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Correction to: SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification

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Medical & biological engineering & computing 2024年第9期62卷 2785页

作者： Akib Zaman Shiu Kumar Swakkhar Shatabda Iman Dehzangi Alok Sharma Computer Science and Artificial Intelligence Laboratory (CSAIL) Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge MA USA. School of Electrical & Electronics Engineering Fiji National University Suva Fiji. shiu.kumar@fnu.ac.fj. Centre for Artificial Intelligence and Robotics (CAIR) United International University Dhaka Bangladesh. Department of Computer Science Rutgers University Camden NJ USA. Center for Computational and Integrative Biology Rutgers University Camden USA. Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230‑0045 Japan. Institute for Integrated and Intelligent Systems Griffith University Nathan Brisbane QLD Australia.

Not all fabrics are created equal: Exploring eFPGA parameters for IP redaction

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arXiv 2021年

作者： Bhandari, Jitendra Khader Thalakkattu Moosa, Abdul Tan, Benjamin Pilato, Christian Gore, Ganesh Tang, Xifan Temple, Scott Gaillardon, Pierre-Emmanuel Karri, Ramesh Center for Cybersecurity New York University New York CityNY11201 United States Department of Electrical and Software Engineering University of Calgary CalgaryABT2N 1N4 Canada Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano20133 Italy Laboratory for NanoIntegrated Systems Electrical and Computer Engineering Department University of Utah Salt Lake CityUT84112 United States

Semiconductor design houses rely on third-party foundries to manufacture their integrated circuits (IC). While this trend allows them to tackle fabrication costs, it introduces security concerns as external (and potentially malicious) parties can access critical parts of the designs and steal or modify the Intellectual Property (IP). Embedded FPGA (eFPGA) redaction is a promising technique to protect critical IPs of an ASIC by redacting (i.e., removing) critical parts and mapping them onto a custom reconfigurable fabric. Only trusted parties will receive the correct bitstream to restore the redacted functionality. While previous studies imply that using an eFPGA is a sufficient condition to provide security against IP threats like reverse-engineering, whether this truly holds for all eFPGA architectures is unclear, thus motivating the study in this paper. We examine the security of eFPGA fabrics generated by varying different FPGA design parameters. We characterize the power, performance, and area (PPA) characteristics and evaluate each fabric’s resistance to SAT-based bitstream recovery. Our results encourage designers to work with custom eFPGA fabrics rather than off-the-shelf commercial FPGAs and reveals that only considering a redaction fabric’s bitstream size is inadequate for gauging security. Copyright © 2021, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

Dynamic Normalization in MOEA/D for Multiobjective optimization

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Dynamic Normalization in MOEA/D for Multiobjective optimizat...

Congress on Evolutionary Computation

作者： Linjun He Hisao Ishibuchi Anupam Trivedi Dipti Srinivasan Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Objective space normalization is important since areal-world multiobjective problem usually has differently scaled objective functions. Recently, bad effects of the commonly used simple normalization method have been reported for the popular decomposition-based algorithm MOEA/D. However, the effects of recently proposed sophisticated normalization methods have not been investigated. In this paper, we examine the effectiveness of these normalization methods in MOEA/D. We find that these normalization methods can cause performance deterioration. We also find that the sophisticated normalization methods are not necessarily better than the simple one. Although the negative effects of inaccurate estimation of the nadir point are well recognized in the literature, no solution has been proposed. In order to address this issue, we propose two dynamic normalization strategies which dynamically adjust the extent of normalization during the evolutionary process. Experimental results clearly show the necessity of considering the extent of normalization.

关键词： Linear programming Sociology Estimation Pareto optimization Measurement

Acoustic Willis metamaterials beyond the passivity bound

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arXiv 2020年

作者： Cho, Choonlae Wen, Xinhua Park, Namkyoo Li, Jensen Department of Physics Hong Kong University of Science and Technology Clear Water Bay Hong Kong Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Acoustic bianisotropy, also known as the Willis parameter, expands the field of acoustics by providing nonconventional couplings between momentum and strain in constitutive relations. Sharing the common ground with electromagnetics, the realization of acoustic bianisotropy enables the exotic manipulation of acoustic waves in cooperation with a properly designed inverse bulk modulus and mass density. While the control of entire constitutive parameters substantiates intriguing theoretical and practical applications, a Willis metamaterial that enables independently and precisely designed polarizabilities has yet to be developed to overcome the present restrictions of the maximum Willis bound and the nonreciprocity inherent to the passivity of metamaterials. Here, by extending the recently developed concept of virtualized metamaterials, we propose acoustic Willis metamaterials that break the passivity and reciprocity limit while also achieving decoupled control of all constitutive parameters with designed frequency responses. By instituting basis convolution kernels based on parity symmetry for each polarization response, we experimentally demonstrate bianisotropy beyond the limit of passive media. Furthermore, based on the notion of inverse design of the frequency dispersion by means of digital convolution, purely nonreciprocal media and media with a broadband, flat-response Willis coupling are also demonstrated. Our approach offers all possible independently programmable extreme constitutive parameters and frequency dispersion tunability accessible within the causality condition and provides a flexible platform for realizing the full capabilities of acoustic metamaterials. Copyright © 2020, The Authors. All rights reserved.

关键词： Metamaterials

Blackhole-Inspired Thermal Trapping with Graded Heat-Conduction Metadevices

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arXiv 2022年

作者： Xu, Liujun Liu, Jinrong Jin, Peng Xu, Guoqiang Li, Jiaxin Ouyang, Xiaoping Li, Ying Qiu, Cheng-Wei Huang, Jiping Fudan University Shanghai200438 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Graduate School of China Academy of Engineering Physics Beijing100193 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Xiangtan University Xiangtan411105 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining314400 China

Black holes are one of the most intriguing predictions of general relativity. So far, metadevices have enabled analogous black holes to trap light or sound in laboratory spacetime. However, trapping heat in a conductive ambient is still challenging because diffusive behaviors are directionless. Inspired by black holes, we construct graded heat-conduction metadevices to achieve thermal trapping, resorting to the imitated advection produced by graded thermal conductivities rather than the trivial solution of using insulation materials to confine thermal diffusion. We experimentally demonstrate thermal trapping for guiding hot spots to diffuse towards the center. Graded heat-conduction metadevices have advantages in energy-efficient thermal regulation because the imitated advection has a similar temperature field effect to the realistic advection that is usually driven by external energy sources. These results also provide insights into correlating transformation thermotics with other disciplines such as cosmology for emerging heat control schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Advection

Upper-Bounded Price of Anarchy in D2D Communications over 5G Edge-Computing Mobile Wireless Networks

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Upper-Bounded Price of Anarchy in D2D Communications over 5G...

2019 INFOCOM IEEE Conference on computer Communications Workshops, INFOCOM WKSHPS 2019

作者： Xu, Xiaohua Lee, Benjamin Chen, Yuanfang Zhang, Xi Department of Computer Science Kennesaw State University Marietta United States School of Cyberspace Hangzhou Dianzi University Hangzhou China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States

ISBN: (纸本)9781728118789

Device to Device (D2D) communications provide a means of boosting communication capacity. As there is a gap between the D2D requests and the available spectrum resource, it is critical to design an allocation rule for user equipment and payment. We design two mechanisms from a linear programming perspective and the maximum in range perspective, respectively. Both mechanisms can achieve the upper-bounded approximation ratios in terms of price of anarchy for D2D communications over 5G edge-computing mobile networks. These two mechanisms have found various applications in other disciplines as well. We conduct numerical studies and compare our work with a greedy method. We show that the proposed algorithm outperforms the greedy method. © 2019 IEEE.

关键词： Edge computing

Decentralizing feature extraction with quantum convolutional neural network for automatic speech recognition

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arXiv 2020年

作者： Yang, Chao-Han Huck Qi, Jun Chen, Samuel Yen-Chi Chen, Pin-Yu Siniscalchi, Sabato Marco Ma, Xiaoli Lee, Chin-Hui School of Electrical and Computer Engineering Georgia Institute of Technology United States Brookhaven National Laboratory NY United States IBM Research Yorktown HeightsNY United States Faculty of Computer and Telecommunication Engineering University of Enna Italy Department of Electronic Systems NTNU Trondheim Norway

We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction, and a recurrent neural network (RNN) based end-to-end acoustic model (AM). To enhance model parameter protection in a decentralized architecture, an input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram, and the corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition. The proposed decentralized framework takes advantage of the quantum learning progress to secure models and to avoid privacy leakage attacks. Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95.12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features. We conduct an in-depth study of different quantum circuit encoder architectures to provide insights into designing QCNN-based feature extractors. Neural saliency analyses demonstrate a high correlation between the proposed QCNN features, class activation maps, and the input Mel-spectrogram. We provide an implementation1 for future studies. © 2020, CC BY-NC-SA.

关键词： Speech recognition

Substation-level grid topology optimization using bus splitting

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arXiv 2020年

作者： Zhou, Yuqi Zamzam, Ahmed S. Bernstein, Andrey Zhu, Hao The Department of Electrical & Computer Engineering The University of Texas at Austin 2501 Speedway AustinTX78712 United States The Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States

Operations of substation circuit breakers are important for maintenance needs and topology reconfiguration in power systems. Bus splitting is one type of topology change where the two bus bars at a substation can become electrically disconnected under certain actions of circuit breakers. Because these events involve detailed substation modeling, they are typically not considered in routine power system operation and control. In this paper, an improved substation-level topology optimization framework is developed by expanding traditional line switching decisions by breaker-level bus splitting, which can further reduce grid congestion and generation costs. A tight McCormick relaxation is proposed to reformulate the bilinear terms in the resultant optimization problem to linear inequality constraints. Thus, a tractable mixed-integer linear program reformulation is attained that allows for efficient solutions in real-time operations. Numerical studies on the IEEE 14-bus and 118-bus systems demonstrate the computational performance and economic benefits of the proposed topology optimization approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Buses

Byzantine-robust variance-reduced federated learning over distributed Non-i.i.d. Data

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arXiv 2020年

作者： Peng, Jie Wu, Zhaoxian Ling, Qing Chen, Tianyi the School of Computer Science and Engineering the Guangdong Provincial Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510006 China the Pazhou Lab Guangzhou510335 China the Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

—We consider the federated learning problem where data on workers are not independent and identically distributed (i.i.d.). During the learning process, an unknown number of Byzantine workers may send malicious messages to the central node, leading to remarkable learning error. Most of the Byzantine-robust methods address this issue by using robust aggregation rules to aggregate the received messages, but rely on the assumption that all the regular workers have i.i.d. data, which is not the case in many federated learning applications. In light of the significance of reducing stochastic gradient noise for mitigating the effect of Byzantine attacks, we use a resampling strategy to reduce the impact of both inner variation (that describes the sample heterogeneity on every regular worker) and outer variation (that describes the sample heterogeneity among the regular workers), along with a stochastic average gradient algorithm to gradually eliminate the inner variation. The variance-reduced messages are then aggregated with a robust geometric median operator. We prove that the proposed method reaches a neighborhood of the optimal solution at a linear convergence rate and the learning error is determined by the number of Byzantine workers. Numerical experiments corroborate the theoretical results and show that the proposed method outperforms the state-of-the-arts in the non-i.i.d. setting. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems