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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiang Li Ming Xu Rongtao Jiang Xuemei Li Vince D. Calhoun Xinyu Zhou Jing Sui National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Radiology and Biomedical Imaging Yale School of Medicine CT United States Department of Psychiatry The First Affiliated Hospital of Chongqing Medical University Chongqing China Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emorv University Atlanta GA United States IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified large scale structural brain alterations in MDD, yet most are group analyses with atlas-parcellated anatomical regions. Here we proposed a method to measure individual difference by independent component analysis (ICA)-based individual difference structural similarity network (IDSSN). This approach provided a data-adaptive, atlas-free solution that can be applied to new individual subjects. Specifically, we constructed individualized whole-brain structural covariance networks based on network perturbation approach using spatially constrained ICA. First, a set of benchmark independent components (ICs) were generated using gray matter volume (GMV) from all healthy controls. Then individual heterogeneity was obtained by calculating differences and other similarity metrics between ICs derived from "each one patient + all controls" and the benchmark ICs, resulting in 32 imaging features and structural similarity networks for each patient, which can be used for predicting multiple clinical symptoms. We estimated IDSSN for 189 adolescent MDD patients aged 10-20 years and compared them to 112 healthy adolescents. We tested their predictability of the Hamilton Anxiety Scale , the 17-item Hamilton Depression Scale and six clinical syndromes using connectome-based predictive modeling. The prediction results showed that ICA-based IDSSN features reveal more disease-specific information than those using other brain templates. We also found that depression-associated networks mainly involved the default-mode network and visual network. In conclusion, our study proposed an adaptive method that improves the ability to detect GMV deviations and specificity between one individual patient and healthy groups, providing a new perspectives and insights for evaluating individual-level clinical heterogeneity based on brain structures.

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MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments (vol 18, e1010241, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第9期18卷 e1010241页

作者： Alinejad-Rokny, Hamid Modegh, Rassa Ghavami Rabiee, Hamid R. Sarbandi, Ehsan Ramezani Rezaie, Narges Tam, Kin Tung Forrest, Alistair R. R. Harry Perkins Institute of Medical Research QEII Medical Centre and Centre for Medical Research The University of Western Australia Perth Australia Bio Medical Machine Learning Lab (BML) The Graduate School of Biomedical Engineering UNSW Sydney Sydney Australia Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia Bioinformatics and Computational Biology Lab Department of Computer Engineering Sharif University of Technology Tehran Iran Center for Complex Biological Systems University of California Irvine Irvine California United States of America

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

关键词： Genomics Chromatin Genome-wide association studies Single nucleotide polymorphisms Histones Gene regulation Deoxyribonucleases Insulators

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Evaluation of data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection

arXiv

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arXiv 2023年

作者： Schlager, Elke Windisch, Andreas Hanna, Lukas Klünsner, Thomas Hagendorfer, Elias Jan Teppernegg, Tamara Know-Center GmbH Sandgasse 36 Graz8010 Austria Graz University of Technology Institute of Interactive Systems and Data Science Sandgasse 36 Graz8010 Austria Washington University in St. Louis Physics Department One Brookings Drive St. LouisMO63130 United States Reinforcement Learning Community AI Austria Wollzeile 24/12 Vienna1010 Austria Materials Center Leoben Forschung GmbH Roseggerstrasse 12 Leoben8700 Austria CERATIZIT Austria GmbH Metallwerk-Plansee-Straße 71 Breitenwang6600 Austria

Tool wear monitoring is crucial for quality control and cost reduction in manufacturing processes, of which drilling applications are one example. In this paper, we present a U-Net based semantic image segmentation pipeline, deployed on microscopy images of cutting inserts, for the purpose of wear detection. The wear area is differentiated in two different types, resulting in a multiclass classification problem. Joining the two wear types in one general wear class, on the other hand, allows the problem to be formulated as a binary classification task. Apart from the comparison of the binary and multiclass problem, also different loss functions, i. e., Cross Entropy, Focal Cross Entropy, and a loss based on the Intersection over Union (IoU), are investigated. Furthermore, models are trained on image tiles of different sizes, and augmentation techniques of varying intensities are deployed. We find, that the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function. Copyright © 2023, The Authors. All rights reserved.

关键词： Wear of materials

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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

arXiv

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arXiv 2022年

作者： Ray, Evan L. Brooks, Logan C. Bien, Jacob Biggerstaff, Matthew Bosse, Nikos I. Bracher, Johannes Cramer, Estee Y. Funk, Sebastian Gerding, Aaron Johansson, Michael A. Rumack, Aaron Wang, Yijin Zorn, Martha Tibshirani, Ryan J. Reich, Nicholas G. School of Public Health and Health Sciences University of Massachusetts Amherst United States Machine Learning Department Carnegie Mellon University United States Department of Data Sciences and Operations University of Southern California United States COVID-19 Response U.S. Centers for Disease Control and Prevention United States London School of Hygiene & Tropical Medicine United Kingdom Statistical Methods and Econometrics Karlsruhe Institute of Technology Germany Computational Statistics Group Heidelberg Institute for Theoretical Studies Germany

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful. © 2022, CC BY.

关键词： COVID-19

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Uncertainty Quantification in Machine learning for Engineering Design and Health Prognostics: A Tutorial

arXiv

引用

arXiv 2023年

作者： Nemani, Venkat Biggio, Luca Huan, Xun Hu, Zhen Fink, Olga Tran, Anh Wang, Yan Zhang, Xiaoge Hu, Chao Department of Mechanical Engineering Iowa State University AmesIA50011 United States Data Analytics Lab ETH Zürich Switzerland Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Industrial and Manufacturing Systems Engineering University of Michigan-Dearborn DearbornMI48128 United States Intelligent Maintenance and Operations Systems EPFL Lausanne12309 Switzerland Scientific Machine Learning Sandia National Laboratories AlbuquerqueNM87123 United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong New Territories Hong Kong Department of Mechanical Engineering University of Connecticut StorrsCT06269 United States

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Toward this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. Two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2). © 2023, CC BY.

关键词： Machine learning

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APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

arXiv

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arXiv 2023年

作者： Park, Hyun Patel, Parth Haas, Roland Huerta, E.A. Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign Illinois Urbana61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. Copyright © 2023, The Authors. All rights reserved.

关键词： Supercomputers

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Federated learning for Inference at Anytime and Anywhere

arXiv

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arXiv 2022年

作者： Liu, Zicheng Li, Da Fernandez-Marques, Javier Laskaridis, Stefanos Gao, Yan Dudziak, Lukasz Li, Stan Z. Hu, Shell Xu Hospedales, Timothy School of Information Science & Electronic Engineering Zhejiang University Hangzhou China Machine Learning & Data Intelligence Samsung AI Center Cambridge United Kingdom Automated AI Samsung AI Center Cambridge United Kingdom Distributed AI Samsung AI Center Cambridge United Kingdom Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom School of Engineering Westlake University Hangzhou China School of Informatics The University of Edinburgh Edinburgh United Kingdom

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Experimental Realization of One Dimensional Helium

arXiv

引用

arXiv 2022年

作者： Del Maestro, Adrian Nichols, Nathan S. Prisk, Timothy R. Warren, Garfield Sokol, Paul E. Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Center for Neutron Research National Institute of Standards and Technology GaithersburgMD20899-6100 United States Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Physics Indiana University BloomingtonIN47408 United States

The realization of experimental platforms exhibiting one dimensional (1D) quantum phenomena has been elusive, due to their inherent lack of stability, with a few notable exceptions including spin chains [1], carbon nanotubes [2] and ultracold low-density gasses [3]. The difficulty of such systems in exhibiting long range order is integral to their effective description in terms of the Tomonaga-Luttinger liquid theory [4-7]. Recently, it has been proposed that the bosonic superuid 4He could realize a 1D quantum system [8] beyond the Luttinger liquid paradigm [9]. Here we describe an experimental observation of this behavior using nanoengineering by preplating a porous material with a noble gas to enhance dimensional reduction. The resulting excitations of the confined 4He are qualitatively different than 3D and 2D superuid helium, and can be analyzed in terms of a mobile impurity in a Luttinger liquid [10] allowing for the characterization of the emergent quantum liquid. The confined helium system offers the possibility of tuning via pressure-from weakly interacting, all the way to the super Tonks-Girardeau gas of strongly interacting hardcore particles. Copyright © 2022, The Authors. All rights reserved.

关键词： Helium

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State of the art of machine learning: An overview of the past, current, and the future research trends in the era of quantum computing

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AIP Conference Proceedings 2022年第1期2641卷

作者： Mohammad Isa Irawan Mohammad Jamhuri 1)Laboratory of Machine Learning and Big Data Departement Mathematics Faculty of Sciences and Analytical Data Institut Teknologi Sepuluh Nopember Surabaya Indonesia. 2)Department of Mathematics Faculty of Science and Technology UIN Maulana Malik Ibrahim Malang Indonesia

This paper describes data science history and behavioral trends on the largest platform for learning and competition in analyzing and modeling data; Kaggle. We analyze the history of methods commonly used in linear predictor to predict, classify, cluster, and explore data sets. In addition, we also examine the use of the most widely used tools and frameworks to help make data modeling easier. The analysis was carried out on the forum discussion data for the last ten years based on the data available on meta-Kaggle. To see the future trend of data science and linear predictor models, we analyzed the abstracts on the articles available on the Elsevier search page. We extracted information from them using a machine learning method.

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End-to-end AI Framework for Interpretable Prediction of Molecular and Crystal Properties

arXiv

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arXiv 2022年

作者： Park, Hyun Zhu, Ruijie Huerta, E.A. Chaudhuri, Santanu Tajkhorshid, Emad Cooper, Donny Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab. Copyright © 2022, The Authors. All rights reserved.

关键词： Organometallics

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