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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-dept. 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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A Lightweight Performance Enhancing Proxy for Evolved Protocols and Networks

A Lightweight Performance Enhancing Proxy for Evolved Protoc...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Zsolt Krämer Sándor Molnár Marcus Pieskä Attila Mihály Dept. of Telecomm. and Media Informatics Budapest University of Technology and Economics Hungary Dept. of Mathematics and Computer Science Karlstad University Sweden Traffic Analysis and Network Performance Laboratory Ericsson Research Hungary

ISBN: (数字)9781728163390

ISBN: (纸本)9781728163406

In this paper we present the design and prototype implementation of a multi-domain congestion control framework based on a non-ossifying Lightweight Performance Enhancing Proxy. We demonstrate that our solution can be both used for evolved protocols such as end-to-end encrypted QUIC and for evolved cellular access networks like 5G. We also show the performance of our Multi-Domain Congestion Control algorithm by a simulation study. Moreover, we expose its behavior in realistic use cases like sudden capacity changes in 5G mmWave cellular networks. Our results highlight that significant performance improvements can be achieved by cooperative traffic management frameworks in protocols and networks of the future.

关键词： Servers 5G mobile communication Prototypes Cryptography Protocols Middleboxes Microsoft Windows

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A Hybrid Approach to Parallel Pattern Discovery in C++

A Hybrid Approach to Parallel Pattern Discovery in C++

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： C. Brown V. Janjic A. Barwell J. Thomson R. Castañeda Lozano M. Cole B. Franke J.D. Garcia-Sanchez D. Del Rio Astorga K. MacKenzie School of Computer Science University of St Andrews St Andrews UK School of Informatics University of Edinburgh Edinburgh UK Computer Science and Engineering Dept. Universidad Carlos III de Madrid Madrid Spain IOHK

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Parallel pattern libraries offer a strong combination of abstraction and performance. However, discovering places in sequential code where parallel patterns should be introduced is still highly non-trivial, often requiring expert manual analysis and profiling. We present a hybrid discovery technique to detect instances of parallel patterns in sequential code. This employs both static and dynamic trace-based analysis, together with hotspot detection. We evaluate our pattern discovery mechanism on a number of representative benchmarks. We evaluate the performance of the resulting parallelised benchmarks on a 24-core parallel machine.

关键词： Pattern matching Benchmark testing Parallel processing Detectors Shape C++ languages Instruments

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Accelerating Cross-Matching Operation of Geospatial Datasets using a CPU-GPU Hybrid Platform

Accelerating Cross-Matching Operation of Geospatial Datasets...

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2018 IEEE International Conference on Big Data, Big Data 2018

作者： Gao, Chao Baig, Furqan Vo, Hoang Zhu, Yangyang Wang, Fusheng Dept. of Computer Science United States Dept. of Biomedical Informatics Stony Brook University United States

ISBN: (纸本)9781538650356

Spatial cross-matching operation over geospatial polygonal datasets is important to a variety of GIS applications. However, it involves extensive computation cost associated with intersection and union of a geospatial polygon pair from large scale datasets. This mandates for exploration of parallel computing capabilities such as GPU to increase the efficiency of such operations. In this paper, we present a CPU-GPU hybrid platform to accelerate the cross-matching operation of geospatial datasets. The computing tasks are dynamically scheduled to be executed either on CPU or GPU. To accommodate geospatial datasets processing on GPU using pixelization approach, we convert the floating point-valued vertices into integer-valued vertices with an adaptive scaling factor as a function of area of minimum bounding box. We test our framework over Natural Earth Dataset and achieve 10x speedup on NVIDIA GeForce GTX750 GPU and 14x speedup on Tesla K80 GPU over 280,000 polygon pairs in one tile and 400 tiles in total. We also investigate the effects of input data size to the IO / computation ratio and note that the sufficiently large input data size is required to better utilize the computing power of GPU. Finally, with comparison between two GPUs, our results demonstrate that the efficient cross-matching comparison can be achieved with a cost-effective GPU. © 2018 IEEE.

关键词： Graphics processing unit

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Quantifying the quality of indoor maps 4

Quantifying the quality of indoor maps

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4th ISPRS Geospatial Week 2019

作者： Assali, M. Pipelidis, G. Podolskiy, V. Iwaszczuk, D. Heinen, L. Gerndt, M. Dept. of Computer Architecture and Parallel Systems Dept. of Informatics Technical University of Munich Munich Germany Software and Systems Engineering Research Group Dept. of Informatics Technical University of Munich Munich Germany Remote Sensing and Image Analysis Group Dept. of Civil and Environmental Engineering Science Technische Universität Darmstadt Germany BMW Group IT Munich Germany

Indoor maps are required for multiple applications, such as, navigation, building maintenance and robotics. One of common methods for map generation is laser scanning. In such maps, not only geometry of the map is of interest, but also its quality. This study aims at developing methods for real-time generation of indoor maps using features extracted from pointclouds obtained by a robot with their simultaneous quality assessment. We investigate, how this quality can be quantified for feature-based maps. First, we introduce a method for modeling 2D maps into 3D models that enable their usage for localization. Second, we review and evaluate a number of algorithms that can enable us to address features in a map. Hence, we enable the generation of objects from a pointcloud that has been sensed. Finally, we study several aspects of the map quality and we formalize them into metrics that can be applied to quantify their quality. © Authors 2019.

关键词： Robots

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Evaluation of eeg connectivity network measures based features in schizophrenia classification

Evaluation of eeg connectivity network measures based featur...

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2019 International Conference on biomedical Innovations and Applications, BIA 2019

作者： Bougou, Vasiliki Mporas, Iosif Schirmer, Pascal Ganchev, Todor KU Leuven Dept. of Biomedical Sciences Leuven Belgium University of Hertfordshire Sch. of Eng/ring and Computer Science Hatfield United Kingdom Technical University of Varna Dept. of Electronics Varna Bulgaria

ISBN: (纸本)9781728147543

In this paper an architecture for the classification of Schizophrenia using EEG-based brain connectivity is proposed. Functional and effective networks were constructed from the EEG using a variety of connectivity measures and with graph theory metrics complex network features were extracted. Several classification algorithms were used for the evaluation of the architecture. Promising results were observed when using connectivity measures that also capture directionality properties of the network. The best classification accuracy was 82.36% and was achieved by Random Forest classifier with Direct Transfer Function as a connectivity measure. © 2019 IEEE.

关键词： Classification (of information)

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

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2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

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RNNs on Monitoring Physical Activity Energy Expenditure in Older People

arXiv

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arXiv 2020年

作者： Paraschiakos, Stylianos de Sá, Cláudio Rebelo Okai, Jeremiah Eline Slagboom, P. Beekman, Marian Knobbe, Arno Leiden Institute of Advanced Computer Science Universiteit Leiden Netherlands Molecular Epidemiology Dept. Biomedical Data Science LUMC Leiden Netherlands Data Science research group University of Twente Enschede Netherlands

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a monitoring environment, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the Recurrent Neural Network (RNN). To train the RNN for an elderly population, we used the Growing Old Together Validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. In this paper, we describe our efforts to go beyond the standard facilities of a GRU-based RNN, with the aim of achieving accuracy surpassing the state of the art. These efforts include switching aggregation function from mean to dispersion measures (SD, IQR, ...), combining temporal and static data (person-specific details such as age, weight, BMI) and adding symbolic activity data as predicted by a previously trained ML model. The resulting architecture manages to increase its performance by approximatelly 10% while decreasing training input by a factor of 10. It can thus be employed to investigate associations

关键词： Recurrent neural networks

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Crossmodal language grounding in an embodied neurocognitive model

arXiv

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arXiv 2020年

作者： Heinrich, Stefan Yao, Yuan Hinz, Tobias Liu, Zhiyuan Hummel, Thomas Kerzel, Matthias Weber, Cornelius Wermter, Stefan Knowledge Technology Group Dept. of Informatics Universität Hamburg Germany Natural Language Processing Lab Dept. of Computer Science and Technology Tsinghua University China International Research Center for Neurointelligence University of Tokyo Japan

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterising the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilise the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children’s playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organise hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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madmpy: A Python library for creating and validating Data Management Plans

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SoftwareX 2025年 31卷

作者： Alberto Ballesteros-Rodríguez Miguel-Ángel Sicilia Elena García-Barriocanal Biomedical Data Science and Engineering Research Group Ramón y Cajal University Hospital and Ramón y Cajal Health Research Institute (IRYCIS) 28034 Madrid Spain Universidad de Alcalá Computer Science dept. Polytechnic Building Ctra. Barcelona km. 33.6 28871 Alcalá de Henares (Madrid) Spain

Data Management Plans (DMPs) are documents that describe the data used and produced during the course of research projects. Machine-actionable DMPs (maDMPs) are plans written in computer-readable formats. They are designed to support the automation of data-generation processes in scientific research. The madmpy Python package validates maDMPs that follow any version of the RDA DMP Common Standard. These plans can be written in JSON format or built programmatically. It also supports institution- or domain-specific extensions and additional validations that adhere to the standard. The library serves as a building block for research data engineering workflows. It promotes data management and accountability through the use of structured DMPs.

关键词： Data Management Plan Machine-actionable Data validation FAIR

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