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arXiv 2023年

作者： El Nahhas, Omar S.M. van Treeck, Marko Wölflein, Georg Unger, Michaela Ligero, Marta Lenz, Tim Wagner, Sophia J. Hewitt, Katherine J. Khader, Firas Foersch, Sebastian Truhn, Daniel Kather, Jakob Nikolas Else Kroener Fresenius Center for Digital Health Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany School of Computer Science University of St Andrews St Andrews United Kingdom Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Institute of Pathology University Medical Center Mainz Mainz Germany Pathology & Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom University Hospital Heidelberg Heidelberg Germany Department of Medicine 1 University Hospital Faculty of Medicine Carl Gustav Carus Technische Universität Dresden Dresden Germany

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic- and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages which have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of MSI-high tumors. Moreover, we provide an open-source codebase which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge. © 2023, CC BY.

关键词： Pathology

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Geometric analysis of fast-slow PDEs with fold singularities via Galerkin discretisation

arXiv

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arXiv 2022年

作者： Engel, Maximilian Hummel, Felix Kuehn, Christian Popović, Nikola Ptashnyk, Mariya Zacharis, Thomas Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Department of Mathematics Technical University of Munich Boltzmannstraße 3 Garching bei München85748 Germany Munich Data Science Institute Walther-von-Dyck Straße 10 Garching bei München85748 Germany Complexity Science Hub Vienna Josefstädter Straße 39 Vienna1080 Austria School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh James Clerk Maxwell Building King’s Buildings Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom School of Mathematical & Computer Sciences Maxwell Institute for Mathematical Sciences Heriot-Watt University EdinburghEH14 4AP United Kingdom Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands

We study a singularly perturbed fast-slow system of two partial differential equations of reaction-diffusion type on a bounded domain via Galerkin discretisation. We assume that the reaction kinetics in the fast variable realise a generic fold singularity, whereas the slow variable takes the role of a dynamic bifurcation parameter, thus extending the classical analysis of the singularly perturbed fold. Our approach combines a spectral Galerkin discretisation with techniques from geometric singular perturbation theory which are applied to the resulting high-dimensional systems of ordinary differential equations. In particular, we show the existence of invariant slow manifolds in the phase space of the original system of PDEs away from the fold singularity, while the passage past the singularity of the Galerkin manifolds obtained after discretisation is described by geometric desingularisation, or blow-up. Finally, we discuss the relation between these Galerkin manifolds and the underlying slow *** Codes 35B25, 35K57, 34Cxx, 34D15, 34E15, 37G10 © 2022, CC BY.

关键词： Perturbation techniques

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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Improved small gain conditions for input-to-state stability with respect to measurement functions: Discrete time networked system

Improved small gain conditions for input-to-state stability ...

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IEEE Conference on Decision and Control

作者： Yuanqiu Mo Changbin Yu Soura Dasgupta Westlake Institute for Advanced Study Westlake University Hangzhou China College of Artificial Intelligence and Big Data Shandong First Medical University & Shandong Academy of Medical Sciences Jinan China Institute for Intelligent Robots Fudan University Shanghai China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks China Faculty of Engineering & the Built Environment University of Johannes-Burg Johannesburg South Africa University of Iowa Iowa City Iowa USA

ISBN: (纸本)9781665436601

In this paper we study input-to-state stability with respect to measurement functions for discrete time networked systems. In such a networked system, the trajectory of each subsystem is affected by another in each time step, and this constraining system may change from step to step. We derive small gain conditions of input-to-state stability with respect to measurement functions for this type of discrete time networked system relying on the construction of dissipative-form finite time Lyapunov function. These conditions loosen those presented in [1] and [2]. Finally, we demonstrate the applicability of our results by applying them to the stability analysis of a distributed graph algorithm which does not satisfy the conditions of [1] and [2].

关键词： Network topology Conferences Gain measurement Stability analysis Time measurement Trajectory Topology

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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UAV Communication Channel Measurement, Modeling, and Application

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Journal of Communications and Information networks 2019年第4期4卷 32-43页

作者： Xiang Cheng Yiran Li Lin Bai State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shenzhen Research Institute of Big Data Shenzhen 518000China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

As unmanned aerial vehicles(UAV)deploy-ment is shifting from military to civilian sectors,and in particular as a means of facilitating more flexible and versatile wireless communications,the UAV channels that were previously not well understood are attracting increasing research and *** the key component facilitating the UAV-aided communications,UAV channel characteristics and modeling are of critical importance to the designed UAV system to achieve satisfactory *** this article,we will provide a comprehensive overview and the future perspective of envisioned UAV communication system benefits,the existing and needed UAV channel measurements and modeling approaches,together with the new viewpoint of UAV channel applications in different communication and networking *** existing work and future directions are extensively covered.

关键词： UAV channel measurements modeling application future challenges

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Emerging Technologies for Next Generation Remote Health Care and Assisted Living

TechRxiv

引用

TechRxiv 2022年

作者： Ahmad, Ijaz Asghar, M. Zeeshan Kumar, Tanesh Li, Gaolei Manzoor, Ahsan Mikhaylov, Konstantin Shah, Syed Attique Höyhtyä, Marko Reponen, Jarmo Huusko, Jyrki Harjula, Erkki The VTT Technical Research Centre of Finland Espoo02150 Finland Navigil Espoo Finland The University of Oulu Oulu Finland The Institute of Cyber Security Shanghai Jiao Tong University Shanghai200240 China Rovio Entertainment Ltd. Espoo Finland The Data Systems Group Institute of Computer Science University of Tartu Delta Centre Narva mnt 18-3123 Tartu51009 Estonia The Research Unit of Medical Imaging Physics and Technology University of Oulu Finland The Medical Research Centre Oulu Oulu University Hospital University of Oulu Finland

Remote health care is currently one of the most promising solutions to ensure a high level of treatment outcome, cost-efficiency and sustainability of the healthcare systems worldwide. Even though research on remote health care can be traced back to the early days of the Internet, the recent COVID-19 has necessitated further improvement in existing health care systems with invigorated research on remote health care technologies. In this article we delve into the state-of-the-art research in latest technologies and technological paradigms that play a vital role in enabling the next generation remote health care and assisted living. First the need of using the latest technological developments in the domain of remote health care is briefly discussed. Then the most important technologies and technological paradigms that are crucial in enabling remote health care and assisted living are emphasised. Henceforth, a detailed survey of existing technologies, potential challenges in those technologies, and possible solutions is conducted. Finally, missing research gaps and important future research directions in each enabling technology are brought forth to motivate further research in remote health care. © 2022, CC BY.

关键词： Internet of things

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Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm

Predicting Large-scale Protein-protein Interactions by Extra...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lun Hu Bo-Wei Zhao Shicheng Yang Xin Luo MengChu Zhou Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

Protein-protein interactions are of great significance for us to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technology, the amount of protein-protein interaction data has become so big that most of existing prediction algorithms are no longer applicable. To address this problem, we develop a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, by using MapReduce. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption cased by the huge sequence information of proteins. After that, the procedure of CoFex is modified by following the paradigm of MapReduce such that the prediction task can be completed in a distributed manner, thus fulfilling the demanding requirements of large-scale protein-protein interaction prediction. A series of experiments have been conducted to evaluate the performance of the proposed distributed framework in terms of both efficiency and effectiveness. Experimental results demonstrate that the proposed framework can considerably improve the efficiency of CoFex by achieving more than two-orders-of-magnitude improvement in computational efficiency while retaining a comparable level of accuracy.

关键词： Proteins Memory management Genomics Predictive models Prediction algorithms data structures Computational efficiency

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Scientific Workflow Scheduling in Mobile Edge Computing Based on a Discrete Butterfly Optimization Algorithm

Research Square

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Research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and computer engineering Cihan university-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Sulaimani Polytechnic University Technical College of Informatics Computer Networks Department Sulaymaniyah Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of computer science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran

Mobile Edge Computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of the Internet of things (IoT) networks. However, MECs contain limited resources, and they should be managed effectively to improve resource utilization. Workflow scheduling is a process that tries to map the workflow tasks to the most proper set of computing resources regarding some objectives. For this purpose, this paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight to improve its convergence speed and prevent the local optima problem. Then, DBOA is applied for DVFS-based data-intensive workflow scheduling and data placement in MEC environments. This scheme also employs the HEFT algorithm's task prioritization method to find the task execution order in the scientific workflows. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that this method can outperform other algorithms regarding energy consumption, data access overheads, etc. © 2021, CC BY.

关键词： Optimization

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Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS

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Journal of Ambient Intelligence and Humanized Computing 2020年第5期12卷 5471-5477页

作者： Suchithra, M. Baskar, M. Ramkumar, J. Kalyanasundaram, P. Amutha, B. Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Kancheepuram India Department of Wireless Sensor Networks Institute of ECE Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai India

The problem of low rate attack detection has been well studied in different situations. However the methods suffer to achieve higher performance in low rate attack detection. The multimedia transmission is focused on transmitting video and audio which claims higher bandwidth conditions. There exists no such algorithm in detecting low rate attacks for invariant network conditions. To solve this issue, an invariant feature based approach is presented in this paper. The method maintains the network features like the routes, bandwidth conditions and traffic. Based on these features, a set of routes has been identified for each data transmission. Here, low rate attack detection is performed at the reception of any packet and the data transmission is performed using cooperative routing. From the packet features, and the route being followed, the method identifies the class of route, traffic and bandwidth conditions of the route. Using these features, the method computes Network Transmission Support measure. Based on the NTS value, the method performs low rate attack detection and improves the performance.

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