Dark Energy from Mass Varying Neutrinos

We show that mass varying neutrinos (MaVaNs) can behave as a negative pressure fluid which could be the origin of the cosmic acceleration. We derive a model independent relation between the neutrino mass and the equation of state parameter of the neutrino dark energy, which is applicable for general theories of mass varying particles. The neutrino mass depends on the local neutrino density and the observed neutrino mass can exceed the cosmological bound on a constant neutrino mass. We discuss microscopic realizations of the MaVaN acceleration scenario, which involve a sterile neutrino. We consider naturalness constraints for mass varying particles, and find that both ev cutoffs and ev mass particles are needed to avoid fine-tuning. These considerations give a (current) mass of order an eV for the sterile neutrino in microscopic realizations, which could be detectable at MiniBooNE. Because the sterile neutrino was much heavier at earlier times, constraints from big bang nucleosynthesis on additional states are not problematic. We consider regions of high neutrino density and find that the most likely place today to find neutrino masses which are significantly different from the neutrino masses in our solar system is in a supernova. The possibility of different neutrino mass in different regions of the galaxy and the local group could be significant for Z-burst models of ultra-high energy cosmic rays. We also consider the cosmology of and the constraints on the ``acceleron'', the scalar field which is responsible for the varying neutrino mass, and briefly discuss neutrino density dependent variations in other constants, such as the fine structure constant.Comment: 26 pages, 3 figures, refs added, typos corrected, comment added about possible matter effect

Title: Statistics of the Visual Normal Flow

Abstract: We describe statistical properties of the visual normal flow. These are used to model space-time texture, such as waterfalls, an open problem in visual motion analysis. The normal flow is perpendicular to edges. It is the only component of the image velocity that can be locally computed, so that it is simple to implement. We analysed the statistical representation of the normal flow magnitude for two regions, foreground and background. The log of the normal flow magnitude was discovered to exhibit a Gaussian fit and strong local correlations describing directional motion preferences. We build a statistical model used for region segmentation. This model is given by a prior representing a segmentation map plus a likelihood term of the log normal flow magnitude given this segmention map. Conclusions: Experiments using different videos with space-time texture resulted in good agreement with visual characteristics of these textured regions, such as, motio

Integrated Multimedia Processing for Topic Segmentation and Classification

In this paper we describe integrated multimedia processing for Video Scout, a system that segments and indexes TV programs according to their audio, visual, and transcript information. Video Scout represents a future direction for personal video recorders. In addition to using electronic program guide metadata and a user profile, Scout allows the users to request specific topics within a program. For example, users can request the video clip of the President speaking from a half-hour news program. Video Scout has three modules: (i) Video Pre-Processing, (ii) Segmentation and Indexing, and (iii) Storage and User Interface. Segmentation and Indexing, the core of the system, incorporates a Bayesian framework that integrates information from the audio, visual, and transcript (closed captions) domains. This framework uses three layers to process low, mid, and high-level multimedia information. The high-level layer generates semantic information about TV program topics. This paper describes the elements of the system and presents results from running Video Scout on real TV programs