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.

Multi-Feature Fusion via Hierarchical Regression for Multimedia Analysis

Abstract: Multimedia data are usually represented by multiple features. In this paper, we propose a new algorithm, namely Multi-feature Learning via Hierarchical Regression for multimedia semantics understanding, where two issues are considered. First, labeling large amount of training data is labor-intensive. It is meaningful to effectively leverage unlabeled data to facilitate multimedia semantics understanding. Second, given that multimedia data can be represented by multiple features, it is advantageous to develop an algorithm which combines evidence obtained from different features to infer reliable multimedia semantic concept classifiers. We design a hierarchical regression model to exploit the information derived from each type of feature, which is then collaboratively fused to obtain a multimedia semantic concept classifier. Both label information and data distribution of different features representing multimedia data are considered. The algorithm can be applied to a wide range of multimedia applications and experiments are conducted on video data for video concept annotation and action recognition. Using Trecvid and CareMedia video datasets, the experimental results show that it is beneficial to combine multiple features. The performance of the proposed algorithm is remarkable when only a small amount of labeled training data are available.

Formal Reasoning Techniques for Goal Models

Abstract: Over the past decade, goal models have been used in Computer Science in order to represent software requirements, business objectives and design qualities. Such models extend traditional AI planning techniques for representing goals by allowing for partially defined and possibly inconsistent goals. This paper presents a formal framework for reasoning with such goal models. In particular, the paper proposes a qualitative and a numerical axiomatization for goal modeling primitives and introduces label propagation algorithms that are shown to be sound and complete with respect to their respective axiomatizations. In addition, the paper reports on experimental results on the propagation algorithms applied to a goal model for a US car manufacturer.

Neurobiological bases of autism-epilepsy comorbidity: a focus on excitation/inhibition imbalance.

Abstract: Autism spectrum disorders (ASD) and epilepsy are common neurological diseases of childhood, with an estimated incidence of approximately 0.5-1% of the worldwide population. Several genetic, neuroimaging and neuropathological studies clearly showed that both ASD and epilepsy have developmental origins and a substantial degree of heritability. Most importantly, ASD and epilepsy frequently coexist in the same individual, suggesting a common neurodevelopmental basis for these disorders. Genome-wide association studies recently allowed for the identification of a substantial number of genes involved in ASD and epilepsy, some of which are mutated in syndromes presenting both ASD and epilepsy clinical features. At the cellular level, both preclinical and clinical studies indicate that the different genetic causes of ASD and epilepsy may converge to perturb the excitation/inhibition (E/I) balance, due to the dysfunction of excitatory and inhibitory circuits in various brain regions. Metabolic and immune dysfunctions, as well as environmental causes also contribute to ASD pathogenesis. Thus, an E/I imbalance resulting from neurodevelopmental deficits of multiple origins might represent a common pathogenic mechanism for both diseases. Here, we will review the most significant studies supporting these hypotheses. A deeper understanding of the molecular and cellular determinants of autism-epilepsy comorbidity will pave the way to the development of novel therapeutic strategies.