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.

Federalism, Chinese Style: The Political Basis for Economic Success in China

Abstract: China's remarkable economic success rests on a foundation of political reform providing a considerable degree of credible commitment to markets. This reform reflects a special type of institutionalized decentralization that the authors call “federalism, Chinese style.” This form of decentralization has three consequences. First, it fosters competition, not only in product markets, but also among local governments for labor and foreign capital. This competition, in turn, encourages local government experimentation and learning with new forms of enterprises, regulation, and economic relationships. Second, it provides incentives for local governments to promote local economic prosperity. Finally, it provides a significant amount of protection to local governments and their enterprises from political intrusion by the central government.

Organization and cell differentiation in lateral roots of Arabidopsis thaliana

Abstract: Lateral root formation in plants involves the stimulation of mature pericycle cells to proliferate and redifferentiate to create a new organ. The simple organization of the root of Arabidopsis thaliana allows the development of lateral root primordia to be characterized histologically. We have divided the process of lateral root development into 8 stages defined by specific anatomical characteristics and cell divisions. To identify the cell types in the developing primordium we have generated a collection of marker lines that express beta-glucuronidase in a tissue- or cell type-specific manner in the root. Using these tools we have constructed a model describing the lineage of each cell type in the lateral root. These studies show that organization and cell differentiation in the lateral root primordia precede the appearance of a lateral root meristem, with differential gene expression apparent after the first set of divisions of the pericycle.

Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies and the Distant Universe

Abstract: We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median $z\sim 0.03$). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between $z\sim 0.6$ and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July.

Get another label? improving data quality and data mining using multiple, noisy labelers

Abstract: This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction. With the outsourcing of small tasks becoming easier, for example via Rent-A-Coder or Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a robust technique that combines different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.