging engineering design problems ranging from structural vibrations to fluid dynamics. Due to its speed, robustness, interpretability, and ease-of-use, ID-DMD in comparison with other leading machine learning methods represents a significant advancement in data-driven methods for inverse design and optimization, promising a paradigm shift in how to approach inverse design in *** Codes 37M05, 37M10, 37M21 Copyright © 2025, The Authors. All rights reserved.

关键词： Dynamic mode decomposition

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Combining Transformers with Natural Language Explanations

arXiv

引用

arXiv 2021年

作者： Ruggeri, Federico Lippi, Marco Torroni, Paolo Department of Computer Science and Engineering viale Risorgimento 2 Bologna Italy Department of Sciences and Methods for Engineering via Amendola 2 Reggio Emilia Italy

Many NLP applications require models to be interpretable. However, many successful neural architectures, including transformers, still lack effective interpretation methods. A possible solution could rely on building explanations from domain knowledge, which is often available as plain, natural language text. We thus propose an extension to transformer models that makes use of external memories to store natural language explanations and use them to explain classification outputs. We conduct an experimental evaluation on two domains, legal text analysis and argument mining, to show that our approach can produce relevant explanations while retaining or even improving classification performance. © 2021, CC BY-SA.

关键词： Text processing

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

引用

arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Exploring the Anti-Biofilm Properties of Austenitic Stainless Steel: A Study on Material Surface Characteristics and Biofilm Resistance via Selective Laser Sintering

Exploring the Anti-Biofilm Properties of Austenitic Stainles...

引用

Steel Properties and Applications in conjunction with Materials science and Technology 2024

作者： Kanematsu, Hideyuki Barry, Dana M. Saiki, Tsunemasa Hirayama, Akihiro Otsu, Akira Ogata, Koichiro Ogawa, Akiko Kogo, Takeshi Konishi, Hirokazu Hirai, Nobumitsu Miura, Hidekazu Nakano, Takayoshi National Institute of Technology Suzuka College Shiroko-cho Suzuka Mie510-0294 Japan Division of Materials & Manufacturing Science Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita565-0871 Japan Department of Electrical & Computer Engineering Clarkson University PotsdamNY United States SUNY Canton CantonNY United States Hyogo Prefectural Institute of Technology 3-1-12 Yukihira-cho Suma-ku Hyogo Kobe654-0037 Japan National Institute of Technology Oita College 1666 Oaza Maki Oita Oita870-0152 Japan Suzuka University of Medical Science 1001-1 Kishiokacho Suzuka Mie510-0293 Japan

ISBN: (纸本)9798331307202

Biofilms, structured communities of bacteria that adhere to and proliferate on medical surfaces, can become sanctuaries for bacteria against antibiotics, mainly when formed on implants and medical devices. This often leads to chronic infections and hospital-acquired infections, making the development of effective countermeasures critical. Given that biofilms result from the interaction between bacteria and material surfaces, manipulating material properties could offer a viable strategy for mitigation. This study investigates the anti-biofilm properties of austenitic stainless steel produced with varying grain sizesusing Selective Laser Sintering (SLS). By examining the relationship between surface characteristics of the materials and their resistance to biofilm formation, the research aims to identify key material attributes that inhibit biofilm development, potentially leading to the design of more hygienic medical devices. © 2024 by the Association for Iron & Steel Technology.

关键词： Biofilms

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Joint Program Partitioning and Resource Allocation for Completion Time Minimization in Multi-MEC Systems

arXiv

引用

arXiv 2022年

作者： Yi, Taizhou Zhang, Guopeng Wang, Kezhi Yang, Kun School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Department of Computer and Information Science Northumbria University NewcastleNE2 1XE United Kingdom Zhongshan528400 China Chengdu611731 China School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

This paper considers a practical mobile edge computing (MEC) system, where edge server does not preinstall the program required to perform user offloaded computing tasks. A partial program offloading (PPO) scheme is proposed, which can divide a user program into two parts, where the first part is executed by the user itself and the second part is transferred to an edge server for remote execution. However, the execution of the latter part requires the results of the previous part (called intermediate result) as the input. We aim to minimize the overall time consumption of a multi-server MEC system to complete all user offloaded tasks. It is modeled as a mixed integer nonlinear programming (MINLP) problem which considers user-and-server association, program partitioning, and communication resource allocation in a joint manner. An effective algorithm is developed to solve the problem by exploiting its structural features. First, the task completion time of a single server is minimized given the computing workload and available resource. Then, the working time of the edge servers are balanced by updating user-and-server association and communication resource allocation. Numerical results show that significant performance improvement can be achieved by the proposed scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile edge computing

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