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SSRN 2024年

作者： Clarke, Emily Laura Magee, Derek Newton-Bishop, Julia Merchant, William Insall, Robert Maher, Nigel G. Scolyer, Richard A. Farnworth, Grace Ali, Anisah O’Shea, Sally Treanor, Darren Department of Histopathology Leeds Teaching Hospitals NHS Trust Leeds United Kingdom Division of Pathology and Data Analytics University of Leeds Leeds United Kingdom School of Computing University of Leeds Leeds United Kingdom Leeds Institute for Medical Research University of Leeds Leeds United Kingdom Department of Cell and Developmental Biology University College London Gower Street London United Kingdom Melanoma Institute Australia The University of Sydney Sydney Australia Faculty of Medicine and Health The University of Sydney Sydney Australia Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital and NSW Health Pathology Sydney Australia Charles Perkins Centre The University of Sydney SydneyNSW Australia Dermatology Department South Infirmary Victoria University Hospital Cork Ireland School of Medicine University College Cork Ireland Department of Clinical Pathology Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Linköping University Linköping Sweden

Background: The current melanoma staging system does not explain 26% of the variance in survival. Melanoma morphology may be of greater significance than in other solid tumours, with Breslow thickness (a subjective measure) remaining the strongest prognostic indicator despite being subject to high levels of inter-observer variation. The application of convolutional neural networks to whole slide images affords objective morphological metrics, which may reveal new insights into patient prognosis. This work outlines the development and evaluation of a convolutional neural network for invasive cutaneous melanoma detection in whole slide images for the generation of objective prognostic biomarkers based on tumour ***: 1,156 WSIs containing cutaneous melanoma from five datasets have been used in the initial development and evaluation of the convolutional neural network. A 2-class tumour segmentation network with a fully convolutional architecture was trained using sparse annotations. The network was evaluated using various methodologies, including comparison at per-pixel and per-tumour levels as compared to manual annotation, as well as variation across 3 scanning ***: The convolutional neural network located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9% respectively across the 5 test sets. There were no statistical differences between the tumour dimensions generated by the model as compared to manual annotation, or between the tumour dimensions generated by the model across three scanning ***: We have developed and performed initial evaluation of a convolutional neural network which accurately detects invasive cutaneous melanoma of no specific type in whole slide images for objective evaluation of tumour morphology. Future work should interrogate these data further for its ability to predict survival ***: This work was supported by the

关键词： Pixels

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On How data are Used in Model Development: The Elephant in the Room

SSRN

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SSRN 2023年

作者： Maier, Holger R. Zheng, Feifei Gupta, Hoshin Chen, Junyi Mai, Juliane Savic, Dragan Loritz, Ralf Wu, Wenyan Guo, Danlu Bennett, Andrew Zhao, Jianshi School of Architecture and Civil Engineering The University of Adelaide Adelaide Australia College of Civil Engineering and Architecture Zhejiang University China Department of Hydrology and Atmospheric Sciences The University of Arizona Tucson United States Computational Hydrosystems Helmholtz Centre for Environmental Research–UFZ Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence–ScaDS.AI Leipzig Germany KWR Water Research Institute Netherlands Centre for Water Systems University of Exeter Exeter United Kingdom Institute of Water and River Basin Management Karlsruhe Germany Department of Infrastructure Engineering The University of Melbourne Melbourne Australia School of Engineering College of Engineering Computing & Cybernetics Australian National University Canberra Australia Department of Hydraulic Engineering Tsinghua University Beijing China

Models are widely used to support the development of understanding regarding the physical nature of Earth and environmental systems, with a view to improving their planning and management. data is crucial to the development of such models, and it is common to partition the available data into separate subsets to be used for model development and evaluation. Although the manner by which the data is partitioned can significantly affect the model’s ability to generalize to novel conditions, this issue has (surprisingly) been largely ignored (i.e., the elephant in the room). Here we highlight the importance of careful attention to this aspect of the model development process and how it can impact the generalization ability of models. We report on the problems with commonly used, as well as more advanced data-splitting approaches. We also map out a way forward to enable us to address the elephant in the room. © 2023, The Authors. All rights reserved.

关键词： data handling

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INFINITE-HORIZON OFF-POLICY POLICY EVALUATION WITH MULTIPLE BEHAVIOR POLICIES 8

INFINITE-HORIZON OFF-POLICY POLICY EVALUATION WITH MULTIPLE ...

引用

8th International Conference on Learning Representations, ICLR 2020

作者： Chen, Xinyun Wang, Lu Hang, Yizhe Ge, Heng Zha, Hongyuan Insitute for Data and Decision Analytics The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Department of Computer Science East China Normal University China Department of Computer Science University of Science and Technology of China China School of Mathematics and Statistics Shandong University China Georgia Institute of Technology United States College of Computing Georgia Institute of Technology United States

We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies. Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation. We propose estimated mixture policy (EMP), a partially policy-agnostic methods to accurately estimate those quantities. With careful analysis, we show that EMP gives rise to estimates with reduced variance for estimating the state stationary distribution correction while it also offers a useful induction bias for estimating the state-action stationary distribution correction. In extensive experiments with both continuous and discrete environments, we demonstrate that our algorithm offers significantly improved accuracy compared to the state-of-the-art methods. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

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Entanglement-Induced Barren Plateaus

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PRX Quantum 2021年第4期2卷 040316-040316页

作者： Carlos Ortiz Marrero Mária Kieferová Nathan Wiebe Data Sciences and Analytics Group Pacific Northwest National Laboratory Richland Washington 99354 USA Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney New South Wales 2007 Australia Department of Computer Science University of Toronto Ontario M5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory Richland Washington 99354 USA

We argue that an excess in entanglement between the visible and hidden units in a quantum neural network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume law in the entanglement entropy will give rise to models that are not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then show that this volume law is typical and that there exists a barren plateau in the optimization landscape due to entanglement. More precisely, we show that for any bounded objective function on the visible layers, the Lipshitz constants of the expectation value of that objective function will scale inversely with the dimension of the hidden subsystem with high probability. We show how this can cause both gradient-descent and gradient-free methods to fail. We note that similar problems can happen with quantum Boltzmann machines, although stronger assumptions on the coupling between the hidden and/or visible subspaces are necessary. We highlight how pretraining such generative models may provide a way to navigate these barren plateaus.

关键词： Machine learning Quantum algorithms

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The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

arXiv

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arXiv 2024年

作者： Haag, David Kumar, Devender Gruber, Sebastian Hofer, Dominik Sareban, Mahdi Treff, Gunnar Niebauer, Josef Bull, Christopher Schmidt, Albrecht Smeddinck, Jan David Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Department of Psychology Paris-Lodron-University of Salzburg Salzburg Austria Digital Health Information Systems Center for Health & Bioresources AIT Austrian Institute of Technology GmbH Graz Austria University of Southern Denmark Odense Denmark Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria Human Motion Analytics Salzburg Research Forschungsgesellschaft Salzburg Austria LMU Munich Munich Germany University Institute for Sports Medicine Prevention and Rehabilitation Salzburg Austria Institute of Molecular Sports and Rehabilitation Medicine Paracelsus Medical University Salzburg Austria Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual’s current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing. © 2024, CC BY.

关键词： Electronic health record

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The EMory BrEast imaging dataset (EMBED): A Racially Diverse, Granular dataset of 3.5M Screening and Diagnostic Mammograms

arXiv

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arXiv 2022年

作者： Jeong, Jiwoong J. Vey, Brianna L. Reddy, Ananth Kim, Thomas Santos, Thiago Correa, Ramon Dutt, Raman Mosunjac, Marina Oprea-Ilies, Gabriela Smith, Geoffrey Woo, Minjae McAdams, Christopher R. Newell, Mary S. Banerjee, Imon Gichoya, Judy Trivedi, Hari Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States College of Computing Georgia Institute of Technology 801 Atlantic Dr NW AtlantaGA30332 United States Department of Pathology Emory University 1364 Clifton Rd AtlantaGA30322 United States School of Data Science and Analytics Kennesaw State University 1000 Chastain Road KennesawGA30144 United States Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States

Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available breast imaging datasets lack in one or more of these areas. Models trained on these data may therefore underperform on patient populations or pathologies that have not previously been encountered. The EMory BrEast imaging dataset (EMBED) addresses these gaps by providing 3650,000 2D and DBT screening and diagnostic mammograms for 116,000 women divided equally between White and African American patients. The dataset also contains 40,000 annotated lesions linked to structured imaging descriptors and 61 ground truth pathologic outcomes grouped into six severity classes. Our goal is to share this dataset with research partners to aid in development and validation of breast AI models that will serve all patients fairly and help decrease bias in medical AI. © 2022, CC BY.

关键词： Mammography

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Machine Learning - Algorithms, Models and Applications

arXiv

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arXiv 2022年

作者： Sen, Jaydip Sen, Rajdeep Dutta, Abhishek Mehtab, Sidra Kherwa, Pooja Singh, Sonali Ahmed, Saheel Berry, Pranay Khurana, Sahil Akinbo, Racheal S. Daramola, Oladunni A. Ost, Kalum J. Anderson, David W. Cadotte, David W. Lainjo, Bongs Department of Data Science Praxis Business School Kolkata India School of Computing and Analytics NASHM Knowledge Campus Kolkata India Maharaja Surajmal Institute of Technology New Delhi India Federal University of Technology Akure Nigeria Hotchkiss Brain Institute CalgaryAB Canada Cumming School of Medicine CalgaryAB Canada Division of Neurosurgery Department of Clinical Neurosciences University of Calgary AB Canada Division of Neurosurgery Department of Radiology University of Calgary AB Canada Combined Orthopedic and Neurosurgery Spine Program University of Calgary AB Canada Cybermatic International MontréalQC Canada

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence. © 2022, CC BY-SA.

关键词： Learning algorithms

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Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

arXiv

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arXiv 2022年

作者： Tahir, Barzan Hussein Rashid, Tarik A. Rauf, Hafiz Tayyab Bacanin, Nebojsa Chhabra, Amit Vimal, S. Yaseen, Zaher Mundher Department of Computer Science and Engineering University of Kurdistan Helwer Erbil Iraq Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia USQ's Advanced Data Analytics Research Group School of Mathematics Physics and Computing University of Southern Queensland QLD4350 Australia New era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced Fitness Dependent Optimizer. Correspondingly, various standard estimations are used to prove the stability

关键词： Decision making

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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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Big POI data integration with linked data technologies 22

Big POI data integration with linked data technologies

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22nd International Conference on Extending database Technology, EDBT 2019

作者： Athanasiou, Spiros Giannopoulos, Giorgos Graux, Damien Karagiannakis, Nikos Lehmann, Jens Ngomo, Axel-Cyrille Ngonga Patroumpas, Kostas Sherif, Mohamed Ahmed Skoutas, Dimitrios Information Management Systems Institute Athena Research Center Greece Enterprise Information Systems Department Fraunhofer IAIS Germany Smart Data Analytics Group University of Bonn Germany Computing Center Universität Leipzig Germany Data Science Group Universität Paderborn Germany

ISBN: (纸本)9783893180813

Point of Interest (POI) data constitutes the cornerstone in many modern applications. From navigation to social networks, tourism, and logistics, we use POI data to search, communicate, decide and plan our actions. POIs are semantically diverse and spatiotemporally evolving entities, having geographical, temporal, and thematic relations. Currently, integrating POI datasets to increase their coverage, timeliness, accuracy and value is a resource-intensive and mostly manual process, with no specialized software available to address the specific challenges of this task. In this paper, we present an integrated toolkit for transforming, linking, fusing and enriching POI data, and extracting additional value from them. In particular, we demonstrate how Linked data technologies can address the limitations, gaps and challenges of the current landscape in Big POI data integration. We have built a prototype application that enables users to define, manage and execute scalable POI data integration workflows built on top of state-of-the-art software for geospatial Linked data. This application abstracts and hides away the underlying complexity, automates quality-assured integration, scales efficiently for world-scale integration tasks, and lowers the entry barrier for end-users. Validated against real-world POI datasets in several application domains, our system has shown great potential to address the requirements and needs of cross-sector, cross-border and cross-lingual integration of Big POI data. © 2019 Copyright held by the owner/author(s).

关键词： data integration

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