- Thomas Jefferson National Accelerator FacilityUS312 papers
- University of VirginiaUS259 papers
- William & MaryUS254 papers
- Florida International UniversityUS249 papers
- A. Alikhanyan National LaboratoryAM246 papers
- Old Dominion UniversityUS242 papers
- George Washington UniversityUS233 papers
- Carnegie Mellon UniversityUS229 papers
- University of New HampshireUS225 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR223 papers
- Catholic University of AmericaUS222 papers
- University of EdinburghGB222 papers
242 papers · 1 filter
Federated Nonconvex Sparse Learning
Qianqian Tong, Guannan Liang, Tan Zhu +1
Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-…
Relational Deep Reinforcement Learning for Routing in Wireless Networks
Victoria Manfredi, Alicia Wolfe, Bing Wang +1
While routing in wireless networks has been studied extensively, existing protocols are typically designed for a specific set of network conditions and so cannot accommodate any dr…
Dirac node engineering and flat bands in doped Dirac materials
Anna Pertsova, Peter Johnson, Daniel P. Arovas +1
We suggest the tried approach of impurity band engineering to produce flat bands and additional nodes in Dirac materials. We show that surface impurities give rise to nearly flat i…
Recent Star-formation in a Massive Slowly-Quenched Lensed Quiescent Galaxy at z=1.88
Mohammad Akhshik, Katherine E. Whitaker, Joel Leja +10
In this letter, we reconstruct the formation pathway of MRG-S0851, a massive, , strongly lensed, red, galaxy at . While the global p…
Regularized Fingerprinting in Detection and Attribution of Climate Change with Weight Matrix Optimizing the Efficiency in Scaling Factor Estimation
Yan Li, Kun Chen, Jun Yan +1
The optimal fingerprinting method for detection and attribution of climate change is based on a multiple regression where each covariate has measurement error whose covariance matr…
Machine-Learning Arithmetic Curves
Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver
We show that standard machine-learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around one hundred thousand, w…