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Studies in Adult Education 2023年第1期55卷

作者： Kaisa Pihlainen Anja Ehlers Rebekka Rohner Katerina Cerna Eija Kärnä Moritz Hess Lisa Hengl Lotta Aavikko Susanne Frewer-Graumann Vera Gallistl Claudia Müller a School of Educational Sciences and Psychology University of Eastern Finland Joensuu Finland b Institute of Gerontology at Technical University Dortmund Research Association for Gerontology Technical University Dortmund Dortmund Germany c Department of General Health Studies Karl Landsteiner University Krems an der Donau Austria d Division of Human-Computer Interaction University of Gothenburg Gothenburg Sweden e Hochschule Niederrhein Krefeld Germany f FOM University of Applied Science Münster Germany g Institute for Information Systems University of Siegen Siegen Germany

The rapid development of digital technologies and their increasing application in many areas of everyday life challenge all citizens to continuously learn digital skills. This also applies to older adults, among whom digital literacy is on average less well-developed than among younger adults. This article investigates why retired older adults participate in opportunities to learn digital skills. Multiple case design with both qualitative and quantitative methods was used to include the views of older adults from Austria, Finland, and Germany. The results of this interdisciplinary study indicated individual, social and technical reasons for their participation in digital skills training. Practical implications and recommendations for future studies are suggested.

关键词： Older adults participation digital skills multiple case study

Retrospective Reader for Machine Reading Comprehension

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arXiv 2020年

作者： Zhang, Zhuosheng Yang, Junjie Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China SJTU-ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai China

Machine reading comprehension (MRC) is an AI challenge that requires machine to determine the correct answers to questions based on a given passage. MRC systems must not only answer question when necessary but also distinguish when no answer is available according to the given passage and then tactfully abstain from answering. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder, though the latest practice on MRC modeling still most benefits from adopting well pre-trained language models as the encoder block by only focusing on the "reading". This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions. Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yield an initial judgment;2) intensive reading that verifies the answer and gives the final prediction. The proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0 and NewsQA, achieving new state-of-the-art results. Significance tests show that our model is significantly better than the strong ALBERT baseline. A series of analysis is also conducted to interpret the effectiveness of the proposed reader. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

Spot that Bird: A Location Based Bird Game

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arXiv 2018年

作者： Mehta, Sneha Department of Computer Science Center for Human Computer Interaction Virginia Tech BlacksburgVA United States

In today’s age of pervasive computing and social media people make extensive use of technology for communicating, sharing media and learning. Yet while in the outdoors, on a hike or a trail we find ourselves inept of information about the natural world surrounding us. In this paper I present in detail the design and technological considerations required to build a location based mobile application for learning about the avian taxonomy present in the user’s surroundings. It is designed to be a game for better engagement and learning. The application makes suggestions for birds likely to be sighted in the vicinity of the user and requires the user to spot those birds and upload a photograph to the system. If spotted correctly the user scores points. I also discuss some design methods and evaluation approaches for the application. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

P-012 Investigating the potential role of microRNAs as biomarkers in idiopathic non-obstructive azoospermia: A systematic review and in-silico analysis of the affected pathways

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human Reproduction 2022年第SUPPLEMENT_1期37卷

作者： Pantou, A Grigoriadis, S Karagkouni, D Pantos, K Maziotis, E Tomara, P Hatzigeorgiou, A Agarwal, A Sfakianoudis, K Simopoulou, M Genesis Genoma Lab Department of Genetic Diagnosis- Clinical Genetics & Research Athens Greece National and Kapodistrian University of Athens Laboratory of Physiology- Medical School Athens Greece University of Thessaly DIANA-Lab- Department of Computer Science and Biomedical Informatics Lamia Greece Genesis Athens Clinic Centre for Human Reproduction Athens Greece Cleveland Clinic American Center for Reproductive Medicine Cleveland Greece

Designing Ecosystems of Intelligence from First Principles

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arXiv 2022年

作者： Friston, Karl J. Ramstead, Maxwell J.D. Kiefer, Alex B. Tschantz, Alexander Buckley, Christopher L. Albarracin, Mahault Pitliya, Riddhi J. Heins, Conor Klein, Brennan Millidge, Beren Sakthivadivel, Dalton A.R. Smithe, Toby St Clere Koudahl, Magnus Tremblay, Safae Essafi Petersen, Capm Fung, Kaiser Fox, Jason G. Swanson, Steven Mapes, Dan René, Gabriel VERSES AI Research Lab Los AngelesCA United States Wellcome Centre for Human Neuroimaging University College London London United Kingdom Department of Philosophy Monash University Melbourne Australia Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science Université du Québec à Montréal MontréalQC Canada Department of Experimental Psychology University of Oxford Oxford United Kingdom Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Network Science Institute Northeastern University BostonMA United States Brain Network Dynamics Unit University of Oxford Oxford United Kingdom Department of Mathematics Stony Brook University Stony BrookNY United States Department of Physics and Astronomy Stony Brook University Stony BrookNY United States Department of Biomedical Engineering Stony Brook University Stony BrookNY United States Topos Institute BerkeleyCA United States Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Philosophy Université du Québec à Montréal MontréalQC Canada

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call "shared intelligence". This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems;namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology. © 2022, CC BY.

关键词： Ecosystems

The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Modeling named entity embedding distribution into hypersphere

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arXiv 2019年

作者： Zhang, Zhuosheng Tang, Bingjie Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Computer Science Department Brown University RI United States

This work models named entity distribution from a way of visualizing topological structure of embedding space, so that we make an assumption that most, if not all, named entities (NEs) for a language tend to aggregate together to be accommodated by a specific hypersphere in embedding space. Thus we present a novel open definition for NE which alleviates the obvious drawback in previous closed NE definition with a limited NE dictionary. Then, we show two applications with introducing the proposed named entity hypersphere model. First, using a generative adversarial neural network to learn a transformation matrix of two embedding spaces, which results in a convenient determination of named entity distribution in the target language, indicating the potential of fast named entity discovery only using isomorphic relation between embedding spaces. Second, the named entity hypersphere model is directly integrated with various named entity recognition models over sentences to achieve state-of-theart results. Only assuming that embeddings are available, we show a prior knowledge free approach on effective named entity distribution depiction. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Dependency and span, cross-style semantic role labeling on PropBank and NomBank

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arXiv 2019年

作者： Li, Zuchao Zhao, Hai Zhou, Junru Parnow, Kevin He, Shexia Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

The latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalisms/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this paper, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role, providing a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation. Our model learns a general semantic argument structure and is capable of outputting in either style. Additionally, we propose a syntax-aided method to uniformly enhance the learning of both dependency and span representations. Experiments show that the proposed methods are effective on both span and dependency SRL benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Syntactics

Multi-span style extraction for generative reading comprehension

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arXiv 2020年

作者： Yang, Junjie Zhang, Zhuosheng Zhao, Hai SJTU-ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Generative machine reading comprehension (MRC) requires a model to generate well-formed answers. For this type of MRC, answer generation method is crucial to the model performance. However, generative models, which are supposed to be the right model for the task, in generally perform poorly. At the same time, single-span extraction models have been proven effective for extractive MRC, where the answer is constrained to a single span in the passage. Nevertheless, they generally suffer from generating incomplete answers or introducing redundant words when applied to the generative MRC. Thus, we extend the single-span extraction method to multi-span, proposing a new framework which enables generative MRC to be smoothly solved as multi-span extraction. Thorough experiments demonstrate that this novel approach can alleviate the dilemma between generative models and single-span models and produce answers with better-formed syntax and semantics. We will open-source our code for the research community. Copyright © 2020, The Authors. All rights reserved.

关键词： Extraction

Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

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Local and global structure-aware entropy regularized mean te...

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting