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The design of a mobile platform providing personalized assistance to older multimorbid patients with mild dementia or mild cognitive impairment (MCI) 22

The design of a mobile platform providing personalized assis...

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Proceedings of the 10th international conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

作者： Mert Gencturk Gokce B. Laleci Erturkmen Henrike Gappa Wolfgang Schmidt-Barzynski Antje Steinhoff Pedro Abizanda Timothy Robbins Omid Pournik Bilal Ahmad Harpal Randeva Oana Cramariuc Theodoros N. Arvanitis Jaouhar Ayadi Yehya Mohamad Mustafa Yuksel SRDC Software Research & Development and Consultancy Corporation TR Fraunhofer Institute for Applied Information Technology (FIT) DE University Hospital OWL DE Complejo Hospitalario Universitario de Albacete Servicio de Salud de Castilla-La Mancha ES and Facultad de Medicina de Albacete Universidad de Castilla-La Mancha Spain Data & Digital Driven Research Unit University Hospitals Coventry & Warwickshire NHS Trust GB Institute of Digital Healthcare WMG University of Warwick GB Centrul IT pentru Stiinta si Technologie RO Eclexys Sagl CH

ISBN: (纸本)9781450398077

Management of multiple chronic conditions introduces demanding challenges for patients. This situation becomes more complex when multimorbidity is associated with dementia. In this paper, we present the design of a mobile Patient Empowerment Platform that enables older multimorbid patients with mild dementia or mild cognitive impairment (MCI) to easily follow their complex care plans and increase their adherence. We focus on the presentation of the human-centered design process that we have followed with the involvement of patients, informal caregivers, and healthcare professionals via the clinical pilot sites of the CAREPATH project. We elaborate the design challenges we have faced and present the iterative mock-ups that have been created in cooperation with end users to address these challenges and the final PEP design.

关键词： Patient empowerment Dementia Multimorbidity Mobile applications

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ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder

ICA-based Individualized Differential Structure Similarity N...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiang Li Ming Xu Rongtao Jiang Xuemei Li Vince D. Calhoun Xinyu Zhou Jing Sui National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Radiology and Biomedical Imaging Yale School of Medicine CT United States Department of Psychiatry The First Affiliated Hospital of Chongqing Medical University Chongqing China Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emorv University Atlanta GA United States IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified large scale structural brain alterations in MDD, yet most are group analyses with atlas-parcellated anatomical regions. Here we proposed a method to measure individual difference by independent component analysis (ICA)-based individual difference structural similarity network (IDSSN). This approach provided a data-adaptive, atlas-free solution that can be applied to new individual subjects. Specifically, we constructed individualized whole-brain structural covariance networks based on network perturbation approach using spatially constrained ICA. First, a set of benchmark independent components (ICs) were generated using gray matter volume (GMV) from all healthy controls. Then individual heterogeneity was obtained by calculating differences and other similarity metrics between ICs derived from "each one patient + all controls" and the benchmark ICs, resulting in 32 imaging features and structural similarity networks for each patient, which can be used for predicting multiple clinical symptoms. We estimated IDSSN for 189 adolescent MDD patients aged 10-20 years and compared them to 112 healthy adolescents. We tested their predictability of the Hamilton Anxiety Scale , the 17-item Hamilton Depression Scale and six clinical syndromes using connectome-based predictive modeling. The prediction results showed that ICA-based IDSSN features reveal more disease-specific information than those using other brain templates. We also found that depression-associated networks mainly involved the default-mode network and visual network. In conclusion, our study proposed an adaptive method that improves the ability to detect GMV deviations and specificity between one individual patient and healthy groups, providing a new perspectives and insights for evaluating individual-level clinical heterogeneity based on brain structures.

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Secure and Efficient Federated Multi-label Propagation via Secret Sharing 19th

Secure and Efficient Federated Multi-label Propagation via...

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19th CCF conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Guo, Chen Hu, Wenbin Xiang, Zhishang Dong, Shiyu Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fuzhou350108 China Xianda College of Economics & Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

The Multi-Label Propagation Algorithm (MLPA) identifies and reveals community structure by passing labels between network nodes, and is also suitable for dealing with complex networks with overlapping communities. Due to its flexibility and effectiveness, the algorithm has been successfully applied in a number of fields, including image segmentation, text classification, and bioinformatics. In today’s society where personal privacy protection is increasingly important, how to detect communities without revealing sensitive information has become a hot issue in the field of network analysis. Existing privacy-preserving multi-label propagation algorithms primarily rely on anonymization and homomorphic encryption techniques. While homomorphic encryption can protect privacy, the complex encryption and decryption processes incur significant computational costs, making it challenging to achieve efficient computation while ensuring accuracy and privacy. In this paper, we propose a Secure and Efficient Federated Multi-Label Propagation Algorithm (SEFMLPA) that combines an anonymization strategy with a secret sharing strategy, considering the attribute similarities between nodes to ensure privacy, accuracy, and efficiency. The experimental results indicate that SEFMLPA achieves an accuracy comparable to the latest algorithms and reduces runtime by 80%. These significant improvements validate the effectiveness and superiority of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Bullying10K: a large-scale neuromorphic dataset towards privacy-preserving bullying recognition 23

Bullying10K: a large-scale neuromorphic dataset towards priv...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Yiting Dong Yang Li Dongcheng Zhao Guobin Shen Yi Zeng School of Future Technology University of Chinese Academy of Sciences and Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences and Center for Long-term Artificial Intelligence School of Artificial Intelligence University of Chinese Academy of Sciences and Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences and Center for Long-term Artificial Intelligence Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences and Center for Long-term Artificial Intelligence School of Future Technology University of Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences and Center for Long-term Artificial Intelligence

The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) camera to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.

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MultiFusion: fusing pre-trained models for multi-lingual, multi-modal image generation 23

MultiFusion: fusing pre-trained models for multi-lingual, mu...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Marco Bellagente Manuel Brack Hannah Teufel Felix Friedrich Björn Deiseroth Constantin Eichenberg Andrew Dai Robert J.N. Baldock Souradeep Nanda Koen Oostermeijer Patrick Schramowski Kristian Kersting Samuel Weinbach Stability AI German Research Center for Artificial Intelligence (DFKI) and Computer Science Department TU Darmstadt Aleph Alpha Computer Science Department TU Darmstadt and Hessian.AI Aleph Alpha and Computer Science Department TU Darmstadt and Hessian.AI University of Texas German Research Center for Artificial Intelligence (DFKI) and Computer Science Department TU Darmstadt and Hessian.AI and LAION German Research Center for Artificial Intelligence (DFKI) and Computer Science Department TU Darmstadt and Hessian.AI and Centre for Cognitive Science TU Darmstadt

The recent popularity of text-to-image diffusion models (DM) can largely be at tributed to the intuitive interface they provide to users. The intended generation can be expressed in natural language, with the model producing faithful interpretations of text prompts. However, expressing complex or nuanced ideas in text alone can be difficult. To ease image generation, we propose MULTIFUSION that allows one to express complex and nuanced concepts with arbitrarily interleaved inputs of multiple modalities and languages. MULTIFUSION leverages pre-trained models and aligns them for integration into a cohesive system, thereby avoiding the need for extensive training from scratch. our experimental results demonstrate the efficient transfer of capabilities from individual modules to the downstream model. Specifically, the fusion of all independent components allows the image generation module to utilize multilingual, interleaved multimodal inputs despite being trained solely on monomodal data in a single language.

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A recurrent neural circuit mechanism of temporal-scaling equivariant representation 23

A recurrent neural circuit mechanism of temporal-scaling equ...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Junfeng Zuo Xiao Liu Ying Nian Wu Si Wu Wen-Hao Zhang Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University Department of Statistics University of California Los Angeles Lyda Hill Department of Bioinformatics UT Southwestern Medical Center and O'Donnell Brain Institute UT Southwestern Medical Center

Time perception is fundamental in our daily life. An important feature of time perception is temporal scaling (TS): the ability to generate temporal sequences (e.g., movements) with different speeds. However, it is largely unknown about the mathematical principle underlying TS in the brain. The present theoretical study investigates temporal scaling from the Lie group point of view. We propose a canonical nonlinear recurrent circuit dynamics, modeled as a continuous attractor network, whose neuronal population responses embed a temporal sequence that is TS equivariant. We find the TS group operators can be explicitly represented by a time-invariant control input to the network, whereby the input gain determines the TS factor (group parameter), and the spatial offset between the control input and the network state on the continuous attractor manifold gives rise to the generator of the Lie group. The recurrent circuit's neuronal responses are consistent with experimental data. The recurrent circuit can drive a feedforward circuit to generate complex sequences with different temporal scales, even in the case of negative temporal scaling ("time reversal"). Our work for the first time analytically links the abstract temporal scaling group and concrete neural circuit dynamics.

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类脑心智计算的科学技术和工程应用的研究与思考

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计算机科学 2023年第2期50卷 364-373页

作者：刘扬刘睿佳周黎鸣左宪禹杨伟周毅河南大学河南省空间信息处理工程研究中心河南开封475004 河南大学河南省大数据分析与处理重点实验室河南开封475004 河南大学河南省车联网协同技术国际联合实验室郑州450046 河南大学计算机与信息工程学院河南开封475004 河南大学软件学院河南开封475004 河南大学人工智能学院郑州450046 河南大学深圳研究院广东深圳518000

发展新一代的类脑智能,需要综合考虑形成自然智能的结构、功能和行为等研究,偏颇任一方向都是不全面的,难以完全触及智能的本质。文中基于神经系统的结构仿真、认知系统的功能模仿和自然智能的行为模拟,定义了类脑心智计算(BMC)的基本概... 详细信息

发展新一代的类脑智能,需要综合考虑形成自然智能的结构、功能和行为等研究,偏颇任一方向都是不全面的,难以完全触及智能的本质。文中基于神经系统的结构仿真、认知系统的功能模仿和自然智能的行为模拟,定义了类脑心智计算(BMC)的基本概念,提出了BMC的假设、模型和框架,研究了BMC的前沿理论。在大脑机制、心智模式和行为控制上,分析了当前BMC研究的技术路线、核心算法和关键技术,综述了BMC的复杂系统和工程应用现状。结合智能科学、神经科学、认知科学、信息科学和计算数学等多学科的交叉融合特征,进一步讨论了BMC的科研范式和跨学科建设问题。BMC研究将有望在新一代类脑智能的科学理论、技术创新和工程系统上取得重大突破。

关键词：类脑心智计算类脑智能跨媒体认知神经计算跨模态神经认知计算跨学科研究

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生成式人工智能对学习分析研究的影响:现状与前瞻——2024年学习分析与知识国际会议(LAK24)述评

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电化教育研究 2024年第12期45卷 113-120页

作者：石琬若韩锡斌清华大学教育研究院北京100084

2011年,首届学习分析与知识国际会议(简称LAK)召开,标志着学习分析研究领域的确立。2022年,生成式人工智能(AIGC)的爆发,给该领域的研究带来了空前的影响。文章基于最新一届LAK会议的主旨报告及论文,从学习分析的技术系统设计、应用场... 详细信息

2011年,首届学习分析与知识国际会议(简称LAK)召开,标志着学习分析研究领域的确立。2022年,生成式人工智能(AIGC)的爆发,给该领域的研究带来了空前的影响。文章基于最新一届LAK会议的主旨报告及论文,从学习分析的技术系统设计、应用场景、效果评价、伦理风险等方面,系统梳理了AIGC对学习分析领域产生的影响以及今后的发展趋势。研究分析结果表明:基于AIGC的学习分析系统在自动化程度和个性化匹配能力方面得到提升;AIGC具有教师、学习者和助教三种角色的功能属性,围绕数据生成、数据测量与分析、学习反馈等环节拥有多种应用的潜能;从AIGC与传统模型算法的比较、学生学习体验等方面来看,其在学习分析中的应用具有一定的优势,然而尚未有实证证据表明AIGC的使用对学习效果有提升作用。AIGC及其相关技术的发展将为探究人机协同的学习过程带来全新的视角和可能,也将为研究学习过程提供更丰富的数据和创新的方法,同时将促使学习分析价值取向与目标定位的重构,不断推动对复杂人机交互行为方式、人智协同的认知变化与学习规律、AIGC融入的新型教学方式等的理解与探索。

关键词：人工智能学习分析教育中的人工智能学习分析与知识国际会议文献综述

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Deep Learning Based Event Reconstruction for the IceCube-Gen2 Radio Detector 38

Deep Learning Based Event Reconstruction for the IceCube-Gen...

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38th international Cosmic Ray conference, ICRC 2023

作者： Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Audehm, J. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Bishop, A. Blaufuss, E. Blot, S. Bohmer, M. Bontempo, F. Book, J.Y. Borowka, J. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bouma, S. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Cataldo, M. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Clark, R. Classen, L. Coleman, A. Collin, G.H. Conrad, J.M. Cowen, D.F. Dasgupta, B. Dave, P. Deaconu, C. De Clercq, C. De Kockere, S. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evans, J.J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Flaggs, B. Fox, D. Franckowiak, A. Fritz, A. Fujii, T. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Gernhaeuser, R. Ghadimi, A. Giri, P. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Guevel, D. Günther, C. Gutjahr, P. Haack, C. Haji Azim, T. Hallgren, A. Halliday, R. Hallmann, S. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haugen, J. Haungs, A. Heinen, D. Helbing, K. Hellrung, J. Hendricks, B. Henningsen, F. Henrichs, J. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hignight, J. Hill, C. III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Dept. of Physics University of Chicago ChicagoIL60637 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio Sta

The planned in-ice radio array of IceCube-Gen2 at the South Pole will provide unprecedented sensitivity to ultra-high-energy (UHE) neutrinos in the EeV range. The ability of the detector to measure the neutrino’s energy and direction is of crucial importance. This contribution presents an end-to-end reconstruction of both of these quantities for both detector components of the hybrid radio array (’shallow’ and’deep’) using deep neural networks (DNNs). We are able to predict the neutrino’s direction and energy precisely for all event topologies, including the electron neutrino charged-current (νe-CC) interactions, which are more complex due to the LPM effect. This highlights the advantages of DNNs for modeling the complex correlations in radio detector data, thereby enabling a measurement of the neutrino energy and direction. We discuss how we can use normalizing flows to predict the PDF for each individual event which allows modeling the complex non-Gaussian uncertainty contours of the reconstructed neutrino direction. Finally, we discuss how this work can be used to further optimize the detector layout to improve its reconstruction performance. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Neutrons

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社会民众新冠病毒疫苗接种意识传播模型与干预策略研究

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中国管理科学 2023年第8期31卷 269-277页

作者：朱宏淼闫辛齐佳音靳祯上海对外经贸大学工商管理学院上海201620 上海对外经贸大学统计与信息学院上海201620 广州大学网络空间安全学院广东广州510006 北京邮电大学可信分布式计算与服务教育部重点实验室北京100876 山西大学复杂系统研究所山西太原030006 山西大学疾病防控的数学技术与大数据分析山西省重点实验室山西太原030006

研究如何通过多种渠道的融合使用以促进新冠疫苗接种意识在民众间传播,对新冠疫情防控有重要意义。首先,基于多重网络理论,建立民众新冠疫苗接种意识的多渠道传播动力学模型。其次,利用该模型对新冠病毒疫病毒苗接种意识在社会民众间的... 详细信息

研究如何通过多种渠道的融合使用以促进新冠疫苗接种意识在民众间传播,对新冠疫情防控有重要意义。首先,基于多重网络理论,建立民众新冠疫苗接种意识的多渠道传播动力学模型。其次,利用该模型对新冠病毒疫病毒苗接种意识在社会民众间的传播过程进行数值仿真,提出相应的干预策略。最后,利用实际数据对模型进行参数估计,验证其有效性。研究表明:在新冠病毒疫苗接种宣传时,过多使用一种渠道或仅使用一种宣传语,容易使民众对疫苗接种产生疲劳心理,导致新冠病毒疫苗接种意识的传播效率下降,甚至使民众失去新冠病毒疫苗接种意识,因此,应适当变换宣传语,同时均衡使用各渠道进行新冠病毒疫苗接种宣传,以使各渠道间疫苗接种意识传播产生相互促进作用,进而使新冠病毒疫苗接种意识的传播效率达到最大。对疫苗接种意识偏差者进行适度心理沟通、监督与管控,使其改变对新冠病毒疫苗接种的认知偏差及抵触情绪,停止散布疫苗接种的负面谣言,将有效促进新冠病毒疫苗接种意识传播。

关键词：突发公共卫生事件新冠病毒疫苗接种意识社会传播多重网络复杂系统仿真

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