图 书 馆开课单位--加利福尼亚大学
31
32
33
34
35
36
37
38
39
40
31The giant component in a random subgraph of a given graph[在一个随机的子图给定图的巨型构件]
Paul Horn;Fan Chung Graham;Linyuan Lu(加利福尼亚大学)
热度：36
Paul Horn;Fan Chung Graham;Linyuan Lu(加利福尼亚大学)
热度：36
32Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction[深时空结构和学习蛋白质结构预测]
Ken Nagata(加利福尼亚大学) Residue-residue contact prediction is a fundamental problem in protein structure prediction. Hower, despite considerable research efforts, contact pre...
热度：70
Ken Nagata(加利福尼亚大学) Residue-residue contact prediction is a fundamental problem in protein structure prediction. Hower, despite considerable research efforts, contact pre...
热度：70
33From the Mega to the Nano: Computer Modeling in Engineering and Sciences / Od mega do nano:računalniško modeliranje v inženirstvu in znanosti[从巨型到纳米：工程和科学的计算机建模]
Satya N. Atluri(加利福尼亚大学) Computer Modeling in Engineering & Sciences (CMES) is a multi-disciplinary enabling- methodology for an integrated process & product simulatio...
热度：67
Satya N. Atluri(加利福尼亚大学) Computer Modeling in Engineering & Sciences (CMES) is a multi-disciplinary enabling- methodology for an integrated process & product simulatio...
热度：67
34CareLog: A Selective Archiving Tool for Behavior Management in Schools[carelog：一个在学校行为管理选择性归档工具]
Gillian R. Hayes(加利福尼亚大学) Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identifythe function is through f...
热度：47
Gillian R. Hayes(加利福尼亚大学) Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identifythe function is through f...
热度：47
35Compositional Noisy-Logical Learning[成分嘈杂的逻辑学习]
Yuille Alan L(加利福尼亚大学) We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our propo...
热度：37
Yuille Alan L(加利福尼亚大学) We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our propo...
热度：37
36Modeling reality without sacrificing data: Inferentially tractable models for complex social systems[模拟现实而不牺牲数据：对复杂社会系统的模型推理]
Carter Butts(加利福尼亚大学) 模拟现实而不牺牲数据：对复杂社会系统的模型推理
热度：38
Carter Butts(加利福尼亚大学) 模拟现实而不牺牲数据：对复杂社会系统的模型推理
热度：38
37The Cost of Interrupted Work: More Speed and Stress[中断工作的成本：更多的速度和压力]
Gloria Mark(加利福尼亚大学) Our study showed interrupted tasks are completed faster, with similar quality. We suggest that people compensate for interruptions by working faster, ...
热度：44
Gloria Mark(加利福尼亚大学) Our study showed interrupted tasks are completed faster, with similar quality. We suggest that people compensate for interruptions by working faster, ...
热度：44
38Approximation and Inference using Latent Variable Sparse Linear Models[隐变量稀疏线性模型的逼近与推理]
David P Wipf(加利福尼亚大学) A variety of Bayesian methods have recently been introduced for performing approximate inference using linear models with sparse priors. We focus on f...
热度：34
David P Wipf(加利福尼亚大学) A variety of Bayesian methods have recently been introduced for performing approximate inference using linear models with sparse priors. We focus on f...
热度：34
39Life = Experiences (Events) + Vision[生活=经验（事件）+想像]
Ramesh Jain(加利福尼亚大学) Dr. Ramesh Jain, Discussion Rooms Chair of ACM Multimedia 2010, received the prestigious ACM Special Interest Group on Multimedia (SIGMM) award for Ou...
热度：48
Ramesh Jain(加利福尼亚大学) Dr. Ramesh Jain, Discussion Rooms Chair of ACM Multimedia 2010, received the prestigious ACM Special Interest Group on Multimedia (SIGMM) award for Ou...
热度：48
40Apical trafficking of endocytosed Delta is required for Notch signaling during fate specification[在规范中，信号需要内吞三角的顶端传输]
An-Chi Tien(加利福尼亚大学) Max-Planck Institute and Faculty of Medicine University of Maribor Partner symposium on Communicating Physiology
热度：35
An-Chi Tien(加利福尼亚大学) Max-Planck Institute and Faculty of Medicine University of Maribor Partner symposium on Communicating Physiology
热度：35