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Customisable sound absorption properties of functionally graded metallic foams

Journal of Materials Science & technology 2022年第13期108卷 196-207页

作者： Jun Wei Chua Xinwei Li Tao Li Beng Wah Chua Xiang Yu Wei Zhai Department of Mechanical Engineering National University of Singapore117575Singapore Agency for Science Technology and Research(A^(*)STAR)Singapore Institute of Manufacturing Technology138634Singapore Agency for Science Technology and Research(A^(*)STAR)Engineering MechanicsInstitute of High-Performance Computing1 Fusionopolis Way138632Singapore

In this study,functionally graded foam made of Inconel 625 superalloy was successfully produced using the template replication method,with open-cell polyurethane foams as a *** products have a similar pore morphology as the templates and adjacent layers were successfully sintered together by particle *** absorption experiments on graded metallic foams reveal that the sound absorption at particular frequency ranges can be improved by various permutations of foam *** graded foam of two distinct pore sizes,a mathematical equation was proposed to predict the location of the intersection point of the sound absorption curves,thereby aiding in graded foam *** increase in sound absorption coefficients by resonance-like effects can be introduced before the intersection points by placing the foam layer of smaller pore size nearer to the sound *** sound absorption performances can be further customized when the thickness proportion of the pore sizes is changed and when the number of distinct pore sizes used is *** sound absorption performance at lower frequencies is generally boosted by resonance-like effects when the layer of foam with the largest pore size is placed furthest from the sound *** the same composition of foam with a fixed thickness proportion of pore sizes,one can introduce resonance-like effects to improve the sound absorption performance compared to other permutations while possibly satisfying weight requirements in practical *** study provides valuable insights and mathematical guidelines in the design and manufacturing of functionally graded metallic foam for specific applications.

关键词： Template replication method Functionally graded foam Sound absorption Transfer matrix method Composite metal foam

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Design and Implementation of a Compiler Supporting RISC-V Custom Cryptographic and Vector Instructions 23

Design and Implementation of a Compiler Supporting RISC-V Cu...

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7th International Conference on Computer Science and Application Engineering, CSAE 2023

作者： Zou, Xiaofeng Peng, Yuanxi Zhang, Qing Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Second Research Department Cloud Computing Equipment Industry Innovation Center Jinan China

ISBN: (纸本)9798400700590

Post-quantum cryptographic algorithms capable of resisting quantum computing attacks have received increasing attention in recent years, especially lattice-based cryptographic algorithms have become mainstream. Since their specialization and complex computation, the cryptographic computation is inefficient on general-purpose processors. In order to improve the performance of the processor in post-quantum cryptography computing, we have extended a set of cryptographic and vector instruction sets for the CRYSTALS-Kyber algorithm, and implemented support for custom cryptographic and vector instructions based on the open-source toolchain. These efforts can realize the compilation and disassembly of the CRYSTALS-Kyber encryption/decryption algorithm programs, and optimized the performance of the compilation. This paper mainly introduces how to design an extended instruction set based on the target algorithm and enable the compiler to support these instructions. Compared to the situation before extending custom instructions, the total number of instructions for key generation, encryption, and decryption assembly programs has decreased by 3651 times, and the total number of execution cycles has decreased by 2195 times. © 2023 ACM.

关键词： Program compilers

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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Turán Numbers of Expanded Intersecting Cliques in 3-graphs

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Journal of the Operations Research Society of China 2024年第4期12卷 952-964页

作者： Yu-Cong Tang Tong Li Gui-Ying Yan School of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing211106JiangsuChina Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) Ministry of Industry and Information TechnologyNanjing211106JiangsuChina School of Mathematical Sciences Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

Let l>r≥*** a 2-graph F,the expansion E^((r))of F is an r-graph obtained from F by adding r-2 new vertices into each *** F is a clique of order l,the Turán number ex(n,F^((r)))was first asymptotically determined by Mubayi(J Comb Theory Ser B 96:122–134,2006)and exactly computed by Pikhurko(J Comb Theory Ser B 103:220–225,2013).Let F_(k,l)be the 2-graph on(l-1)k+1 vertices consisting of k cliques of order l intersecting at exactly one *** determine the exact Turán number ex(n,F_(k,l)^((3)))for all l>3,k≥1,and sufficiently large n,as well as the corresponding extremal graphs.

关键词： Expansion Turán number 3-Graph Intersecting cliques Stability

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Online Optimization in Power systems With high Penetration of Renewable Generation:Advances and Prospects

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 839-858页

作者： Zhaojian Wang Wei Wei John Zhen Fu Pang Feng Liu Bo Yang Xinping Guan Shengwei Mei Key Laboratory of System Control and Information Processing Ministry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Power System and the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Institute of High Performance Computing(IHPC) Agency for Science Technology and Research(A*STAR)Singapore 138632Singapore

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable *** increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization ***,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical *** paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques *** particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period ***,we recommend some potential future directions for online optimization in the power systems domain.

关键词： optimization Lyapunov optimization online convex optimization online optimization optimization-guided control

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Medical image registration and its application in retinal images:a review

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： Computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Novel energy dissipative method on the adaptive spatial discretization for the Allen–Cahn equation

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Chinese Physics B 2021年第7期30卷 107-115页

作者： Jing-Wei Sun Xu Qian Hong Zhang Song-He Song Department of Mathematics National University of Defense TechnologyChangsha 410073China State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China

We propose a novel energy dissipative method for the Allen–Cahn equation on nonuniform *** spatial discretization,the classical central difference method is utilized,while the average vector field method is applied for time *** with the average vector field method on the uniform mesh,the proposed method can involve fewer grid points and achieve better numerical performance over long time *** is due to the moving mesh method,which can concentrate the grid points more densely where the solution changes *** experiments are provided to illustrate the advantages of the proposed concrete adaptive energy dissipative scheme under large time and space steps over a long time.

关键词： moving mesh energy dissipative average vector field method Allen–Cahn equation

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Mobility-Aware Deep Reinforcement Learning with Seq2seq Mobility Prediction for Offloading and Allocation in Edge computing

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IEEE Transactions on Mobile computing 2024年第6期23卷 6803-6819页

作者： Wu, Chao-Lun Chiu, Te-Chuan Wang, Chih-Yu Pang, Ai-Chun Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan National Taiwan University Institute of Networking and Multimedia Taipei10617 Taiwan National Taiwan University High Performance and Scientific Computing Center Taipei10617 Taiwan

Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading decision and resource allocation need to be jointly handled to optimize the service provision efficiency within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision in the MEC system. M-DRL is composed of two parts: glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a 'glimpse' of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements. © 2002-2012 IEEE.

关键词： Reinforcement learning

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LightTraffic: On Optimizing CPU-GPU Data Traffic for Efficient Large-scale Random Walks 39

LightTraffic: On Optimizing CPU-GPU Data Traffic for Efficie...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Xing, Yipeng Li, Yongkun Wang, Zhiqiang Xu, Yinlong Lui, John C. S. University of Science and Technology of China China Anhui Province Key Laboratory of High Performance Computing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350322279

As a fundamental tool for graph analysis, random walk receives extensive attention in both industry and academia. For computing massive random walks, recent works show that GPUs provide a good option to accelerate the performance. However, due to the limited memory space of modern GPUs, it is infeasible to have both the graph data and walk index fully reside in GPU memory when running large-scale random walks. Thus, it necessitates an out-of-GPU-memory design, but this inevitably induces large amounts of CPU-GPU data transmission traffic and thus hinders the overall performance. In this paper, we develop LightTraffic, which optimizes the data transmission between CPU and GPU memory under the constraint of GPU memory capacity with various system designs, including a memory-efficient scheme for partition-based management and multiple scheduling techniques. LightTraffic is a fully out-of-GPU-memory design, so it supports running large-scale random walks on GPUs. Experiments on our prototype show that LightTraffic outperforms various state-of-the-art CPU-based in-memory systems which also support large-scale random walks. For example, compared to the CPU-based systems FlashMob and ThunderRW, which are highly optimized for random walks, LightTraffic achieves 1.7-5.0× and 1.4 - 12.8× performance speedup, respectively. It also achieves up to an order of magnitude speedup when compared to the GPU-based system Subway which also supports large-scale random walks with an out-of-GPU-memory design for graph data. © 2023 IEEE.

关键词： Graphics processing unit

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Can Justice be a measurable value for AI? Proposed evaluation of the relationship between NLP models and principles of Justice 1

Can Justice be a measurable value for AI? Proposed evaluatio...

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1st Workshop on User Perspectives in Human-Centred Artificial Intelligence, HCAI4U 2023

作者： Marassi, Lidia Patwardhan, Narendra Gargiulo, Francesco University of Naples Federico II Department of Electrical Engineering and Information Technology Naples Italy Institute for High Performance Computing and Networking of National Research Council ICAR-CNR Naples Italy

NLP models, such as chat-based generative ones, have gained widespread use and influence in various fields. Considering AI as a valuable resource, it seems critical to determine the ethical principles to which these models should adhere to be considered usable. Applying the philosophical concept of justice to the evaluation of NLP models can help improve their functioning, performance, and overall ethical implications. Although strict adherence to traditional codes of justice may not be sufficient, it is proposed that the concept be adapted to evaluate the performance of NLP systems. Creating a rating scale based on these values may help estimate the "amount of Justice" or fairness demonstrated by different NLP systems. By emphasizing the importance of certain values central to assessing the fairness of NLP models, it becomes possible to develop parameters for evaluation. As these models become increasingly pervasive, this research can help improve this models’ performance and their socio-cultural significance. The awareness of building more equitable technologies raises consciousness about the potential consequences of irresponsible use of these models, highlighting the significance of ethical considerations. This approach can lead to a more comprehensive understanding of these systems, promoting their improvement and fostering greater knowledge of the ethical implications associated with their functioning. © 2023 CEUR-WS. All rights reserved.

关键词： Natural language processing systems

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