Primordial Power Spectrum Reconstruction

In order to reconstruct the initial conditions of the universe it is important to devise a method that can efficiently constrain the shape of the power spectrum of primordial matter density fluctuations in a model-independent way from data. In an earlier paper we proposed a method based on the wavelet expansion of the primordial power spectrum. The advantage of this method is that the orthogonality and multiresolution properties of wavelet basis functions enable information regarding the shape of $P_{\rm in}(k)$ to be encoded in a small number of non-zero coefficients. Any deviation from scale-invariance can then be easily picked out. Here we apply this method to simulated data to demonstrate that it can accurately reconstruct an input $P_{\rm in}(k)$, and present a prescription for how this method should be used on future data.Comment: 4 pages, 2 figures. JCAP accepted versio

Locally adaptive image denoising by a statistical multiresolution criterion

We demonstrate how one can choose the smoothing parameter in image denoising by a statistical multiresolution criterion, both globally and locally. Using inhomogeneous diffusion and total variation regularization as examples for localized regularization schemes, we present an efficient method for locally adaptive image denoising. As expected, the smoothing parameter serves as an edge detector in this framework. Numerical examples illustrate the usefulness of our approach. We also present an application in confocal microscopy

A Common-Factor Approach for Multivariate Data Cleaning with an Application to Mars Phoenix Mission Data

Data quality is fundamentally important to ensure the reliability of data for stakeholders to make decisions. In real world applications, such as scientific exploration of extreme environments, it is unrealistic to require raw data collected to be perfect. As data miners, when it is infeasible to physically know the why and the how in order to clean up the data, we propose to seek the intrinsic structure of the signal to identify the common factors of multivariate data. Using our new data driven learning method, the common-factor data cleaning approach, we address an interdisciplinary challenge on multivariate data cleaning when complex external impacts appear to interfere with multiple data measurements. Existing data analyses typically process one signal measurement at a time without considering the associations among all signals. We analyze all signal measurements simultaneously to find the hidden common factors that drive all measurements to vary together, but not as a result of the true data measurements. We use common factors to reduce the variations in the data without changing the base mean level of the data to avoid altering the physical meaning.Comment: 12 pages, 10 figures, 1 tabl