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 & Context (language use). 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.

Navier-Stokes equations for compressible fluids: global existence and qualitative properties of the solutions in the general case

Abstract: We consider the equations which describe the motion of a viscous compressible fluid, taking into consideration the case of inflow and/or outflow through the boundary. By means of some a priori estimates we prove the existence of a global (in time) solution. Moreover, as a consequence of a stability result, we show that there exist a periodic solution and a stationary solution.

Upper Limits on the Stochastic Gravitational-Wave Background from Advanced LIGO's First Observing Run

Abstract: A wide variety of astrophysical and cosmological sources are expected to contribute to a stochastic gravitational-wave background. Following the observations of GW150914 and GW151226, the rate and mass of coalescing binary black holes appear to be greater than many previous expectations. As a result, the stochastic background from unresolved compact binary coalescences is expected to be particularly loud. We perform a search for the isotropic stochastic gravitational-wave background using data from Advanced Laser Interferometer Gravitational Wave Observatory’s (aLIGO) first observing run. The data display no evidence of a stochastic gravitational-wave signal. We constrain the dimensionless energy density of gravitational waves to be Ω 0 < 1.7 × 10 − 7 with 95% confidence, assuming a flat energy density spectrum in the most sensitive part of the LIGO band (20–86 Hz). This is a factor of ∼ 33 times more sensitive than previous measurements. We also constrain arbitrary power-law spectra. Finally, we investigate the implications of this search for the background of binary black holes using an astrophysical model for the background.

Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions

Abstract: Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of the-art HR estimation methods in naturalistic conditions.

NADEEF: a commodity data cleaning system

Abstract: Despite the increasing importance of data quality and the rich theoretical and practical contributions in all aspects of data cleaning, there is no single end-to-end off-the-shelf solution to (semi-)automate the detection and the repairing of violations w.r.t. a set of heterogeneous and ad-hoc quality constraints. In short, there is no commodity platform similar to general purpose DBMSs that can be easily customized and deployed to solve application-specific data quality problems. In this paper, we present NADEEF, an extensible, generalized and easy-to-deploy data cleaning platform. NADEEF distinguishes between a programming interface and a core to achieve generality and extensibility. The programming interface allows the users to specify multiple types of data quality rules, which uniformly define what is wrong with the data and (possibly) how to repair it through writing code that implements predefined classes. We show that the programming interface can be used to express many types of data quality rules beyond the well known CFDs (FDs), MDs and ETL rules. Treating user implemented interfaces as black-boxes, the core provides algorithms to detect errors and to clean data. The core is designed in a way to allow cleaning algorithms to cope with multiple rules holistically, i.e. detecting and repairing data errors without differentiating between various types of rules. We showcase two implementations for core repairing algorithms. These two implementations demonstrate the extensibility of our core, which can also be replaced by other user-provided algorithms. Using real-life data, we experimentally verify the generality, extensibility, and effectiveness of our system.