An Open-Domain Dialog Act Taxonomy

This document defines the taxonomy of dialog acts that are necessary to encode domain-independent dialog moves in the context of a task-oriented, open-domain dialog. Such taxonomy is formulated to satisfy two complementary requirements: on the one hand, domain independence, i.e. the power to cover all the range of possible interactions in any type of conversation (particularly conversation oriented to the performance of tasks). On the other hand, the ability to instantiate a concrete set of tasks as defined by a specific knowledge base (such as an ontology of domain concepts and actions) and within a particular language. For the modeling of dialog acts, inspiration is taken from several well-known dialog annotation schemes, such as DAMSL (Core & Allen, 1997), TRAINS (Traum, 1996) and VERBMOBIL (Alexandersson et al., 1997)

A Learned-SVD approach for Regularization in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is an emerging technology in medical imaging which employs light in the NIR spectrum to estimate the distribution of optical coefficients in biological tissues for diagnostic and monitoring purposes. DOT reconstruction implies the solution of a severely ill-posed inverse problem, for which regularization techniques are mandatory in order to achieve reasonable results. Traditionally, regularization techniques put a variance prior on the desired solution/gradient via regularization parameters, whose choice requires a fine tuning, specific for each case. In this work we explore deep learning techniques in a fully data-driven approach, able of reconstructing the generating signal (optical absorption coefficient) in an automated way. We base our approach on the so-called Learned Singular Value Decomposition, which has been proposed for general inverse problems, and we tailor it to the DOT application. We perform tests with increasing levels of noise on the measure, and compare it with standard variational approaches

Quantitative abilities in a reptile (Podarcis sicula)

The ability to identify the largest amount of prey available is fundamental for optimizing foraging behaviour in several species. To date, this cognitive skill has been observed in all vertebrate groups except reptiles. In this study we investigated the spontaneous ability of ruin lizards to select the larger amount of food items. In Experiment 1, lizards proved able to select the larger food item when presented with two alternatives differing in size (0.25, 0.50, 0.67 and 0.75 ratio). In Experiment 2 lizards presented with two groups of food items (1 versus 4, 2 versus 4, 2 versus 3 and 3 versus 4 items) were unable to select the larger group in any contrast. The lack of discrimination in the presence ofmultiple items represents an exception in numerical cognition studies, raising the question as to whether reptiles' quantitative abilities are different from those of other vertebrate groups