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International Conference on Display Technology, 2024

作者： Tang, Shu Tuen Chiu, Hon Wah Tseng, Man Chun Kwok, Hoi Sing State Key Laboratory on Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

Vertically Aligned LCD is one of the most widely used optical modes in LCD TV and mobile devices. It is also a very important category of passive LC displays used in Automobiles. It has the advantages of perfect dark state and very high contrast ratio for normal viewing. However, single domain VA LCD has the intrinsic issue of dead zone (contrast vanishing zone) in certain viewing direction in the voltage-on state, which seriously limits its viewing angle performance. Recently, the State Key laboratory on Advanced Displays and Optoelectronics Technologies of the Hong Kong University of Science and Technology has developed an easy-to-apply multi-domain VA LCD technology that can be readily implemented in LCD factories without major investment and change in current production process. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Azo dyes

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Task Placement and Resource Allocation for Edge Machine Learning: A GNN-Based Multi-Agent Reinforcement Learning Paradigm

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IEEE Transactions on Parallel and Distributed Systems 2023年第12期34卷 3073-3089页

作者： Li, Yihong Zhang, Xiaoxi Zeng, Tianyu Duan, Jingpu Wu, Chuan Wu, Di Chen, Xu Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Peng Cheng Laboratory Department of Communications Shenzhen518066 China Southern University of Science and Technology Institute of Future Networks Shenzhen518055 China University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources for ML tasks. This paper proposes TapFinger, a distributed scheduler for edge clusters that minimizes the total completion time of ML tasks through co-optimizing task placement and fine-grained multi-resource allocation. To learn the tasks' uncertain resource sensitivity and enable distributed scheduling, we adopt multi-agent reinforcement learning (MARL) and propose several techniques to make it efficient, including a heterogeneous graph attention network as the MARL backbone, a tailored task selection phase in the actor network, and the integration of Bayes' theorem and masking schemes. We first implement a single-task scheduling version, which schedules at most one task each time. Then we generalize to the multi-task scheduling case, in which a sequence of tasks is scheduled simultaneously. Our design can mitigate the expanded decision space and yield fast convergence to optimal scheduling solutions. Extensive experiments using synthetic and test-bed ML task traces show that TapFinger can achieve up to 54.9% reduction in the average task completion time and improve resource efficiency as compared to state-of-the-art schedulers. © 1990-2012 IEEE.

关键词： Reinforcement learning

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On Differentiation Among Pilot Signals of Multiple Mobile Targets in Retro-Reflective Beamforming for Wireless Power Transmission

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engineering 2023年第11期30卷 55-62页

作者： Xin Wang Long Li Tie Jun Cui Mingyu Lu School of Electrical Engineering Chongqing UniversityChongqing 400044China School of Electronic Engineering Xidian UniversityXi’an 710071China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Department of Electrical and Computer Engineering West Virginia University Institute of TechnologyBeckleyWV 25801USA

An experimental study is conducted on several retro-reflective beamforming schemes for wireless power transmission to multiple wireless power receivers(referred to herein as“targets”).The experimental results demonstrate that,when multiple targets broadcast continuous-wave pilot signals at respective frequencies,a retro-reflective wireless power transmitter is capable of generating multiple wireless power beams aiming at the respective targets as long as the multiple pilot signals are explicitly separated from one another by the wireless power ***,various practical complications are identified when the pilot signals of multiple targets are not appropriately differentiated from each other by the wireless power ***,when multiple pilot signals are considered to be carried by the same frequency,the wireless power transmission performance becomes heavily dependent on the interaction among the pilot signals,which is highly undesirable in *** conclusion,it is essential for a retro-reflective wireless power transmitter to explicitly discriminate multiple targets’pilot signals among each other.

关键词： Wireless power transmission Microwave antenna array Retro-reflective beamforming Pilot signal Multiple access

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yawen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350386226

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature's contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG's predictive prowess, with additional support from other omics features. © 2024 IEEE.

关键词： Adversarial machine learning

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Asymmetric Hong-Ou-Mandel Revival Interferences in a Singly-Filtered Biphoton Frequency Comb

Asymmetric Hong-Ou-Mandel Revival Interferences in a Singly-...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Chang, Kai-Chi Cheng, Xiang Chen, Yujie Chin, Hsiao-Hsuan Sarihan, Murat Can Song, Sophi Chen Wong, Chee Wei Fang Lu Mesoscopic Optics and Quantum Electronics Laboratory Department of Electrical Computer and Engineering University of California Los AngelesCA90095 United States

We observed first asymmetric Hong-Ou-Mandel (HOM) revival interferences in a singly-filtered biphoton frequency comb. We measure up to 10 time-bins, and the witnessed central HOM dip has an accidental-subtracted visib... 详细信息

关键词： Comb filters

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer Science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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In-situ fabrication of on-chip 1T'-MoTe_(2)/Ge Schottky junction photodetector for self-powered broadband infrared imaging and position sensing

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Nano Research 2024年第6期17卷 5587-5594页

作者： Menglei Zhu Kunxuan Liu Di Wu Yunrui Jiang Xue Li Pei Lin Zhifeng Shi Xinjian Li Ran Ding Yalun Tang Xuechao Yu Longhui Zeng School of Physics and Microelectronics and Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China

High-sensitivity room-temperature multi-dimensional infrared(IR)detection is crucial for military and civilian ***,the gapless electronic structures and unique optoelectrical properties have made the two-dimensional(2D)topological semimetals promising candidates for the realization of multifunctional optoelectronic ***,we demonstrated the in-situ construction of high-performance 1T’-MoTe_(2)/Ge Schottky junction device by inserting an ultrathin AlOx passivation *** good detection performance with an ultra-broadband detection wavelength range of up to 10.6 micron,an ultrafast response time of~160 ns,and a large specific detectivity of over 109 Jones in mid-infrared(MIR)range surpasses that of most 2D materials-based IR sensors,approaching the performance of commercial IR *** on-chip integrated device arrays with 64 functional detectors feature high-resolution imaging capability at room *** these outstanding detection features have enabled the demonstration of position-sensitive detection *** demonstrates an exceptional position sensitivity of 14.9 mV/mm,an outstanding nonlinearity of 6.44%,and commendable trajectory tracking and optoelectronic demodulation *** study not only offers a promising route towards room-temperature MIR optoelectronic applications,but also demonstrates a great potential for application in optical sensing systems.

关键词： MoTe_(2) broadband photodetection Schottky junction imaging position sensitive detector

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Performance Evaluation of Evolutionary Multi-Objective Algorithms Using Real-World Problems with an Additional Total Constraint Violation Objective 13

Performance Evaluation of Evolutionary Multi-Objective Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Nan, Yang Ishibuchi, Hisao Shu, Tianye Southern University of Science and Technology Guangdong Provinial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350308365

In the community of evolutionary multi-objective optimization (EMO), one important issue is the choice of test problems for performance evaluations of EMO algorithms. This is because performance evaluation results of EMO algorithms totally depend on the choice of test problems. This means that the research on new EMO algorithm design is also influenced by the choice of test problems. Recently, researchers have started to use real-world problems for performance evaluation of EMO algorithms. Among them, a real-world problem suite RE has attracted much attention and has been used in many studies. However, most RE problems have been created from real-world constrained problems by using the total constraint violation as an additional objective. That is, the original versions of most RE problems are not unconstrained multi-objective problems. Thus, even when a good solution set is obtained by an EMO algorithm for an RE problem, it can be a poor solution set for its original constrained problem. This is because many well-distributed solutions over the entire Pareto front of the transformed unconstrained problem are usually infeasible solutions of the original constrained problem with some positive total constraint violation values. In this paper, we examine whether good solution sets obtained by EMO algorithms for RE problems are also good solution sets for their original constrained problems. Our experimental results show that good solutions sets for most RE problems include good feasible solution sets for their original constrained problems. However, for a few RE problems, good solution sets obtained by some high performance EMO algorithms do not include good feasible solution sets. Our results show that high-performance EMO algorithms on most RE problems generate good feasible solution sets for their original constrained versions. This observation supports the usefulness of those RE problems as test problems for performance evaluation of EMO algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Distance-Based Subset Selection With a Knee Point for Multi-Objective Optimization 13

Distance-Based Subset Selection With a Knee Point for Multi-...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Pang, Lie Meng Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350373332

Recently, the use of an unbounded external archive in the design of evolutionary multi-objective optimization (EMO) algorithms has received increasing attention. An important component in the use of an unbounded external archive is its subset selection method used to select a pre-specified number of solutions from the archive. One of the most commonly used subset selection method is distance-based subset selection (DSS) since it is very easy to calculate and can easily scale to many-objective optimization. However, the DSS's selection mechanism usually lead to a small hypervolume value for the selected subset even when the optimal subset with respect to the minimum distance among the selected solutions is obtained. In this paper, we propose a modification to the DSS algorithm by selecting a knee point as the first solution. The knee point is often a solution of interest to decision makers in the absence of preference information. Experimental results show that the inclusion of a knee point generally has a positive effect on improving the hypervolume value of the selected subset. © 2024 IEEE.

关键词： Optimization algorithms

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Using Deep Learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems 23

Using Deep Learning and Object-Oriented Metrics to Identify ...

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5th World Symposium on Software engineering, WSSE 2023

作者： Tete, Akpedje Touré, Fadel Badri, Mourad Software Engineering Research laboratory Department of Mathematics and Computer Science University of Quebec Trois-RivièresQC Canada

ISBN: (纸本)9798400708053

This paper aims at studying the ability of deep machine learning to predict software faults based on object-oriented metrics. This research investigated software faults from the perspective of fault-proneness, faults number and faults frequency, and used data collected from several versions of a Java open-source software system. This study relied on Chidamber and Kemerer suite of metrics as proxy to capture various software characteristics. In this study, the deep learning regression and classification results were compared to linear and logistic regressions. Auto-Encoders (AE) and Principal Components Analysis (PCA) have been used to reduce redundant information from the dataset. To evaluate the prediction ability of the models, this research used the inter-version validation strategy. The results showed that the models can achieve a significant average performance up to 89%. © 2023 Copyright held by the owner/author(s).

关键词： Principal component analysis

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