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On Ultrafast X-ray Methods for Magnetism

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arXiv 2023年

作者： Plumley, R. Chitturi, S.R. Peng, C. Assefa, T.A. Burdet, N. Shen, L. Reid, A.H. Dakovski, G.L. Seaberg, M.H. O'Dowd, F. Montoya, S.A. Chen, H. Okullo, A. Mardanya, S. Kevan, S.D. Fischer, P. Fullerton, E.E. Sinha, S.K. Colocho, W. Lutman, A. Decker, F.-J. Roy, S. Fujioka, J. Tokura, Y. Minitti, M.P. Johnson, J.A. Hoffmann, M. Amoo, M.E. Feiguin, A. Yoon, C. Thayer, J. Nashed, Y. Jia, C. Bansil, A. Chowdhury, S. Lindenberg, A.M. Dunne, M. Blackburn, E. Turner, J.J. Stanford Institute for Materials and Energy Science Stanford University Slac National Accelerator Laboratory Menlo ParkCA94025 United States Linac Coherent Light Source Slac National Accelerator Laboratory Menlo ParkCA94720 United States Physics Dept. Carnegie Mellon University PittsburghPA15213 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Division of Synchrotron Radiation Research Department of Physics Lund University Solvegatan 14 Lund22100 Sweden Center for Memory and Recording Research University of California-San Diego La Jolla CA92093 United States Physics Department Northeastern University BostonMA02115 United States Department of Physics and Astronomy Howard University WashingtonDC20059 United States Department of Physics University of Oregon EugeneOR97401 United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical and Computer Engineering University of California-San Diego La Jolla CA92093 United States Department of Physics University of California-San Diego La Jolla CA92093 United States University of Tokyo Hongo Tokyo113-8656 Japan Graduate School of Pure and Applied Science University of Tsukuba Ibaraki Tsukuba305-8573 Japan Wako351-0198 Japan Villigen PSI5232 Switzerland Department of Mechanical Engineering Howard University WashingtonDC20059 United States Slac National Accelerator Laboratory Menlo ParkCA94025 United States

With the introduction of x-ray free electron laser sources around the world, new scientific approaches for visualizing matter at fundamental length and time-scales have become possible. As it relates to magnetism and 'magnetic-type' systems, advanced methods are being developed for studying ultrafast magnetic responses on the time-scales at which they occur. We describe three capabilities which have the potential to seed new directions in this area and present original results from each: pump-probe x-ray scattering with low energy excitation, x-ray photon fluctuation spectroscopy, and ultrafast diffuse x-ray scattering. By combining these experimental techniques with advanced modeling together with machine learning, we describe how the combination of these domains allows for a new understanding in the field of magnetism. Finally, we give an outlook for future areas of investigation and the newly developed instruments which will take us there. © 2023, CC BY.

关键词： Electrons

Scoring the terabit/s goal: Broadband connectivity in 6G

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Bicaïs, Simon Björnson, Emil Bourdoux, André Buzzi, Stefano D'Andrea, Carmen Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Makki, Behrooz Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, Mohammadjavad Sarieddeen, Hadi Shahabuddin, Shahriar Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland CEA-Leti France KTH Royal Institute of Technology Sweden Linköping University Sweden IMEC Belgium University of Cassino and Southern Latium Italy Italy Department of Electrical and Electronics Engineering Kadir Has University Turkey Fivecomm Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Institute for the Wireless Internet of Things Department of Electrical and Computer Engineering Northeastern University United States Ericsson Research Gothenburg Sweden Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Nokia Finland Department of Electrical Engineering Chalmers University of Technology Sweden State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Huawei Technologies Germany

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity. © 2020, CC BY.

关键词： MIMO systems

Prototype Framework of Low-Latency Collection of Multi-Point Video Stream over Multi-Hop Multi-Channel Wireless Network

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Prototype Framework of Low-Latency Collection of Multi-Point...

作者： Aung Myo Htut Juseong Kim JongWon Kim Chaodit Aswakul Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Networked Computing Systems (NetCS) Laboratory School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology

Big cities are facing heavy road traffic congestion. The main reason is the increasing number of vehicles beyond the road network capacity, which forces traffic police at times to be incapable of controlling the inevitable traffic jams. To resolve those traffic jams, traffic police need the near real-time information of the road network e.g. from traffic cameras. In this paper, a prototype framework of low-latency collection of multi-point video streams over a multi-hop multi-channel wireless network is proposed. Video frames are captured by Raspberry Pi cameras and sent by the wireless link to the herein-called IoT Concentrators. IoTCloud Hub then pulls and stores those video frames from the IoT Concentrators. Users（traffic police） can watch traffic videos from the display screen of the IoT-Cloud Hub. As a proof-of-concept implementation, two Pi Cameras have been set up with Apache Kafka Producers and two additional Raspberry Pi's have been set up as the IoT Concentrators, which run the leader and follower Apache Kafka Brokers. To complete our system, an Intel NUC as IoT-Cloud Hub and another Intel NUC as Orchestration-Hub have been also set up. We assume the available wired communication linkage between the Orchestration-Hub and the other devices. However, the links between the Pi Cameras, IoT Concentrators and IoT-Cloud Hub are wireless with Wi-Fi. To achieve a high performance, multiple Wi-Fi channels can be dynamically configured by the Orchestration-Hub to steer the VDO traffic（based on TCP） away from the congested channels. Based on the implementation prototype, the average TCP throughputs over all the wireless links have been improved by 56.046% on average, compared with the case without the dynamic Wi-Fi channel selection.

关键词： Apache Kafka Raspberry Pi video streaming wireless

AIM 2020 Challenge on Image Extreme Inpainting 16th

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AIM 2020 Challenge on Image Extreme Inpainting

Workshops held at the 16th European Conference on computer vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

Convolutional neural network for behavioral modeling and predistortion of wideband power amplifiers

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arXiv 2020年

作者： Hu, Xin Liu, Zhijun Yu, Xiaofei Zhao, Yulong Chen, Wenhua Hu, Biao Du, Xuekun Li, Xiang Helaoui, Mohamed Wang, Weidong Ghannouchi, Fadhel M. School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China Intelligent RF Radio Laboratory Department of Electrical and Computer Engineering Schulish School of Engineering University of Calgary CalgaryABT2N 1N4 Canada Department of Electronic Engineering Tsinghua University Beijing100084 China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN models, have the problems of high complexity. In this paper, we firstly propose a novel behavior model for wideband PAs using a real-valued time-delay convolutional neural network (RVTDCNN). The input data of the model are sorted and arranged as the graph composed of the in-phase and quadrature (I/Q) components and envelope-dependent terms of current and past signals. We design a pre-designed filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling. Then, the generated rich basis functions are modeled using a simple fully connected layer. Because of the weight sharing characteristics of the convolutional model's structure, the strong memory effect does not lead to a significant increase in the complexity of the model. Meanwhile, the extraction effect of the pre-designed filter also reduces the training complexity of the model. The experimental results show that the performance of the RVTDCNN model is almost the same as the NN models and the multilayer NN models. Meanwhile, compared with the models mentioned above, the coefficient number and computational complexity of the RVTDCNN model are significantly reduced. This advantage is noticeable when the memory effects of PA are increased by using wider signal bandwidths. Copyright © 2020, The Authors. All rights reserved.

关键词： Timing circuits

DeePMD-kit v2: A software package for Deep Potential models

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

The Effects of Prenatal Bisphenol a Exposure on Brain Volume of Children and Young Mice

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SSRN

SSRN 2022年

作者： Zheng, Jing Reynolds, Jess E. Long, Madison Ostertag, Curtis Pollock, Tyler Hamilton, Max Dunn, Jeff F. Liu, Jiaying Martin, Jonathan Grohs, Melody Landman, Bennett Huo, Yuankai Dewey, Deborah Kurrasch, Deborah Lebel, Catherine Department of Radiology Cumming School of Medicine University of Calgary CalgaryAB Canada Department of Medical Genetics Cumming School of Medicine University of Calgary CalgaryAB Canada Alberta Children’s Hospital Research Institute University of Calgary CalgaryAB Canada Hotchkiss Brain Institute University of Calgary CalgaryAB Canada Department of Paediatrics Cumming School of Medicine University of Calgary CalgaryAB Canada Department of Laboratory Medicine and Pathology Faculty of Medicine and Dentistry University of Alberta EdmontonAB Canada University of Calgary Canada Department of Electrical Engineering & Computer Science Vanderbilt University NashvilleTN United States Telethon Kids Institute The University of Western Australia PerthWA Australia Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryAB Canada Division of Allergy Pulmonary and Critical Care Medicine United States Department of Biochemistry and Molecular Biology Canada

Bisphenol A (BPA) is a synthetic chemical used for the manufacturing of plastics, epoxy resin, and many personal care products. This ubiquitous endocrine disruptor is detectable in the urine of over 80% of North Americans. Although adverse neurodevelopmental outcomes have been observed in children with high gestational exposure to BPA, the effects of prenatal BPA on brain structure remain unclear. Here, using magnetic resonance imaging (MRI), we studied the impact of maternal BPA exposure on children’s brain structure, as well as the impact of comparable BPA levels in a mouse model. Our human data showed that maternal BPA exposure was weakly associated (0.10.4). In mice, gestational exposure to an equivalent level of BPA (2.25 μg BPA/kg bw/day) induced structural alterations in brain regions including the superior olivary complex (SOC) and bed nucleus of stria terminalis (BNST);however, no changes were significant after correction for multiple comparison ( q >0.25). Human sample sizes (n = 87) provided >80% power to detect medium differences, and animal sample size (n = 8) is considered adequate. Thus, these human and mouse data suggest that gestational exposure to low levels of BPA may impact the developing brain in subtle ways, but do not lead to large morphological changes detectable at the resolution of MRI. © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

User cooperation for IRS-aided secure SWIPT MIMO Systems

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arXiv 2020年

作者： Zhou, Gui Pan, Cunhua Ren, Hong Wang, Kezhi Chai, Kok Keong Wong, Kai-Kit The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Computer and Information Sciences Northumbria University United Kingdom The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom

In this paper, intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel where the angular direction of the eavesdropper is aligned with a legitimate user. In this scenario, we consider a two-phase communication system under the active attacks and passive eavesdropping. Particularly, in the first phase, the base station avoids direct transmission to the attacked user. While, in the second phase, other users cooperate to forward signals to the attacked user with the help of IRS and energy harvesting technology. Under the active attacks, we investigate an outage constrained beamforming design problem under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. As for the passive eavesdropping, an average secrecy rate maximization problem is formulated, which is addressed by a low complexity algorithm. Numerical results show that the negative effect of the eavesdropper's channel error is greater than that of the legitimate user. © 2020, CC BY.

关键词： Energy harvesting

Online multi-object tracking and segmentation with GMPHD filter and mask-based affinity fusion

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arXiv 2020年

作者： Song, Young-Min Yoon, Young-Chul Yoon, Kwangjin Jeon, Moongu Lee, Seong-Whan Pedrycz, Witold The School of Electrical Engineering and Computer Science Gwangju Institute of Technology Gwangju61005 Korea Republic of The Robotics Laboratory Hyundai Motor Company Gyeonggi-do 16082 Korea Republic of SI Analytics Co. Ltd. Daejeon34051 Korea Republic of The Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2H5 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU-only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets. Copyright © 2020, The Authors. All rights reserved.

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