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Data-driven modeling of dislocation mobility from atomistics using physicsinformed machine learning

npj computational Materials 2024年第1期10卷 937-948页

作者： Yifeng Tian Soumendu Bagchi Liam Myhill GiacomoPo Enrique Martinez Yen Ting Lin Nithin Mathew Danny Perez Information Sciences Group ComputerComputational and Statistical Sciences Division(CCS-3)Los Alamos National LaboratoryLos Alamos87545 NMUSA Physics and Chemistry of Materials Theoretical Division(T-1)Los Alamos National LaboratoryLos Alamos87545 NMUSA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeUSA Department of Materials Science and Engineering Clemson UniversityClemson29623 SCUSA Department of Mechanical and Aerospace Engineering University of MiamiMiami33146 FLUSA

Dislocationmobility,which dictates the response of dislocations to an applied stress,is a fundamental property of crystalline materials that governs the evolution of plastic *** approaches for deriving mobility laws rely on phenomenological models of the underlying physics,whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and *** tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress,temperature,and local environment,such as body-centered cubic crystals(BCC)metals and *** this paper,we present a novel,uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations,using Graph Neural Networks(GNN)with a physics-informed *** demonstrate that this Physics-informed Graph Neural Network(PI-GNN)framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

关键词： stress dislocation consuming

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A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Artificial Intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Adaptive Neuro Fuzzy-Based Depression Detection Model for Students in Tertiary Education 1

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6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Udoh, Samuel S. Usip, Patience U. George, Uduak D. Akpan, Imeobong E. Department of Computer Science Faculty of Science University of Uyo Uyo Nigeria TETFund Center of Excellence in Computational Intelligence Research University of Uyo Uyo Nigeria

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Depression is a severe mental disorder with characteristic symptoms such as sadness, feeling of emptiness, anger, anxiety and sleep disturbance as well as general loss of initiative and interest in activities. The effects of late diagnosis of depression have culminated in many students dropping out of school, becoming unfulfilled in life and thereby posing a threat to human and environmental stability. Linear models for depression detection lack the intelligence to decode the non-linear interactions and imprecision proliferated in depression data. This work is aimed at utilizing Adaptive Neuro Fuzzy Inference (ANFIS) model furnished with intelligence for handling imprecision and non-linear modelling for diagnosis of depression. ANFIS model for depression detection was designed to provide a means of handling imprecision that characterizes depression detection attributes. Data collected from questionnaires administered to students in University of Uyo were analyzed. MATLAB programming tools were deployed for implementation of the model. The proposed fuzzy model identified and classified depression cases with 94.21% accuracy. The system would assist in early detection of depression in students studying in tertiary institutions and would guide health workers in administration of therapy to depressed students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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Can Automated Metadata Extraction Make Scientific Data More Navigable? 19

Can Automated Metadata Extraction Make Scientific Data More ...

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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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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2504-2514页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Fudan University Institute of Science and Technology for Brain-inspired Intelligence Moe Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Ninth People's Hospital Department of Oral Maxillofacial Head and Neck Oncology China Shanghai Jiao Tong University School of Medicine College of Stomatology China Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai200011 China Sichuan University School of Cyber Science and Engineering Chengdu Sichuan610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo groundtruths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 2025 IEEE.

关键词： computerized tomography

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PokkahScan - An Intelligent Drone for detection of Pokkah Boeng Disease in Sugarcane Using Transfer Learning and eXplainable AI 15

PokkahScan - An Intelligent Drone for detection of Pokkah Bo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ravikumar, Rahul Sujith Kumaar, M.V. Krishna, Shirish Jayasurkanth, M. Yasshwanth, V.R. Saravanan, Sanjay Kishore Thangavel, Senthil Kumar Thangeswari, S. Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Center for Computational Engineering & Networking Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Aerospace Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Tamilnadu Agricultural University Department of Plant Pathology Coimbatore India Amrita School of Physical Sciences Department of Mathematics Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning becomes apparent as it allows for leveraging pre-existing knowledge from large datasets to improve the performance of models trained on smaller, domain-specific datasets. IoT has also played a significant role in our automation process. Transfer learning leveraging MobilenetV2 is particularly beneficial for this problem of detecting Pokkah Boeng disease in sugarcane crops, as it enables the adaptation of pretrained models, such as MobileNetV2, to accurately identify Pokkah Boeng disease even with limited set of data. LIME is employed to provide transparent explanations for the model's predictions, enhancing its interpretability and facilitating informed decision-making in agricultural management. By combining transfer learning with xAI and aerial drone technology, having add-ons of IoT and facilitating a comprehensive comparative study, PokkahScan offers a comprehensive solution for timely disease detection and crop management in sugarcane farming. © 2024 IEEE.

关键词： Agricultural Analytics Crop Management Disease Detection Early Detection IoT MobilenetV2 Pokkah Boeng Disease Sugarcane Crop Transfer Learning xAI

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Breaking Isometric Ties and Introducing Priors in Gromov-Wasserstein Distances 27

Breaking Isometric Ties and Introducing Priors in Gromov-Was...

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27th International Conference on Artificial Intelligence and Statistics, AISTATS 2024

作者： Demetci, Pinar Tran, Quang Huy Redko, Ievgen Singh, Ritambhara Eric and Wendy Schmidt Center 1 Broad Institute of MIT and Harvard United States Université Bretagne-Sud IRISA CMAP Ecole Polytechnique IP Paris France Paris Noah’s Ark Lab Huawei Technologies France Department of Computer Science Center for Computational Molecular Biology Brown University United States

Gromov-Wasserstein distance has many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformations. However, in certain applications, this invariant property can be too flexible, thus undesirable. Moreover, the Gromov-Wasserstein distance solely considers pairwise sample similarities in input datasets, disregarding the raw feature representations. We propose a new optimal transport formulation, called Augmented Gromov-Wasserstein (AGW), that allows for some control over the level of rigidity to transformations. It also incorporates feature alignments, enabling us to better leverage prior knowledge on the input data for improved performance. We first present theoretical insights into the proposed method. We then demonstrate its usefulness for single-cell multi-omic alignment tasks and heterogeneous domain adaptation in machine learning. Copyright 2024 by the author(s).

关键词： Machine learning

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Decoding Speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Mahmud, M. Hasan, M. Yeasin, M. Bidelman, G. Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Department Of Electrical And Computer Engineering TN United States Indiana University Language And Hearing Sciences Department Of Speech BloomingtonIN United States

ISBN: (纸本)9798350388572

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing. © 2024 IEEE.

关键词： Hidden Markov models

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Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

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Physical Review Applied 2024年第2期22卷 024063页

作者： Henri Tertilt Jonas Mensing Marlon Becker Wilfred G. van der Wiel Peter A. Bobbert Andreas Heuer Institute for Physical Chemistry Department of Physics NanoElectronics Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) Department of Applied Physics Center for Computational Energy Research (CCER)

Nonlinear behavior in the hopping transport of interacting charges enables reconfigurable logic in disordered dopant network devices, where voltages applied at control electrodes tune the relation between voltages applied at input electrodes and the current measured at an output electrode. From kinetic Monte Carlo simulations we analyze the critical nonlinear aspects of variable-range hopping transport for realizing Boolean logic gates in these devices on three levels. First, we quantify the occurrence of individual gates for random choices of control voltages. We find that linearly inseparable gates such as the xor gate are less likely to occur than linearly separable gates such as the and gate, despite the fact that the number of different regions in the multidimensional control voltage space for which and or xor gates occur is comparable. Second, we use principal-component analysis to characterize the distribution of the output current vectors for the (00,10,01,11) logic input combinations in terms of eigenvectors and eigenvalues of the output covariance matrix. This allows a simple and direct comparison of the behavior of different simulated devices and a comparison to experimental devices. Third, we quantify the nonlinearity in the distribution of the output current vectors necessary for realizing Boolean functionality by introducing three nonlinearity indicators. The analysis provides a physical interpretation of the effects of changing the hopping distance and temperature and is used in a comparison with data generated by a deep neural network trained on a physical device.

关键词： Carrier dynamics Dynamics of networks Electrical conductivity Fitness Hopping transport Network optimization Optimization problems Temperature

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