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 & Machine translation. The organization has 1145 authors who have published 4730 publications receiving 94404 citations. The organization is also known as: Trentino Institute of Culture.
Topics: Silicon photomultiplier, Machine translation, Detector, Deep learning, Ontology (information science)
Papers published on a yearly basis
Papers
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01 May 2014TL;DR: This work aims to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.
Abstract: Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.
732 citations
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University of Glasgow1, University of Salerno2, Max Planck Society3, University of Southampton4, University of Paris-Sud5, University of Nice Sophia Antipolis6, Washington State University7, Istituto Nazionale di Fisica Nucleare8, University of Warsaw9, University of Naples Federico II10, University of Birmingham11, Cardiff University12, University of Rome Tor Vergata13, Moscow State University14, California Institute of Technology15, VU University Amsterdam16, fondazione bruno kessler17, Leibniz University of Hanover18, University of Cambridge19, University of Tübingen20, University of Urbino21, University of Jena22, University of the Balearic Islands23, Northwestern University24, University of Minnesota25, University of Savoy26, Pennsylvania State University27, University of Pisa28, Roma Tre University29, Sapienza University of Rome30, University of Mississippi31
TL;DR: In this article, a special focus is set on evaluating the frequency band below 10 Hz where a complex mixture of seismic, gravity gradient, suspension thermal and radiation pressure noise dominates, including the most relevant fundamental noise contributions.
Abstract: Advanced gravitational wave detectors, currently under construction, are expected to directly observe gravitational wave signals of astrophysical origin. The Einstein Telescope (ET), a third-generation gravitational wave detector, has been proposed in order to fully open up the emerging field of gravitational wave astronomy. In this paper we describe sensitivity models for ET and investigate potential limits imposed by fundamental noise sources. A special focus is set on evaluating the frequency band below 10 Hz where a complex mixture of seismic, gravity gradient, suspension thermal and radiation pressure noise dominates. We develop the most accurate sensitivity model, referred to as ET-D, for a third-generation detector so far, including the most relevant fundamental noise contributions.
682 citations
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12 Aug 2016
TL;DR: The results of the WMT16 shared tasks are presented, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task.
Abstract: This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.
616 citations
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TL;DR: In this paper, the authors compare the performance of R-squared and SMAPE with respect to the distribution of ground truth elements, and show that the coefficient of determination is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE.
Abstract: Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R 2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.
568 citations
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Rotem Botvinik-Nezer1, Rotem Botvinik-Nezer2, Felix Holzmeister3, Colin F. Camerer4 +217 more•Institutions (78)
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
551 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 |