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arXiv 2022年

作者： Chmiela, Stefan Vassilev-Galindo, Valentin Unke, Oliver T. Kabylda, Adil Sauceda, Huziel E. Tkatchenko, Alexandre Müller, Klaus-Robert Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data – BIFOLD Germany Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg Google Research Brain Team Berlin Germany Departamento de Materia Condensada Instituto de Física Universidad Nacional Autónoma de México Cd. de MéxicoC.P. 04510 Mexico BASLEARN - TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany Max Planck Institute for Informatics Stuhlsatzenhausweg Saarbrücken66123 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of

Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms due a considerable growth of the model complexity with system size. For larger molecules, locality assumptions are typically introduced, with the consequence that non-local interactions are poorly or not at all described, even if those interactions are contained within the reference ab initio data. Here, we approach this challenge and develop an exact iterative parameter-free approach to train global symmetric gradient domain machine learning (sGDML) force fields for systems with up to several hundred atoms, without resorting to any localization of atomic interactions or other potentially uncontrolled approximations. This means that all atomic degrees of freedom remain fully correlated in the global sGDML FF, allowing the accurate description of complex molecules and materials that present phenomena with far-reaching characteristic correlation lengths. We assess the accuracy and efficiency of our MLFFs on a newly developed MD22 benchmark dataset containing molecules from 42 to 370 atoms. The robustness of our approach is demonstrated in nanosecond long path-integral molecular dynamics simulations for the supramolecular complexes in the MD22 dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Insightful analysis of historical sources at scales beyond human capabilities using unsupervised machine learning and XAI

arXiv

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arXiv 2023年

作者： Eberle, Oliver Büttner, Jochen El-Hajj, Hassan Montavon, Grégoire Müller, Klaus-Robert Valleriani, Matteo Machine Learning Group Technische Universität Berlin Marchstr. 23 Berlin10587 Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Max Planck Institute for the History of Science Boltzmannstr. 22 Berlin14195 Germany Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Stuhlsatzenhausweg 4 Saarbrücken66123 Germany Institute of History and Philosophy of Science Technology and Literature Faculty I - Humanities and Educational Sciences Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany The Cohn Institute for the History and Philosophy of Science and Ideas Faculty of Humanities Tel Aviv University P.O.B. 39040 Ramat Aviv Tel Aviv6139001 Israel

Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only be very selectively addressed. The vast volume of materials precludes comprehensive studies, given the restricted number of human specialists. However, as large amounts of historical materials are now available in digital form there is a promising opportunity for AI-assisted historical analysis. In this work, we take a pivotal step towards analyzing vast historical corpora by employing innovative machine learning (ML) techniques, enabling in-depth historical insights on a grand scale. Our study centers on the evolution of knowledge within the ‘Sacrobosco Collection’ – a digitized collection of 359 early modern printed editions of textbooks on astronomy used at European universities between 1472 and 1650 – roughly 76,000 pages, many of which contain astronomic, computational tables. An ML based analysis of these tables helps to unveil important facets of the spatio-temporal evolution of knowledge and innovation in the field of mathematical astronomy in the period, as taught at European universities. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

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VAE-GAN based zero-shot outlier detection 4

VAE-GAN based zero-shot outlier detection

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4th International Symposium on Computer science and Intelligent Control, ISCSIC 2020

作者： Ibrahim, Bekkouch Imad Nicolae, Dragos Constantin Khan, Adil Ali, Syed Imran Khattak, Asad Machine Learning and Knowledge Representation Lab Institute of Data Science and AI Innopolis Tatarstan Russia Institutul de Cercetari Pentru Inteligenta Artificiala Mihai Draganescu Romania Department of Computer Engineering Kyung Hee University Yongin-si Korea Republic of College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets. © 2020 ACM.

关键词： Anomaly detection

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Fast emulation of two-point angular statistics for photometric galaxy surveys

arXiv

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arXiv 2022年

作者： Bonici, Marco Biggio, Luca Carbone, Carmelita Guzzo, Luigi INAF-IASF Milano Via Alfonso Corti 12 MilanoI-20133 Italy Data Analytics Lab Institute of Machine Learning Department of Computer Science ETH Zürich Switzerland Dipartimento di Fisica Aldo Pontremoli Università Degli Studi di Milano INFN Sez. di Milano Via Celoria 16 MilanoI-20133 Italy INAF Osservatorio Astronomico di Brera Milano Italy

We develop a set of machine-learning based cosmological emulators, to obtain fast model predictions for the C() an-gular power spectrum coefficients characterising tomographic observations of galaxy clustering and weak gravitational lensing from multi-band photometric surveys (and their cross-correlation). A set of neural networks are trained to map cosmological parameters into the coefficients, achieving a speed-up O(103) in computing the required statistics for a given set of cosmological parameters, with respect to standard Boltzmann solvers, with an accuracy better than 0.175% ( Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Towards DMC accuracy across chemical space with scalable ∆-QML

arXiv

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arXiv 2022年

作者： Huang, Bing von Lilienfeld, O. Anatole Krogel, Jaron T. Benali, Anouar University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to under-gird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for ∆-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Risk-based decision making: estimands for sequential prediction under interventions

arXiv

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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Towards Ground Truth Explainability on Tabular data

arXiv

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arXiv 2020年

作者： Barr, Brian Xu, Ke Silva, Claudio Bertini, Enrico Reilly, Robert Bayan Bruss, C. Wittenbach, Jason D. Center for Machine Learning Capital One New YorkNY United States Tandon School of Engineering New York University NY United States Center for Data Science New York University NY United States Card Machine Learning and Technology Capital One PlanoTX United States Center for Machine Learning Capital One Maclean VA United States Center for Machine Learning Capital One CambridgeMA United States

In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes from recent work in explainability. Today’s datasets are typically larger and more complex - requiring less interpretable models. In the setting of post hoc explainability, there is no ground truth for explanations. Inspired by recent work in explaining image classifiers that does provide ground truth, we propose a similar solution for tabular data. Using copulas, a concise specification of the desired statistical properties of a dataset, users can build intuition around explainability using controlled data sets and experimentation. The current capabilities are demonstrated on three use cases: one dimensional logistic regression, impact of correlation from informative features, impact of correlation from redundant variables. Copyright © 2020, The Authors. All rights reserved.

关键词： machine learning

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Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

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Revue d'Intelligence Artificielle 2019年第6期33卷 461-466页

作者： Reddy Chirra, Venkata Rami Uyyala, Srinivasulu Reddy Kishore Kolli, Venkata Krishna Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Department of Computer Science and Engineering VFSTR Guntur522213 India

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

关键词： Face recognition

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A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes

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Computers in Biology and Medicine 2024年 173卷 108306-108306页

作者： Kazemi, Azar Rasouli-Saravani, Ashkan Gharib, Masoumeh Albuquerque, Tomé Eslami, Saeid Schüffler, Peter J. Department of Medical Informatics School of Medicine Mashhad University of Medical Sciences Mashhad Iran Institute of General and Surgical Pathology Technical University of Munich Munich Germany Student Research Committee Department of Immunology School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Department of Pathology Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran INESC TEC -Rua Dr. Roberto Frias Porto Portugal Pharmaceutical Sciences Research Center Institute of Pharmaceutical Technology Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics University of Amsterdam Amsterdam Netherlands TUM School of Computation Information and Technology Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany

The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods. © 2024 Elsevier Ltd

关键词： Tumors

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Nanoscale chemical imaging with structured X-ray illumination

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Proceedings of the National Academy of sciences of the United States of America 2023年第49期120卷 e2314542120-e2314542120页

作者： Li, Jizhou Chen, Si Ratner, Daniel Blu, Thierry Pianetta, Piero Liu, Yijin Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park 94025 CA United States School of Data Science City University of HongKong Hong Kong X-ray Science Division Argonne National Laboratory Lemont 60439 IL United States Machine Learning Initiative SLAC National Accelerator Laboratory Menlo Park 94025 CA United States Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Walker Department of Mechanical Engineering The University of Texas at Austin Austin 78705 TX United States

High-resolution imaging with compositional and chemical sensitivity is crucial for a wide range of scientific and engineering disciplines. Although synchrotron X-ray imaging through spectromicroscopy has been tremendously successful and broadly applied, it encounters challenges in achieving enhanced detection sensitivity, satisfactory spatial resolution, and high experimental throughput simultaneously. In this work, based on structured illumination, we develop a single-pixel X-ray imaging approach coupled with a generative image reconstruction model for mapping the compositional heterogeneity with nanoscale resolvability. This method integrates a full-field transmission X-ray microscope with an X-ray fluorescence detector and eliminates the need for nanoscale X-ray focusing and raster scanning. We experimentally demonstrate the effectiveness of our approach by imaging a battery sample composed of mixed cathode materials and successfully retrieving the compositional variations of the imaged cathode particles. Bridging the gap between structural and chemical characterizations using X-rays, this technique opens up vast opportunities in the fields of biology, environmental, and materials science, especially for radiation-sensitive samples. © 2023 the Author(s).

关键词： image reconstruction synchrotron X-rays transmission X-ray microscopy X-ray chemical imaging X-ray fluorescence

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