dataesr/scientific-paragraphs-categorization huggingface dataset:
In a Bayesian framework, one could instead marginalise over the dimensions not appearing on the axes of each plane, to determine the posterior mass in excluded scenarios; we leave such an analysis for future work. We note that in order to obtain a large enough dataset to produce Figure  6 , we include all parameter samples with at least 500 000 MC events in the LHC likelihood calculation. This should be contrasted with the other results in this paper, where only samples with at least 4 million MC events are used. Because the profile likelihood picks out the least constrained parameter sample for every point in the (m χ± 1 , m χ0 1 ) plane, this larger MC uncertainty implies that the result in Figure  6  should be viewed as a somewhat conservative estimate of the constraining power of the combined data.
The grey bars show the total SM background (taken from Ref. [116] ) and the stacked blue bars show the signal for our best-fit point. The hatched red bands show the 1σ uncertainty on the total number of expected events, found by summing in quadrature the background uncertainty and the signal statistical uncertainty for our best-fit point. The black points show the ATLAS data. matrix element. We have checked the missing energy distributions for the two lepton plus jets and three lepton signal regions of the traditional multilepton analysis (Figures  11  and 12 , respectively), as well as the distri-butions of several variables for the two lepton and three lepton signal regions of the recursive jigsaw analysis (Figures  13  and 14 , respectively). We see that the shape of the total expected contribution to data (SM back-   ground plus SUSY signal) is well compatible with the observed data in all cases.
Conclusions We are in a very interesting period in the hunt for electroweakinos at the Large Hadron Collider, as the large accumulated datasets at a centre of mass energy of 13 TeV offer real potential for discovering weaklyproduced sparticles. In this paper, we have performed a comprehensive global statistical fit of a 4D MSSM model in which M 1 , M 2 , µ and tan β are varied, whilst other MSSM parameters are held at fixed values in order to decouple all sparticles except the electroweakinos. In interpreting the results, we have considered both the case where one assumes that supersymmetry is not realised at a scale accessible by the LHC (in which case we are testing the exclusion power of current LHC searches), and the case where one allows the presence of a possible signal in the LHC data.
Our best-fit point has neutralino masses of (m χ0 1 , m χ0 2 , m χ0 3 , m χ0 4 ) ≈ (49. 4, 141. 6, 270. 3, 290. 2) GeV, and chargino masses of (m χ± 1 , m χ± 2 ) ≈ (142. 1, 293. 9) GeV. We find a local significance of 3. 3σ for this excess. If there is indeed a supersymmetric signal resembling these properties the ATLAS and CMS experiments should be sensitive to it using the full LHC Run 2 dataset. If one includes LHC searches for charginos and neutralinos conducted with proton-proton collision data collected at a centre of mass energy of 8 TeV, the local significance reduces to 2. 9σ, but the general details of our best fit region apparently remain intact. Analysis of the DM implications of our points is complicated by the fact that the particular values of the MSSM parameters that are held fixed in our analysis might influence the ability of our models to generate the correct relic density.
Nevertheless, we find that a subset of the area around our best-fit point is very much consistent with both the observed relic density and constraints from direct and indirect searches for DM -even assuming that only electroweakinos and SM particles play a role in setting the relic density. Excellent fits to both the DM and collider likelihoods are possible in the so-called Z-and h-funnel regions, where the lightest neutralino has a mass equal to approximately half the mass of either the Z or Higgs boson. Many of these models will be accessible to the next generation of direct detection experiments, raising the possibility of a simultaneous confirmation of the putative LHC signal in future datasets from both the LHC and dark matter experiments. tions to the global likelihood for all parameter samples within the 1σ preferred region of our main fit, generating 64 million Monte Carlo events per parameter point.
The signal region indices and labels correspond to those in the public dataset provided with this paper  [70]. 0, 0. 50 ± 0. 14 0. 02 ± 0. 00 0. 03 ± 0. 00 0. 01 ± 0. 00 0. 04 ± 0. 01 0. 03 ± 0. 00 0. 02 ± 0. 00 64, SS1tau_mT<120_ETmiss150-200_mll<100 0, 2. 0 ± 0. 4 0. 16 ± 0. 01 0. 12 ± 0. 01 0. 05 ± 0. 00 0. 16 ± 0. 01 0. 13 ± 0. 01 0. 07 ± 0. 00 65, SS1tau_mT<120_ETmiss150-200_mll>100 0, 0. 11 ± 0. 07 0. 01 ± 0. 00 0. 01 ± 0. 00 0. 00 ± 0. 00 0. 01 ± 0. 00 0. 01 ± 0. 00 0. 01 ± 0. 00 66, SS1tau_mT<120_ETmiss200-250_mll<100 0, 0. 90 ± 0. 24 0. 06 ± 0. 01 0. 04 ± 0. 00 0. 02 ± 0. 00 0. 06 ± 0. 01 0. 04 ± 0. 00 0. 04 ± 0. 00 67, SS1tau_mT<120_ETmiss200-250_mll>100 0, 0. 042 ± 0. 021 0. 01 ± 0. 00 0. 00 ± 0. 00 0. 00 ± 0. 00 0. 01 ± 0. 00 0. 00 ± 0. 00 0. 00 ± 0. 00 68, SS1tau_mT120-160_ETmiss50-100_mll<100 6, 5.
[70]icted signal counts for the 13 TeV analyses ATLAS_MultiLep_2lep_jet, ATLAS_MultiLep_3lep, ATLAS_RJ_2lep_ 2jet, ATLAS_RJ_3lep, CMS_1lep_2b, CMS_2lep_soft, CMS_2OSlep, CMS_MultiLep_2SSlep and CMS_MultiLep_3lep, for all six benchmark points. For comparison, the second column lists the observed event count and background prediction for each signal region, taken from the corresponding ATLAS or CMS analysis. The signal region indices and labels correspond to those in the public dataset provided with this paper[70].
Acknowledgements We thank  Will Handley  for interfacing polychord in GAMBIT 1. 2. 0, Peter Skands  for discussions on ISR jets, and the rest of the  GAMBIT Community  for helpful discussions and comments. We acknowledge  PRACE  for awarding us access to the Marconi supercluster at CINECA, Italy. This work was supported by  STFC (UK ;  ST/K00414X/1 ,  ST/N000838/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ,  UF160548 ), the  Research Council of Norway (Norway ;  FRIPRO 230546/F20 ), NOTUR (Norway;  NN9284K ),  Red Española de Supercomputación  (Spain;  FI-2016-1-0021 ), the  Horizon 2020 Marie Skłodowska-Curie  actions (EU;  H2020-MSCA-IF-2016-752162 ), the  Australian Research Council (Australia ;  CE110001004 ,  DP180100031 ,  FT130100018 ,  FT140100244 ,  FT160100274 ), the  Labex ILP part of the Idex SUPER (France ;  ANR-10-LABX-63 ), the  Agence Nationale de la Recherche  as part of the  programme Investissements d'avenir (France ;  ANR-11-IDEX-0004-02 ), and the  National Sciences and Engineering Research Council (NSERC) of Canada.
Inlet air humidity brought by ventilation can also have an impact on air conditioning latent heat load  [8]. • Thermal conduction through walls: internal or external insulation lead to different internal and external surface temperatures, thus also influencing the exchanges between the indoor and outdoor environments. • Latent heat fluxes due to hygrothermal transfers: similar to heat transfer, moisture transfer may occur through the wall and depends on internal and external relative humidity. These fluxes are usually evaluated for stand-alone buildings by using weather datasets including air temperature and relative humidity, wind velocity and direction and solar radiation at least. Nowadays, numerous datasets are available for multiple regions worldwide in viable format (TRY, TMY, etc. ), even if they are provided for reference weather stations, that are located outside the cities.
• CIM The Canopy Interface Model (CIM) is a 1D meteorological model  [67]  that can be either coupled with a 3D meteorological model (such as the WRF  [54, 68] ) or used in an offline mode as a stand-alone module  [11, 69]. In such a case, the meteorological variables used as boundary conditions for the top cell of the column are usually taken from a climatic dataset. The district or block geometrical characteristics (building characteristics, street widths, and number of cells where buildings are present) are also given as input to the model. The CIM stands out from other canopy models by its ability to show different building sizes in the x or y-direction, and it can also calculate an adaptive mixing length along the z-axis based on the density in each cell. The CIM solves a diffusion equation derived from the Navier-Stokes equation reduced to one-dimension and calculates high-resolution vertical profiles of meteorological variables (wind speed, wind direction, and air temperature).
The surface-plant-air interactions model is very detailed and documented to assess the effects of green architecture visions on local comfort conditions. The typical simulation period is 24-48 h with a time step of 1-5 s. • SOLENE-Microclimat SOLENE-Microclimat is the result of the coupling of two tools, Code-Saturne  [73]  (a CFD open source code developed by EDF) and SOLENE (a radiative tool  [74] ), to which different modules have been added to represent conventional built elements (conduction in paved soil, building thermal models) as well as natural elements and climate adaptation solutions (trees, natural soil, green roofs and walls, water ponds, pavement watering). Because of a flexible geometry representation, all kinds of geometry aspects can be handled. The canopy is meshed to run CFD simulation and the lower boundary of this mesh corresponds to the surface mesh used by SOLENE and the building model.
In this paper, the potential, the assumptions, and the accuracy level are detailed for the following widespread models: TRNSYS  [85] , Design Builder, Pleiades  [84] , EnergyPlus  [83] , BuildSysPro  [86] , CitySim  [87] , and Virtual Environment. Since Design Builder uses EnergyPlus as a calculation model and facilitates simulation with its user-friendly interface, the capabilities of EnergyPlus and Design Builder are deemed to be the same; hence, they are examined as one software (the last version of Design Builder V5 allows people to load their own EnergyPlus model). Although CitySim  [87]  is not primarily used at the building scale, we included it in our analysis as there are not many BEMs working at the district scale. Before exploring the BEMs, we can already differentiate BuildSysPro and EnergyPlus from the others as they are mostly used in research.
Even if the others are also used in research projects, they are first adapted to be convenient and user friendly for engineers working in a consulting agency. Tittelein  [88]  classifies the dynamic BEMs into two categories: the "monolithic" software tools and the modular ones. Pleiades, in the first category, does a good job for building envelope design. In the second category, BuildSysPro, TRNSYS, and EnergyPlus, with all their modules, can offer coupled studies as for example between a building and a specific Heating Ventilation and Air-Conditioning (HVAC) system. As specified in the introduction section, we only focus on the way BEMs take into account local climate conditions. The following subsections show the comparisons of BEMs regarding the different fluxes, summarized in tables: conductive heat transfer, solar radiations, infrared radiations, flux linked to aeraulic systems, and hygrometry.
Chaining strategies 4. 1. 1. UWG and TRNSYS Aim and method: Palme et al. [102]  present a methodology for considering the urban heat island (UHI) to enhance a building performance simulation. The method is applied to four different residential building types (two-story family housing, semi-detached house of two floors, block of apartments of five floors and tall building of twenty floors). The urban weather files are generated by using the UWG software for numerous urban tissue categories (UTCs). In this case, UTCs are classified according to their morphological characteristics, i. e. the built-up area, the green area, and the facade to site area ratio. Then, a building performance simulation is performed for each building type with TRNSYS using the generated weather files. In this work, the UCM is a global parametric model and the used BEM is a detailed multizone model.
OpenFoam (CFD) + TRNSYS Aim and method: Dorer et al. [106]  analyze the impact of the urban microclimate and in particular the UHI on the space heating and cooling energy demand for typical office buildings in street canyon configurations. Therefore, the open source CFD software package Openfoam which is a district model with explicit geometry (OpenFoam, 2012  [109] ) is chained to TRNSYS 17. 0.
Analysis of the coupling: The convective heat fluxes and data are transferred to the BEM, either through CHTC or by directly coupling CFD and BEM for street canyon case modeling  [106]. CFD simulations are performed using the RANS models of the open source CFD software package OpenFoam (OpenFoam, 2012  [109] ). The UHI intensity is approximated by using results of the BUBBLE project  [110]. A diurnal rural-urban temperature is generated for each month. For this study, TRNSYS 17. 0 (TRNSYS 17. 0, 2010)  [111]  is employed. For the external radiation fluxes, the authors use the TRNSYS 3D radiation model that has been developed to assess reflections within interior cells. To this end, they view the building surrounding spaces as an atrium with an open ceiling, so that finally, the shadowing by the neighboring buildings and the SW and LW radiation exchanges between the different Infiltrations are modeled assuming a homogeneous wall permeability (K) and a power law for infiltrated air flow rates: = Q K P P n.
[67] , the CIM is then used to simulate the flow in the column module and to recalculate high resolution vertical profiles of meteorological variables, such as the air temperature and the wind speed. Finally, CitySim is provided with localized meteorological data to simulate the energy demand. The modification of the variables influences the computation of the CHTC and the calculation of the LW radiation. Capabilities and limits: This example focuses on the energy needs of several buildings through CitySim tool which has an explicit geometry of the study area allowing for radiation exchanges. It estimates flux exchanges at the external envelop surfaces of buildings and at the ground surface. Combined with the CIM that calculated air temperature and wind speed at local scale, this coupling seems interesting to generate hourly microclimate data (air temperature and wind speed from CIM, SW and LW radiations from CitySim) and this for one year.
Other strategies The above-mentioned reasons certainly explain why some authors aiming to take the local urban context into account do not propose complex chaining or coupling processes. Two other ways are explored: developing external functions in BEMs or developing BEMs in UCMs. Introducing some patches or modifying some equations in the BEM is the strategy chosen by Vallati et al. [15]  who model a street canyon in TRNSYS as an internal zone with an open air ceiling that is an atrium. Djedjig et al. [118]  also develop a new module in TRNSYS so as to include the canyon street confinement effect. Several modifications to EnergyPlus are proposed by Evins et al. [115] ) because EnergyPlus posits that the ground and the obstructions to the building (i. e. the urban scene) are at the same temperature as the air.
Delineation, classification, segmentation, object identification, partitioning or scale management are some of the capabilities of GEOBIA to "derive meaningful objects". But what is a meaningful spatial object? On a semantic point of view, it is something that refers to symbolic representation and language, something anchored, apprehensive ans shared by people (e. g. , a forest or a house on an image or on a map). On a more technical point of view in the field of image analysis, it is a composition, a texture and an organization of pixels with different radiometric signatures, that draws a contour (the object boundary) and a polygon over which the texture shows significant differences, this discontinuity enabling to pass to another object. To provide a meaningful spatial partitioning, the resulting object shape and composition should be identical or at least quite similar in a given class of objects and at a given scale.
Although the role of human perception and interpretation remains crucial to fix the final segmentation, spatial analysts have to base their decision on their knowledge and also on objective metrics  [33] , especially if one wants to automatize mapping. So, notably in GEOBIA, (good) map generation is tightly linked to (good) object identification. Since an object from a segmentation process is mainly made of two complementary features-itself and the set of pixels it comprises-and regardless of scale issues and object semantic uncertainty, this leads to deal with two types of metrics, whether we consider the object or its pixels. Those metrics are well-known and available in different software of GEOBIA (e-Cognition, GEODMA, etc. ). A first set of metrics describes the objects geometry in the spatial partition: size, shape, orientation, compacity, etc.
Material and Data The reference is a 960 × 700 pixels aerial image (cf. Figure  2 ) with a pixel resolution of 2 m from the Institut Géographique National (IGN, French mapping and survey agency) web portal. It is an interesting location, because there are distinct land uses: forest, meadows, copses, alpine ski runs, pathways, road, fallen rocks and chalets  [35]. Also, there are little vs large, long linear vs more compact objects. Both pixel values and object characteristics can be studied. We used the free-ware GEODMA  [36]. This software is useful for little images processing and sufficient for our methodological survey. It provides interesting metrics according to several algorithms. We focus on two distinct and complementary approaches: the Baatz and Schape algorithm  [37] : BS-S, where S is the varying scale parameter in GEODMA, see Figures  4  and 5 ; for most of the BS-S partitions, the color and the shape parameters are fixed; -Self Organizing Maps adapted to GEOBIA  [38, 39] : SOM-R-A-K, where R is the radius of the neighborhood Gaussian function of the neuron, A is alpha, the learning rate used to update the neural network and K is the radius of the geographical best matching unit to be searched (Figure  6 ).
Covariates We extracted demographic data (e. g. sex, date of birth, last known vital status), as well as both qualitative (e. g. the admission at any point during the stay to an ICU, the type of opioid drug received) and quantitative time-dependent variables (e. g. biological results, vital sign values, intraveinous opiod syringes parameters) regarding each stay. We also extracted all the free text reports from the patients' EHR regardless of the source department and the stay. In order to facilitate variable extraction from such textual reports, we used a locally developed browser-accessible tool called FASTVISU  [15]. This software is linked with the CDW, and allowed us to quickly check throughout these textual reports for highlighted information and to vote for variable status (e. g. SCD genotype) or value (e.
Metrics used for analysis In the binary outcome setting, the natural metric used to evaluate performances is the AUC  [7]. In the survival analysis setting, the natural equivalent is the C-index (implemented in the python package lifelines), that is P[M i > M j |Y i < Y j , Y i < τ ] with i = j two independent patients, τ corresponding to the follow-up period duration  [24] , and M i the natural risk marker of the model for patient i: exp(x i β) for the Cox PH model, the probability of being uncured for the CURE model and π β (x i ) for the C-mix. To compare the two settings, one can predict the survival function Ŝi for each model and for patients i in the test set. Then, for a given threshold , one can now use Ŝi ( |X i = x i ) for each model to predict whether or not T i ≤ on the test set -relaying to the binary outcome setting -thus assessing performances using the classical AUC score.
Discussion In this paper, rather than trying to be exhaustive in terms of considered methods, we choose, accordingly with the aim of this paper, to offer a methodology for fairly comparing methods in the two considered settings. Also, we do not try different values, as it is done in Chen et al. [12]  (where emphasis is on performance metrics), since our focus is to propose a general comparison and interpretation methodology, with an analysis that remains valid for any choice of value. In the binary outcome setting, classifiers highly depend on how the risk groups are defined: a slight change of the survival threshold for assignment of classes can lead to different prediction results  [12]. In our dataset, only 5. 2% of the visits lead to a readmission within 30 days. We are then in a classical setup where the adverse event appears rarely in the data at our disposal.
This would suggest that the proportional hazard ratio assumption for Cox PH model (or its related models like the competing risks model, the marginal model or the frailty model; for this reason not considered in this study) is not respected in this situation. The CURE model main hypothesis being that a proportion of the patient is cured is questionable too. Those reasons partly explain the good performances of the C-mix model that does not rely on any restrictive hypothesis. In this study, data extraction was performed with no a priori on the relevance of each variable. For instance, we extracted all biological covariates that have been measured during a patient's stay, without presuming of their importance on readmission risk. Selected variables in our use case are relevant from a clinical point of view, highlighting the capacity of regularization methods to pinpoint clinically relevant covariates.
Background: Salmonella enterica subsp. enterica is a public health issue related to food safety, and its adaptation to animal sources remains poorly described at the pangenome scale. Firstly, serovars presenting potential mono-and multi-animal sources were selected from a curated and synthetized subset of Enterobase. The corresponding sequencing reads were downloaded from the European Nucleotide Archive (ENA) providing a balanced dataset of 440 Salmonella genomes in terms of serovars and sources (i). Secondly, the coregenome variants and accessory genes were detected (ii). Thirdly, single nucleotide polymorphisms and small insertions/deletions from the coregenome, as well as the accessory genes were associated to animal sources based on a microbial Genome Wide Association Study (GWAS) integrating an advanced correction of the population structure (iii).
Lastly, a Gene Ontology Enrichment Analysis (GOEA) was applied to emphasize metabolic pathways mainly impacted by the pangenomic mutations associated to animal sources (iv). Results: Based on a genome dataset including Salmonella serovars from mono-and multi-animal sources (i), 19,130 accessory genes and 178,351 coregenome variants were identified (ii). Among these pangenomic mutations, 52 genomic signatures (iii) and 9 over-enriched metabolic signatures (iv) were associated to avian, bovine, swine and fish sources by GWAS and GOEA, respectively. Conclusions: Our results suggest that the genetic and metabolic determinants of Salmonella adaptation to animal sources may have been driven by the natural feeding environment of the animal, distinct livestock diets modified by human, environmental stimuli, physiological properties of the animal itself, and work habits for health protection of livestock.