dataesr/scientific-paragraphs-categorization huggingface dataset:
Threshold decryption for Kyber and Saber We have implemented the threshold decryption function th for Kyber and Saber, which is a core component of the IND-CPA decryption. For Kyber we have used the approach described in Section 8. 2 with Algorithm 13, where we first perform a modulus switching to a smaller modulus 2 , and then compute the threshold function f : 2 → {0, 1} from arithmetic to Boolean masking. We see in Table  8  that the function must be computed for = 6, 7, 8 for a number of shares ≤ 11. For = 6, we can therefore use the register optimization from Section 6. 3, and the table with 2 = 64 rows can fit in a physical register or at least be smoothly managed by the compiler using the appropriate data type. The cases = 7, 8 are somewhat trickier. Even if some architecture might support 128-bit or 256-bit integers, these widths are less common.
Caenorhabditis elegans lifespan assay Synchronous L4 worms were transferred [~50 worms per well (6-well plate)] to NGMY with 0. 12 mM FUdR to avoid egg hatching and seeded with 100 μl of microbes at 100 mg/ml E. coli OP50, L. rhamnosus Lcr35 ® or C. albicans as previously stated. The plates were kept at 20°C (15°C for the CB1370 strain), and live worms were scored each day until the death of all animals. An animal was scored as dead when it did not respond to a gentle mechanical stimulation. This assay was performed as three independent experiments with three plates per condition. The C. elegans lifespan assay was examined using the Kaplan-Meier method, and differences were determined using the log-rank test with R software version 3. 6. 1 (R Core Team, 2018), and the survival  (Therneau, 2015)  and survminer  (Kassambara and Kosinski, 2017)  packages.
12 mM FUdR liquid assay medium per well and incubated at 20°C (15°C for the CB1370 strain). During a curative treatment, young adult worms were placed on plates containing C. albicans at 20°C for 2 h and 4 h with L. rhamnosus Lcr35 ® at 20°C after washing. For the control groups (i. e. , E. coli OP50 + C. albicans, E. coli OP50 only, L. rhamnosus Lcr35 ® only and C. albicans only), worms were treated in the same way. Nematodes were observed daily and were considered dead when they did not respond to a gentle mechanical stimulation. This assay was performed as three independent experiments containing three wells per condition. The C. elegans survival assay was examined using the Kaplan-Meier method, and differences were determined using the log-rank test with R software version 3. 6. 1 (R Core Team, 2018), and the survival  (Therneau, 2015)  and survminer  (Kassambara and Kosinski, 2017)  packages.
Determination of Caenorhabditis elegans transcriptome by RNA sequencing The following steps were performed by the MGX Platform (Montpellier GenomiX, CNRS, Montpellier, France) using Illumina kits, devices and softwares (Illumina Inc. , San Diego, CA, United States): construction of the mRNA library using a TruSeq Stranded mRNA Sample Preparation Kit; cluster generation with the cBot system using a Cluster Generation Kit; hybridization of the sequencing primer on the flow cell; 50-bp single-read sequencing using a HiSeq 2,500 device with Sequence by Synthesis technology; informatic pretreatments, i. e. , image analysis with the HiSeq Control Software and Real-Time Analysis component, base-calling with the RTA software and demultiplexing with bcl2fastq. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al.
, 2002)  and accessible through GEO Series accession number GSE202877. 1 The quality scores across all bases of all reads and the N (non-attributed bases) content across all bases were determined for each condition with the FastQC software from the Babraham Institute. The research of contaminants was carried out using FastQ Screen. The reads were aligned on reference genomes using Bowtie2 software: C. elegans (WBcel235, UCSC), and C. albicans SC5314 Assembly 22 (Candida Genome Database). Reads which did not align on C. elegans genome were excluded from further analysis. Retained reads were counted using feature counts software (version 1. 6. 4) and analysis of differential expression was achieved by the package DESeq2  (Love et al. , 2014). Gene expression changes with adjusted value of p of less than 0.
05 were declared differentially expressed and considered for further analysis. Using significantly differentially expressed genes (adjusted value of p less than 0. 05), a Gene Ontology analysis was performed. For this, the genes were sorted according to their expression (under-expression or overexpression) and according to whether the worms were in contact with a single microbial strain or a sequential culture. The lists obtained were then analyzed using GO Enrichment Analysis. Three levels of analysis were considered: biological process, molecular function and 1 https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE202877 cellular component. Only the GO terms significantly enriched (Fisher test and FDR, p < 0. 05) were retained and represented graphically. The representation of gene expression in the form of Volcano plots and GO terms were carried using GraphPad Prism version 9.
Finally, these insights could be transposed to the in-depth study of the action mechanisms of other Lacticaseibacillus spp. against Candida spp. or other pathogens through the establishment of new targets for the screening of strains of interest. This work was supported by the European funds FEDER and the Auvergne-Rhône-Alpes Region in the form of grants to CP as well as biose ® as part of the doctoral thesis of CP. The funder biose ® provided support in the form of salaries for AN. Some strains were provided by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). MGX acknowledges financial support from France Génomique National infrastructure, funded as part of Investissement d'avenir program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09).
Model-Based System Engineering (MBSE) The usual approaches for system engineering have been using documents to describe the requirements, environment, architecture, and other views of the system. Although those documents are sometimes created in tools such as Excel or Doors, they are textual and rely on natural language. The model-based approach used in system engineering finds its origin in model-based software development  [6]. Software developers use such approaches to represent software architecture, with tools such as the UML notation  [7]. This language was derived into the SysML notation  [8] , which system engineers use to describe complex systems. The model-based system engineering tools such as SysML or Capella use diagrams to represent different system views. Structural and behavioral aspects are represented, with diagrams such as requirement diagrams allowing for representation of the requirements, activities diagrams allowing for representation of the functions, and block definition and internal block diagrams allowing for representation of the architecture.
Synchronisation Methodologies Different approaches exist to assert consistency between heterogeneous models. The model federation  [2]  approach uses a database to store all data from a system's models. Within that database, the user can assert consistency and traceability between the models. Proof theory is used by  [3]  in their inconsistency management approach. This methodology interprets models as first-order logics, identifying inconsistencies through a computeraided proof tool. The lack of expressivity of proof theory limits this approach, making the interpretation and proofs difficult. A third approach to heterogeneous models consistency is model synchronisation  [4]. The synchronization consists of a three-step process, which is illustrated in Figure  1   The model synchronization approach is the base for different methodologies.
In this work, we intend to interpret them in a mathematical framework. Regarding the SmartSync synchronization framework  [20] , the authors of the methodology provide a methodology and tools for the synchronization of the MBSE and MBSA models  [21]. The comparison is operated on abstracted models written with the S2ML language  [22]. The S2ML comparison models used in SmartSync are described with a set-theoretical point of view as a quintuple < P, C, B, α, r  [5] , where: • P and B are sets of symbols called ports and blocks, respectively; • C is a multiset of subsets of P called connections; • α is a subset of B × (P ∪ C ∪ B) such as any element of (P ∪ C ∪ B) that is associated with, at most, a unique element of B called its parent, and there exists a unique block r ∈ B with no parent. SmartSync relies on the user to manually align model elements, as described in Section 4.
Visualisation of S2ML + Cat Models with NetworkX In order to achieve this goal, we used oriented graphs that give a visual representation of the categorical S2ML models. We created this representation at each step during the comparison to visualize which parts of the model the comparison traversed. Because SmartSync makes the comparison in a way that resembles a breadth-first search, this corresponds to taking the model with only its first level of abstraction (the main and its children) and then adding a level of abstraction for each iteration. To obtain the graph representations, we wrote a python script that converts the CSV files created by the SmartSync tool to directed graphs over the underlying S2ML + Cat models. This script used the NetworkX package  [32]  as its basis for graph classes and visualization methods.
This package is a library for the study of graphs and networks. It features classes for the representation of graphs and digraphs, and methods for graph analysis and visualisation. The graphs we show here over the S2ML + Cat models only show direct belonging relations, blocks, and ports. The absence of composed morphisms is because we wanted to minimize the number of edges to allow for good readability. The absence of connections is related to the comparison made by SmartSync, which does not consider them yet. For each iteration, the script also computed a correspondence matrix between objects of both models, which corresponds to giving the F and G functors from Figure  11. This matrix is represented as a table. Its element A n+1,m+1 will contain -1 if the nth element of the first model or the mth element of the second has the attribute forget, 1 if they are aligned and 0 otherwise.
Discussion The map-based (template-fitting) and harmonic-domain (SMICA) component separation techniques agree well in their estimation of dust model parameters and CMB spectra. The template-fitting method imposes no model for dust's spectral scaling, and the SMICA method makes no assumptions about the spatial distribution of dust. Thus, this agreement is evidence that their differing assumptions are valid for the SPIDER data set in combination with Planck. Further, the level of synchrotron emission is constrained to be well below that of dust in this region of sky at SPIDER frequencies. This justifies the assumption of dust-dominant foregrounds made in the template-fitting and SMICA pipelines. Having established confidence in and consistency among the methods for calculating CMB component power spectra, we then use each to construct likelihoods for the tensor-to-scalar ratio, r.
XFaster The XFaster power spectrum estimator can naturally provide a likelihood for a parameterized model directly, rather than fitting for the maximum likelihood bandpower deviations. The generalized XFaster parameter likelihood has a form similar to that in Equation  2 : -2 ln L(θ| d d d) = ,k (2 + 1)g k C C C -1 (θ) C C C + ln C C C (θ) kk , ( 10 ) where θ is a set of parameters, C C C is the model pseudo-C matrix, C C C is the data pseudo-C matrix, g is the mode count recalibration factor, and the index k labels the maps used (for this paper, four 95 GHz and four 150 GHz maps). The likelihood is based on the XFaster approximation of the likelihood for the observed pseudo-a m s and is therefore also Gaussian without loss of generality. The model pseudo-C s are computed using the same transfer functions, beam window functions, and mode-coupling kernels used for the bandpower computation, with the tensor contributions to the EE and BB power spectra modeled as a function of r.
Foreground fitting could, in principle, be accomplished by adding a scaled dust template spectrum to the model and fitting for the scale factor α at each frequency. However, because all terms in the signal model are treated as Gaussian random fields, XFaster's estimated error would include sample variance proportional to the foreground amplitudes, which is not appropriate for the fit to a non-Gaussian template. Therefore we instead subtract the scaled template from the data at the map level and model the residuals as CMB and noise. Because the template subtraction is not accounted for in the covariance, the additional error from foreground cleaning must be calibrated using an ensemble of CMB, noise, and template simulations. The data terms after template subtraction are C C C i× j = (d d d i -α i t t t i ) × (d d d j -α j t t t j ) , (11) where d d d is a data map, t t t is a template map, α is the frequencydependent template scaling, and i and j are indices of the eight maps.
The template maps used for each cross-spectrum are separate Planck half-missions to avoid contributions from Planck noise auto-spectra. In order to compute the maximum likelihood parameter estimates, an MCMC sampler steps through values of r, α 95 , and α 150 , recomputing the data and model terms in the likelihood at each step. Monte Carlo simulations of this process are used to propagate the uncertainty in template fitting to the likelihood derived from the data. The same process is repeated for 300 simulations using different realizations of CMB, SPIDER noise, and Planck noise from the FFP10 simulation ensemble. The parameter distributions recovered from these simulations are used to estimate the added covariance from uncertainty in the template fit. The magnitude of the additional covariance was found not to change significantly within reasonable ranges of r or α, or with different morphologies of the simulated foregrounds.
2, this implementation of SMICA requires a choice of noise model at 353 GHz. Lacking strong justification in preferring one over the other, and in the spirit of reporting a conservative upper limit, we run the analysis with both choices and report the less stringent result. Consequently, the reported SMICA results come from the auto-cross noise model. The resulting SMICA r-likelihood is shown in Figure  12. yields a maximum likelihood estimate of r mle = 0. 06 ± 0. 11. Subject to a physical prior that r ≥ 0, this corresponds to a 95% Bayesian upper limit of r < 0. 24. A Feldman-Cousins constraint is computationally impractical in the SMICA framework. In Figure  12  we further explore the effect on the SMICA likelihood of incorporating different subsets of the Planck data. The Planck 100 and 143 GHz data are particularly interesting in this regard, as they contribute appreciably to the SMICA CMB component but not directly to the template analysis.
At the angular scales of interest, the SMICA algorithm assigns weights to the SPIDER data at 95 (150) GHz that are approximately five (three) times those applied to the Planck data at the nearest corresponding frequency. Omitting the Planck data at 100 and 143 GHz shifts the r-estimate downward to r mle = -0. 03 ± 0. 12, in closer agreement with the template results. A configuration similar to the template methods (Planck 143 and 217 GHz data omitted) results in a similar value (r mle = -0. 02 +0. 12  -0. 11 ). Even in this configuration, however, Planck 100 GHz still has substantial influence on CMB recovery, with a weight approximately four times that in the template methods. When omitting Planck 100, 143 and 217 GHz data, SMICA recovers an r mle closest to that from the template methods: r mle = -0.
Discussion Table  5  presents the maximum likelihood r for each of the three pipelines, both in their standard configurations and in various modified configurations chosen to explore the impact of their structural differences (discussed further below). The nominal configuration of each pipeline was chosen prior to running the estimator on data and includes its maximal data set-all EE and BB science bins for XFaster, and the full set of SPIDER and Planck maps for SMICA. The same table reports the 95% upper limit for each pipeline in its the nominal configuration. In all cases, XFaster, NSI, and SMICA are found to return unbiased r posteriors that are broadly consistent with one another when run on time-domain simulations. We adopt the XFaster pipeline as our primary result due to the more formally correct construction of its likelihood.
Insofar as each of the above pipelines is unbiased on simulations and makes relatively simple (and non-contradictory) assumptions, the observed difference in r mle when restricted to a closely comparable subset of data merits investigation. An important question is whether the observed discrepancy between methods is consistent with expected variation given the difference in methodologies alone. We address this question below in two ways: by observing the effects on the data's r mle from slight modifications of each pipeline, and (where feasible) by comparing the results of each pipeline when applied to identical simulated maps. We first compare the two template-subtraction methods, XFaster and NSI. XFaster's tighter upper limit results primarily from a lower r mle , as illustrated in Figure  11.
The NSI limit is also increased slightly by having a broader distribution than XFaster, as a result of less-optimal weighting of the available data. Table  5  highlights the effects of modifying some of the assumptions that differ between the two meth- The nominal cases correspond to the "Nominal" row for each in Table 5, using the maximal data set for each pipeline. Posteriors are also shown for configurations in which the pipelines use approximately the same data inputs. For XFaster, this corresponds to case a in Table  5 , limiting the r fit to the BB spectrum. For SMICA, this corresponds to the final case in Table  5 , removing all Planck data except at 353 GHz (reproduced from the red line in Figure  12 ). ods. When XFaster is run in a more NSI-like configurationfitting r from BB only, with independently estimated EE and BB noise-its nominal r mle = -0.
21 shifts to -0. 15, in better agreement with NSI's r mle = -0. 09. This shift of 0. 06 in r mle is consistent with expectations from simulations, which show random fluctuations with σ = 0. 06 between the two different XFaster configurations when applied to the same simulated maps. SMICA differs from the template subtraction methods in the quantity of Planck data that are incorporated in the analysis. As shown in Table  5  and Figure  12 , changing the selection of Planck data to better match the template methods results in a downward shift of r mle by 0. 13, in the direction of the template results, implying that fluctuations in the SPIDER and Planck data drive the BB spectrum in opposite directions. The difference in r mle between SMICA and XFaster is not entirely resolved through the inclusion of common data products.
This is shown most clearly in Figure  13. To quantify the significance of the remaining difference (δr mle = 0. 14), we compare XFaster and SMICA in simulation by applying them to a nearly identical set of CMB, noise, and foreground simulation maps. For SMICA, we simulate the configuration in which the only Planck map used is 353 GHz; for XFaster, the simulated template includes both 353 and 100 GHz simulated Planck noise. Both estimators recover r mle without bias and with partially-correlated variance. The covariance between the two estimators can be written as: Σ Σ Σ XF,SMICA = σ 2 XF ρ σ XF σ SMICA ρ σ XF σ SMICA σ 2 SMICA. ( 13 ) For a 200-simulation ensemble with input r = 0, we find σ XF = 0. 13, σ SMICA = 0. 13, and ρ = 0. 74. The uncorrelated variance between the two estimators (ρ < 1) captures the degree to which each is sensitive to a different projection of the data when estimating r mle , leading to statistical variation between the methods even when given nearly identical input data; it also suggests some degree of non-optimality in the estimators.
Comparing this ensemble to XFaster's nominal result and SMICA's template-like result in Table  5 , we find that the observed difference between these estimators (0. 14) is consistent with the range of differences seen in simulations (σ = 0. 1). Further, if we compare the pair of observed estimator values to the simulation ensemble in two dimensions, we find that about one in six of the simulated pairs result in a difference that is equal to or greater than that obtained on the data. 10. CONCLUSION The data from SPIDER's first flight have returned maps of the intensity and polarization at 95 and 150 GHz that are substantially deeper than the Planck data in the same region of sky. A rigorous suite of consistency tests have been used to define a subset of these data that can be reliably used for cosmological analysis.
These maps, in concert with data from Planck, are used to constrain the amplitude of any cosmological B-mode signal in the cosmic microwave background. As anticipated, polarized Galactic dust emission is observed with high signal-to-noise. In SPIDER's sky region, the Galactic E-mode component has roughly twice the power of the B-mode component, and is found to be dominated by thermal dust emission at 95 GHz and above at all angular scales probed; Galactic synchrotron radiation is found to be strongly subdominant. Separating the dust component from the cosmological signal is the principal challenge of the present analysis. To this end, two basic approaches are employed: map-based template subtraction and SMICA, an internal linear combination applied in the harmonic domain. While the SPIDER and Planck B-mode data are found to push the constraints in opposite directions, the r mle derived from the template methods and SMICA are found to be consistent, subject to the assumptions made in each.
ACKNOWLEDGMENTS SPIDER is supported in the U. S. by the  National Aeronautics and Space Administration  under grants  NNX07AL64G ,  NNX12AE95G , and  NNX17AC55G  issued through the  Science Mission Directorate  and by the  National Science Foundation  through  PLR-1043515. Logistical support for the Antarctic deployment and operations is provided by the  NSF  through the  U. S. Antarctic Program. Support in Canada is provided by the  Natural Sciences and Engineering Research Council  and the  Canadian Space Agency. Support in Norway is provided by the  Research Council of Norway. Support in Sweden is provided by the  Swedish Research Council through the Oskar Klein Centre  (Contract No. 638-2013-8993 ) as well as a grant from the  Research Council  (dnr. 2019-93959 ) and a grant from the  Swedish Space Agency (dnr.
In this note, we report the results of our initial exploration of a collection of 1,600 plant extracts in the frame of a drugdiscovery effort. After describing the taxonomic coverage of this collection, we present the results of its liquid chromatography highresolution mass spectrometric profiling and the exploitation of these profiles using computational solutions. The resulting annotated mass spectral dataset and associated chemical and taxonomic metadata are made available to the community, and data reuse cases are proposed. We are currently continuing our exploration of this plant extract collection for drug-discovery purposes (notably looking for novel antitrypanosomatids, anti-infective and prometabolic compounds) and ecometabolomics insights. We believe that such a dataset can be exploited and reused by researchers interested in computational natural products exploration.