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.

Adaptive deconvolutional networks for mid and high level feature learning

Abstract: We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level object parts and complete objects. To build our model we rely on a novel inference scheme that ensures each layer reconstructs the input, rather than just the output of the layer directly beneath, as is common with existing hierarchical approaches. This makes it possible to learn multiple layers of representation and we show models with 4 layers, trained on images from the Caltech-101 and 256 datasets. When combined with a standard classifier, features extracted from these models outperform SIFT, as well as representations from other feature learning methods.

A PANIC Attack on Unit Roots and Cointegration

Abstract: This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC—Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable-specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid pooling of individual statistics and thus panel tests can be constructed. A distinctive feature of PANIC is that it tests the unobserved components of the data instead of the observed series. The key to PANIC is consistent estimation of the space spanned by the unobserved common factors and the idiosyncratic errors without knowing a priori whether these are stationary or integrated processes. We provide a rigorous theory for estimation and inference and show that the tests have good finite sample properties.

Predictors of sleep-disordered breathing in community-dwelling adults: the Sleep Heart Health Study.

Abstract: Background Sleep-disordered breathing (SDB) is common, but largely undiagnosed in the general population. Information on demographic patterns of SDB occurrence and its predictive factors in the general population is needed to target high-risk groups that may benefit from diagnosis. Methods The sample comprised 5615 community-dwelling men and women aged between 40 and 98 years who were enrolled in the Sleep Heart Health Study. Data were collected by questionnaire, clinical examinations, and in-home polysomnography. Sleep-disordered breathing status was based on the average number of apnea and hypopnea episodes per hour of sleep (apnea-hypopnea index [AHI]). We used multiple logistic regression modeling to estimate cross-sectional associations of selected participant characteristics with SDB defined by an AHI of 15 or greater. Results Male sex, age, body mass index, neck girth, snoring, and repeated breathing pause frequency were independent, significant correlates of an AHI of 15 or greater. People reporting habitual snoring, loud snoring, and frequent breathing pauses were 3 to 4 times more likely to have an AHI of 15 or greater vs an AHI less than 15, but there were weaker associations for other factors with an AHI of 15 or greater. The odds ratios (95% confidence interval) for an AHI of 15 or greater vs an AHI less than 15 were 1.6 and 1.5, respectively, for 1-SD increments in body mass index and neck girth. As age increased, the magnitude of associations for SDB and body habitus, snoring, and breathing pauses decreased. Conclusions A significant proportion of occult SDB in the general population would be missed if screening or case finding were based solely on increased body habitus or male sex. Breathing pauses and obesity may be particularly insensitive for identifying SDB in older people. A better understanding of predictive factors for SDB, particularly in older adults, is needed.

De novo mutations in epileptic encephalopathies

Abstract: Epileptic encephalopathies are a devastating group of severe childhood epilepsy disorders for which the cause is often unknown. Here we report a screen for de novo mutations in patients with two classical epileptic encephalopathies: infantile spasms (n = 149) and Lennox-Gastaut syndrome (n = 115). We sequenced the exomes of 264 probands, and their parents, and confirmed 329 de novo mutations. A likelihood analysis showed a significant excess of de novo mutations in the ∼4,000 genes that are the most intolerant to functional genetic variation in the human population (P = 2.9 × 10(-3)). Among these are GABRB3, with de novo mutations in four patients, and ALG13, with the same de novo mutation in two patients; both genes show clear statistical evidence of association with epileptic encephalopathy. Given the relevant site-specific mutation rates, the probabilities of these outcomes occurring by chance are P = 4.1 × 10(-10) and P = 7.8 × 10(-12), respectively. Other genes with de novo mutations in this cohort include CACNA1A, CHD2, FLNA, GABRA1, GRIN1, GRIN2B, HNRNPU, IQSEC2, MTOR and NEDD4L. Finally, we show that the de novo mutations observed are enriched in specific gene sets including genes regulated by the fragile X protein (P < 10(-8)), as has been reported previously for autism spectrum disorders.