L
Liang Ding
Researcher at National University of Defense Technology
Publications - 170
Citations - 2334
Liang Ding is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Thin film & Computer science. The author has an hindex of 22, co-authored 152 publications receiving 1782 citations. Previous affiliations of Liang Ding include University of Texas–Pan American & Nanyang Technological University.
Papers
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Posted ContentDOI
Single-cell analysis identifies TCF4 and ID3 as a molecular switch of mammary epithelial stem cell differentiation.
Koon-Kiu Yan,Erin A. Nekritz,Bensheng Ju,Xinran Dong,Rachel L Werner,Dayanira Alsina-Beauchamp,Celeste Rosencrance,Partha Mukhopadhyay,Qingfei Pan,Andrej Gorbatenko,Liang Ding,Yanyan Wang,Chenxi Qian,Hao Shi,Bridget Shaner,Sivaraman Natarajan,Hongbo Chi,John Easton,Jose M. Silva,Jiyang Yu +19 more
TL;DR: This study identified a distinct cell population presenting molecular features of MaSCs, and identified E2-2 (Tcf4) and ID3 as a potential molecular switch of mammary epithelial stem cell differentiation.
Proceedings ArticleDOI
Demonstration of OSAT compatible 300 mm through Si interposer
TL;DR: In this paper, a fine pitch TSV interposer (TSI) with OSAT-only infra-structure has been demonstrated, and a comprehensive fabrication and characterization has been presented for process modules, integration, and fabricated interposers.
Proceedings ArticleDOI
Progressive Multi-Granularity Training for Non-Autoregressive Translation
TL;DR: The authors propose progressive multi-granularity training for non-autoregressive translation (NAT) to learn fine-grained lower-mode knowledge, such as words and phrases, compared with sentences.
Posted Content
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding.
TL;DR: In this article, the authors propose a method from the perspective of domain adaptation, by which both high and low-quality samples are embedding into similar vector space, and design a denoising generation model to reduce the impact of the low quality samples, which not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness.
Book ChapterDOI
O((log n) 2 ) time online approximation schemes for bin packing and subset sum problems
TL;DR: An online approximate algorithm for the function bp(L) in the bin packing problem, where L is the list of the items that have been received, and it updates in O(log n) updating time and gives a (1 + e)-approximation solution app(L), which is presented as an online approximation scheme for this problem.