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Probing dynamics and ion structuring of imidazolium ionic liquid confined at charged graphene surfaces using graphene colloid probe AFM

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Friction 2025年第6期13卷 151-161页

作者： Muqiu Wu Zhongyang Dai Fan Zhang Faiz Ullah Shah Enrico Gnecco Yijun Shi Braham Prakash Rong An 2State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Lanzhou 730000 China 3Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai 264006 China 1School of Materials Science and Engineering/Herbert Gleiter Institute of Nanoscience Nanjing University of Science and Technology Nanjing 210094 China 4High Performance Computing Department National Supercomputing Center in Shenzhen Shenzhen 518055 China 5Department of Engineering and Design School of Engineering and Information University of Sussex Brighton BN1 9RH UK 6Chemistry of Interfaces Luleå University of Technology Luleå 97187 Sweden 7Marian Smoluchowski Institute of Physics Jagiellonian University Krakow 30-348 Poland 8Division of Machine Elements Luleå University of Technology Luleå 97187 Sweden

Driven by the potential applications of ionic liquid (IL) flow for charging graphene-based surfaces in many emerging technologies, recent research efforts have focused on understanding ion dynamics and structuring at IL–graphene interfaces. Here, graphene colloid probe (GrP) atomic force microscopy (AFM) was used to probe the dynamics and ion structuring of 1-butyl-3-methylimidazolium tetrafluoroborate at graphene surfaces under various bias voltages. In particular, the AFM-measured nanofriction provides a good measure of the dynamic properties of the ILs at graphene surfaces. Compared with the IL at the unbiased graphene surface (0 V), the charged graphene surfaces with either negative (–1, –2 V) or positive (+1, +2 V) voltages favor a reduction in the friction coefficient by the IL. A higher magnitude of the bias voltage applied on the graphene surface with either sign (–2 or +2 V) results in a smaller friction coefficient than that at –1 and +1 V. In combination with the AFM-probed contact stiffness, adhesion forces, and ion structuring force curves with an ion orientational distribution according to molecular dynamics (MD) simulations, we discovered that the unbiased graphene surface (0 V) possesses randomly structured IL ions and that the graphene colloid probe is more likely to become stuck, resulting in more energy dissipation to contribute to a larger friction coefficient. Biasing of the graphene surface under either negative or positive voltages resulted in uniformly arranged ions, which produced a more ordered ion structure and, thus, a smoother sliding plane to reduce the friction coefficient. Electrochemical impedance spectroscopy (EIS) for the IL with graphene as an electrode demonstrated a greater ionic conductivity in the IL paired with the biased graphene than in the unbiased one, implying faster ion movement at the charged graphene, which is beneficial for reducing the friction coefficient.

关键词： nanofriction graphene charge ionic liquid (IL) IL arrangement simulation

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Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning

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Journal of computer science & Technology 2019年第5期34卷 1039-1062页

作者： Zhou Xu Shuai Pang Tao Zhang Xia-Pu Luo Jin Liu Yu-Tian Tang Xiao Yu Lei Xue College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science Wuhan UniversityWuhan 430072China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong 999077China Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of Sciences Beijing 100190China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Science City University of Hong KongHong Kong 999077China

Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no historical labeled defect data, cross project defect prediction (CPDP) is an alternative technique for this scenario. CPDP utilizes labeled defect data of an external project to construct a classification model to predict the module labels of the current project. Transfer learning based CPDP methods are the current mainstream. In general, such methods aim to minimize the distribution differences between the data of the two projects. However, previous methods mainly focus on the marginal distribution difference but ignore the conditional distribution difference, which will lead to unsatisfactory performance. In this work, we use a novel balanced distribution adaptation (BDA) based transfer learning method to narrow this gap. BDA simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them. To evaluate the effectiveness of BDA for CPDP performance, we conduct experiments on 18 projects from four datasets using six indicators (i.e., F-measure, g-means, Balance, AUC, EARecall, and EAF-measure). Compared with 12 baseline methods, BDA achieves average improvements of 23.8%, 12.5%, 11.5%, 4.7%, 34.2%, and 33.7% in terms of the six indicators respectively over four datasets.

关键词： cross-project defect prediction transfer learning balancing distribution effort-aware indicator

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Recent progress of integrated circuits and optoelectronic chips

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science China(information sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic photonic neural computing

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Implementing the matrix multiplication with dfc on kunlun small scale computer 20

Implementing the matrix multiplication with dfc on kunlun sm...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Du, Zheng Zhang, Jing Sha, Shihao Luo, Qiuming Department of Computer Science and Technology Tsinghua University China NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512. © 2019 IEEE.

关键词： Matrix algebra

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Cross-modal consensus network for weakly supervised temporal action localization

arXiv

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arXiv 2021年

作者： Hong, Fa-Ting Feng, Jia-Chang Xu, Dan Shan, Ying Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Tencent PCG Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science and Engineering HKUST Hong Kong Pazhou Lab Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Previous works use the appearance and motion features extracted from pre-trained feature encoder directly, e.g., feature concatenation or score-level fusion. In this work, we argue that the features extracted from the pre-trained extractors, e.g., I3D, which are trained for trimmed video action classification, but not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization. Therefore, the feature re-calibration is needed for reducing the task-irrelevant information redundancy. Here, we propose a cross-modal consensus network (CO2-Net) to tackle this problem. In CO2-Net, we mainly introduce two identical proposed cross-modal consensus modules (CCM) that design a cross-modal attention mechanism to filter out the task-irrelevant information redundancy using the global information from the main modality and the cross-modal local information from the auxiliary modality. Moreover, we further explore inter-modality consistency, where we treat the attention weights derived from each CCM as the pseudo targets of the attention weights derived from another CCM to maintain the consistency between the predictions derived from two CCMs, forming a mutual learning manner. Finally, we conduct extensive experiments on two commonly used temporal action localization datasets, THUMOS14 and ActivityNet1.2, to verify our method, which we achieve the state-of-the-art results. The experimental results show that our proposed cross-modal consensus module can produce more representative features for temporal action localization. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

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Editorial: special issue on “The security, privacy, and digital forensics of mobile networks and mobile cloud”

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Digital Communications and Networks 2019年第4期5卷 203-204页

作者： Lei Chen Guofei Gu Wenjia Li Rami J.Haddad the Department of Information Technology Allen E. Paulson College of Engineering and Computing at Georgia Southern University USA the Department of Computer Science & Engineering at Texas A&M University Computer Science at New York Institute of Technology (NYIT) since the Department of Electrical and Computer Allen E. Paulson College of Engineering and Computing at Georgia Southern University the Optical Networks and Smart Grid Applications (ONSmart) Laboratory GSU

To many people, smartphones and other mobile devices have been an indispensable part of their daily lives. With personal financial and medical information and job-related data processed on mobile devices,systems and networks as well as the connected mobile cloud, it is critically important that secure mobile operating systems, secure mobile applications, secure smartphone devices, secure cellular networks and secure mobile cloud all keep up with the ever-growing mobile usage.

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SA-ConvONet: Sign-agnostic optimization of convolutional occupancy networks

arXiv

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arXiv 2021年

作者： Tang, Jiapeng Lei, Jiabao Xu, Dan Ma, Feiying Jia, Kui Zhang, Lei School of Electronic and Information Engineering South China University of Technology Department of Computer Science and Engineering HKUST Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong DAMO Academy Alibaba Group Pazhou Lab Guangzhou China Peng Cheng Laboratory Shenzhen China

Surface reconstruction from point clouds is a fundamental problem in the computer vision and graphics community. Recent state-of-the-arts solve this problem by individually optimizing each local implicit field during inference. Without considering the geometric relationships between local fields, they typically require accurate normals to avoid the sign conflict problem in overlapped regions of local fields, which severely limits their applicability to raw scans where surface normals could be unavailable. Although SAL breaks this limitation via sign-agnostic learning, further works still need to explore how to extend this technique for local shape modeling. To this end, we propose to learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks, to simultaneously achieve advanced scalability to large-scale scenes, generality to novel shapes, and applicability to raw scans in a unified framework. Concretely, we achieve this goal by a simple yet effective design, which further optimizes the pre-trained occupancy prediction networks with an unsigned cross-entropy loss during inference. The learning of occupancy fields is conditioned on convolutional features from an hourglass network architecture. Extensive experimental comparisons with previous state-of-the-arts on both object-level and scene-level datasets demonstrate the superior accuracy of our approach for surface reconstruction from un-orientated point clouds. The code is available at https://***/tangjiapeng/SA-ConvONet. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Content Distribution based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks

arXiv

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arXiv 2022年

作者： Su, Lan Niu, Yong Han, Zhu Ai, Bo He, Ruisi Wang, Yibing Wang, Ning Su, Xiang The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Norwegian University of Science and Technology Norway and University of Oulu Finland

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets

arXiv

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arXiv 2024年

作者： Perkowski, Ernest Pan, Rui Nguyen, Tuan Dung Ting, Yuan-Sen Kruk, Sandor Zhang, Tong O’Neill, Charlie Jablonska, Maja Sun, Zechang Smith, Michael J. Liu, Huiling Schawinski, Kevin Iyer, Kartheik Ciucă, Ioana Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy The Ohio State University ColumbusOH43210 United States The Ohio State University ColumbusOH43210 United States Department of Computer Science University of Illinois Urbana-Champaign United States Mathematical Science Institute Australian National University ActonACT2601 Australia Department of Astronomy MongManWai Building Tsinghua University Beijing100084 China Aspia Space Tremough Innovation Centre PenrynTR10 9TA United Kingdom Department of Modern Physics University of Science and Technology of China Anhui Hefei230026 China Modulos Technoparkstrasse 1 Zurich8005 Switzerland Columbia Astrophysics Laboratory Columbia University New YorkNY10027 United States

We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2 model and focusing exclusively on a curated set of astronomy corpora—comprising abstracts, introductions, and conclusions—we achieve notable improvements in specialized topic comprehension. While general LLMs like GPT-4 excel in broader question-answering scenarios due to superior reasoning capabilities, our findings suggest that continual pre-training with limited resources can still enhance model performance on specialized topics. Additionally, we present an extension of AstroLLaMA: the fine-tuning of the 7B LLaMA model on a domain-specific conversational dataset, culminating in the release of the chat-enabled AstroLLaMA for community use. Comprehensive quantitative benchmarking is currently in progress and will be detailed in an upcoming full paper. The model, AstroLLaMA-Chat, is now available at https://***/universeTBD, providing the first open-source conversational AI tool tailored for the astronomy community. Copyright © 2024, The Authors. All rights reserved.

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Quingo: A programming framework for heterogeneous quantum-classical computing with NISQ features

arXiv

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arXiv 2020年

作者： Fu, Xiang Yu, Jintao Su, Xing Jiang, Hanru Wu, Hua Cheng, Fucheng Deng, Xi Zhang, Jinrong Jin, Lei Yang, Yihang Xu, Le Hu, Chunchao Huang, Anqi Huang, Guangyao Qiang, Xiaogang Deng, Mingtang Xu, Ping Xu, Weixia Liu, Wanwei Zhang, Yu Deng, Yuxin Wu, Junjie Feng, Yuan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Computer National University of Defense Technology Changsha410073 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China School of Information Engineering Zhengzhou University Zhengzhou450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Computing Science College of Computer National University of Defense Technology Changsha410073 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Centre for Quantum Software and Information University of Technology Sydney Sydney2007 Australia

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