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arXiv 2020年

作者： Faldu, Priyank Philosophy Institute of Computing Systems Architecture School of Informatics University of Edinburgh United Kingdom

Last-Level Cache (LLC) represents the bulk of a modern CPU processor’s transistor budget and is essential for application performance as LLC enables fast access to data in contrast to much slower main memory. Problematically, technology constraints make it infeasible to scale LLC capacity to meet the ever-increasing working set size of the applications. Thus, future processors will rely on effective cache management mechanisms and policies to get more performance out of the scarce LLC capacity. Applications with large working set size often exhibit streaming and/or thrashing access patterns at LLC. As a result, a large fraction of the LLC capacity is occupied by dead blocks that will not be referenced again, leading to inefficient utilization of the LLC capacity. To improve cache efficiency, the state-of-the-art cache management techniques employ prediction mechanisms that learn from the past access patterns with an aim to accurately identify as many dead blocks as possible. Once identified, dead blocks are evicted from LLC to make space for potentially high reuse cache blocks. In this thesis, we identify variability in the reuse behavior of cache blocks as the key limiting factor in maximizing cache efficiency for state-of-the-art predictive techniques. Variability in reuse prediction is inevitable due to numerous factors that are outside the control of LLC. The sources of variability include control-flow variation, speculative execution and contention from cores sharing the cache, among others. Variability in reuse prediction challenges existing techniques in reliably identifying the end of a block’s useful lifetime, thus causing lower prediction accuracy, coverage, or both. To address this challenge, this thesis aims to design robust cache management mechanisms and policies for LLC in the face of variability in reuse prediction to minimize cache misses, while keeping the cost and complexity of the hardware implementation low. To that end, we propose two cache man

关键词： Forecasting

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Blockchain based Public Auditing Outsourcing for Cloud Storage 27

Blockchain based Public Auditing Outsourcing for Cloud Stora...

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27th IEEE International Conference on Parallel and Distributed systems, ICPADS 2021

作者： Lin, Yangfei Li, Jie Kimura, Shigetomo Zhang, Yongbing Ji, Yusheng Yang, Yang Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Tsukuba Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan School of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9781665408783

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user. © 2021 IEEE.

关键词： Smart contract

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Heterogeneous message passing for heterogeneous networks

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Physical Review E 2023年第3期108卷 034310-034310页

作者： George T. Cantwell Alec Kirkley Filippo Radicchi Santa Fe Institute 1399 Hyde Park Road Santa Fe New Mexico 87501 USA Department of Engineering University of Cambridge Cambridge CB2 1PZ United Kingdom Institute of Data Science University of Hong Kong Hong Kong Department of Urban Planning and Design University of Hong Kong Hong Kong Urban Systems Institute University of Hong Kong Hong Kong Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

Message passing (MP) is a computational technique used to find approximate solutions to a variety of problems defined on networks. MP approximations are generally accurate in locally treelike networks but require corrections to maintain their accuracy level in networks rich with short cycles. However, MP may already be computationally challenging on very large networks and additional costs incurred by correcting for cycles could be prohibitive. We show how the issue can be addressed. By allowing each node in the network to have its own level of approximation, one can focus on improving the accuracy of MP approaches in a targeted manner. We perform a systematic analysis of 109 real-world networks and show that our node-based MP approximation is able to increase both the accuracy and speed of traditional MP approaches. We find that, compared to conventional MP, a heterogeneous approach based on a simple heuristic is more accurate in 81% of tested networks, faster in 64% of cases, and both more accurate and faster in 49% of cases.

关键词： Complex networks Real world networks

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Impact of Cardiac Dose on Overall Survival in Lung Stereotactic Body Radiotherapy (SBRT) Compared to Conventionally Fractionated Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer (LA-NSCLC)

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Journal of Cancer Therapy 2021年第7期12卷 409-423页

作者： Justin D. Anderson Jiuyun Hu Jing Li Steven E. Schild Mirek Fatyga Department of Radiation Oncology Mayo Clinic Phoenix Arizona USA School of Computing Informatics and Decision Systems Engineering Tempe Arizona USA School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta Georgia USA

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patie... 详细信息

Purpose: To examine possible association between heart irradiation and Overall Survival (OS) in lung SBRT patients and to compare observed associations with cardiac toxicity models previously derived in LA-NSCLC patient studies. Materials and Methods: 197 Patients treated with lung SBRT at Mayo Clinic Arizona were selected for this IRB-approved study. Multivariate Cox model with Akaike Information Criterion (AIC) was used to select patient specific covariates associated with OS. Heart dosimetry was represented by V_D indices, which is a percentage of volume exposed to dose D or greater. Multivariate Cox models with patient specific cova

关键词： Lung Cancer Lung SBRT Cardiac Toxicity Lung Radiation Therapy Non-Small Cell Lung Cancer

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning

Reconstructing Retinal Visual Images from 3T fMRI Data Enhan...

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IEEE International Symposium on Biomedical Imaging

作者： Yujian Xiong Wenhui Zhu Zhong-Lin Lu Yalin Wang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science and Department of Psychology New York University New York USA NYU Shanghai NYU-ECNU Institute of Brain and Cognitive Science Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechanisms of the human visual system. Despite the significant strides made by deep learning methods in improving the quality and interpretability of visual reconstruction, there remains a substantial demand for high-quality, long-duration, subject-specific 7-Tesla fMRI experiments. The challenge arises in integrating diverse smaller 3-Tesla datasets or accommodating new subjects with brief and low-quality fMRI scans. In response to these constraints, we propose a novel framework that generates enhanced 3T fMRI data through an unsupervised Generative Adversarial Network (GAN), leveraging unpaired training across two distinct fMRI datasets in 7T and 3T, respectively. This approach aims to overcome the limitations of the scarcity of high-quality 7-Tesla data and the challenges associated with brief and low-quality scans in 3-Tesla experiments. In this paper, we demonstrate the reconstruction capabilities of the enhanced 3T fMRI data, highlighting its proficiency in generating superior input visual images compared to data-intensive methods trained and tested on a single subject.

关键词： Training Visualization Functional magnetic resonance imaging Visual systems Generative adversarial networks Data models Task analysis

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Enhancing 3T Retinotopic Maps Using Diffeomorphic Registration

Enhancing 3T Retinotopic Maps Using Diffeomorphic Registrati...

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IEEE International Symposium on Biomedical Imaging

作者： Negar Jalili-Mallak Yanshuai Tu Zhong-Lin Lu Yalin Wang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science and Department of Psychology New York University New York USA NYU-ECNU Institute of Brain and Cognitive Science NYU Shanghai Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Retinotopic mapping aims to uncover the relationship between visual stimuli on the retina and neural responses on the visual cortical surface. This study advances retinotopic mapping by applying diffeomorphic registration to the 3T NYU retinotopy dataset, encompassing analyze-PRF and mrVista data. Diffeomorphic Registration for Retinotopic Maps (DRRM) quantifies the diffeomorphic condition, ensuring accurate alignment of retinotopic maps without topological violations. Leveraging the Beltrami coefficient and topological condition, DRRM significantly enhances retinotopic map accuracy. Evaluation against existing methods demonstrates DRRM’s superiority on various datasets, including 3T and 7T retinotopy data. The application of diffeomorphic registration improves the interpretability of low-quality retinotopic maps, holding promise for clinical applications.

关键词： Visualization Accuracy Oxygen Time series analysis Functional magnetic resonance imaging Retina Iterative methods

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Facilitating Bioinformatics Reproducibility

arXiv

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arXiv 2023年

作者： Keefe, Christopher R. Dillon, Matthew R. Herman, Chloe Jewell, Mary Wood, Colin V. Bolyen, Evan Caporaso, J. Gregory Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University FlagstaffAZ United States Department of Epidemiology University of Washington SeattleWA United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States

Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally many biologists aren't trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software. © 2023, CC BY.

关键词： Bioinformatics

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Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning

arXiv

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arXiv 2024年

作者： Sun, Rui Wang, Zhipeng Zhang, Hengrui Jiang, Ming Wen, Yizhe Zhang, Jiqun Sun, Jiahao Zhang, Shuoying Liu, Erwu Li, Kezhi School of Computing Newcastle University Newcastle upon Tyne United Kingdom Department of Computing Imperial College London London United Kingdom Institute of Health Informatics University College London London United Kingdom Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China FLock.io London United Kingdom College of Electronics and Information Engineering Tongji University Shanghai China University College London Hospital London United Kingdom

One of the biggest challenges of building artificial intelligence (AI) model in healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausted, costly, and sometimes impossible. In this paper, we propose a framework for global healthcare modelling using datasets from multi-continents (Europe, North America and Asia) while without sharing the local datasets, and choose glucose management as a study model to verify its effectiveness. Technically, blockchain-enabled federated learning is implemented with adaption to make it meet with the privacy and safety requirements of healthcare data, meanwhile rewards honest participation and penalize malicious activities using its on-chain incentive mechanism. Experimental results show that the proposed framework is effective, efficient, and privacy preserved. Its prediction accuracy is much better than the models trained from limited personal data and is similar to, and even slightly better than, the results from a centralized dataset. This work paves the way for international collaborations on healthcare projects, where additional data is crucial for reducing bias and providing benefits to humanity. © 2024, CC BY.

关键词： Federated learning

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Discovering collective narratives shifts in online discussions

arXiv

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arXiv 2023年

作者： Zhao, Wanying Guo, Fiona Lerman, Kristina Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University United States Department of Computer Science University of Southern California United States Information Sciences Institute University of Southern California United States Network Science Institute Indiana University BloomingtonIN United States

Narrative is a foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can have profound societal impact. Yet, systematic and computational understanding of online narratives faces critical challenge of the scale and dynamics;how can we reliably and automatically extract narratives from massive amount of texts? How do narratives emerge, spread, and die? Here, we propose a systematic narrative discovery framework that fill this gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks. We evaluate our model with synthetic and empirical data-two Twitter corpora about COVID-19 and 2017 French Election. Results demonstrate that our approach can recover major narrative shifts that correspond to the major events. © 2023, CC BY-NC-ND.

关键词： Decision making

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