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Given a set P of n points in R-d and epsilon > 0, we consider the problem of constructing weak E-nets for P. We show the following: pick a random sample Q of size O(1/epsilon log(1/epsilon)) from P. Then, with constant probability, a weak epsilon-net of P ...
This study quantifies the uncertainty involved in predicting micropollutant oxidation during drinking water ozonation in a pilot plant reactor. The analysis is conducted for geosmin, methyl tert-butyl ether (MTBE), isopropylmethoxypyrazine (IPMP), bezafibr ...
We study continuity properties of law-invariant (quasi-)convex functions f : L1(Ω,F, P) to ( ∞,∞] over a non-atomic probability space (Ω,F, P) .This is a supplementary note to [12] ...
In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in ...
The Helimak experiment produces a toroidal plasma with a helical magnetic field. A simulation-experiment comparison of turbulence in this device is presented, focusing on parameter regimes in which the turbulence is dominated by interchange modes with k(pa ...
Many applications that require distributed optimization also include uncertainty about the problem and the optimization criteria themselves. However, current approaches to distributed optimization assume that the problem is entirely known before optimizati ...
We present the optical tomography of the probability density of quasiparticles, the microcavity polaritons, confined in three dimensions by cylindrical traps. Collecting the photoluminescence emitted by the quasimodes under continuous nonresonant laser exc ...
We investigate the problem of optimal risk sharing between agents endowed with cash-invariant choice functions which are law-invariant with respect to different reference probability measures. We motivate a discrete setting both from an operational and a t ...
We formulate and investigate a statistical inverse problem of a random tomographic nature, where a probability density function on R3 is to be recovered from observation of finitely many of its two-dimensional projections in random and unobserv ...
It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low-quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weigh ...
