Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.Comment: 20 pages, 6 figures, AISTATS 2010, Revise

Development and Enhancement to a Pilot Selection Battery for a University Aviation Program

There exists an imbalance between the number of pilots trained to practice in the field of aviation and the amount of those individuals who are qualified to fly airplanes. By putting a systematic selection system in place, it helps to ensure that the best possible candidates fill open positions in the field. Specifically developing a selection system to train and acclimate future pilots while they are in a university setting will not only help select top-tier candidates into the aviation program, but also prepare them for what to expect when they enter the job market. This research study built upon two iterations of a pilot selection battery for a Midwestern university aviation program. Participants completed a battery that was then used for research purposes to obtain information about the potential predictors of pilot performance. The measures include the IPIP Five Factor Scale, Assertive Interpersonal Schema Questionnaire, Cockpit Management Attitudes Questionnaire, Proactive Personality Scale - Short Version, Block Counting Measure, and Rotated Blocks Measure. Additionally, flight instructors evaluated their students based on several aspects of effective performance. Data from 30 student pilots were examined with bivariate correlations and linear regression and the results from the current sample indicated that a pilot personality profile, assertiveness, proactivity, cockpit management skills, and spatial reasoning did not consistently predict flight performance. Further research is warranted to accumulate a larger sample size in order to determine if these characteristics do, indeed, predict performance in the field

Development and Enhancement to a Pilot Selection Battery for a University Aviation Program

There exists an imbalance between the number of pilots trained to practice in the field of aviation and the amount of those individuals who are qualified to fly airplanes. By putting a systematic selection system in place, it helps to ensure that the best possible candidates fill open positions in the field. Specifically developing a selection system to train and acclimate future pilots while they are in a university setting will not only help select top-tier candidates into the aviation program, but also prepare them for what to expect when they enter the job market. This research study built upon two iterations of a pilot selection battery for a Midwestern university aviation program. Participants completed a battery that was then used for research purposes to obtain information about the potential predictors of pilot performance. The measures include the IPIP Five Factor Scale, Assertive Interpersonal Schema Questionnaire, Cockpit Management Attitudes Questionnaire, Proactive Personality Scale - Short Version, Block Counting Measure, and Rotated Blocks Measure. Additionally, flight instructors evaluated their students based on several aspects of effective performance. Data from 30 student pilots were examined with bivariate correlations and linear regression and the results from the current sample indicated that a pilot personality profile, assertiveness, proactivity, cockpit management skills, and spatial reasoning did not consistently predict flight performance. Further research is warranted to accumulate a larger sample size in order to determine if these characteristics do, indeed, predict performance in the field

On the Evaluation of Plug-in Electric Vehicle Data of a Campus Charging Network

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.Comment: Accepted by IEEE Energycon 201

Unintended Effects of Residential Energy Storage on Emissions from the Electric Power System.

In many jurisdictions, policy-makers are seeking to decentralize the electric power system while also promoting deep reductions in the emission of greenhouse gases (GHG). We examine the potential roles for residential energy storage (RES), a technology thought to be at the epicenter of these twin revolutions. We model the impact of grid-connected RES operation on electricity costs and GHG emissions for households in 16 of the largest U.S. utility service territories under 3 plausible operational modes. Regardless of operation mode, RES mostly increases emissions when users seek to minimize their electricity cost. When operated with the goal of minimizing emissions, RES can reduce average household emissions by 2.2-6.4%, implying a cost equivalent of $180 to$5160 per metric ton of carbon dioxide avoided. While RES is costly compared with many other emission-control measures, tariffs that internalize the social cost of carbon would reduce emissions by 0.1-5.9% relative to cost-minimizing operation. Policy-makers should be careful about assuming that decentralization will clean the electric power system, especially if it proceeds without carbon-mindful tariff reforms

Combining Sentiment Lexica with a Multi-View Variational Autoencoder

When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.Comment: To appear in NAACL-HLT 201

Incentives Work: Getting Teachers to Come to School

We use a randomized experiment and a structural model to test whether monitoring and financial incentives can reduce teacher absence and increase learning in India. In treatment schools, teachers' attendance was monitored daily using cameras, and their salaries were made a nonlinear function of attendance. Teacher absenteeism in the treatment group fell by 21 percentage points relative to the control group, and the children's test scores increased by 0.17 standard deviations. We estimate a structural dynamic labor supply model and find that teachers respond strongly to financial incentives. Our model is used to compute cost-minimizing compensation policies.John D. and Catherine T. MacArthur Foundatio