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CERN-SPS NA62 experiment. Upper limits at 90% CL on the branching ratio for the decay K+ -> pi+X vs mass for a set of rest lifetime values tau_X of an invisible particle X. Exclusion limits as ALP/dark scalar production searches, as a function of the coupling g_Y/sin^2theta vs mass. Two curves for the ALP search, corresponding to ALP widths dominated by coupling to SM fermions or to invisible particles. The expected upper limit refers to absence of signal.

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MS2-MCP live imaging data of the synthetic Z6 MS2 reporter used for the manuscript: Fernandes, Tran et al. Synthetic deconstruction of hunchback regulation by Bicoid

Behaviours quantified from GPS tracking data and dual motion sensors of 42 adult, solitary brown bears moitored in south-central Sweden by the Scandinavian Brown Bear Research Project (SBBRP). Behaviours were quantified and averaged within six 10-day periods between 21. June and 19. August 2007 - 2016. We quantified individual repeatability of each behaviour and correlations among them (i.e. behavioural syndromes).

MS2-MCP live imaging data of the synthetic B9 MS2 reporter used for the manuscript: Fernandes, Tran et al. Synthetic deconstruction of hunchback regulation by Bicoid

Dataset related to article: Heavy-oxide glasses with superior mechanical assets for nonlinear fiber applications in the mid-infrared. Cl��ment Strutynski, Florian Calzavara, Th��o Guerineau, Laura Loi, Romain Laberdesque, Jean-Michel Rampnoux, Steeve Morency, Yannick Ledemi, Yannick Petit, Marc Dussauze, Fr��d��ric D��s��v��davy, Fr��d��ric Smektala, Sylvain Danto, Lionel Canioni, Younes Messaddeq, Evelyne Fargin, Thierry Cardinal. Optical Materials Express. 2021, volume 11, issue 5, pp. 1420-1430 https://doi.org/10.1364/ome.417699 The data were measured internally with instrumentations belonging to the tem in Bordeaux. UV-Visible-NIR transmission spectra of the glass were recorded in the 0.2 ��m - 3.3 ��m range using an Agilent Cary 5000 UV-Vis-NIR spectrometer. Transmission in the Mid-IR was collected using a Bruker FTIR Optical losses measurements of single-index fibers were carried out using the cutback method on several meters-long samples. The Raman gain cross-section was calculated from spontaneous scattering spectra measured in in VV (vertical polarization for the excitation and the analysis) configuration using a micro-Raman setup. A spectroscopic phase modulated ellipsometric measurement had thus been performed with an UVISEL apparatus (HORIBA Jobin-Yvon) to determine the refractive index n of the GGBLaY glass, after a fine calibration of the ellipsometre with the reference Silica glass over the spectral range between 0.260 ��m to 2 ��m thanks to the comparison to literature.

This repository provides the data used for the experiments of the paper "Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning" by Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh, and Lionel Briand appearing in IEEE Transactions on Reliability (doi: 10.1109/TR.2021.3074750) Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components. We observe three major challenges with existing practices regarding DNNs in safety-critical systems: (1) scenarios that are underrepresented in the test set may lead to serious safety violation risks, but may, however, remain unnoticed; (2) char- acterizing such high-risk scenarios is critical for safety analysis; (3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine. To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors. HUDD identifies root causes by applying a clustering algorithm to heatmaps capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters. We evaluated HUDD with DNNs from the automotive domain. HUDD was able to identify all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches.

VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'The LOFAR Two Metre Sky Survey: Deep Fields Data Release 1. III. Host-galaxy identifications and value added catalogues.' (bibcode: 2021A&A...648A...3K)