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arXiv 2024年

作者： Bauer, Stefan Benner, Peter Bereau, Tristan Blum, Volker Boley, Mario Carbogno, Christian Catlow, C.A. Richard Dehm, Gerhard Eibl, Sebastian Ernstorfer, Ralph Fekete, Ádám Foppa, Lucas Fratzl, Peter Freysoldt, Christoph Gault, Baptiste Ghiringhelli, Luca M. Giri, Sajal K. Gladyshev, Anton Goyal, Pawan Hattrick-Simpers, Jason Kabalan, Lara Karpov, Petr Khorrami, Mohammad S. Koch, Christoph Kokott, Sebastian Kosch, Thomas Kowalec, Igor Kremer, Kurt Leitherer, Andreas Li, Yue Liebscher, Christian H. Logsdail, Andrew J. Lu, Zhongwei Luong, Felix Marek, Andreas Merz, Florian Mianroodi, Jaber R. Neugebauer, Jörg Pei, Zongrui Purcell, Thomas A.R. Raabe, Dierk Rampp, Markus Rossi, Mariana Rost, Jan-Michael Saal, James Saalmann, Ulf Sasidhar, Kasturi Narasimha Saxena, Alaukik Sbailò, Luigi Scheidgen, Markus Schloz, Marcel Schmidt, Daniel F. Teshuva, Simon Trunschke, Annette Wei, Ye Weikum, Gerhard Xian, R. Patrick Yao, Yi Yin, Junqi Zhao, Meng Scheffler, Matthias School of Computation Information and Technology Technical University of Munich & Helmholtz AI Munich Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Institute for Theoretical Physics Heidelberg University Heidelberg Germany Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Monash University Department of Data Science and AI Melbourne Australia The NOMAD Laboratory The FHI of the Max-Planck-Gesellschaft IRIS-Adlershof of the Humboldt-Universität zu Berlin Berlin Germany School of Chemistry Cardiff University Cardiff United Kingdom Max-Planck-Institut für Eisenforschung GmbH Düsseldorf Germany Max Planck Computing and Data Facility Garching Germany Institute for Optics and Atomic Physics Technical University of Berlin Berlin Germany Physics Department and IRIS-Adlershof Humboldt Universität zu Berlin Berlin Germany Max Planck Institute of Colloids and Interfaces Potsdam Germany Department of Materials Imperial College London United Kingdom Department of Materials Science and Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Germany Department of Chemistry Northwestern University Illinois United States Humboldt-Universität zu Berlin Department of Physics Berlin Germany Department of Materials Science and Engineering University of Toronto Toronto Canada STFC Hartree Centre Daresbury Laboratory Daresbury Warrington United Kingdom Molecular Simulations from First Principles e.V. Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Max Planck Institute for Polymer Research Mainz Germany ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona Spain Lenovo HPC Innovation Center Stuttgart Germany New York University New York United States University of Arizona Biochemistry Department Arizona United States Max Planck Institute for the Structure an

science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (ai) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on data-Centric Materials science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. © 2024, CC BY.

关键词： data Analytics

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DiaBD: A diabetes dataset for enhanced risk analysis and research in Bangladesh

引用

data in Brief 2025年 61卷

作者： Tabia Tanzin Prama Md. Jobayer Rahman Marzia Zaman Farhana Sarker Khondaker A. Mamun Advanced Intelligent Multidisciplinary Systems (AIMS) Lab Institute of Research Innovation Incubation and Commercialization (IRIIC) United International University Dhaka 1212 Bangladesh CMED Health Ltd. Mohakhali DOHS Dhaka 1206 Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Dhaka 1229 Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka 1229 Bangladesh Department of Computer Science and Engineering United International University Dhaka 1212 Bangladesh

Diabetes is a chronic condition affecting millions worldwide and severely impacts health and quality of life. According to the International Diabetes Federation (IDF), over 463 million adults, which is 9.3% of the global population, live with diabetes. Diabetes ranks among the most prevalent chronic diseases and was the ninth-leading cause of mortality in 2019, with 4.2 million deaths reported. This article introduces DiaBD, a novel dataset of 5,288 patient records from Bangladesh, designed to address critical gaps in diabetes research and aid in healthcare planning, risk analysis, and predictive modelling. The dataset comprises 14 attributes including age, gender, clinical vitals (pulse rate, systolic and diastolic blood pressure, glucose levels), anthropometrics (height, weight, body mass index (BMI)), family history of diabetes and hypertension, cardiovascular disease (CVD), and stroke, with a dependent attribute, Diabetic, indicates whether an individual has diabetes or not. The dataset ensures demographic diversity and precise measurements, supporting the study of diabetes and its related health issues. Features like CVD and stroke enable broader research on comorbidities. This dataset facilitates machine learning applications, risk assessment, and personalized healthcare strategies. researchers can explore the links between diabetes, hypertension, CVD, and stroke, while healthcare providers and policymakers can leverage DiaBD to identify trends, allocate resources efficiently, and enhance public health strategies.

关键词： Diabetes Medical care Biometric data standardization Risk factor analysis Predictive analysis

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and engineering Technology Association. All rights reserved.

关键词： Wind power

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Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation

arXiv

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arXiv 2024年

作者： Li, Cheng-Yi Chang, Kao-Jung Yang, Cheng-Fu Wu, Hsin-Yu Chen, Wenting Bansal, Hritik Chen, Ling Yang, Yi-Ping Chen, Yu-Chun Chen, Shih-Pin Lirng, Jiing-Feng Chang, Kai-Wei Chiou, Shih-Hwa Department of Computer Science University of California Los Angeles United States Department of Medical Research Taipei Veterans General Hospital Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taiwan Big Data Center Taipei Veterans General Hospital Taiwan Department of Ophthalmology Taipei Veterans General Hospital Taiwan Department of Electrical Engineering City University of Hong Kong Hong Kong Institute of Hospital and Health Care Administration National Yang Ming Chiao Tung University Taiwan Department of Family Medicine Taipei Veterans General Hospital Taiwan Department of Neurology Neurological Institute Taipei Veterans General Hospital Taiwan Department of Radiology School of Medicine National Yang Ming Chiao Tung University Taiwan College of Medicine National Yang Ming Chiao Tung University Taiwan

Multi-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation. Nevertheless, the preliminary MLLM successful attempts in 2D medical image-text pair captioning are incompetent to reflect the real-world diagnostic challenge in the volumetric 3D anatomy. Toward deploying MLLM for more applicable diagnostic context, we noticed that the (1) scarcity of 3D image training dataset, (2) the direct use of undifferentiated foundation MLLMs, and (3) the lack of pertinent caption evaluation metrics were independent domain-specific constraints that integratively hobbles the iteration of next-generation medical MLLM research. In this regard, this study collected a 3D-BrainCT dataset (18,885 text-scan pairs) and applied clinical visual instruction tuning (CVIT) to train volumetric anatomy-sensible BrainGPT models to generate radiology-adherent 3D brain CT reports. Statistically, our BrainGPT model scored BLEU-1 = 44.35, BLEU-4 = 20.38, METEOR = 30.13, ROUGE-L = 47.6, and CIDEr-R = 211.77 during internal testing and demonstrated an accuracy of 0.91 in captioning midline shifts on the external validation CQ500 dataset. By further inspecting the captioned report, we reported that the traditional metrics appeared to measure only the surface text similarity and failed to gauge the information density of the diagnostic purpose. To close this gap, we proposed a novel Feature-Oriented Radiology Task Evaluation (FORTE) to estimate the clinical relevance (lesion feature and landmarks) of the report. Notably, the BrainGPT model scored an average FORTE 0.71 F1-score (degree=0.661;landmark=0.706;feature=0.693, and impression=0.779). To demonstrate that BrainGPT models possess objective readiness to generate human-like radiology reports, we conducted a Turing test that enrolled 11 physician evaluators, and around 74% of the BrainGPT-generated captions were indistinguishable from those written by hu

关键词： Radiology

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv

引用

arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao Xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, Luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang Xu, Yan Xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of ai models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

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Prediction of Oral Food Challenge Outcomes via Ensemble Learning

arXiv

引用

arXiv 2022年

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann ArborMI United States Department of Internal Medicine University of Michigan Ann ArborMI United States Department of Pediatrics University of Michigan Ann ArborMI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Computer Science and Engineering University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States Mary H. Weiser Food Allergy Center University of Michigan Ann ArborMI United States Department of Computer Science Queens College City University of New York New YorkNY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥ 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

arXiv

引用

arXiv 2022年

作者： Wehbi, Osama Arisdakessian, Sarhad Wahab, Omar Abdel Otrok, Hadi Otoum, Safa Mourad, Azzam Guizani, Mohsen Cyber Security Systems and Applied AI Research Center Department of CSM Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal MontrealQC Canada Khalifa University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Dubai United Arab Emirates Division of Science New York University Abu Dhabi United Arab Emirates Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model. © 2022, CC BY-NC-SA.

关键词： Game theory

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Final Report of the NTCIR-14 FinNum Task: Challenges and Current Status of Fine-Grained Numeral Understanding in Financial Social Media data 14th

Final Report of the NTCIR-14 FinNum Task: Challenges and Cur...

引用

14th International Conference on NII Testbeds and Community for Information Access research, NTCIR 2019

作者： Chen, Chung-Chi Huang, Hen-Hsen Takamura, Hiroya Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan MOST Joint Research Center for AI Technology and All Vista Healthcare Taipei Taiwan

ISBN: (纸本)9783030368043

NTCIR-14 FinNum task aims to disambiguate the meanings of the numerals in financial social media data from both coarse-grained taxonomy and fine-grained taxonomy. This task attracted 13 teams from 15 institutions in 6 different countries to register, received 16 submissions from 9 participants, and finally accepted 6 papers from participants. In this paper, we provide the analysis of the proposed dataset in depth, and discuss the challenges of fine-grained numeral understanding in financial social media data. © 2019, Springer Nature Switzerland AG.

关键词： Taxonomies

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