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arXiv 2023年

作者： Capraro, Valerio Lentsch, Austin Acemoglu, Daron Akgun, Selin Akhmedova, Aisel Bilancini, Ennio Bonnefon, Jean-François Brañas-Garza, Pablo Butera, Luigi Douglas, Karen M. Everett, Jim A.C. Gigerenzer, Gerd Greenhow, Christine Hashimoto, Daniel A. Holt-Lunstad, Julianne Jetten, Jolanda Johnson, Simon Kunz, Werner H. Longoni, Chiara Lunn, Pete Natale, Simone Paluch, Stefanie Rahwan, Iyad Selwyn, Neil Singh, Vivek Suri, Siddharth Sutcliffe, Jennifer Tomlinson, Joe van der Linden, Sander Van Lange, Paul A.M. Wall, Friederike Van Bavel, Jay J. Viale, Riccardo Department of Psychology University of Milan-Bicocca Italy Department of Economics MIT United States Institute Professor and Department of Economics MIT United States College of Education Michigan State University United States IMT School of Advanced Studies Lucca Italy Toulouse School of Economics France Department of Economics Loyola Andalucia University Spain Department of Economics Copenhagen Business School Denmark School of Psychology University of Kent United Kingdom Max Planck Institute for Human Development Berlin Germany Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery Perelman School of Medicine University of Pennsylvania United States Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania United States Department of Psychology and Neuroscience Brigham Young University United States School of Psychology University of Queensland Australia MIT Sloan School of Management United States Department of Marketing University of Massachusetts Boston United States Department of Marketing Bocconi University Italy Economic & Social Research Institute Dublin Ireland Department of Humanities University of Turin Italy Department of Service and Technology Marketing Aarhus University Denmark Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Faculty of Education Monash University Australia Microsoft Research United States York Law School United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Experimental and Applied Psychology Vrije Universiteit Amsterdam Netherlands Department of Management Control and Strategic Management University of Klagenfurt Austria Department of Psychology Center for Neural Science New York University United States Norwegian School of Economics Bergen Norway Department of Economics University of Milan-Bicocca Italy

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. © 2023, CC BY.

关键词： Artificial intelligence

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Successive cohorts of twitter users show increasing activity and shrinking content horizons

arXiv

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arXiv 2021年

作者： Wolf, Frederik Lorenz-Spreen, Philipp Lehmann, Sune Member of the Leibnitz Association Telegrafenberg Potsdam14473 Germany Department of Physics Humboldt University Newtonstraße 15 Berlin12489 Germany Center for Adaptive Rationality Max Planck Institute for Human Development Berlin14195 Germany Department of Applied Mathematics and Computer Science Technical University of Denmark LyngbyDK-2800 Denmark Center for Social Data Science University of Copenhagen Copenhagen1353 Denmark

The global public sphere has changed dramatically over the past decades: A significant part of public discourse now takes place on algorithmically driven platforms owned by a handful of private companies. Despite its growing importance, there is scant large-scale academic research on the long-term evolution of user behaviour on these platforms, because the data are often proprietary to the platforms. Here, we evaluate the individual behaviour of 600,000 Twitter users between 2012 and 2019 and find empirical evidence for an acceleration of the way Twitter is used on an individual level. This manifests itself in the fact that cohorts of Twitter users behave differently depending on when they joined the platform. Behaviour within a cohort is relatively consistent over time and characterised by strong internal interactions, but over time behaviour from cohort to cohort shifts towards increased activity. Specifically, we measure this in terms of more tweets per user over time, denser interactions with others via retweets, and shorter content horizons, expressed as an individual's decaying autocorrelation of topics over time. Our observations are explained by a growing proportion of active users who not only tweet more actively but also elicit more retweets. These behaviours suggest a collective contribution to an increased flow of information through each cohort's news feed-an increase that potentially depletes available collective attention over time. Our findings complement recent, empirical work on social acceleration, which has been largely agnostic about individual user activity. © 2021, CC BY.

关键词： Social networking (online)

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A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

arXiv

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arXiv 2023年

作者： Di Liberto, Giovanni M. Nidiffer, Aaron Crosse, Michael J. Zuk, Nathaniel J. Haro, Stephanie Cantisani, Giorgia Winchester, Martin M. Igoe, Aoife McCrann, Ross Chandra, Satwik Lalor, Edmund C. Baruzzo, Giacomo School of Computer Science and Statistics University of Dublin Trinity College Ireland ADAPT Centre Trinity College Institute of Neuroscience Ireland Dept Biomedical Engineering Dept Neuroscience Del Monte Institute for Neuroscience Center for Visual Science University of Rochester NY United States Segotia Galway Ireland Department of Mechanical Manufacturing and Biomedical Engineering TCBE Trinity College Dublin Ireland Department of Psychology Nottingham Trent University Nottingham United Kingdom Human Health and Performance Systems MIT Lincoln Laboratory LexingtonMA United States Speech and Hearing Bioscience and Technology Harvard Medical School BostonMA United States Laboratoire des systémes perceptifs Département d’études cognitives ENS PSL University CNRS Paris75005 France Dept Biomedical Engineering Center for Visual Science University of Rochester NY United States Department of Information Engineering University of Padova Padova Italy

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis. © 2023, CC BY-NC-ND.

关键词： Data assimilation

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Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks

arXiv

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arXiv 2023年

作者： Iuso, Domenico Chatterjee, Soumick Cornelissen, Sven Verhees, Dries De Beenhouwer, Jan Sijbers, Jan Imec-Vision Lab Department of Physics University of Antwerp Antwerp2610 Belgium Faculty of Computer Science Otto von Guericke University Street Magdeburg39106 Germany Genomics Research Centre Human Technopole V.le Rita Levi-Montalcini 1 Milan20157 Italy Materialise NV. Technologielaan 15 Leuven3001 Belgium Flanders Make vzw. Oude Diestersebaan 133 Lommel3920 Belgium DynXlab Center for 4D Quantitative X-ray Imaging and Analysis Antwerp2610 Belgium

Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all manufactured samples of a batch, X-ray computed tomography (X-CT) is often used combined with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly, as they can be trained to be robust to the material being analysed and resilient towards poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information. This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch pipeline for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models is post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was UNet++ with an average precision of 0.751 ± 0.030, while the best unsupervised model was the post-processed ceVAE with 0.830 ± 0.003. The VAE/ceVAE models demonstrated superior capabilities, particularly when leveraging post-processing techniques. © 2023, CC BY-NC-ND.

关键词： 3D printing

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Impact of delayed response on Wearable Cognitive Assistance

arXiv

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arXiv 2020年

作者： Muñoz, Manuel Olguín Klatzky, Roberta Wang, Junjue Pillai, Padmanabhan Satyanarayanan, Mahadev Gross, James Division of Information Science and Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Psychology Carnegie Mellon University PittsburghPA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States School of Computer Science Carnegie Mellon University PittsburghPA United States Intel Labs PittsburghPA United States

Wearable Cognitive Assistants (WCA) are anticipated to become a widely-used application class, in conjunction with emerging network infrastructures like 5G that incorporate edge computing capabilities. While prototypical studies of such applications exist today, the relationship between infrastructure service provisioning and its implication for WCA usability is largely unexplored despite the relevance that these applications have for future networks. This paper presents an experimental study assessing how WCA users react to varying end-to-end delays induced by the application pipeline or infrastructure. Participants interacted directly with an instrumented task-guidance WCA as delays were introduced into the system in a controllable fashion. System and task state were tracked in real time, and biometric data from wearable sensors on the participants were recorded. Our results show that periods of extended system delay cause users to correspondingly (and substantially) slow down in their guided task execution, an effect that persists for a time after the system returns to a more responsive state. Furthermore, the slow-down in task execution is correlated with a personality trait, neuroticism, associated with intolerance for time delays. We show that our results implicate impaired cognitive planning, as contrasted with resource depletion or emotional arousal, as the reason for slowed user task executions under system delay. The findings have several implications for the design and operation of WCA applications as well as computational and communication infrastructure, and additionally for the development of performance analysis tools for WCA. © 2020, CC-BY.

关键词： Quality of service

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Accuracy of 3-dimensional freehand ultrasound to estimate anatomical landmarks in children and adolescents with obesity

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Gait & Posture 2021年 90卷 232-233页

作者： Schwab, C. Durstberger, S. Kainz, H. Baca, A. Thajer, A. Greber-Platzer, S. Ilse, J. Horsak, B. Kranzl, A. St. Pölten University of Applied Science Department of Health Sciences St. Pölten Austria Orthopedic Hospital Vienna - Speising Laboratory of Gait and Movement Analysis Vienna Austria University of Vienna - Center for Sports Science and University Sports Department of Biomechanics- Kinesiology and Applied Computer Science Vienna Austria Medical University of Vienna Department of Pediatrics and Adolescent Medicine Vienna Austria KU Leuven Department of Movement Sciences - Human Movement Biomechanics Research Group Leuven Belgium

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Comment to Federal Trade Commission on Disclosure Guidelines and Dark Patterns

SSRN

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SSRN 2025年

作者： Burke, Caitlin Gunawan, Johanna King, Jennifer Kshirsagar, Mihir Acquisti, Alessandro Chetty, Marshini Choffnes, David Egelman, Serge Gray, Colin M. Hartzog, Woodrow Im, Jane Mayer, Jonathan Mhaidli, Abraham Narayanan, Arvind Roesner, Franziska Schaffner, Brennan Schaub, Florian Strahilevitz, Lior Tong, Nicole Wang, Charlie Wilson, Christo Zeng, Eric Department of Communication Stanford University United States Khoury College of Computer Sciences Northeastern University United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States Heinz College Carnegie Mellon University United States Department of Computer Science University of Chicago United States Khoury College of Computer Sciences Northeastern University United States United States Purdue University United States Boston University School of Law United States School of Information Division of Computer Science and Engineering University of Michigan United States Princeton University United States School of Information University of Michigan United States Princeton University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States Department of Computer Science University of Chicago United States School of Information University of Michigan United States University of Chicago United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States CyLab Carnegie Mellon University United States Stanford University School of Law United States Princeton University United States

As documented in several research studies, consumers may encounter dark patterns in many online contexts, such as when making choices to consent to the disclosure of personal information or to cookies, when interacting with services and applications like games or content feeds that seek to capture and extend consumer attention and time spent, and in e-commerce, including at multiple points along a purchasing journey. Dark patterns may start with the advertising of a product or service, and can be present across the whole customer path, including sign-up, purchase, and cancellation. As a result, the Commission’s guidance should cover the business’s entire interaction with the consumer to ensure that they are allowed to engage in free and informed transactions. © 2025, The Authors. All rights reserved.

关键词： Sales

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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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In vivo high-resolution multimodal nonlinear optical microscopy of spinal cord in mice

In vivo high-resolution multimodal nonlinear optical microsc...

引用

Advanced Chemical Microscopy for Life science and Translational Medicine 2020

作者： Wu, Wanjie Qin, Zhongya Wu, Junqiang Chen, Congping Chen, Weitao Liu, Kai Qu, Jianan Y. Biophotonics Research Laboratory Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Division of Life Science State Key Laboratory of Molecular Neuroscience Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center of Systems Biology and Human Health Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9781510632677

Chronic in vivo optical imaging of the spinal cord is an effective way to study the biological processes during and after spinal cord injury (SCI) in mouse models. It normally relies on an implanted spinal chamber to provide continuous optical access to the spinal cord. However, the chronic window consists of multiple layers of transparent materials with various optical properties and irregular thickness, which induce large optical aberration. Therefore, the image quality of multiphoton microscopy as well as the precision of femtosecond laser axotomy were dramatically degraded. In this work, we developed an adaptive optics (AO) microscope system integrating stimulated Raman scattering (SRS) and twophoton excited fluorescence (TPEF). Using our system, the aberrations induced by the spinal cord window were measured and compensated accordingly, enabling both high-resolution imaging and precise laser axotomy of the mouse spinal cord. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Adaptive optics

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