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arXiv 2023年

作者： Cheng, Yuan Yang, Jing Liang, Yingbin Integrative Sciences and Engineering Programme NUS Graduate School Singapore School of Electrical Engineering and Computer Science The Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering The Ohio State University OH43210 United States

Reinforcement learning (RL) under changing environment models many real-world applications via nonstationary Markov Decision Processes (MDPs), and hence gains considerable interest. However, theoretical studies on nonstationary MDPs in the literature have mainly focused on tabular and linear (mixture) MDPs, which do not capture the nature of unknown representation in deep RL. In this paper, we make the first effort to investigate nonstationary RL under episodic low-rank MDPs, where both transition kernels and rewards may vary over time, and the low-rank model contains unknown representation in addition to the linear state embedding function. We first propose a parameter-dependent policy optimization algorithm called PORTAL, and further improve PORTAL to its parameter-free version of Ada-PORTAL, which is able to tune its hyper-parameters adaptively without any prior knowledge of nonstationarity. For both algorithms, we provide upper bounds on the average dynamic suboptimality gap, which show that as long as the nonstationarity is not significantly large, PORTAL and Ada-PORTAL are sample-efficient and can achieve arbitrarily small average dynamic suboptimality gap with polynomial sample complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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The Best Interval Representation of Fuzzy S-Implications and Automorphisms

The Best Interval Representation of Fuzzy S-Implications and...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Benjamin C. Bedregal Regivan H. N. Santiago Renata H. S. Reiser Gracaliz P. Dimuro Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Graduate Programme in Computer Science Catholic University of Pelotas Pelotas Rio Grande do Sul Brazil

The aim of this work is to analyze interval fuzzy S-implications and interval automorphisms. Starting from any fuzzy S-implication, it is shown how to obtain an interval fuzzy S-implication canonically. We proved that such interval fuzzy S-implications meet the optimality property and preserve the same properties satisfied by fuzzy S-implications. In addition, commutative diagrams are used in order to relate fuzzy S-implications to interval fuzzy S-implications, and to understand how interval automorphisms act on interval S-implications, generating other interval fuzzy S-implications.

关键词： Fuzzy logic Uncertainty Mathematics Fuzzy sets Fuzzy set theory Fuzzy control Control systems Humans Accuracy Optimal control

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Digitally-defined ultrathin transparent wireless sensor network for room-scale imperceptible ambient intelligence

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npj Flexible Electronics 2024年第1期8卷 869-878页

作者： Yunxia Jin Mengxia Yu Dat T.Nguyen Xin Yang Zhipeng Li Ze Xiong Chenhui Li Yuxin Liu Yong Lin Kong John S.Ho Institute for Health Innovation and Technology National University of SingaporeSingapore 117599Singapore Department of Biomedical Engineering National University of SingaporeSingapore 119276Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate SchoolSingapore 119077Singapore The N.1 Institute for Health National University of SingaporeSingapore 117456Singapore Institute of Materials Research and Engineering Agency for Science Technology and ResearchSingapore 138634Singapore Department of Mechanical Engineering University of UtahSalt Lake CityUT 84112USA

Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments,accommodate daily activities,and functionally integrate with ***,existing RF sensors are unable to simultaneously achieve high transparency,flexibility,and the electrical conductivity required for remote room-scale ***,we report 4.5μm RF tag sensors achieving transparency exceeding 90%that provide capabilities in room-scale ambient wireless *** develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at *** individually tagging multiple objects and regions of the human body,we demonstrate multiplexed wireless tracking of humanenvironment interactions and physiological signals at a range of up to *** radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.

关键词： network ambient transparent

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Robust triboelectric information-mat enhanced by multi-modality deep learning for smart home

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InfoMat 2023年第1期5卷 139-160页

作者： Yanqin Yang Qiongfeng Shi Zixuan Zhang Xuechuan Shan Budiman Salam Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingaporeSingapore National University of Singapore Suzhou Research Institute(NUSRI) SuzhouPeople's Republic of China Singapore Institute of Manufacturing Technology and National University of Singapore(SIMTech-NUS)Joint Lab on Large-area Flexible Hybrid Electronics National University of SingaporeSingaporeSingapore Printed Intelligent Device Group Singapore Institute of Manufacturing TechnologyAgency for ScienceTechnology and Research(A*STAR)SingaporeSingapore NUS Graduate School-Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingaporeSingapore

In metaverse,a digital-twin smart home is a vital platform for immersive communication between the physical and virtual *** nanogenerators(TENGs)sensors contribute substantially to providing smart-home ***,TENG deployment is hindered by its unstable out-put under environment ***,we develop a digital-twin smart home using a robust all-TENG based information mat(InfoMat),which consists of an in-home mat array and an entry *** interdigital electrodes design allows environment-insensitive ratiometric readout from the mat array to can-cel the commonly experienced environmental *** position sensing is also achieved because of the interval arrangement of the mat ***,the two-channel entry mat generates multi-modality informa-tion to aid the 10-user identification accuracy to increase from 93% to 99% compared to the one-channel ***,a digital-twin smart home is visualized by real-time projecting the information in smart home to virtual reality,including access authorization,position,walking trajectory,dynamic activities/sports,and so on.

关键词： digital twin environment-insensitive multi-modality deep learning scalability smart home triboelectric information-mat

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Computation-Efficient Model Predictive Control of a Multilevel Current Source Converter

Computation-Efficient Model Predictive Control of a Multilev...

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Annual Conference of Industrial Electronics Society

作者： Muyu Chen Zhige Yuan Amer M. Y. M. Ghias Vun Chan Hua Nicholas School of Electrical and Electronic Engineering Collab. Initiative Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

This paper presents a modified finite control set model predictive control (FCS-MPC) that can significantly reduce computation time and maintain good quality of output voltage. The switching states of a three-phase five-level current source converter (5L-CSC) are sorted according to the corresponding output converter current level. The influences of every switching state on the converter inductor current are analyzed. Then the optimal switching state can be anchored based on the converter current level that minimizes the cost function and converter inductor current conditions. The proposed method eliminates the need for the discrete-time model establishment for the converter. It avoids the ergodic search through all switching states, easing the controller's computation burden. Besides, this algorithm achieves multiple control goals without assigning different weighting factors to various control objectives. The simulated output voltage total harmonics distortion (THD), the inductor current ripple, and the execution time of the modified model predictive control and traditional model predictive control are compared to validate the effectiveness of the proposed algorithm.

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Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization

Covid-19 Identification with Chest X-Ray Images merging Hand...

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IEEE International Conference on Computing, Communication and Security (ICCCS)

作者： Rik Das Mohammad Arshad P.K. Manjhi Sudeep D. Thepade Post Graduate Programme in Information Technology Xavier Institute of Social Service Ranchi India Faculty of Science (Computer Applications) Vinoba Bhave University Hazaribag India Vinoba Bhave University Hazaribag India Pimpri Chinchwad College of Engineering Pune Maharashtra

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. Nevertheless, quick identification of the disease can lead to faster recovery with medical help. Unfortunately, the ratio of COVID infection to methods of early medical identification of disease is quite feeble. This has motivated researchers in designing computer aided diagnosis (CAD) systems which can identify the disease from easily available chest X-Ray images of patients. However, the success rate of identification of COVID cases is not satisfactory since the proposed systems are emphasizing on identifying pneumonia rather than separating the cause of it as COVID or non COVID. In this paper, the authors have investigated the cause of such misclassification and have identified feature generalization as one of the reasons. A feature fusion based approach is proposed by the authors to encourage generalization of input features to CAD for better identification of COVID 19. The reason for proposing the fusion based feature formation is encompassing diverse X-Ray image information by extracting descriptors with assorted techniques and uniting them with fusion. The classification results with fusion based approach have reported substantial improvements compared to individual techniques. This in turn indicates better generalization of input features with feature fusion.

关键词： Feature extraction COVID-19 Lung X-ray imaging Support vector machines Histograms Convolutional neural networks

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

Spin-UP: Spin Light for Natural Light Uncalibrated Photometr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zongrui Li Zhan Lu Haojie Yan Boxin Shi Gang Pan Qian Zheng Xudong Jiang Rapid-Rich Object Search (ROSE) Lab Interdisciplinary Graduate Programme Nanyang Technological University School of Electrical and Electronic Engineering Nanyang Technological University The State Key Lab of Brain-Machine Intelligence Zhejiang University College of Computer Science and Technology Zhejiang University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised / unsupervised NaUPS meth-ods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP.

关键词： Reflectivity Training Shape Supervised learning Anisotropic Rendering (computer graphics) Structural engineering

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Room-Temperature Switching of Perpendicular Magnetization by Magnon Torques

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Physical Review Applied 2023年第3期19卷 034039-034039页

作者： Guoyi Shi Fei Wang Hui Ru Tan Shishun Zhao Yakun Liu Dongsheng Yang Kyusup Lee Yuchen Pu Shuhan Yang Anjan Soumyanarayanan Hyunsoo Yang Integrative Sciences and Engineering Programme NUS Graduate School National University of Singapore Singapore 119077 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore Institute of Materials Research & Engineering Agency for Science Technology & Research (A*STAR) Singapore 138634 Singapore Department of Physics National University of Singapore Singapore 117551 Singapore

Electron-mediated spin torque provides a fast and efficient method to manipulate magnetization; however, electron motion inevitably brings about the generation of Joule heat and corresponding power consumption. Magnon-mediated spin torque, without involving moving electrons, could circumvent the energy dissipation issue. In this work, we fabricate a sandwich structure of topological insulator/antiferromagnetic insulator/ferromagnet with perpendicular magnetic anisotropy. We find that the magnon current with spin angular momentum can traverse a 25-nm-thick antiferromagnetic NiO layer and effectively switch the perpendicular magnetization of Co−Fe−B at room temperature with a critical switching current density of 4.1 × 106A/cm2. The magnon torque efficiency is characterized using spin-torque ferromagnetic resonance measurements to be 0.33 with a magnon diffusion length of 26.6 nm. Our work paves the way for manipulating perpendicular magnetization via magnon torques, facilitating the exploration of magnon-based spintronics with low power consumption.

关键词： Magnetic anisotropy Magnetization switching Magnons Spin-orbit torque Ferromagnets Magnetic insulators Magnetic multilayers Topological insulators

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An Energy-Efficient Convolution Unit for Depthwise Separable Convolutional Neural Networks

An Energy-Efficient Convolution Unit for Depthwise Separable...

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IEEE International Symposium on Circuits and Systems

作者： Yi Sheng Chong Wang Ling Goh Yew Soon Ong Vishnu P. Nambiar Anh Tuan Do School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore Energy Research Institute Interdisciplinary Graduate Programme NTU Singapore Nanyang Technological University Singapore Singapore School of Computer Science and Engineering NTU Singapore Institute of Microelectronics A*STAR Singapore

High performance but computationally expensive Convolutional Neural Networks (CNNs) require both algorithmic and custom hardware improvement to reduce model size and to improve energy efficiency for edge computing applications. Recent CNN architectures employ depthwise separable convolution to reduce the total number of weights and MAC operations. However, depthwise separable convolution workload does not run efficiently in existing CNN accelerators. This paper proposes an energy-efficient CONV unit for pointwise and depthwise operation. The CONV unit utilizes weight stationary to enable high efficiency. The row partial sum reduction is engaged to increase parallelism in pointwise convolution thereby lightening the memory requirements on output partial sums. Our design achieves a maximum efficiency of 3.17 TOPS/W at 0.85V/40nm CMOS which is well-suited for energy constrained edge computing applications.

关键词： Convolution Memory management Prototypes Parallel processing Energy efficiency Convolutional neural networks Edge computing

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Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders

arXiv

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arXiv 2024年

作者： Yu, Yi Wang, Yufei Xia, Song Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore PengCheng Laboratory Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore

Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks can be categorized based on whether specific interventions are adopted during training. The first approach is training-time defense, such as adversarial training, which can mitigate poisoning effects but is computationally intensive. The other approach is pre-training purification, e.g., image short squeezing, which consists of several simple compressions but often encounters challenges in dealing with various UEs. Our work provides a novel disentanglement mechanism to build an efficient pre-training purification method. Firstly, we uncover rate-constrained variational autoencoders (VAEs), demonstrating a clear tendency to suppress the perturbations in UEs. We subsequently conduct a theoretical analysis for this phenomenon. Building upon these insights, we introduce a disentangle variational autoencoder (D-VAE), capable of disentangling the perturbations with learnable class-wise embeddings. Based on this network, a two-stage purification approach is naturally developed. The first stage focuses on roughly eliminating perturbations, while the second stage produces refined, poison-free results, ensuring effectiveness and robustness across various scenarios. Extensive experiments demonstrate the remarkable performance of our method across CIFAR-10, CIFAR-100, and a 100-class ImageNet-subset. Code is available at https://***/yuyi-sd/D-VAE. Copyright © 2024, The Authors. All rights reserved.

关键词： Purification

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