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作者： Yun, Daqing Wu, Chase Q. Rao, Nageswara S. V. Kettimuthu, Rajkumar Computer and Information Sciences Program Harrisburg University HarrisburgPA17101 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States

Big data transfer in next-generation scientific applications is now commonly carried out over dedicated channels in high-performance networks (HPNs), where transport protocols play a critical role in maximizing application-level throughput. Optimizing the performance of these protocols is challenging: i) transport protocols perform differently in various network environments, and the protocol choice is not straightforward;ii) even for a given protocol in a given environment, different parameter settings of the protocol may lead to significantly different performance and oftentimes the default setting does not yield the best performance. However, it is prohibitively time-consuming to conduct exhaustive transport profiling due to the large parameter space. In this paper, we propose a PRofiling Optimization Based data Transfer Advisor (Probdata) to help end users determine the most effective transport method with the most appropriate parameter settings to achieve satisfactory performance for big data transfer over dedicated connections in HPNs. Probdata employs a fast profiling scheme based on the Simultaneous Perturbation Stochastic Approximation algorithm, namely, FastProf, to accelerate the exploration of the optimal operational zones of various transport methods to improve profiling efficiency. We first present a theoretical background of the optimized profiling approach in Probdata and then detail its design and implementation. The advising procedure and performance benefits of FastProf and Probdata are illustrated and evaluated by both extensive emulations based on real-life performance measurements and experiments over various physical connections in existing production HPNs. © 1993-2012 IEEE.

关键词： data transfer

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Evaluating Large Language Models for Enhancing Radiology Specialty Examination: A Comparative Study with Human Performance

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Academic Radiology 2025年

作者： Liu, Hao-Yun Chen, Shyh-Jye Wang, Weichung Lee, Chung-Hsi Hsu, Hsian-He Shen, Shu-Huei Chiou, Hong-Jen Lee, Wen-Jeng Department of Medical Imaging National Taiwan University Hospital Hsin-Chu Branch Br. No.25 Lane 442 Sec.1 Jingguo Rd Hsinchu City 300 Taiwan National Taiwan University College of Medicine No.1 Jen Ai Road Section 1 Taipei 100 Taiwan Department of Medical Imaging National Taiwan University Hospital No. 7 Zhongshan S. Rd. Zhongzheng Dist. Taipei City 100229 Taiwan Institute of Applied Mathematical Sciences Department of Mathematics and Data Science Degree Program National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei 106319 Taiwan Graduate Institute of Health and Biotechnology Law Taipei Medical University 250 Wuxing Street Taipei City Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center No.325 Sec.2 Chenggong Rd. Neihu District Taipei City Taiwan Department of Radiology Taipei Veterans General Hospital No.201 Sec. 2 Shipai Rd. Beitou District Taipei City Taiwan National Yang Ming Chiao Tung University School of Medicine No. 118 Sec. 1 Zhongxiao West Road Taipei City 100 Taiwan

Rationale and Objectives: The radiology specialty examination assesses clinical decision-making, image interpretation, and diagnostic reasoning. With the expansion of medical knowledge, traditional test design faces challenges in maintaining accuracy and relevance. Large language models (LLMs) demonstrate potential in medical education. This study evaluates LLM performance in radiology specialty exams, explores their role in assessing question difficulty, and investigates their reasoning processes, aiming to develop a more objective and efficient framework for exam design. Materials and Methods: This study compared the performance of LLMs and human examinees in a radiology specialty examination. Three LLMs (GPT-4o, o1-preview, and GPT-3.5-turbo-1106) were evaluated under zero-shot conditions. Exam accuracy, examinee accuracy, discrimination index, and point-biserial correlation were used to assess LLMs’ ability to predict question difficulty and reasoning processes. The data provided by the Taiwan Radiological Society ensures comparability between AI and human performance. Results: As for accuracy, GPT-4o (88.0%) and o1-preview (90.9%) outperformed human examinees (76.3%), whereas GPT-3.5-turbo-1106 showed significantly lower accuracy (50.2%). Question difficulty analysis revealed that newer LLMs excel in solving complex questions, while GPT-3.5-turbo-1106 exhibited greater performance variability. Discrimination index and point-biserial Correlation analyses demonstrated that GPT-4o and o1-preview accurately identified key differentiating questions, closely mirroring human reasoning patterns. These findings suggest that advanced LLMs can assess medical examination difficulty, offering potential applications in exam standardization and question evaluation. Conclusion: This study evaluated the problem-solving capabilities of GPT-3.5-turbo-1106, GPT-4o, and o1-preview in a radiology specialty examination. LLMs should be utilized as tools for assessing exam question dif

关键词： Accuracy Evaluation GPT LLMs Radiology specialty examination

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Reconstruction of Agents’ Corrupted Trajectories of Collective Motion Using Low-rank Matrix Completion

Reconstruction of Agents’ Corrupted Trajectories of Collect...

引用

IEEE International Conference on Big data

作者： Kelum Gajamannage Randy Paffenroth Department of Mathematics and Statistics Texas A&M University-Corpus Christi Corpus Christi USA Department of Mathematical Sciences Department of Computer Science and Data Science Program Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Tracking of trajectories of mutually interacted collectively moving agents such as fish, birds, insects, and even humans is an active field in computer vision. However, the trajectories produced by multi-object tracking methods might consist of unconstructed segments of trajectories due to the natural phenomena such as occlusion, change of illumination, etc., which require robust tracking methods. Some tracking methods employ computationally expensive approximation schemes to connect these segments. In this work, we utilize mutual interactions and dependencies between the agents to reconstruct the missing segments of the trajectories. Due to these interactions, the coordinate matrix representing the particles' trajectories of collective motion is often low-rank. Thus, we utilize a low-rank matrix completion technique to reconstruct incomplete trajectories. We apply this approach for two representative self-propelled particle swarms, simulated by the classic Vicsek model, that imitate two real-life collective motion scenarios and use low-rank approximations to analyze their incomplete trajectories.

关键词： Trajectory Motion segmentation Particle swarm optimization Tracking Biological system modeling Birds Manifolds

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Sustainable improvement of food SMEs through halal certification: A meta-analysis

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AIP Conference Proceedings 2024年第1期2957卷

作者： Rizal Bahara Muhammad N. Aidi Khaswar Syamsu Euis Sunarti Anuraga Jayanegara Natural Resources and Environmental Management Science Study Program Graduate School IPB University Jl Pajajaran No 1 Kampus IPB Baranangsiang Bogor West Java 1614 Indonesia Statistics and Data Science Department Mathematics and Natural Science Faculty IPB University Jl. Lingkar Kampus IPB Dramaga Bogor West Java 16680 Indonesia Halal Science Center LPPM IPB. Gedung Pascasarjana Gedung Kimia IPB University Jl Pajajaran No 1 Kampus IPB Baranangsiang Bogor West Java 1644 Indonesia Department of Family and Consumer Sciences Faculty of Human Ecology IPB University Jl. Lingkar Kampus IPB Dramaga Bogor West Java 16610 Indonesia Department of Nutrition and Feed Technology Faculty of Animal Science IPB University Jl. Lingkar Kampus IPB Dramaga Bogor West Java 16680 Indonesia

Small and medium enterprises (SMEs) are one of the important economic drivers in Indonesia, including during the Covid-19 pandemic. Indonesia as the country with the largest Muslim population requires halal certificates for all food products sold in Indonesia. Obstacles to the implementation and certification of halal in the food SME sector are still encountered due to, among others, the cost of certification, knowledge from human resources, ease of access and information. The purpose of this study was to evaluate the role of halal certification on financial performance in Food SMEs by employing the meta-analysis method. A number of six articles out of 55 articles were selected and integrated into a database. The data were analyzed based on the Hedges’ d (standardized mean difference/SMD) effect size values and confidence interval (CI) values by using the Open MEE software. The results showed that halal-certified food SMEs significantly increased sales (SMD: 3.182; 95% CI:1.228 to 5.136; p<0.001) as compared to those of food SMEs that were not halal-certified. In the location in the Indonesia subgroup, halal certification at Food SMEs in Bogor city had a higher sale (SMD: 12.105; 95% CI: 10.289 to 13.922; p<0.001) compared to other cities. The conclusion of this study is that halal certification enhances sales and thus contributes to the sustainability of food SMEs.

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Opinion dynamics on tie-decay networks

arXiv

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arXiv 2020年

作者： Sugishita, Kashin Porter, Mason A. Beguerisse-Díaz, Mariano Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Mathematics University of California Los AngelesCA90095 United States Mathematical Institute University of Oxford OxfordOX2 6GG United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

In social networks, interaction patterns typically change over time. We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks. First, we compare the spectral gaps of the Laplacian matrices of tie-decay networks that we construct from empirical data with the spectral gaps for corresponding randomized and aggregate networks. We find that the spectral gaps for the empirical networks tend to be smaller than those for the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time. Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction between two nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interactions occur, we expect eventual convergence. However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Laplace transforms

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Concurrency measures in the era of temporal network epidemiology: A review

arXiv

引用

arXiv 2020年

作者： Masuda, Naoki Miller, Joel C. Holme, Petter Department of Mathematics State University of New York Buffalo United States Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States School of Engineering and Mathematical Sciences La Trobe University Australia Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency-quantifying individuals' tendency to form time-overlapping "partnerships". Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches. © 2020, CC BY.

关键词： Epidemiology

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Fast Communication-efficient Spectral Clustering Over Distributed data

arXiv

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arXiv 2019年

作者： Yan, Donghui Wang, Yingjie Wang, Jin Wu, Guodong Wang, Honggang Department of Mathematics and Program in Data Science Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States Lovelace Respiratory Research Institute AlbuquerqueNM United States

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume all the data are already in one place, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with "minimal" communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing;the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Hyper-molecules: On the representation and recovery of dynamical structures, with application to flexible macro-molecular structures in cryo-EM

arXiv

引用

arXiv 2019年

作者： Lederman, Roy R. Andén, Joakim Singer, Amit Department of Statistics and Data Science Yale University New HavenCT United States Center for Computational Mathematics Flatiron Institute New YorkNY United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States

Cryo-electron microscopy (cryo-EM), the subject of the 2017 Nobel Prize in Chemistry, is a technology for determining the 3-D structure of macromolecules from many noisy 2-D projections of instances of these macromolecules, whose orientations and positions are unknown. The molecular structures are not rigid objects, but flexible objects involved in dynamical processes. The different conformations are exhibited by different instances of the macromolecule observed in a cryo-EM experiment, each of which is recorded as a particle image. The range of conformations and the conformation of each particle are not known a priori;one of the great promises of cryo-EM is to map this conformation space. Remarkable progress has been made in determining rigid structures from homogeneous samples of molecules in spite of the unknown orientation of each particle image and significant progress has been made in recovering a few distinct states from mixtures of rather distinct conformations, but more complex heterogeneous samples remain a major challenge. We introduce the "hyper-molecule" framework for modeling structures across different states of heterogeneous molecules, including continuums of states. The key idea behind this framework is representing heterogeneous macromolecules as high-dimensional objects, with the additional dimensions representing the conformation space. This idea is then refined to model properties such as localized heterogeneity. In addition, we introduce an algorithmic framework for recovering such maps of heterogeneous objects from experimental data using a Bayesian formulation of the problem and Markov chain Monte Carlo (MCMC) algorithms to address the computational challenges in recovering these high dimensional hyper-molecules. We demonstrate these ideas in a prototype applied to synthetic data. Copyright © 2019, The Authors. All rights reserved.

关键词： Medical imaging

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Small inter-event times govern epidemic spreading on networks

引用

Physical Review Research 2020年第2期2卷 023163-023163页

作者： Naoki Masuda Petter Holme Department of Mathematics University at Buffalo State University of New York Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New York 14260-5030 USA Tokyo Tech World Research Hub Initiative (WRHI) Institute of Innovative Research Tokyo Institute of Technology Yokohama 226-8503 Japan

Many aspects of human and animal interaction, such as the frequency of contacts of an individual, the number of interaction partners, and the time between the contacts of two individuals, are characterized by heavy-tailed distributions. These distributions affect the spreading of, e.g., infectious diseases or rumors, often because of impacts of the right tail of the distributions (i.e., the large values). In this paper we show that when it comes to inter-event time distributions, it is not the tail but the small values that control spreading dynamics. We investigate this effect both analytically and numerically for different versions of the susceptible-infected-recovered model on different types of networks.

关键词： Epidemic Epidemic evolution Epidemic growth Epidemic prevalence

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