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Author Correction: Gate-tunable third-order nonlinear optical response of massless Dirac fermions in graphene

Nature Photonics 2018年第10期12卷 634-634页

作者： Tao Jiang Di Huang Jinluo Cheng Xiaodong Fan Zhihong Zhang Yuwei Shan Yangfan Yi Yunyun Dai Lei Shi Kaihui Liu Changgan Zeng Jian Zi J. E. Sipe Yuen-Ron Shen Wei-Tao Liu Shiwei Wu State Key Laboratory of Surface Physics Key Laboratory of Micro and Nano Photonic Structures (MOE) Department of Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China International Center for Quantum Design of Functional Materials Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei China CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics and Department of Physics University of Science and Technology of China Hefei China State Key Laboratory for Mesoscopic Physics and School of Physics Peking University Beijing China Collaborative Innovation Center of Advanced Microstructures Nanjing China Department of Physics University of Toronto Toronto Canada Physics Department University of California Berkeley USA

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Perovskite Solar Cells: Unique Seamlessly Bonded CNT@Graphene Hybrid Nanostructure Introduced in an Interlayer for Efficient and Stable Perovskite Solar Cells (Adv. Funct. Mater. 32/2018)

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advanced Functional Materials 2018年第32期28卷

作者： Xuanhua Li Tengteng Tong Qinjiang Wu Shaohui Guo Qiang Song Jian Han Zhixiang Huang State Key Laboratory of Solidification Processing Research and Development Institute of Northwestern Polytechnical University In Shenzhen Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China

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功耗约束下的3D多核芯片芯核级测试调度算法

功耗约束下的3D多核芯片芯核级测试调度算法

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第七届中国测试学术会议

作者： Wang Wei 王伟 Lin Zhuo-wei 林卓伟 Chen Tian 陈田 Liu Jun 刘军 Fang Fang 方芳 Wu Xi 吴玺 AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine HeFei AnHui H 合肥工业大学情感计算与先进智能机器安徽省重点实验室安徽合肥230009 合肥工业大学计算机与信息学院安徽合肥230009 School of Management Hefei University of Technology HeFei 230009 合肥工业大学管理学院安徽合肥230009

三维堆叠集成电路测试中的一个关键的挑战是在功耗约束下,在绑定前测试和绑定后测试中,协同优化测试应用时间和测试硬件开销.将传统的二维芯片的绑定前和绑定后测试调度方法运用于三维堆叠集成电路的测试调度会导致测试应用时间的延长.... 详细信息

三维堆叠集成电路测试中的一个关键的挑战是在功耗约束下,在绑定前测试和绑定后测试中,协同优化测试应用时间和测试硬件开销.将传统的二维芯片的绑定前和绑定后测试调度方法运用于三维堆叠集成电路的测试调度会导致测试应用时间的延长.我们分别针对未堆叠的集成电路和N(N≥2)层芯片堆叠的3D-SICs,提出了一种功耗约束下的测试调度优化算法.在ITC'02基准电路的实验结果表明,算法在功耗约束下,测试应用时间和测试数据寄存器个数分别减少多达33.8％和28.6％,证明算法能有效地权衡测试应用时间和硬件开销.

关键词：集成电路测试测试调度优化算法功耗约束

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Water Splitting: Optical and Electrical Enhancement of Hydrogen Evolution by MoS2@MoO3Core–Shell Nanowires with designed Tunable Plasmon Resonance (Adv. Funct. Mater. 32/2018)

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advanced Functional Materials 2018年第32期28卷

作者： Shaohui Guo Xuanhua Li Xingang Ren Lin Yang Jinmeng Zhu Bingqing Wei State Key Laboratory of Solidification Processing Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China Department of Mechanical Engineering University of Delaware Newark DE 19716 USA

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Accurate RNA 3D structure prediction using a language model-based deep learning approach

arXiv

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arXiv 2022年

作者： Shen, Tao Hu, Zhihang Sun, Siqi Liu, Di Wong, Felix Wang, Jiuming Chen, Jiayang Wang, Yixuan Hong, Liang Xiao, Jin Zheng, Liangzhen Krishnamoorthi, Tejas King, Irwin Wang, Sheng Yin, Peng Collins, James J. Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Zelixir Biotech Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Systems Biology Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Synthetic Biology Center Massachusetts Institute of Technology CambridgeMA United States Integrated Biosciences Redwood CityCA United States OneAIM Ltd. Hong Kong Shanghai AI Laboratory Shanghai China The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China Shenzhen Institute of Advanced Technology Guangdong Shenzhen China School of Molecular Sciences Center for Molecular Design and Biomimetics the Biodesign Institute Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ∼23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+’s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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

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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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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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Resolving the Origins of Superior Cycling Performance of Antimony Anode in Sodium-ion Batteries: A Comparison with Lithium-ion Batteries

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Angewandte Chemie 2024年第11期136卷

作者： Ruiwen Shao Zhefei Sun Lei Wang Jianhai Pan Luocai Yi Yinggan Zhang Jiajia Han Zhenpeng Yao Jie Li Zhenhai Wen Shuangqiang Chen Shu-Lei Chou Dong-Liang Peng Qiaobao Zhang Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Medical Technology Beijing Institute of Technology Beijing China State Key Laboratory of Physical Chemistry of Solid Surfaces College of Materials Xiamen University Xiamen Fujian 361005 China Institute for Carbon Neutralization College of Chemistry and Materials Engineering Wenzhou University Wenzhou 325035 China Department of Chemical Engineering School of Environmental and Chemical Engineering Shanghai University Shangda Road 99 Shanghai 200444 China CAS Key Laboratory of Design and Assembly of Functional Nanostructures and Fujian Provincial Key Laboratory of Materials and Techniques toward Hydrogen Energy Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou Fujian 350002 China Center of Hydrogen Science Shanghai Jiao Tong University Shanghai China State Key Laboratory of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Energy Politecnico di Milano Via Lambruschini 4 20156 Milano Italy

Alloying-type antimony (Sb) with high theoretical capacity is a promising anode candidate for both lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). Given the larger radius of Na + (1.02 Å) than Li + (0.76 Å), it was generally believed that the Sb anode would experience even worse capacity degradation in SIBs due to more substantial volumetric variations during cycling when compared to LIBs. However, the Sb anode in SIBs unexpectedly exhibited both better electrochemical and structural stability than in LIBs, and the mechanistic reasons that underlie this performance discrepancy remain undiscovered. Here, using substantial in situ transmission electron microscopy, X-ray diffraction, and Raman techniques complemented by theoretical simulations, we explicitly reveal that compared to the lithiation/delithiation process, sodiation/desodiation process of Sb anode displays a previously unexplored two-stage alloying/dealloying mechanism with polycrystalline and amorphous phases as the intermediates featuring improved resilience to mechanical damage, contributing to superior cycling stability in SIBs. Additionally, the better mechanical properties and weaker atomic interaction of Na−Sb alloys than Li−Sb alloys favor enabling mitigated mechanical stress, accounting for enhanced structural stability as unveiled by theoretical simulations. Our finding delineates the mechanistic origins of enhanced cycling stability of Sb anode in SIBs with potential implications for other large-volume-change electrode materials.

关键词： Antimony anode Lithium-ion batteries Sodium-ion batteries In situ transmission electron microscopy Electrochemical performance

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Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

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ACM Transactions on Internet Technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

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Advances in information processing and biological imaging using flat optics

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年 391-411页

作者： Xinwei Wang Huijie Hao Jian Liu Jiubin Tan Haoyu Li Xumin Ding Xiaoyuan He Peng Xie Guangwei Hu Hao Wang Patrice Genevet Yu Luo Advanced Microscopy and Instrumentation Research Center School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China Key Lab of Ultra-Precision Intelligent Instrumentation Harbin Institute of Technology Ministry of Industry and Information Technology Harbin China Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Product Development Singapore University of Technology and Design Singapore Singapore Physics Department Colorado School of Mines Golden CO USA National Key Laboratory of Microwave Photonics Nanjing University of Aeronautics and Astronautics Nanjing China

Flat optical components (metasurfaces) made from artificial electromagnetic materials (metamaterials) have opened new possibilities for the manipulation of electromagnetic waves within compact multifunctional devices. This field encompasses the development of individual optical elements and their integration into systems for use in real-world applications, especially in optical information processing, light detection and ranging (LiDAR), augmented or virtual reality, and biological imaging. This comprehensive Review highlights advances in the use of flat optics in analog computational information processing and imaging applications and emphasizes their fundamental role in transfer function engineering. The natural synergy between flat optics and computational technologies is described in relation to advanced imaging and microscopy solutions that have the potential for simultaneous information acquisition and processing. An outlook on future developments, including critical insights for both newcomers and experts in this field, is also provided.

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