Sampling is a common way to collect execution information with performance event counters. However, the sampling data generated from performance event counters tend to be massive if sampling with high frequencies. Storing and processing a large amount of sampling data require much disk space and computing time. In this paper, we propose the online linear regression method to reduce the size of ...

We present a method to decompose a set of multivariate real polynomials into linear combinations of univariate polynomials in linear forms of the input variables. The method proceeds by collecting the first-order information of the polynomials in a set of sampling points, which is captured by the Jacobian matrix evaluated at the sampling points. The canonical polyadic decomposition of the three...

We study the effect of additive noise to inversion FIOs associated a diffeomorphic canonical relation. use microlocal defect measures measure power spectrum in phase space and analyze how that is transformed under inversion. In general, white noise, for example, mapped depending on position direction. particular, we compute standard deviation, locally, added as function deviation data. As an Ra...

We derive an alternative proof for the regret of Thompson sampling (TS) in the stochastic linear bandit setting. While we obtain a regret bound of order e O(d3/2 p T ) as in previous results, the proof sheds new light on the functioning of the TS. We leverage on the structure of the problem to show how the regret is related to the sensitivity (i.e., the gradient) of the objective function and h...

In this paper we investigate the linear sampling method by focusing on energy conservation inside a lossless background, in the case of a three-dimensional, impenetrable and acoustic scattering set-up. We analyze, from both a numerical and a theoretical viewpoint, how average power fluxes are carried throughout the host medium by the flow tubes of radiating fields. As a result, the far-field eq...