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Kun Tan

Researcher at Huawei

Publications -  169
Citations -  7852

Kun Tan is an academic researcher from Huawei. The author has contributed to research in topics: Wireless network & Network packet. The author has an hindex of 37, co-authored 162 publications receiving 7000 citations. Previous affiliations of Kun Tan include China Agricultural University & Microsoft.

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Journal ArticleDOI

Nonsense-mediated RNA decay: an emerging modulator of malignancy

TL;DR: Findings suggest that NMD-modulatory therapy has the potential to provide widespread therapeutic benefit against diverse tumour types, however, whether NMD should be stimulated or repressed requires careful analysis of the tumour to be treated.
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Dynamic integrated analysis of DNA methylation and gene expression profiles in in vivo and in vitro fertilized mouse post-implantation extraembryonic and placental tissues

TL;DR: Dynamic functional profiling of high-throughput data, together with molecular detection, and morphometric and phenotypic analyses, showed that differentially expressed genes dysregulated by DNA methylation were functionally involved in: actin cytoskeleton disorganization in IVF extraembryonic tissues, which may impair allantois or chorion formation, and chorioallantoic fusion.
Proceedings ArticleDOI

One More Config is Enough: Saving (DC)TCP for High-speed Extremely Shallow-buffered Datacenters

TL;DR: This work presents BCC1, a simple yet effective solution that requires just one more ECN config (i.e., shared buffer ECN/RED) over prior solutions, and shows that BCC maintains low packet loss rate while slightly degrading throughput when the available buffer becomes insufficient.
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Natriuretic peptide receptor 2 (NPR2) localized in bovine oocyte underlies a unique mechanism for C-type natriuretic peptide (CNP)-induced meiotic arrest.

TL;DR: A novel mechanism for CNP-induced meiotic arrest that appears to be unique to bovine oocytes is identified and a natural factor synchronized in vitro oocyte maturation (NFSOM) system is established, which can significantly improve the developmental competence of matured oocytes, thereby resulting in higher in Vitro embryo production efficiency.
Proceedings ArticleDOI

Empowering Sketches with Machine Learning for Network Measurements

TL;DR: This paper presents a generalized machine learning framework that increases the accuracy of sketches significantly and shows that machine learning helps decrease the error rates of existing sketches by up to 202 times.