University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

Numerical observation of Hawking radiation from acoustic black holes in atomic Bose?Einstein condensates

Abstract: We report numerical evidence of Hawking emission of Bogoliubov phonons from a sonic horizon in a flowing one-dimensional atomic Bose–Einstein condensate. The presence of Hawking radiation is revealed from peculiar long-range patterns in the density–density correlation function of the gas. Quantitative agreement between our fully microscopic calculations and the prediction of analog models is obtained in the hydrodynamic limit. New features are predicted and the robustness of the Hawking signal against a finite temperature discussed.

Spatial constraints on visual-tactile cross-modal distractor congruency effects.

Abstract: Across three experiments, participants made speeded elevation discrimination responses to vibrotactile targets presented to the thumb (held in a lower position) or the index finger (upper position) of either hand, while simultaneously trying to ignore visual distractors presented independently from either the same or a different elevation. Performance on the vibrotactile elevation discrimination task was slower and less accurate when the visual distractor was incongruent with the elevation of the vibrotactile target (e.g., a lower light during the presentation of an upper vibrotactile target to the index finger) than when they were congruent, showing that people cannot completely ignore vision when selectively attending to vibrotactile information. We investigated the attentional, temporal, and spatial modulation of these cross-modal congruency effects by manipulating the direction of endogenous tactile spatial attention, the stimulus onset asynchrony between target and distractor, and the spatial separation between the vibrotactile target, any visual distractors, and the participant’s two hands within and across hemifields. Our results provide new insights into the spatiotemporal modulation of crossmodal congruency effects and highlight the utility of this paradigm for investigating the contributions of visual, tactile, and proprioceptive inputs to the multisensory representation of peripersonal space.

Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

Abstract: The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.

Improving recommender systems with adaptive conversational strategies

Abstract: Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.