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arXiv 2021年

作者： Wang, Qisheng Zhang, Zhicheng Chen, Kean Guan, Ji Fang, Wang Liu, Junyi Ying, Mingsheng The Department of Computer Science and Technology Tsinghua University Beijing China Centre for Quantum Software and Information University of Technology Sydney Australia The University of Chinese Academy of Sciences China The State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

For two unknown mixed quantum states ρ and σ in an N -dimensional Hilbert space, computing their fidelity F (ρ, σ) is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in poly(log(N), r, 1/Ε) time, where r is the lower rank of ρ and σ, and Ε is the desired precision, provided that the purifications of ρ and σ are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in N. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

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The Evolution of Quantum Secure Direct Communication: On the Road to the Qinternet

arXiv

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arXiv 2023年

作者： Pan, Dong Long, Gui-Lu Yin, Liuguo Sheng, Yu-Bo Ruan, Dong Ng, Soon Xin Lu, Jianhua Hanzo, Lajos Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low-dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Beijing National Research Center for Information Science and Technology Frontier Science Center for Quantum Information Beijing100084 China School of Information Science and Technology Tsinghua University Beijing100084 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Communication security has to evolve to a higher plane in the face of the threat from the massive computing power of the emerging quantum computers. Quantum secure direct communication (QSDC) constitutes a promising branch of quantum communication, which is provably secure and overcomes the threat of quantum computing, whilst conveying secret messages directly via the quantum channel. In this survey, we highlight the motivation and the status of QSDC research with special emphasis on its theoretical basis and experimental verification. We will detail the associated point-to-point communication protocols and show how information is protected and transmitted. Finally, we discuss the open challenges as well as the future trends of QSDC networks, emphasizing again that QSDC is not a pure quantum key distribution (QKD) protocol, but a fully-fledged secure communication scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum channel

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Deep spatiooral attention model for grain storage temperature forecasting 26

Deep spatiooral attention model for grain storage temperatur...

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26th IEEE International Conference on Parallel and Distributed systems, ICPADS 2020

作者： Duan, Shanshan Yang, Weidong Wang, Xuyu Mao, Shiwen Zhang, Yuan Henan Key Laboratory of Grain Photoelectric Detection Control Key Laboratory of Grain Information Processing Control College of Information Science Engineering Henan University of Technology Zhengzhou Henan China California State University Department of Computer Science SacramentoCA95819-6021 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781728190747

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatiooral attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods. © 2020 IEEE.

关键词： Piles

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Bidirectional Optimization Coupled Ligh tweight Net works for Efficient and Robust Multi-Person 2D Pose Estimation

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Journal of computer science & technology 2019年第3期34卷 522-536页

作者： Shnai Li Zheng Fang Wen-Feng Song Ai-Min Hao Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Beihang University Qingdao Research Institute Qingdao 266000China Department of Computer Science Stony Brook UniversityStony Brook 11790U.S.A.

For multi-person 2D pose estimation,current deep learning baised methods have exhibited impressive performance,but the trade-offs among efficiency,robustness,and accuracy in the existing approaches remain *** principle,bottom-up methods are superior to top-down methods in efficiency,but they perform worse in *** make full use of their respective advantages,in this paper we design a novel bidirectional optimization coupled lightweight network(BOCLN)architecture for efficient,robust,and general-purpose multi-person 2D(2-dimensional)pose estimation from natural *** the BOCLN framework,the bottom-up network focuses oil global features,while the top-down net work places emphasis on det ailed *** entire framework shares global features along the bottom-up data stream,while the top-down data stream aims to accelerate the accurate pose *** particular,to exploit the priors of human joints'relationship,we propose a probability limb heat map to represent the spatial context of the joints and guide the overall pose skeleton prediction,so that each person's pose estimation in cluttered scenes(involving crowd)could be as accurate and robust as ***,benefiting from the novel BOCLN architecture,the tinie-consuming refinement procedure could be much simplified to an efficient lightweight *** experiments and evaluations on public benchmarks have confirmed that our new method is more efficient and robust,yet still attain competitive accuracy performance compared with the state-of-the-art *** BOCLN shows even greater promise in online applications.

关键词： bidirectional optimization computer vision deep learning probability limb heat map 2D multi-person pose estimation

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Multi-modal remote sensory learning for multi-objects over autonomous devices

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Frontiers in bioengineering and biotechnology 2025年 13卷 1430222页

作者： Aysha Naseer Naif Almudawi Hanan Aljuaid Abdulwahab Alazeb Yahay AlQahtani Asaad Algarni Ahmad Jalal Hui Liu Department of Computer Science Air University Islamabad Pakistan. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia. Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia. Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia. Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea. Guodian Nanjing Automation Co. Ltd. Nanjing China. Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and technology Nanjing China. Cognitive Systems Lab University of Bremen Bremen Germany.

Introduction:There has been an increasing focus on object segmentation within remote sensing images in recent years due to advancements in remote sensing technology and the growing significance of these images in both military and civilian realms. In these situations, it is critical to accurately and quickly identify a wide variety of objects. In many computer vision applications, scene recognition in aerial-based remote sensing imagery presents a common issue. Method:However, several challenging elements make this work especially difficult: (i) Different objects have different pixel densities; (ii) objects are not evenly distributed in remote sensing images; (iii) objects can appear differently depending on viewing angle and lighting conditions; and (iv) there are fluctuations in the number of objects, even the same type, in remote sensing images. Using a synergistic combination of Markov Random Field (MRF) for accurate labeling and Alex Net model for robust scene recognition, this work presents a novel method for the identification of remote sensing objects. During the labeling step, the use of MRF guarantees precise spatial contextual modeling, which improves comprehension of intricate interactions between nearby aerial objects. By simultaneously using deep learning model, the incorporation of Alex Net in the following classification phase enhances the model's capacity to identify complex patterns in aerial images and adapt to a variety of object attributes. Results:Experiments show that our method performs better than others in terms of classification accuracy and generalization, indicating its efficacy analysis on benchmark datasets such as UC Merced Land Use and AID. Discussion:Several performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets,

关键词： autonomous devices deep learning computer vision likelihood estimation multi-modal multi-objects posterior probability remote sensing scene analysis

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Theoretical and Experimental Investigation of On-Chip Mid-Infrared Chalcogenide Waveguide Ch4 Sensor Based on Wavelength Modulation Spectroscopy

SSRN

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SSRN 2022年

作者： Pi, Mingquan Huang, Yijun Zhao, Huan Peng, Zihang Lang, Jiaming Ji, Jialin Teng, Ling Song, Fang Liang, Lei Zhang, Yu Zheng, Chuantao Wang, Yiding Tittel, Frank K. State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University 2699 Qianjin Street Changchun130012 China School of Mathematics and Statistics Changchun University of Science and Technology Changchun130022 China State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States

Compared with direct absorption spectroscopy (DAS), wavelength modulation spectroscopy (WMS) with good ability of suppressing noise is rarely used in waveguide sensor. The influence of waveguide parameters on WMS sensing performance is required to clarify in both theory and experiment. Three mid-infrared waveguide methane (CH4) sensors (trapezoid, rectangular, suspended) were proposed and optimized for WMS simulation. Theoretical formulation and the experimental sensing noise were used to numerically evaluate the key figure of merit more accurately. Simulation results were presented to show the influence of waveguide parameters on sensing performance. Two chalcogenide on magnesium fluoride (ChG-on-MgF2) trapezoid waveguide sensors were fabricated using lift-off method, and CH4 measurement was carried out using WMS. The waveguide loss was measured to be 1.57 dB/cm, leading to an optimal sensor length of 2.7 cm. The limit of detection (LoD) for the 1cm- and 2cm-long waveguide sensors are 0.68% and 0.17% with an averaging time of 0.2 s. The experimentally achieved second harmonic (2f) signal amplitude and LoD show good agreement with the theoretical results, verifying the feasibility of the design and analysis model of the WMS-based waveguide sensor. Due to the use of WMS and a reduction of waveguide loss from 3.6 to 1.57 dB/cm, the LoD of the 2cm-long ChG-on-MgF2 sensor is 24 times lower than our previously reported 2cm-long ChG-on-SiO2 sensor based on DAS. This work provides a systematic guidance for the design of waveguide gas sensor based on WMS and contributes to improving the sensitivity of on-chip gas sensing. © 2022, The Authors. All rights reserved.

关键词： Chalcogenides

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Long-term in vivo imaging of mouse spinal cord through an optically cleared intervertebral window (vol 13, 1959, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1959-1959页

作者： Wu, Wanjie He, Sicong Wu, Junqiang Chen, Congping Li, Xuesong Liu, Kai Qu, Jianan Y. Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China Department of Biology School of Life Sciences Southern University of Science and Technology Shenzhen China Division of Life Science The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China Center of Systems Biology and Human Health The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong P. R. China

The spinal cord accounts for the main communication pathway between the brain and the peripheral nervous system. Spinal cord injury is a devastating and largely irreversible neurological trauma, and can result in lifelong disability and paralysis with no available cure. In vivo spinal cord imaging in mouse models without introducing immunological artifacts is critical to understand spinal cord pathology and discover effective treatments. We developed a minimally invasive intervertebral window by retaining the ligamentum flavum to protect the underlying spinal cord. By introducing an optical clearing method, we achieve repeated two-photon fluorescence and stimulated Raman scattering imaging at subcellular resolution with up to 15 imaging sessions over 6–167 days and observe no inflammatory response. Using this optically cleared intervertebral window, we study neuron-glia dynamics following laser axotomy and observe strengthened contact of microglia with the nodes of Ranvier during axonal degeneration. By enabling long-term, repetitive, stable, high-resolution and inflammation-free imaging of mouse spinal cord, our method provides a reliable platform in the research aiming at interpretation of spinal cord physiology and pathology. Wu et al. developed a technique for longitudinal imaging with subcellular resolution of the spinal cord without causing inflammation or microglia activation in live mouse through an optically cleared intervertebral window.

关键词： Multiphoton microscopy Cellular neuroscience Optical imaging

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Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

arXiv

引用

arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point Computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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Is Out-of-Distribution Detection Learnable?

arXiv

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arXiv 2022年

作者： Fang, Zhen Li, Yixuan Lu, Jie Dong, Jiahua Han, Bo Liu, Feng Australian Artificial Intelligence Institute University of Technology Sydney Australia Department of Computer Sciences University of Wisconsin-Madison United States State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China ETH Zurich Switzerland Department of Computer Science Hong Kong Baptist University Hong Kong School of Mathematics and Statistics University of Melbourne Australia

Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability and diversity of OOD data, good generalization ability is crucial for effective OOD detection algorithms. To study the generalization of OOD detection, in this paper, we investigate the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem. First, we find a necessary condition for the learnability of OOD detection. Then, using this condition, we prove several impossibility theorems for the learnability of OOD detection under some scenarios. Although the impossibility theorems are frustrating, we find that some conditions of these impossibility theorems may not hold in some practical scenarios. Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios. Lastly, we also offer theoretical supports for several representative OOD detection works based on our OOD theory. © 2022, CC BY-NC-SA.

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