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Improved hybrid Allen-Cahn phase-field-based lattice Boltzmann method for incompressible two-phase flows

Physical Review E 2023年第3期107卷 035308-035308页

作者： Xi Liu Zhenhua Chai Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China and Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China

In this work we develop an improved phase-field based lattice Boltzmann (LB) method where a hybrid Allen-Cahn equation (ACE) with a flexible weight instead of a global weight is used to suppress the numerical dispersion and eliminate the coarsening phenomenon. Then two LB models are adopted to solve the hybrid ACE and the Navier-Stokes equations, respectively. Through the Chapman-Enskog analysis, the present LB model can correctly recover the hybrid ACE, and the macroscopic order parameter used to label different phases can be calculated explicitly. Finally, the present LB method is validated by five tests, including the diagonal translation of a circular interface, two stationary bubbles with different radii, a bubble rising under the gravity, the Rayleigh-Taylor instability in two-dimensional and three-dimensional cases, and the three-dimensional Plateau-Rayleigh instability. The numerical results show that the present LB method has a superior performance in reducing the numerical dispersion and the coarsening phenomenon.

关键词： Multiphase flows Lattice-Boltzmann methods Two-fluid & multi-fluid model

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Tomo-NeRL: Tomographic neural representation learning for implicit CT image reconstruction

Tomo-NeRL: Tomographic neural representation learning for im...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Xiaoqin Tan, Boheng Guo, Yuanhao Yu, Xianping Kendrick, Jake Reynolds, Mark Southwest University College of Computer and Information Science Chongqing China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Research Institute of Highway Ministry of Transport Beijing China The University of Western Australia School of Physics Mathematics and Computing Perth Australia Perth Australia

ISBN: (纸本)9798350386226

CT image reconstruction is a challenging inverse problem of estimating image intensities from sensor signals. While deep learning-based methods hold promise, they are typically classified into fully-supervised, self-supervised, and case-by-case approaches, each offering distinct advantages and limitations. Among these, case-by-case reconstruction techniques, exemplified by models like NeRP, provide the advantage of flexible data-sampling and image sizes, making them well-suited for the complex CT inverse problem. However, existing case-by-case models encounter difficulties in achieving precise reconstructions, particularly in sparse-view scenarios. To address this limitation, we propose tomo-NeRL, a novel implicit reconstruction model that incorporates tomographic information of individual pixels within the reconstruction network. By harnessing this tomo-graphic data for implicit inverse learning, tomo-NeRL enhances reconstruction quality while effectively mitigating artifacts. The core innovation of tomo-NeRL lies in defining and extracting tomographic insights, such as visual characteristics and spatial variations specific to tomographic imaging, from the sinogram data for individual pixels. Moreover, our work introduces an adaptive inversion framework that amalgamates tomographic information from diverse projection angles, constructing a latent feature space that enhances the reconstruction process. Extensive experiments unequivocally showcase tomo-NeRL's outstanding reconstruction performance, excelling in artifact suppression and detail preservation, especially in sparse-view scenarios. © 2024 IEEE.

关键词： Image reconstruction

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XMiner: Efficient Directed Subgraph Matching with Pattern Reduction

arXiv

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arXiv 2024年

作者： Yuan, Pingpeng Wang, Yujiang Ma, Tianyu He, Siyuan Liu, Ling School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China College of Computing Georgia Institute of Technology Atlanta United States

Graph pattern matching, one of the fundamental graph mining problems, aims to extract structural patterns of interest from an input graph. The state-of-the-art graph matching algorithms and systems are mainly designed for undirected graphs. Directed graph matching is more complex than undirected graph matching because the edge direction must be taken into account before the exploration of each directed edge. Thus, the technologies (e.g. storage, exploiting symmetry for graph matching) for undirected graph matching may not be fully applicable to directed graphs. For example, the redundancy implied in directed graph pattern can not be detected using the symmetry breaking for undirected pattern graph. Here, we present XMiner for efficient directed graph pattern matching whose core idea is 'pattern reduction'. It first analyzes the relationship between constraints implied in a pattern digraph. Then it reduces the pattern graph into a simplified form by finding a minimum constraint cover. Finally, XMiner generates an execution plan and follows it to extract matchings of the pattern graph. So, XMiner works on simplified pattern graph and avoids much data access and redundant computation throughout the matching process. Our experimental results show that XMiner outperforms state-of the-art stand-alone graph matching systems, and scales to complex graph pattern matching tasks on larger graphs. © 2024, CC BY-SA.

关键词： Directed graphs

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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A Simulated Annealing approach for the Circle Bin Packing Problem with Rectangular Items

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期176卷

作者： Tole, Kevin Moqa, Rashad Zheng, Jiongzhi He, Kun [a]School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China [b]Institute of Computing and Informatics Technical University of Mombasa Mombasa 90420-80100 Kenya

We introduce a new variant of Bin Packing Problem (BPP) called the Circular Bin Packing Problem with Rectangular Items (CBPP-RI). CBPP-RI involves the dense orthogonal packing of rectangular items into a minimum number of bins. To our knowledge, there is no existing literature addressing this NP-hard problem. To fill this gap, we study CBPP-RI and propose an algorithm consisting of two stages, an initialization stage and an improvement stage. We propose a first-fit grid search (FGS) algorithm to generate the initial solution, then we design a Simulated Annealing (SA) algorithm to explore the solution space from the initial solution by the proposed novel local neighborhood search strategy called the circular quadrant perturbation (CQP). The SA algorithm always accepts the current solution if it is better than the previous one, and otherwise accepts based on a probability function. Experimental results show that the proposed algorithm is effective for not only the introduced CBPP-RI problem, but also other related packing problems.

关键词： Circle Bin Packing Problem NP-hard First-fit Grid Search Simulated Annealing

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Emerging theory and phenomena in thermal conduction: A selective review

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science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 2-17页

作者： Jie Chen Jia He Dongkai Pan Xiaotian Wang Nuo Yang Jiaojiao Zhu Shengyuan A.Yang Gang Zhang Center for Phononics and Thermal Energy Science China-EU Joint Lab for NanophononicsMOE Key Laboratory of Advanced Microstructured MaterialsSchool of Physics Science and EngineeringTongji UniversityShanghai 200092China School of Energy and Power Engineering Huazhong University of Science and Technology(HUST)Wuhan 430074China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Laboratory for Quantum Materials Singapore University of Technology and DesignSingapore 487372Singapore Institute of High Performance Computing Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore

Recently, emerging phonon phenomena have been discovered and rapidly developed, which have become an active hot research topic. In this review article, we present state-of-the-art advances in several fascinating phonon transport phenomena. First, we summarize the recent progress on the wave nature of phonons, including phonon coherence and its effects on thermal conductivity and the topological properties of phonons. Then, we discuss the particle nature of phonons, including the weak coupling of phonons and the high-order phonon anharmonicity. Finally, we present the summary and a brief outlook. This review presents the advanced understanding of some emerging phonon phenomena in solid materials, which provides new opportunities for further advancement in a wide variety of applications.

关键词： phonon transport coherence topological phonon high-order phonon interaction

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InstAttention: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference

InstAttention: In-Storage Attention Offloading for Cost-Effe...

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IEEE Symposium on High-Performance Computer Architecture

作者： Xiurui Pan Endian Li Qiao Li Shengwen Liang Yizhou Shan Ke Zhou Yingwei Luo Xiaolin Wang Jie Zhang Peking University University of Electronic Science and Technology of China Institute of Computing Technology Chinese Academy of Sciences Huawei Cloud Wuhan National Laboratory for Optoelectronics of Huazhong University of Science and Technology

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

The widespread of Large Language Models (LLMs) marks a significant milestone in generative AI. Nevertheless, the increasing context length and batch size in offline LLM inference escalate the memory requirement of the key-value (KV) cache, which imposes a huge burden on the GPU VRAM, especially for resource-constrained scenarios (e.g., edge computing). Several cost-effective solutions leverage host memory or SSDs to reduce storage costs for offline inference scenarios and improve the throughput. Nevertheless, they suffer from significant performance penalties imposed by intensive KV cache accesses due to limited PCIe bandwidth. To address these issues, we propose InstAttention, a novel LLM inference system that offloads the most performance-critical computation (i.e., attention in decoding phase) and data (i.e., KV cache) parts to Computational Storage Drives (CSDs), which minimize the enormous KV transfer overheads. InstAttention designs a dedicated flashaware in-storage attention engine with KV cache management mechanisms to exploit the high internal bandwidths of CSDs instead of being limited by the PCIe bandwidth. The optimized P2P transmission between GPU and CSDs further reduces data migration overheads. Experimental results demonstrate that for a 13B model using an NVIDIA A6000 GPU, InstAttention improves throughput for long-sequence inference by up to $11.1 \times$, compared to existing SSD-based solutions such as FlexGen.

关键词： Costs Generative AI Large language models Memory management Graphics processing units Bandwidth Throughput Decoding Engines Edge computing

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Subspace Collision: An Efficient and Accurate Framework for High-dimensional Approximate Nearest Neighbor Search

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Proceedings of the ACM on Management of Data 2025年第1期3卷 1-29页

作者： Jiuqi Wei Xiaodong Lee Zhenyu Liao Themis Palpanas Botao Peng Institute of Computing Technology Chinese Academy of Sciences Beijing China & University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China & Fuxi Institution Beijing China Huazhong University of Science and Technology Wuhan China LIPADE Université Paris Cité Paris France Institute of Computing Technology Chinese Academy of Sciences Beijing China

Approximate Nearest Neighbor (ANN) search in high-dimensional Euclidean spaces is a fundamental problem with a wide range of applications. However, there is currently no ANN method that performs well in both indexing and query answering performance, while providing rigorous theoretical guarantees for the quality of the answers. In this paper, we first design SC-score, a metric that we show follows the Pareto principle and can act as a proxy for the Euclidean distance between data points. Inspired by this, we propose a novel ANN search framework called Subspace Collision (SC), which can provide theoretical guarantees on the quality of its results. We further propose SuCo, which achieves efficient and accurate ANN search by designing a clustering-based lightweight index and query strategies for our proposed subspace collision framework. Extensive experiments on real-world datasets demonstrate that both the indexing and query answering performance of SuCo outperform state-of-the-art ANN methods that can provide theoretical guarantees, performing 1-2 orders of magnitude faster query answering with only up to one-tenth of the index memory footprint. Moreover, SuCo achieves top performance (best for hard datasets) even when compared to methods that do not provide theoretical guarantees.

关键词： ann search high-dimensional spaces subspace collision

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A data-assisted physics-informed neural network(DA-PINN)for fretting fatigue lifetime prediction

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International Journal of Mechanical System Dynamics 2024年第3期4卷 361-373页

作者： Can Wang Qiqi Xiao Zhikun Zhou Yongyu Yang Gregor Kosec Lihua Wang Magd Abdel Wahab Soete Laboratory Department of Electrical EnergyMetalsMechanical Constructions and SystemsFaculty of Engineering and ArchitectureGhent UniversityGentBelgium Department of Engineering Mechanics School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanChina Key Laboratory for Mechanics Anhui Polytechnic UniversityWuhuChina Parallel and Distributed Systems Laboratory Jožef Stefan InstituteLjubljanaSlovenia School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghaiChina College of Engineering Yuan Ze UniversityTaiwanChina

In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue *** PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting *** propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation *** traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction *** DA-PINN method,employed in this study,consists of two main ***,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other ***,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue *** results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is ***,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.

关键词： crack initiation data-assisted fretting fatigue finite element method physics-informed neural network

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