Institution
fondazione bruno kessler
Facility•Trento, Italy•
About: fondazione bruno kessler is a facility organization based out in Trento, Italy. It is known for research contribution in the topics: Silicon photomultiplier & Detector. The organization has 1145 authors who have published 4730 publications receiving 94404 citations. The organization is also known as: Trentino Institute of Culture.
Papers published on a yearly basis
Papers
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TL;DR: MaSIF (molecular surface interaction fingerprinting) is presented, a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions that will lead to improvements in the understanding of protein function and design.
Abstract: Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein's modes of interactions with other biomolecules. We hypothesize that proteins participating in similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.
389 citations
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06 Jun 2017
TL;DR: A set of parameters are defined based on which one of these implementations can be chosen optimally given a particular use-case or application and a decision tree for the selection of the optimal implementation is presented.
Abstract: When it comes to storage and computation of large scales of data, Cloud Computing has acted as the de-facto solution over the past decade. However, with the massive growth in intelligent and mobile devices coupled with technologies like Internet of Things (IoT), V2X Communications, Augmented Reality (AR), the focus has shifted towards gaining real-time responses along with support for context-awareness and mobility. Due to the delays induced on the Wide Area Network (WAN) and location agnostic provisioning of resources on the cloud, there is a need to bring the features of the cloud closer to the consumer devices. This led to the birth of the Edge Computing paradigm which aims to provide context aware storage and distributed Computing at the edge of the networks. In this paper, we discuss the three different implementations of Edge Computing namely Fog Computing, Cloudlet and Mobile Edge Computing in detail and compare their features. We define a set of parameters based on which one of these implementations can be chosen optimally given a particular use-case or application and present a decision tree for the selection of the optimal implementation.
387 citations
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TL;DR: A novel double fusion framework is introduced, combining the benefits of traditional early fusion and late fusion strategies, which is extensively evaluated on publicly available video surveillance datasets including UCSD pedestian, Subway, and Train, showing competitive performance with respect to state of the art approaches.
385 citations
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Osaka University1, Technische Universität Darmstadt2, Texas A&M University–Commerce3, University of the Witwatersrand4, Kyushu University5, Tohoku University6, Kyoto University7, GSI Helmholtz Centre for Heavy Ion Research8, Goethe University Frankfurt9, University of Tokyo10, fondazione bruno kessler11, University of Valencia12, Niigata University13, National Institute of Radiological Sciences14
TL;DR: The extracted E1 polarizability leads to a neutron skin thickness close to that of a neutron star, thereby constraining the symmetry energy and its density dependence relevant to the description of neutron stars.
Abstract: A benchmark experiment on Pb-208 shows that polarized proton inelastic scattering at very forward angles including 0 degrees is a powerful tool for high-resolution studies of electric dipole (E1) and spin magnetic dipole (M1) modes in nuclei over a broad excitation energy range to test up-to-date nuclear models. The extracted E1 polarizability leads to a neutron skin thickness r(skin) = 0.156(-0.021)(+0.025) fm in Pb-208 derived within a mean-field model [Phys. Rev. C 81, 051303 (2010)], thereby constraining the symmetry energy and its density dependence relevant to the description of neutron stars.
362 citations
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22 Sep 2008TL;DR: The IRSTLM toolkit supports distribution of ngram collection and smoothing over a computer cluster, language model compression through probability quantization, lazy-loading of huge language models from disk.
Abstract: Research in speech recognition and machine translation is boosting the use of large scale n-gram language models. We present an open source toolkit that permits to efficiently handle language models with billions of n-grams on conventional machines. The IRSTLM toolkit supports distribution of ngram collection and smoothing over a computer cluster, language model compression through probability quantization, lazy-loading of huge language models from disk. IRSTLM has been so far successfully deployed with the Moses toolkit for statistical machine translation and with the FBK-irst speech recognition system. Efficiency of the tool is reported on a speech transcription task of Italian political speeches using a language model of 1.1 billion four-grams.
354 citations
Authors
Showing all 1174 results
Name | H-index | Papers | Citations |
---|---|---|---|
Luca Benini | 101 | 1453 | 47862 |
Gianluigi Casse | 98 | 1150 | 46476 |
Lorenzo Bruzzone | 86 | 699 | 33030 |
Wolfram Weise | 71 | 463 | 18090 |
Achim Richter | 61 | 654 | 16937 |
Nicola M. Pugno | 61 | 730 | 18985 |
Alessandro Tredicucci | 57 | 329 | 16545 |
Alessandro Cimatti | 57 | 277 | 17459 |
Patrizio Pezzotti | 56 | 260 | 10698 |
Tommaso Calarco | 53 | 192 | 9077 |
Paolo Tonella | 53 | 289 | 9155 |
Alessandro Moschitti | 52 | 308 | 11378 |
Marco Roveri | 51 | 213 | 13029 |
Fabio Remondino | 50 | 321 | 12087 |
Gert Aarts | 48 | 232 | 6462 |