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19th IEEE International Conference on e-science, e-science 2023

作者： Skluzacek, Tyler J. Chard, Kyle Foster, Ian National Center for Computational Sciences Oak Ridge National Lab Oak RidgeTN United States University of Chicago Department of Computer Science ChicagoIL United States Data Science and Learning Division Argonne National Lab LemontIL United States

ISBN: (纸本)9798350322231

FAIR principles require that scientific data be findable, discoverable, and reusable by users. To enable FAIRness, practioners of a science repository will often construct a rich, searchable index of metadata derived from the data. Unfortunately, manual metadata annotation methods do not scale to the many data files generated by many projects;and instead automated extraction systems are needed to scalably parse these files - often with nonstandard schema requiring specialized parsing strategies - and deposit representative metadata into a search index. In this work, we evaluate whether, and the extent to which, automatically extracted metadata make research repositories more navigable. We present a two-part user study conducted with scientists at two U.S. national laboratories from projects spanning spectroscopy and battery modeling. We constructed research indexes automatically by using the Xtract metadata extraction system. In the first part of our study, we learned about each user's role and identified key navigation concerns for scientists. We found that participants wished to navigate for purposes of discovery, retrieval, and organization. In the second part, participants completed simulated research data navigation tasks crafted to reflect real-world navigability concerns. We found that regardless of the interface used, participants consistently solved navigation tasks with high degrees of confidence and correctness, and significantly (1.2X-50×) faster than via their alternative methods (e.g., manual directory scans or designing a customized navigational tool). © 2023 IEEE.

关键词： Metadata

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Machine learning for automated experimentation in scanning transmission electron microscopy

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npj computational Materials 2023年第1期9卷 25-40页

作者： Sergei V.Kalinin Debangshu Mukherjee Kevin Roccapriore Benjamin J.Blaiszik Ayana Ghosh Maxim A.Ziatdinov Anees Al-Najjar Christina Doty Sarah Akers Nageswara S.Rao Joshua C.Agar Steven R.Spurgeon Department of Materials Science and Engineering University of TennesseeKnoxvilleTN 37996USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Argonne National Laboratory Data Science and Learning DivisionChicagoIL 60439USA University of Chicago GlobusChicagoIL 60637USA National Security Directorate Pacific Northwest National LaboratoryRichlandWA 99352USA Department of Materials Science and Engineering Drexel UniversityPhiladelphiaPA 19104USA Department of Physics University of WashingtonSeattleWA 98195USA

Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and *** emerging trend is the transition to real-time analysis and closed-loop microscope *** effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and ***,we discuss the associated challenges with the transition to active ML,including sequential data analysis and out-of-distribution drift effects,the requirements for edge operation,local and cloud data storage,and theory in the loop ***,we discuss the relative contributions of human scientists and ML agents in the ideation,orchestration,and execution of experimental workflows,as well as the need to develop universal hyper languages that can apply across multiple *** considerations will collectively inform the operationalization of ML in next-generation experimentation.

关键词： optimization automated execution

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A Decreasing Upper Bound of the Energy for Time-Fractional Phase-Field Equations

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Communications in computational Physics 2023年第4期33卷 962-991页

作者： Chaoyu Quan Tao Tang Boyi Wang Jiang Yang International Center for Mathematics Southern University of Science and TechnologyShenzhen 518055China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics Southern University of Science and TechnologyShenzhen 518055GuangdongChina Department of Mathematics National University of SingaporeSingapore 119076Singapore Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhen 518055China

In this article,we study the energy dissipation property of time-fractional Allen–Cahn *** the continuous level,we propose an upper bound of energy that decreases with respect to time and coincides with the original energy at t=0 and as t tends to∞.This upper bound can also be viewed as a nonlocal-in-time modified energy which is the summation of the original energy and an accumulation term due to the memory effect of time-fractional *** particular,the decrease of the modified energy indicates that the original energy indeed decays *** in a small neighborhood at t=*** illustrate the theory mainly with the time-fractional Allen-Cahn equation but it could also be applied to other time-fractional phase-field models such as the Cahn-Hilliard *** the discrete level,the decreasing upper bound of energy is useful for proving energy dissipation of numerical ***-order L1 and second-order L2 schemes for the time-fractional Allen-Cahn equation have similar decreasing modified energies,so that stability can be *** numerical results are provided to illustrate the behavior of this modified energy and to verify our theoretical results.

关键词： Time-fractional Allen-Cahn equation energy dissipation L1 approximation L2 approximation

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Fair and Efficient Allocation of Scarce Resources Based on Predicted Outcomes: Implications for Homeless Service Delivery

Fair and Efficient Allocation of Scarce Resources Based on P...

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作者： Kube, Amanda R. Das, Sanmay Fowler, Patrick J. Division of Computational and Data Sciences Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States Department of Computer Science George Mason University 4400 University Drive FairfaxVA22030 United States Brown School of Social Work Public Health and Social Policy Division of Computational and Data Sciences Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States

Artificial intelligence, machine learning, and algorithmic techniques in general, provide two crucial abilities with the potential to improve decision-making in the context of allocation of scarce societal resources. They have the ability to flexibly and accurately model treatment response at the individual level, potentially allowing us to better match available resources to individuals. In addition, they have the ability to reason simultaneously about the effects of matching sets of scarce resources to populations of individuals. In this work, we leverage these abilities to study algorithmic allocation of scarce societal resources in the context of homelessness. In communities throughout the United States, there is constant demand for an array of homeless services intended to address different levels of need. Allocations of housing services must match households to appropriate services that continuously fluctuate in availability, while inefficiencies in allocation could "waste"scarce resources as households will remain in-need and reenter the homeless system, increasing the overall demand for homeless services. This complex allocation problem introduces novel technical and ethical challenges. Using administrative data from a regional homeless system, we formulate the problem of "optimal"allocation of resources given data on households with need for homeless services. The optimization problem aims to allocate available resources such that predicted probabilities of household reentry are minimized. The key element of this work is its use of a counterfactual prediction approach that predicts household probabilities of reentry into homeless services if assigned to each service. Through these counterfactual predictions, we find that this approach has the potential to improve the efficiency of the homeless system by reducing re-entry, and, therefore, system-wide demand. However, efficiency comes with trade-offs - a significant fraction of households are assigned to serv

关键词： Efficiency

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Accelerating VQE Algorithm via Parameters and Measurement Reuse

Accelerating VQE Algorithm via Parameters and Measurement Re...

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IEEE International Conference on Rebooting Computing (ICRC)

作者： Xinpeng Li Ji Liu Ethan H. Hansen Shuai Xu Paul Hovland Vipin Chaudhary Department of Computer and Data Science Case Western Reserve University Mathematics and Computational Research Division Argonne National Laboratory

The Variational Quantum Eigensolver (VQE), a quantum-classical hybrid algorithm, holds significant potential for advancements in quantum chemistry and optimization tasks. Our study focuses on accelerating the VQE in combinatorial optimization tasks such as Max-Cut, k-Clique, and k-SAT, while addressing the challenges of scalability and measurement overhead in quantum devices. Notably, while solving one problem, it allows us to concurrently compute essential information like expectation values and gradients for multiple other problems. This capability is made feasible because combinatorial optimization problems often share the same measurement basis. Drawing inspiration from this insight, we introduce a method that efficiently solves multiple problems by appropriately selecting their initial points. Subsequently, we propose two selection strategies for the method. Employing these strategies can, to some extent, help avoid the issue of becoming trapped in local optima, thus leading to improved accuracy. Our tests on 5-node and 10-node graphs for Max-Cut and k-Clique show that, in some cases, our method outperforms random initialization in terms of both accuracy and number of iterations.

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Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats 22

Transferable Graph Neural Fingerprint Models for Quick Respo...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Chen, Wei Ren, Yihui Kagawa, Ai Carbone, Matthew R. Chen, Samuel Yen-Chi Qu, Xiaohui Yoo, Shinjae Clyde, Austin Ramanathan, Arvind Stevens, Rick L. Van Dam, Hubertus J. J. Lu, Deyu Center for Functional Nanomaterials Brookhaven National Laboratory UptonNY United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States Argonne National Laboratory Data Science and Learning Division LemontIL United States Condensed Matter Physics & Materials Science Brookhaven National Laboratory UptonNY United States

ISBN: (纸本)9798350345346

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fin-gerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than 0.21 kcal/mol for most of the docking targets, showing significant improvement over conventional circular fin-gerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the training and data efficiency of the transferable model is several times higher. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats. © 2023 IEEE.

关键词： Bioinformatics Graph Neural Networks Transfer Learning

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Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?

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Current Gastroenterology Reports 2024年第5期26卷 137-144页

作者： Ahmed, Mehwish Stone, Molly L. Stidham, Ryan W. Division of Gastroenterology Department of Internal Medicine Michigan Medicine Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan 3912 Taubman Center 1500 East Medical Center Drive Ann Arbor 48109 MI United States Michigan Institute for Data Science (MIDAS) University of Michigan Ann Arbor MI United States

Purpose of Review: Artificial intelligence (AI) is quickly demonstrating the ability to address problems and challenges in the care of IBD. This review with commentary will highlight today’s advancements in AI applications for IBD in image analysis, understanding text, and replicating clinical knowledge and experience. Recent Findings: Advancements in machine learning methods, availability of high-performance computing, and increasing digitization of medical data are providing opportunities for AI to assist in IBD care. Multiple groups have demonstrated the ability of AI to replicate expert endoscopic scoring in IBD, with expansion into automated capsule endoscopy, enterography, and histologic interpretations. Further, AI image analysis is being used to develop new endoscopic scoring with more granularity and detail than is possible using conventional methods. Advancements in natural language processing are proving to reduce laborious tasks required in the care of IBD, including documentation, information searches, and chart review. Finally, large language models and chatbots that can understand language and generate human-like replies are beginning to exhibit clinical intelligence that will revolutionize how we deliver IBD care. Summary: Today, AI is being deployed to replicate expert judgement in specific tasks where disagreement, subjectivity, and bias are common. However, the near future will herald contributions of AI doing what we cannot, including new detailed measures of IBD, enhanced analysis of images, and perhaps even fully automating care. As we speculate on future technologic capabilities that may improve how we care for IBD, this review will also consider how we will implement and fairly use AI in practice. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Artificial intelligence Clinical trials Crohn’s disease Decision support Endoscopic scoring Inflammatory bowel disease Machine learning Prediction models Segmentation Ulcerative colitis

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Interpolatory model order reduction of large-scale dynamical systems with root mean squared error measures

arXiv

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arXiv 2024年

作者： Reiter, Sean Werner, Steffen W.R. Department of Mathematics Virginia Tech BlacksburgVA24061 United States Department of Mathematics Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech BlacksburgVA24061 United States

The root mean squared error is an important measure used in a variety of applications such as structural dynamics and acoustics to model averaged deviations from standard behavior. For large-scale systems, simulations of this quantity quickly become computationally prohibitive. Classical model order reduction techniques attempt to resolve this issue via the construction of surrogate models that emulate the root mean squared error measure using an intermediate linear system. However, this approach requires a potentially large number of linear outputs, which can be disadvantageous in the design of reduced-order models. In this work, we consider directly the root mean squared error as the quantity of interest using the concept of quadratic-output models and propose several new model reduction techniques for the construction of appropriate surrogates. We test the proposed methods on a model for the vibrational response of a plate with tuned vibration absorbers. Copyright © 2024, The Authors. All rights reserved.

关键词： Structural dynamics

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TIME-DOMAIN ITERATIVE RATIONAL KRYLOV METHOD

arXiv

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arXiv 2024年

作者： Ackermann, Michael S. Gugercin, Serkan Department of Mathematics Virginia Tech BlacksburgVA24061 United States Department of Mathematics Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech BlacksburgVA24061 United States

The Realization Independent Iterative Rational Krylov Algorithm (TF-IRKA) is a frequency-based data-driven reduced order modeling (DDROM) method that constructs H2 optimal DDROMs. However, as the H2 optimal approximation theory dictates, TF-IRKA requires repeated sampling of frequency data, that is, values of the system transfer function and its derivative, outside the unit circle. This repeated evaluation of frequency data requires repeated full model computations and may not be feasible. The data-informativity framework for moment matching provides a method for obtaining such frequency data from a single time-domain simulation. However, this framework usually requires solving linear systems with prohibitively ill-conditioned matrices, especially when recovering frequency data from off the unit circle as required for optimality. In this paper, building upon our previous work with the data informativity framework for moment matching, we provide a formula for the nonzero extreme eigenvalues of a symmetric rank-1 perturbation to an orthogonal projection, which then leads to an optimal scaling of the aforementioned linear systems. We also establish connections between the underlying dynamical system and conditioning of these linear systems. This analysis then leads to our algorithmic development, time-domain IRKA, which allows us to implement a time-domain variant of TF-IRKA, constructing H2 optimal DDROMs from a single time-domain simulation without requiring repeated frequency data evaluations. The numerical examples illustrate the effectiveness of the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear systems

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Identify Non-mutational p53 Functional Deficiency in Human Cancers

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Genomics, Proteomics & Bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： Cancer Composite expression DNA mutation Machine learning p53 deficiency

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