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arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

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data prioritization aware resource allocation in internet of vehicles using multi-agent deep reinforcement learning

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107671页

作者： Cong Wang Yingshan Guan Sancheng Peng Hao Chen Guorui Li School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 Hebei China Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao 066004 Hebei China. Electronic address: congw@***. School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 Hebei China. Electronic address: ***@***. Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou 510006 Guangdong China. Electronic address: psc346@***. College of Information Science and Engineering Ritsumeikan University Ibaraki 567-8570 Osaka Japan. Electronic address: ***@***. Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao 066004 Hebei China. Electronic address: lgr@***.

Intelligent transportation systems (ITS) are facing the limitation of spectral resources and stringent real time communication requirements. How to effectively allocate system resources for maximizing the performance in Internet of Vehicles (IoV) is still a substantial challenge, particularly the priority and urgency of different types of data need to be focused. To improve the allocation spectrum resources and optimize transmission power while taking into the dynamic characteristics of vehicles and data priorities account, we design a time-series-based multi-agent deep reinforcement learning framework (NL-MAPPO for short), in this paper. First, we formulate the joint optimization problem as a multi-agent Markov decision process to ensure the minimization of transmission delays and energy consumption when the total vehicle-to-vehicle (V2V) link capacity is maximized. Here, V2V link capacity refers to the maximum achievable data rate for direct communication between vehicles, which depends on factors such as signal strength, interference, and available bandwidth. Then, we design a multi-agent resource allocation algorithm based on a shared-critic mechanism to realize the global sharing of channel information and solve the optimization problem. Finally, to improve efficiency, we also introduce a time series-based channel information extraction mechanism to capture the temporal characteristics of channel information. The simulation experiments were conducted and the results demonstrated that our proposed NL-MAPPO can demonstrate superiority in multiple metrics.

关键词： data prioritization Internet of vehicles Mobile edge computing Multi-agent deep reinforcement learning Spectrum resource allocation

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Towards a General Prediction System for the Primary Delay in Urban Railways

Towards a General Prediction System for the Primary Delay in...

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International Conference on Intelligent Transportation

作者： Jianqing Wu Luping Zhou Chen Cai Fang Dong Jun Shen Geng Sun School of Computing and Information Technology University of Wollongong Wollongong Australia Advanced Data Analytics in Transport Data 61 CSIRO Sydney Australia School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781538670255

Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing `big data' methods meet the specific needs in railways. We propose a comprehensive and general data-driven Primary Delay Prediction System (PDPS) framework, which combines General Transit Feed Specification (GTFS), Critical Point Search (CPS), and deep learning models to leverage the data fusion. Based on this framework, we have also developed an open source data collection and processing tool that reduces the barrier to the use of the different open data sources. Finally, we demonstrate an advanced deep learning model, the novel ConvLSTM Encoder-Decoder model with CPS for better primary delay predictions.

关键词： Delays Rail transportation Machine learning Prediction algorithms Predictive models Schedules

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Informal caregivers' attitudes and compliance towards a connected health platform for home care support: Insights from a long-term exposure

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Gerontechnology 2019年第4期18卷 231-242页

作者： Guisado-Fernández, Estefanía Giunti, Guido Mackey, Laura Silva, Paula Alexandra Blake, Catherine Caulfield, Brian Insight Centre for Data Analytics University College Dublin 3rd floor O'Brien Science Building East Dublin Ireland M3S Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Department of Computer Science Center for Informatics and Systems of the University of Coimbra (CISUC) University of Coimbra Coimbra Portugal

Background When designing Connected Health (CH) solutions for home care, it is vital to focus on usability and user experience to ensure that technologies are easy to use and meet users' expectations and needs. Generally, the usability and user experience tests are conducted during short-term exposure, which does not allow a true understanding of how the technology can help with the home caring tasks. Research aim We aim to investigate informal caregivers' feedback on the utility and usability of a CH monitoring platform for People with Dementia (PwD) during a period of extended use in the natural living context, and to understand how this was related to compliance patterns. Methods Informal caregiver's feedback about the CH platform, usability, and the impact of short-term versus long-term exposure were investigated through semi-structured individual interviews at the beginning and end of a 6-month deployment in the home care setting. Informal caregivers' compliance with the CH platform was analysed from their daily platform utilization during the deployment time. Results 11 informal caregivers agreed to participate. There was a change in the participants' opinions about the CH platform between the short-term and the long-term exposure feedback. Their initial impressions about what the CH platform could offer them to improve their delivery of home care for the PwD did not correspond with what they found that the CH platform could provide them following the long-term exposure. If at the beginning they saw the CH platform as a helpful tool to facilitate home care delivery and to improve their self-efficacy, after the deployment they expressed that because of the way the platform was designed it was mainly conceived for dementia research benefit and not to fulfill their caring needs. Compliance with the CH platform was quite low and similar between all participants. Conclusions Most contemporary CH studies are not conducted in real-life settings and without enough dur

关键词： Connected health Dementia Informal caregiver Usability User experience

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A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

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Communications in Theoretical Physics 2018年第10期70卷 384-390页

作者： Hong Lai Ming-Xing Luo Yong-Jian Xu Josef Pieprzyk Jun Zhang Lei Pan Mehmet A.Orgun School of Computer and Information Science and Centre for Research and Innovation in Software Engineering（RISE） Southwest UniversityChongqing 400715China Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China Data61 Commonwealth Scientific and Industrial Research OrganisationSydneyAustralia Institute of Computer Science Polish Academy of SciencesWarsaw01-248Poland School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVIC3122Australia School of Information Technology Deakin UniversityGeelongVIC3220Australia Department of Computing Macquarie UniversitySydneyNSW 2109Australia Faculty of Information Technology Macao University of Science and TechnologyAvenida Wai LongTaipa999078Macao

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.

关键词： spin-orbit entanglement multiple spin states Fibonacci compression encoding quantum secretsharing

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Quantifying fermionic nonlinearity of quantum circuits

arXiv

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arXiv 2021年

作者： Hakkaku, Shigeo Tashima, Yuichiro Mitarai, Kosuke Mizukami, Wataru Fujii, Keisuke Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Osaka Toyonaka560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midoricho Musashino180-8585 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan JST PRESTO 4-1-8 Honcho Saitama Kawaguchi332-0012 Japan Hirosawa 2-1 Saitama Wako351-0198 Japan Fujitsu Quantum Computing Joint Research Division QIQB Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan

Variational quantum algorithms (VQAs) have been proposed as one of the most promising approaches to demonstrate quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, it has been unclear whether VQAs can maintain quantum advantage under the intrinsic noise of the NISQ devices, which deteriorates the quantumness. Here we propose a measure, called fermionic nonlinearity, to quantify the classical simulatability of quantum circuits designed for simulating fermionic Hamiltonians. Specifically, we construct a Monte Carlo type classical algorithm based on the classical simulatability of fermionic linear optics, whose sampling overhead is characterized by the fermionic nonlinearity. As a demonstration of these techniques, we calculate the upper bound of the fermionic nonlinearity of a rotation gate generated by four fermionic modes under the dephasing noise. Moreover, we estimate the sampling costs of the unitary coupled cluster singles and doubles quantum circuits for hydrogen chains subject to the dephasing noise. We find that, depending on the error probability and atomic spacing, there are regions where the fermionic nonlinearity becomes very small or unity, and hence the circuits are classically simulatable. We believe that our method and results help to design quantum circuits for fermionic systems with potential quantum advantages. Copyright © 2021, The Authors. All rights reserved.

关键词： Timing circuits

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Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action

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Methods of information in Medicine 2021年第6期59卷 183-192页

作者： Luis Fernandez-Luque Andre W. Kushniruk Andrew Georgiou Arindam Basu Carolyn Petersen Charlene Ronquillo Chris Paton Christian Nøhr Craig E. Kuziemsky Dari Alhuwail Diane Skiba Elaine Huesing Elia Gabarron Elizabeth M. Borycki Farah Magrabi Kerstin Denecke Linda W. P. Peute Max Topaz Najeeb Al-Shorbaji Paulette Lacroix Romaric Marcilly Ronald Cornet Shashi B. Gogia Shinji Kobayashi Sriram Iyengar Thomas M. Deserno Tobias Mettler Vivian Vimarlund Xinxin Zhu Adhera Health Inc. Palo Alto California United States School of Health Information Science University of Victoria Victoria Canada Australian Institute of Health Innovation Macquarie University Macquarie New South Wales Australia School of Health Sciences University of Canterbury Christchurch New Zealand Division of Biomedical Statistics and Informatics Mayo Clinic Rochester Minnesota United States Daphne Cockwell School of Nursing Ryerson University Ryerson Toronto Canada Department of Information Science University of Otago Dunedin New Zealand Nuffield Department of Medicine University of Oxford Oxford United Kingdom Centre for Health Informatics and Technology Maersk McKinney Moller Institute University of Southern Denmark Denmark Office of Research Services MacEwan University Edmonton AB Canada Department of Information Science Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait University of Colorado Denver Colorado United States IMIA CEO Norwegian Centre for E-health Research University Hospital of North Norway Tromsø Norway Institute for Medical Informatics Bern University of Applied Sciences Bern Switzerland Medical Informatics Amsterdam UMC University of Amsterdam Amsterdam The Netherlands Columbia University Medical Center Data Science Institute Columbia University Columbia United States Amman Jordan University of Victoria Victoria Canada Univ. Lille Inserm CHU Lille ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales F-59000 Lille France Society for Administration of Telemedicine and Healthcare Informatics New Delhi India National Institute of Public Health Japan The University of Arizona Arizona United States Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Braunschweig Germany Swiss Graduate School of Public Administration University of Lausanne Lausanne Switzerland Department of Computer and Information Science (IDA)

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Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

Accelerated Latent Factor Analysis for Recommender Systems v...

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IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models data models Sparse matrices Acceleration Recommender systems Convergence

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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 110406页

作者： Saleh, Hager El-Sappagh, Shaker McCann, Michael Alsamhi, Saeed Hamood Breslin, John G. Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road GalwayH91 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 Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 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 Seoul02841 Korea Republic of 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： Electrotherapeutics

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Corrigendum to “Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization” [Eng. Appl. Artif. Intell. 124 (2023) 106643]

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Engineering Applications of Artificial Intelligence 2024年 128卷

作者： A.A. Zaidan Alhamzah Alnoor O.S. Albahri R.T. Mohammed A.H. Alamoodi A.S. Albahri B.B. Zaidan Salem Garfan Hamsa Hameed Mohammed S. Al-Samarraay Ali Najm Jasim R.Q. Malik SP Jain School of Global Management Sydney Australia Management Technical College Southern Technical University Basrah Iraq Department of Computer Science and Information Technology La Trobe University Melbourne VIC Australia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Science Faculty of Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq Department of Computing and META Technology (FKMT) Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia MEU Research Unit Middle East University Amman Jordan Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Faculty of Human Development Sultan Idris University of Education (UPSI) Tanjung Malim Malaysia Foundation of Alshuhda Baghdad Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

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