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arXiv 2018年

作者： He, Rui Zhong, Shazhou Kim, Hyun Ho Ye, Gaihua Ye, Zhipeng Winford, Logan McHaffie, Daniel Rilak, Ivana Chen, Fangchu Luo, Xuan Sun, Yuping Tsen, Adam W. Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Physics University of Northern Iowa Cedar FallsIA50614 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Key Laboratory of Materials Physics Institute of Solid State Physics Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Centre of Advanced Microstructures Nanjing University Nanjing210093 China

We use a combination of Raman spectroscopy and transport measurements to study thin flakes of the type-II Weyl semimetal candidate MoTe2 protected from oxidation. In contrast to bulk crystals, which undergo a phase transition from monoclinic to the inversion symmetry breaking, orthorhombic phase below ~250 K, we find that in moderately thin samples below ~12 nm, a single orthorhombic phase exists up to and beyond room temperature. This could be due to the effect of c-axis confinement, which lowers the energy of an out-of-plane hole band and stabilizes the orthorhombic structure. Our results suggest that Weyl nodes, predicated upon inversion symmetry breaking, may be observed in thin MoTe2 at room temperature. Copyright © 2018, The Authors. All rights reserved.

关键词： Crystal symmetry

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Machine Learning in Nuclear Physics

arXiv

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arXiv 2021年

作者： Boehnlein, Amber Diefenthaler, Markus Sato, Nobuo Schram, Malachi Ziegler, Veronique Fanelli, Cristiano Hjorth-Jensen, Morten Horn, Tanja Kuchera, Michelle P. Lee, Dean Nazarewicz, Witold Ostroumov, Peter Orginos, Kostas Poon, Alan Wang, Xin-Nian Scheinker, Alexander Smith, Michael S. Pang, Long-Gang Thomas Jefferson National Accelerator Facility 12000 Jefferson Avenue Newport NewsVA United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Facility for Rare Isotope Beams Department of Physics and Astronomy Michigan State University MI48824 United States Department of Physics Center for Computing in Science Education University of Oslo OsloN-0316 Norway Department of Physics The Catholic University of America N. E. Washington DC20064 United States Department of Physics Department of Mathematics & Computer Science Davidson College NC28035 United States Department of Physics William & Mary WilliamsburgVA United States Nuclear Science Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Accelerator Operations and Technology Division Applied Electrodynamics Group Los Alamos National Laboratory Los AlamosNM87544 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37831-6354 United States Key Laboratory of Quark and Lepton Physics Institute of Particle Physics Central China Normal University Wuhan430079 China

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research which has been transformed by machine learning techniques. © 2021, CC BY.

关键词： Machine learning

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

arXiv

引用

arXiv 2024年

作者： Hu, Yaowen Song, Yunxiang Zhu, Xinrui Guo, Xiangwen Lu, Shengyuan Zhang, Qihang He, Lingyan Franken, C.A.A. Powell, Keith Warner, Hana Assumpcao, Daniel Renaud, Dylan Wang, Ying Magalhães, Letícia Rosborough, Victoria Shams-Ansari, Amirhassan Li, Xudong Cheng, Rebecca Luke, Kevin Yang, Kiyoul Barbastathis, George Zhang, Mian Zhu, Di Johansson, Leif Beling, Andreas Sinclair, Neil Loncar, Marko John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Beijing100871 China Quantum Science and Engineering Harvard University CambridgeMA02138 United States Department of Electrical and Computing Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States HyperLight Corporation 501 Massachusetts Ave CambridgeMA02139 United States Laser Physics and Nonlinear Optics Group Department of Science and Technology MESA+ Institute of Nanotechnology University of Twente Enschede Netherlands Freedom Photonics 41 Aero Camino GoletaCA United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Singapore138634 Singapore

The surge in artificial intelligence applications, encompassing machine vision, autonomous driving, remote sensing, and process simulation, relies on efficient and large-scale data processing. This requires highly scalable computation with high speed and low energy consumption, calling for innovative solutions to overcome the limitations of electronic systems1–9. Integrated photonics may play a pivotal role in addressing this challenge, leveraging the intrinsic properties of photons, such as natural parallelism, high bandwidth, and low latency10–15. While current photonic computing approaches show promise, they are critically limited by the speed and energy consumption of mapping information from the electronic to optical domain (i.e., electro-optic conversion)1–3,6–8,16 . Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photonic computation at speeds up to 1.36 TOPS while consuming 0.057 pJ/OP. Our system features more than 100 thin-film lithium niobate high-performance components working synergistically, surpassing state-of-the-art systems on this platform. We further demonstrate binary-classification, handwritten-digit classification, and image classification with remarkable accuracy, showcasing our system’s capability of executing real algorithms. Finally, we investigate the opportunities offered by combining our system with a hybrid-integrated distributed feedback laser source and a heterogeneous-integrated modified uni-traveling carrier photodiode. Our results illustrate the promise of thin-film lithium niobate as a computational platform, addressing current bottlenecks in both electronic and photonic computation. Its unique properties of high-performance electro-optic weight encoding and conversion, wafer-scale scalability, and compatibility with integrated lasers and

关键词： Distributed feedback lasers

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Corrigendum to “Trajectory design and resource allocation for UAV energy minimization in a rotary-wing UAV-enabled WPCN” [Alex. Eng. J. 60(1) (2021) 1787–1796]

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Alexandria Engineering Journal 2022年第12期61卷 11003-11003页

作者： Zhen Wang Miaowen Wen Shuping Dang Lisu Yu Yuhao Wang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing 100876 China School of Information Engineering Nanchang University Nanchang 330031 China School of Electronic and Information Engineering South China University of Technology Guangzhou 510641 China Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1UB UK State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

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基于漏电变化表征的参数偏差在线预测方法

基于漏电变化表征的参数偏差在线预测方法

引用

第七届中国测试学术会议

作者： Jin Song 靳松 Han Yinhe 韩银和 Xu Qiang 徐强 Department of Electronic and Communication Engineering School of Electrical and Electronic Engineer 华北电力大学电气与电子工程学院电子与通信工程系保定071003 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室北京100190 CUhk REliable Computing Laboratory(CURE) Department of Computer Science&Engineering The Chinese Un 香港中文大学计算机科学与工程系

负偏置温度不稳定(NBTI)引发的动态参数偏差给集成电路的生命期可靠性带来严重威胁。已有的方法主要通过设计片上检测电路实时监测关键通路的时延偏差来阻止NBTI导致的电路失效。然而，不断恶化的静、动参数偏差，不断增加的设计复杂度... 详细信息

负偏置温度不稳定(NBTI)引发的动态参数偏差给集成电路的生命期可靠性带来严重威胁。已有的方法主要通过设计片上检测电路实时监测关键通路的时延偏差来阻止NBTI导致的电路失效。然而，不断恶化的静、动参数偏差，不断增加的设计复杂度以及片上检测电路所引入的面积开销会影响上述方法的应用效果。为了弥补已有方法的不足，本文提出以测量到的电路漏电变化为表征，预测NBTI导致的电路时延偏差。首先，通过施加一组测量向量得到的电路漏电变化量被联立成一个方程组;求解方程组后可以得到关键通路上关键门的漏电变化量。随后，根据描述漏电变化与时延偏差相关性的分析表达式来预测任意关键通路的时延偏差。提出的方法对电路工作过程中的实时噪声免疫，且可以通过增加漏电测量时间抵消参数偏差对预测结果的影响。实验结果表明，提出的方法能够以可接受的精度损失有效地预测NBTI导致的电路时延偏差。

关键词：纳米集成电路负偏置温度不稳定性漏电变化时延监测

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Author Correction: Determining protein structures in cellular lamella at pseudo-atomic resolution by GisSPA

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Nature communications 2024年第1期15卷 3423页

作者： Jing Cheng Tong Liu Xin You Fa Zhang Sen-Fang Sui Xiaohua Wan Xinzheng Zhang National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China. High Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China. State Key Laboratory of Membrane Biology Beijing Advanced Innovation Center for Structural Biology School of Life Sciences Tsinghua University Beijing 100084 China. Beijing Institute of Technology Beijing 100081 China. Beijing Institute of Technology Beijing 100081 China. wanxiaohua@***. National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China. xzzhang@***.

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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

引用

Digital Communications and Networks 2024年

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel state Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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Author Correction: Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

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Nature communications 2021年第1期12卷 4370页

作者： Xueyi Zheng Zhao Yao Yini Huang Yanyan Yu Yun Wang Yubo Liu Rushuang Mao Fei Li Yang Xiao Yuanyuan Wang Yixin Hu Jinhua Yu Jianhua Zhou Department of Ultrasound Sun Yat-Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. Department of Electronic Engineering Fudan University Shanghai China. Paul C. Lauterbur Research Center for Biomedical Imaging Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. The key laboratory of medical imaging computing and computer assisted intervention of Shanghai Shanghai China. Department of Electronic Engineering Fudan University Shanghai China. jhyu@***. The key laboratory of medical imaging computing and computer assisted intervention of Shanghai Shanghai China. jhyu@***. Department of Ultrasound Sun Yat-Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. zhoujh@***.

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Enhancing quantum control by bootstrapping a quantum processor of 12 qubits

arXiv

引用

arXiv 2017年

作者： Lu, Dawei Li, Keren Li, Jun Katiyar, Hemant Park, Annie Jihyun Feng, Guanru Xin, Tao Li, Hang Long, Guilu Brodutch, Aharon Baugh, Jonathan Zeng, Bei Laflamme, Raymond Department of Physics Southern University of Science and Technology Shenzhen518055 China Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Max-Planck-Institutfür Quantenoptik GarchingD-85748 Germany Center for Quantum Information and Quantum Control Department of Physics Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3H6 Canada Department of Mathematics and Statistics University of Guelph GuelphONN1G 2W1 Canada Perimeter Institute for Theoretical Physics Waterloo ONN2L 2Y5 Canada

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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