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.

Supramodal Representations of Perceived Emotions in the Human Brain

Abstract: Basic emotional states (such as anger, fear, and joy) can be similarly conveyed by the face, the body, and the voice. Are there human brain regions that represent these emotional mental states regardless of the sensory cues from which they are perceived? To address this question, in the present study participants evaluated the intensity of emotions perceived from face movements, body movements, or vocal intonations, while their brain activity was measured with functional magnetic resonance imaging (fMRI). Using multivoxel pattern analysis, we compared the similarity of response patterns across modalities to test for brain regions in which emotion-specific patterns in one modality (e.g., faces) could predict emotion-specific patterns in another modality (e.g., bodies). A whole-brain searchlight analysis revealed modality-independent but emotion category-specific activity patterns in medial prefrontal cortex (MPFC) and left superior temporal sulcus (STS). Multivoxel patterns in these regions contained information about the category of the perceived emotions (anger, disgust, fear, happiness, sadness) across all modality comparisons (face-body, face-voice, body-voice), and independently of the perceived intensity of the emotions. No systematic emotion-related differences were observed in the overall amplitude of activation in MPFC or STS. These results reveal supramodal representations of emotions in high-level brain areas previously implicated in affective processing, mental state attribution, and theory-of-mind. We suggest that MPFC and STS represent perceived emotions at an abstract, modality-independent level, and thus play a key role in the understanding and categorization of others' emotional mental states.

The Gender We Think, The Gender We Do in Our Everyday Organizational Lives

Abstract: We “do gender” while we are at work, while we produce an organizational culture and its rules governing what is fair in the relationship between the sexes. The inner ambiguity of gender construction is expressed in the dilemma: how can we do gender without second-sexing the female? The management of cross-gendered situations (dual presence) is based on a two-stage ritual involving the ceremonial work of paying homage to the symbolic order of gender (a deep trans-psychic structure) and the remedial work of repairing the inequality inherent in gender difference. Studying the ambiguity of gender symbols enables us to use indirect speech and discoursively to change gender relationships in organizations.

Exploring 4D quantum Hall physics with a 2D topological charge pump.

Abstract: The discovery of topological states of matter has greatly improved our understanding of phase transitions in physical systems Instead of being described by local order parameters, topological phases are described by global topological invariants and are therefore robust against perturbations A prominent example is the two-dimensional (2D) integer quantum Hall effect: it is characterized by the first Chern number, which manifests in the quantized Hall response that is induced by an external electric field Generalizing the quantum Hall effect to four-dimensional (4D) systems leads to the appearance of an additional quantized Hall response, but one that is nonlinear and described by a 4D topological invariant-the second Chern number Here we report the observation of a bulk response with intrinsic 4D topology and demonstrate its quantization by measuring the associated second Chern number By implementing a 2D topological charge pump using ultracold bosonic atoms in an angled optical superlattice, we realize a dynamical version of the 4D integer quantum Hall effect Using a small cloud of atoms as a local probe, we fully characterize the nonlinear response of the system via in situ imaging and site-resolved band mapping Our findings pave the way to experimentally probing higher-dimensional quantum Hall systems, in which additional strongly correlated topological phases, exotic collective excitations and boundary phenomena such as isolated Weyl fermions are predicted

Towards Robust Linguistic Analysis using OntoNotes

Abstract: Large-scale linguistically annotated corpora have played a crucial role in advancing the state of the art of key natural language technologies such as syntactic, semantic and discourse analyzers, and they serve as training data as well as evaluation benchmarks. Up till now, however, most of the evaluation has been done on monolithic corpora such as the Penn Treebank, the Proposition Bank. As a result, it is still unclear how the state-of-the-art analyzers perform in general on data from a variety of genres or domains. The completion of the OntoNotes corpus, a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information, makes it possible to perform such an evaluation. This paper presents an analysis of the performance of publicly available, state-of-the-art tools on all layers and languages in the OntoNotes v5.0 corpus. This should set the benchmark for future development of various NLP components in syntax and semantics, and possibly encourage research towards an integrated system that makes use of the various layers jointly to improve overall performance.

A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images

Abstract: This paper proposes a novel pixel-based system for the supervised classification of very high geometrical (spatial) resolution images. This system is aimed at obtaining accurate and reliable maps both by preserving the geometrical details in the images and by properly considering the spatial-context information. It is made up of two main blocks: 1) a novel feature-extraction block that, extending and developing some concepts previously presented in the literature, adaptively models the spatial context of each pixel according to a complete hierarchical multilevel representation of the scene and 2) a classifier, based on support vector machines (SVMs), capable of analyzing hyperdimensional feature spaces. The choice of adopting an SVM-based classification architecture is motivated by the potentially large number of parameters derived from the contextual feature-extraction stage. Experimental results and comparisons with a standard technique developed for the analysis of very high spatial resolution images confirm the effectiveness of the proposed system