Sensitivity of DF-ICP-MS, PERALS and alpha-spectrometry for the determination of actinides: A comparison

We applied three techniques (DF-ICP-MS, PERALS and alpha-spectrometry) for the determination of minor actinides at environmental levels. For each method the limit of detection and the resolution were estimated in order to study the content and isotopic composition of the actinides. Two international reference materials, IAEA-135 (Irish Sea Sediment) and IAEA-300 (Baltic Sea sediment) were analyzed for activity concentrations of 238Pu, 239Pu, 240Pu, 241Pu and 241Am. The sensitivities of the three determination techniques were compare

Learning Poisson Binomial Distributions

We consider a basic problem in unsupervised learning: learning an unknown \emph{Poisson Binomial Distribution}. A Poisson Binomial Distribution (PBD) over $\{0,1,\dots,n\}$ is the distribution of a sum of $n$ independent Bernoulli random variables which may have arbitrary, potentially non-equal, expectations. These distributions were first studied by S. Poisson in 1837 \cite{Poisson:37} and are a natural $n$-parameter generalization of the familiar Binomial Distribution. Surprisingly, prior to our work this basic learning problem was poorly understood, and known results for it were far from optimal. We essentially settle the complexity of the learning problem for this basic class of distributions. As our first main result we give a highly efficient algorithm which learns to \eps-accuracy (with respect to the total variation distance) using \tilde{O}(1/\eps^3) samples \emph{independent of $n$}. The running time of the algorithm is \emph{quasilinear} in the size of its input data, i.e., \tilde{O}(\log(n)/\eps^3) bit-operations. (Observe that each draw from the distribution is a $\log(n)$-bit string.) Our second main result is a {\em proper} learning algorithm that learns to \eps-accuracy using \tilde{O}(1/\eps^2) samples, and runs in time (1/\eps)^{\poly (\log (1/\eps))} \cdot \log n. This is nearly optimal, since any algorithm {for this problem} must use \Omega(1/\eps^2) samples. We also give positive and negative results for some extensions of this learning problem to weighted sums of independent Bernoulli random variables.Comment: Revised full version. Improved sample complexity bound of O~(1/eps^2

Accuracy and performance issues of spectral preconditioners in semiconductor device simulation

In most numerical simulations in computational science and engineering, a preconditioner is mandatory to iteratively solve large sparse linear systems. In theory, a good preconditioner clusters all eigenvalues of the iteration matrix around one. However, in practice, there may still be a few outliers. In particular, small eigenvalues deteriorate the convergence speed and may affect the ultimate accuracy. Spectral preconditioners can be used to circumvent these problems. They are constructed with the help of an invariant subspace that contains the eigenvectors corresponding to the small eigenvalues. In this paper, we investigate spectral preconditioners in the field of semiconductor device simulation. We look into different formulations and their influence on the accuracy. Performance issues of spectral preconditioners are the second topic we investigate

Exponential approximation by stein's method and spectral graph theory

Alea81197-22

Determination of 241Pu in nuclear waste slurries: a comparative study using LSC and ICP-MS.

(241)Pu was determined in slurry samples from a nuclear reactor decommissioning project at the Paul Scherrer Institute (Switzerland). To validate the results, the (241)Pu activities of five samples were determined by LSC (TriCarb and Quantulus) and ICP-MS, with each instrument at a different laboratory. In lack of certified reference materials for (241)Pu, the methods were further validated using the (241)Pu information values of two reference sediments (IAEA-300 and IAEA-384). Excellent agreement with the results was found between LSC and ICP-MS in the nuclear waste slurries and the reference sediments