The Statefinder hierarchy: An extended null diagnostic for concordance cosmology

We show how higher derivatives of the expansion factor can be developed into a null diagnostic for concordance cosmology (LCDM). It is well known that the Statefinder -- the third derivative of the expansion factor written in dimensionless form, a^{(3)}/aH^3, equals unity for LCDM. We generalize this result to higher derivatives of the expansion factor and demonstrate that the hierarchy, a^{(n)}/aH^n, can be converted to a form that stays pegged at unity in concordance cosmology. This remarkable property of the Statefinder hierarchy enables it to be used as an extended null diagnostic for the cosmological constant. The Statefinder hierarchy combined with the growth rate of matter perturbations defines a composite null diagnostic which can distinguish evolving dark energy from LCDM.Comment: 6 pages, 6 figures; to appear in Phys. Rev.

Semantic Video CNNs through Representation Warping

In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to augment existing architectures with very little extra computational cost. This module is called NetWarp and we demonstrate its use for a range of network architectures. The main design principle is to use optical flow of adjacent frames for warping internal network representations across time. A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to-end training. Experiments validate that the proposed approach incurs only little extra computational cost, while improving performance, when video streams are available. We achieve new state-of-the-art results on the CamVid and Cityscapes benchmark datasets and show consistent improvements over different baseline networks. Our code and models will be available at http://segmentation.is.tue.mpg.deComment: ICCV 201

Video Propagation Networks

We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a 'Video Propagation Network' that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17

Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters

Possible use of self-calibration to reduce systematic uncertainties in determining distance-redshift relation via gravitational radiation from merging binaries

By observing mergers of compact objects, future gravity wave experiments would measure the luminosity distance to a large number of sources to a high precision but not their redshifts. Given the directional sensitivity of an experiment, a fraction of such sources (gold plated -- GP) can be identified optically as single objects in the direction of the source. We show that if an approximate distance-redshift relation is known then it is possible to statistically resolve those sources that have multiple galaxies in the beam. We study the feasibility of using gold plated sources to iteratively resolve the unresolved sources, obtain the self-calibrated best possible distance-redshift relation and provide an analytical expression for the accuracy achievable. We derive lower limit on the total number of sources that is needed to achieve this accuracy through self-calibration. We show that this limit depends exponentially on the beam width and give estimates for various experimental parameters representative of future gravitational wave experiments DECIGO and BBO.Comment: 6 pages, 2 figures, accepted for publication in PR

A Visionary Organization: From Donor Intent to New Horizons of Race and Gender Equity

This article documents the unique trajectory of the Leeway Foundation and its transition from sole-director family foundation to an independent foundation. Over 25 years, Leeway shifted in structure and grantmaking, yet has remained in line with its founder’s original mission: to fund women artists in the Philadelphia region. This article focuses on the shift from the founder’s initial intentions to what is now an organization informed by models of racial and gender equity, funding women, trans, and gender nonconforming artists working for social change. Leeway thus serves as a case study for examining transformational shifts in mission, vision, and constituency with leadership after an initial donation. Through analysis of qualitative data, this article addresses donor intent and (unintentional) legacy in changing social and political circumstances. We consider how the organization’s development was enabled but not constrained by the circumstances of its founding and identify strategies and best practices for other foundations in transition, whether in terms of population served or organizational structure