Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression

We design a new approach that allows robot learning of new activities from unlabeled human example videos. Given videos of humans executing the same activity from a human's viewpoint (i.e., first-person videos), our objective is to make the robot learn the temporal structure of the activity as its future regression network, and learn to transfer such model for its own motor execution. We present a new deep learning model: We extend the state-of-the-art convolutional object detection network for the representation/estimation of human hands in training videos, and newly introduce the concept of using a fully convolutional network to regress (i.e., predict) the intermediate scene representation corresponding to the future frame (e.g., 1-2 seconds later). Combining these allows direct prediction of future locations of human hands and objects, which enables the robot to infer the motor control plan using our manipulation network. We experimentally confirm that our approach makes learning of robot activities from unlabeled human interaction videos possible, and demonstrate that our robot is able to execute the learned collaborative activities in real-time directly based on its camera input

Forecasting Hands and Objects in Future Frames

This paper presents an approach to forecast future presence and location of human hands and objects. Given an image frame, the goal is to predict what objects will appear in the future frame (e.g., 5 seconds later) and where they will be located at, even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene information in its frame and that (2) we can predict (i.e., regress) such representations corresponding to the future frames based on that of the current frame. We design a new two-stream convolutional neural network (CNN) architecture for videos by extending the state-of-the-art convolutional object detection network, and present a new fully convolutional regression network for predicting future scene representations. Our experiments confirm that combining the regressed future representation with our detection network allows reliable estimation of future hands and objects in videos. We obtain much higher accuracy compared to the state-of-the-art future object presence forecast method on a public dataset

Empirical Performance of the Czech and Hungarian Index Options under Jump

This paper analyses Czech and Hungarian index options that are traded on the Austrian Futures and Options Exchange. We find that the Poisson jump-diffusion and not the GARCH (1,1) process lends statistical support for the data description. We estimate that approximately four-fifth of 4 percent underpricing (for the Czech Index) and 18 percent overpricing (for the Hungarian Index) biases reported for the short term out-of-the-money call options can be explained by the Jump option pricing model. However, we question whether the mispricings from the jump model are operational, especially, in these emerging financial markets.Leptokurtosis, Poisson jump-diffusion, GARCH, Equity index

Cross-sectional study of risky substance use by injured emergency department patients

INTRODUCTION: Survey data regarding the prevalence of risky substance use in the emergency department (ED) is not consistent. The objective of this study was to identify the prevalence of risky substance use among injured ED patients based on the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST v3.0). A secondary objective was to report on the feasibility of administering the ASSIST to this population, based on the time to conduct screening. METHODS: This cross-sectional study used screening data from a randomized controlled trial. Injured ED patients completed the ASSIST on a tablet computer, and an ASSIST score was computed that indicated the need for a brief or intensive treatment intervention (risky use) for alcohol and other substances. For a subsample, data on time to complete each step of screening was recorded. RESULTS: Between July 2010 and March 2013, 5,695 patients completed the ASSIST. Most (92%) reported lifetime use of at least one substance and 51% reported current risky use of at least one substance. Mean time to complete the ASSIST was 5.4 minutes and screening was considered feasible even when paused for clinical care to proceed. CONCLUSION: Estimates of risky substance use based on the ASSIST in our large sample of injured ED patients were higher than previously reported in other studies of ED patients, possibly due to the current focus on an injured population. In addition, it was feasible to administer the ASSIST to patients in the course of their clinical care.Published versio

Efficient Universal Noiseless Source Codes

Although the existence of universal noiseless variable-rate codes for the class of discrete stationary ergodic sources has previously been established, very few practical universal encoding methods are available. Efficient implementable universal source coding techniques are discussed in this paper. Results are presented on source codes for which a small value of the maximum redundancy is achieved with a relatively short block length. A constructive proof of the existence of universal noiseless codes for discrete stationary sources is first presented. The proof is shown to provide a method for obtaining efficient universal noiseless variable-rate codes for various classes of sources. For memoryless sources, upper and lower bounds are obtained for the minimax redundancy as a function of the block length of the code. Several techniques for constructing universal noiseless source codes for memoryless sources are presented and their redundancies are compared with the bounds. Consideration is given to possible applications to data compression for certain nonstationary sources

Temperature Dependence Of The Electrical Resistivity Of LaxLu1-xAs

We investigate the temperature-dependent resistivity of single-crystalline films of LaxLu1-xAs over the 5-300 K range. The resistivity was separated into lattice, carrier and impurity scattering regions. The effect of impurity scattering is significant below 20 K, while carrier scattering dominates at 20-80 K and lattice scattering dominates above 80 K. All scattering regions show strong dependence on the La content of the films. While the resistivity of 600 nm LuAs films agree well with the reported bulk resistivity values, 3 nm films possessed significantly higher resistivity, suggesting that interfacial roughness significantly impacts the scattering of carriers at the nanoscale limit. (C) 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.Microelectronics Research Cente