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arXiv 2024年

作者： Attafi, Omar Abdelghani Clementel, Damiano Kyritsis, Konstantinos Capriotti, Emidio Farrell, Gavin Fragkouli, Styliani-Christina Castro, Leyla Jael Hatos, András Lenaerts, Tom Mazurenko, Stanislav Mozaffari, Soroush Pradelli, Franco Ruch, Patrick Savojardo, Castrense Turina, Paola Zambelli, Federico Piovesan, Damiano Monzon, Alexander Miguel Psomopoulos, Fotis Tosatto, Silvio C.E. Department of Biomedical Sciences University of Padova Italy Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece Department of Pharmacy and Biotechnology University of Bologna Bologna Italy ELIXIR Hub Hinxton Cambridge United Kingdom Department of Biology National and Kapodistrian University of Athens Athens Greece ZB Med Information Centre for Life Sciences Cologne Germany Department of Oncology Geneva University Hospitals Geneva Switzerland Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Swiss Cancer Center Léman Lausanne Switzerland Interuniversity Institute of Bioinformatics in Brussels Université Libre de Bruxelles Vrije Universiteit Brussel Brussels1050 Belgium Machine Learning Group Université Libre de Bruxelles Street Belgium Artificial Intelligence Laboratory Vrije Universiteit Brussels Brussels Belgium Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Czech Republic Masaryk University Brno Czech Republic International Clinical Research Centre St. Anne's Hospital Brno Czech Republic HES-SO - HEG Geneva Geneva Switzerland SIB Swiss Institute of Bioinformatics Geneva Switzerland Dept. of Biosciences University of Milan Italy Bari Italy Department of Information Engineering University of Padova Italy Bari Italy

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry, a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences. The DOME Registry can be freely accessed from URL: ***. © 2024, CC BY-NC-ND.

关键词： Domes

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From Chaos to Coherence: Effects of High-Order Synaptic Correlations on Neural Dynamics

arXiv

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arXiv 2025年

作者： Sherf, Nimrod Pitkow, Xaq Josić, Krešimir Bassler, Kevin E. Department of Mathematics University of Houston HoustonTX United States Department of Neuroscience Baylor College of Medicine HoustonTX United States Neuroscience Institute Carnegie Mellon University PittsburghPA United States Department of Machine Learning The School of Computer Science Carnegie Mellon University PittsburghPA United States Departments of Electrical and Computer Engineering and Computer Science Rice University HoustonTX United States NSF AI Institute for Artificial and Natural Intelligence United States Department of Biology and Biochemistry University of Houston HoustonTX United States Department of Physics University of Houston HoustonTX United States Texas Center for Superconductivity University of Houston HoustonTX United States

Recurrent Neural Network models have elucidated the interplay between structure and dynamics in biological neural networks, particularly the emergence of irregular and rhythmic activities in cortex. However, most studies have focused on networks with random or simple connectivity structures. Experimental observations find that high-order cortical connectivity patterns affect the temporal patterns of network activity, but a theory that relates such complex structure to network dynamics has yet to be developed. Here, we show that third- and higher-order cyclic correlations in synaptic connectivities greatly impact neuronal dynamics. Specifically, strong cyclic correlations in a network suppress chaotic dynamics, and promote oscillatory or fixed activity. The change in dynamics is related to the form of the unstable eigenvalues of the random connectivity matrix. A phase transition from chaotic to fixed or oscillatory activity coincides with the development of a cusp at the leading edge of the eigenvalue support. We also relate the dimensions of activity to the network structure. © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Structuring the Problem Space for Model-Based Systems engineering

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Insight 2019年第1期22卷 45-50页

作者： Wrigley, J. Craig Wide-ranging experience of systems engineering for over 35 years through innovative work on systems ranging from new demand access protocols for military data links through to large scale intelligence processing systems applying artificial intelligence and machine learning and assessments of quantum technologies.

Systems engineering is evolving from a largely text-based endeavour towards a more graphical and model-based approach. While modelling of the structural aspects of a system is well developed, the modelling of requirements is still evolving. This paper proposes a traceable method for transforming textual functional requirements or business activities into required behaviours and proposes a multiscale approach to refining the behavioural descriptions into "atomic" behavioural requirements which can be satisfied either by existing systems or which can form the basis for development of new subsystems. © 2019 by J. Craig Wrigley. Published and used by INCOSE with permission.

关键词： Requirements engineering

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Variational pretraining for semi-supervised text classification

arXiv

引用

arXiv 2019年

作者： Gururangan, Suchin Dang, Tam Card, Dallas Smith, Noah A. Allen Institute for Artificial Intelligence SeattleWA United States Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States Machine Learning Department Carnegie Mellon University PittsburghPA United States

We introduce VAMPIRE,1a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier. Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. We also find that fine-tuning to indomain data is crucial to achieving decent performance from contextual embeddings when working with limited supervision. We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Optimal operation of cryogenic calorimeters through deep reinforcement learning

arXiv

引用

arXiv 2023年

作者： Angloher, G. Banik, S. Benato, G. Bento, A. Bertolini, A. Breier, R. Bucci, C. Burkhart, J. Canonica, L. D’Addabbo, A. Di Lorenzo, S. Einfalt, L. Erb, A. Feilitzsch, F.V. Fichtinger, S. Fuchs, D. Garai, A. Ghete, V.M. Gorla, P. Guillaumon, P.V. Gupta, S. Hauff, D. Ješkovský, M. Jochum, J. Kaznacheeva, M. Kinast, A. Kuckuk, S. Kluck, H. Kraus, H. Langenkämper, A. Mancuso, M. Marini, L. Mauri, B. Meyer, L. Mokina, V. Niedermayer, K. Olmi, M. Ortmann, T. Pagliarone, C. Pattavina, L. Petricca, F. Potzel, W. Povinec, P. Pröbst, F. Pucci, F. Reindl, F. Rothe, J. Schäffner, K. Schieck, J. Schönert, S. Schwertner, C. Stahlberg, M. Stodolsky, L. Strandhagen, C. Strauss, R. Usherov, I. Wagner, F. Wagner, V. Willers, M. Zema, V. Heitzinger, C. Waltenberger, W. Max-Planck-Institut für Physik MünchenD-80805 Germany Institut für Hochenergiephysik der Österreichischen Akademie der Wissenschaften WienA-1050 Austria Atominstitut Technische Universität Wien WienA-1020 Austria INFN Laboratori Nazionali del Gran Sasso AssergiI-67100 Italy Comenius University Faculty of Mathematics Physics and Informatics Bratislava84248 Slovakia Physik-Department TUM School of Natural Sciences Technische Universität München GarchingD-85747 Germany Eberhard-Karls-Universität Tübingen TübingenD-72076 Germany Department of Physics University of Oxford OxfordOX1 3RH United Kingdom Institute of Information Systems Engineering Technische Universität Wien WienA-1040 Austria Center for Artificial Intelligence and Machine Learning Technische Universität Wien WienA-1040 Austria LIBPhys-UC Departamento de Fisica Universidade de Coimbra CoimbraP3004 516 Portugal Walther-Meißner-Institut für Tieftemperaturforschung GarchingD-85748 Germany Dipartimento di Ingegneria Civile e Meccanica Universitá degli Studi di Cassino e del Lazio Meridionale CassinoI-03043 Italy

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c2 dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three CRESST detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Combining machine learning and computational chemistry for predictive insights into chemical systems

arXiv

引用

arXiv 2021年

作者： Keith, John A. Vassilev-Galindo, Valentin Cheng, Bingqing Chmiela, Stefan Gastegger, Michael Müller, Klaus-Robert Tkatchenko, Alexandre Department of Chemical and Petroleum Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh United States Department of Physics and Materials Science University of Luxembourg Luxembourg CityL-1511 Luxembourg Accelerate Programme for Scientific Discovery Department of Computer Science and Technology 15 J. J. Thomson Avenue CambridgeCB3 0FD United Kingdom Cavendish Laboratory University of Cambridge J. J. Thomson Avenue CambridgeCB3 0HE United Kingdom Department of Software Engineering and Theoretical Computer Science Technische Universität Berlin Berlin10587 Germany Machine Learning Group Technische Universität Berlin Berlin10587 Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul02841 Korea Republic of Max-Planck-Institut für Informatik Saarbrücken Germany Google Research Brain team Berlin Germany

machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design. © 2021, CC BY-SA.

关键词： Computational chemistry

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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How compact?: Assessing compactness of representations through layer-wise pruning

arXiv

引用

arXiv 2019年

作者： Jung, Hyun-Joo Kim, Jaedeok Choe, Yoonsuck Machine Learning Lab Artificial Intelligence Center Samsung Research Samsung Electronics Co. 56 Seongchon-gil Secho-gu Seoul06765 Korea Republic of Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

Various forms of representations may arise in the many layers embedded in deep neural networks (DNNs). Of these, where can we find the most compact representation? We propose to use a pruning framework to answer this question: How compact can each layer be compressed, without losing performance? Most of the existing DNN compression methods do not consider the relative compressibility of the individual layers. They uniformly apply a single target sparsity to all layers or adapt layer sparsity using heuristics and additional training. We propose a principled method that automatically determines the sparsity of individual layers derived from the importance of each layer. To do this, we consider a metric to measure the importance of each layer based on the layer-wise capacity. Given the trained model and the total target sparsity, we first evaluate the importance of each layer from the model. From the evaluated importance, we compute the layer-wise sparsity of each layer. The proposed method can be applied to any DNN architecture and can be combined with any pruning method that takes the total target sparsity as a parameter. To validate the proposed method, we carried out an image classification task with two types of DNN architectures on two benchmark datasets and used three pruning methods for compression. In case of VGG-16 model with weight pruning on the ImageNet dataset, we achieved up to 75% (17.5% on average) better top-5 accuracy than the baseline under the same total target sparsity. Furthermore, we analyzed where the maximum compression can occur in the network. This kind of analysis can help us identify the most compact representation within a deep neural network. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Developing an Intelligent Resume Screening Tool With AI-Driven Analysis and Recommendation Features

Applied AI Letters

引用

Applied AI Letters 2025年第2期6卷

作者： K. L. Abhishek M. Niranjanamurthy Shonit Aric Syed Immamul Ansarullah Anurag Sinha G. Tejani Mohd Asif Shah Department of Artificial Intelligence & Machine Learning BMS Institute of Technology Affiliated to Visvesvaraya Technological University Bangalore Karnataka India Software Engineer Fidelity Investments Bangalore India Department of Management Studies University of Kashmir North Campus Srinagar Jammu and Kashmir India Tech School Computer Science Department ICFAI University Ranchi Jharkhand India Department of Computer Science RC University Palamue Bishrampur Jharkhand India Jadara University Research Center Jadara University Amman Jordan Dean of Faculty Department of Economics Kardan University Kabul Afghanistan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India

Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face challenges in identifying qualified candidates due to oversight and time constraints. Inconsistent evaluation criteria hinder decision-making. These issues result in prolonged hiring processes, missed opportunities, and potential bias in candidate selection. The goal of this project is to develop an AI-powered Resume Analysis and Recommendation Tool, catering to the trend of recruiters spending less than 2 min on each CV. The tool will rapidly analyze all resume components while providing personalized predictions and recommendations to applicants for improving their CVs. It will present user-friendly data for recruiters, facilitating export to CSV for integration into their recruitment processes. Additionally, the tool will offer insights and analytics on popular roles and skills within the job market. Its user section will enable applicants to continually test and track their resumes, encouraging repeat usage and driving traffic. Colleges can benefit from gaining insights into students' resumes before placements. Overall, this AI-powered tool aims to enhance the resume evaluation process, benefiting both job seekers and employers. The primary aim of this project is to develop a Resume Analyzer using Python, incorporating advanced libraries such as Pyresparser, NLTK (Natural Language Toolkit), and MySQL. This automated system offers an efficient solution for parsing, analyzing, and extracting essential information from resumes. The user-friendly interface, developed using Streamlit, allows for seamless resume uploading, insightful data visualization, and analytics. The Resume Analyzer significantly streamlines the resume screening process, providing recruiters with valuable insights and enhancing their decision-making capabilities.

关键词： natural language processing parsing resume analysis tokenization

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