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arXiv 2024年

作者： Glaubitz, Jan Ranocha, Hendrik Winters, Andrew R. Schlottke-Lakemper, Michael Öffner, Philipp Gassner, Gregor Department of Aeronautics and Astronautics Massachusetts Institute of Technology United States Department of Mathematics Linköping University Sweden Institute of Mathematics Johannes Gutenberg University Mainz Germany High-Performance Scientific Computing University of Augsburg Germany High-Performance Computing Center Stuttgart Germany Institute of Mathematics TU Clausthal Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

high-order numerical methods for conservation laws are highly sought after due to their potential efficiency. However, it is challenging to ensure their robustness, particularly for under-resolved flows. Baseline high-order methods often incorporate stabilization techniques that must be applied judiciously—sufficient to ensure simulation stability but restrained enough to prevent excessive dissipation and loss of resolution. Recent studies have demonstrated that combining upwind summation-by-parts (USBP) operators with flux vector splitting can increase the robustness of finite difference (FD) schemes without introducing excessive artificial dissipation. This work investigates whether the same approach can be applied to nodal discontinuous Galerkin (DG) methods. To this end, we demonstrate the existence of USBP operators on arbitrary grid points and provide a straightforward procedure for their construction. Our discussion encompasses a broad class of USBP operators, not limited to equidistant grid points, and enables the development of novel USBP operators on Legendre–Gauss–Lobatto (LGL) points that are well-suited for nodal DG methods. We then examine the robustness properties of the resulting DG-USBP methods for challenging examples of the compressible Euler equations, such as the Kelvin–Helmholtz instability. Similar to high-order FD-USBP schemes, we find that combining flux vector splitting techniques with DG-USBP operators does not lead to excessive artificial dissipation. Furthermore, we find that combining lower-order DG-USBP operators on three LGL points with flux vector splitting indeed increases the robustness of nodal DG methods. However, we also observe that higher-order USBP operators offer less improvement in robustness for DG methods compared to FD schemes. We provide evidence that this can be attributed to USBP methods adding dissipation only to unresolved modes, as FD schemes typically have more unresolved modes than nodal DG *** Codes 65M1

关键词： Vectors

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Towards Harmless Rawlsian Fairness Regardless of Demographic Prior

arXiv

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arXiv 2024年

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024, CC BY.

关键词： Differential privacy

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SegMAE-Net: A Hybrid Method Using Masked Autoencoders for Consistent 3D Medical Image Segmentation

SegMAE-Net: A Hybrid Method Using Masked Autoencoders for Co...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Zheng Kai Liaw Ankit Das Shaista Hussain Feng Yang Yong Liu Rick Siow Mong Goh Department of Mathematics National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Republic of Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Volumetric medical image segmentation has been particularly challenging as both local and global features are important in producing an accurate and consistent segmentation output. However, 2D CNNs often ignore the global contextual information of the volumetric input while the use of 3D CNNs is heavily limited by the large computational needs and GPU restrictions. In this paper, we propose SegMAE-Net, which combines 2D and 3D methods to leverage on the strengths of both approaches. Specifically, SegMAE-Net consists of two branches, 1) slice-centric branch made up of an encoder-decoder architecture to learn the local features of each image slice, 2) volumecentric branch utilising masked autoencoders to capture longrange dependencies. We evaluate SegMAE-Net on the RETOUCH dataset, and the experimental results show that our proposed method achieves state-of-the-art performance. We also show that our method is able to produce segmentation outputs with a higher consistency across the volume level.

关键词： Image segmentation Solid modeling Three-dimensional displays Neural networks Surgery Feature extraction Planning

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ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING 12

ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-O...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ye, Feiyang Lyu, Yueming Wang, Xuehao Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Shanghai Artificial Intelligence Laboratory China School of Computer Science and Engineering Nanyang Technological University Singapore

Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multiobjective optimization

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Swarm Intelligence Model for Securing Healthcare Ecosystem 13th

Swarm Intelligence Model for Securing Healthcare Ecosystem

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13th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN / The 12th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2022 / Affiliated Workshops

作者： Ribino, Patrizia Ciampi, Mario Islam, Shareeful Papastergiou, Spyridon Institute for High Performance Computing and Networking National Research Council of Italy Italy School of Computing and Information Science Anglia Ruskin University United Kingdom Department of Informatics University of Piraeus Greece Focal Point Belgium

The healthcare sector is constantly facing challenges in ensuring security due to the sophisticated attacks by the threat actor for acquiring sensitive patient data. An attack on the system can pose any potential risk to the business continuity. The increased use of information technology in the modern healthcare system makes medical devices and systems more vulnerable to exploitation and possible cyber-security attacks. This paper proposes a flexible and decentralized cyber-security model based on the integration of multi-agent systems and swarm intelligence for tackling the propagation of attacks inside interconnected healthcare organizations and ensuring the whole healthcare ecosystem's security and resilience. The proposed model is based on the collaboration between the agents with different functions and cognitive capabilities, named primary and supervisor agents. Primary agents are lightweight BDI (Belief-Desire-Intention) agents implementing a minimum set of capabilities for monitoring a specific area of the healthcare system;supervisor agents incorporate an extended version of the BDI reasoning to provide advanced capabilities for securing the overall healthcare system by enabling collective intelligence and overall cyber-security awareness. The preliminary experimental results show that the model is robust and responsive for securing the ecosystem. © 2022 Elsevier B.V.. All rights reserved.

关键词： Multi agent systems

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SOCA-DOM:A Mobile System-on-Chip Array System for Analyzing Big data on the Move

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Journal of Computer science & Technology 2022年第6期37卷 1271-1289页

作者： Le-Le Li Jiang-Yi Liu Jian-Ping Fan Xue-Hai Qian Kai Hwang Yeh-Ching Chung Zhi-Bin Yu Center for Heterogeneous and Intelligent Computing Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Science and Engineering The Chinese University of Hong KongShenzhenShenzhen 518172China Center for High Performance Computing Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen 518055China Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos Angeles CA 90089-0001U.S.A. Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089-0001U.S.A. School of Data and Science The Chinese University of Hong KongShenzhenShenzhen 518172China

Recently,analyzing big data on the move is *** requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be flexible and consume little hardware *** meet these requirements,we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier“software-defined”resource manager named ***,we design an Ethernet communication board to support an array of mobile ***,we propose a two-tier software architecture for Chameleon to make it ***,we devise data,configuration,and control planes for Chameleon to make it“software-defined”and in turn consume hardware resources on ***,we design an accurate synthetic metric that represents the computational power of a computing *** employ 12 Apache Spark benchmarks to evaluate ***,SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource *** addition,we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon *** on the results,we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.

关键词： edge computing mobile architecture resource management big data analytics software-defined systems

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Cyber threat assessment and management for securing healthcare ecosystems using natural language processing

Cyber threat assessment and management for securing healthca...

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作者： Silvestri, Stefano Islam, Shareful Amelin, Dmitry Weiler, Gabriele Papastergiou, Spyridon Ciampi, Mario Institute for High Performance Computing and Networking of the National Research Council of Italy ICAR-CNR Via Pietro Castellino 111 Naples80131 Italy School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Focal Point Waterloo Belgium Department of Informatics University of Piraeus Piraeus Greece Fraunhofer Institute for Biomedical Engineering IBMT Sulzbach Germany

The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security-related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security-related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security-related information on Internet (e.g. cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering, considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions. ©

关键词： Ecosystems

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Mining Top-k Frequent Patterns over Streaming Graphs 28th

Mining Top-k Frequent Patterns over Streaming Graphs

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Xi Zhang, Qianzhen Guo, Deke Zhao, Xiang Institute for Quantum Information and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency. Most existing work assumes a static graph. However, graphs are dynamic in nature, which is described as streaming graphs. Mining top-k frequent patterns in streaming graphs is challenging due to the streaming nature of the input and the exponential time complexity of the problem. A naive solution is to calculate approximations of the frequent patterns in the streaming graph and then find the top-k answers, which is a memory- and time-consuming method. In this paper, we design a novel auxiliary data structure, FPC, to detect valid subgraph patterns and their frequency in real-time. We first convert each newly produced subgraph into a sequence and then map it into corresponding tracks in FPC based on hash functions. We theoretically prove that FPC can provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve FPC with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-quality results compared to the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Evaluating Energy Efficiency of GPUs using Machine Learning Benchmarks

Evaluating Energy Efficiency of GPUs using Machine Learning ...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Brett Foster Shubbhi Taneja Joseph Manzano Kevin Barker Department of Computer Science Worcester Polytechnic Institute High Performance Computing Group Pacific Northwest National Laboratory

As we enter the exascale era, the energy efficiency and performance of high-performance computing (HPC) systems, especially running Machine Learning (ML) applications, are becoming increasingly important. Nvidia recently released its 9th-generation HPC-grade Graphics Processing Unit (GPU) microarchitecture, Ampere, claiming significant improvements over the previous generation’s Volta architecture. In this paper, we perform fine-grained power collection and assess the performance of these two HPC architectures’ performance by profiling ML benchmarks. In addition, we analyze various hyperparameters, primarily the batch size and the number of GPUs, to determine their impact on these systems’ performance and power efficiency. While Ampere is 3.16x more energy-efficient than Volta in isolation, this is counteracted by the PCIe interconnects of the A100s as the ML tasks are parallelized to run on more GPUs.

关键词：

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Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons

arXiv

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arXiv 2024年

作者： Liu, Jiao Sun, Zhu Feng, Shanshan Chen, Caishun Ong, Yew-Soon College of Computing & Data Science Nanyang Technological University Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore Centre for Frontier AI Research A*STAR The Institute of High Performance Computing A*STAR Singapore

In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code. Although this field is still in its early stages, its impressive potential has led to the development of various LLM-based EAs. To effectively evaluate the performance and practical applicability of these LLM-based EAs, benchmarks with real-world relevance are essential. In this paper, we focus on LLM-based recommender systems (RSs) and introduce a benchmark problem set, named RSBench, specifically designed to assess the performance of LLM-based EAs in recommendation prompt optimization. RSBench emphasizes session-based recommendations, aiming to discover a set of Pareto optimal prompts that guide the recommendation process, providing accurate, diverse, and fair recommendations. We develop three LLM-based EAs based on established EA frameworks and experimentally evaluate their performance using RSBench. Our study offers valuable insights into the application of EAs in LLM-based RSs. Additionally, we explore key components that may influence the overall performance of the RS, providing meaningful guidance for future research on the development of LLM-based EAs in RSs. © 2024, CC BY.

关键词： Algorithmic languages

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