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arXiv 2022年

作者： Saha, Kamal K. Robson, Walter Howington, Connor Suh, In-Saeng Wang, Zhimin Nabrzyski, Jaroslaw Center for Research Computing University of Notre Dame Notre DameIN46556 United States Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far either suffer from lack of accuracy and/or are limited to very small sizes of the problem, and thus have no practical usage. Here we present an advanced quantum algorithm for solving the Poisson equation with high accuracy and dynamically tunable problem size. After converting the Poisson equation to a linear system through the finite difference method, we adopt the HHL algorithm as the basic framework. Particularly, in this work we present an advanced circuit that ensures the accuracy of the solution by implementing non-truncated eigenvalues through eigenvalue amplification, as well as by increasing the accuracy of the controlled rotation angular coefficients, which are the critical factors in the HHL algorithm. Consequently, we are able to drastically reduce the relative error in the solution while achieving higher success probability as the amplification level is increased. We show that our algorithm not only increases the accuracy of the solutions but also composes more practical and scalable circuits by dynamically controlling problem size in NISQ devices. We present both simulated and experimental results and discuss the sources of errors. Finally, we conclude that though overall results on the existing NISQ hardware are dominated by the error in the CNOT gates, this work opens a path to realizing a multidimensional Poisson solver on near-term quantum hardware. © 2022, CC BY.

关键词： Poisson equation

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Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders 23rd

Abnormality Detection in Chest X-Ray Images Using Uncertaint...

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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Mao, Yifan Xue, Fei-Fei Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi Liu, Hongmei School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Pazhou Lab Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783030597245

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Double JPEG Compression Detection Based on Markov Model 18th

Double JPEG Compression Detection Based on Markov Model

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18th International Workshop on Digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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State Permutation Control in Non-Hermitian Multiqubit Systems with Suppressed Non-Adiabatic Transitions

arXiv

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arXiv 2025年

作者： Arkhipov, Ievgen I. Lewalle, Philippe Nori, Franco Özdemir, Şahin K. Whaley, K. Birgitta Joint Laboratory of Optics Palacký University Institute of Physics of CAS Faculty of Science Palacký University 17. listopadu 12 Olomouc771 46 Czech Republic Department of Chemistry University of California BerkeleyCA94720 United States Berkeley Center for Quantum Information and Computation BerkeleyCA94720 United States Theoretical Quantum Physics Laboratory Cluster for Pioneering Research RIKEN Saitama Wako-shi351-0198 Japan Quantum Information Physics Theory Research Team Quantum Computing Center RIKEN Wakoshi Saitama351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States Department of Electrical and Computer Engineering Saint Louis University St. LouisMO63103 United States

Non-Hermitian systems have been at the focus of intense research for over a decade, partly due to their nontrivial energy topology formed by intersecting Riemann manifolds with branch points known as exceptional points (EPs). This spectral property can be exploited, e.g., to achieve controlled state permutations that are necessary for implementing a wide class of classical and quantum information protocols. However, the imaginary spectra of typical non-Hermitian systems lead to instabilities and breakdown of adiabaticity, which impedes the practical use of EP-induced energy topologies in quantum information protocols that explicitly rely on symmetric state flips. On the other hand, techniques that help to suppress non-adiabatic transitions in non-Hermitian systems have also been developed, but these advances have so far been limited to single qubits. In this work, we address this long-standing problem by introducing a model of interacting qubits governed by an effective non-Hermitian Hamiltonian that hosts EPs and possesses a completely real energy spectrum. We demonstrate that such non-Hermitian Hamiltonians enable realization of permutation groups in the multi-qubit eigenspace. Our findings indicate that, contrary to previous beliefs, non-Hermiticity can be utilized to achieve controlled state permutations in time-modulated multiqubit systems, thus paving the way for the advancement and development of novel quantum information protocols. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer

arXiv

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arXiv 2023年

作者： Li, Zheng Zhu, Zhengyu Chu, Zheng Guan, Yingying Mi, De Liu, Fan Yang, Lie-Liang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China National Mobile Communications Research Laboratory Southeast University Nanjing210019 China University of Surrey GuildfordGU2 7XH United Kingdom School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy transfer

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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2020 International Conference on computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

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Deep Attention-guided Graph Clustering with Dual Self-supervision

arXiv

引用

arXiv 2021年

作者： Peng, Zhihao Liu, Hui Jia, Yuheng Hou, Junhui The Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong The School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Existing deep embedding clustering methods fail to sufficiently utilize the available off-the-shelf information from feature embeddings and cluster assignments, limiting their performance. To this end, we propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC). Specifically, DAGC first utilizes a heterogeneity-wise fusion module to adaptively integrate the features of the autoencoder and the graph convolutional network in each layer and then uses a scale-wise fusion module to dynamically concatenate the multi-scale features in different layers. Such modules are capable of learning an informative feature embedding via an attention-based mechanism. In addition, we design a distribution-wise fusion module that leverages cluster assignments to acquire clustering results directly. To better explore the off-the-shelf information from the cluster assignments, we develop a dual self-supervision solution consisting of a soft self-supervision strategy with a Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Extensive experiments on nine benchmark datasets validate that our method consistently outperforms state-of-the-art methods. Especially, our method improves the ARI by more than 10.29% over the best baseline. The code will be publicly available at https://***/ZhihaoPENG-CityU/DAGC. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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