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arXiv 2023年

作者： Caufield, J. Harry Putman, Tim Schaper, Kevin Unni, Deepak R. Hegde, Harshad Callahan, Tiffany J. Cappelletti, Luca Moxon, Sierra A.T. Ravanmehr, Vida Carbon, Seth Chan, Lauren E. Cortes, Katherina Shefchek, Kent A. Elsarboukh, Glass Balhoff, James P. Fontana, Tommaso Matentzoglu, Nicolas Bruskiewich, Richard M. Thessen, Anne E. Harris, Nomi L. Munoz-Torres, Monica C. Haendel, Melissa A. Robinson, Peter N. Joachimiak, Marcin P. Mungall, Christopher J. Reese, Justin T. Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States University of Colorado Anschutz Medical Campus AuroraCO80045 United States Swiss Institute of Bioinformatics Basel Switzerland Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Department of Computer Science University of Milano Milan Italy Department of Lymphoma-Myeloma MD Anderson Cancer Center HoustonTX77030 United States College of Public Health and Human Sciences Oregon State University CorvallisOR97331 United States Helix Genomics Inc. San MateoCA94401 United States Renaissance Computing Institute University of North Carolina Chapel HillNC27517 United States Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Semanticly Ltd. Athens Greece STAR Informatics Delphinai Corporation SookeBCV9Z 0M3 Canada Department of Biomedical Informatics University of Colorado Anschutz Medical Campus AuroraCO80045 United States The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. © 2023, CC BY.

关键词： Data integration

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Semantic Role Labeling with Associated Memory Network

arXiv

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arXiv 2019年

作者： Guan, Chaoyu Cheng, Yuhao 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

Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented. This paper proposes a novel syntax-agnostic SRL model enhanced by the proposed associated memory network (AMN), which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. In detail, we use sentences and their labels from train dataset as an associated memory cue to help label the target sentence. Furthermore, we compare several associated sentences selecting strategies and label merging methods in AMN to find and utilize the label of associated sentences while attending them. By leveraging the attentive memory from known training data, Our full model reaches state-of-the-art on CoNLL-2009 benchmark datasets for syntax-agnostic setting, showing a new effective research line of SRL enhancement other than exploiting external resources such as well pre-trained language models. Copyright © 2019, The Authors. All rights reserved.

关键词： Syntactics

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Inception classification and object detection based joint-CNN for indoor scene classification

Inception classification and object detection based joint-CN...

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2018 International MultiConference of Engineers and computer Scientists, IMECS 2018

作者： Tian, Yarding Zhang, Weitong Zhang, Qieshi Lu, Gang School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9789881404886

While convolutional neural network (CNN) has been successfully used in many fields including single-label scene classification, it is vital to note that real world scenes generally contain multiple semantics and multi-label, especially in the indoor scene classification due to its content complexity. At the same time, most approaches try to make the network much deeper to make sure that they can extract more detail information. However, the deeper network will cause a lot of problems such as the increase of computational costs and network costs and so on. In order to solve these problems, this paper presents a novel framework which called Joint-CNN based on the proposed special label extraction and network structure. Extensive experiments on various data sets show that our method has enhanced the performance on MIT indoor67 and SUN397 data sets. © 2018 Newswood Limited. All rights reserved.

关键词： Object detection

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Revisiting metric learning for few-shot image classification

arXiv

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arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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A scalable method for estimating the regional polygenicity of complex traits 24th

A scalable method for estimating the regional polygenicity o...

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24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

作者： Johnson, Ruth Burch, Kathryn S. Hou, Kangcheng Paciuc, Mario Pasaniuc, Bogdan Sankararaman, Sriram Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA90095 United States Department of Statistics Rice University HoustonTX77005 United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States

ISBN: (纸本)9783030452568

A key question in human genetics is understanding the proportion of SNPs modulating a particular phenotype or the proportion of susceptibility SNPs for a disease, termed polygenicity. Previous studies have observed that complex traits tend to be highly polygenic, opposing the previous belief that only a handful of SNPs contribute to a trait [1–4]. Beyond these genome-wide estimates, the distribution of polygenicity across genomic regions as well as the genomic factors that affect regional polygenicity remain poorly understood. © Springer Nature Switzerland AG 2020.

关键词： Genome

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Retrospective Reader for Machine Reading Comprehension

arXiv

引用

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

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Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

arXiv

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arXiv 2020年

作者： Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States

Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios. Cop

关键词： Electrophysiology

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Older adults’ reasons to participate in digital skills learning: An interdisciplinary, multiple case study from Austria, Finland, and Germany

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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

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SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition

arXiv

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arXiv 2025年

作者： Lei, Mengqi Wu, Yihong Li, Siqi Zheng, Xinhu Wang, Juan Gao, Yue Du, Shaoyi BNRist THUIBCS BLBCI School of Software Tsinghua University Beijing100084 China Department of Mechanical Engineering Taiyuan University of Technology Taiyuan030024 China Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ultrasound the Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China Department of Ultrasound the Second Affiliated Hospital Xi’an Jiaotong University China State Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Visual recognition relies on understanding both the semantics of image tokens and the complex interactions among them. Mainstream self-attention methods, while effective at modeling global pair-wise relations, fail to capture high-order associations inherent in real-world scenes and often suffer from redundant computation. Hypergraphs extend conventional graphs by modeling high-order interactions and offer a promising framework for addressing these limitations. However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, leading to excessive and redundant hyperedges with hard binary vertex memberships that overlook the continuity of visual semantics. To overcome these issues, we present Soft Hypergraph Neural Networks (SoftHGNNs), which extend the methodology of hypergraph computation, to make it truly efficient and versatile in visual recognition tasks. Our framework introduces the concept of soft hyperedges, where each vertex is associated with hyperedges via continuous participation weights rather than hard binary assignments. This dynamic and differentiable association is achieved by using the learnable hyperedge prototype. Through similarity measurements between token features and the prototype, the model generates semantically rich soft hyperedges. By using these soft hyperedges as the medium for message aggregation and dissemination, SoftHGNN models various high-order semantic relationships within visual features. To further enhance efficiency when scaling up the number of soft hyperedges, we incorporate a sparse hyperedge selection mechanism that activates only the top-k important hyperedges, along with a load-balancing regularizer to ensure adequate and balanced hyperedge utilization. Experimental results across three tasks on five datasets demonstrate that SoftHGNN efficiently captures high-order associations in visual scenes, achieving significant performance improvements. The source code of our framework is availa

关键词： computer vision

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GREGoR: Accelerating Genomics for Rare Diseases

arXiv

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arXiv 2024年

作者： Dawood, Moez Heavner, Ben Wheeler, Marsha M. Ungar, Rachel A. LoTempio, Jonathan Wiel, Laurens Berger, Seth Bernstein, Jonathan A. Chong, Jessica X. Délot, Emmanuèle C. Eichler, Evan E. Gibbs, Richard A. Lupski, James R. Shojaie, Ali Talkowski, Michael E. Wagner, Alex H. Wei, Chia-Lin Wellington, Christopher Wheeler, Matthew T. Carvalho, Claudia M.B. Gifford, Casey A. May, Susanne Miller, Danny E. Rehm, Heidi L. Sedlazeck, Fritz J. Vilain, Eric O'Donnell-Luria, Anne Posey, Jennifer E. Chadwick, Lisa H. Bamshad, Michael J. Montgomery, Stephen B. Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Biostatistics University of Washington SeattleWA United States Department of Genetics School of Medicine Stanford University StanfordCA United States Department of Pathology School of Medicine Stanford University Stanford CA United States Stanford Center for Biomedical Ethics School of Medicine Stanford University Stanford CA United States Institute for Clinical and Translational Science University of California IrvineCA United States Division of Cardiovascular Medicine School of Medicine Stanford University Stanford CA United States Division of Genetics and Metabolism Children’s National Rare Disease Institute WashingtonDC United States Center for Genetic Medicine Research Children’s National Rare Disease Institute WashingtonDC United States Department of Genomics and Precision Medicine George Washington University WashingtonDC United States Department of Pediatrics School of Medicine Stanford University Stanford CA United States Department of Pediatrics Dvision of Genetic Medicine University of Washington SeattleWA United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Howard Hughes Medical Institute University of Washington SeattleWA United States Department of Pediatrics Baylor College of Medicine HoustonTX United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard BostonMA United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Stanley Center for

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://***/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics. © 2024, CC BY.

关键词： Genome

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