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NEXT:a neural network framework for next POI recommendation

Frontiers of Computer Science 2020年第2期14卷 314-333页

作者： Zhiqian ZHANG Chenliang LI Zhiyong WU Aixin SUN Dengpan YE Xiangyang LUO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China Department of Computer Science The University of Hong KongPokfulam RoadHong Kong 999077China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou 450001China

The task of next POI recommendations has been studied extensively in recent ***,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,because of the heterogeneity nature of these ***,effective mechanisms to smoothly handle cold-start cases are also a difficult *** by the recent success of neural networks in many areas,in this paper,we propose a simple yet effective neural network framework,named NEXT,for next POI *** is a unified framework to learn the hidden intent regarding user's next move,by incorporating different factors in a unified ***,in NEXT,we incorporate meta-data information,e.g.,user friendship and textual descriptions of POIs,and two kinds of temporal contexts(i.e.,time interval and visit time).To leverage sequential relations and geographical influence,we propose to adopt DeepWalk,a network representation learning technique,to encode such *** evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based *** results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI *** experiments show inherent ability of NEXT in handling cold-start.

关键词： POI neural networks POI recommendation

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Deep generative network for image inpainting with gradient semantics and spatial-smooth attention

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Journal of Visual Communication and Image Representation 2024年 98卷

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei Ye, Long School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

As a powerful forgery operation in the image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, especially in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering both the spatial structural relations and the surrounding information in the hole region. Experimental results on public datasets show the superiority of the proposed method, especially in large-hole region tasks. © 2023 Elsevier Inc.

关键词： Semantics

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CFTD from TQFTD+1 via Holographic Tensor Network, and Precision Discretization of CFT2

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Physical Review X 2024年第4期14卷 041033页

作者： Lin Chen Kaixin Ji Haochen Zhang Ce Shen Ruoshui Wang Xiangdong Zeng Ling-Yan Hung State Key Laboratory of Surface Physics Department of Physics and Center for Field Theory and Particle Physics School of Physics and Optoelectronics Department of Physics International Centre for Theoretical Physics Asia-Pacific Ithaca New York 14853 USA Institute for Advanced Study Institute for Nanoelectronic Devices and Quantum Computing Yau Mathematical Sciences Center Beijing Institute of Mathematical Sciences and Applications Beijing 101408 China

We show that the path integral of conformal field theories in D dimensions (CFTD) can be constructed by solving for eigenstates of a renormalization group (RG) operator following from the Turaev-Viro formulation of a topological field theory (topological quantum field theory) (TQFT) in D+1 dimensions (TQFTD+1), explicitly realizing the holographic sandwich relation between a symmetric theory and a TQFT. Generically, exact eigenstates corresponding to symmetric TQFTD follow from Frobenius algebra in TQFTD+1. For D=2, we construct eigenstates that produce 2D rational CFT path integrals exactly, which curiously connect a continuous-field theoretic path integral with the Turaev-Viro state sum. We also devise and illustrate numerical methods for D=2, 3 to search for CFTD as phase transition points between symmetric TQFTD. Finally, since the RG operator is in fact an exact analytic holographic tensor network, we compute “bulk-boundary” correlators and compare them with the AdS/CFT dictionary at D=2. Promisingly, they are numerically compatible given our accuracy, although further works will be needed to explore the precise connection to the AdS/CFT correspondence.

关键词： Conformal field theory Gauge-gravity dualities Tensor network renormalization Topological field theories Topological order

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Projected gradient descent algorithm for ab initio crystal structure relaxation under a fixed unit cell volume

arXiv

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arXiv 2024年

作者： Hu, Yukuan Yin, Junlei Gao, Xingyu Liu, Xin Song, Haifeng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Solidification Processing Northwestern Polytechnical University Shaanxi Xi’an710072 China Innovation Center NPU Chongqing Chongqing401135 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China

This paper is concerned with ab initio crystal structure relaxation under a fixed unit cell volume, which is a step in calculating the static equations of state and forms the basis of thermodynamic property calculations for materials. The task can be formulated as an energy minimization with a determinant constraint. Widely used line minimization-based methods (e.g., conjugate gradient method) lack both efficiency and convergence guarantees due to the nonconvex nature of the feasible region as well as the significant differences in the curvatures of the potential energy surface with respect to atomic and lattice components. To this end, we propose a projected gradient descent algorithm named PANBB. It is equipped with (i) search direction projections onto the tangent spaces of the nonconvex feasible region for lattice vectors, (ii) distinct curvature-aware initial trial step sizes for atomic and lattice updates, and (iii) a nonrestrictive line minimization criterion as the stopping rule for the inner loop. It can be proved that PANBB favors theoretical convergence to equilibrium states. Across a benchmark set containing 223 structures from various categories, PANBB achieves average speedup factors of approximately 1.41 and 1.45 over the conjugate gradient method and direct inversion in the iterative subspace implemented in off-the-shelf simulation software, respectively. Moreover, it normally converges on all the systems, manifesting its unparalleled robustness. As an application, we calculate the static equations of state for the high-entropy alloy AlCoCrFeNi, which remains elusive owing to 160 atoms representing both chemical and magnetic disorder and the strong local lattice distortion. The results are consistent with the previous calculations and are further validated by experimental thermodynamic data. © 2024, CC BY.

关键词： Conjugate gradient method

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HELCFL: high-efficiency and low-cost federated learning in heterogeneous mobile-edge computing 22

HELCFL: high-efficiency and low-cost federated learning in h...

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Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe

作者： Yangguang Cui Kun Cao Junlong Zhou Tongquan Wei East China Normal University Shanghai China and State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Jinan University Guangzhou China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783981926361

Federated Learning (FL), an emerging distributed machine learning (ML), empowers a large number of embedded devices (e.g., phones and cameras) and a server to jointly train a global ML model without centralizing user private data on a server. However, when deploying FL in a mobile-edge computing (MEC) system, restricted communication resources of the MEC system, heterogeneity and constrained energy of user devices have a severe impact on FL training efficiency. To address these issues, in this article, we design a distinctive FL framework, called HELCFL, to achieve high-efficiency and low-cost FL training. Specifically, by analyzing the theoretical foundation of FL, our HELCFL first develops a utility-driven and greedy-decay user selection strategy to enhance FL performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCFL introduces a device operating frequency determination approach to reduce training energy costs. Experiments verify that our HELCFL can enhance the highest accuracy by up to 43.45%, realize the training speedup of up to 275.03%, and save up to 58.25% training energy costs compared to state-of-the-art baselines.

关键词： high efficiency user selection federated learning low costs mobile-edge computing frequency determination

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Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval

arXiv

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arXiv 2024年

作者： Zeng, Gangyan Zhang, Yuan Wei, Jin Yang, Dongbao Zhang, Peng Gao, Yiwen Qin, Xugong Zhou, Yu School of Cyber Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Media Convergence and Communication Communication University of China China Institute of Information Engineering Chinese Academy of Sciences China Laboratory for Advanced Computing and Intelligence Engineering China TMCC College of Computer Science Nankai University China

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP’s particular advantages in handling diverse forms of query text. The source code will be publicly available at https://***/Gyann-z/FDP. © 2024, CC BY-NC-SA.

关键词： Semantics

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通过表面终止化学设计实现高度可逆的锌金属阳极

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Science Bulletin 2023年第23期68卷 2993-3002,M0005页

作者：刘宇陈树麟袁豪熊方宇刘琴安永康张建勇吴璐孙建国 Yong-Wei Zhang 安琴友 John Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Materials Science and Engineering National University of SingaporeSingapore 117574Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*Star)Singapore 138632Singapore Institute of Materials Research and Engineering(IMRE) Agency for ScienceTechnology and Research(A*Star)Singapore 138634Singapore

锌表面自然形成的非均匀氧化层(ZnO)会与电解液发生反应形成Zn_(4)(OH)_(6)SO_(4)·xH_(2)O(ZSH)副产物,导致锌负极表面非均匀沉积,从而阻碍水性锌离子电池实际应用.为了排除氧化层分布形式的影响,本文利用表面终止化学设计策略在... 详细信息

锌表面自然形成的非均匀氧化层(ZnO)会与电解液发生反应形成Zn_(4)(OH)_(6)SO_(4)·xH_(2)O(ZSH)副产物,导致锌负极表面非均匀沉积,从而阻碍水性锌离子电池实际应用.为了排除氧化层分布形式的影响,本文利用表面终止化学设计策略在锌负极表面制备了一层完全氧化的ZnO层,研究发现锌负极均匀分布的ZnO层会原位演化形成致密的ZSH功能层.均匀且致密的ZSH能够有效隔绝电解液的腐蚀,从而有效提高锌负极界面的稳定性.此外,ZSH(002)晶面边缘的不饱和悬空键(如-O)更容易和Zn(H_(2)O)_(6)^(2+)结构中的锌离子形成强Zn-O键,可以促进Zn(H_(2)O)_(6)^(2+)的脱溶剂化和锌离子沿边扩散,实现了高度可逆的锌沉积行为并限制了副反应.基于此,本文提出与“表面脱水-晶体通道转移”机制明显不同的“边缘脱水-沿边缘转移”锌离子输运新机制.更重要的是,基于此组装的全电池显示出高循环稳定性(超过1000次循环)和高库仑效率(99.07%),验证了其潜在的实际应用价值.

关键词： Zn-O termination Zn Anode Solid electrolyte interphase Dendrite-free Zn ion battery

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A New Adaptive Balanced Augmented Lagrangian Method with Application to ISAC Beamforming Design

arXiv

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arXiv 2024年

作者： Wu, Jiageng Jiang, Bo Li, Xinxin Liu, Ya-Feng Yuan, Jianhua School of Mathematics Jilin University Changchun130012 China Ministry of Education Key Laboratory for NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Sciences Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, we consider a class of convex programming problems with linear equality constraints, which finds broad applications in machine learning and signal processing. We propose a new adaptive balanced augmented Lagrangian (ABAL) method for solving these problems. The proposed ABAL method adaptively selects the stepsize parameter and enjoys a low per-iteration complexity, involving only the computation of a proximal mapping of the objective function and the solution of a linear equation. These features make the proposed method wellsuited to large-scale problems. We then custom-apply the ABAL method to solve the ISAC beamforming design problem, which is formulated as a nonlinear semidefinite program in a previous work. This customized application requires careful exploitation of the problem's special structure such as the property that all of its signal-to-interference-and-noise-ratio (SINR) constraints hold with equality at the solution and an efficient computation of the proximal mapping of the objective function. Simulation results demonstrate the efficiency of the proposed ABAL method. © 2024, CC BY.

关键词： Signal to noise ratio

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Self-Attention Prediction Correction with Channel Suppression for Weakly-Supervised Semantic Segmentation

Self-Attention Prediction Correction with Channel Suppressio...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guoying Sun Meng Yang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

Single-stage weakly-supervised semantic segmentation (WSSS) with image-level labels has become a new research hotspot in the community for its lower cost and higher training efficiency. However, the pseudo label of WSSS generally suffers from somewhat noise, which limits the segmentation performance. In this paper, to explore the integral foreground activation, we propose the Channel Suppression (CS) module for preventing only activating the most discriminative regions, thereby improving the initial pseudo labels. To rectify the in-correct prediction, we explore the Self-Attention Prediction Correction (SAPC) module, which adaptively generates the category-wise prediction rectification weights. After extensive experiments, the proposed efficient single-stage framework achieves excellent performance with 67.6% mIoU and 39.9% mIoU on PASCAL VOC 2012 and MS COCO 2014 datasets, significantly exceeding several recent single-stage methods.

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Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud

Security-as-a-Service with Cyberspace Mimic Defense Technolo...

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2021国际计算机前沿大会

作者： Junchao Wang Jianmin Pang Jin Wei State Key Laboratory of Mathematical Engineering and Advanced Computing School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application's security. Cyberspace mimic defense(CMD) technologies can help to enhance the application's security despite the existence of the vulnerability. In this paper,the concept of SECurity-as-a-Service(SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application's security is greatly improved to meet the user's security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements(monetary costs,required security level, etc.) and automate the process of application orchestration.

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