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arXiv 2024年

作者： Graham, J.N. Islam, S.S. Sazgari, V. Li, Y. Deng, H. Janka, G. Zhong, Y. Gerguri, O. Král, P. Doll, A. Bialo, I. Chang, J. Salman, Z. Suter, A. Prokscha, T. Yao, Y. Okazaki, K. Luetkens, H. Khasanov, R. Wang, Z. Yin, J.-X. Guguchia, Z. PSI Center for Neutron and Muon Sciences CNM Villigen5232 Switzerland Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of Education School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Department of Physics Southern University of Science and Technology Guangdong Shenzhen518055 China Institute for Solid States Physics The University of Tokyo Kashiwa Japan Physik-Institut Universität Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland International Center for Quantum Materials Beijing Institute of Technology Zhuhai319000 China Trans-scale Quantum Science Institute The University of Tokyo Tokyo Japan Material Innovation Research Center The University of Tokyo Kashiwa Japan

The experimental realisation of unconventional superconductivity and charge order in kagome systems AV3Sb5 is of critical importance. We conducted a highly systematic study of Cs(V1−xNbx)3Sb5 with x=0.07 (Nb0.07-CVS) by employing a unique combination of tuning parameters such as doping, hydrostatic pressure, magnetic fields, and depth, using muon spin rotation, AC susceptibility, and STM. We uncovered tunable magnetism in the normal state of Nb0.07-CVS, which transitions to a time-reversal symmetry (TRS) breaking superconducting state under pressure. Specifically, our findings reveal that the bulk of Nb0.07-CVS (at depths >20 nm from the surface) experiences TRS breaking below T∗ = 40 K, lower than the charge order onset temperature, TCO = 58 K. However, near the surface ( © 2024, CC BY.

关键词： Magnetic susceptibility

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BTI Aging Monitoring based on SRAM Start-up Behavior

BTI Aging Monitoring based on SRAM Start-up Behavior

引用

Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging System-on-chip

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Multi-Objective Squirrel Search Algorithm for EEG Feature Selection

SSRN

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SSRN 2022年

作者： Wang, Chao Li, Songjie Shi, Miao Zhao, Jie Acharya, U. Rajendra Xie, Neng-Gang Cheong, Kang Hao College of Civil Engineering and Architecture Anhui Polytechnic University Anhui Wuhu241000 China Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems Anhui Ma’anshan 243002 China Department of Management science and Engineering Anhui University of Technology Anhui Maanshan243002 China 8 Somapah Road Singapore487372 Singapore Department of Mechanical Engineering Anhui University of Technology Maanshan243002 China Department of Electronics and Computer Engineering Ngee Ann Polytechnic S599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Feature selection plays a critical role in the application of Brain computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualise controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online © 2022, The Authors. All rights reserved.

关键词： Brain computer interface

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Practical Stabilization of Passive Nonlinear systems with Limited Control

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IFAC-PapersOnLine 2019年第16期52卷 460-465页

作者： B. Jayawardhana M.Z. Almuzakki A. Tanwani Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen The Netherlands Department of Computer Science Faculty of Science and Computer Universitas Pertamina Jakarta Indonesia Laboratory for Analyses and Architecture of Systems (LAAS) –CNRS Université de Toulouse France

This article addresses output feedback stabilization of continuous-time nonlinear systems by choosing control actions from a finite set. Working under the assumption that the system under consideration is passive and large-time norm observable, we propose a static feedback mapping, from the output space to the finite set of control actions, which is shown to be practically stabilizing if the convex hull of certain control actions (in the chosen finite set) contains the origin in its interior. Consequently, to construct this stabilizing feedback, it suffices to have, in addition to a zero symbol, another m + 1 elements in the control set which form an m -simplex in ℝ m (the input, and output space).

关键词： Nonlinear passive systems finite control set output feedback binary control

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

arXiv

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

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Sparse code multiple access for 6G wireless communication networks: Recent advances and future directions

arXiv

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arXiv 2021年

作者： Yu, Lisu Liu, Zilong Wen, Miaowen Cai, Donghong Dang, Shuping Wang, Yuhao Xiao, Pei School of Information Engineering Nanchang University Nanchang330031 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China College of Cyber Security Jinan University Guangzhou510632 China Thuwal23955-6900 Saudi Arabia Institute for Communication Systems 5G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom

As 5G networks rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity. We propose to apply SCMA to a massively distributed access system (MDAS), whose architecture is based on fiber-based visible light communication (FVLC), ultra-dense network (UDN), and NOMA. Under this framework, we consider the interactions between optical front-hauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks. © 2021, CC BY-NC-ND.

关键词： Visible light communication

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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A simplified approach for presenting the differences between ionising and non-ionising electromagnetic radiation

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Physics Education 2020年第2期55卷 1-1页

作者： Kontomaris, S.V. Malamou, A. Balogiannis, G. Antonopoulou, N. Athens Metropolitan College Faculty of Architecture Engineering and Built Environment Athens Greece Radar Systems and Remote Sensing Lab School of Electrical and Computer Engineering of National Technical University of Athens Athens Greece Biomedical Optics and Applied Biophysics Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Molecular Biology and Genetics Democritus University of Thrace Alexandroupolis Greece

Electromagnetic radiation can be classified into two major types depending on its ability to detach electrons from atoms: ionising and non-ionising. The aforementioned categorization is significant due to the effects of ionising radiation on human tissue (e.g. carcinogenesis). However, many students around the globe cannot distinguish these two types of electromagnetic radiation and as a result significant misconceptions arise regarding this topic (for example radiation emitted by mobile phones is considered by many students as ionising). Hence, in this paper a teaching approach specifically designed and implemented for the secondary education level is presented, based on the absorbed energy distribution by atoms/molecules in each radiation type. More specifically, the number of atoms/molecules that absorb equal doses of energy per unit mass can be used in order to explain the difference in the effects caused by the two abovementioned radiation types. Also, two analogies from classical mechanics are presented to provide a full insight into the energy distribution concept from a teaching perspective. © 2019 IOP Publishing Ltd.

关键词： Science Instruction Energy Magnets Scientific Concepts Misconceptions Secondary School Science Molecular Structure Radiation Teaching Methods

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Thermoelectric properties of layered ternary telluride Nb3SiTe6

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Physical Review Materials 2020年第9期4卷 094205-094205页

作者： Yahui Pang Emad Rezaei Dongyun Chen Si Li Yu Jian Qinsheng Wang Zhiwei Wang Junxi Duan Mona Zebarjadi Yugui Yao Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Micronano Centre Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China Department of Electrical and Computer Engineering University of Virginia Charlottesville Virginia 22932 USA School of Physics and Electronics Hunan Normal University Changsha Hunan 410081 China Department of Materials Science and Engineering University of Virginia Charlottesville Virginia 22932 USA

We report the study of the thermoelectric properties of layered ternary telluride Nb3SiTe6. The temperature dependence of the thermoelectric power (TEP) evolves from nonlinear to linear when the thickness of the devices is reduced, consistent with the suppression of electron-phonon interaction caused by quantum confinement. The magnitude of TEP strongly depends on the hole density. It increases with decreasing hole density when the hole density is low, as observed in ionic-liquid-gated thin flakes. However, the device with the largest hole density possesses the highest TEP. Theoretical analysis suggests that the high TEP in the device with the largest hole density can be ascribed to the phonon-mediated intervalley scatterings. The highest TEP reaches ∼230μV/K at 370 K while the electrical resistivity of the device is maintained below 1.5mΩcm. Therefore, a large power factor PF ∼36μWcm−1K−2 comparable to the record values reported in p-type materials is obtained.

关键词： Seebeck effect Thermoelectric effects Topological materials Transport phenomena

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Exploring Fault-Tolerant Erasure Codes for Scalable All-Flash Array Clusters

arXiv

引用

arXiv 2019年

作者： Koh, Sungjoon Zhang, Jie Kwon, Miryeong Yoon, Jungyeon Donofrio, David Kim, Nam Sung Jung, Myoungsoo Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology School of Integrated Technology Yonsei Institute Convergence Technology Yonsei University Lawrence Berkeley National Laboratory Georgia Institute of Technology University of Illinois Urbana-Champaign

—Large-scale systems with all-flash arrays have become increasingly common in many computing segments. To make such systems resilient, we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to replication because erasure coding incurs a significantly lower storage overhead than replication. To understand the impact of using erasure coding on the system performance and other system aspects such as CPU utilization and network traffic, we build a storage cluster that consists of approximately 100 processor cores with more than 50 high-performance solid-state drives (SSDs), and evaluate the cluster with a popular open-source distributed parallel file system, called Ceph. Specifically, we analyze the behaviors of a system adopting erasure coding from the following five viewpoints, and compare with those of another system using replication: (1) storage system I/O performance;(2) computing and software overheads;(3) I/O amplification;(4) network traffic among storage nodes, and (5) impact of physical data layout on performance of RS-coded SSD arrays. For all these analyses, we examine two representative RS configurations, used by Google file systems, and compare them with triple replication employed by a typical parallel file system as a default fault tolerance mechanism. Lastly, we collect 96 block-level traces from the cluster and release them to the public domain for the use of other researchers. Copyright © 2019, The Authors. All rights reserved.

关键词： File organization

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