New York University

About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.

Dynamic conditional correlation - a simple class of multivariate garch models

Abstract: Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.

Macropinocytosis of protein is an amino acid supply route in Ras-transformed cells

Abstract: Macropinocytosis is a highly conserved endocytic process by which extracellular fluid and its contents are internalized into cells through large, heterogeneous vesicles known as macropinosomes. Oncogenic Ras proteins have been shown to stimulate macropinocytosis but the functional contribution of this uptake mechanism to the transformed phenotype remains unknown. Here we show that Ras-transformed cells use macropinocytosis to transport extracellular protein into the cell. The internalized protein undergoes proteolytic degradation, yielding amino acids including glutamine that can enter central carbon metabolism. Accordingly, the dependence of Ras-transformed cells on free extracellular glutamine for growth can be suppressed by the macropinocytic uptake of protein. Consistent with macropinocytosis representing an important route of nutrient uptake in tumours, its pharmacological inhibition compromises the growth of Ras-transformed pancreatic tumour xenografts. These results identify macropinocytosis as a mechanism by which cancer cells support their unique metabolic needs and point to the possible exploitation of this process in the design of anticancer therapies.

Fast Image Deconvolution using Hyper-Laplacian Priors

Abstract: The heavy-tailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as denoising, deblurring and super-resolution. These distributions are well modeled by a hyper-Laplacian (p(x) ∝ e-k|x|α ), typically with 0.5 ≤ α ≤ 0.8. However, the use of sparse distributions makes the problem non-convex and impractically slow to solve for multi-megapixel images. In this paper we describe a deconvolution approach that is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors. We adopt an alternating minimization scheme where one of the two phases is a non-convex problem that is separable over pixels. This per-pixel sub-problem may be solved with a lookup table (LUT). Alternatively, for two specific values of α, 1/2 and 2/3 an analytic solution can be found, by finding the roots of a cubic and quartic polynomial, respectively. Our approach (using either LUTs or analytic formulae) is able to deconvolve a 1 megapixel image in less than ~3 seconds, achieving comparable quality to existing methods such as iteratively reweighted least squares (IRLS) that take ~20 minutes. Furthermore, our method is quite general and can easily be extended to related image processing problems, beyond the deconvolution application demonstrated.

Learning by knowledge‐intensive firms*

Abstract: Knowledge-intensive firms (KIFs) specialize in expertise, especially esoteric knowledge that few other firms possess. KIFs must keep their knowledge up-to-date, so they need to learn continually. However, their ability to satisfy clients depends on the close relations that some of KIFs' personnel develop with specific clients.