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SSRN 2023年

作者： Shi, Ling Wang, Jiang Xu, Songzhe Li, Jingjing Chen, Chaoyue Hu, Tao Sundar, Hari Ren, Zhongming State Key Laboratory of Advanced Special Steels School of Materials Science and Engineering Shanghai University Shanghai China School of Computing University of Utah Salt Lake City United States

Single crystal (SX) nickel-based superalloys have been used for turbine blades fabrication in recent years because of their superior mechanical performance at high temperature. The restoration of SX blades is significant considering the high cost to re-manufacture the blade. Direct energy deposition (DED), as a laser additive manufacturing technique, is a promising method for repairing the SX blades. Continuous epitaxial growth from the substrate to deposited layers is vital for successful restoration of SX blades. In this work, a novel single crystal texture cellular automata (SITCA) method is developed to simultaneously predict the columnar to equiaxed transition (CET) and crystal growth direction at the molten pool scale. Based on the grain envelope simplification, the SITCA method introduces a random variable to trace the growth trajectory, and therefore indicates the partition of the growth regions in the molten pool. A continuous nucleation model is introduced for nucleation simulation, and the crystal growth is simulated using the KurzGiovanola-Trivedi (KGT) model. The validation of SITCA model is performed by casting solidification cases under uniform temperature and uniform temperature gradient. The SITCA method is then integrated with a thermo-fluid molten pool simulation using finite element method to comprehensively compute the epitaxial growth. SX remelting experiments are carried out with different laser scanning speeds using a flat top laser source, and compared with the numerical results. The experimental and numerical results both indicate that increasing laser scanning speed will increase the ratio of crystals growing in the [001] direction while facilitate the CET on the other hand. This work shows the capacity of the SITCA method to predict epitaxial growth in DED process, and we anticipate to deploy the SITCA method to complex SX additive manufacturing. © 2023, The Authors. All rights reserved.

关键词： Epitaxial growth

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4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images

arXiv

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arXiv 2024年

作者： Liu, Zhentao Zha, Ruyi Zhao, Huangxuan Li, Hongdong Cui, Zhiming School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Computing Australian National University Canberra Australia School of Computer Science Wuhan University Wuhan China

Reconstructing 3D vessel structures from sparse-view dynamic digital subtraction angiography (DSA) images enables accurate medical assessment while reducing radiation exposure. Existing methods often produce suboptimal results or require excessive computation time. In this work, we propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality reconstruction efficiently. In detail, we represent the vessels with 4D radiative Gaussian kernels. Each kernel has time-invariant geometry parameters, including position, rotation, and scale, to model static vessel structures. The time-dependent central attenuation of each kernel is predicted from a compact neural network to capture the temporal varying response of contrast agent flow. We splat these Gaussian kernels to synthesize DSA images via X-ray rasterization and optimize the model with real captured ones. The final 3D vessel volume is voxelized from the well-trained kernels. Moreover, we introduce accumulated attenuation pruning and bounded scaling activation to improve reconstruction quality. Extensive experiments on real-world patient data demonstrate that 4DRGS achieves impressive results in 5 minutes training, which is 32× faster than the state-of-the-art method. This underscores the potential of 4DRGS for real-world clinics. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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A Neighbor Immunization Strategy to Block Virus Spreading on Networks

A Neighbor Immunization Strategy to Block Virus Spreading on...

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Information Communication Technologies Conference (ICTC)

作者： Hao Yaohui Liu Yingze Zhao Baole Wang Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China Information Engineering University Zhengzhou China

In order to achieve better effect on blocking virus propagation, a neighbor immunization strategy was proposed and verified in this paper. On email-EU real networks and on scale-free model network, the efficiencies of the neighbor immunization strategy and the random immunization strategy in SIR and SEIR virus spreading models were compared and analyzed by simulation experiment. The result indicates that the neighbor immunization strategy is a feasible and effective. This phenomenon shows that neighbor immunization strategy can improve to the future virus and effectively prevent the virus spreading.

关键词： Analytical models Communications technology Viruses (medical) Immune system

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NEXT:a neural network framework for next POI recommendation

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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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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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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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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

引用

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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