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Institution

Tsinghua University

EducationBeijing, Beijing, China
About: Tsinghua University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Catalysis & Graphene. The organization has 129978 authors who have published 200506 publications receiving 4549561 citations. The organization is also known as: Tsinghua & THU.


Papers
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Journal ArticleDOI
TL;DR: The epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of patients with laboratory-confirmed 2019-nCoV infection in Wuhan, China, were reported.

36,578 citations

Proceedings ArticleDOI
21 Jul 2017
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Abstract: Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.

27,821 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death, including older age, high SOFA score and d-dimer greater than 1 μg/mL.

20,189 citations

Journal ArticleDOI
15 Feb 2013-Science
TL;DR: The type II prokaryotic CRISPR (clustered regularly interspaced short palindromic repeats)/Cas adaptive immune system has been shown to facilitate RNA-guided site-specific DNA cleavage as discussed by the authors.
Abstract: Functional elucidation of causal genetic variants and elements requires precise genome editing technologies. The type II prokaryotic CRISPR (clustered regularly interspaced short palindromic repeats)/Cas adaptive immune system has been shown to facilitate RNA-guided site-specific DNA cleavage. We engineered two different type II CRISPR/Cas systems and demonstrate that Cas9 nucleases can be directed by short RNAs to induce precise cleavage at endogenous genomic loci in human and mouse cells. Cas9 can also be converted into a nicking enzyme to facilitate homology-directed repair with minimal mutagenic activity. Lastly, multiple guide sequences can be encoded into a single CRISPR array to enable simultaneous editing of several sites within the mammalian genome, demonstrating easy programmability and wide applicability of the RNA-guided nuclease technology.

12,265 citations

Journal ArticleDOI
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.

10,401 citations


Authors

Showing all 131304 results

NameH-indexPapersCitations
Yi Cui2201015199725
Yi Chen2174342293080
Jing Wang1844046202769
Joel Schwartz1831149109985
Xiaohui Fan183878168522
Jie Zhang1784857221720
Lei Jiang1702244135205
Yang Gao1682047146301
Qiang Zhang1611137100950
Wei Li1581855124748
Rui Zhang1512625107917
Zhenwei Yang150956109344
Philip S. Yu1481914107374
Hui-Ming Cheng147880111921
Yoshio Bando147123480883
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023536
20223,108
202116,995
202016,971
201916,082