More Like this

1. Matrix Completion With Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling

   https://doi.org/10.1109/TPAMI.2023.3261185  

   Cai, HanQin; Huang, Longxiu; Li, Pengyu; Needell, Deanna (August 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence)
2. Practical guide to replica exchange transition interface sampling and forward flux sampling

   https://doi.org/10.1063/5.0080053  

   Hall, Steven W.; Díaz Leines, Grisell; Sarupria, Sapna; Rogal, Jutta (May 2022, The Journal of Chemical Physics)

   Path sampling approaches have become invaluable tools to explore the mechanisms and dynamics of the so-called rare events that are characterized by transitions between metastable states separated by sizable free energy barriers. Their practical application, in particular to ever more complex molecular systems, is, however, not entirely trivial. Focusing on replica exchange transition interface sampling (RETIS) and forward flux sampling (FFS), we discuss a range of analysis tools that can be used to assess the quality and convergence of such simulations, which is crucial to obtain reliable results. The basic ideas of a step-wise evaluation are exemplified for the study of nucleation in several systems with different complexities, providing a general guide for the critical assessment of RETIS and FFS simulations. 

   more » « less
3. Quantum sampling and entropic uncertainty

   https://doi.org/10.1007/s11128-019-2481-5  

   Krawec, Walter O. (December 2019, Quantum Information Processing)
4. Strong self-concordance and sampling

   https://doi.org/10.1145/3357713.3384272  

   Laddha, Aditi; Lee, Yin Tat; Vempala, Santosh (June 2020, ACM Symposium on the Theory of Computing)
5. Spatially distributed sampling and reconstruction

   https://doi.org/https://doi.org/10.1016/j.acha.2017.07.007  

   Cheng, Cheng; Jiang, Yinchun; Sun, Qiyu (July 2019, Applied and computational harmonic analysis)

   A spatially distributed network contains a large amount of agents with limited sensing, data processing, and communication capabilities. Recent technological advances have opened up possibilities to deploy spatially distributed networks for signal sampling and reconstruction. In this paper, we introduce a graph structure for a distributed sampling and reconstruction system by coupling agents in a spatially distributed network with innovative positions of signals. A fundamental problem in sampling theory is the robustness of signal reconstruction in the presence of sampling noises. For a distributed sampling and reconstruction system, the robustness could be reduced to the stability of its sensing matrix. In this paper, we split a distributed sampling and reconstruction system into a family of overlapping smaller subsystems, and we show that the stability of the sensing matrix holds if and only if its quasi-restrictions to those subsystems have uniform stability. This new stability criterion could be pivotal for the design of a robust distributed sampling and reconstruction system against supplement, replacement and impairment of agents, as we only need to check the uniform stability of affected subsystems. In this paper, we also propose an exponentially convergent distributed algorithm for signal reconstruction, that provides a suboptimal approximation to the original signal in the presence of bounded sampling noises. 

   more » « less