An MDL framework for sparse coding and dictionary learning

The power of sparse signal modeling with learned over-complete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inference and machine learning. However, the statistical properties of these models, such as under-fitting or over-fitting given sets of data, are still not well characterized in the literature. As a result, the success of sparse modeling depends on hand-tuning critical parameters for each data and application. This work aims at addressing this by providing a practical and objective characterization of sparse models by means of the Minimum Description Length (MDL) principle -- a well established information-theoretic approach to model selection in statistical inference. The resulting framework derives a family of efficient sparse coding and dictionary learning algorithms which, by virtue of the MDL principle, are completely parameter free. Furthermore, such framework allows to incorporate additional prior information to existing models, such as Markovian dependencies, or to define completely new problem formulations, including in the matrix analysis area, in a natural way. These virtues will be demonstrated with parameter-free algorithms for the classic image denoising and classification problems, and for low-rank matrix recovery in video applications

The Efficiency Loss of Capital Income Taxation under Imperfect Loss Offset Provisions

The importance of capital loss offset provisions in a world of risk is well documented in the tax literature. However, the potential deadweight losses owing to imperfect offset has not been fully explored. This paper develops a framework whereby that investigation can be carried out and utilizes numerical simulations to investigate the size of potential losses. Results show that when the government and private sector are equally efficient in handling market risk, welfare losses owing to the absence of offset provisions could be substantial. Under plausible assumptions about attitudes towards risk and time preference, and with a capital income tax rate of forty percent, over sixty cents per dollar of tax revenue raised would be dissipated. In contrast, full loss offset would reduce that loss to approximately fourteen cents.capital income taxation, uncertainty, deadweight loss, loss offset provisions

Super- and Anti-Principal Modes in Multi-Mode Waveguides

We introduce a new type of states for light in multimode waveguides featuring strongly enhanced or reduced spectral correlations. Based on the experimentally measured multi-spectral transmission matrix of a multimode fiber, we generate a set of states that outperform the established "principal modes" in terms of the spectral stability of their output spatial field profiles. Inverting this concept also allows us to create states with a minimal spectral correlation width, whose output profiles are considerably more sensitive to a frequency change than typical input wavefronts. The resulting "super-" and "anti-principal" modes are made orthogonal to each other even in the presence of mode-dependent loss. By decomposing them in the principal mode basis, we show that the super-principal modes are formed via interference of principal modes with closeby delay times, whereas the anti-principal modes are a superposition of principal modes with the most different delay times available in the fiber. Such novel states are expected to have broad applications in fiber communication, imaging, and spectroscopy.Comment: 8 pages, 5 figures, plus supplementary materia