We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data di...

BACKGROUND In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed. METHOD Here we review all cluster-based correction for multiple comparison m...

BACKGROUND Testing for clustering at multiple ranges within a single dataset is a common practice in spatial epidemiology. It is not documented whether this approach has an impact on the type 1 error rate. METHODS We estimated the family-wise error rate (FWE) for the difference in Ripley's K functions test, when testing at an increasing number of ranges at an alpha-level of 0.05. Case and con...

This is a study concerning the identification of the heterogeneous flexural rigidity of a beam governed by the steady-state Euler-Bernoulli fourth order ordinary differential equation. We use the method of Variational Imbedding (MVI) to deal with the inverse problem for the coefficient identification from over-posed data. The method is identifying the coefficient by approximating it with a piec...

We will now consider the problem of creating a set of bitstrings such that the bits are almost k-wise independent. Our method consists of two steps. In the first step we create a set of bitstrings such that parity on every fixed index set is almost even. In the second step we present a method to convert such a set into a set where the bits are almost k-wise independent. For presentational reaso...

Two general classes of multiple decision functions, where each member of the first class strongly controls the family-wise error rate (FWER), while each member of the second class strongly controls the false discovery rate (FDR), are described. These classes offer the possibility that optimal multiple decision functions with respect to a pre-specified Type II error criterion, such as the missed...

In phase II platform trials, 'many-to-one' comparisons are performed when K experimental treatments are compared with a common control to identify the most promising treatment(s) to be selected for Phase III trials. However, when sample sizes are limited, such as when the disease of interest is rare, only a single Phase II/III trial addressing both treatment selection and confirmatory efficacy ...

Gene expression microarrays have been the vanguard of new analytic approaches in high-dimensional biology. Draft sequences of several genomes coupled with new technologies allow study of the influences and responses of entire genomes rather than isolated genes. This has opened a new realm of highly dimensional biology where questions involve multiplicity at unprecedented scales: thousands of ge...

We report our results on the THUMOS Challenge 2014 Action Recognition Task. Given an untrimmed video as input, our method recognizes its major action category in four main steps: (1) detect shot boundaries within the video; (2) decode pre-computed indices of iDTFs within each shot; (3) encode the recovered iDTFs into shot-wise fisher vectors and compute the shot-to-category classification score...

Camera stabilization is an important task for video analysis. In this report, we describe a method for estimating and stabilizing camera motion between two images via 2D image registration. Our approach is based on a pair-wise registration of images parameterized by 2D affine or projective transformation. Pair-wise image registration estimates transformation parameters for each pair of consecut...