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arXiv 2020年

作者： Alshaabi, Thayer Arnold, Michael V. Minot, Joshua R. Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Muhamad, Roby Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 MassMutual Data Science AmherstMA01002 International Class Program Faculty of Social and Political Sciences University of Indonesia Jakarta Indonesia Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most ‘important’ for March 2020 with respect to March 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for ‘virus’ in January followed by a decline through February and a recent surge through March. The world’s collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

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EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case

arXiv

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arXiv 2021年

作者： Díaz-Rodríguez, Natalia Lamas, Alberto Sanchez, Jules Franchi, Gianni Donadello, Ivan Tabik, Siham Filliat, David Cruz, Policarpo Montes, Rosana Herrera, Francisco U2IS ENSTA Institut Polytechnique Paris Inria Flowers Palaiseau91762 France Free University of Bozen-Bolzano 39100 Italy DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Art History University of Granada Granada18071 Spain Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain Department of Software Engineering University of Granada Granada18071 Spain

The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols –such as knowledge graphs– are easier to explain. However, they present lower generalisation and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: 1) EXPLANet: Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and 2) SHAP-Backprop, an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that with our approach, it is possible to i

关键词： Classification (of information)

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Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications

arXiv

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arXiv 2025年

作者： Tang, Xiao Zhao, Kexin Shen, Chao Du, Qinghe Wang, Yichen Niyato, Dusit Han, Zhu School of Information and Communication Engineering Xi’an Jiaotong University Xi’an710049 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronics and Information Northwestern Polytechinical University Xi’an710072 China Faculty of Electronics and Information Engineering Xi’an Jiaotong University Xi’an710049 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Houston Houston77004 United States

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

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The Lancet Psychiatry 2022年第6期9卷 477-486页

作者： Gumley, Andrew I Bradstreet, Simon Ainsworth, John Allan, Stephanie Alvarez-Jimenez, Mario Aucott, Lorna Birchwood, Maximillian Briggs, Andrew Bucci, Sandra Cotton, Sue M Engel, Lidia French, Paul Lederman, Reeva Lewis, Shôn Machin, Matthew MacLennan, Graeme McLeod, Hamish McMeekin, Nicola Mihalopoulos, Cathy Morton, Emma Norrie, John Schwannauer, Matthias Singh, Swaran P Sundram, Suresh Thompson, Andrew Williams, Chris Yung, Alison R Farhall, John Gleeson, John Glasgow Institute of Health and Wellbeing University of Glasgow Glasgow United Kingdom Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Orygen Melbourne Melbourne VIC Australia Centre for Youth Mental Health University of Melbourne Melbourne VIC Australia School of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Melbourne VIC Australia Centre for Healthcare Randomised Trials (CHaRT) University of Aberdeen Aberdeen United Kingdom Centre for Mental Health and Wellbeing Research Warwick Medical School University of Warwick Warwick United Kingdom Department of Health Services Research and Policy London School of Hygiene & Tropical Medicine London United Kingdom Greater Manchester Mental Health NHS Foundation Trust Manchester United Kingdom School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Psychiatry School of Clinical Sciences Monash University Melbourne VIC Australia School of Medicine Deakin University Melbourne VIC Australia Department of Psychiatry Manchester Metropolitan University Manchester United Kingdom Department of Psychiatry University of British Columbia Vancouver BC Canada The Usher Institute University of Edinburgh Edinburgh United Kingdom School for Health in Social Sciences University of Edinburgh Edinburgh United Kingdom Mental Health Program Monash Health Melbourne VIC Australia Department of Psychology and Counselling La Trobe University Melbourne VIC Australia NorthWestern Mental Health The Royal Melbourne Hospital Epping VIC Australia Healthy Brain and Mi

Background: Early warning signs monitoring by service users with schizophrenia has shown promise in preventing relapse but the quality of evidence is low. We aimed to establish the feasibility of undertaking a definitive randomised controlled trial to determine the effectiveness of a blended digital intervention for relapse prevention in schizophrenia. Methods: This multicentre, feasibility, cluster randomised controlled trial aimed to compare Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) with treatment as usual in community mental health services (CMHS) in Glasgow and Melbourne. CMHS were the unit of randomisation, selected on the basis of those that probably had five or more care coordinators willing to participate. Participants were eligible if they were older than 16 years, had a schizophrenia or related diagnosis confirmed via case records, were able to provide informed consent, had contact with CMHS, and had had a relapse within the previous 2 years. Participants were randomised within stratified clusters to EMPOWER or to continue their usual approach to care. EMPOWER blended a smartphone for active monitoring of early warning signs with peer support to promote self-management and clinical triage to promote access to relapse prevention. Main outcomes were feasibility, acceptability, usability, and safety, which was assessed through face-to-face interviews. App usage was assessed via the smartphone and self-report. Primary end point was 12 months. Participants, research assistants and other team members involved in delivering the intervention were not masked to treatment conditions. Assessment of relapse was done by an independent adjudication panel masked to randomisation group. The study is registered at ISRCTN (99559262). Findings: We identified and randomised eight CMHS (six in Glasgow and two in Melbourne) comprising 47 care coordinators. We recruited 86 service users between Jan 19 and Au

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Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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Computers in Biology and Medicine 2025年 194卷

作者： Hager Saleh Shaker El-Sappagh Michael McCann Saeed Hamood Alsamhi John G. Breslin Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road Galway H91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha 13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul 02841 Republic of Korea Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person’s blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model’s generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system’s potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Real-time patient monitoring Time-series data forecasting Intensive care unit Sequence-to-sequence modeling Multi-horizon forecasting Attention-based modeling Deep learning

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Optimal control of microalgae growth using linear quadratic regulator method with firefly algorithm optimization

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Journal of Physics: Conference Series 2020年第1期1538卷

作者： Suci Yongki Setyowati Mardlijah Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember

Microalgae is an important potential resource for various industrial applications. In the industry, there are a variety of processed products from algae biomass. Lipids from microalgae used for biofuell, humans and animals in food supplements while in the pharmaceutical, lipids used as a vitamin or protein. The other application of microalgae captures CO2 and wastewater treatment. This research discusses the optimal control of microalgae growth with variable control are carbon dioxide and nutrients. In this case, the methodology is the Linear Quadratic Regulator method. In the Linear Quadratic Regulator method, there are weights in which the value is determined by trial and error in the matrices Q and R. The method of optimizing the weight value is required to optimize the weight. In this paper, the firefly algorithm is used to optimize the matrix values for Q and R. Application of firefly algorithm is able to optimally increase microalgae growth, so that the biomass produced is also abundant. The using of Firefly Algorithms optimization in the LQR method can increase algal concentration by 63.3% of the initial concentration.

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Wind disturbance elimination on dual axis solar tracker using fuzzy logic control 3

Wind disturbance elimination on dual axis solar tracker usin...

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3rd International Conference on Natural and Environmental sciences 2019, ICONES 2019

作者： Ikhwan, M. Rizal, S. Ramli, M. Muchlisin, Z.A. Mardlijah, M. Graduate School of Mathematics and Applied Science Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Marine Sciences Faculty of Marine and Fisheries Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Science Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Aquacultures Faculty of Marine and Fisheries Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

The use of solar energy using solar panels began to be encouraged. Solar panels utilize solar radiation and convert it into clean and renewable energy. The problems of the accuracy of the panel facing right towards radiation are a challenge in the development of a driving system. Solar panels have limited motion, which is a maximum of phi, so that wind disturbances are very dangerous for motor drives and solar panels. This paper aims to eliminate wind disturbances in solar panel drive systems. The fuzzy logic control is used to determine the amount of power given to the driving motor to direct the panel towards solar radiation. Inputs and outputs of fuzzy controllers are arranged by dividing into each of the five members. Simulation results show that the position error of solar panels without wind disturbances reaches 0.3325° in zonal and 0.5210° in meridional. By using wind disturbances, this system managed to maintain errors smaller than 0.4285° in zonal and 0.6116° in meridional. After modifying the rule base and membership function, systems show decreasing of error half of recent trial. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Solar panels

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Cell membrane vaccine delivery system: A review of the recent advances against breast cancer

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International Journal of Pharmaceutics 2025年 681卷

作者： Ayah Rebhi Hilles Syed Mahmood Riyanto Teguh Widodo Ahmad Khusairi Azemi Abu Hassan Nordin Abdin Shakirin Mohamad Norpi Muhammad Fauzi Abd Jalil Zarif Mohamed Sofian Mazlan Mohamed Kamarul Ariffin Hambali Nani Izreen Mohd Sani Roslina Rashid Muhammad Luqman Nordin INHART International Islamic University Malaysia Jalan Gombak 53100 Kuala Lumpur Malaysia Department of Pharmaceutical Technology Faculty of Pharmacy Universiti Malaya 50603 Kuala Lumpur Malaysia Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Advanced Materials (CAM) Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia Faculty of Pharmaceutical Sciences Chulalongkorn University Pathum Wan Bangkok 10330 Thailand Institute of Climate Change Adaptation and Marine Biotechnology Universiti Malaysia Terengganu 21030 Kuala Terengganu Terengganu Malaysia Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Campus Arau 02600 Perlis Malaysia Faculty of Pharmacy and Health Sciences Universiti Kuala Lumpur Royal College of Medicine Perak Perak 30450 Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kota Bharu 16100 Kelantan Malaysia Faculty of Earth Science Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Animal and Wildlife Research Group Faculty of Earth Science Jeli Campus Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Faculty of Veterinary Medicine Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kelantan Malaysia

Natural cells exhibit unique properties, such as homotypic targeting and prolonged circulation in the bloodstream, making them attractive candidates for breast cancer therapy vaccine delivery systems (VDSs). To improve delivery efficiency, these cells can be integrated with nanocarriers, paving the way for next-generation VDSs in the form of biomimetic nanomedicines. This review highlights recent progress in cell membrane-based vaccine delivery systems (CVDSs) and other types of vaccine delivery systems as a potential for breast cancer vaccine development. Cell membrane sources, including red blood cells (RBCs), cancer cells, and platelets are explored. Additionally, the methods for extracting these membranes and the mechanisms driving the functionality of biomimetic CVDSs are detailed. This review also highlights recent advancements and cutting-edge strategies of cell membrane vaccine delivery systems, with a particular focus on the use of cell-derived biomimetic nanotechnology for breast cancer. It also highlights breast cancer vaccines under development encompass protein-based (e.g., peptide vaccines), nucleic acid-based (DNA and RNA vaccines), immune cell-based (including dendritic cell and autologous tumor cell vaccines), and bacterial/viral vector-based vaccines. Each platform aims to elicit targeted anti-tumor immune responses.

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Federated learning for 6G communications: Challenges, methods, and future directions

arXiv

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arXiv 2020年

作者： Liu, Yi Yuan, Xingliang Xiong, Zehui Kang, Jiawen Wang, Xiaofei Niyato, Dusit School of Data Science of Technology Heilongjiang University Harbin China Faculty of Information Technology Monash University ClaytonVIC3800 Australia School of Computer Science and Engineering Nanyang Technological University Singapore Singapore College of Intelligence and Computing Tianjin University Tianjin China Energy Research Institute Nanyang Technological University Singapore

—As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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