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2023 International Conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Ferlanti, Erik Allen, William J. Lima, Ernesto A. B. F. Wang, Yinzhi Fonner, John M. Texas Advanced Computing Center AustinTX United States

ISBN: (纸本)9798400707858

Containers are becoming essential to support the diversity of scientific computing workloads at academic computing centers. Here, we offer perspectives and experiences from the Texas advanced computing center on: the installation, configuration, and support of select containerization platforms;incorporation of containers into the module system to improve their discoverability and usability;facilitation of advanced use cases including MPI containers, GPU containers, and support for multiple instruction set architectures;and finally instruction on best practices to end users through workshops and university courses. We will briefly discuss case studies that highlight the importance of supporting containers for research computing. © 2023 Owner/Author.

关键词： Containers

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PyTACC: HPC python at the Texas advanced computing center 5

PyTACC: HPC python at the Texas advanced computing center

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5th Workshop on Python for High-Performance and Scientific computing, PyHPC 2015

作者： Evans, Todd Gómez-Iglesias, Antonio Proctor, Cyrus Texas Advanced Computing Center University of Texas at Austin AustinTX United States

ISBN: (纸本)9781450340106

Python-based applications at the Texas advanced computing center (TACC) consume a significant and growing fraction of our computational resources. To meet this demand, TACC has developed an approach to provide its users a robust, performant, and exible Python ecosystem. HPC centers such as TACC have unique concerns when supporting Python due to their complex system environments and diverse user base: maintenance and usage tracking of multiple Python versions/distributions/compiler-bases and associated packages, deployment of this software in a manner that is compatible with our systems and readily usable to our users, optimization of the software to maximize scientific throughout, and user support and education. © 2015 ACM.

关键词： Python

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Polymorphic signal responses of phase-change quantum dot string upon stochastic resonance

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Nano Research 2025年第2期18卷 717-724页

作者： Qin Wan Fei Zeng Ziao Lu Junwei Yu Feng Pan Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Center for Brain Inspired Computing Research(CBICR) Tsinghua UniversityBeijing 100084China

We successfully constructed phase-change quantum dots string(PCQDS)systems and studied their signal *** PCQDs actually is a cascaded structure consisted of several stochastic resonance(SR)two-state systems,in which inherent non-linearity,i.e.,phase-change of quantum dots(QDs),plays elementary and important roles to modulate signal *** established an SR model to simulate signal responses depending on stimulation *** know that some QDs will oscillate with input forcing frequency,while certain QDs will oscillate in their own frequency triggered by phase *** two effectscooperate togeneratepolymorphic response patterns,including action potential patterns exhibited by envelope of spike peak *** interesting and important simulation is that we replicate the memory effect in Nb-doped AINO,i.e.,a QDs dispersed *** result indicates that memory can occur in a system only constructed by volatile elementary units,implying memory existing in ***-term plasticity and spike-rate dependent plasticity can also be realized by using frequency and phase *** study provides a new scope to study signal handling and memory effect in quantum system.

关键词： quantum-dot string phase-change inherent nonlinearity signal modulation niobium oxide stochastic resonance

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Application of sequence analysis pipelining tools for high-throughput sample processing at the advanced biomedical computing center

Application of sequence analysis pipelining tools for high-t...

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Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, GBGIST 2001

作者： Stephens, Robert M. Smythers, Gary W. Advanced Biomedical Computing Center SAIC Frederick NCI Frederick Cancer Res. Devmt. Ctr PO Box B Frederick MD 21702 United States

ISBN: (纸本)0970789009

As a result of the relatively recent explosion in sequencing technologies, it has become possible for the rate of sequence production to out-strip the rate of sequence analysis using conventional tools. In order to decrease the time spent by researchers performing the analysis of these sequences it has been necessary to apply pipelining capability to conventional analysis methods. This has required the development and use of tools allowing user flexibility in analysis options coupled with mechanisms for data archiving, viewing and retrieval. In order to accomplish this task, we have developed a system that incorporates sequence sample transfer (using a RDBMS system), the Washington University analysis programs (Phred, 1, Phrap, 2, Polyphred, 3, Repeatmasker, 4, and Consed, 5) as well as sequence querying tools such as Blast (6), Fasta (7), Genscan (8) and the Wisconsin (GCG, 9) sequence analysis package. The current system allows for the user to describe a sequencing/analysis project selecting options such as vector masking, calls to Polyphred (3, for SNP analysis) and assembly, calls to Genscan (8) and other analysis programs. Additional analysis pipelines have also been built, for example calls to primer prediction programs, calls to antigenic epitope selection programs and calls to cluster analysis programs. The results from these analyses are then parsed, filtered and converted to web-viewable outputs that are password-protected allowing the user to view both the full dataset on a unix file system (eg. for Consed, 5) or via the web.

关键词： Biomedical engineering

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Decision making module based on stochastic magnetic tunnel junctions

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 198-205页

作者： Yifan Miao Li Zhao Yajun Zhang Zhe Yuan Center for Advanced Quantum Studies and Department of Physics Beijing Normal UniversityBeijing 100875China Institute for Nanoelectric Devices and Quantum Computing Fudan UniversityShanghai 200433China Interdisciplinary Center for Theoretical Physics and Information Sciences Fudan UniversityShanghai 200433China

In biological neural systems,noise is ubiquitous but does not affect the correct decisions made in the complex cognitive ***-making in biological neural system is typically achieved by accumulating input information over a period of *** by recent developments in neurosciences,we design a decision-making module based on spintronic devices,utilizing superparamagnetic tunnel junctions as artificial *** feasibility of this decision-making module is verified through circuit *** a multi-layer perceptron as an example,the module significantly improves the accuracy of the perceptron in the handwritten digit recognition ***,the spintronic decision-making module offers advantages over the conventional pooling methods,such as adaptive decision time,high performance and the absence of analog-to-digital *** decision-making module is flexible to be integrated into artificial neural networks and provides a general yet effective solution to enhance performance against device noise.

关键词： neuromorphic computing spintronics magnetic tunnel junction neural networks

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Explainable One Class Classification for ATM Fraud Detection 17

Explainable One Class Classification for ATM Fraud Detection

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Vivek, Yelleti Ravi, Vadlamani Mane, Abhay Naidu, Laveti Ramesh Institute for Development and Research in Banking Telangana Hyderabad India Center for Development of Advanced Computing Karnataka Bangalore India

ISBN: (纸本)9798331531195

Effective risk management and compliance adherence are paramount for the success of financial institutions and organizations. However, they often face significant challenges due to fraudulent activities, with ATM fraud, among others, emerging as a prevalent issue in today's banking landscape. We proposed a novel profiling-based one-class classification (OCC) method to solve this problem. Then training phase of our approach employs the K-Means clustering algorithm to cluster non-fraudulent transactions exhibiting similar characteristics and patterns. A rule is generated from each cluster, thereby in a rule set comprising K rules, each consisting of conditions based on the lower and upper bounds on all features. This rule set is employed to identify fraudulent transactions presented in the test phase because ours is an OCC method. One distinctive feature of our approach is its interpretability and explainability, which is crucial for understanding the model's predictions. Overall, our proposed approach demonstrates the best performance vis-à-vis that of various state-of-the-art OCC methods in terms of classification rate. Additionally, we provide sensitivity analysis by varying the number of conditions violated across the K rules. © 2025 IEEE.

关键词： Sensitivity analysis

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Dynamic Incentive Model for Federated Learning Model Trading via Evolutionary Game Theory

Dynamic Incentive Model for Federated Learning Model Trading...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Tianxiang Wang, Feng Hou, Wenjie Tang, Shaoting Zheng, Zhiming Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

ISBN: (纸本)9798350368741

Federated Learning (FL) is an emerging decentralized machine learning paradigm that addresses the data-silo problem through privacy-preserving collaborative model training, attracting significant attention from academia and industry. However, model trading in FL involves multiple stakeholders, including data owners, model requesters, and the cloud service platform, whose conflicting interests hinder the sustainability and stability of FL. To address these challenges, this paper considers the bounded rationality of the three parties involved in long-term dynamic decision-making and constructs a tripartite evolutionary game model based on evolutionary game theory, further taking into account collusion between data owners and cloud service platforms. We analyze the evolutionary dynamics involved, theoretically revealing the social dilemma of dishonesty in FL model trading. To prevent dishonest behaviors such as free-riding and false reporting, we apply the replicator dynamics and Lyapunov method to analyze the impact of rewards, punishments, and collusion costs on the evolutionary stable strategies of the three parties and propose incentive strategies. Simulation experimental results validate that our incentive model is effective in alleviating dishonest social dilemmas and improving social welfare. © 2025 IEEE.

关键词： Evolutionary game Federated learning Incentive model Social dilemma

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Simulating Social Pressure: Evaluating Risk Behaviors in Construction Using Augmented Virtuality

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2494-2503页

作者： Pooladvand, Shiva Hasanzadeh, Sogand Takahashi, George Park, Kenneth Jongwon Marroquin, Jacob School of Sustainable Engineering and the Built Environment Arizona State University United States Lyles School of Civil and Construction Engineering Purdue University United States Rosen Center for Advanced Computing Purdue University United States

Drawing on social influence and behavioral intention theories, coworkers' risk-taking serves as an "extra motive" —an exogenous factor—for risk-taking behaviors among workers in the workplace. Social influence theories have shown that social factors, such as social pressure and coworker risk-taking, may predict risk-taking behaviors and significantly affect decision-making. While immersive technologies have been widely used to create close-to-real simulations for construction safety-related studies, there is a paucity of research considering the impact of social presence in evaluating workers' risk decision-making within immersive environments. To bridge this gap, this study developed a state-of-the-art Augmented Virtuality (AV) environment to investigate roofers' risk-taking behaviors when exposed to social stressors (working alongside a safe/unsafe peer). In this augmented virtuality environment, a virtual peer with safe and unsafe behaviors was simulated in order to impose peer pressure and increase participants' sense of social presence. Participants were asked to install asphalt shingles on a physical section of a roof (passive haptics) while the rest of the environment was projected virtually. During shingle installation, participants' cognitive and behavioral responses were captured using psychophysiological wearable technologies and self-report measures. The results demonstrated that the developed AV model could successfully enhance participants' sense of presence and social presence while serving as an appropriate platform for assessing individuals' decision-making orientations and behavioral changes in the presence of social stressors. Such information shows the value of immersive technologies to examine the naturalistic responses of individuals without exposing them to actual risks. © 1995-2012 IEEE.

关键词： Augmented reality

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Performance Analysis of Scientific Applications on an NVIDIA Grace System 24

Performance Analysis of Scientific Applications on an NVIDIA...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Amit Ruhela John Cazes John McCalpin Carlos del-Castillo-Negrete Junjie Li Hang Liu Hanning Chen Chun-Yaung Lu Kent Milfeld Wenyang Zhang Ian Wang Lars Koesterke John DeSantis Nic Lewis Sean Hempel Dan Stanzione Texas Advanced Computing Center Austin Texas

ISBN: (纸本)9798350355543

The NSF funded Frontera system has supported many important scientific applications over the last five years, not only in HPC but also in the area of Big Data and Machine Learning. Applications leveraged the petascale computational capabilities of the system to achieve new breakthroughs in science and engineering. Frontera is currently the fastest academic supercomputer in the US and is used by hundreds of scientists and engineers daily, enabling researchers to tackle more significant and complex challenges than ever before. As Frontera is nearing the end of its production life, it will be replaced by a new system, "Horizon". An intermediate system, "Vista", will bridge the gap by enabling researchers to access updated software and hardware technologies before Horizon is available. This paper summarizes early experiences on Vista by reporting on the performance of key applications and presenting its design and architecture. Early results are presented from the CPU architecture of Vista, "Grace-Grace", and compared with other processor technologies at TACC from Intel and AMD. A future paper will discuss the performance of the "Grace-Hopper" component of Vista.

关键词： Grace-Hopper

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Data Sharing and Evaluation: Evaluation's Role in Improving the Quality of Open Science Data Sharing Platforms

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New Directions for Evaluation 2025年第0期 65-71页

作者： Jacobson, Miriam R. Baker, Stephanie N. Yang, Yiwen Childs, Joshua Wang, Zhuoying Garbrecht, Lisa S. Texas Advanced Computing Center The University of Texas at Austin Austin TX United States Department of Educational Leadership and Policy The University of Texas at Austin Austin TX United States

Data sharing platforms are vital open science tools that facilitate research collaboration and data dissemination. Evaluating these platforms helps ensure they meet open science standards by assessing their accessibility, utility, and impact. This article shares a case example from a user experience evaluation of an open science data sharing platform, which collected mixed methods data from users, stakeholders, and the platform's website. In describing the case example, the authors aimed to understand the role of evaluation in helping the platform adhere to open science practices and to identify opportunities for evaluators to use similar types of platforms to enhance their evaluation practice. The evaluation team used open science principles to design their evaluation, which aligned with evaluation values of inclusion, transparency, and utility. The evaluation provided useful data for understanding platform users’ perspectives on the platform and perceptions of its impact. The case example suggests that open science data sharing platforms could provide many benefits for the evaluation field, such as offering ways to collaborate with others, strengthening the quality of evaluation studies, and improving the availability of data. However, to increase engagement of evaluators in data sharing platforms, it is important to build partnerships between evaluators and platform developers, design training and outreach for evaluators, and research how existing data sharing platforms can be further developed to serve the evaluation community. © 2025 American Evaluation Association and Wiley Periodicals LLC.

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