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arXiv 2023年

作者： Aknin, Eliel Larey, Ariel Caldwell, Julie M. Collins, Margaret H. Rothenberg, Marc E. Savir, Yonatan Abonia, Juan P. Aceves, Seema S. Arva, Nicoleta C. Chehade, Mirna Dellon, Evan S. Gonsalves, Nirmala Gupta, Sandeep K. Leung, John Peterson, Kathryn A. Shoda, Tetsuo Spergel, Jonathan M. Department of Physiology Biophysics and System Biology Faculty of Medicine Technion Israel Institute of Technology Haifa Israel Faculty of Industrial Engineering Technion Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion Israel Institute of Technology Haifa Israel Division of Allergy and Immunology Cincinnati Children's Hospital Medical Center CincinnatiOH United States Dept. of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Division of Pathology and Laboratory Medicine Cincinnati Children's Hospital Medical Center CincinnatiOH United States Division of Allergy and Immunology University of California at San Diego Rady Children's Hospital San DiegoCA United States Dept. of Pathology and Laboratory Ann and Robert H Lurie Children's Hospital Northwestern University Feinberg School of Medicine ChicagoIL United States Mount Sinai Center for Eosinophilic Disorders Icahn School of Medicine at Mount Sinai New YorkNY United States Division of Gastroenterology and Hepatology University of North Carolina School of Medicine Chapel HillNC United States Division of Gastroenterology and Hepatology Northwestern University Feinberg School of Medicine ChicagoIL United States Division of Pediatric Gastroenterology Hepatology and Nutrition Children's of Alabama University of Alabama at Birmingham BirminghamAL United States Boston Specialists BostonMA United States Division of Gastroenterology University of Utah Salt Lake CityUT United States Division of Allergy and Immunology Children's Hospital of Philadelphia Dept. of Pediatrics Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of EoE relies on counting eosinophils in histological slides, a manual and time-consuming task that limits the ability to extract complex patient-dependent features. The treatment of EoE includes medication and food elimination. A personalized food elimination plan is crucial for engagement and efficiency, but previous attempts failed to produce significant results. In this work, on the one hand, we utilize AI for inferring histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This study illustrates the significance of AI in enhancing treatment planning by analyzing molecular features' distribution in histological slides through causal learning. Our approach can be harnessed for other conditions that rely on histology for diagnosis and treatment. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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A Simulation Study of Performances for Different Substrates Sizes and Copper Thickness For PD Antenna Design

A Simulation Study of Performances for Different Substrates ...

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Student Conference on Research and Development (SCOReD)

作者： Z. H. Bohari M. Isa P. J. Soh A. Z. Abdullah A. Ab Rahman M. N. M. Nasir CeRIA Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia Melaka Pauh Perlis Center of Renewable Energy(CERE) School of Electrical System Engineering Universiti Malaysia Perlis Pauh Perlis Advanced Communication Engineering Centre (ACE) School of Computer and Communication Engineering Universiti Malaysia Perlis Pauh Perlis CeRIA Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka

ISBN: (数字)9781728193175

ISBN: (纸本)9781728193182

Partial discharge (PD) is a phenomenon that consistently happens throughout the millennium in the power equipment. This phenomenon has an unpredictable and intermittent characteristic that hard to be detected easily. There are various available methods to detect this phenomenon, but each technique has its own flaws and drawback. This paper explored the early step to select, design, and simulate a PD UHF antenna for power transformer through CST MWS simulation. This task is crucial to choose the optimized substrate size and copper thickness for the PD antenna design. Based on the detailed analysis, it is substantially proven that there is an important relationship between substrate size towards antenna performance and characteristics to design an improved and efficient PD antenna.

关键词： Partial discharges Resonant frequency Copper Broadband antennas Power transformers Substrates Wideband

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Rubber Plant Disease Diagnostic system Using Technique for Order Preference by Similarity to Ideal Solution

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Procedia computer Science 2019年 161卷 484-492页

作者： Ramadiani M. Syahrir Ramadhani Muhammad Labib Jundillah Azainil Faculty of Computer Science and Information Technology Mulawarman University Samrinda Indonesia Magister Program of Information System School of Post Graduate Studies Diponegoro University Semarang Indonesia Faculty of Teacher Training and Education Mulawarman University Samarinda Indonesia

Data from the Plantation Office of East Kalimantan Province, the area of rubber plantations has decreased every year. One of the factors that make productivity of rubber plants low is the presence of pests and diseases. The time limit of an expert is an obstacle in identifying rubber plantations. To overcome this problem, we need an expert system that can identify rubber plant diseases such as an expert. This system was developed to be able to provide solutions in diagnosing rubber plant diseases such as White Root Fungus (Rigidoporus micropus), Upas Mushroom Disease (Corticium salmonicolor), Antraknosa Disease (Colletorichum gloeosporoides), Skin Necrosis (Fusarium sp.), And Cancer Lines (Phytoptora palmivore). This study uses the TOPSIS method. There are four variables used as assessment criteria in this study, namely: the level of damage to the root, the level of damage to the stem, the level of damage to the leaves and the level of damage to the tapping path or the intensity of tapping, with preference values from 0 to 100; none, Very Light Intensity (1), Mild Intensity (3), Medium Intensity (5), Weight Intensity (7) and Very Heavy Intensity (9). Each weight value C1= 0.3; C2= 0.22; C3= 0.28; C4= 0.2. The results of this study compare manual calculations and calculations on the system obtained 99.99% accuracy. This system is expected to help facilitate rubber farmers in identifying diseases and can help experts.

关键词： Rubber Plant Disease East Kalimantan TOPSIS Expert system

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Clarity in chaos: Boosting few-shot classification through information suppression and sparsification

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Pattern Recognition 2025年 167卷

作者： Li, Xiaoxu Ji, Luchen Zhu, Rui Ma, Zhanyu Xue, Jing-Hao School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China Faculty of Actuarial Science and Insurance Bayes Business School City St George's University of London LondonEC1Y 8TZ United Kingdom Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Statistical Science University College London LondonWC1E 6BT United Kingdom

The advance of deep learning has invigorated the research of few-shot classification. However, the interference of non-target information in feature representations hampers classification generalization. To tackle this issue, we propose an irrelevant information suppression (IIS) module, which is focused on suppressing the weight of unimportant information and elevating the sparsity of feature representations. An IIS network with three consecutive IIS modules is developed, to illustrate the progressive suppression of unimportant information and highlighting of key discriminative features of the target. Extensive experiments showcase the superior performance of our IIS network on five widely-used benchmark datasets. Furthermore, we show that the IIS module can be readily used as a plug-in module by state-of-the-art few-shot classifiers, and can clearly further improve their performance. Our code is available on GitHub at https://***/LC4188/IISNet. © 2025

关键词： Taxonomies

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Retraction Note: An architecture of agent-based multi-layer interactive e-learning and e-testing platform

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Quality & Quantity 2019年第1期54卷 189-190页

作者： Muhammad Arif Manzoor Illahi Ahmad Karim Shahaboddin Shamshirband Khubaib Amjad Alam Shahid Farid Salman Iqbal Zolkepli Buang Valentina Emilia Balas Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science COMSATS Institute of Information Technology Islamabad Islamabad Pakistan Faculty of Computer Science and Information Technology Department of Computer System and Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science Chalous Branch Islamic Azad University (IAU) Chalous Iran Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia Department of Automation and Applied Informatics University of Arad Arad Romania

The Editor-in-Chief has retracted this article.

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Preliminary Research Design on Sensor Data Gathering for Air Quality Text Generation

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： Indra Aulia Ainul Hizriadi Seniman Muhibuddin Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan 20155 Indonesia Data Science and Intelligent System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan 20155 Indonesia Computer System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan 20155 Indonesia

Air pollution is one of the environmental problems faced by many countries (i.n. the developing countries). It often harms the health of people, plants, and animals as well as our environment. To decrease the risks, e ective and e cient technology for air quality monitoring is indispensable. In Indonesia, it is still limited in the case of the number of devices and the dissemination of air quality information. This paper describes the preliminary research design of air quality monitoring technology based on the Internet of Things (IoT) and Natural Language Generation (NLG) for solving these limitations. It de nes air quality knowledge in the form of a set of rules and presents the architecture design for data gathering and text generation. Data gathering uses the related sensors which are able to collect the values of parameters as air quality indicators. Then, the collected values are identi ed to be used for generating the textual information in Indonesian language to social media, such as twitter. So, in this research, the proposed solutions are able to solve the problems properly.

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High Performance Computing (HPC) Implementation: A Survey 1

High Performance Computing (HPC) Implementation: A Survey

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1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Assiroj, Priati Hananto, April Lia Fauzi, Ahmad Hendric Spits Warnars, Harco Leslie Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Information System Faculty of Technology and Computer Science United States Buana Perjuangan University Faculty of Technology and Computer Science Information Technology Karawang Indonesia

ISBN: (纸本)9781538694220

To maintain excellence in global hyper-competitive economies in the upcoming decades, manufacturers must improve the design, development, and distribution of subsequent product generations, production technologies, equipment, and processes. High-performance computing (HPC) solves the application of significant challenges using computer modeling, simulation, and analytical technology. By using HPC technology, products design will be faster, making and testing prototypes will save time, the production process can be simplified, the cost of innovation will decrease, and high-value innovation will be quickly developed. This study uses the PRISMA method, based on predetermined keywords and analyzes the algorithms, frameworks and programming languages and the processes that are most commonly used to optimize the Graphics Processing Unit (GPU). The results of this study can be a reference for future research on the implementation of High-Performance Computing (HPC) Application by combine modeling, simulation, and analysis. © 2018 IEEE.

关键词： Graphics processing unit

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Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

Deep Sequence Labelling Model for Information Extraction in ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiayin Lin Zhexuan Zhou Geng Sun Jun Shen David Pritchard Tingru Cui Dongming Xu Li Li Ghassan Beydoun School of Computing and Information Techonology University of Wollongong Wollongong Australia Research Lab of Electronics Massachusetts Institute of Technology Cambridge MA University of Melbourne Melbourne Australia UQ Business School The University of Queensland Brisbane Australia Faculty of Computer and Information Science Southwest University Chongqing China School of Information System and Modelling University of Technology Sydney Sydney Australia

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant pre-processing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services.

关键词： Hidden Markov models Data mining Feature extraction Information retrieval Labeling Task analysis Data models

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Scalable communication for high-order stencil computations using CUDA-aware MPI

arXiv

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arXiv 2021年

作者： Pekkilä, Johannes Väisälä, Miikka S. Käpylä, Maarit J. Rheinhardt, Matthias Lappi, Oskar Department of Computer Science Aalto University Konemiehentie 2 Espoo02150 Finland Academia Sinica Institute of Astronomy and Astrophysics Roosevelt Rd Taipei10617 Taiwan Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 GöttingenD-37077 Germany Nordita KTH Royal Institute of Technology and Stockholm University Roslagstullsbacken 23 StockholmSE-10691 Sweden Faculty of Science and Engineering Åbo Akademi University Tuomiokirkontori 3 Turku20500 Finland

Modern compute nodes in high-performance computing provide a tremendous level of parallelism and processing power. However, as arithmetic performance has been observed to increase at a faster rate relative to memory and network bandwidths, optimizing data movement has become critical for achieving strong scaling in many communication-heavy applications. This performance gap has been further accentuated with the introduction of graphics processing units, which can provide by multiple factors higher throughput in data-parallel tasks than central processing units. In this work, we explore the computational aspects of iterative stencil loops and implement a generic communication scheme using CUDA-aware MPI, which we use to accelerate magnetohydrodynamics simulations based on high-order finite differences and third-order Runge-Kutta integration. We put particular focus on improving intra-node locality of workloads. Our GPU implementation scales strongly from one to 64 devices at 50%-87% of the expected efficiency based on a theoretical performancemodel. Compared with a multi-core CPU solver, our implementation exhibits 20-60× speedup and 9-12× improved energy efficiency in compute-bound benchmarks on 16 nodes. © 2021, CC BY-NC-ND.

关键词： Program processors

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