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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning

Unsupervised Person Re-Identification by Camera-Aware Simila...

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International Conference on computer Vision (ICCV)

作者： Ancong Wu Wei-Shi Zheng Jian-Huang Lai School of Electronics and Information Technology Sun Yat-sen University China School of Data and Computer Science Sun Yat-sen University China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

For matching pedestrians across disjoint camera views in surveillance, person re-identification (Re-ID) has made great progress in supervised learning. However, it is infeasible to lab.l data in a number of new scenes when extending a Re-ID system. Thus, studying unsupervised learning for Re-ID is important for saving lab.lling cost. Yet, cross-camera scene variation is a key challenge for unsupervised Re-ID, such as illumination, background and viewpoint variations, which cause domain shift in the feature space and result in inconsistent pairwise similarity distributions that degrade matching performance. To alleviate the effect of cross-camera scene variation, we propose a Camera-Aware Similarity Consistency Loss to learn consistent pairwise similarity distributions for intra-camera matching and cross-camera matching. To avoid learning ineffective knowledge in consistency learning, we preserve the prior common knowledge of intra-camera matching in the pretrained model as reliable guiding information, which does not suffer from cross-camera scene variation as cross-camera matching. To learn similarity consistency more effectively, we further develop a coarse-to-fine consistency learning scheme to learn consistency globally and locally in two steps. Experiments show that our method outperformed the state-of-the-art unsupervised Re-ID methods.

关键词： Cameras Feature extraction Reliability Computational modeling lab.ling data models Lighting

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Evaluation of Age of information for LDPC Coded Transmission over AWGN Channels

Evaluation of Age of Information for LDPC Coded Transmission...

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IEEE Vehicular technology Conference

作者： Mangang Xie Qianfan Wang Jie Gong Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Age of information (AoI) is an important metric in real-time status update communication system to assess the freshness of information. Different from previous works, this paper focuses on the average AoI over additive white Gaussian noise (AWGN) channels. A fixed redundancy (FR) coding scheme is considered, which encodes each k-bits update as an n-bits packet by a low-density parity-check (LDPC) code. By using the renewal-reward theory, a closed-form expression of the average AoI under the FR scheme is derived. Simulation results show that the average AoI relies on E_b/N_0, especially in the low E_b/N_0 region. For different E_b/N_0, an optimal code length always exists to minimize the average AoI. In addition, the same average AoI can be achieved with different code lengths in the high E_b/N_0 region. Hence, in the high E_b/N_0 region, short codes are preferred especially when the transmission delay is taken into account.

关键词： Encoding Parity check codes AWGN channels Monitoring Real-time systems Binary phase shift keying Measurement

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A Provable Secure Server Friendly Two-Party SM2 Singing Protocol for Blockchain IoT

A Provable Secure Server Friendly Two-Party SM2 Singing Prot...

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IEEE Globecom Workshops

作者： Haibo Tian Yinxue Su Zhenquan Liang Guangdong Province Key Laboratory of Information Security Technology School of Data and Computer Science Sun Yat-Sen University Guangzhou Guangdong P. R. China School of Data and Computer Science Sun Yat-Sen University Guangzhou Guangdong P. R. China Shenzhen Digital Certificate Authority Center Co. Ltd Guangzhou Shenzhen P. R. China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

The blockchain transaction protocol of IETF shows us an emerging paradigm of blockchain based Internet of Things (BIoT). In this paradigm, a constraint node sends transactions including their device data. Different from a wallet in a computing device of a user, an IoT node could be controlled by an adversary for a while and the private keys in the device may be revealed. This paper proposes a two- party SM2 signing protocol so that each transaction has to be signed by two parties collab.ratively. The server of the protocol only needs to compute one scalar multiplication and two finite filed operations for an online signing, which makes it suitable for large scale BIoT applications. We also give a proof about the security of our singing procedure.

关键词： Protocols Servers Games Encryption Bitcoin

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Deep Learning-Based Decoding of Block Markov Superposition Transmission

Deep Learning-Based Decoding of Block Markov Superposition T...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Sheng Bi Qianfan Wang Zengzhe Chen Jiachen Sun Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

In this paper, we present deep learning-based decoding of block Markov superposition transmission (BMST). For the basic code, a deep neural network (DNN) is trained, providing with good decoding performance. The complexity of the trained DNN can be reduced by removing those negligibly small coefficients. For the BMST code, we introduce a sliding window decoding algorithm, which integrates the DNN decoder of the basic code in an iterative manner. Numerical results show that the performance of our proposed scheme is close to that of the BMST system with conventional decoders and matches well with the corresponding lower bound of BMST. The above results verify that the extra gain and lower bound of BMST are effective for neural network decoder and thus demonstrate the universality of BMST.

关键词： Decoding Machine learning Encoding Iterative decoding Microsoft Windows Neural networks Markov processes

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Unsupervised person re-identification by deep asymmetric metric embedding

arXiv

引用

arXiv 2019年

作者： Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Guangdong Province Key Laboratory of Information Security China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing NUDT China School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Person re-identification (Re-ID) aims to match identities across non-overlapping camera views. Researchers have proposed many supervised Re-ID models which require quantities of cross-view pairwise lab.lled data. This limits their scalab.lities to many applications where a large amount of data from multiple disjoint camera views is availab.e but unlab.lled. Although some unsupervised Re-ID models have been proposed to address the scalab.lity problem, they often suffer from the view-specific bias problem which is caused by dramatic variances across different camera views, e.g., different illumination, viewpoints and occlusion. The dramatic variances induce specific feature distortions in different camera views, which can be very disturbing in finding cross-view discriminative information for Re-ID in the unsupervised scenarios, since no lab.l information is availab.e to help alleviate the bias. We propose to explicitly address this problem by learning an unsupervised asymmetric distance metric based on cross-view clustering. The asymmetric distance metric allows specific feature transformations for each camera view to tackle the specific feature distortions. We then design a novel unsupervised loss function to embed the asymmetric metric into a deep neural network, and therefore develop a novel unsupervised deep framework named the DEep Clustering-based Asymmetric MEtric Learning (DECAMEL). In such a way, DECAMEL jointly learns the feature representation and the unsupervised asymmetric metric. DECAMEL learns a compact cross-view cluster structure of Re-ID data, and thus help alleviate the view-specific bias and facilitate mining the potential cross-view discriminative information for unsupervised Re-ID. Extensive experiments on seven benchmark datasets whose sizes span several orders show the effectiveness of our framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Characterizing quantum correlation with the correlated part of partial coherence

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Physical Review A 2019年第3期100卷 032334-032334页

作者： Chunhe Xiong Sunho Kim Daowen Qiu Institute of Computer Science Theory School of Data and Computer Science Sun Yat-Sen University Guangzhou 510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou 510006 China School of Mathematical Sciences Harbin Engineering University Harbin 150001 China Instituto de Telecomunicações Departamento de Matemática Instituto Superior Técnico Av. Rovisco Pais 1049-001 Lisbon Portugal

One of the fundamental features of the quantum world that distinguishes it from the classical counterpart is the existence of quantum correlations. In this paper, we study the quantum correlation from the viewpoint of partial coherence. Partial coherence in a bipartite system often contains local coherence of a subsystem. By removing the local coherence, we show that the remaining part of partial coherence can be regarded as quantum correlations. In this way, we introduce two quantum correlations: partial correlated coherence (PCC) and minimal partial correlated coherence (MPCC). Furthermore, we prove that PCC and MPCC induced by contractive pseudodistances are good candidates of quantum correlations. For contractive pseudodistances, we also show that the induced geometric partial correlated coherence (GPCC) and geometric minimal partial correlated coherence are actually the upper and lower bound of corresponding geometric quantum discord. Particularly, GPCCs based on a family of distances are proved to be bona fide measures of quantum correlations. By connecting to local quantum uncertainty, we provide an operational meaning for geometric quantum discord and GPCC induced by affinity of distance.

关键词： Quantum correlations in quantum information Quantum discord Quantum entanglement Resource theories

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Ultra-scalab.e spectral clustering and ensemble clustering

arXiv

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arXiv 2019年

作者： Huang, Dong Wang, Chang-Dong Wu, Jian-Sheng Lai, Jian-Huang Kwoh, Chee-Keong College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Information Engineering Nanchang University Nanchang China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

This paper focuses on scalab.lity and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalab.e spectral clustering (U-SPEC) and ultra-scalab.e ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalab.lity and robustness of our algorithms. The MATlab.code and experimental data are availab.e at https://***/publication/330760669. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Generalized Block Markov Superposition Transmission over Free-Space Optical Links

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China Communications 2017年第9期14卷 80-93页

作者： Jinshun Zhu Shancheng Zhao Xiao Ma School of Electronics and Information Technology Sun Yat-sen University College of Information Science and Technology Jinan University School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University

In free-space optical(FSO) communications, the performance of the communication systems is severely degraded by atmospheric turbulence. Channel coding and diversity techniques are commonly used to combat channel fading induced by atmospheric turbulence. In this paper, we present the generalized block Markov superposition transmission(GBMST) of repetition codes to improve time diversity. In the GBMST scheme, information sub-blocks are transmitted in the block Markov superposition manner, with possibly different transmission memories. Based on analyzing an equivalent system, a lower bound on the bit-error-rate(BER) of the proposed scheme is presented. Simulation results demonstrate that, under a wide range of turbulence conditions, the proposed scheme improves diversity gain with only a slight reduction of transmission rate. In particular, with encoding memory sequence(0, 0, 8) and transmission rate 1/3, a diversity order of eleven is achieved under moderate turbulence conditions. Numerical results also show that, the GBMST systems with appropriate settings can approach the derived lower bound, implying that full diversity is achievable.

关键词： atmospheric turbulence channel block Markov superposition transmission(BMST) free-space optical communications gamma-gamma channel model sliding-window decoding algorithm time diversity

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Fast stochastic ordinal embedding with variance reduction and adaptive step size

arXiv

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalab.lity, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalab.lity via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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