dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). We used this resolution because it was consistent with the largest DPS data of our larger study. We also believe that this 1° pixel resolution provides the best opportunity for DOD to be associated with dust emission. We note the difference between our use of collection 6. 1 and collection 5. 1 used by  Ginoux et al. (2012). We assume that the criteria we used following  Ginoux et al. (2012)  are applicable to the later collection. The DOD was retrieved from those pixels in which dust emission was observed from DPS in space and time throughout 2001-2016. The identification of DPS data is a highly time-consuming and labor-intensive activity (Text S5 in Supporting Information S1). Consequently, there are few (published) studies relative to the large number of global dust source regions.
These findings have considerable implications for the use of the exemplar TEM in large-scale dust climate projections of ESMs. 10 ) varying aerodynamic roughness (c) and with exemplar TEM (Equations 2 and 5) z 0 fixed and static over time  (d). Wind data are from ERA5-Land (Source: ECMWF). See Text S3 in Supporting Information S1 for details on the calculation of the MDC.
Introduction The Run 3 data taking period of the LHC has started in 2022 at an unprecedented centre-of-mass energy of √ S = 13. 6 TeV. The two main general-purpose LHC experiments ATLAS and CMS are expected to collect an integrated luminosity of about 300 fb -1 , which will complement the 140 fb -1 already collected at 13 TeV during Run 2. This increase in luminosity and centre-of-mass energy will thus make it possible to further explore extensions of the Standard Model (SM) of partia e-mail: fiaschi@liverpool. ac. uk (corresponding author) b e-mail: fuks@lpthe. jussieu. fr c e-mail: michael. klasen@uni-muenster. de d e-mail: alexander. neuwirth@uni-muenster. de cle physics, such as supersymmetry (SUSY) and its minimal incarnation dubbed the Minimal Supersymmetric Standard Model (MSSM)  [1, 2].
Installation The Resummino package is a high-energy physics program whose source code is written in C++, that is publicly available for download at https://resummino. hepforge. org/, and that is licensed under the European Union Public Licence v1. 1. It can be compiled with the GNU compiler collection (GCC) and a working installation of CMake (version 3. 0 or more recent)  [66]. It requires as external dependencies the GNU Scientific Library GSL (version 2. 0 or more recent)  [67]  and the Boost package (version 1. 70. 0 or more recent)  [68]. The former allows Resummino to make use of the VEGAS Monte Carlo routines for numerical integration  [69] , and of the Levenberg-Marquardt algorithm  [70, 71]  to fit non-linear functions like parton distribution functions  [72]. On the other hand, the Boost package is a dependency necessary to read supersymmetric spectra encoded in the SLHA format  [73, 74]  through the SLHAea library  [72].
In addition, Resummino must be linked to LHAPDF (version 6. 0 or more recent)  [75]  for PDF handling, and LoopTools (version 2. 15 or more recent)  [76]  for the evaluation of one-loop integrals. The code can easily be installed by typing the following commands in a shell, once the Resummino zipped sources have been downloaded: unzip Resummino-X. Y. Z. zip cd Resummino* mkdir build cd build cmake.
make make install The last step is optional, and it is only relevant to install the code system-wide. The cmake command can be cast with options dictating the behaviour of Resummino relative to its two external dependencies LHAPDF and Loop-Tools. The program is equipped with LHAPDF version 6. 2. 3 and LoopTools version 2. 15, and the usage of those built-in libraries can be enforced through the cmake options -DBUILD_LHAPDF=ON (default: OFF) and -DBUILD_LOOPTOOLS=ON (default: ON) respectively. Different versions of these packages can be used by providing information about the path to existing installations through the options -DLHAPDF=PATH and -DLOOPTOOLS=PATH if libraries and headers are installed in the same folder, or through LHAPDF_INCLUDE_DIR/LHAPDF_LIB_DIR and LOOPTOOLS_INCLUDE_DIR/LOOPTOOLS_LIB_ DIR if not.
The Resummino binary, called resummino, is located in the folder build/bin. After completion of compilation, users can test their local installation by running (from the installation folder). /bin/resummino --help. /bin/resummino input/resummino. in The source files of the code are collected in the folder src whose architecture and structure is described in Appendix B. The folder input includes exemplary input files, whereas the folder external contains necessary external packages such as LoopTools and LHAPDF. Finally, the folder scripts provides a set of docker and apptainer scripts related to the usage of portable installations of Resummino.
166379e -5GeV -2 , sin 2 θ W = 1 - M 2 W M 2 Z , g 2 = 2M W √ 2G F. ( 9 ) following the prescription introduced in Prospino  [79]. In these expressions, M W and M Z stand for the W -boson and Z -boson masses, G F is the Fermi constant, g 2 is the weak coupling constant, and θ w is the electroweak mixing angle. All Yukawa couplings of light quarks (for a number of active quark flavours n f = 5) are set to zero, and the strong coupling constant α s is computed using the value returned by LHAPDF. Consequently, Resummino does not use the values of α s and α included in the SMINPUTS block of the SLHA input file. For computations in the presence of extra gauge bosons, the code uses instead sin 2 θ W = 1 - M 2 W M 2 Z , g 1 = 4πα em / cos θ W , g 2 = 4πα em / sin θ W , ( 10 ) where g 1 stands for the hypercharge coupling constant.
This is achieved through the options --pdfset_lo (or -a) and --pdfset_nlo (or -b), both followed by an integer, and --mu_f (or -f) and --mu_r (or -r), both followed by a double-precision number. The former two flags allow users to change the identifier of the PDF member set (within a specific LHAPDF collection of parton densities) used for calculations at LO and beyond, whereas the latter two flags provide multiplicative factors to include when fixing the factorisation and renormalisation scales. In addition, the code can also be started with the options --version (or -v), --help (or -h), --parameterlog (or -p) followed by a string, and --output (or -o) followed by a string. The first of these displays the Resummino release number to the screen, whereas the second of them prints a help message indicating how to run the code.
Docker containers We provide convenient 'docker' containers that allow users to employ Resummino without the need to compile it from its source code. This requires to have either Docker or Singularity/Apptainer available on the system, the latter choice being recommended for secured highperformance computing. In this case, Resummino can be run from the scripts localised in the scripts folder, # if Docker is available source scripts/docker_alias. sh # if Singularity/Apptainer is available source scripts/apptainer_alias. sh These scripts automatically detect a local LHAPDF installation, and then enable the usage of any PDF set available within the system. If no installed version of LHAPDF is found, then Resummino is restricted to make use of the default PDF sets included within the docker images.
In particular, it will be important to assess the phenomenology of non-minimal model spectra exhibiting several light strongly-interacting and non-strongly-interacting SUSY states. In this case, rarely-studied semi-strong processes (such as squark-electroweakino associated production) could be relevant, and worth to be explored. The package Resummino and its current release 3. 1. 2 is ready to achieve related calculations. The source code is publicly available from HEPForge and can be used for aNNLO + NNLL calculations for slepton and electroweakino pair production (as studied in this article), as well as for NLO + NLL calculations for the associated production of a squarkelectroweakino and gluino-electroweakino pair. Moreover, non-supersymmetric models featuring additional W and Z boson could also be investigated at aNNLO + NNLL accuracy, and in addition to total rates Resummino can be used to calculate invariant-mass and transverse-momentum distributions for all implemented processes (with an NLO + NLL precision).
or the data will not be deposited. [Authors' comment: The software RESUMMINO is publicly available for download at https://resummino. hepforge. org/, as specified in the beginning of Sect. 3. 1. A digital, extended version of the Tables in Appendix F is publicly available for downloat at https://github. com/APN-Pucky/HEPi/tree/master/ hepi/data/json, as specified in the beginning of Appendix F. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indi-cated otherwise in a credit line to the material.
-integration_method. cc: General-purpose wrapper for Monte Carlo integration through the GSL routine VEGAS. main. cc: Initialisation of the code within its main function. maths. h: Useful mathematical functions. options. h: Different options for the implementation of on-shell resonance subtraction from the real emission corrections to squark-electroweakino associated production. params. cc: Values of all SUSY and SM couplings and masses (see also details in Sect. 3. 2. 2). pdf. cc: Initialisation of the PDF functions, interface with LHAPDF, and fit of the PDFs in Mellin space. resummation. cc: Implementation of the resummed exponent defined in  (6) , and the hard-matching coefficients defined in (  5 ). resummino. cc: Parsing of the input file and calls to the functions appropriate for the process and calculation chosen (total rate, p T distribution, etc.
Acknowledgements This work has been supported by the  BMBF  under contract  05P21PMCAA , by the  DFG  through the  Research Training Network 2149 "Strong and Weak Interactions -from Hadrons to Dark Matter " and by the  French ANR  (grant  ANR-21-CE31-0013  "DMwithLLPatLHC"). The work of JF has been supported by  STFC  under the  Consolidated Grant   ST/T000988/1.
We compare the two models in terms of their inner workings, results, and interpretive utility. We also use an updated database of Angkorian temples as the training dataset, allowing us to produce the most current estimate for temple foundations and historic spatiotemporal urban growth patterns at Angkor. Our results demonstrate that, in principle, predictive statistical and machine learning methods could be used to rapidly add chronological information to large lidar datasets and a Bayesian paradigm makes it possible to incorporate important uncertainties-especially chronological-into modelled temporal estimates. Lidar (light-detection and ranging) has become widely appreciated as a revolutionary new tool for archaeological discovery and heritage management 1. It is a sophisticated laser scanning technology that can be used to produce 3D models of the Earth's surface even through dense forest canopies.
1 ). The city may have been home to as Figure  1. Map showing location of Angkor and the distribution of temple locations for data used in this study. This map was created with QGIS 3. 28. 10 (https://www. qgis. org). Point data for temple locations and vector data for Angkor's infrastructure (roads, reservoirs, etc) were provided by three of the authors (SK, JW, and DE) and first reported in  11  ; inset map hillshade and vector data are in the public domain and free for use from Natural Earth (https:// www. natur alear thdata. com/)  25  ; main map hillshade was derived from lidar data  7  and created in QGIS. many as 900,000 inhabitants at the height of its influence and prosperity  15. Unlike most other cities at that time, Angkor's population was spread out over more than 1000 km 2 encompassing a complex hydrological management system comprising hundreds of kilometres of canals and channels  7, 16  and a web of roads and embankments connecting ancient Angkorians in a vast urban network  14.
11  attempted to predict the founding dates of the hundreds of un-dated temples identified over decades of pedestrian surveys and in the 2012 lidar survey. Their premise was that observable traits might be useful for predicting temple founding dates, meaning that they could rapidly add temporal information (albeit estimates) to the recently acquired lidar data, especially newly identified habitation features associated with the temples. This notion, that urban developmental trajectory could be reconstructed by leveraging observable temporal patterns, has antecedents in archaeological, historical, and urban studies research-e. g. Lilley's  26  work on using street/property grid orientations to identify stages of urban development in English medieval cities. Klassen et al. 's dataset comprised 1431 temples, 105 of which had dates derived from inscriptional or art-historical analyses.
When the aim is purely predictive, the opacity is not a problem. But, if there are alternative approaches that can predict and explain (in the statistical sense) variation, we can get even more information out of our data. Considering these limitations, we explored an additional approach involving Bayesian regression that could also be applied broadly to lidar-derived datasets. Bayesian analyses are based on probability theory and usually concern full posterior distributions for model parameters  28, 29. The paradigm is, therefore, fundamentally about uncertainty quantification, expression, and propagation. Additionally, most modern Bayesian analyses involve Monte-Carlo simulation, which is an iterative procedure for exploring model likelihoods that rely on randomness  30. As part of the process, parameter values are sampled repeatedly.
Here we present the results of a study in which we compare the hybrid GLM-GSSL model's predictions with those of the Bayesian regression model. Importantly, we applied both models to a new and updated database of Angkorian temples and, as a result, produced the most up-to-date trajectories for temple foundation dates published so far. We also fully explore and compare the two approaches so that they can be further developed and applied to other archaeological case studies involving lidar and ground survey data.
On average, temples with the "square" morphotype were the oldest, followed by "causeway-4" types, "horseshoe-east", and then "causeway-2" and "blob" types. However, there was significant overlap between many of the types, and some of these differences in mean foundation date were very slight. There were also too few examples of "circle" and "horseshoe-north" types in the training dataset to be certain about their relative positions in the morphotype chronology. The visualizations were also revealing. Plotting the counts of temple foundation events per period exposed important differences between the predictive approaches despite some overall similarity in the trajectories (see Fig. 3 ). Only the plot produced with the Bayesian model included information about temporal uncertainty propagated into the per-period counts.
Such differences would become particularly important for high-resolution comparisons between temple foundation trajectories and climatic or historical datasets because the short-term patterns might matter more. As noted, the Bayesian approach also has certain other scientific advantages. One is that the importance of predictive variables can be easily assessed. In doing so, we found that the presence of brick and thma phom have predictive utility. The model's posteriors also tells us, though, about which way the age estimates move on average given the presence of each material-i. e. the marginal effect on temple foundation date for each variable. Brick, for instance, was used more frequently in early temple construction, leading on average to foundation estimates that were 150 years older for temples with brick holding other variables constant-a finding in line with earlier research indicating temporal patterns in construction material of temples at Angkor and in Southeast Asia more generally  32, 33.
It is important to point out, nonetheless, that both models suffer from some of the same limitations. Chiefly, biases in the training sample will propagate into the predictions of both models. The only data relating foundation dates to temple traits come from the dated temples. Consequently, strong biases in the sample of dated temples could lead to poor out-of-sample estimates regardless of the approach used. Such a bias can be seen in the predictions of both models we evaluated when comparing their cross-validation predictions to the known foundation dates of temples in the training dataset (see Fig. 5 ). In both cases, we can see that prediction error is higher for older and younger temples. This pattern probably occurred for two reasons: (1) there are simply fewer older and younger temples in the training data on which to base predictions for undated older and younger temples, respectively; and (2) the relationship between predictors (temple traits) and the foundation dates may have changed over time, meaning that the models have less predictive utility for temples of certain ages.
The first is further empirically testing model predictions about foundation dates. This will entail randomly sampling temples and deploying a combination of chronological methods to produce evidence-based foundation dates for comparison with corresponding model predictions. As this research goes on-likely in tandem with other field-based research at Angkor-the model(s) can be continuously evaluated and updated, as we have done here by including newly available training dataset. It would also be enlightening to examine any deviations between model predictions for specific temples to investigate the potential importance temples that may appear to be chronological outliers with respect to one of the models. Another direction for future research should be the continued search for, and evaluation of, alternative modelling approaches.
Temple data The original temple dataset from Klassen et al. 11  included 1431 temples, of which 105 had founding dates. Since that paper was published, additional research has revealed founding dates for a further 58 temples bringing the total number to 163  15. Some of these 163 temples had their consecration dates commemorated in ancient Khmer or Sanskrit inscribed directly into architectural elements or associated stone stelae  22. Such inscriptions provide fairly direct chronological evidence for founding a new temple. Even though it is possible that the actual construction date and the religious consecration date may have been different, the discrepancy is unlikely to be very large in most cases. Other temples may only have inscriptions indicating the date of a particular transaction, or reference to another significant event.
In these cases, the earliest date in evidence could be used to estimate the temple's foundation date with the caveat that such a date is only a terminus ante quem. Still, other temples can be dated indirectly by comparing certain art-architectural elements with those of dated temples  41, 42. Consistent styles of relief sculpture on door lintels, for example, emerged at different times  41. An undated temple could, therefore, be assigned a date based on similarities between its door lintel designs and those of a dated temple. The 163 dated temples formed the new/updated training dataset used in all of our analyses so that the results from the two models would be directly comparable. The database used included 11 predictors, as in Klassen et al. 11. The first was temple morphology, a categorical variable with 8 categories: "square", "circle" , "two-causeways", "four-causeways", "horseshoe (north)", "horseshoe (east)", "horseshoe (west)", and "blob".
e. dated temples with no missing predictor variables) and then used to predict dates for undated temples that also had no missing predictors. Producing predictions with GSSL label propagation required further consideration. As mentioned, label propagation is a technique that uses structure in a graph-based representation of a dataset containing labelled and unlabelled observations to predict labels for the unlabelled data. A graph is simply a network composed of nodes connected by edges (lines between the nodes)  27. Each temple in the graph representation would be a node in the network. For our purposes, each node is then connected to every other node by an edge, and the edges are given weights proportional to the similarity between the temples. The similarity metric defines the structure of the graph and ultimately determines how labels (dates in our case) are propagated over the graph from labelled to unlabelled nodes.
The minimum total similarity between any pair of temples could be 0 and the maximum could be 11 corresponding to the total number of predictor variables being compared. To represent the graph mathematically, label propagation algorithms make use of a "graph Laplacian"  27. A graph Laplacian is a square matrix where the diagonal elements indicate the degree of connectedness between a given node and all the others in the graph, and the off-diagonal elements indicate whether a given pair of nodes is connected. For label propagation, the matrix is modified such that its elements refer to similarities instead of connectedness. Following Klassen et al. 11  , the diagonal elements in our graph Laplacian indicate the overall similarity between a given unlabelled temple and all other labelled and unlabelled temples in the dataset.
Following Klassen et al. 11  , we then combined the GLM and GSSL-label propagation estimates. Klassen et al. 11  found that GLM yields a lower mean absolute deviation than GSSL-label propagation. It was, therefore, reasoned that GLM-based estimates should be used where possible and GSSL estimates otherwise. So, for the present study, GLM estimates were produced for temples that had the full suite of predictor variables available, as described earlier, and then the entire dataset was used to produce estimates with the GSSL approach. Then, the GSSL estimates for temples with no missing variables were replaced with their corresponding GLM-based estimates.
For the azimuth prior, we used a uniform distribution bounded by 1 on the low end and by 360 on the upper end. This reflects the fact that azimuth-degrees clockwise from North-is a bounded variable. Of course, it does not account for the fact that 0 = 360 , or that the boundaries used exclude potential azimuths between 360 and 1 degree. However, none of the temples in the dataset with azimuth entries have orientations between 360 and 1 degree, with the vast majority tightly clustered around a south-east orientation. For area, we used a log-normal prior with a log-mean of 7. 7 and log-standard-deviation of 1. 02, values chosen based on an examination of the distribution of logged areas for the temple data. This prior reflects the fact that the distribution of temple areas is highly skewed and always positive, with a few massive temples pulling the tail of the distribution upwards.
The distribution has one parameter, θ , which is the probability of a "success", or obtaining a 1. Rather than explicitly determin- ing θ for each variable, we used a Beta distribution prior and estimated each θ from the data during MCMC. Nevertheless, the posterior distribution for a given θ would be approximately the empirical frequency of the relevant trait in the database. These presence/absence data and their priors were as follows (for the jth variable),.