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arXiv 2024年

作者： Mao, Lingchao Wang, Hairong Hu, Leland S. Tran, Nhan L. Canoll, Peter D. Swanson, Kristin R. Li, Jing H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Mathematical NeuroOncology Lab Precision Neurotherapeutics Innovation Program Mayo Clinic Arizona 5777 East Mayo Blvd Support Services Building Suite 2-700 PhoenixAZ85054 United States Department of Radiology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Neurosurgery Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Cancer Biology Mayo Clinic Arizona 5777 E. Mayo Blvd PhoenixAZ85054 United States Department of Pathology and Cell Biology Columbia University Medical Center 630 West 168th Street New YorkNY10032 United States

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine *** Codes 92B99 Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge representation

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Ultrafast circuit protection for unmanned systems

Ultrafast circuit protection for unmanned systems

引用

2019 International Conference on Communications, Information System, and Computer engineering, CISCE 2019

作者： Kao, Chih-Hong Huang, Sy-Ruen He, Bo-Rong Lin, Hsiung-Cheng School of Computer Science Minnan Normal University Lab of Granular Computing ZhangZhou Fujjan China Department of Electrical Engineering Feng Chia University Taichung Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering Chin-Yi University of Technology Taichung Taiwan

ISBN: (纸本)9781728136813

With increasing dependence on precision electronic equipment, self-protection is becoming essential. The protection circuits commonly used before were fuses and circuit breakers which, however, they are not enough for the current need of high-speed protection circuits and automated engineering logic. In this paper, we develop a new ultrahigh-speed protection circuit, which is over exceeded 20 times faster than traditional circuits and has the ability to self-recover when any failure occurs. With the new circuit developed, it is not necessary to do manual restart and will meet the future needs of protection for electronic precision equipment. This design provides greater convenience and reliability for the unmanned equipment for the future. © 2019 IEEE.

关键词： Timing circuits

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A Knowledge Visualization Course for the Era of Data Science

A Knowledge Visualization Course for the Era of Data Science

引用

Conferencia Latinoamericana En Informatica (CLEI)

作者： Ismar Frango Silveira Electrical Engineering and Computing Postgraduate Program Mackenzie Presbyterian University São Paulo SP Brazil

ISBN: (数字)9781728155746

ISBN: (纸本)9781728155753

Big data applications, as well as the whole myriad of applications arisen through the evolution of the body of knowledge commonly known as Data Science brings new challenges to CS-related courses curricula. The demands imposed in CS-related courses by the recent development of data-oriented application makes that visualization skills became an important part of both undergraduate and graduate formation. In this sense, this paper presents a syllabus for a Knowledge Visualization course, adaptable to both undergraduate and graduate levels, which aims to contribute to systematize the competences, skills and attitudes regarding data, information and knowledge visualization that are expected to be developed by future professionals.

关键词： Data visualization Visualization Big Data Proposals Tools Data science programming

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Multimodal deep learning framework for image popularity prediction on social media

arXiv

引用

arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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Urban Trees Mapping Using Multi-Scale Rgb Image and Deep Learning Vision Transformer-Based

SSRN

引用

SSRN 2022年

作者： Gomes, Felipe David Georges Furuya, Michelle Taís Garcia Junior, José Marcato Gonçalves, Diogo Nunes Martins, José Augusto Correa Silva, Paulo Antonio Gonçalves, Wesley Nunes Osco, Lucas Prado Ramos, Ana Paula Marques Program of Environment and Regional Development University of Western São Paulo Raposo Tavares km 572SP Presidente Prudente19067-175 Brazil Program of Agronomy University of Western São Paulo Raposo Tavares km 572SP Presidente Prudente19067-175 Brazil Faculty of Engineering and Architecture and Urbanism University of Western São Paulo Raposo Tavares km 572SP Presidente Prudente19067-175 Brazil Federal University of Rio de Janeiro Rua Athos da Silveira Ramos 274RJ Rio de Janeiro21941-916 Brazil Faculty of Computing Federal University of Mato Grosso do Sul Av. Costa e SilvaMS Campo Grande79070-900 Brazil Faculty of Engineering Architecture and Urbanism and Geography Federal University of Mato Grosso do Sul Av. Costa e Silva Campo Grande79070-900 MS Brazil

Despite the fact that deep neural networks performed well in arboreous urban vegetation semantic segmentation, they fell short of expectations. As a result, we evaluate the performance of SegFormer, a vision transformer-based deep learning. We compare the evaluation metrics of SegFormer with the deep learning architecture DeepLabv3+. Two sets of image data, each having a different GSD, were employed (0.2 m and 0.5 m). In the semantic segmentation of tree vegetation in urban environments the SegFormer outperforms the state-of-the-art convolutional neural network, proving its capacity to map urban forests in RGB pictures. The SegFormer reported the best results including when we combined the different GSDs to train the model, which proved to be just as effective as training and testing with only the 0.20 m GSD. © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Deep Hierarchical Embedding for Simultaneous Modeling of GPCR Proteins in a Unified Metric Space

Research Square

引用

Research Square 2021年

作者： Lee, Taeheon Lee, Sangseon Kang, Minji Kim, Sun Looxid Labs Seoul06099 Korea Republic of BK21 FOUR Intelligence Computing Seoul National University Seoul08826 Korea Republic of Department of Computer Science Stanford University StanfordCA94305 United States Bioinformatics Institute Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately at each level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects. © 2021, CC BY.

关键词： Proteins

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The regulation of cancer-associated thrombosis by podoplanin

引用

Thrombosis Update 2024年 15卷 100174-100174页

作者： Gupta, Neha Saifi, Mohd Faiz Wilson, Kiesha Hisada, Yohei Evans, Colin E. Department of Biosciences Faculty of Natural Sciences Jamia Millia Islamia New Delhi India Department of Gastroenterology and Human Nutrition Unit All India Institute of Medical Sciences New Delhi India Department of Pathology Microbiology and Immunology University of South Carolina School of Medicine Columbia SC United States Division of Hematology Department of Medicine University of North Carolina at Chapel Hill Chapel Hill NC United States UNC Blood Research Center University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Cell Biology and Anatomy University of South Carolina School of Medicine Columbia SC United States Cardiovascular Translational Research Center University of South Carolina Columbia SC United States Institute on Cardiovascular Disease Research University of South Carolina Columbia United States Biomedical Engineering Program College of Engineering and Computing University of South Carolina Columbia SC United States

The incidence of venous thromboembolism (VTE) in cancer patients is 4–9 fold higher compared with the general population. The mortality rate of patients with cancer and VTE is more than 2-fold greater versus cancer patients without VTE. Given that the pathophysiology of thrombosis in cancer is multi-faceted, investigations of the mechanisms that regulate cancer-associated thrombosis (CAT) could improve the understanding and treatment of CAT. These mechanisms include activation of the coagulation and fibrinolytic systems. Tumor cells activate coagulation by expressing procoagulant molecules, releasing pro-inflammatory and pro-angiogenic cytokines, and adhering to vascular and blood cells. Tumor-secreted and tissue factor-positive extracellular vesicles are another major driver of CAT, while emerging studies have discovered a role for podoplanin (PDPN) in intratumoral thrombosis, hyper-coagulation, and enhanced VTE risk. In this article, we will review studies of PDPN in CAT, which together suggest that PDPN contributes not only to cancer progression and metastasis, but also to CAT. PDPN may therefore represent an attractive putative target for therapies that aim to simultaneously reduce cancer progression and associated VTE. © 2024 The Authors

关键词： Cancer-associated thrombosis Podoplanin Thrombosis

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Partial-Order, Partially-Seen Observations of Fluents or Actions for Plan Recognition as Planning

arXiv

引用

arXiv 2019年

作者： Nelson, Jennifer M. Cardona-Rivera, Rogelio E. School of Computing Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

This work aims to make plan recognition as planning more ready for real-world scenarios by adapting previous compilations to work with partial-order, half-seen observations of both fluents and actions. We first redefine what observations can be and what it means to satisfy each kind. We then provide a compilation from plan recognition problem to classical planning problem, similar to original work by Ramírez and Geffner, but accommodating these more complex observation types. This compilation can be adapted towards other planning-based plan recognition techniques. Lastly we evaluate this method against an "ignore complexity" strategy that uses the original method by Ramírez and Geffner. Our experimental results suggest that, while slower, our method is equally or more accurate than baseline methods;our technique sometimes significantly reduces the size of the solution to the plan recognition problem, i.e, the size of the optimal goal set. We discuss these findings in the context of plan recognition problem difficulty and present an avenue for future work. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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