
Jing Lu - Google Scholar
Proceedings of the 46th International ACM SIGIR Conference on Research and … O Chaparro, C Bernal-Cárdenas, J Lu, K Moran, A Marcus, M Di Penta, ... Proceedings of the 2019 27th …
吕劲 中文主页--首页 - pku.edu.cn
团队介绍: 本团队主要致力于利用DFT+NEGF研究低维纳米体系的量子调控、准粒子效应和激子效应、能谷电子学、拓扑性质、光电性质、旋轨耦合性质、电声耦合性质等,并利用这些纳米材料设计新型纳米电子学器件、纳米光电子学器件、自旋电子学器件和储能器件等。 尤其致力于研发高性能和低功耗的逻辑型器件,希望把摩尔定律延续到10纳米以下。
Jing Lu (吕静) - Google 学术搜索
Proceedings of the 17th ACM International Conference on Web Search and Data …
Jing Lu - Google 学术搜索
J Lu, A Moussard, S Guo, Y Lee, GM Bidelman, S Moreno, C Skrotzki, ... How do musical tonality and experience affect visual working memory? R Ding, H Tang, Y Liu, Y Yin, B Yan, Y Jiang, …
Jing LU | Peking University, Beijing | PKU | Research profile
Jing LU | Cited by 14,671 | of Peking University, Beijing (PKU) | Read 310 publications | Contact Jing LU
lvjing Home--Home - pku.edu.cn
Dr. Jing Lu “Hanjiang Scholars" Distinguished Professor. New Century Talent of The Ministry of Education of China. Zhong Chengbiao Teachers Scientific Research Prize Winner, Peking...
Jing Lu | IEEE Xplore Author Details
Jing Lu received the Ph.D. degree from the University of Science and Technology Beijing, Beijing, China, in 2012, and served as a Postdoctoral Researcher with Peking University, in 2016. He is a Senior Researcher with News Algorithm Center, Department of PCG, Tencent, Beijing, China.
吕劲 中文主页--首页 - pku.edu.cn
本团队主要致力于利用DFT+NEGF研究低维纳米体系的量子调控、准粒子效应和激子效应、能谷电子学、拓扑性质、光电性质、旋轨耦合性质、电声耦合性质等,并利用这些纳米材料设计新型纳米电子学器件、纳米光电子学器件、自旋电子学器件和储能器件等。 尤其致力于研发高性能和低功耗的逻辑型器件,希望把摩尔定律延续到10纳米以下。 目前兴趣: 1. 二维晶体管的性能极限. 2. 二维材料的电接触. 3....
Jing Lu - Postdoctoral Researcher at Michigan State University ...
2025年1月23日 · Postdoctoral researcher at Michigan State University · - Proficient in data visualization and analysis of complex data set using Python. - Experienced with many statistical and machine learning...
Jing Lu - Google Research
Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space.