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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Accuracy-Guaranteed Event Detection via Collaborative Mobile Crowdsensing with Unreliable Users 15th

Accuracy-Guaranteed Event Detection via Collaborative Mobile...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Liu, Tong Wu, Wenbin Zhu, Yanmin Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9783030301453

Recently, mobile crowdsensing has become a promising paradigm to collect rich spatial sensing data, by taking advantage of widely distributed sensing devices like smartphones. Based on sensing data, event detection can be conducted in urban areas, to monitor abnormal incidents like traffic jam. However, how to guarantee the detection accuracy is still an open issue, especially when unreliable users who may report wrong observations are considered. In this work, we focus on the problem of user recruitment in collaborative mobile crowdsensing, aiming to optimize the fine-grained detection accuracy in a large urban area. Unfortunately, the problem is proved to be NP-hard, which means there is no polynomial-time algorithm to achieve the optimal solution unless P NP. To meet the challenge, we first employ a probabilistic model to characterize the unreliability of users, and measure the uncertainty of inferring event occurrences given collected observations by Shannon entropy. Then, by leveraging the properties of adaptive monotonicity and adaptive submodularity, we propose an adaptive greedy algorithm for user recruitment, which is theoretically proved to achieve a constant approximation ratio guarantee. Extensive simulations are conducted, which show our proposed algorithm outperforms baselines under different settings. © 2019, ICST institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Polynomial approximation

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Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Dalal, Surjeet Manoharan, Poongodi Kumar, Lilhore Umesh Seth, Bijeta alsekait, Deema Mohammed Simaiya, Sarita Hamdi, Mounir Raahemifar, Kaamran Department of Computer Science and Engineering Amity University Haryana Gurugram India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Computing University of Louisiana Lafayette USA Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering B. M. Institute of Engineering & Technology Sonipat India Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia College of Information Sciences and Technology Data Science and Artificial Intelligence Program State College USA School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Faculty of Engineering University of Waterloo Waterloo Canada

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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

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

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

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

关键词： Metabolites

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Confidence intervals uncovered: Are we ready for real-world medical imaging AI?

arXiv

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arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

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Adaptive Handover Decision Inspired By Biological Mechanism in Vehicle Ad-hoc Networks

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Computers, Materials & Continua 2019年第9期61卷 1117-1128页

作者： Xuting Duan Jingyi Wei Daxin Tian Jianshan Zhou Haiying Xia Xin Li Kunxian Zheng Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&SafetyControl Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry Transport Beijing100088China Department of Computer Science and Technology Tsinghua UniversityHaidian DistrictBeijing100088China

In vehicle ad-hoc networks(VANETs),the proliferation of wireless communication will give rise to the heterogeneous access environment where network selection becomes *** by the self-adaptive paradigm of cellular attractors,this paper regards an individual communication as a cell,so that we can apply the revised attractor selection model to induce each connected *** at improving the Quality of Service(QoS),we presented the bio-inspired handover decision-making *** addition,we employ the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)for any vehicle to choose an access *** paper proposes a novel framework where the bio-inspired mechanism is combined with *** a dynamic and random mobility environment,our method achieves the coordination of performance of heterogeneous networks by guaranteeing the efficient utilization and fair distribution of network resources in a global *** experimental results confirm that the proposed method performs better when compared with conventional schemes.

关键词： Revised attraction selection model TOPSIS VANETs QoS

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

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SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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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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