MULTIDIMENSIONAL OUTPUT INDICES

This abstract describes alternative output aggregates that provide both cross-sectional and temporal comparisons appropriate for the analysis of panel data sets. Several of these multidimensional output indices are constructed using detailed data on agricultural production to illustrate the effects of fixing price weights over time, employing sample average price weights, and choosing between alternative approximations of Divisia indices.Research Methods/ Statistical Methods,

On the spatial evolution of long-wavelength Goertler vortices governed by a viscous-inviscid interaction

The generation of long-wavelength, viscous-inviscid interactive Goertler vortices is studied in the linear regime by numerically solving the time-dependent governing equations. It is found that time-dependent surface deformations, which assume a fixed nonzero shape at large times, generate steady Goertler vortices that amplify in the downstream direction. Thus, the Goertler instability in this regime is shown to be convective in nature, contrary to the earlier findings of Ruban and Savenkov. The disturbance pattern created by steady and streamwise-elongated surface obstacles on a concave surface is examined in detail, and also contrasted with the flow pattern due to roughness elements with aspect ratio of order unity on flat surfaces. Finally, the applicability of the Briggs-Bers criterion to unstable physical systems of this type is questioned by providing a counterexample in the form of the inviscid limit of interactive Goertler vortices

PATTERNS OF AGRICULTURAL DEVELOPMENT IN THE UNITED STATES

International Development,

Catchment boundaries of ocean drainage basins

This dataset contains discretized estimates of the catchment boundaries surrounding the five ocean drainage basins (Atlantic, Pacific, Indian, Arctic and Southern). The catchment boundaries were used to calculate atmospheric moisture fluxes across the catchment boundaries and to initialize airmass trajectories from each point on multiple vertical levels

Using Word Embedding to Evaluate the Coherence of Topics from Twitter Data

Scholars often seek to understand topics discussed on Twitter using topic modelling approaches. Several coherence metrics have been proposed for evaluating the coherence of the topics generated by these approaches, including the pre-calculated Pointwise Mutual Information (PMI) of word pairs and the Latent Semantic Analysis (LSA) word representation vectors. As Twitter data contains abbreviations and a number of peculiarities (e.g. hashtags), it can be challenging to train effective PMI data or LSA word representation. Recently, Word Embedding (WE) has emerged as a particularly effective approach for capturing the similarity among words. Hence, in this paper, we propose new Word Embedding-based topic coherence metrics. To determine the usefulness of these new metrics, we compare them with the previous PMI/LSA-based metrics. We also conduct a large-scale crowdsourced user study to determine whether the new Word Embedding-based metrics better align with human preferences. Using two Twitter datasets, our results show that the WE-based metrics can capture the coherence of topics in tweets more robustly and efficiently than the PMI/LSA-based ones

INTERNATIONAL AGRICULTURAL PRODUCTIVITY PATTERNS

In this paper we present measures of land and labor productivity for a group of 98 developed and developing countries using an entirely new data set with annual observations spanning the past three decades. The substantial cross-country and intertemporal variation in productivity in our sample is linked to both natural and economic factors. We extend previous work by dealing with multiple sources of measurement error in conventional agricultural inputs when accounting for observed differences in productivity. In addition to the mix of conventional inputs in agriculture, we find that indicators of quality change in these inputs and the amount of publicly provided infrastructure are significant in explaining cross-sectional differences in productivity patterns.Productivity Analysis,

Topic-centric Classification of Twitter User's Political Orientation

In the recent Scottish Independence Referendum (hereafter, IndyRef), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweets---their short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions ("Yes"--in favour of Independence vs. "No"--Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline

The phase and volumetric behavior of natural gases at low temperatures and high pressures, including the critical states

Dissertation (Ph.D.)--University of Kansas, Chemical Engineering, 1951