Institution
University of Trento
Education•Trento, Italy•
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.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors use empirical evidence on networks of voluntary organizations mobilizing on ethnic minority, environmental, and social exclusion issues in two British cities, to differentiate between social movement processes and other, cognate collective action dynamics.
Abstract: This article uses empirical evidence on networks of voluntary organizations mobilizing on ethnic minority, environmental, and social exclusion issues in two British cities, to differentiate between social movement processes and other, cognate collective action dynamics. Social movement processes are identified as the building and reproducing of dense informal networks between a multiplicity of actors, sharing a collective identity, and engaged in social and/or political conflict. They are contrasted to coalitional processes, where alliances to achieve specific goals are not backed by significant identity links, and organizational processes, where collective action takes place mostly in reference to specific organizations rather than broader, looser networks.
239 citations
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05 Sep 2010TL;DR: A mechanism to automatically determine characteristic fixation seeds for segmentation is proposed and it is shown that the use of fixation seeds generated from multiple fixation clusters on the salient object can lead to a 10% improvement in segmentation performance over the state-of-the-art.
Abstract: To learn the preferential visual attention given by humans to specific image content, we present NUSEF- an eye fixation database compiled from a pool of 758 images and 75 subjects. Eye fixations are an excellent modality to learn semantics-driven human understanding of images, which is vastly different from feature-driven approaches employed by saliency computation algorithms. The database comprises fixation patterns acquired using an eye-tracker, as subjects free-viewed images corresponding to many semantic categories such as faces (human and mammal), nudes and actions (look, read and shoot). The consistent presence of fixation clusters around specific image regions confirms that visual attention is not subjective, but is directed towards salient objects and object-interactions.
We then show how the fixation clusters can be exploited for enhancing image understanding, by using our eye fixation database in an active image segmentation application. Apart from proposing a mechanism to automatically determine characteristic fixation seeds for segmentation, we show that the use of fixation seeds generated from multiple fixation clusters on the salient object can lead to a 10% improvement in segmentation performance over the state-of-the-art.
239 citations
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TL;DR: This paper adopts a formal goal model defined and analyzed in (J.Tropos), to make the goal analysis process concrete through the use of forward and backward reasoning for goal models.
239 citations
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TL;DR: A systematic, large-scale structure is found in the neural responses related to the interaction between two major cognitive dimensions of object representation: animacy and real-world size that reflect the major joints in the representational structure of objects and place informative constraints on the nature of the underlying cognitive architecture.
Abstract: Occipito-temporal cortex is known to house visual object representations, but the organization of the neural activation patterns along this cortex is still being discovered. Here we found a systematic, large-scale structure in the neural responses related to the interaction between two major cognitive dimensions of object representation: animacy and real-world size. Neural responses were measured with functional magnetic resonance imaging while human observers viewed images of big and small animals and big and small objects. We found that real-world size drives differential responses only in the object domain, not the animate domain, yielding a tripartite distinction in the space of object representation. Specifically, cortical zones with distinct response preferences for big objects, all animals, and small objects, are arranged in a spoked organization around the occipital pole, along a single ventromedial, to lateral, to dorsomedial axis. The preference zones are duplicated on the ventral and lateral surface of the brain. Such a duplication indicates that a yet unknown higher-order division of labor separates object processing into two substreams of the ventral visual pathway. Broadly, we suggest that these large-scale neural divisions reflect the major joints in the representational structure of objects and thus place informative constraints on the nature of the underlying cognitive architecture.
239 citations
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TL;DR: In this article, a theory of slice regular functions on a real alternative algebra A is developed based on a well-known Fueter's construction, which permits to extend the range of these function theories and to obtain new results, including a strong form of the fundamental theorem of algebra for an ample class of polynomials with coefficients in A and a Cauchy integral formula for slice functions of class C 1.
238 citations
Authors
Showing all 10758 results
Name | H-index | Papers | Citations |
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Yi Chen | 217 | 4342 | 293080 |
Jie Zhang | 178 | 4857 | 221720 |
Richard B. Lipton | 176 | 2110 | 140776 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
J. N. Butler | 172 | 2525 | 175561 |
Andrea Bocci | 172 | 2402 | 176461 |
P. Chang | 170 | 2154 | 151783 |
Bradley Cox | 169 | 2150 | 156200 |
Marc Weber | 167 | 2716 | 153502 |
Guenakh Mitselmakher | 165 | 1951 | 164435 |
Brian L Winer | 162 | 1832 | 128850 |
J. S. Lange | 160 | 2083 | 145919 |
Ralph A. DeFronzo | 160 | 759 | 132993 |
Darien Wood | 160 | 2174 | 136596 |
Robert Stone | 160 | 1756 | 167901 |