Community Benefits Agreements

Community Benefits Agreements (CBA’s) are legally enforceable contracts between community groups and developers in which community groups promise to support the developers in seeking approvals, permits, or subsidies, and the developers promise to provide certain benefits to the surrounding community. CBA’s are outgrowths of the civil rights, labor, and community organizing movements. CBA’s often represent community-based unionism, where organized labor supports initiatives beyond the walls of the workplace and the collective bargaining agreement

Community Development Block Grants and Buffalo

The Community Development Block Grant (CDBG) Program is a federal program run by the United States Department of Housing and Urban Development (HUD). CDBG grants are provided to state and local governments for the purpose of addressing community needs such as affordable housing, job creation, and retention and expansion of business activity. Grants are available for projects lasting from one to three years. Seventy percent of the funding must be used for the benefit of low and moderate income individuals. Ventures funded by CDBG must also further one of the national goals of the CDBG Program, described below

Economic Inequality in New York State

New York State was the state with the greatest income disparity between the rich and poor in the mid-2000s. At that time incomes in the bottom fifth of the population were 8.7 times lower than those in the top fifth. In New York City this gap was even wider. In the mid-2000s the City’s top income quintile had an average income 9.5 times higher than the average income of the bottom quintile. Overall income in New York State grew between the 1980s and the mid-2000s but those at the top took the majority of this increase. The richest 20 percent’s share of total personal income grew from 42 percent in the late 1980s to 47 percent in the mid-2000s with the share of the richest 5 percent increasing the most, from 16 percent in the late 1980s to 21 percent in the mid-2000s. Meanwhile, the other 80 percent of New Yorkers saw their total share of personal income decline

Preventing Deterioration and Abandonment of Rental Properties in Buffalo

The City of Buffalo faces a severe abandoned housing crisis. One component of this immense problem is the abandonment of rental housing due to dilapidated conditions. Forcing landlords to keep their properties in good repair will help to reduce abandonment of rental housing. Several mechanisms hold landlords in Buffalo accountable for the poor conditions of their buildings. The warranty of habitability requires landlords to maintain decent, safe, and sanitary housing. However, remedies available to tenants under this legal doctrine, such as rent withholding and repair and deduct, are inadequate and dangerous. The lack of adequate protections for tenants seeking to address the poor conditions in their apartments is a major deterrent to tenant initiation of action

Noise Corruption of Empirical Mode Decomposition and Its Effect on Instantaneous Frequency

Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing nonstationary data that provides a localized time-frequency representation by decomposing the data into adaptively defined modes. EMD can be used to estimate a signal's instantaneous frequency (IF) but suffers from poor performance in the presence of noise. To produce a meaningful IF, each mode of the decomposition must be nearly monochromatic, a condition that is not guaranteed by the algorithm and fails to be met when the signal is corrupted by noise. In this work, the extraction of modes containing both signal and noise is identified as the cause of poor IF estimation. The specific mechanism by which such "transition" modes are extracted is detailed and builds on the observation of Flandrin and Goncalves that EMD acts in a filter bank manner when analyzing pure noise. The mechanism is shown to be dependent on spectral leak between modes and the phase of the underlying signal. These ideas are developed through the use of simple signals and are tested on a synthetic seismic waveform.Comment: 28 pages, 19 figures. High quality color figures available on Daniel Kaslovsky's website: http://amath.colorado.edu/student/kaslovsk

Non-Asymptotic Analysis of Tangent Space Perturbation

Constructing an efficient parameterization of a large, noisy data set of points lying close to a smooth manifold in high dimension remains a fundamental problem. One approach consists in recovering a local parameterization using the local tangent plane. Principal component analysis (PCA) is often the tool of choice, as it returns an optimal basis in the case of noise-free samples from a linear subspace. To process noisy data samples from a nonlinear manifold, PCA must be applied locally, at a scale small enough such that the manifold is approximately linear, but at a scale large enough such that structure may be discerned from noise. Using eigenspace perturbation theory and non-asymptotic random matrix theory, we study the stability of the subspace estimated by PCA as a function of scale, and bound (with high probability) the angle it forms with the true tangent space. By adaptively selecting the scale that minimizes this bound, our analysis reveals an appropriate scale for local tangent plane recovery. We also introduce a geometric uncertainty principle quantifying the limits of noise-curvature perturbation for stable recovery. With the purpose of providing perturbation bounds that can be used in practice, we propose plug-in estimates that make it possible to directly apply the theoretical results to real data sets.Comment: 53 pages. Revised manuscript with new content addressing application of results to real data set

Geometric Sparsity in High Dimension

While typically complex and high-dimensional, modern data sets often have a concise underlying structure. This thesis explores the sparsity inherent in the geometric structure of many high-dimensional data sets. Constructing an efficient parametrization of a large data set of points lying close to a smooth manifold in high dimension remains a fundamental problem. One approach, guided by geometry, consists in recovering a local parametrization (a chart) using the local tangent plane. In practice, the data are noisy and the estimation of a low-dimensional tangent plane in high dimension becomes ill posed. Principal component analysis (PCA) is often the tool of choice, as it returns an optimal basis in the case of noise-free samples from a linear subspace. To process noisy data, PCA must be applied locally, at a scale small enough such that the manifold is approximately linear, but at a scale large enough such that structure may be discerned from noise. We present an approach that uses the geometry of the data to guide our definition of locality, discovering the optimal balance of this noise-curvature trade-off. Using eigenspace perturbation theory, we study the stability of the subspace estimated by PCA as a function of scale, and bound (with high probability) the angle it forms with the true tangent space. By adaptively selecting the scale that minimizes this bound, our analysis reveals the optimal scale for local tangent plane recovery. Additionally, we are able to accurately and efficiently estimate the curvature of the local neighborhood, and we introduce a geometric uncertainty principle quantifying the limits of noise-curvature perturbation for tangent plane recovery. An algorithm for partitioning a noisy data set is then studied, yielding an appropriate scale for practical tangent plane estimation. Next, we study the interaction of sparsity, scale, and noise from a signal decomposition perspective. Empirical Mode Decomposition is a time-frequency analysis tool for nonstationary data that adaptively defines modes based on the intrinsic frequency scales of a signal. A novel understanding of the scales at which noise corrupts the otherwise sparse frequency decomposition is presented. The thesis concludes with a discussion of future work, including applications to image processing and the continued development of sparse representation from a geometric perspective

Signaling via β2 Integrins Triggers Neutrophil-Dependent Alteration in Endothelial Barrier Function

Activation of polymorphonuclear leukocytes (PMNs) and adhesion to the endothelial lining is a major cause of edema formation. Although known to be dependent on the function of β2 integrins (CD11/CD18), the precise mechanisms by which adherent PMNs may impair endothelial barrier capacity remain unclear. Here, the role of transmembrane signaling by β2 integrins in PMN-induced alterations in tight junctional permeability of cultured endothelial cell (EC) monolayers was investigated. PMN activation, in the absence of proinflammatory stimuli, was accomplished through antibody cross-linking of CD11b/CD18, mimicking adhesion-dependent receptor engagement. CD18 cross-linking in PMNs added to the EC monolayer provoked a prompt increase in EC permeability that coincided with a rise in EC cytosolic free Ca2+ and rearrangement of actin filaments, events similar to those evoked by chemoattractant PMN activation. Cell-free supernatant obtained after CD18 cross-linking in suspended PMNs triggered an EC response indistinguishable from that induced by direct PMN activation, and caused clear-cut venular plasma leakage when added to the hamster cheek pouch in vivo preparation. The PMN-evoked EC response was specific to β2 integrin engagement inasmuch as antibody cross-linking of l-selectin or CD44 was without effect on EC function. Our data demonstrate a causal link between outside-in signaling by β2 integrins and the capacity of PMNs to induce alterations in vascular permeability, and suggest a paracrine mechanism that involves PMN-derived cationic protein(s) in the cellular crosstalk between PMNs and ECs