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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

computing and Software for Big science 2024年第1期8卷 1-21页

作者： Allaire, C. Ammendola, R. Aschenauer, E.-C. Balandat, M. Battaglieri, M. Bernauer, J. Bondì, M. Branson, N. Britton, T. Butter, A. Chahrour, I. Chatagnon, P. Cisbani, E. Cline, E.W. Dash, S. Dean, C. Deconinck, W. Deshpande, A. Diefenthaler, M. Ent, R. Fanelli, C. Finger, M. Fol, E. Furletov, S. Gao, Y. Giroux, J. Waduge, N. C. Gunawardhana Hassan, O. Hegde, P.L. Hernández-Pinto, R.J. Blin, A. Hiller Horn, T. Huang, J. Jalotra, A. Jayakodige, D. Joo, B. Junaid, M. Kalantarians, N. Karande, P. Kriesten, B. Elayavalli, R. Kunnawalkam Li, Y. Lin, M. Liu, F. Liuti, S. Matousek, G. McEneaney, M. McSpadden, D. Menzo, T. Miceli, T. Mikuni, V. Montgomery, R. Nachman, B. Nair, R.R. Niestroy, J. Oregon, S. A. Ochoa Oleniacz, J. Osborn, J.D. Paudel, C. Pecar, C. Peng, C. Perdue, G.N. Phelps, W. Purschke, M.L. Rajendran, H. Rajput, K. Ren, Y. Renteria-Estrada, D.F. Richford, D. Roy, B.J. Roy, D. Saini, A. Sato, N. Satogata, T. Sborlini, G. Schram, M. Shih, D. Singh, J. Singh, R. Siodmok, A. Stevens, J. Stone, P. Suarez, L. Suresh, K. Tawfik, A.-N. Acosta, F. Torales Tran, N. Trotta, R. Twagirayezu, F.J. Tyson, R. Volkova, S. Vossen, A. Walter, E. Whiteson, D. Williams, M. Wu, S. Zachariou, N. Zurita, P. Université Paris-Saclay CNRS/IN2P3 IJCLAB Orsay 91400 France Sezione di Roma Tor Vergata INFN Rome 00133 Italy Brookhaven National Lab Upton 11973 NY United States Meta Menlo Park 935025 CA United States National Institute for Nuclear Physics Genoa 16146 Italy Center for Frontiers in Nuclear Science Stony Brook University Stony Brook 11790-3800 NY United States Stony Brook University Stony Brook 11794 NY United States National Institute for Nuclear Physics Catania 95125 Italy Drexel University Philadelphia 19104 PA United States Messiah University Mechanicsburg 17055 PA United States Jefferson Lab Newport News 293606 VA United States Laboratory Nuclear and High-Energy Physics CNRS Paris 75005 France University of Michigan Ann Arbor 48104 MI United States National Institute for Nuclear Physics Roma Rome 00185 Italy Indian Institute of Technology Bombay Maharashtra Mumbai 400076 India Massachusetts Institute of Technology Cambridge 021339 MA United States Department of Physics University of Manitoba Winnipeg R3T 2N2 MB Canada William & amp Charles University Faculty of Mathematics and Physics Prague 18000 Czech Republic Accelerators and Beam Physics Group CERN Geneva 1211 Switzerland University of Regina Regina S4S0A2 SK Canada University of Virginia Charlottesville 22904 VA United States University of Victoria Victoria V8P 5C2 Canada Central University of Karnataka Kalaburagi 585367 India Facultad de Ciencias Físico-Matemáticas Universidad Autónoma de Sinaloa Ciudad Universitaria Sinaloa Culiacán 80000 Mexico Institute for Theoretical Physics TübingenUniversity Tübingen 72076 Germany The Catholic University of America Washington 20064 DC United States University of Jammu Jammu and Kashmir Jammu 180006 India Hampton University Hampton 23668 VA United States Oak Ridge National Laboratory Oak Ridge 37830 TN United States Virginia Union University Richmond 23220 VA United States Lawrence Livermore N

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. © The Author(s) 2024.

关键词： Artificial Intelligence Deep learning EIC ePIC Machine learning Physics QCD

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A review on image processing for fish disease detection

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： S N Pauzi M G Hassan N Yusoff N H Harun A H Abu Bakar B C Kua School of Technology Management and Logistics Universiti Utara Malaysia 06010 Sintok Kedah Malaysia Department of Data Science Universiti Malaysia Kelantan 16100 Kota Bharu Kelantan Malaysia School of Computing Universiti Utara Malaysia 06010 Sintok Kedah Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis 01000 Kangar Perlis Malaysia National Fish Health Research Centre Fisheries Research Institute Department of Fisheries Malaysia 11960 Batu Maung Penang Malaysia

Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance.

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IoT Based Smart Agriculture Monitoring, Automation and Intrusion Detection System

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Journal of Physics: Conference Series 2021年第1期1962卷

作者： Ahmad Faisol Suhaimi Naimah Yaakob Sawsan Ali Saad Khairul Azami Sidek Mohamed Elobaid Elshaikh Alaa K.Y. Dafhalla Ong Bi Lynn Mahathir Almashor Faculty of Electronic Engineering technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Advanced Computing Centre of Excellence (CoE) Universiti malaysia Perlis (UniMAP) Perlis Malaysia Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Kingdom of Saudi Arabia Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia (IIUM) Selangor Malaysia Data61 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia

Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive watering. Moreover, it is difficult for farmers to predict suitable quantities at the appropriate time. Manual monitoring of the crop field may also lead to human error and is potentially risky for rural areas. Farmers may also not be aware of intrusions if they are not on location. Therefore, this project is designed to develop a smart monitoring and automated irrigation system to provide not only efficient water consumption based on specific conditions, but also enables real-time monitoring of the environment. Furthermore, this system prevents damage to plants and reduces the likelihood of plant theft. This system uses NodeMCU ESP32 as a microcontroller that collects environmental data such as humidity, temperature, soil moisture levels from sensors. The NodeMCU is integrated with a relay and RTC module to irrigate plants at specific times and is also equipped with a passive infrared sensor to detect intruders near the crop-field. Upon detection, an ESP32 camera is used to automatically capture the current conditions and farmers will be subsequently notified. Warnings are also sent to farmers upon detection of unwanted circumstances such as extreme temperature, which could prevent instances of open burning. The utility of the developed prototype is evident in the way it automatically irrigates the crop field without human intervention. Farmers may monitor and manually control the irrigation process using an attached Android application. Additionally, they may manually activate a buzzer warn off any potential malicious actors.

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

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Computers in Biology and Medicine 2024年 176卷 108588-108588页

作者： Gómez-Pascual, Alicia Naccache, Talel Xu, Jin Hooshmand, Kourosh Wretlind, Asger Gabrielli, Martina Lombardo, Marta Tiffany Shi, Liu Buckley, Noel J. Tijms, Betty M. Vos, Stephanie J.B. ten Kate, Mara Engelborghs, Sebastiaan Sleegers, Kristel Frisoni, Giovanni B. Wallin, Anders Lleó, Alberto Popp, Julius Martinez-Lage, Pablo Streffer, Johannes Barkhof, Frederik Zetterberg, Henrik Visser, Pieter Jelle Lovestone, Simon Bertram, Lars Nevado-Holgado, Alejo J. Gualerzi, Alice Picciolini, Silvia Proitsi, Petroula Verderio, Claudia Botía, Juan A. Legido-Quigley, Cristina Department of Information and Communications Engineering Faculty of Informatics University of Murcia Murcia Spain Steno Diabetes Center Copenhagen Herlev Denmark Department of Data Science City University of London United Kingdom Institute of Pharmaceutical Science King's College London London United Kingdom CNR Institute of Neuroscience Vedano al Lambro20854 Italy School of Medicine and Surgery University of Milano-Bicocca 20126 Italy Oxford United Kingdom Department of Psychiatry University of Oxford United Kingdom Kavli Institute for Nanoscience Discovery Denmark Alzheimer Center VU University Medical Center Amsterdam Netherlands Department of Psychiatry and Neuropsychology Alzheimer Centrum Limburg Maastricht University Maastricht Netherlands Department of Biomedical Sciences University of Antwerp Antwerp Belgium Vrije Universiteit Brussel Brussels Belgium Complex Genetics Group VIB Center for Molecular Neurology VIB Antwerp Belgium Institute Born-Bunge Department of Biomedical Sciences University of Antwerp Antwerp Belgium University of Geneva Geneva Switzerland IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Institute of Neuroscience and Physiology Sahlgrenska Academy at University of Gothenburg Gothenburg Sweden Barcelona Spain Old age psychiatry University Hospital of Lausanne University of Lausanne Switzerland Department of Geriatric Psychiatry University Hospital of Psychiatry Zürich University of Zürich Switzerland CITA-Alzheimer Foundation San Sebastian Spain AC Immune SA formerly Janssen R&D LLC. Beerse Belgium at the time of study conduct Lausanne Switzerland Department of Radiology and Nuclear Medicine Amsterdam UMC Vrije Universiteit Netherlands Queen Square Institute of Neurology and Centre for Medical Image Computing University College London United Kingdom Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Mölndal Sweden Department of Psychiatry and Neurochemistry Institute of

Background: Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed. Method: Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis. Results: Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others. Conclusions: This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways. © 2024 The Authors

关键词： Metabolites

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Mobile laser scanning systems for GPS/GNSS-denied environment mapping

Mobile laser scanning systems for GPS/GNSS-denied environmen...

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2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications

作者： Wen, Chenglu Xia, Yan Lian, Yuhan Dai, Yudi Tan, Jinbin Wang, Cheng Li, Jonathan Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Faculty of Environment University of Waterloo WaterlooONN2L 3G1 Canada

Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy. © Authors 2018. CC BY 4.0 License.

关键词： Mapping

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P0578QUININE EXPOSURE AND THE RISK OF ACUTE KIDNEY INJURY: A POPULATION BASED OBSERVATIONAL STUDY OF OLDER PEOPLE

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Nephrology Dialysis Transplantation 2020年第SUPPLEMENT_3期35卷

作者： Duncan, Andrew Hapca, Simona De Souza, Nicosha Morales, Daniel Bell, Samira University of Dundee Division of Population Health and Genomics School of Medicine Dundee United Kingdom University of Stirling Division of Computing Science and Mathematics Faculty of Natural Sciences Stirling United Kingdom Health Data Research U.K. Scotland NHS Tayside Renal Unit Dundee

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Topic modeling Twitter data using Latent Dirichlet Allocation and Latent Semantic Analysis

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AIP Conference Proceedings 2019年第1期2194卷

作者： Siti Qomariyah Nur Iriawan Kartika Fithriasari Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Kampus ITS-Sukolilo 60111 Surabaya Indonesia

The industrial world has entered the era of industrial revolution 4.0. In this era, there is an urgent data requirement from the community to support service policies. Because of that, Surabaya Government made Media Center Surabaya. This media is used to accommodate all the aspiration of Surabaya citizen. To access this media, a citizen can use Twitter. The topic which is discussed in Twitter is important information that we need to know. The information can be used to improve the performance of Surabaya Government services. Twitter data is a text data that consists of thousands of variables. Text mining is frequently used to analyze this kind of data, including topic modeling and sentiment analysis. This study would work on topic modeling focused on the algorithm employing Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The evaluation of the algorithm performance uses the topic coherence. As unstructured data, the Twitter data need preprocessing before the analysis. The stages of preprocessing include cleansing, stemming, and stop words. The advantages of LSA are fast and easy to implement. LSA, on the other hand, doesn’t consider the relationship between documents in the corpus, while LDA does. This study shows that LDA gives a better result than LSA.

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Cross-Lingual Ontology Enrichment Based on Multi-Agent Architecture

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Procedia Computer science 2018年 137卷 127-138页

作者： Mohamed Ali Said Fathalla Shimaa Ibrahim Mohamed Kholief Yasser Hassan Faculty of Science University of Alexandria Egypt Smart Data Analytics (SDA) University of Bonn Germany Institute of Graduate Studies and Research University of Alexandria Egypt College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to contribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process. This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology. This has several advantages: 1) an ontology is used to enrich another one, written in a different natural language, and 2) several ontologies could be enriched at the same time using a single chunk of text ( Simultaneous Ontology Enrichment ). A prototype for the proposed approach has been implemented in order to enrich several ontologies using English, Arabic and German text. Evaluation results are promising and showing that CLOE performs well in comparison with four state-of-the-art approaches.

关键词： cross-lingual ontology enrichment multi-agent knowledge management ontology learning.

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Malytics: A malware detection scheme

arXiv

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arXiv 2018年

作者： Yousefi-Azar, Mahmood Hamey, Len Varadharajan, Vijay Chen, Shiping Department of Computing Faculty of Science and Engineering Macquarie University SydneyNSW Australia Faculty of Engineering and Built Environment University of Newcastle Commonwealth Scientific and Industrial Research Organisation CSIRO Data61

An important problem of cyber-security is malware analysis. Besides good precision and recognition rate, a malware detection scheme needs to be able to generalize well for novel malware families (a.k.a zero-day attacks). It is important that the system does not require excessive computation particularly for deployment on the mobile devices. In this paper, we propose a novel scheme to detect malware which we call Malytics. It is not dependent on any particular tool or operating system. It extracts static features of any given binary file to distinguish malware from benign. Malytics consists of three stages: feature extraction, similarity measurement and classification. The three phases are implemented by a neural network with two hidden layers and an output layer. We show feature extraction, which is performed by tf-simhashing, is equivalent to the first layer of a particular neural network. We evaluate Malytics performance on both Android and Windows platforms. Malytics outperforms a wide range of learning-based techniques and also individual state-of-the-art models on both platforms. We also show Malytics is resilient and robust in addressing zero-day malware samples. The F1-score of Malytics is 97.21% and 99.45% on Android dex file and Windows PE files respectively, in the applied datasets. The speed and efficiency of Malytics are also evaluated. Copyright © 2018, The Authors. All rights reserved.

关键词： Feature extraction

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