检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Wang, Zhikang Gao, Qian Yi, Xiaoping Zhang, Xinyu Zhang, Yiwen Zhang, Daokun Liò, Pietro Bain, Christopher Bassed, Richard Li, Shanshan Guo, Yuming Imoto, Seiya Yao, Jianhua Daly, Roger J. Song, Jiangning Xiangya Hospital Central South University Hunan Changsha China Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University Melbourne Australia Faculty of Information Technology Monash University Melbourne Australia Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom Victorian Institute of Forensic Medicine Melbourne Australia Human Genome Center Institute of Medical Science The University of Tokyo Japan Tencent AI Lab Tencent Shenzhen China

High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognostication. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer, a portmanteau of survival and transformer) for cancer survival prediction from WSIs. Notably, it can quantify specific histological patterns and explicitly interpret the model prediction with statistical analysis, all of which are achieved through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross- attention module (RRCA) detects valuable features and quantifies them in the form of ratio factors (rfs), in which process both global and multiple local features are generated. Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting RRCA in achieving more explicit quantification. Extensive benchmarking experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its excellent interpretability and utility by showing attentive visualizations and analysis. Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states. © 2023, The Authors. All rights reserved.

关键词： Decision making

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TORCHTREE: FLEXIBLE PHYLOGENETIC MODEL DEVELOPMENT AND INFERENCE USING PYTORCH

arXiv

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arXiv 2024年

作者： Fourment, Mathieu Macaulay, Matthew Swanepoel, Christiaan J. Ji, Xiang Suchard, Marc A. Matsen IV, Frederick A. Australian Institute for Microbiology and Infection University of Technology Sydney UltimoNSW Australia Centre for Computational Evolution The University of Auckland Auckland New Zealand School of Computer Science The University of Auckland Auckland1010 New Zealand Public Health Sciences Division Fred Hutchinson Cancer Research Center SeattleWA United States Department of Mathematics Tulane University New Orleans United States Department of Human Genetics University of California Los Angeles United States Department of Computational Medicine University of California Los Angeles United States Department of Biostatistics University of California Los Angeles United States Department of Statistics University of Washington Seattle United States Department of Genome Sciences University of Washington Seattle United States Howard Hughes Medical Institute Fred Hutchinson Cancer Research Center SeattleWA United States

Bayesian inference has predominantly relied on the Markov chain Monte Carlo (MCMC) algorithm for many years. However, MCMC is computationally laborious, especially for complex phylogenetic models of time trees. This bottleneck has led to the search for alternatives, such as variational Bayes, which can scale better to large datasets. In this paper, we introduce torchtree, a framework written in Python that allows developers to easily implement rich phylogenetic models and algorithms using a fixed tree topology. One can either use automatic differentiation, or leverage torchtree's plug-in system to compute gradients analytically for model components for which automatic differentiation is slow. We demonstrate that the torchtree variational inference framework performs similarly to BEAST in terms of speed and approximation accuracy. Furthermore, we explore the use of the forward KL divergence as an optimizing criterion for variational inference, which can handle discontinuous and non-differentiable models. Our experiments show that inference using the forward KL divergence tends to be faster per iteration compared to the evidence lower bound (ELBO) criterion, although the ELBO-based inference may converge faster in some cases. Overall, torchtree provides a flexible and efficient framework for phylogenetic model development and inference using PyTorch. Copyright © 2024, The Authors. All rights reserved.

关键词： Phylogenetic

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A decision aid for the port placement problem in robot-assisted hysterectomy

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Laparoscopic, Endoscopic and Robotic Surgery 2023年第2期6卷 43-56页

作者： Mohammad R.Maddah Jean-Marc Classe Isabelle Jaffre Keith A.Watson Katherine S.Lin Damien Chablat Cedric Dumas Caroline G.L.Cao Department of Biomedical Industrialand Human Factors EngineeringWright State University3640 Colonel Glenn HwyDaytonOHUSA Department of Automatique Productique et InformatiqueIMT Atlantique4 Rue Alfred KastlerNantesFrance Laboratoire des Sciences Du Numerique de Nantes(LS2N) 2 Chemin de La Houssini?ereNantesFrance Centre Rene Gauducheau ICO Institut de Cancerologie de L'Ouest BD Professeur Jacques MonodSaint-HerblainFrance Brethen Center for Surgical Advancement in Robotics and Minimally Invasive Surgery Miami Valley HospitalOne Wyoming StreetDaytonOHUSA Department of Computer Science IMT AtlantiqueTechnopole Brest-IroiseBrestFrance Laboratoire des Sciences et Techniques de L'information de La Communication et de La Connaissance(Lab-STICC) Technopole Brest-IroiseBrestFrance Future Industries Institute STEMUniversity of South AustraliaMawson Lakes BlvdMawson LakesAustralia

Objective:In robot-assisted minimally invasive surgery,proper port positioning ensures that surgical tools have adequate access to the surgical site and avoids mid-surgery collisions of robotic *** date,several guidelines have been proposed for more accurate port ***,challenges remain due to variations in patient morphology,anatomy,and,in particular,organ displacement due to insuf-flation in certain laparoscopic *** objective of this study was to design and develop a de-cision aid for optimal port placement in robot-assisted hysterectomy that accounts for patient variability and organ displacement due to ***:Three components were constructed:a robot model,a patient-specific model,and an opti-mization *** three components were integrated,and the system was verified using four different patients who underwent robotic *** verified,two expert surgeons were asked to evaluate the decision aid for face and construct validity.A usability test was conducted using a torso phantom with target organs located in three different *** expert surgeons performed a simulated hysterectomy task with and without the decision aid to evaluate performance and ***:The optimization algorithm was sensitive to individual differences in anatomy in the four *** surgeons successfully established face and construct *** test results showed a 28%-40% reduction in time to task completion with the optimized ports compared to expert-determined port locations without using the decision ***:The decision aid,based on an individualized patient-specific model,robot model,and optimization algorithm,was shown to be effective at addressing the challenges of displaced organs due to insufflation in robot-assisted *** face and construct validity of the decision aid was established by expert surgeons,while efficiency gains in task performance were demonstrated experim

关键词： Optimization Planning tool Patient model Robotic hysterectomy

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Ferroelectrets:Heterogenous polymer electrets with high piezoelectric sensitivity for transducers

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Journal of Advanced Dielectrics 2023年第4期13卷 3-26页

作者： Xunlin Qiu Peng Fang Axel Mellinger Ruy Alberto Pisani Altafim Werner Wirges Gunnar Gidion Dmitry Rychkov Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237P.R.China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055P.R.China Department of Physics Central Michigan University 223 Dow Science ComplexMount PleasantMI 48859USA Computer Systems Department Informatics Center Federal University of ParáıbaJoão Pessoa-PBBrazil Department of Physics and Astronomy University of Potsdam PotsdamBrandenburgGermany Laboratory for Electrical Instrumentation Department of Microsystems Engineering-IMTEK University of FreiburgGeorges-Köhler-Allee 106Freiburg 79110Germany Technology and Study Center Weissenburg Deggendorf Institute of Technology WeissenburgBavariaGermany

Nowadays,the demand for advanced functional materials in transducer technology is growing *** materials transform mechanical variables(displacement or force)into electrical signals(charge or voltage)and vice *** are interesting from both fundamental and application points of ***(also called piezoelectrets)are a relatively young group of piezo-,pyro-and ferroelectric *** exhibit ferroic behavior phenomenologically undistinguishable from that of traditional ferroelectrics,although the materials per se are essentially non-polar space-charge electrets with artificial macroscopic dipoles(i.e.,internally charged cavities).A lot of work has been done on ferroelectrets and their applications up to *** this paper,we review and discuss mostly the work done at University of Potsdam on the research and development of *** will,however,also mention important results from other teams,and prospect the challenges and future progress trend of the field of ferroelectret research.

关键词： Ferroelectret piezoelectret space-charge electret transducer advanced functional materials

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Designing Troubleshooting Support Cards for Novice End-User Developers of Physical Computing Prototypes 1

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7th International Symposium on End-User Development, IS-EUD 2019

作者： Booth, Tracey Bird, Jon Stumpf, Simone Jones, Sara Centre for Human-Computer Interaction Design City University of London London United Kingdom Centre for Creativity in Professional Practice City University of London London United Kingdom Department of Computer Science University of Bristol Bristol United Kingdom

ISBN: (数字)9783030247812

ISBN: (纸本)9783030247805

Previous work has shown that end-user developers (EUDs) find diagnosing and fixing circuit bugs in physical computing prototypes challenging. This paper reports on the design of a card deck to support troubleshooting by novice EUDs. The deck provides EUDs with ideas for different troubleshooting tactics and guides them in their use by encouraging reflection to help build EUDs’ troubleshooting knowledge and skill. We describe the design process and the resulting card deck. Our work contributes a new way of supporting EUDs in troubleshooting physical computing prototypes. © 2019, Springer Nature Switzerland AG.

关键词： Design

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Workshop Program on Disaster Prevention and Mitigation for Young Generation Utilizing Disaster Information Tweeting and Mapping System 1

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4th IFIP TC 5 DCITDRR International Conference on Information Technology in Disaster Risk Reduction, ITDRR 2019

作者： Uchida, Osamu Tajima, Sachi Kajita, Yoshitaka Utsu, Keisuke Murakami, Yuji Yamada, Sanetoshi Department of Human and Information Science Tokai University HiratsukaKanagawa Japan Center for Liberal Arts Tokai University HiratsukaKanagawa Japan Department of Civil Engineering Tokai University HiratsukaKanagawa Japan Department of Communication and Network Engineering Tokai University MinatoTokyo Japan Department of Electrical Engineering and Computer Science Tokai University Kumamoto Japan Research and Information Center Tokai University HiratsukaKanagawa Japan

ISBN: (数字)9783030489397

ISBN: (纸本)9783030489380

Various large-scale natural disasters occur every year in Japan. Then, various disaster education is conducted at schools and in local communities for alleviating the damage caused by such natural disasters. However, according to various surveys, disaster prevention awareness among young people is known to be lower than in other age groups in Japan. Therefore, we worked with Kanagawa Prefecture, Japan, on establishing a program of workshops on disaster prevention and mitigation utilizing ICT equipment such as smartphones or tablet PCs to raise awareness of disaster prevention and mitigation among young people. The program is based on town watching and group discussion using DITS and DIMS (disaster information tweeting and mapping system) proposed in the previous studies. In this paper, we introduce the contents of the developed workshop program using DITS/DIMS. Moreover, we report on the results of the workshops for disaster prevention and mitigation using this program at several junior high and high schools. The results of the post-questionnaire show that many participants of the workshops had a positive impression on the disaster prevention and mitigation workshops using DITS/DIMS. © 2020, IFIP International Federation for Information Processing.

关键词： Disasters

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Correction: AI content detection in the emerging information ecosystem: new obligations for media and tech companies

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Ethics and Information Technology 2024年第4期26卷 1-2页

作者： Knott, Alistair Pedreschi, Dino Jitsuzumi, Toshiya Leavy, Susan Eyers, David Chakraborti, Tapabrata Trotman, Andrew Sundareswaran, Sundar Baeza-Yates, Ricardo Biecek, Przemyslaw Weller, Adrian Teal, Paul D. Basu, Subhadip Haklidir, Mehmet Morini, Virginia Russell, Stuart Bengio, Yoshua Social Media Governance Project Global Partnership on AI Montreal Canada School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand University of Pisa Pisa Italy Chuo University Tokyo Japan Insight SFI Research Centre for Data Analytics School of Information and Communication University College Dublin Dublin Ireland School of Computing University of Otago Dunedin New Zealand Alan Turing Institute London United Kingdom University College London London United Kingdom Institute for Experiential AI Northeastern University Silicon Valley USA Warsaw University of Technology Warsaw Poland University of Cambridge Cambridge United Kingdom Computer Science and Engineering Department Jadavpur University Kolkata India Artificial Intelligence Institute Tubitak Bilgem Gebze Türkiye Center for Human-Compatible AI UC Berkeley Berkeley USA Mila - Quebec AI Institute Montreal Canada University of Montreal Montreal Canada

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Are 1,000 features worth a picture? Combining crowdsourcing and face recognition to identify Civil War soldiers

Are 1,000 features worth a picture? Combining crowdsourcing ...

引用

2018 HCOMP Works in Progress and Demonstration Papers, HCOMP WIP and DEMO 2018

作者： Mohanty, Vikram Thames, David Luther, Kurt Department of Computer Science Center for Human-Computer Interaction Virginia Tech ArlingtonVA United States

We introduce a web-based platform called Civil War Photo Sleuth for helping users identify unknown soldiers in portraits from the American Civil War era. Our system employs a novel person identification pipeline by leveraging the complementary strengths of crowdsourced human vision and face recognition algorithms. Copyright © 2018 for this paper by its authors.

关键词： Face recognition

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Designing with Genes in Early Childhood: An exploratory user study of the tangible CRISPEE technology

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International Journal of Child-computer interaction 2020年 26卷

作者： Strawhacker, Amanda Verish, Clarissa Shaer, Orit Bers, Marina Umaschi Department of Child Study and Human Development Tufts University 105 College Ave Medford 02155 MA United States Department of Computer Science Human-Computer Interaction Lab 106 Central Street Wellesley 02481 MA United States

Biodesign, a speculative and creative offshoot from the field of bioengineering, is an area of STEM that is growing in popularity in education settings, primarily because of its unique interdisciplinary lens that connects STEM disciplinary knowledge and creative design practices. Although this trend is currently limited to middle school, high school, and higher education, prior research suggests that children 5 years and older, may yield long-term gains from exploring developmentally-appropriate concepts from novel STEM fields. Although there is little research on educational technologies or resources to support young children's curiosity and learning in this novel domain, some research suggests that young children may already be forming preconceptions about genetics and biology (e.g., from popular media). Tangible technologies, which provide children qualitatively new, developmentally appropriate ways to engage with ideas and techniques, have been shown to support children's engagement with foundational ideas relevant to biodesign, including the engineering design process. By applying developmentally appropriate constraints to our technology development (e.g., through frameworks such as the Positive Technological Development), the research team developed and evaluated a novel tangible technology called CRISPEE to introduce young children to concepts of biology and engineering. This article describes an experimental pilot study to investigate (1) how young children interact with the CRISPEE technological prototype, and (2) what prior knowledge the average child might bring to an educational biodesign activity. Implications for ongoing technology development and developmentally appropriate learning goals are discussed. © 2020 Elsevier B.V.

关键词： Biodesign Child–computer interaction Early childhood Novel technology STEM education

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Global guidance network for breast lesion segmentation in ultrasound images

arXiv

引用

arXiv 2021年

作者： Xue, Cheng Zhu, Lei Fu, Huazhu Hu, Xiaowei Li, Xiaomeng Zhang, Hai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Applied Mathematics and Theoretical Physics University of Cambridge Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen People's Hospital Second Clinical College of Jinan University First Affiliated Hospital of Southern University of Science and Technology Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks. © 2021, CC BY.

关键词： Deep neural networks

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