dataesr/scientific-paragraphs-categorization huggingface dataset:
Existing databases of impact craters on Mars do not cover the range of diameters (less than 1 km) that are relevant to find radial patterns of secondaries associated with primary craters <30 km  11, 12. The use of high-resolution imagery would address this issue, but manual mapping of the tens of millions of secondary impact craters constellating the surface of Mars is not feasible. In this work we adapt a Crater Detection Algorithm (CDA)  17  to detect craters <1 km on the whole surface of Mars. We build a database of 90 million impact craters and identify secondary crater rays system to locate the crater candidates responsible of the ejection of martian meteorites. We show that Tooting and 09-000015 craters are the most likely source of the depleted shergottites ejected 1. 1 Ma ago. We discuss the relationship between this group of meteorites and the Tharsis volcanic province, where these two craters are located.
Results Machine learning approach to pinpoint the meteorite crater sources. We retrained a Convolutionnal Neural Network  17  (Methods, subsection The Crater Detection Algorithm) to identify craters down to 25 m in diameter across the entire surface of Mars. The algorithm was trained using High-Resolution Imaging Science Experiment (HiRISE) images (25 cm/pixel) and applied on the global Context Camera (CTX) mosaic  18  (5 m/pixel), thus generating a database of ~90 million detections (Methods, subsection Application to the CTX global mosaic and evaluation, Supplementary Table  1  and Supplementary Figs. 1 2 3 4 ). To visualize secondaries, and therefore recent primary of different sizes, the density of secondaries for three ranges of sizes (150 < D < 300 m, 75 < D < 150 m and <75 m) are represented as a map (Figs.
Methods The Crater Detection Algorithm. The Crater Detection Algorithm has been previously described in detail  17. The key features are summarized here followed by a description of the adaptation achieved for the purpose of this study. The CDA was initially trained on Thermal Emission Imagery System (THEMIS) Day IR images (100 m/px) covering 1762 impact craters ≥1 km in diameter from the Mars crater database  12. To detect smaller impact craters and thus be able to identify rays of sub-kilometre-sized secondary craters, higher resolution datasets, such as HiRISE (High-Resolution Imaging Science Experiment) and CTX (Context Camera) are necessary. The high degree of detail found in these datasets makes the current training for the CDA inefficient due to the presence of decameter to hectometer-sized circular structures that are not of impact origin.
This include landscapes within field dunes exhibiting circular features or structures formed by the erosion, essential to include in the training dataset to avoid detecting them. Retraining the algorithm is therefore essential to accurately detect impact craters smaller than 1 km in diameter. Two significant hurdles were required for retraining: (1) creating a new set of labelled images, i. e. a training library where all impact craters visible on a set of images are identified using HiRISE imagery  42  and (2) increasing the speed of the training-validation and test steps. The first challenge was achieved using the HiRISE mosaic  42  of the Jezero crater (E77-5-N18-0) where 2142 craters have been manually identified in the aim to train the CNN (Convolutional Neural Network), of which 550 have been held out for validation.
This labelled dataset was also augmented by applying a range of transformations (rotate, shear, scale and translate) using YOLOv3  43. To address the second challenge, we updated the CDA to achieve a higher modularity of the components, but at the same time made the software more user friendly and able to run both on a desktop for development as well as the High Performance Computing (HPC) clusters at the Pawsey Supercomputer Centre in Western Australia. This is particularly critical regarding the test step as well as the scoring of the entire CTX mosaic, when a few hundred gigabytes of imagery datasets (>5 TB in the case of the CTX global mosaic  18  ) must be analysed in a timely fashion. To that end, we broke down all the processing steps (image reprojection and downsampling, tiling, scoring) of the previous version of the CDA  17  to individual tasks, all with their own container.
Supplementary Table  1  shows how many craters have been manually identified for each terrain as well as the number of true positive (TP), false detection or false positive (FP) and missed craters or false negative (FN) for different diameter ranges from D > 60 m. We cannot guarantee the validity of the manually mapped crater population <60 m and the associated metrics due to the resolution of the image. We calculated the true positive rate (or recall) defined as TP P , the precision TP TPþFP and F1 score TP TPþ  1  2 ðFPþFNÞ. The recall expresses the ability of the CDA to find all relevant craters in our dataset, while the precision expresses the proportion of relevant craters detected by the CDA. F1 score combines both metrics using the harmonic mean of the recall and precision, thus allowing to punish extreme values.
2 , where three close-ups of impact craters detected by the CDA illustrate different precision in the diameter estimation. Our validation test shows that our database is statistically robust and complete from 100 m in diameter. The CDA estimates crater diameter with a precision of ~25% for craters larger than 100 m. While the counting accuracy decreases with decreasing diameter (in particular close to 10x the native resolution of the image, i. e. 50 m), true positive rates are equal to or better than average manual counting differences between two humans  45  (~±30%) for craters larger than 70 m in diameter (Supplementary Table  1 ). Degraded crater morphologies, mostly represented at mid and high latitudes (>45 degrees), are the main source of error in absolute counts and diameter estimation compared to those closer to the equator, as described in the work presented in a previous version of our algorithm  17.
Also, while the number of detections in the 25-75 m is 32,889,699 (corresponding to one third of the total detections), the number of craters detected within the higher bracket of this range (60-75 m, N = 16,615,952) is approximately equal to those smaller than 60 m (N = 16,273,747) (see Supplementary Fig. 4  for a plot of the Crater Size-Frequency Distribution of the detections). The population of small craters <60 m, where the diameter is generally overestimated up to 50% does not dominate the population of craters used to populate the blue band of the density map shown on Fig. 1a. We note that our catalogue accounts for nearly all craters down to 70-100 m diameter range as shown on Supplementary Fig. 4  and fills the gap of sub-kilometre craters existing in manual crater database  11, 12.
Nevertheless, since craters >200 m constitutes a minor portion of the detections tested in the present work, an extensive evaluation of larger craters is needed and left for future investigation. It is of note that the usual caveats and limitations of an automatic algorithm, including proportion of false positives, have negligible consequences on recognizing the main systems of secondary craters, which is the purpose of this study. Given the large numbers of secondaries associated with a given primary, a complete dataset is not required when using the CDA as a visualization tool, and the performance of the CDA is appropriate for the detection of secondary crater field. Crater density map computation. The crater density map presented on Fig. 1a  has been computed using Matlab by defining a grid where each cell is 0.
05°× 0. 05°. The number of craters contained in each cell and falling into each of the diameter range (25-75 m, 75-150 m and 150-300 m) is computed. These values are then divided by the area of each cell using the mars ellipsoid to obtain the crater density for each cell (Supplementary Fig. 5 ). The division of the data in different ranges is achieved to make sure to identify primary craters to different sizes. Results are stored in a georeferenced RGB raster and loaded on ArcGIS (ESRI) to adjust the stretch of the values in each histogram band. For each band, values between the average ±2 standard deviations are stretched between 0 and 255 (values beyond this range are assigned either 0 and 255). The final product is the RGB crater density map presented on Fig. 1a  (see Supplementary Figs.
8-26 for a close-up of the crater density around each crater candidate), and available at high resolution on a CESIUM interface at http://craters. computation. org. au/, accessible upon registration, and at http://HIVE. curtin. edu. au/research/CDA-94M-release. Model age derivation and uncertainties. We derived the formation model age of the 19 impact craters exhibiting rays of small secondaries (<300 m) by counting craters superposed on their ejecta blankets and excluded areas corresponding to the impact crater floor and its rims  46  using the CraterTools software  47. In order to determine the age of these young craters, we need an accurate crater count superposed on the ejecta of these craters  48. Given the young age of these surfaces, a small number of craters are expected, and the result is very sensitive to any misidentification of craters (missing craters or false detections).
Therefore, CDA results were used as a first pass to pinpoint the primary impact craters superposed on each ejecta blanket and were then corrected and completed by a manual verification count. The age of terrains surrounding each large crater has been derived using the manual crater database complete down to 1 km in diameter  11, 12  where secondary craters are marked and discarded from the counting. Each Crater Size-Frequency Distribution was loaded into CraterStats II  19  and fitted with an isochron using a standard chronology model  21. We derived all model ages using the differential representation  49, 50  defined at a given diameter Dx as N(Dx) = n(Dx1, Dx2)/(Dx2-Dx1). Compared to cumulative plot, this method allows to plot each point independently from subsequent diameter bins, and an easier recognition of a resurfacing events contribution or a contamination by secondary craters  49.
To account for these potential sources of variability in the model ages we report in this study, we varied the impact rate by a factor of 2 above and below the reference rate  21  between 0 and ~3 Gyr and recomputed all model ages of impact craters and their host units. Results are presented on Fig. 3  in the form of curves (straight lines for young ages) intersecting each dot, where the top right end corresponds to the combination of model ages obtained for an impact cratering rate two times lower than that generally assumed  21  , and the lower left end of the curves correspond to a cratering rate two times higher. Impact cratering simulation. We used the shock physics code iSALE-2D (https:// isale-code. github. io/) to model complex impact craters. The iSALE-2D code  [62] [63] [64]  is a multi-rheology, multi-material extension to the finite-difference hydrocode  61.
Acknowledgements A. L. and G. K. B. are funded by the  Australian Research Council  ( DP170102972  and  DP210100336 ), the  Australian and Western Australian Government ,  Curtin University , and supported by the  Pawsey Supercomputing Centre  and ADACS (Astronomy Data and Compute Services). K. S. is supported by the  Pawsey Supercomputing Centre , ARTICLE NATURE COMMUNICATIONS | https://doi. org/10. 1038/s41467-021-26648-38 NATURE COMMUNICATIONS | (2021 ) 12:6352 | https://doi. org/10. 1038/s41467-021-26648-3|  www. nature. com/naturecommunications ADACS, and  CSIRO. Raw data were generated at  Pawsey Supercomputing Centre. We thank  Wesley Lamont  and the  Curtin Hub for Immersive Visualization and eResearch (HIVE)  for their help in the visualization of our crater detection having allowed the best use of our algorithm.
Data availability Correspondence and material requests should be addressed to Anthony Lagain at anthony. lagain@gmail. com. Remote sensing data (HiRISE and CTX imagery) are available at http://murray-lab. caltech. edu/CTX/tiles/ and at http://murraylab. caltech. edu/Mars2020/. The crater density map presented on Fig. 1a  is available at high resolution at http://HIVE. curtin. edu. au/research/CDA-94M-release and at http:// craters. computation. org. au/. The algorithm training dataset that support the findings of this study is available in Zenodo with the identifier: https://doi. org/10. 5281/ zenodo. 5514313. Derived data supporting the findings of this study are available within the paper, the Supplementary Information file, in Supplementary Data, and from the corresponding author A. L. on request.
Code availability The Crater Detection Algorithm code supporting the findings in this study is available upon request from the corresponding author A. L. The numerical impact crater formation were made using the iSALE shock physics hydrocode. At present, iSALE is not fully open source. Application for use of iSALE can be made via https://isale-code. github. io/. Any recent stable release can be used to reproduce the data presented. We used the IDL 5. 2 software (L3Harris geospatial https://www. l3harrisgeospatial. com/Software-Technology/IDL) to run the CraterStats II software available at https://www. geo. fuberlin. de/en/geol/fachrichtungen/planet/softwarealgorithm and the ESRI's ArcGIS 10. 8. 1 software suite (ESRI https://www. esri. com/en-us/arcgis/about-arcgis/overview) and Matlab (https://au.
Case isolation involves a complete reduction in their contact rate for the duration of infection. Positive test results also lead to contact tracing. Contact tracing was simulated for screening and testing strategies by assuming public health authorities could elicit 2 contacts per case detected with 75% of those successfully traced and quarantined. The number of contacts per case is based on data from Emory University's ongoing contract-tracing program. Quarantine, like isolation, was modeled as a complete reduction in the contact rate for the duration of infection. Some of those quarantined contacts might have been incubating but are now no longer able to infect since they are under quarantine.
Finally, we then used partial rank correlation coefficient to determine how much the modeled variation in cumulative incidence among students and faculty/staff depends on random parameter values. We recommend that these results be interpreted qualitatively, since there is considerable uncertainty in these projections stemming from lack of precision of parameter inputs (e. g. true R0 in this population). The model was built and simulated in the EpiModel package in the R statistical computing platform  23  ; the LHS package was used to perform Latin Hypercube Sampling  24. We also built an interactive web app for model Table  2. Cumulative outcomes at end of the semester on medium size university campus (approx. 15,000 students and 15,000 staff/faculty). Values are medians and 2. 5 and 97. 5th percentiles of 1000 model samples.
Sero-survey data can inform the proportion of the university community that is immune upon campus opening. These parameters may be updated in our accompanying web application (https ://epimo del. shiny apps. io/covid -unive rsity /). Emory University implemented a plan for Fall 2020 semester that included screening of all returning student, repeat screening of those residing on-campus. Staff, faculty and all students were also able to access optional screening using a rapid antigen test. In addition, testing of all symptomatic staff, students and faculty by PCR was prioritized. Up until time of writing (November 2020) the program was successful in that there was a moving 7-day average of about 1 staff/faculty and 1 student case per day and no large clusters were identified through October  27.
The dominant one-loop corrections to the Higgs mass matrix in the presence of complex parameters, under various approximations, were computed between the late 1990s and the early 2000s in refs. [78] [79] [80] [81] [82] [83] [84] [85] [86]  (the calculations of refs. [80, 82, 85]  also included two-loop leading-logarithmic terms). Full calculations of the one-loop corrections became available in 2004  [87]  and in 2006  [88] , and the two-loop O(α t α s ) corrections were computed in 2007  [89]. The results of refs. [81, 82, 85, 87]  were implemented in the public code CPsuperH  [90] [91] [92] , whereas the results of refs. [88, 89]  were implemented in FeynHiggs. For the two-loop corrections other than O(α t α s ), which were only known for real MSSM parameters at the time, the dependence on the relevant phases was approximated in FeynHiggs through an interpolation between the corrections obtained with positive and with negative values of the corresponding parameters.
[134]  were implemented in NMSSMTools  [139, 140]  and in the NMSSM-specific version of SOFTSUSY  [141]. These codes included also the MSSM results of ref. [37]  for the remaining two-loop corrections controlled by the third-family Yukawa couplings, in the gaugeless and vanishingmomentum limits. The inclusion of these additional corrections, applied only to the 2×2 sub-matrix that involves the Higgs doublets, allowed the codes to better reproduce the MSSM predictions for the Higgs masses in the "MSSM limit" of the NMSSM. The one-loop corrections in the DR scheme from ref. [135]  were made available in the NMSSM-specific version of SPheno that is generated automatically by SARAH. These one-loop corrections, combined with the two-loop corrections of ref. [134]  and, for the MSSM limit, ref. [37] , were also implemented in FlexibleSUSY  [142, 143] , a package that relies on SARAH to generate C++ spectrum generators for generic, user-specified BSM models.
Finally, the one-loop corrections of refs. [136, 137]  were implemented in NMSSMCALC  [144] , which computes masses and decay widths of the Higgs bosons in the NMSSM with either real or complex parameters. The code adopts a variant of the mixed OS-DR scheme defined in ref. [136] , slightly modified to comply with the input format for complex parameters established in the "SUSY Les Houches Accord" (SLHA)  [145, 146]. In addition, NMSSMCALC provides the option to use the DR parameter A λ instead of the pole mass M H ± as input for the mixed OS-DR scheme. Advances during KUTS: Broadly speaking, the recent developments in the Higgs-mass calculations for the NMSSM followed four directions which we will discuss separately below: the calculation of two-loop corrections tailored for inclusion in SARAH/SPheno and in NMSSMCALC, respectively; the development of an NMSSM-specific version of FeynHiggs; detailed comparisons between the predictions of the available codes.
[271]  reproduced the MSSM results of ref. [227] , and obtained novel results for the one-loop matching condition for the quartic Higgs coupling in the scenario where the highenergy theory is the MDGSSM and the EFT is the SM plus higgsinos. Ref. [272]  focused instead on the implementation of the general one-loop matching conditions in the package SARAH. In addition to reproducing the results of ref. [227]  for the MSSM scenario with one light Higgs doublet and those of ref. [211]  for the MSSM scenario with two light Higgs doublets, ref. [272]  provided a novel NLL calculation of the Higgs mass in the scenario where the high-energy theory is the NMSSM and the EFT is the SM. In 2019, a follow-up paper  [273]  employed SARAH to study the Split-SUSY scenario where the high-energy theory is the GNMSSM and the EFT is the SM plus higgsinos, gauginos, and all of the components of the singlet superfield.
[246] , which covers eight different EFT towers depending on the relative position of the thresholds for the heavy Higgs doublet, the higgsinos and EW gauginos, and the gluino. The matching conditions for the quartic Higgs couplings of the 2HDM also include the two-loop O(y 4 t g 2 s ) and O(y 6 t ) contributions from ref. [248]. • FlexibleSUSY contains several modules for the EFT calculation of the MSSM Higgs masses. For the simplest heavy-SUSY scenario where the EFT valid below the matching scale is the SM, the module HSSUSY implements a full NLL and "gaugeless" NNLL calculation that relies on the one-and two-loop corrections from refs. [227, 235] , plus a partial N 3 LL calculation that relies on the three-loop corrections from ref. [236] , provided by the code Himalaya. For the Split-SUSY scenario with only one light Higgs doublet, the module SplitMSSM implements oneand two-loop corrections from refs.
The package comes with model files for several heavy-SUSY scenarios. In the case where the theory valid above the matching scale is the MSSM, these cover six different EFT towers depending on the relative position of the thresholds for the heavy Higgs doublet and for the SUSY fermions (in contrast to FeynHiggs, the gluino is always decoupled at the same scale as the EW gauginos). There are also model files for the NMSSM matched either directly to the SM, or to the SM plus higgsinos, gauginos, singlet and singlino. Finally, SARAH allows for automated "hybrid" Higgs-mass calculations similar to those in FlexibleEFTHiggs, but the accuracy of the resummation of the large logarithmic effects is only LL in this case. While the EFT calculations implemented in the codes listed above differ from each other in several aspects -e.
g. , in the classes of threshold corrections that they include at the SUSY scale, and in the renormalization scheme adopted for some of the SUSY parameters -their predictions for the Higgs masses are generally in good agreement with each other in the appropriate limits. For the heavy-SUSY scenario where the high-energy theory is the MSSM and the EFT is the SM, a comparison between SusyHD and HSSUSY was presented in ref. [277] , a comparison between SusyHD and FeynHiggs was presented in ref. [278] , and a comparison between SusyHD and the relevant module of SARAH was presented in ref. [272]. For the scenario where the high-energy theory is the MSSM and the EFT is a 2HDM, a comparison between MhEFT and the relevant module of FlexibleSUSY was presented in ref. [143] , a comparison between MhEFT and FeynHiggs was presented in ref.
The hybrid approach of FlexibleEFTHiggs An alternative method to combine the EFT resummation of large logarithmic corrections with the FO calculation of corrections suppressed by powers of v 2 /M 2 S was proposed in 2016 in ref. [277] , and it was implemented in the FlexibleSUSY module FlexibleEFTHiggs. In 2017 a similar approach was implemented in SARAH  [294]. The main idea of this approach consists in incorporating the corrections to the Higgs mass suppressed by powers of v 2 /M 2 S into the boundary condition for the quartic Higgs coupling at the SUSY scale, λ SM (M S ), then proceeding as in a regular EFT calculation (i. e. , evolving λ SM down to the EW scale and computing there the pole Higgs mass M h ). The boundary condition is determined by the requirement that the FO result for the pole Higgs mass computed at the SUSY scale be the same in the low-energy EFT (which is assumed to be the SM) and in the high-energy SUSY model.
In particular, the term ∆ 0 +1 v accounts for the treelevel and one-loop effects, and is obtained by subtracting the result of the pure EFT calculation of M 2 h provided by HSSUSY from the result of the hybrid calculation of M 2 h provided by FlexibleEFTHiggs, including only the NLL resummation of logarithmic effects (i. e. , one-loop threshold corrections and twoloop RGEs) in each of the calculations. The term ∆ 2 v contains instead the two-loop, M S -suppressed effects controlled by y 4 t g 2 s or by y 6 t , and is computed from the difference between the known analytic formulas for the two-loop corrections to the lighter Higgs mass in the gaugeless limit and the same formulas expanded to the first order in v 2. We remark that the proposal of ref. [296]  remains at the level of "proof of concept", as the script that combines the various ingredients entering eq.