dataesr/scientific-paragraphs-categorization huggingface dataset:
• Rather than importing external fragments of code written in C, as in LOTOS-18 and LNT-17, to implement for instance blocks and arithmetic operations on blocks, REC-17 and Lustre/SCADE models implement the algorithm entirely, they are self-contained and fully-formal. Even if using external fragments would allow a shorter formalization and generation of more efficient code. We prefer to avoid the risks arising when formal and non-formal codes are mixed (e. g. , an error was found in the external implementation in C  [8] ). • Contrary to REC 17, LNT-18 and LOTOS-18 specifications, the Lustre/SCADE models contains duplicated computations. Effectively, the MAC node is defined as a sequential function; at each instant, the MAC calculates prelude and coda, even if prelude is only needed at the beginning of each message sequence, and the latter is only needed at the end.
Such "trick" was necessary, since modelling cryptographical protocol is not the usual application domain of Lustre/SCADE. • SCADE and LNT-18 models have a more disciplined specification style than the three other executable specifications, because of the numerous static-analysis checks (e. g. , unused variables, useless assignments, etc. ) performed by their compilers. • Finally, the SCADE model contains graphical representations, which makes the model readable and easier to use for non-specialists. To the best of our knowledge, this feature is absent in other specification languages.
Validation of the Lustre model When automating the test execution process, we use the testing tool Lurette  [14]  and take advantage of its connection with the Lustre language. Figure  6  gives an overview of Lurette in the context of testing our MAA model. Lurette takes two inputs: (i) a specification of the environment constraints in Lutin  [25]  to dynamically constrain the inputs, (ii) an oracle implementing the test decision, in our case the 12 possible pairs (one key K J, and a message of two blocks), and the expected resulting MAC; and some parameters controlling the execution and the coverage of the generated input sequences, e. g. , the number of steps (n) in the execution sequence. Lurette interacts with the Lustre implementation of the MAA, logs the generated sequence of inputs and their corresponding outputs in a file, and displays the test decisions.
Following  [7] , we add three supplementary test vectors that operate on messages of 16, 256, and 4100 blocks containing bit patterns not preserved by permutations to detect implementation mistakes arising from byte permutations (e. g. , endianness issues) or incorrect segmentation of messages longer than 1024 bytes (i. e. , 256 blocks). Originally in  [7] , it was done by introducing a function acting as a pseudo-random message generator, which is only possible to implement in Lustre with recursive nodes. Thus, we rewrite the node MAC as the recursive Lustre MAC long messages node as below: When defining the recursive node in the code above, we specify the integer depth of recursion in triangular brackets, the depth of the recursion. The MAC long messages has three inputs: the blocks INIT and INCR to generates a message of the given length ( it ), the blocks of which follow an arithmetic progression, and the key KJ.
Conclusion and Future Work In this paper we have presented a formal SCADE model of the Message Authenticator Algorithm (MAA). First, we gave an overview of this cryptographic functions for computing a Message Authentication Code, then we discussed the choices and the challenges of this implementation in SCADE and compared it to the Lustre implementation. Finally we validated our model using the test test vectors derived from the specification in  [6]  The modelling of the MAA and its validation enabled us to find three errors in the Lustre v6 toolbox: (i) lv6 compiler generates invalid C code if a variable identifier uses quotes ('); (ii) another issue with constant structures and the compiler lv6; and (iii) Lurette tool ignoring -2c-exec option; all these errors have been communicated to the Lustre v6 developers and have been quickly fixed by the team.
The modelling of the SCADE model enabled us to improve the Lustre model, more precisely by detecting potentially non-initialized variables and unused variables. Since neither SCADE, nor Lustre implements the list data type, we had to consider only messages with fixed size in our SCADE model. However, in our Lustre model, we could generate pseudo-random messages by adding a recursive node. It is however fair to warn the reader that testing our Lustre/SCADE MAA models was a tedious task, i. e. , we had to manually define more than 400 constants. In this paper we only have written the main node computing the MAC with the graphical language SCADE. As future work, the full MAA algorithm or at least its main functions could be designed with the graphical language SCADE and some test scenarios could also be simulated on the graphical nodes.
Background: Pruritus (itch) is a cardinal symptom in atopic dermatitis (AD). Objective: To evaluate the timing and effect of dupilumab on itch. Methods: Analysis of data from 1505 patients with moderate to severe AD included in 4 randomized controlled studies, treated for up to 52 weeks. Adults received dupilumab 300 mg every 2 weeks or placebo monotherapy (SOLO 1: NCT02277743; SOLO 2: NCT02277769), with concomitant topical corticosteroids (CHRONOS: NCT02260986); adolescents ($12 to \18 y) were treated with dupilumab monotherapy every 2 weeks (200 mg for baseline weight of \60 kg; 300 mg for baseline weight of $60 kg) or placebo (AD ADOL: NCT03054428). Results: Dupilumab showed significant rapid improvements from baseline in daily Peak Pruritus Numerical Rating Scale scores versus placebo, by day 2 in adults and day 5 in adolescents.
16 upilumab is also approved by the US Food and Drug Administration as an add-on maintenance treatment in patients aged 12 years or older with moderate to severe asthma with an eosinophilic phenotype or oral corticosteroid-dependent asthma regardless of eosinophilic phenotype. 15 inally, dupilumab is approved by the US Food and Drug Administration as an add-on maintenance treatment in adult patients with inadequately controlled chronic rhinosinusitis with nasal polyps. 15 8] [19] [20]  Safety data for the LIBERTY AD SOLO 1 and SOLO 2, LIBERTY AD ADOL, and LIBERTY AD CAPSULE SUMMARY d This analysis from 4 randomized phase 3 trials of dupilumab presents higherresolution data on the onset, duration, and magnitude of itch reduction in patients with moderate to severe atopic dermatitis (AD). d These data confirm that dupilumab treatment has a rapid and sustained effect on itch in patients with moderate to severe AD.
CHRONOS studies showed dupilumab to be generally well tolerated, with an acceptable safety profile. 17, 18, 20 he objective of this article is to report the timing, magnitude, and sustainability of the effect of dupilumab on different measures of itch, using data from 3 phase 3 trials of dupilumab in adults with moderate to severe AD and 1 phase 3 trial in adolescents with moderate to severe AD.
METHODS Detailed descriptions of the study populations and methodology were previously published and are briefly summarized. 17, 18, 20 udy design LIBERTY AD SOLO 1 (NCT02277743) and SOLO 2 (NCT02277769) were 2 identically designed phase 3 randomized, multicenter, double-blind, placebocontrolled trials in adults with moderate to severe AD. LIBERTY AD ADOL (NCT03054428) was a phase 3 randomized, multicenter, double-blind, parallelgroup, placebo-controlled trial in adolescents (aged $12 to \18 years) with moderate to severe AD. In these 3 trials, patients received dupilumab or placebo monotherapy for 16 weeks. LIBERTY AD CHRONOS (NCT02260986) was a phase 3 randomized, multicenter, double-blind, placebo-controlled trial in adults with moderate to severe AD in which patients were randomly assigned to receive dupilumab with topical corticosteroids (TCSs) or placebo with TCSs (control) for 52 weeks.
Outcomes assessed in this analysis The Peak Pruritus Numerical Rating Scale (PP-NRS) is a well-defined, valid, and reliable, single-item measure to evaluate patient-reported intensity of the worst itch in the previous 24 hours on a scale of 0 to 10 (with 0 being no itch and 10 being worst itch imaginable) in adults with moderate to severe AD. 21 he measure was completed daily from baseline through to week 16 in SOLO 1 and 2, AD ADOL, and CHRONOS, and weekly from week 17 to week 52 in CHRONOS, by using the ClinPhone (Parexel, Waltham, MA) interactive voice response system for SOLO 1 and 2 and CHRONOS, and an eDiary for AD ADOL. Patients were instructed to call in at about the same time on each scheduled data entry day between the hours of 5 AM and 11 PM. Clinically relevant within-person meaningful response was defined as a change from baseline of at least 2 to 4 points in the PP-NRS score.
Statistical analysis For the SOLO studies, efficacy data through week 16 were pooled for the dupilumab 300 mg every 2 weeks and placebo groups through week 16; for the AD ADOL and CHRONOS studies, data were collected through week 16 and week 52, respectively. Analyses were carried out with the full analysis set, which included all randomized patients. Detailed descriptions of the statistical analyses for the primary and secondary endpoints of all 4 individual studies have been published previously. 17  All analyses were performed with SAS software, version 9. 4 (SAS Institute Inc, Cary, NC).
Patients A total of 1379 adults were enrolled in SOLO 1 and 2 and received monotherapy, 251 adolescents were randomized in AD ADOL, and 740 adults received concomitant TCSs in CHRONOS. This article presents data for the control arm and the approved dupilumab every 2 weeks treatment groups. A total of 1505 patients were included in the analysis, and 645 of these received treatment with dupilumab every 2 weeks. Baseline demographics and disease characteristics for patients enrolled in SOLO 1, SOLO 2, AD ADOL, and CHRONOS were generally balanced among treatment groups. 17, 18, 20.
Previously, significant improvements in itch were also reported for dupilumab by using the Scoring Atopic Dermatitis (SCORAD) Itch Visual Analog Scale score versus placebo, which has a recall period of 3 days. Assessment of the SCORAD Visual Analog Scale itch data from 4 dupilumab trials showed that a majority of patients treated with dupilumab achieved no or mild pruritus by the end of treatment. 35 oupled with the long-term favorable safety of dupilumab treatment,  36  these results support the notion that treatment with dupilumab provides rapid improvements and sustained itch control for long periods of time. Most adults enrolled in these trials had AD for more than 20 years,  17, 18  whereas adolescents had a mean disease duration of more than 14 years. 20 lthough the rapid onset of effect on itch is highly beneficial in those patients who had AD for large parts of their life, this beneficial effect was also comprehensive (including clinically meaningful responses, assessed by at least a 3-or 4-point improvement in PP-NRS score) and sustained through the end of treatment, coupled with a favorable safety profile.
17, 18, 20 Although not as detailed as the analyses presented here, comprehensive dupilumab effects on pruritus were also shown in adults among different racial subgroups (white, Asian, and black/ African American)  37  and among Japanese patients. 38 he very rapid effect on itch in these trials might be explained by the administration of a dupilumab loading dose on day 1 of the trials, because the loading dose allows systemic concentrations to reach a steady state more quickly. The strength of this analysis lies in the fact that data were analyzed from prospective, randomized, placebo-controlled studies and that a large number of patients were evaluated in these analyses, totaling 1505 patients across all treatment groups and studies, including 645 patients treated with dupilumab every 2 weeks.
Sensitivity to the main atmospheric and AO system parameters was analysed and the code was re-optimised to account for the sensitivity found. Finally, the optimised algorithm was tested and commissioned using more than one year of on-sky MUSE data. Results. We demonstrate with an on-sky data analysis that our algorithm meets all the requirements imposed by the MUSE scientists, namely an accuracy better than a few percent on the critical PSF parameters including full width at half maximum and global PSF shape through the kurtosis parameter of a Moffat function. Conclusions. The PSFR algorithm is publicly available and is used routinely to assess the MUSE image quality for each observation. It can be included in any post-processing activity which requires knowledge of the PSF.
Choice of PSF model One of the critical issues for any PSF reconstruction algorithm is the choice of PSF model. The most natural basis for describing the PSF is a pixel-wise basis. Although by definition, working on a pixel-wise basis removes the need for a model-based approximation, it is often not very well adapted to operational constraints because it requires a lot of memory and storage capacity. This is especially true for multi-wavelength instruments where one (or several if there are field variations to account for) PSF has to be computed and stored per wavelength bin. The choice of PSF model also strongly depends on the type of AO system (and thus AO correction) considered as well as the observation and post-processing requirements related to the astrophysical science cases. For MUSE WFM, the GLAO system only provides a very partial AO correction, and therefore the two critical parameters that have been identified to cover most of the science case requirements in terms of PSF knowledge are the PSF FWHM, which provides information on the data quality and final image resolution; and the level of the PSF wings, which provides information on the energy spread by the PSF in the FoV (spaxel contamination).
On-sky data The previous sections give an overview of the PSFR algorithm and of its performance measured on simulated data. This algorithm has been implemented in Python (see Section 6) and has been tested and validated on real MUSE-WFM data acquired during commissioning, science verification, and the early operation periods. The available data can be split into two main categories: -The RTC (also known as SPARTA) data. The instantaneous WFS measurement and DSM command are used to compute statistics on which turbulence models are fitted  (Fusco et al. 2004b,a; Fedrigo et al. 2006). For each LGS-WFS signal atmospheric parameters (r 0 , L 0 , GLF wind speed) as well as WFS information (e. g. WFS noise) are saved every 30 seconds. An example of a statistical analysis obtained from these data is plotted in Figure  10  and described in Section 4.
Adaptive optics telemetry and environmental data The SPARTA RTC system does continuously (every 30 seconds) provide information on the main atmospheric parameters derived from the LGS WFS and DSM command recorded in closed loop after a pseudo open-loop reconstruction. In the following, we use 392 data sets associated to observations of Globular Clusters performed by MUSE during the periods of October 1, 2017, to August 31, 2018; we kept 355 of them. We discarded data with (i) computation issues (aberrant values) and (ii) very large seeing (> 1. 5"). From the remaining RTC data, turbulence parameters are estimated (in the LOS) and a statistical analysis is provided (see Figure  10 ). The median values of seeing (0. 83"), L 0 (16 m) and GLF (72%) are fully compatible with the common Paranal values now recorded for more than 20 years.
The seeing proba- bility density function (PDF) has a typical Poisson distribution shape. The L 0 PDF is almost symmetric around its median value with a tight FWHM of only ±3m. Only a very small percentage of the measurements exhibit an outer scale smaller than 8m and the validity of those data points remains questionable. Finally, the GLF PDF is more structured, with some kind of bi-modal shape, and a significant part of the occurrences are found in the 80-90% domain. This aspect could be important in the following. The combination of this large GLF with small seeing values should lead to very good GLAO performance and produce neardiffraction limit images (at least at the AO system focal plan output before entering the MUSE spectrograph). This has two main implications: (i) a higher sensitivity to MUSE internal defects (see Section 5.
MUSE Wide Field Mode 3D data The data were obtained within the MUSE globular cluster survey  (Kamann et al. 2018a) , which is carried out as part of the MUSE guaranteed time observations. The survey targets the central regions of Galactic globular clusters with a series of relatively short exposures. In order to detect variable stars, the observations of each cluster are split into different epochs, with time lags of hours to months between them. Each individual epoch is split into three exposures, in between which derotator offsets of 90 • are applied. For this work, we consider all the data taken with the WFM AO system between October 1, 2017, and August 31, 2018, that is, in observing periods P100 and P101. In total, 413 individual exposures were analysed.
Point-spread-function fit on MUSE data cube We performed a fit of the PSF on the MUSE data cube using Pam-pelMuse  (Kamann 2018; Kamann et al. 2013 ), a software package designed for the analysis of integral field data of crowded stellar fields such as globular clusters. PampelMuse uses a reference catalogue containing the world coordinates and magnitudes of the sources in the observed field as input and first identifies the subset of available sources that can be resolved from the integral field data. In a subsequent step, PampelMuse determines the coordinate transformation from the input catalogue to the integral field data as well as the PSF as a function of wavelength. This information is finally used to optimally extract the spectra of the resolved sources. The MUSE data considered in this study were analysed using an analytical Moffat profile as PSF.
Both the width of the Moffat (parametrised by the FWHM) and its kurtosis (parametrised by the parameter β) were optimised during the analysis and were allowed to change with wavelength. As mentioned above, a standard observation of a globular cluster consists of three exposures with derotator offsets. By default, the exposures are combined before the analysis, in order to homogenise the image quality across the FOV. However, for this project we analysed the individual exposures, which allows for a more direct comparison with the atmospheric parameters gathered during the observations. As the resampling into a data cube can produce artefacts if only a single exposure is used, Pampel-Muse has been modified to work on pixel tables, an intermediate data format used by the MUSE pipeline that does not require resampling (see  Weilbacher et al.
Non-atmospheric part to the PSF The bias observed in the previous section is mainly attributed to the MUSE internal PSF. The very nature of the instrument makes a precise measurement of the internal PSF, both in the FoV and for each wavelength channel, very challenging (if not impossible). No such measurement was available with a sufficient spatial and spectral resolution. To deal with this specific issue we decided to measure the overall MUSE internal PSF (including both the optics and the detector) using on-sky data. To do so, the following multi-step process was applied: -Identification of a subset of data points (among the 355 available). We chose the best data points of the data set (those for which, at the reddest wavelength, the PSF FWHM computed on the MUSE images is better than 0.
4 arcsec). This corresponds to 95 of the 392 data points, that is typically 27% of the data. = PS F T el ⋆ PS F GLAO (λ i ) ⋆ M(FWHM MUSE (λ i ), β MUSE (λ i )). (11) This process allows us to obtain an associated MUSE internal PSF characterised by its FWHM and β parameter for each MUSE wavelength channel. This PSF includes both detectors and optical defects. The final results are plotted in Figure  13. The results are fully compatible with the MUSE original specification and with the very few and incomplete MUSE PSF internal measurements made in the laboratory during the Assembly Integration and Test (AIT) period. The MUSE internal PSF (detector included) goes from 0. 35" to 0. 3" (in the reddest part of the instrument spectrum). Assuming a 0. 2" detector FWHM, this corresponds to a full optical error budget of between 0.
The comparison between our measurements and some partial (for only a very limited number of the MUSE channels) internal data acquired during the AIT stage of the instrument seems to fully confirm this hypothesis. In any case, a follow up of the internal aberrations with time could be organised using the same procedure proposed here in order to fully validate the hypothesis. The MUSE PSF is now included in the complete PSFR algorithm in order to obtain the final performance. Although it has been computed using only the best available data, it is now applied to all the data, assuming that this MUSE internal PSF remains constant during the lifetime of the project (or at least between two re-calibration process).
Final performance Let us now use the MUSE internal PSF in the full PSFR process and re-process all the data (the 355 available) with the final version of the algorithm. The results are plotted in Figure  14. Results can be compared to Figure  12. The correlation of both FWHM and β remains identical but the bias has completely disappeared for ALL the processed data, showing the pertinence of the MUSE internal PSF for the whole set of data. In order to obtain more quantitative results, we propose in the following to compute an error metric between the final computed parameters using the full PSFR process (including the MUSE internal PSF) and the measured parameters obtained on the Globular Cluster images: err p = p measured -p predicted. (12) Figure  14  shows the PDF of the err p for both FWHM and β for several wavelength ranges.
It also shows the PDF cumulative function in each case. From these various plots, we can extract the final on-sky performance of our PSFR algorithm: -For FWHM, the bias is 10mas and the 1 and 2 σ dispersion are respectively 60 mas and 160 mas (which means that the error is smaller than 60 mas in 68% of the cases and smaller than 160 mas in 95. 4% of the cases). -For β, the bias is -0. 1 and the 1 and 2 σ dispersions are respectively 0. 26 and 0. 6 (which means that the error is smaller than 0. 26 in 68% of the cases and smaller than 0. 6 in 95. 4% of the cases). These results, combined with the simulation analysis, fully demonstrate (with respect to our initial scientific requirements) the accuracy and reliability of the PSFR algorithm. This study validates the proposed strategy and allow us to pass to the next level of the project: the final implementation in the MUSE-WFM pipeline and the use of PSFR for scientific observations and final astrophysical data processing.
-  -------------------------------------   -------------------------------------- The last option is to use the Python API directly, which gives access to more parameters: -Number of reconstructed wavelengths: To reduce computation time, the muse-psfr command reconstructs the PSF at three wavelengths: 500, 700, and 900 nm. But it is possible to reconstruct the PSF at any wavelength, with the compute_psf_from_sparta function. This function reconstructs by default for 35 wavelengths between 490nm and 930nm (which can be specified with the lmin, lmax, and nl parameters) -Number of reconstructed directions: Since the spatial variation is negligible over the MUSE FOV, the reconstruction is done by default only at the centre of the FOV. This can be changed in compute_psf_from_sparta with the npsflin parameter.