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A Recurrent Neural Network Model for Battery Capacity Fade Curve Prediction Using Early Life data

ECS Meeting Abstracts 2021年第1期MA2021-01卷

作者： Saurabh Saxena Logan Ward Joseph Kubal Hong-Keun Kim Wenquan Lu Susan Babinec Noah Paulson Applied Materials Division Argonne National Laboratory USA Data Science and Learning Division Argonne National Laboratory USA CSE Division Argonne National Laboratory USA ACCESS Argonne National Laboratory USA

Batteries are often considered the most performance and reliability-limiting component of any electronic and electrical assembly. Thus their degradation trend characterization through extensive testing and development of life prediction algorithms using testing data have been at the forefront of battery research. However, battery testing is also time-consuming, and it can take at least few months, even years, to test a battery for cycle life performance under normal operating conditions. Hence, there is a need to develop tools for battery life and degradation trend prediction using limited cycle data. Early life prediction tools can facilitate battery qualification, timely replacement of batteries in critical applications, and even the secondary use market. In this study, we present a recurrent neural network-linear network model to predict the entire battery capacity fade curve using less than 100 cycles of data (~ 1 month of testing). The discharge voltage-capacity curves have been used as input to the model. Our approach is also able to predict the rollover point (knee point) in the capacity fade curve of the battery, which indicates the onset of increased capacity fade rate. We use the publicly available MIT dataset to show the validity of the approach and extend the previous work from Severson et al. [1] on the end-of-life prediction to the entire capacity fade trend prediction. The recurrent neural network can capture the sequential changes in discharge voltage-capacity curves with cycles and provide flexibility in terms of the input data size. References [1] Kristen A Severson, Peter M Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, Michael H Chen, Muratahan Aykol, Patrick K Herring, Dimitrios Fraggedakis, Martin Z Bazant, Stephen J Harris, William C Chueh, and Richard D Braatz, 'data-driven prediction of battery cycle life before capacity degradation,' Nature Energy, volume 4, pages 383–391(2019).

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Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

arXiv

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arXiv 2020年

作者： Ramanathan, Arvind Ma, Heng Parvatikar, Akash Chakra Chennubhotla, S. Data Science & Learning Division Argonne National Laboratory LemontIL60439 United States University of Chicago Hyde ParkIL United States Department of Computational and Systems Biology University of Pittsburgh PittsburghPA15260 United States

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena. © 2020, CC BY.

关键词： Proteins

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Joint ptycho-tomography with deep generative priors

arXiv

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arXiv 2020年

作者： Aslan, Selin Liu, Zhengchun Nikitin, Viktor Bicer, Tekin Leyffer, Sven Gürsoy, Doğa X-ray Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Department of Electrical Engineering and Computer Science Northwestern University 2145 Sheridan Road EvanstonIL60208 United States

Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more efficient subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a denoiser parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data. Copyright © 2020, The Authors. All rights reserved.

关键词： Tomography

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Serverless science for Simple, Scalable, and Shareable Scholarship

Serverless Science for Simple, Scalable, and Shareable Schol...

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IEEE International Conference on e-science and Grid Computing

作者： Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory IL USA

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

The adoption of computation- and data-intensive science, or escience, makes research progress increasingly dependent on the availability, management, and use of sophisticated cyberinfrastructure. An unfortunate consequence is that researchers face increasingly burdensome demands for managing and maintaining cyberinfrastructure. The advent of virtualization and cloud computing has helped, by allowing outsourcing of some such tasks to reliable and scalable cloud providers. But much more progress is needed before we can create a research cyberinfrastructure that allows researchers to focus on creative thought rather than systems management. We examine here how the emerging paradigm of serverless computing, in which arbitrary functions can be dispatched seamlessly to scalable, secure, and reliable service providers, can move us in that direction. To demonstrate how serverless computing can transform scientific computing, we describe three serverless computing models: service-oriented computing, research automation, and function as a service, presenting illustrative case studies for each.

关键词： Cloud computing Automation Reliability Software as a service Computational modeling Task analysis

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Advising big data transfer over dedicated connections based on profiling optimization

Advising big data transfer over dedicated connections based ...

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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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Active learning yields better training data for scientific named entity recognition 15

Active learning yields better training data for scientific n...

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15th IEEE International Conference on escience, escience 2019

作者： Tchoua, Roselyne Ajith, Aswathy Hong, Zhi Ward, Logan Chard, Kyle Audus, Debra Patel, Shrayesh De Pablo, Juan Foster, Ian Department of Computer Science University of Chicago ChicagoIL United States Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory ArgonneIL United States Materials Science and Engineering Division National Institute of Standards and Technology GaithersburgMD United States Institute for Molecular Engineering University of Chicago ChicagoIL United States

ISBN: (纸本)9781728124513

Despite significant progress in natural language processing, machine learning models require substantial expertannotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is often more complicated when working with scientific text. For example, in polymer science, chemical structure may be encoded using nonstandard naming conventions, the same concept can be expressed using many different terms (synonymy), and authors may refer to polymers with ad-hoc labels. These challenges, which are not unique to polymer science, make it difficult to generate training data, as specialized skills are needed to label text correctly. We have previously designed polyNER, a semi-Automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance. Our approach requires just five hours of expert time to achieve discrimination capacity comparable to that of a state-of-The-Art chemical NER toolkit. © 2019 IEEE.

关键词： Machine learning

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FTK: A simplicial spacetime meshing framework for robust and scalable feature tracking

arXiv

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arXiv 2020年

作者： Guo, Hanqi Lenz, David Xu, Jiayi Liang, Xin He, Wenbin Grindeanu, Iulian R. Shen, Han-Wei Peterka, Tom Munson, Todd Foster, Ian the Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States the Department of Computer Science and Engineering the Ohio State University ColumbusOH43210 United States the Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States Bosch Research North America SunnyvaleCA94085 United States the Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States

We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our simplicial spacetime meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open sourced under the MIT license: https://***/hguo/ftk. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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Robust charge-density wave correlations in the electron-doped single-band Hubbard model

arXiv

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arXiv 2022年

作者： Mai, Peizhi Nichols, Nathan S. Karakuzu, Seher Bao, Feng Maestro, Adrian Del Maier, Thomas A. Johnston, Steven Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831-6494 United States Department of Physics Institute of Condensed Matter Theory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Mathematics Florida State University TallahasseeFL32306 United States Department of Physics and Astronomy The University of Tennessee KnoxvilleTN37996 United States Institute of Advanced Materials and Manufacturing The University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

There is growing evidence that the hole-doped single-band Hubbard and t-J models do not have a superconducting ground state reflective of the high-temperature cuprate superconductors but instead have striped spin- and charge-ordered ground states. Nevertheless, it is proposed that these models may still provide an effective low-energy model for electron-doped materials. Here we study the finite temperature spin and charge correlations in the electron-doped Hubbard model using quantum Monte Carlo dynamical cluster approximation calculations and contrast their behavior with those found on the hole-doped side of the phase diagram. We find evidence for a charge modulation with both checkerboard and unidirectional components decoupled from any spin-density modulations. These correlations are inconsistent with a weak-coupling description based on Fermi surface nesting, and their doping dependence agrees qualitatively with resonant inelastic x-ray scattering measurements. Our results provide evidence that the single-band Hubbard model describes the electron-doped cuprates. © 2022, CC BY.

关键词： Ground state

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Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems

npj Health Systems

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npj Health Systems 2025年第1期2卷 1-3页

作者： Rui Zhang Genevieve B. Melton James Zou Ashley N. Beecy Yiye Zhang Jiang Bian Cui Tao Department of Surgery and Center for Learning Health System Sciences University of Minnesota Minneapolis MN USA Institute for Health Informatics University of Minnesota Minneapolis MN USA Department of Biomedical Data Science Stanford University Stanford CA USA Department of Medicine Division of Cardiology Weill Cornell Medicine New York NY USA Information Technology NewYork-Presbyterian Hospital New York NY USA Department of Population Health Sciences Weill Cornell Medicine New York New York NY USA Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA Regenstrief Institute Indianapolis IN USA Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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