Charge Ordering in Amorphous WOx_{x} Films

We report on the observation of highly anisotropic viscous electronic conducting phase in amorphous WO$_{1.55}$ films that occurs below a current (I)- and frequency (f)- dependent temperature T*(I, f). At T < T*(I, f) the rotational symmetry of randomly disordered electronic background is broken leading to the appearance of mutually perpendicular metallic- and insulating-like states. A rich dynamic behavior of the electronic matter occurring at T < T*(I, f) provides evidence for an interplay between pinning effects and electron-electron interactions. The results strongly suggest a dynamic crystallization of the disordered electronic matter, viz. formation of sliding Wigner crystal, as well as the occurrence of quantum smectic or stripe phase in the pinning-dominated regime.Comment: 17 pages including 5 figure

Study of one class boundary method classifiers for application in a video-based fall detection system

In this paper, we introduce a video-based robust fall detection system for monitoring an elderly person in a smart room environment. Video features, namely the centroid and orientation of a voxel person, are extracted. The boundary method, which is an example one class classification technique, is then used to determine whether the incoming features lie in the ‘fall region’ of the feature space, and thereby effectively distinguishing a fall from other activities, such as walking, sitting, standing, crouching or lying. Four different types of boundary methods, k-center, k-th nearest neighbor, one class support vector machine and single class minimax probability machine are assessed on representative test datasets. The comparison is made on the following three aspects: 1). True positive rate, false positive rate and geometric means in detection 2). Robustness to noise in the training dataset 3). The computational time for the test phase. From the comparison results, we show that the single class minimax probability machine achieves the best overall performance. By applying one class classification techniques with 3-d features, we can obtain a more efficient fall detection system with acceptable performance, as shown in the experimental part; besides, it can avoid the drawbacks of other traditional fall detection methods

Design, Development and Test of Engineering Models of Tethered Nanosatellites to Observe the Solar Corona

The present work, which is based on a cooperative effort between the Group of Space Solar Physics at the Osservatorio Astronomico di Torino (OATo), and the AeroSpace Systems Engineering Team (ASSET), at Politecnico di Torino, is part of a wider research program known as "Studies of Solar System Exploration," funded in 2007 by ASI (Italian Space Agency). The request made by OATo was the development of the capability of taking pictures of the solar corona by means of simple and low cost optical components. ASSET developed the concept of a system consisting of two low-cost tethered nanosatellites, the Occulting and the Observing. The research program, which ended in 2010, envisaged both the conceptual design of the system and the development of experimental test-benches to test critical technologies. The following tests were performed in the Aerospace Systems Laboratory: attitude determination and control of the Occulting engineering model on a frictionless table; acquisition and transmission of pictures from the Observing engineering model to the control station; rotation of the Observing engineering model on a frictionless table; translation of the Observing engineering model on a frictionless table; and separation between the Occulting and Observing engineering models. This article summarizes the research activities carried out within the program and the primary results obtaine

Bayesian modeling and inference for one-shot experiments

In one-shot experiments, units are subjected to varying levels of stimulus and their binary response (go/no-go) is recorded. Experimental data is used to estimate the “sensitivity function”, which characterizes the probability of a “go” for a given level of stimulus. We review the current GLM approaches to modeling and inference, and identify some deficiencies. To address these, we propose a novel Bayesian approach using an adjustable number of cubic splines, with physically-plausible smoothness, monotonicity, and tail constraints introduced through the prior distribution on the coefficients. Our approach runs “out of the box,” and in roughly the same time as the GLM approaches. We illustrate with two contrasting datasets, and show that our more flexible Bayesian approach gives different inferences to the GLM approaches for both the sensitivity function and its inverse