fondazione bruno kessler

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.

The nuXmv Symbolic Model Checker

Abstract: This paper describes the nuXmv symbolic model checker for finite- and infinite-state synchronous transition systems. nuXmv is the evolution of the nuXmv open source model checker. It builds on and extends nuXmv along two main directions. For finite-state systems it complements the basic verification techniques of nuXmv with state-of-the-art verification algorithms. For infinite-state systems, it extends the nuXmv language with new data types, namely Integers and Reals, and it provides advanced SMT-based model checking techniques. Besides extended functionalities, nuXmv has been optimized in terms of performance to be competitive with the state of the art. nuXmv has been used in several industrial projects as verification back-end, and it is the basis for several extensions to cope with requirements analysis, contract based design, model checking of hybrid systems, safety assessment, and software model checking.

SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment

Abstract: This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval2014. Participation was open to systems based on any approach. Systems were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (i) semantic relatedness and (ii) entailment. The task attracted 21 teams, most of which participated in both subtasks. We received 17 submissions in the relatedness subtask (for a total of 66 runs) and 18 in the entailment subtask (65 runs).

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Abstract: The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Observation of a Narrow Pentaquark State, Pc (4312)+, and of the Two-Peak Structure of the Pc (4450)+

Abstract: A narrow pentaquark state, P_{c}(4312)^{+}, decaying to J/ψp, is discovered with a statistical significance of 7.3σ in a data sample of Λ_{b}^{0}→J/ψpK^{-} decays, which is an order of magnitude larger than that previously analyzed by the LHCb Collaboration. The P_{c}(4450)^{+} pentaquark structure formerly reported by LHCb is confirmed and observed to consist of two narrow overlapping peaks, P_{c}(4440)^{+} and P_{c}(4457)^{+}, where the statistical significance of this two-peak interpretation is 5.4σ. The proximity of the Σ_{c}^{+}D[over ¯]^{0} and Σ_{c}^{+}D[over ¯]^{*0} thresholds to the observed narrow peaks suggests that they play an important role in the dynamics of these states.

Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation

Abstract: This paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through extensive experimental evaluation we demonstrate the effectiveness of the proposed approach and establish new state of the art results on publicly available datasets.