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Ethical Aspects of Computational Modelling in science, Decision Support and Communication 15th

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Ethical Aspects of Computational Modelling in Science, Decis...

15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

Pirate: A blockchain-based secure framework of distributed machine learning in 5G networks

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arXiv 2019年

作者： Zhou, Sicong Huang, Huawei Chen, Wuhui Zheng, Zibin Guo, Song School of Data and Computer Science Sun Yat-Sen University China Department of Computing Hong Kong Polytechnic University

—In the fifth-generation (5G) networks and the beyond, communication latency and network bandwidth will be no more bottleneck to mobile users. Thus, almost every mobile device can participate in the distributed learning. That is, the availability issue of distributed learning can be eliminated. However, the model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients amongst multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE. A case-study shows how the proposed PIRATE contributes to the distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine-resilient learning framework. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

A foundation model for human-AI collaboration in medical literature mining

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arXiv 2025年

作者： Wang, Zifeng Cao, Lang Jin, Qiao Chan, Joey Wan, Nicholas Afzali, Behdad Cho, Hyun-Jin Choi, Chang-In Emamverdi, Mehdi Gill, Manjot K. Kim, Sun-Hyung Li, Yijia Liu, Yi Ong, Hanley Rousseau, Justin Sheikh, Irfan Wei, Jenny J. Xu, Ziyang Zallek, Christopher M. Kim, Kyungsang Peng, Yifan Lu, Zhiyong Sun, Jimeng School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL United States Division of Intramural Research National Library of Medicine National Institutes of Health BethesdaMD United States Kidney Diseases Branch National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health BethesdaMD United States Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States National Eye Institute National Institutes of Health BethesdaMD United States Department of Ophthalmology Northwestern University Feinberg School of Medicine ChicagoIL United States Division of Pulmonary and Critical Care Medicine Department of Medicine Chungbuk National University Hospital Chungbuk National University College of Medicine Cheongju Korea Republic of Department of Medicine University of Pittsburgh Medical Center PittsburghPA United States Department of Medicine Weill Cornell Medicine New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Neurology UT Southwestern Medical Center DallasTX United States Department of Dermatology University of Washington SeattleWA United States Department of Dermatology NYU Langone Health New YorkNY United States OSF HealthCare Illinois Neurological Institute PeoriaIL United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Carle Illinois College of Medicine University of Illinois Urbana-Champaign UrbanaIL United States

Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature. The model is trained on 633,759 instruction data points in LEADSInstruct, curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. We showed that LEADS demonstrates consistent improvements over four cutting-edge generic large language models (LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing supportive references following expert requests, streamlining processes while maintaining high-quality results. A study with 16 clinicians and medical researchers from 14 different institutions revealed that experts collaborating with LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in study selection, with a time savings of 22.6%. In data extraction tasks, experts using LEADS achieved an accuracy of 0.85 versus 0.80 without using LEADS, alongside a 26.9% time savings. These findings highlight the potential of specialized medical literature foundation models to outperform generic models, delivering significant quality and efficiency benefits when integrated into expert workflows for medical literature mining. © 2025, CC BY.

关键词： Diagnosis

Segmentation-based method combined with dynamic programming for brain midline delineation

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arXiv 2020年

作者： Wang, Shen Liang, Kongming Pan, Chengwei Ye, Chuyang Li, Xiuli Liu, Feng Yu, Yizhou Wang, Yizhou Center for Data Science Peking University Beijing China Deepwise AI Lab Beijing China Computer Science Department School of EECS Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Advanced Institute of Information Technology Peking University Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI). The automated midline delineation not only improves the assessment and clinical decision making for patients with stroke symptoms or head trauma but also reduces the time of diagnosis. Nevertheless, most of the previous methods model the midline by localizing the anatomical points, which are hard to detect or even missing in severe cases. In this paper, we formulate the brain midline delineation as a segmentation task and propose a three-stage framework. The proposed framework firstly aligns an input CT image into the standard space. Then, the aligned image is processed by a midline detection network (MD-Net) integrated with the CoordConv Layer and Cascade AtrousCconv Module to obtain the probability map. Finally, we formulate the optimal midline selection as a pathfinding problem to solve the problem of the discontinuity of midline delineation. Experimental results show that our proposed framework can achieve superior performance on one in-house dataset and one public dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamic programming

Optimizing data Placement on Hierarchical Storage Architecture via Machine Learning 16th

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Optimizing Data Placement on Hierarchical Storage Architectu...

16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Cheng, Peng Lu, Yutong Du, Yunfei Chen, Zhiguang Liu, Yang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Changsha China National Supercomputer Center in Guangzhou School of Data and Computer Science Sun Yat-Sen University Guangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783030307080

As storage hierarchies are getting deeper on modern high-performance computing systems, intelligent data placement strategies that can choose the optimal storage tier dynamically is the key to realize the potential of hierarchical storage architecture. However, providing a general solution that can be applied in different storage architectures and diverse applications is challenging. In this paper, we propose adaptive storage learner (ASL), which explores the idea of using machine learning techniques to mine the relationship between data placement strategies and I/O performance under varied workflow characteristics and system status, and uses the learned model to choose the optimal storage tier intelligently. We implement a prototype and integrate it into an existing data management system. Empirical comparison based on real scientific workflows tests shows that ASL is capable of combining workflow characteristics and real-time system status to make optimal data placement decisions. © 2019, IFIP International Federation for Information Processing.

关键词： Real time systems

Correction: Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

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Human-Centric Intelligent Systems 2023年第2期3卷 160-160页

作者： Munawar, Hafiz Suliman Hammad, Ahmed W. A. Waller, S. Travis Shahzad, Danish Islam, Md. Rafiqul School of the Built Environment University of New South Wales Sydney Australia Lighthouse Professor and Chair of Transport Modelling and Simulation “Friedrich List” Faculty of Transport and Traffic Sciences Technische Universität Dresden Dresden Germany Department of Visual Computing University of Saarland Saarbrücken Germany Data Science Institute (DSI) University of Technology Sydney (UTS) Sydney Australia

Cross-modal data fusion and deep learning techniques enabled non-destructive fish vitality recognition during cryogenic waterless live transport

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Biosystems Engineering 2025年 257卷

作者： Zhu, Yanfei Zhang, Wenguan Zhang, Yongjun Zhang, Xiaoshuan Zhang, Mengjie College of Engineering China Agricultural University Beijing100083 China College of Animal Science and Technology China Agricultural University Beijing100193 China Sanya Research Institute of China Agricultural University Sanya572000 China School of Information Engineering Shandong Youth University of Political Science Jinan250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan250103 China

Cryogenic waterless live transport (CWLT) is widely regarded as a green, efficient and low-carbon transportation strategy, which can ensure a certain degree of fish survival rates, enhance water resource utilisation and reduce transportation costs. This study focuses on the pearl gentian grouper, integrating multi-modal sensors, deep learning and cross-modal data fusion techniques for non-destructive vitality recognition during the live transport process. Changes in fish surface and physiological characteristics were captured based on multi-modal sensors. A cross-modal vitality recognition network (CmVRNet) was developed based on cross-modal data fusion and multi-input deep learning techniques. The enhanced whale optimisation algorithm based on multi-strategy fusion (EWOAm) was developed to optimise hyperparameters of the model (EWOAm-CmVRNet). The results showed that: (1) Cluster analysis classified physiological status of the grouper into four categories: high vitality (HV), medium vitality (MV), weak vitality (WV) and near death (ND), with significant differences between physiological data of fish with different vitality levels but the same size (P © 2025 IAgrE

关键词： Physiological models

An effective POS-CNN-based adaptive model for classifying brain tumour from MRI images

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AIP Conference Proceedings 2023年第1期2914卷

作者： Sajeev Ram Arumugam L. Thanga Mariappan K. Makanyadevi R. Balakrishna Sankar Ganesh Karuppasamy Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Smart Computing School of Information Technology and Engineering Vellore Institute of Technology vellore India Department of Computer science and Engineering M. Kumarasamy College of Engineering Karur India Department of Artificial Intelligence and Data Science Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

One of the crucial reasons for death in people is a brain tumour. It will likely develop into malignancy if not appropriately and promptly treated. As a result, early brain tumour diagnosis is a crucial requirement. The Convolutional Neural Network and Particle Swarm Optimization are combined to construct a proposed framework for Brain Tumour Detection from MRI Images. PSO is used to increase segmentation precision and to train the proposed system using CNN. Along with PSO, Scale Invariant Feature Transform (SIFT) is employed as a feature descriptor to improve the system’s classification accuracy. Brain tumour MRI images from the BraTS 2018 dataset were used as input sets for this research. A diagnosis accuracy of 98.76 % was obtained from the results. The proposed technique has better accuracy when the assessment features of the proposed system are evaluated with a few existing approaches.

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The First GECAM Observation Results on Terrestrial Gamma-ray Flashes and Terrestrial Electron Beams

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arXiv 2023年

作者： Zhao, Y. Liu, J.C. Xiong, S.L. Xue, W.C. Yi, Q.B. Lu, G.P. Xu, W. Lyu, F.C. Sun, J.C. Peng, W.X. Zheng, C. Zhang, Y.Q. Cai, C. Xiao, S. Xie, S.L. Wang, C.W. Tan, W.J. An, Z.H. Chen, G. Du, Y.Q. Huang, Y. Gao, M. Gong, K. Guo, D.Y. He, J.J. Li, B. Li, G. Li, X.Q. Li, X.B. Liao, J.Y. Liang, J. Liang, X.H. Liu, Y.Q. Ma, X. Qiao, R. Song, L.M. Song, X.Y. Sun, X.L. Wang, J. Wang, J.Z. Wang, P. Wen, X.Y. Wu, H. Xu, Y.B. Yang, S. Zhang, B.X. Zhang, D.L. Zhang, F. Zhang, P. Zhang, H.M. Zhang, Z. Zhao, X.Y. Zheng, S.J. Zhang, K.K. Han, X.B. Wu, H.Y. Hu, T. Geng, H. Zhang, H.B. Lu, F.J. Zhang, S.N. Yu, H. Department of Astronomy Beijing Normal University Beijing Beijing100875 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing Beijing100049 China University of Chinese Academy of Sciences Beijing Beijing100049 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China School of Earth and Space Sciences University of Science and Technology of China Anhui Hefei230026 China Electronic Information School Wuhan University Hubei Wuhan430072 China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences Jiangsu Nanjing210000 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University GuiZhou Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University GuiZhou Guiyang550001 China Institute of Astrophysics Central China Normal University HuBei Wuhan430079 China School of Computing and Artificial Intelligence Southwest Jiaotong University SiChuan Chengdu611756 China College of Electronic and Information Engineering Tongji University Shanghai Shanghai201804 China Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai Shanghai201304 China National Space Science Center Chinese Academy of Sciences Beijing Beijing100190 China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing Beijing100029 China

Gravitational-wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a space-borne instrument dedicated to monitoring high-energy transients, including Terrestrial Gamma-ray Flashes (TGFs) and Terrestrial Electron Beams (TEBs). We implemented a TGF/TEB search algorithm for GECAM, with which 147 bright TGFs, 2 typical TEBs and 2 special TEB-like events are identified during an effective observation time of ∼9 months. We show that, with gamma-ray and charged particle detectors, GECAM can effectively identify and distinguish TGFs and TEBs, and measure their temporal and spectral properties in detail. A very high TGF-lightning association rate of ∼80% is obtained between GECAM and GLD360 in east Asia region. Copyright © 2023, The Authors. All rights reserved.

关键词： Electron beams