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arXiv 2019年

作者： Rabbi, Mohammad Shifat E. Yin, Xuwang Fitzgerald, Cailey Elizabeth Rohde, Gustavo K. Imaging and Data Science Lab CharlottesvilleVA22903 United States Department of Biomedical Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical & Computer Engineering University of Virginia CharlottesvilleVA22903 United States

Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. We review three different approaches for cell image classification: numerical feature extraction, end to end classification with neural networks, and transport-based morphometry. In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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CoinNet: Deep ancient Roman Republican coin classification via feature fusion and attention

arXiv

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arXiv 2019年

作者： Anwar, Hafeez Anwar, Saeed Zambanini, Sebastian Porikli, Fatih Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Germany Data61-CSIRO Australia College of Engineering and Computer Science Australian National University Australia Computer Vision Lab TU Wien Austria

We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than 98% and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷 103551-103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom

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Poster: Semantically enhanced tag recommendation for software CQAs via deep learning 40

Poster: Semantically enhanced tag recommendation for softwar...

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40th ACM/IEEE International Conference on Software engineering, ICSE 2018

作者： Zhang, Jian Sun, Hailong Tian, Yanfei Liu, Xudong SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China

ISBN: (纸本)9781450356633

Most software CQAs (e.g. Stack Overflow) mainly rely on users to assign tags for posted questions. This leads to many redundant, inconsistent and inaccurate tags that are detrimental to the communities. Therefore tag quality becomes a critical challenge to deal with. In this work, we propose STR, a deep learning based approach that automatically recommends tags through learning the semantics of both tags and questions in such software CQAs. First, word embedding is employed to convert text information to high-dimension vectors for better representing questions and tags. Second, a Multi-Tasking-like Convolutional Neural Network, the core modules of STR, is designed to capture short and long semantics. Third, the learned semantic vectors are fed into a gradient descent based algorithm for classification. Finally, we evaluate STR on three datasets collected from popular software CQAs, and experimental results show that STR outperforms state-of-The-Art approaches in terms of Precision@k, Recall@k and F1-Measure@k. © 2018 Authors.

关键词： Semantics

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Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big data Earth system emergency geospatial sciences epidemics applications

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Multi-Agent Genetic Algorithm for Sparse Optical Phased Array Optimization

Multi-Agent Genetic Algorithm for Sparse Optical Phased Arra...

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Asia Communications and Photonics Conference

作者： Chuxin Liu Weihan Xu Linjie Zhou Liangjun Lu Jianping Chen Shanghai Institute for Advanced Communication and Data Science Shanghai Key Lab of Navigation and Location Services State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering SJTU Shanghai China

We propose a multi-agent Genetic algorithm to suppress the peak sidelobe ratio (PSLR) in the OPA far-field diffraction pattern. The optimized 128-channel sparse array achieves beam-steering range of ±60° wit... 详细信息

ISBN: (纸本)9781943580705

关键词： Far field diffraction Free space optics Genetic algorithms Optical coherence tomography Phased arrays Ptychography

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Correction to: NoRCE: non‑coding RNA sets cis enrichment tool

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BMC bioinformatics 2021年第1期22卷 393页

作者： Gulden Olgun Afshan Nabi Oznur Tastan Department of Computer Engineering Bilkent University Ankara Turkey. Cancer Data Science Lab National Cancer Institute National Institute of Health Bethesda MD USA. Faculty of Engineering and Natural Sciences Sabanci University 34956 Istanbul Turkey. Faculty of Engineering and Natural Sciences Sabanci University 34956 Istanbul Turkey. otastan@sabanciuniv.edu.

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Domain Knowledge Guided Deep Learning with Electronic Health Records

Domain Knowledge Guided Deep Learning with Electronic Health...

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IEEE International Conference on data Mining (ICDM)

作者： Changchang Yin Rongjian Zhao Buyue Qian Xin Lv Ping Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China The Ohio State University Columbus Ohio USA

Due to their promising performance in clinical risk prediction with Electronic Health Records (EHRs), deep learning methods have attracted significant interest from healthcare researchers. However, there are 4 challenges: (i) data insufficiency. Many methods require large amounts of training data to achieve satisfactory results. (ii) Interpretability. Results from many methods are hard to explain to clinicians (e.g., why the models make particular predictions and which events cause clinical outcomes). (iii) Domain knowledge integration. No existing method dynamically exploits complicated medical knowledge (e.g., relations such as cause and is-caused-by between clinical events). (iv) Time interval information. Most existing methods only consider the relative order of visits from EHRs, but ignore the irregular time intervals between neighboring visits. In the study, we propose a new model, Domain Knowledge Guided Recurrent Neural Networks (DG-RNN), by directly introducing domain knowledge from the medical knowledge graph into an RNN architecture, as well as taking the irregular time intervals into account. Experimental results on heart failure risk prediction tasks show that our model not only outperforms state-of-the-art deep-learning based risk prediction models, but also associates individual medical events with heart failure onset, thus paving the way for interpretable accurate clinical risk predictions.

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Metal Organic Framework Cubosomes

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Angewandte Chemie 2023年第11期135卷

作者： Chen Li Yi Pan Tianyu Xiao Luoxing Xiang Dr. Qian Li Dr. Feng Tian Prof. Ian Manners Prof. Yiyong Mai School of Chemistry and Chemical Engineering Frontiers Science Center for Transformative Molecules Shanghai Key Laboratory of Electrical Insulation and Thermal Ageing Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Department of Chemistry Centre for Advanced Materials and Related Technology (CAMTEC) University of Victoria 3800 Finnerty Road Victoria BC V8P 5C2 Canada Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Data curation (supporting) Investigation (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Shanghai Synchrotron Radiation Facility Zhangjiang Lab Shanghai Advanced Research Institute Chinese Academy of Sciences 239 Zhangheng Road Shanghai 201204 China Contribution: Data curation (supporting) Formal analysis (supporting) Writing - original draft (supporting) Contribution: Conceptualization (supporting) Funding acquisition (supporting) Project administration (equal) Supervision (equal) Writing - original draft (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Data curation (supporting) Formal analysis (lead) Funding acquisition (lead) Investigation (supporting) Methodology (lead) Project administration (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead)

We demonstrate a general strategy for the synthesis of ordered bicontinuous-structured metal organic frameworks (MOFs) by using polymer cubosomes (PCs) with a double primitive structure ( Im m symmetry) as the template. The filling of MOF precursors in the open channel of PCs, followed by their coordination and removal of the template, generates MOF cubosomes with a single primitive topology ( Pm m ) and average mesopore diameters of 60–65 nm. Mechanism study reveals that the formation of ZIF-8 cubosomes undergoes a new MOF growth process, which involves the formation of individual MOF seeds in the template, their growth and eventual fusion into the cubosomes. Their growth kinetics follows the Avrami equation with an Avrami exponent of n =3 and a growth rate of k =1.33×10 −4 , indicating their fast 3D heterogeneous growth mode. Serving as a bioreactor, the ZIF-8 cubosomes show high loading of trypsin enzyme, leading to a high catalytic activity in the proteolysis of bovine serum albumin.

关键词： Bicontinuous Structure Block Copolymer Cubosome Metal Organic Framework Proteolysis

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Regulating the Mechanical and Optical Properties of Polymer-based Nanocomposites by Sub-Nanowires

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Angewandte Chemie 2022年第5期135卷

作者： Feng Yuan Dr. Chen Ouyang Minzheng Yang Prof. Wenxiong Shi Dr. Weibin Ren Prof. Yang Shen Prof. Yan Wei Prof. Xuliang Deng Prof. Xun Wang Institute of Medical Technology Peking University Health Science Center Beijing 100191 China Beijing Laboratory of Biomedical Materials Department of Geriatric Dentistry Peking University School and Hospital of Stomatology Beijing 100081 China Engineering Research Center of Advanced Rare Earth Materials Department of Chemistry Tsinghua University Beijing 100084 China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) Contribution: Data curation (supporting) Formal analysis (supporting) State Key Lab of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 China Institute for New Energy Materials and Low Carbon Technologies School of Materials Science and Engineering Tianjin University of Technology Tianjin 300384 China Contribution: Software (supporting) Contribution: Formal analysis (supporting) Contribution: Formal analysis (supporting) Supervision (supporting) Contribution: Conceptualization (equal) Formal analysis (lead) Supervision (lead) Contribution: Conceptualization (lead) Supervision (lead) Contribution: Conceptualization (lead) Data curation (lead) Investigation (lead) Methodology (lead) Project administration (lead) Supervision (lead) Validation (lead) Writing - review & editing (lead)

Sub-nanowires (SNWs) exhibit great potential applications in nanocomposites owing to their high specific surface area, high flexibility, and similarity to polymer chains in dimension, which are a good entry point to bridge inorganic materials and polymer materials. Herein, we synthesized hydroxyapatite sub-nanowires (HAP SNWs) and engineered hydroxyapatite sub-nanowires/polyimide (HSP) gels and films by simple mixing of HAP SNWs and polyimide (PI). Benefiting from the interactions between HAP SNWs and PI, these nanocomposites were a continuous hybrid network. As the increase of HAP SNWs contents, the viscosity and modulus of HSP gels were greatly improved by one or two orders of magnitude compared with PI gel. HSP films not only maintained high transparency but also gained high haze, as well as exhibited enhanced Young's modulus. Thus, both HSP gels and films developed in this work are promising for various applications in coatings and high-performance films.

关键词： Gels Nanocomposites Nanowires Thin Films

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