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arXiv 2023年

作者： Nakaji, Kouhei Bagherimehrab, Mohsen Aspuru-Guzik, Alán Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoON Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Computer Science University of Toronto TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoON Canada Department of Materials Science & Engineering University of Toronto TorontoON Canada Canadian Institute for Advanced Research TorontoON Canada

Hamiltonian simulation is known to be one of the fundamental building blocks of a variety of quantum algorithms such as its most immediate application, that of simulating many-body systems to extract their physical properties. In this work, we present qSWIFT, a high-order randomized algorithm for Hamiltonian simulation. In qSWIFT, the required number of gates for a given precision is independent of the number of terms in Hamiltonian, while the systematic error is exponentially reduced with regards to the order parameter. In this respect, our qSWIFT is a higher-order counterpart of the previously proposed quantum stochastic drift protocol (qDRIFT), in which the number of gates scales linearly with the inverse of the precision required. We construct the qSWIFT channel and establish a rigorous bound for the systematic error quantified by the diamond norm. qSWIFT provides an algorithm to estimate given physical quantities using a system with one ancilla qubit, which is as simple as other product-formula-based approaches such as regular Trotter-Suzuki decompositions and qDRIFT. Our numerical experiment reveals that the required number of gates in qSWIFT is significantly reduced compared to qDRIFT. Particularly, the advantage is significant for problems where high precision is required;for example, to achieve a systematic relative propagation error of 10−6, the required number of gates in third-order qSWIFT is 1000 times smaller than that of qDRIFT. © 2023, CC BY.

关键词： Hamiltonians

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Median Filtering Detection Based on Quaternion Convolutional Neural Network

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computers, Materials & Continua 2020年第10期65卷 929-943页

作者： Jinwei Wang Qiye Ni Yang Zhang Xiangyang Luo Yunqing Shi Jiangtao Zhai Sunil Kr Jha Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Department of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing100093China Department of Electrical Computer Engineering New Jersey Institute of TechnologyNew JerseyNJ07102USA

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image ***,more attention has been paid to the forensics research of median *** this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is *** median filtering residuals(MFR)are used to preprocess the *** the output of MFR is expanded to four channels and used as the input of *** QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional *** quaternion pooling layer is designed to evaluate the result of quaternion *** is proposed to features well combine the three-channel information of color image and fully extract forensics *** show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.

关键词： Median filtering forensics quaternion convolution layer quaternion pooling layer color image

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Visible Light Positioning Based on a Single Luminaire: A Novel Visual Odometry Assisted Algorithm

Visible Light Positioning Based on a Single Luminaire: A Nov...

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IEEE International Conference on Communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

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SpEx+: A complete time domain speaker extraction network 21

SpEx+: A complete time domain speaker extraction network

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21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

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Optically-Sampled Superconducting-Nanostrip Photon-Number Resolving Detector for Non-Classical Quantum State Generation

arXiv

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arXiv 2024年

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Photon number-resolving detectors (PNRDs) are the ultimate optical sensors. Superconducting-nanostrip photon detectors (SNSPDs), traditionally known as ON-OFF detectors, have recently been found to have photon number resolving capability without multiplexing. This discovery positions them to become true PNRDs. However, their practical use is limited by the need to precisely detect tiny signal differences with low signal-to-noise ratios within sub-nanosecond time frames. We overcome this challenge using optical sampling with a dual-output Mach Zehnder modulator (DO-MZM) and ultra-short pulsed laser. By adjusting the DO-MZM’s bias voltage to nearly balance the outputs, this method enables sensitive detection of picosecond-order signal differences, achieving a temporal resolution of 1.9 ps and facilitating real-time photon number resolution. We applied this method to produce various non-classical quantum states, enhancing their non-classicality through photon number resolution. This advancement marks a significant shift from principle verification to practical application for SNSPD-type PNRDs in diverse quantum optics fields. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum optics

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Uncertainty Quantification of Signal Integrity Analysis for Neuromorphic Chips

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2022年 1卷 160-169页

作者： Hanzhi Ma Da Li Tuomin Tao Xingjian Shangguan En-Xiao Liu Jose Schutt-Aine Andreas C. Cangellaris Er-Ping Li College of Information Science and Electronic Engineering ZJU-UIUC Institute Zhejiang University Hangzhou China Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang University Hangzhou China Zhejiang-Singapore Innovation and AI Joint Research Lab China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA A*STAR Institute of High Performance Computing Singapore

A dimensionality reduction based neural network framework is introduced for uncertainty quantification of time-domain response based on system uncertain design parameters for neuromorphic chips. The proposed method firstly makes use of the singular value decomposition (SVD) method to find the basis functions and corresponding coefficients of time-domain response, of which coefficients are used as a lower dimensional target outputs in neural network model compared with time sampling points prediction. This newly proposed method then develops an integrated neural network structure to simultaneously find the mean and variance of target coefficients with a combined definition of loss function, which can be utilized together with basis functions to construct the prediction interval of time-domain response. A memrisor-based crossbar array is applied in this work to verify the performance of the proposed method with the comparison of Monte Carlo method.

关键词： Biological neural networks Uncertainty Memristors Time-domain analysis Integrated circuit modeling Neuromorphics Signal integrity Neural networks Neuromorphics

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Streaming quantum state purification

arXiv

引用

arXiv 2023年

作者： Childs, Andrew M. Fu, Honghao Leung, Debbie Li, Zhi Ozols, Maris Vyas, Vedang Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Perimeter Institute for Theoretical Physics 31 Caroline St. N. WaterlooONN2L 2Y5 Canada QuSoft University of Amsterdam Science Park 123 Amsterdam1098 XG Netherlands Concordia Institute of Information Systems Engineering Concordia University 1455 Blvd. De Maisonneuve Ouest MontrealQCH3G 1M8 Canada

Quantum state purification is the task of recovering a nearly pure copy of an unknown pure quantum state using multiple noisy copies of the state. This basic task has applications to quantum communication over noisy channels and quantum computation with imperfect devices, but has primarily been studied previously for the case of qubits. We derive an efficient purification procedure based on the swap test for qudits of any dimension, starting with any initial error parameter. Treating the initial error parameter and the dimension as constants, we show that our procedure has sample complexity asymptotically optimal in the final error parameter. Our protocol has a simple recursive structure that can be applied when the states are provided one at a time in a streaming fashion, requiring only a small quantum memory to implement. © 2023, CC BY.

关键词： Quantum communication

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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A Smart Health Care Monitor System in IoT Based Human Activities of Daily Living: A Review

A Smart Health Care Monitor System in IoT Based Human Activi...

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2019 International Conference on Machine Learning, Big Data, Cloud and Parallel computing, COMITCon 2019

作者： Jose Reena, K. Parameswari, R. Department of Computer Science School of Computing Science Vels Institute of Science Technology Advanced Studies Chennai India

ISBN: (纸本)9781728102115

IoT is extensively used in all applications. In the healthcare system IoT plays a major role of connecting doctors to the patients by using health monitoring devices. The elderly and the disabled people find this to be very economic and beneficial. There are various methods that monitor the wellbeing of the elderly and in this paper comparison various data mining methods that is being used from the data that is got from things like smart meter, appliance usage, video surveillance and their prediction accuracy is reviewed. © 2019 IEEE.

关键词： Internet of things

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Where’s Wally: A Gigapixel Image Study for Face Recognition in Crowds 15th

Where’s Wally: A Gigapixel Image Study for Face Recognition...

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15th International Symposium on Visual computing, ISVC 2020

作者： Ferreira, Cristiane B. R. Pedrini, Helio de Souza Alencar, Wanderley Ferreira, William D. Carvalho, Thyago Peres Sousa, Naiane Soares, Fabrizzio Instituto de Informática Universidade Federal de Goiás GoianiaGO Brazil Institute of Computing University of Campinas CampinasSP Brazil Department of Computer Science Southern Oregon University AshlandOR United States

ISBN: (纸本)9783030645588

Several devices are capable of capturing images with a large number of people, including those of high resolution known as gigapixel images. These images can be helpful for studies and investigations, such as finding people in a crowd. Although they can provide more details, the task of identifying someone in the crowd is quite challenging and complex. In this paper, we aim to assist the work of a human observer with larger images with crowds by reducing the search space for several images to a ranking of ten images related to a specific person. Our model collects faces in a crowded gigapixel image and then searches for people using three different poses (front, right and left). We built a handcraft dataset with 42 people to evaluate our method, achieving a recognition rate of 69% in the complete dataset. We highlight that, from the 31% "not found" among the top ten in the ranking, many of them are very close to this boundary and, in addition, 92% of non-matched are occluded by some accessory or another face. Experimental results showed great potential for our method to support a human observer to find people in the crowd, especially cluttered images, providing her/him with a reduced search space. © 2020, Springer Nature Switzerland AG.

关键词： Face recognition

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