Simplified Neural Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that can predict the values of subset of important features called "pivot features." In this work, we show that it is possible to improve on these methods by jointly training the representation learner with the task learner, and examine the importance of existing pivot selection methods.Comment: To be presented at NAACL 201

Beyond the Tunnel Problem, Addressing Cross-Cutting Issues that Impact Vulnerable Youth

Across the country, mayors, commissioners, superintendents, governors, and state policymakers are innovating to address the needs of vulnerable youth. These efforts take many forms: restructuring high schools to improve graduation rates, creating developmentally appropriate interventions to reduce juvenile delinquency, and revamping child welfare practices to keep more youth safely in their homes are just a few of these strategies. Many initiatives, however, are plagued by "crosscutting problems" -- issues that cut across the different agencies that serve youth. Unless crosscutting issues are addressed proactively, they may undermine systemic reforms. This short paper is the first in a series of briefing papers designed to inform officials, practitioners, funders, advocates, scholars and the general public about crosscutting problems and possible solutions to these problems. This paper focuses primarily on the authors' experiences in New York City, though many of the crosscutting problems discussed are known to occur in many jurisdictions large and small. The series starts by presenting a typology of crosscutting issues. The next paper in this series will elaborate on a specific area -- namely, juvenile justice and education. Additional briefing papers will focus on local initiatives that tackle specific problems and more systemic attempts to solve crosscutting issues

Exact Computation of a Manifold Metric, via Lipschitz Embeddings and Shortest Paths on a Graph

Data-sensitive metrics adapt distances locally based the density of data points with the goal of aligning distances and some notion of similarity. In this paper, we give the first exact algorithm for computing a data-sensitive metric called the nearest neighbor metric. In fact, we prove the surprising result that a previously published $3$-approximation is an exact algorithm. The nearest neighbor metric can be viewed as a special case of a density-based distance used in machine learning, or it can be seen as an example of a manifold metric. Previous computational research on such metrics despaired of computing exact distances on account of the apparent difficulty of minimizing over all continuous paths between a pair of points. We leverage the exact computation of the nearest neighbor metric to compute sparse spanners and persistent homology. We also explore the behavior of the metric built from point sets drawn from an underlying distribution and consider the more general case of inputs that are finite collections of path-connected compact sets. The main results connect several classical theories such as the conformal change of Riemannian metrics, the theory of positive definite functions of Schoenberg, and screw function theory of Schoenberg and Von Neumann. We develop novel proof techniques based on the combination of screw functions and Lipschitz extensions that may be of independent interest.Comment: 15 page

Upper atmosphere dynamics

The spatial distribution of stratospheric ozone is useful in diagnosis of some features of the large scale atmospheric circulation, and the ozone may also interact with the atmospheric general circulation. Local maxima in the column ozone distribution are often associated with disturbances in the lower stratosphere and upper troposphere, which may herald cyclone development in the troposphere. One research objective is to explore these issues by means of time series analysis of a zonal index of total column ozone, to suggest the existence or nonexistence of relationships between column ozone and dynamical processes which are known to occur on various time scales. Another objective is to investigate the correlation between the ozone mixing ratio on the 350 K isentropic surface and the column integrated ozone, and to investigate the use of an easily derived parameter as a proxy for ozone mixing ratio, which is conserved in the stratosphere for time scales shorter than the photochemical time scale. The source of data for these studies is the Total Ozone Mapping Spectrometer (TOMS) data set

Stochastic Infinite Horizon Forecasts for Social Security and Related Studies

This paper consists of three reports on stochastic forecasting for Social Security, on infinite horizons, immigration, and structural time series models. 1) In our preferred stochastic immigration forecast, total net immigration drops from current levels down to about one million by 2020, then slowly rises to 1.2 million at the end of the century, with 95% probability bounds of 800,000 to 1.8 million at the century's end. Adding stochastic immigration makes little difference to the probability distribution of the old age dependency ratio. 2) We incorporate parameter uncertainty, stochastic trends, and uncertain ultimate levels in stochastic models of wage growth and fertility. These changes sometimes substantially affect the probability distributions of the individual input forecasts, but they make relatively little difference when embedded in the more fully stochastic Social Security projection. 3) Using a 500-year stochastic projection, we estimate an infinite horizon balance of -5.15% of payroll, compared to the -3.5% of the 2004 Trustees Report, probably reflecting different mortality projections. Our 95% probability interval bounds are -10.5 and -1.3%. Such forecasts, which reflect only "routine" uncertainty, have many problems but nonetheless seem worthwhile.

Recent Radar Observations of the Sub-Centimeter Orbital Debris Environment

The NASA Orbital Debris Program Office (ODPO) has conducted radar observations of the orbital debris environment since the early 1990s to provide measurement data that supports orbital debris models and risk mitigation activities in support of NASA mission objectives. Orbital debris radar observations are a unique mode for radar operation, employing a fixed beam configuration to statistically sample the environment. An advantage of conducting operations in this fashion is that it enables observations of smaller classes of orbital debris than would otherwise be available from the same sensor operating in a traditional tracking mode. Orbital debris-mode radar observations are used to fill in the gaps, which exist in the currently available data from the Space Surveillance Network (SSN), on small size orbital debris populations that represent significant risk to NASA programs. These gaps have typically covered orbital debris with characteristic sizes less than approximately 10 cm down to approximately 3 mm in low Earth orbit (LEO) depending upon the altitude and sensor configuration. The value of orbital debris radar measurements lies in the ability to extract partial orbital element information about orbital debris in the centimeter to several millimeter size regimes in low Earth orbit which are not available from other measurement sources. This paper will discuss observations of this smaller class of orbital debris observed in recent years from the radars at the MIT Haystack Observatory in Westford, Massachusetts, and the Goldstone Solar System Radar near Barstow, California. The former radar is able to observe orbital debris down to approximately 5 mm, and the latter, orbital debris with characteristic sizes near 3 mm at altitudes less than 1000 km. The characteristics and inferences about the current LEO orbital debris environment, and the different subpopulations that are identifiable in the observations are highlighted