dataesr/scientific-paragraphs-categorization huggingface dataset:
QC checking and clone exclusion Given that roses are clonally propagated and used commercially worldwide, several varieties can be registered under different cultivar names. In addition, spontaneous somatic mutants exhibiting different traits (e. g. flower color or shape) are frequent in roses and could have therefore been registered with two different names. To exclude potential clones in the dataset that could bias our quantitative and population genomics analyses, we computed the proportion of similar calls between all pairs of individuals and considered all samples exhibiting a value higher than 0. 965 as potential clones. To define this value, we considered the observed distribution of this metric and the lower observed value for a replicate of the analysis (0. 973 for 'Persian yellow'), see Fig.
S3  and Table  S1  for a list. As a consequence, our clone-corrected dataset contains 204 unique samples. All subsequent analyses are based on this clone-corrected dataset. Population structure We used the R package adegenet  (Jombart, 2008)  to explore the population structure among the 204 individuals. We considered the whole set of 92,007 markers for the analysis. We first used the df2genind (with ploidy=4) to generate the input files before using the dudi. pca function of the adegenet package. PCA results were then plotted using both the ggplot2 and ggrepel packages  (Wickham, 2016; Slowikowski, 2023). To confirm the population structure observed with the PCA, we also used faststructure  (Raj et al. , 2014) , with K ranging from 2 to 5 groups (Fig. S5 ).
GWAS Before performing GWAS analyses, we further filtered the dataset in order to exclude poorly informative markers. SNPs with a missing rate greater than 10% and/or a Minor Allele Frequency (MAF) < 0. 05 were discarded, resulting in a set of 46,691 markers. GWAS was then performed with GWASpoly  (Rosyara et al. , 2016)  for all traits using the kinship matrix and the structure inferred with a Principal Coordinates  Analysis (dudi. pco, adegenet, Jombart, 2008). Considering both the tetraploidy and the absence of strong expectations regarding the additive vs. dominant effects of the genes for the studied traits, we considered all the potential models of gene action (i. e. general, additive, diplo-additive, diplo-general, simplex and duplex-dominant models, for details see  Rosyara et al. , 2016).
To explore the results, we first generated the qqplot and the Manhattan plot using R base, but we then generated circular representations of all the results under the R package circlize  (Gu et al. , 2014). In addition to the Manhattan plots, these circular views include density curves in order to identify some potentially interesting regions, even when the empirical background noise is high, generally associated with a general inflation of p-values (as observed with the qqplots). Given the number of traits analyzed, as well as the number of models explored for each, all the results were made available thanks to a dedicated website (https://roseGWASbrowser. github. io/).
Population structure & kinship Population genetic structure among the 32 cultivars was performed using Principal Component Analysis (PCA). Analyses were run on a pruned set of 50,369 biallelic SNPs, which was generated by randomly selecting 50k SNPs across the genome. We used the snpgdsPCA function from the SNPRelate R package  (Zheng et al. , 2012)  to generate the PCA. We then used ggplot2  (Wickham, 2016)  to generate the final plots. Given that the reference corresponds to an ancient Asian rose, we also empirically checked that the observed population structure could not have been generated by a strong mapping bias (Fig. S13 ). As a control of the population structure observed using PCA, we also used faststructure  (Raj et al. , 2014)  on the same 50k SNP set. To do so, we first used plink (v2.
00,  Purcell et al. , 2007)  to generate the input files and then inferred the population structure for K=1 to K=6. Barplots of the inferred individual ancestry memberships were also generated using ggplot2. We performed a family relationship inference by estimating kinship coefficients under KING  (Manichaikul et al. , 2010). The initial objective of this analysis was to ensure that no clones were present in our sequencing dataset (see also above). But given that KING is able to infer relationships up to the third-degree (i. e. great grandparents and great grandchildren, first cousins, or similar), we used KING to infer some potential family relationships. We first generated the input files with plink (v2. 00) and then estimated kinship coefficients. Kinship values higher than >0. 354 correspond to clones, while coefficients ranging from [0.
177, 0. 354], [0. 0884, 0. 177] and [0. 0442, 0. 0884] correspond to 1st-degree, 2nd-degree, and 3rd-degree relationships, respectively. Based on our data, KING was able to infer a remarkably high number of family relationships among our dataset composed of "star varieties" (see results). As a consequence, we used the R package igraph  (Csárdi & Nepusz, 2006)  to generate a comprehensive network based on all the inferred family relationships.
Diagnostic alleles For each filtered SNP among the final list of 54,481,222 SNPs, allele frequencies of the four groups were computed directly based on the filtered joint vcf thanks to a home-made script (script_freqgroup4localancestryprop. py), which was made available on the github repository (https://github. com/roseGWASbrowser/PopGen_GWAS_19thcentury_roses). Given that the number of genotypes available per group is limited, allele frequency was only computed if the number of missing alleles was lower or equal to 4 (i. e. corresponding to a maximum of 1 tetraploid individual or 2 diploid individuals without genotyping calls). We then subsampled SNPs exhibiting diagnostic alleles, i. e. an allele frequency of the reference allele of 0 and 1 in one group and in the other. In this specific example, all diagnostic SNPs identified -at the notable exception of 2 SNPs that were subsequently excluded -exhibit an allele frequency of 0 in the European and of 1 in the Asian group.
Indeed, all the raw sequencing data were mapped against the genome of an ancient Asian genotype  ('Old Blush'; Hibrand-Saint Oyant et al. , 2018). Consequently, the reference alleles are expected to be more associated with the ancient Asian background. The match is almost perfect here given that we also used the Old Blush variety as a member of the ancient Asian group in our study. As a consequence, all diagnostic alleles are expected to have an allele frequency of 1 in the ancient Asian group and, consequently, 0 in the ancient European group. In total, we identified 170,637 diagnostic SNPs on our sampling. Diagnostic SNPs are observed on all chromosomes (Table  S4 ). Importantly, the diagnostic SNPs are based on our limited panel of varieties. Consequently, all are not expected to be truly diagnostic based on a larger panel.
Acknowledments This research was conducted in the framework of the regional programme " Objectif Végétal, Research, Education and Innovation in Pays-de-la-Loire ", supported by the  French Region Pays-de-la-Loire ,  Angers Loire Métropole  and the  European Regional Development Fund. A part of the phenotypic data was supported by the  FLORHIGE (French Region Pays-de-la-Loire) , the  SIFLOR (BAP division of INRAE)  and  Rosascent  projects ( ANR , grant number  ANR-16-CE20-0024-01 ). We acknowledge the  Biological Resource Center (Pome Fruits and Roses, Angers, France ) for managing rose genetic resources. Thibault Leroy  is also grateful to the  university of Vienna, Austria  for its financial support regarding the whole-genome sequencing of the rose cultivars. We would like to thank the  Next Generation Sequencing Facility at Vienna BioCenter Core Facilities (VBCF) , member of the  Vienna BioCenter (VBC), Austria.
Organization of the Wind-STICS Measurements in Energy, Angular, Mass per Charge, and Mass Bins The electrostatic analyzer of STICS steps through 32 voltages corresponding to 32 ranges of energy per charge logarithmically spaced between 6 keV/q and 230 keV/q. Each voltage is kept constant during two spins of the Wind spacecraft; hence, a full sweep of energy per charge is achieved every 3 min. Ion measurements are organized in 48 angular sectors, with 16 azimuthal bins covering each 22. 5° and 3 elevation bins corresponding to the three telescopes of STICS. The 16 azimuthal bins are labeled from "0" to "15" with sector "9" pointing toward the Sun. Ion counts are sorted by the STICS Data Processing Unit (DPU) into boxes of E/q, TOF, and residual energy. Each box is then assigned on the ground to a specific ion species and charge state, based on the observed distribution of counts in the m/q -m space and E/q-TOF tracks.
Section 4 will present a comparison of Wind-STICS oxygen measurements with higher-energy observations by ACE-ULEIS, which will represent a preliminary in-flight calibration for oxygen. Double and triple coincidence measurements have the advantage of being almost unaffected by penetrating radiation. However, data in a given m/q -m box (or E/q, TOF, and SSD box) can suffer from spillover from neighboring boxes. Furthermore, double coincidence measurements of heavy ions can be contaminated by a fraction of the intense flux of light ions that experience energy and direction straggling when passing through the Start carbon foil. The two latter issues can be estimated with the not-standard Pulse Height Analysis data set (PHA) for which the entire information of all ion events (E/q, TOF, SSD energy) is downlinked to Earth, rather than just binned by the DPU.
Mewaldt et al. (2001)  present all ion charge states together (noted A n+ for specie A), while the Wind-STICS data presented here show only a few charge states. For suprathermal protons and helium ions, double coincidence and triple coincidence fluxes (black, red, magenta, and blue curves on panels a and b) are in agreement, which tends to confirm that the instrumental response for protons and helium ions is correctly estimated, at least for energies greater than 70 keV. Below this energy, the decreasing flux of triple coincidence helium ions with decreasing energy indicates that the SSD sensitivity estimated by Chotoo (1998) may be overestimated for helium ions. The issue of falling fluxes close to the SSD threshold is also seen in the triple coincidence spectra of oxygen (panel c) and iron ions (panel d).
Previously,  Poppe et al. (2018)  and  Nénon and Poppe (2020)  investigated the importance of the solar wind suprathermal light and heavy ions for space weathering based on years of ion measurements collected by several spacecraft, and numerical simulations of ion-matter interactions. However, the long-term flux of ions heavier than protons in the suprathermal energy range of around 10 keV/nucleon to 100 keV/nucleon was poorly constrained due to the lack of publicly available data. Nénon and Poppe (2020)    In this article, we take advantage of the 17-year Wind-SMS-STICS data set to constrain the long-term energy spectrum and anisotropy of suprathermal H + , He ++ , He + , O 6+ , and Fe 10+ ions. Our investigation reveals that future work may revisit the absolute calibration of the STICS triple coincidence systems for ions heavier than helium because the triple coincidence fluxes of O 6+ ions are a factor of 3-5 lower than not only the STICS double coincidence fluxes, but also of the higher-energy observations of ACE-ULEIS.
Firstly, suprathermal ions with a kinetic energy greater than a few keV/nucleon contribute to less than 1% to ion sputtering (see Figure  2b  of  Nénon and Poppe (2020) ). Secondly, the heavy 'minor' ions of the solar wind likely contribute to more than 20%-50% of atomic displacements at depth greater than 100 nm (assuming a 3. 8 g/cc olivine), where they can therefore accelerate the development of amorphous rims and may alter the optical reflectance and composition of the material. Future work may attempt to convolve the long-term fluxes presented here with laboratory estimates of implantation rate, defect generation, and radiolysis effects of heavy ions on lunar rocks and ice, as done at the outer planet icy moons for instance (e. g. ,  Cooper et al. , 2001; Teolis et al. , 2017). The long-term-averaged anisotropy of suprathermal light and heavy ions is investigated for the first time thanks to the Wind-SMS-STICS data set, with the motivation to constrain the out-of-ecliptic ion fluxes that directly bombard the PSRs of the lunar polar craters.
Compaire et al. (2021)  and  Schimmel et al. (2021)  analyzed ambient field autocorrelations and identified reflection signals consistent with the first two interfaces. Li et al. (2022)  confirmed the first interface at ∼ 8 km depth and the anisotropic nature of the layer above based on SH-wave reflections. Kim et al. (2021)  and  Durán et al. (2022)  later used updated RF datasets with more events to provide additional constraints and connoted a preference for the three-layer crustal model. Khan et al. (2021) and Drilleau et al. (2022)  arrived at similar results using body wave travel-times. In this paper, we build upon the previous work of  Knapmeyer-Endrun et al. (2021)  to infer further constraints on the crustal structure of Mars using new techniques and additional data from the InSight mission.
We first focus on the problem of detection of multiple phases in our selected RF dataset. For this, we propose a method that utilises recent results from the random matrix theory to extract coherent signals in the RF waveforms. Assuming the observed signal to be a superposition of random noise and an underlying low-rank signal, the eigenvalues of the data covariance matrix follow a well behaved and deterministic limiting spectral distribution dictated by the generalized Marchencko-Pastur law. This information can be effectively used to decouple and identify coherent signal eigenvalues reflecting primary subsurface features from a bulk spectrum formed by incoherent scattering, random noise, and small-scale heterogeneity with distinct eigenvector rotation properties. Once identified, the secondary phase arrivals together with the primary conversions from crustal interfaces can be used to invert for the structure.
RF processing InSight has identified 1244 marsquakes (InSight Marsquake Service, 2022) since its operations started in 2018. Each quake is assigned a type and quality depending on its energy content and uncertainty in location estimate  (Giardini et al. , 2020). Only a few of these marsquakes generate waves that propagate through the mantle like teleseismic earthquakes, most of which do not have a precise location. Our database for Mars thus consists of 8 LF and BB seismic events (InSight Mars SEIS Data Service, 2019) with high SNR and event quality A-B (J. F. Clinton et al. , 2021). Most of these events have similar distances and back-azimuths as they all originate in the Cerberus Fosse region which is a young tectonic structure located to the east of the lander. S0183a is located farther away but we nevertheless use it as its inclusion does not have a significant effect on the results.
For the terrestrial example, we use data from seismic station VSU in Vasula, Estonia. We select events with a similar back-azimuth and distance range to mimic the InSight data. Details of the events used in this study are provided in the Supplementary Material (Tables  S1, S3 , and S4). To calculate RFs, we apply a time-domain Wiener filter for deconvolution as described by  Hannemann et al. (2017). We first remove the transfer functions from the individual components of the data, rotate to ZNE coordinates as VBB uses the U, V, W component system, and filter the seismograms between the corner frequencies (Table  S1  in Supplementary Material) using a zero-phase Butterworth filter. Subsequently, the ZNE coordinate system is rotated into ZRT to obtain radial and transverse components using the backazimuth estimates provided by the Marsquake Service (MQS) (J.
Phase identification In RF data, the travel-times of the converted phases relative to the direct P arrival depend on the epicentral distance. This is generally seen as phase move-out which is different for direct and multiple phases, and helps to distinguish between these. For complex structures with dipping interfaces and seismic anisotropy, the travel-times and amplitudes of conversions also vary with back-azimuth. Although events generated from similar epicentral distance and back-azimuths should theoretically have coherent conversions and multiples, interference with the scattered wave-field, small-scale heterogeneity, and random noise generates variations which can be seen as perturbations superimposed on the response of the primary sub-surface feature. The observed RF data matrix, Y n×m = X n×m + σZ n×m , can now be modelled as a fixed rank perturbation (rank[X] = r ≤ n) of the random noise matrix Z ∼ N (0, 1).
A mean RF is calculated from all the raw RF waveforms in the dataset. This is then used to compute a v S,app curve which is jointly inverted together with the mean RF. We measure the dominant period of the spike in the mean ZRF and discard the values of filter periods smaller than that. given by Bayes's rule. Each layer is parameterised by depth, V s and the v P /v S ratio. Density is not inverted for but is calculated using Birch's law  (Birch, 1961). We use flat model priors and their ranges for depth, V s and v P /v S ratio were set to 0 -100 km, 1 -5 km/s and 1. 4 -2. 2, respectively. A maximum of 20 layers was imposed and the range for the noise amplitude was set to 0. 01 -0. 5 with correlation values of RF and v S,app data fixed to 0. 96 and 0, respectively. For calculating synthetic RFs, we use the forward calculation module implemented by  Shibutani et al.
Apart from the three primary phases at 2. 4 s, 4. 8 s, and 7. 2 s previously identified in  Knapmeyer-Endrun et al. (2021) , the raw RF data (subplot(a)) does not seem to contain any consistent phases after the initial 8 s. From subplot (b), we see that the first principal component is sufficient to identify the main phase arrivals within the first 30s of the RF waveforms. This is expected as all the events considered here have similar distances and back-azimuths. The reconstructed RF waveforms are shown in subplot (c). In addition to the three primary phases, we report three new multiple phases at 15 s, 20 s, and 23 s. We interpret these as the P 2 pPs, P 2 pSs and P 3 pPs phases, where the sub-scripted numeral in the phase name denotes the generating interface. Note that the P 3 pPs phase holds significant importance as it corroborates the existence of the much speculated third inter-crustal layer below the InSight landing site.
Here we used events with varying distances and similar back-azimuths to establish an equivalent idea. A few points are, however, worth noting. Occasionally, data reconstruction with a few principal components can lead to an erroneous broadening of phases. Though it does not affect the detection of phases, it could sometimes lead to an unwanted merge of very close arrivals. We also find that the singular spectrum, and hence the resulting threshold, can show slight variations based on the dataset's quality. For highly irregular RF waveforms, The number of events is also an essential factor. As the size of the dataset (m,n) increases, the fluctuations of the Tracy-Widom distribution decrease. Thus, the larger the dataset, the higher the stability of the threshold. For a small dataset, the assumption of the spiked covariance model can break down.
Summary and Conclusion The receiver function method has played an important role in the analysis of the Martian crustal structure using data from the InSight mission. In order to contribute to that effort, here we present a method to identify coherent phase arrivals in noisy RF waveforms by modelling data noise as samples from an independent and identically distributed random matrix and using this information to jointly invert the RFs with apparent velocity In conclusion, the results presented here agree well with previous work from  Knapmeyer-Endrun et al. (2021). The identified P 3 pPs phase suggests a strong preference for the threelayer model in their study. The inversion results presented here have further constrained the subsurface velocities at the InSight landing site. Our preferred interpretation of the observed crustal layering beneath InSight considers this a result of a series of transitions from sediments or pyroclastic deposits that experienced aqueous alterations to less porous Utopia ejecta and finally to the pre-existing crustal materials from early differentiation of Borealis impact melt  (Wieczorek et al.
Acknowledgments R. J. acknowledges the funding provided by the  IMPRS  and the  Emeritus group. The  MPS MPG SEIS  team acknowledges funding for development of the SEIS leveling system by the  DLR German Space Agency. We acknowledge  NASA ,  CNES , their partner agencies and Institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS , ETHZ, IC, MPS-MPG) and the flight operations team at  JPL , SISMOC, MSDS,  IRIS-DMC  and PDS. This paper is InSight Contribution Number 188.
curves. With examples from terrestrial data, we first show how only a few singular values can help reconstruct coherent parts of the signals enabling the detection of phase arrivals in RF waveforms. The number of singular values needed for this depends on the range of the distance and back-azimuths of the events in the dataset, with often a single value being sufficient in the special case of closely located events. We then apply this method to a set of 8 marsquakes detected by the InSight mission and evaluate the crustal structure below the landing site based on these data. Three new crustal phases were identified in the RF waveforms, which we interpret as P 2 pPs, P 2 pSs and P 3 pPs phases. A subsequent joint inversion of the RFs with the mean v S,app curve shows that the crust of Mars below the InSight landing site is composed of three distinct layers with increasing velocity.
7  The Graph 1 , below, illustrates these five stages. In this paper we focus mostly on the first four stages, which are those related to the effective functioning of an AI system. In each stage, different aspects must be monitored to ensure that an AI system complies with the proposed EU AI Regulation. In the design stage, the model, data and variables to be employed by the AI system are defined, specifying the problem to be dealt with. The development stage involves different steps regarding the preparation of the data (sourcing, cleansing, analysis) and the training, validation and tuning of the model. In the evaluation stage, the dataset and models are analysed in detail against the testing dataset, examining its performance in relation to targets such as robustness  and fairness. In the operation stage, the AI system is deployed in practice, and monitoring and feedback mechanisms are established, as well as procedures for problem-resolution and updates of the system.
The X-ray dataset In this study, we used sources that lie within the COSMOS field  (Scoville et al. 2007 ). The X-ray AGNs have been observed by the COSMOS-Legacy survey  (Civano et al. 2016). This is a 4. 6 Ms Chandra programme that covers 2. 2 deg 2 of the COS-MOS region. The central area has been observed with an exposure time of ≈160 ks, while the remaining area has an exposure time of ≈80 ks. The limiting depths are 2. 2 × 10 -16 , 1. 5 × 10 -15 , and 8. 9 × 10 -16 erg cm -2 s -1 in the soft (0. 5-2 keV), hard (2-10 keV), and full (0. 5-10 keV) bands, respectively. The X-ray catalogue includes 4016 sources. Marchesi et al. (2016)  matched the X-ray sources with optical and infrared counterparts using the likelihood ratio technique  (Sutherland & Saunders 1992). Of the sources, 97% have an optical and IR counterpart and a photometric redshift (photo-z), and ≈54% have spectroscopic redshift (spec-z).