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arXiv 2022年

作者： Marinazzo, Daniele Van Roozendaal, Jan Rosas, Fernando E. Stella, Massimo Comolatti, Renzo Colenbier, Nigel Stramaglia, Sebastiano Rosseel, Yves Department of Data Analysis Ghent University Belgium Data Science Institute Imperial College London London United Kingdom Centre for Psychedelic Research Imperial College London London United Kingdom Centre for Complexity Science Imperial College London London United Kingdom Department of Informatics University of Sussex Brighton United Kingdom CogNosco Lab Dept. of Computer Science University of Exeter United Kingdom Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano Italy IRCCS San Camillo Hospital Via Alberoni 70 Venice30126 Italy Physics Department Universitá degli Studi di Bari Aldo Moro INFN Sezione di Bari Italy

Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to assess the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, focusing the modelling solely on pairwise relationships can neglect potentially critical information shared by groups of three or more variables in the form of higher-order interdependencies. To overcome this important limitation, here we propose an information-theoretic framework based on hypergraphs as psychometric models. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer representation of the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-art, re-analyzed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, extending the psychometric toolbox and opening promising avenues for future investigation. © 2022, CC BY.

关键词： Information theory

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Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

arXiv

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arXiv 2022年

作者： Xu, Shaoqing Li, Fang Song, Ziying Fang, Jin Wang, Sifen Yang, Zhi-Xin The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China School of Mechanical Engineering Beijing Institute of Technology China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Transportation Science and Engineering Beihang University Beijing100083 China Inceptio United States

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Quantifying high-order interdependencies on individual patterns via the local O-information: Theory and applications to music analysis

arXiv

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arXiv 2021年

作者： Scagliarini, Tomas Marinazzo, Daniele Guo, Yike Stramaglia, Sebastiano Rosas, Fernando E. Dipartimento Interateneo di Fisica Universitá degli Studi Aldo Moro Bari Italy INFN Italy Department of Data Analysis Ghent University Belgium Department of Computer Science Hong Kong Baptist University Hong Kong Data Science Institute Imperial College London United Kingdom Universitá degli Studi Aldo Moro Italy Centre for Psychedelic Research Department of Brain Science Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom

High-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer, and control such systems. This paper presents a framework to measure high-order interdependence that disentangles their effect on each individual pattern exhibited by a multivariate system. The approach is centred on the local O-information, a new measure that assesses the balance between synergistic and redundant interdependencies at each pattern. To illustrate the potential of this framework, we present a detailed analysis of music scores from J.S. Bach, which reveals how high-order interdependence is deeply connected with highly non-trivial aspects of the musical discourse. Our results place the local O-information as a promising tool of wide applicability, which opens new perspectives for analysing high-order relationships in the patterns exhibited by complex systems. © 2021, CC BY.

关键词： Music

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Reauthorise PEPFAR to prevent death, orphanhood, and suffering for millions of children

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Lancet (London, England) 2023年第10404期402卷 769-770页

作者： Lucie Cluver Jeffrey W Imai-Eaton Lorraine Sherr Mary Mahy Seth Flaxman Department of Social Policy and Intervention University of Oxford Oxford OX1 2ER UK Department of Psychiatry and Mental Health University of Cape Town Cape Town South Africa. Electronic address: lucie.cluver@spi.ox.ac.uk. Center for Communicable Disease Dynamics Department of Epidemiology Harvard T H Chan School of Public Health Harvard University Boston MA USA MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London UK. Institute for Global Health Faculty of Population Health Sciences University College London London UK. Data for Impact UNAIDS Geneva Switzerland. Department of Computer Science University of Oxford Oxford OX1 2ER UK.

Since PEPFAR's implementation, AIDS deaths in infants and young children have fallen by 80% (figure D). PEPFAR's direct effects extend further to the reduction of subsequent negative outcomes on children's lives, such as avoidance of HIV-related developmental delays, family breakdown and separation, abuse and mental health distress, and reduction of poverty-driven sexual exploitation of children. Abrupt halting of development aid can cause health system collapses.6 PEPFAR, the Global Fund to fight AIDS, Tuberculosis and Malaria, national governments, and donors such as the Bill & Melinda Gates Foundation are a crucial partnership for global health security.7 In 2022, PEPFAR provided life-saving antiretroviral medication to 20 million people8—most of them parents and children.

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Spin-Phonon Interactions Induced Anomalous Thermal Conductivity in Nickel (Ii) Oxide

SSRN

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SSRN 2023年

作者： Sun, Qiyang Hou, Songrui Wei, Bin Su, Yaokun Ortiz, Victor Sun, Bo Lin, Jiao Y.Y. Smith, Hillary Danilkin, Sergey Abernathy, Douglas L. Wilson, Richard Li, Chen Mechanical Engineering University of California Riverside RiversideCA92521 United States Materials Science and Engineering University of California Riverside RiversideCA92521 United States Henan Key Laboratory of Materials on Deep-Earth Engineering School of Materials Science and Engineering Henan Polytechnic University Jiaozuo454000 China Tsinghua-Berkeley Shenzhen Institute Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials TSIGS Tsinghua University Shenzhen518055 China Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak RidgeTN37830 United States Department of Physics and Astronomy Swarthmore College SwarthmorePA19081 United States Australian Centre for Neutron Scattering Australian Nuclear Science and Technology Organization Lucas HeightsNSW2234 Australia Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

Nickel (II) oxide is a prominent candidate for spintronic and spin-caloritronic applications operating at room temperature. Although there are extensive studies on nickel oxide, the roles of magnon- and spin-phonon interactions on thermal transport are not well understood. In the present work, the relationship between spin-phonon interactions and thermal transport is investigated by performing inelastic neutron scattering, time-domain thermoreflectance thermal conductivity measurements, and atomistic thermal transport calculations. Inelastic neutron scattering measurements of the magnon lifetime imply that magnon thermal conductivity is trivial, and so heat is conducted only by phonons. Time-domain thermoreflectance measurements of the thermal conductivity vs. temperature follow T-1.47 in the antiferromagnetic phase. This temperature dependence cannot be explained by phonon-isotope and phonon-defect scattering or phonon softening. Instead, we attribute this to magnon-phonon scattering and spin-induced dynamic symmetry breaking. The spin-phonon interactions are saturated in the paramagnetic phase and lead to a weaker temperature dependence of T-1.00 at 550-700 K. These results reveal the importance of spin-phonon interactions on lattice thermal transport, shedding light on the engineering of functional antiferromagnetic spintronic and spin-caloritronic materials through these interactions. © 2023, The Authors. All rights reserved.

关键词： Nickel oxide

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IoT-enabled social relationships meet artificial social intelligence

arXiv

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arXiv 2021年

作者： Dhelim, Sahraoui Ning, Huansheng Farha, Fadi Chen, Liming Atzori, Luigi Daneshmand, Mahmoud The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing China The School of Computing Ulster University NewtownabbeyBT37 0QB United Kingdom The Department of Electrical and Electronic Engineering University of Cagliari piazza d'Armi Cagliari09123 Italy The Department of Business Intelligence and Analytics The Department of Computer Science Stevens Institute of Technology Hoboken United States

With the recent advances of the Internet of Things, and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This paper discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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The medical algorithmic audit (vol 4, pg e384, 2022)

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LANCET DIGITAL HEALTH 2022年第6期4卷 E405-E405页

作者： Liu, X. Glocker, B. McCradden, M. M. Ghassemi, M. Denniston, A. K. Oakden-Rayner, L. Academic Unit of Ophthalmology Institute of Inflammation and Ageing College of Medical and Dental Sciences University of Birmingham UK Department of Ophthalmology University Hospitals Birmingham NHS Foundation Trust Birmingham UK Moorfields Eye Hospital NHS Foundation Trust London UK Health Data Research UK London UK Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK Biomedical Image Analysis Group Department of Computing Imperial College London London UK The Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health Toronto ON Canada Institute for Medical Engineering and Science and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA National Institute of Health Research Biomedical Research Centre for Ophthalmology Moorfields Hospital London NHS Foundation Trust London UK University College London Institute of Ophthalmology London UK Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia. lauren.oakden-rayner@adelaide.edu.au

Artificial intelligence systems for health care, like any other medical device, have the potential to fail. However, specific qualities of artificial intelligence systems, such as the tendency to learn spurious correlates in training data, poor generalisability to new deployment settings, and a paucity of reliable explainability mechanisms, mean they can yield unpredictable errors that might be entirely missed without proactive investigation. We propose a medical algorithmic audit framework that guides the auditor through a process of considering potential algorithmic errors in the context of a clinical task, mapping the components that might contribute to the occurrence of errors, and anticipating their potential consequences. We suggest several approaches for testing algorithmic errors, including exploratory error analysis, subgroup testing, and adversarial testing, and provide examples from our own work and previous studies. The medical algorithmic audit is a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. We propose that safety monitoring and medical algorithmic auditing should be a joint responsibility between users and developers, and encourage the use of feedback mechanisms between these groups to promote learning and maintain safe deployment of artificial intelligence *** © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

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Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression data 23rd

Pathway Activity Score Learning for Dimensionality Reduction...

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23rd International Conference on Discovery science, DS 2020

作者： Karagiannaki, Ioulia Pantazis, Yannis Chatzaki, Ekaterini Tsamardinos, Ioannis Department of Computer Science University of Crete Heraklion Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece Laboratory of Pharmacology Medical School Democritus University of Thrace Alexandroupoli Greece Institute of Agri-food and Life Sciences University Research Center Hellenic Mediterranean University HeraklionCrete71410 Greece Gnosis Data Analysis PC HeraklionCrete Greece

ISBN: (纸本)9783030615260

Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biological interpretation. As a use-case, PASL is applied on two collections of breast cancer and leukemia gene expression datasets. We show that PASL does retain the predictive information for disease classification on new, unseen datasets, as well as outperforming PLIER, a recently proposed competitive method. We also show that differential activation pathway analysis provides complementary information to standard gene set enrichment analysis. The code is available at https://***/mensxmachina/PASL. © 2020, The Author(s).

关键词： Activation analysis

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A new cognitive clock matching phenotypic and epigenetic ages (vol 12, 364, 2022)

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TRANSLATIONAL PSYCHIATRY 2022年第1期12卷 364页

作者： Krivonosov, M. I. Kondakova, E. V. Bulanov, N. A. Polevaya, S. A. Franceschi, C. Ivanchenko, M. V. Vedunova, M. V. Institute of Biology and Biomedicine Department of Neurotechnology N. I. Lobachevsky State University Nizhny Novgorod Russia. krivonosov@itmm.unn.ru. Laboratory of Systems Medicine of Healthy Aging Institute of Biology and Biomedicine N. I. Lobachevsky State University Nizhny Novgorod Russia. Faculty of Computer Science School of Data Analysis and Artificial Intelligence HSE University Moscow Russia. Faculty of Social Sciences Department of Psychophysiology N. I. Lobachevsky State University Nizhny Novgorod Russia. Institute of Information Technology Mathematics and Mechanics Department of Applied Mathematics N. I. Lobachevsky State University Nizhny Novgorod Russia.

Cognitive abilities decline with age, constituting a major manifestation of aging. The quantitative biomarkers of this process, as well as the correspondence to different biological clocks, remain largely an open problem. In this paper we employ the following cognitive tests: 1. differentiation of shades (campimetry); 2. evaluation of the arithmetic correctness and 3. detection of reversed letters and identify the most significant age-related cognitive indices. Based on their subsets we construct a machine learning-based Cognitive Clock that predicts chronological age with a mean absolute error of 8.62 years. Remarkably, epigenetic and phenotypic ages are predicted by Cognitive Clock with an even better accuracy. We also demonstrate the presence of correlations between cognitive, phenotypic and epigenetic age accelerations that suggests a deep connection between cognitive performance and aging status of an individual.

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