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This talk is concerned with a direct sampling method for imaging the support of a frequency-dependent source term embedded in a homogeneous and isotropic medium. The source term is given by the Fourie...
Adaptive computation is of great importance in numerical simulations. The ideas for adaptive computations can be dated back to adaptive finite element methods in 1970s. In this talk, we shall first re...
Boson sampling solves a classically intractable problem by sampling from a probability distribution given by matrix permanents. We propose a scalable implementation of boson sampling using local trans...
In this paper, we simulate numerically the catastrophic disruption of a large asteroid as a result of a collision with a smaller projectile and the subsequent reaccumulation of fragments as a result o...
This study is carried out in the framework of sample-return missions to asteroids that use a low-speed projectile as the primary component of its sampling mechanism (e.g., JAXA’s Hayabusa and Hayabusa...
Consider a large scale sensor array having N sensors that monitors a surveillance area. Using all sensors simultaneously may be unreasonable in terms of power consumption and data processing.For examp...
Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
Respondent-driven sampling (RDS) is a network-based method for sampling hidden and hard-to-reach populations that has been shown to produce asymptotically unbiased population estimates when its ass...
We analyse a microgravity data set acquired from two spring LaCoste & Romberg gravity meters operated in parallel at the same site on Etna volcano (Italy) for about two months (August September 2005)....
We consider the situation in semi-supervised learning, where the “label sampling” mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of...
We propose a sketch-based sampling algorithm, which effectively exploits the data sparsity. Sampling methods have become popular in large-scale data mining and information retrieval, where high data s...
Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise (l2, l1) distances, in static, large-scale, and sparse data. This study modifies the original CRS and exten...

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