dataesr/scientific-paragraphs-categorization huggingface dataset:
The comparison of the structural data collected at different P allows us to describe the evolution of Sb, Pb and Fe polyhedra. 6 ). Note that the three shortest Sb-S bonds from which the LEP is pointing include the apical atom of the square pyramid and the two atoms in its base; in α-jamesonite, these are the two below or above Sb (looking along the rod extension), whereas in the β polymorph, they are both on the side of the Sb atom. In other words, the LEP becomes perpendicular to the rods, which is a consequence of their mutual repulsion from Sb atoms to those Sb on the opposite side of a micelle (Figure  7 ). With increasing P, the distances between Sb and S on the opposite side of the micelle drastically decrease: at 1. 6 GPa and 16. 6 GPa, Sb1-S1 changes from 4. 125(5) to 3. 234(  5 ) Å, Sb2-S6 from 3.
This behavior can be also evaluated by the reduction in distortion (Figure  9 ) and eccentricity evolution (Table  4 ) with P. The regularization of polyhedra is a common behavior in high-pressure structures  [32] , as regular polyhedra have lower volume with respect to distorted polyhedra with the same superficial extension. The comparison of the structural data collected at diffe the evolution of Sb, Pb and Fe polyhedra.
Introduction In high energy physics (HEP), characterizing a signal hypothesis requires distinguishing its signature from a large number of background processes with similar final states. Observables of physics objects are used to construct classification algorithms that can discriminate the signal signatures from the background processes. An accurate description of these final states and their observables with the detailed modelling of the detector responses is crucial for the sensitivity of the analysis. Monte Carlo techniques are widely used in HEP experiments to simulate a process from a physics model of interest (denoted as signal) and other Standard Model (SM) a e-mail: victor. lohezic@cern. ch (corresponding author) processes (denoted as background). Software libraries such as GEANT4  [1, 2]  are used to model detailed descriptions of the modern colossal particle detectors, and thereby an accurate simulation of the detector responses to the aforementioned processes.
Generative adversarial networks A GAN consists of two neural networks competing in a zero sum game  [11]. In its basic form, the generator network takes a latent space of randomly distributed features. The discrim-inator network compares the generator output with the input sample to distinguish between the generated and the original samples. The two networks are trained consecutively at each iteration (epoch) by minimizing loss metrics (one per network). A loss metric describes the difference between the objective and the value returned by the network. The generator produces samples more and more similar to the input sample with the feedback it received from the discriminator network. Ideally, after a certain number of epochs the discriminator and generator networks should reach an optimal state where no further improvement can be obtained with the given dataset and number of parameters of the networks.
A description of this optimization and of the final set of hyperparameters for a concrete application are given in Sect. 3. 1. In addition to this stabilization method and the tuning of hyperparameters, the preprocessing of the input features was studied. The goal of the preprocessing step is to transform the original input vectors into a representation more suitable for the training of neural networks. This transformation needs to be bijective so that a transformed vector can be processed back to its original values in a unique way. It can compensate rapidly falling or non smooth distributions of the physics observables -those being harder to learn for a network. Multiple preprocessing methods are tested from the Scikit-learn module  [22]  and best performance is obtained with the quantile transformation to a uniform output.
Off diagonal: Contour lines containing 25%, 50% and 75% of the events and showing correlations between pairs of observables photon, thus transforming an event residing in the CR into an event located in the SR. As the CR with given criteria is exclusively composed of γ + jets events, the proposed technique allows evaluation of the γ + jets background in the SR with a data-driven method. This is particularly handy when the analysis focuses on SRs with additional selection criteria, e. g. on the jet selection. Indeed in these cases the γ + jets simulation suffers from both a poor modeling of the physics and a lack of statistics. To demonstrate this, a γ + jets simulated dataset from CMS open data  [8]  containing around 2. 7 million events was used.
Surface snow is assumed to be clean (which is often the case in Antarctica,  Warren et al. , 2006) , vertically and horizontally homogeneous over a flat, 300 m pixel. OLCI dataset are 0. 36 mm and 0. 16 mm for D5 and D17 respectively. Still, we believe the RMSE estimate at D17 to be more realistic than the one at D5. Indeed, the 300 m pixel of SLSTR including D5 may not be completely representative of the snow reflectance because of the recurrent presence of blue ice areas and containers, as well as the strong average North-South slope of the area covered by the pixel (8. 3%), that is almost double the average pixel slope in D17 (4. 3%). Thus, we conclude that Multiband measurements reproduce well the SICE product with an overall error of 0. 16 mm RMS that can be considered as an upper bound, since both the satellite and in-situ measurements are subject to independent errors.
(2013)  (dotted blue line). For both, the grain growth predicted by the model is slightly faster than the measured one. Figure  11c  and 11d show a decrease of the d opt during a 2-4 day period under the sole action of wind drift. Four similar episodes of decreasing d opt lasting 2-3 days were identified throughout the complete dataset. The snow height, during these three first periods, decreases at an average rate of -5. 39 mm day -1 , in line with the mean ∆SH of -5. 22 mm day -1 observed for all the days with only snow drift. However, the snow height measured during this short timeseries occasionally increases, clearly showing the deposition of drifting snow.
Conclusions The spectral albedo of surface snow at D5 and D17, two windy locations in Adélie Land, East Antarctica, was measured over a small area (a few square meters) during several years. To acquire this unique dataset, a specifically designed autonomous spectral radiometer (named Multiband) was developed and was presented here. Instrumental and environmental artifacts were corrected to retrieve timeseries of accurate effective optical diameter of snow for five summer seasons. The analysis of the uncertainties, the comparison with satellite-retrieved snow grain size and with in-situ broadband albedo measurements, as well as the coherency between the grain size observed at D5 and D17 allow us to conclude that Multiband is a reliable instrument for the retrieval of the daily grain size and the snow albedo.
Acknowledgements. This study has been supported by the  Agence Nationale de la Recherche  projects  14-CE01-0001 ASUMA  and  1-JS56-005-01   MONISNOW , the  Centre National d'Etudes Spatiales project Trishna , the  Insitut Polaire Emile Victor  project  1110   NIVO , the  European Space Agency  project  4D   Antarctica , the  GLACIOCLIM  project  SAMBA. The authors acknowledge  Eric Lefebvre  for the development of the electronics for the Multiband instrument,  Charles Amory  for his contribution in the exploitation of the FlowCapt measurements and  Baptiste Vandecrux  for his contribution in the comparison of in-situ measurements with SICE and for his valuable feedback on the manuscript.
Acknowledgments We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. J. M. was supported by  ANR-19-JPOC-003  JPI climate/JPI ocean ROADMAP and by the  ARCHANGE  project of the " Make our planet great again" program  ( ANR-18-MPGA-0001 , France). EM acknowledges the funding provided by the  Spanish Ministry of Science and Competitiveness DISTRO-PIA  ( PID2021-125806NB-I00 ) project. G. Z. acknowledges funding from the  Italian Ministry of Education, University and Research (MIUR)  through the  JPI Oceans/Climate ROADMAP Project  ( D.
, 2016; Servais et al. , 2019). Most previous work on Silurian-Devonian palaeophytogeography was done over 30 years ago  (Raymond et al. , 1985; Raymond, 1987; Edwards, 1990a; Edwards and Berry, 1991) , and so requires reevaluation. Many of these earlier studies used non-numerical approaches (e. g. ,  Petrosyan, 1967; Edwards, 1973; Gensel, 1986; Edwards, 1990a; Edwards and Berry, 1991) , at least partly because of the limited number of fossil occurrences that were then available. However, there are now substantial new datasets especially from previously under-sampled areas (e. g. , Gondwana and the South China block) that make it more feasible to use multivariate analyses (e. g. ,  Pardoe et al. , 2021)  to assess the phytogeographical relationships. In addition, most of these earlier studies looked at the Silurian-Early Devonian fossil record, whilst Middle and Late Devonian palaeophytogeography were only partially evaluated, despite this being a time-interval that was arguably just as critical in early plant evolution, for instance seeing the development of the first-forested ecosystems  (Stein et al.
Data A new occurrence-based compilation of plant macrofossils was developed from the Silurian-Devonian global-scale dataset of  Capel et al. (2022)  and primary literature (Data S1, Text S1). Plant richness was slightly modified by inclusion of recently described taxa, such as Teyoua  (Huang et al. , 2022)  or Taungurungia  (McSweeney et al. , 2022a). In total, the raw data consists of nearly 900 fossil occurrences belonging to 410 plant genera (Data S1), tabulated across 48 different geographical units (Table  1 ). Each geographical unit corresponds to one or more plant fossil-bearing localities often grouped at the regional (e. g. , Svalbard) or at  national-level (e. g. , Morocco). Previous studies demonstrated that grouping closely situated localities is preferable, as assemblages of identical age, only separated by a short distance, may be very distinct in terms of composition due to non-spatial factors (e.
Methods Firstly, we plotted the sampled in-bin diversity patterns of Silurian-Devonian plant genera at both a global scale, and at regional scales based on Scotese (2021) main palaeogeographical regions, i. e. , Laurussia, Siberia, Kazakhstania and Gondwana (Fig. 1 ). The former Gondwanan continent, being presumably spread from the South Pole to the Equator and therefore reflecting a wide variety of climates and landscapes, was divided into several areas: northwest (NW), northeast (NE) and South Gondwana (Fig. 1, Table 1 ). A dataset of global genus-level diversity was compiled based on the age-range of each occurrence and sub-datasets were created for each palaeogeographical unit (Table  1 , Data S2). In each dataset, biological affinity of genera was added based on  Capel et al. (2021). Genus-level diversity for major clades (i.
These analyses required the elaboration of matrices composed of incidence (presence/absence) data for each studied time interval (Data S3). Taxa known to only occur in one geographic unit (endemics) were excluded from analyses, as they do not provide useful information to reconstruct spatial relationships. List and number of endemic taxa per phytochorion are nonetheless available in Data S4. Taxa under open nomenclature and occurrences where the reliability has been questioned (marked as (?) in Data S1) were also excluded to avoid spurious associations. Multivariate statistics were performed using PAST software  (Hammer et al. , 2001), v. 3. 26. Each analysis was carried out for the Silurian-Lochkovian, Pragian-Emsian, Middle Devonian and Late Devonian time intervals, independently. These temporal divisions were adopted because they (1) broadly represent the subdivisions previously used in the literature  (Raymond et al.
The very warm climate during this period is probably the main factor leading to this reduction in latitudinal differentiation compared to the Middle Devonian. This first re-assessment of Silurian-Devonian paleophytogeography, using classical methods to recover diversity patterns and phytochoria, suggests a significant role of climate into dispersion and diversification of early land plants, that merits further evaluation. Innovative methods requiring higher geographical resolution occurrence-based datasets could more precisely locate centres of originations and diversification. Likewise, using both empirical (plant fossils) and modelled diversity to characterize the former latitudinal diversity gradient and its evolution through time, may shed further light on climate change impact over early land plant evolution.
ACKNOWLEDGMENTS The authors thank the study participants. Ankita Baru  and  Beatrice Redi  from IQVIA helped develop, and provided and implemented the online platform for, the screening questionnaire. John Crawford  from IQVIA the platform. Urzula Blak  from  IQVIA  identified the consumer panel. Aditi Saxena  and  Shruti Saxena  from IQVIA collected and analyzed the data and prepared the final presentation of study results. Gillian Gummer  assisted with the writing of this manuscript, fully funded by  Bayer Consumer Care AG, Basel, Switzerland. The study was fully funded by  Bayer Consumer Care AG, Basel, Switzerland.
Methods We constructed interhospital network graphs using data from the Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey for Florida (2014) and California (2011). The exposure of interest was hospital centrality, defined as weighted degree (sum of all ties to a given hospital from other hospitals). The outcomes were in-hospital mortality and length of stay with sub-analyses for four acute medical conditions: pneumonia, heart failure, ischemic stroke, myocardial infarction. We compared outcomes for each quartile of hospital centrality relative to the most central quartile (Q4), independent of patient-and hospital-level characteristics, in this retrospective cross-sectional study.
Yet, most current hospital quality measurements emphasize the importance of accounting for differences in patient case mix  [12]  and hospital characteristics but not interhospital network features  [13]. Hospital performance metrics for example rankings are based on hospital-acquired infections  [14] , thrombolysis rates  [15] , and other metrics highly dependent on network position and patient flow. Network metrics may highlight the importance of implementing patient-sharing protocols that optimize resilience and performance of the whole ecosystem. A network approach could benefit the system at large by improving patient outcomes and costs. In this paper, we examine a method to incorporate hospital network structure into the risk adjustment of patient mortality and length of stay (LOS). Our objective is to determine whether a hospital's centrality in an inter-hospital network was associated with inpatient mortality and LOS in 2 states, Florida and California using state inpatient databases.
Data source We used administrative claims data from the State Inpatient Database (SID)  [16]  and State Emergency Department Database (SEDD)  [17]  from the Healthcare Cost and Utilization Project (HCUP) for network creation and SID alone for patient outcomes in this retrospective cross-sectional study. HCUP is the largest collection of multiyear, all-payer discharge data in the United States and includes state-level datasets from participating states. The SID "contain [s] all inpatient care records in participating states. more than 95 percent of all U. S. hospital discharges  [18]. " Specific states such as Florida and California include an encrypted identifier (visitlink variable) to link encounters longitudinally. These states were chosen because of this longitudinal identifier and their large sizes.
The SEDD includes all state-specific treat-andrelease hospital emergency department patient visits (ED visits resulting in admission are included in the SID). SID contains all state-specific inpatient patient visits to acute care community hospitals. We used the 2014 SEDD and SID from Florida (January 1-December 31, 2014). We validated the analysis by using the 2011 SEDD and SID from California (January 1-December 31, 2011), the most recent year available through HCUP at the time of this study. We chose the two states because of their size, diversity of demographics, and the limited movement of patients for care out of state. Additionally, these two state datasets have unique patient identifiers and timing indicators, variables called 'visitlink' and 'daystoevent', allowing tracking of individuals longitudinally within a state.
Links were treated as undirected and included all movement between hospitals including hospital transfers, physician-referred care, and patient-determined care. Each state was analyzed separately. The SID data does not include encounters from other states, and therefore all data only reflect healthcare use within the state, excluding the contribution of out-of-state hospitals on the local network structure. To build the network, we began by creating an adjacency network in which every hospital was listed as a row and also as a column. A numerical value was assigned equivalent to the number of patients shared by each of two hospitals. The R program igraph was used to calculate global network characteristics including weighted degree as the centrality measure. Weighted degree is the sum of all weighted connections to a given hospital in the network (Fig  1 )  [20].
Study population For the analysis of outcomes, we included all patients � 18 years old who had at least one hospital discharge from the SID file during the study time for each specific state (Florida: 2014; California: 2011). Patients with multiple discharges were counted once (the first visit) in line with standard hospital outcome studies. This approach avoided co-linearity of patient data for patients with multiple discharges and removed the need to censor patients with an initial outcome of death. We completed analysis on the overall cohort and four acute medical conditionspecific subgroups: heart failure, myocardial infarction, or pneumonia  [23]  or ischemic stroke  [24]. These conditions were chosen through review of the most frequent in-hospital mortality diagnoses in the US, choosing the top four with validated diagnostic codes.
Statistical analyses We conducted a multilevel logistic regression to determine the association of hospital centrality and inpatient mortality in the 2014 Florida cohort. A multilevel model helps to account for this hierarchical structure. The reference category was the most central quartile (Q4) as we expected this quartile to have the largest number of admissions. We used multilevel logistic regression analyses to account for hospital-level and patient-level factors. Specifically, hospital clustering was accounted for using a random-effect model. We used negative binomial regressions to determine the association of hospital centrality with LOS because this model better fits the data after adjusting for all potential risk factors. We calculated the incidence rate ratio (IRR) for length of stay by hospital quartile relative to the most central quartile.
Additionally, comorbid diseases may interact, leading to elevated complexity beyond the additive nature of the CCI. These limitations may lead to under-reporting of patient complexity, which may be particularly pertinent for complex patients at central hospitals, leading to under-adjustment of their risk. As stated above, future analyses should be conducted in richer clinical datasets to validate the findings of this study. Second, our attempt to control for hospital-related factors is limited by collinearity between central hospitals and large hospitals. Third, the HCUP SID datasets include all payers for all inpatient care obtained within the state but does not include information for patients who receive care in multiple states. Therefore, the networks we have created are restricted to patient sharing between hospitals within the state.
Nonetheless, we expect that the number of patients who have been hospitalized in Florida and California and are then hospitalized in a different state would be limited. Fourth, these two state-specific networks may not generalize to other states or the US as a whole, though we did include states with large and heterogenous populations (~18% of the US population). Further work should include states with differing populations, locations, and healthcare system organization, as well as newer datasets or datasets from other countries. Fifth, these networks do not represent patient-sharing across all settings of healthcare use, but rather, only inpatient and emergency department utilization. Future analyses would benefit from inclusion of ambulatory settings, post-acute care settings such as skilled nursing facilities, and multi-year longitudinal data.
Conclusion Hospitals are interdependent based on patient-sharing. Our results show that the position of hospitals within an inter-hospital network is associated with patient outcomes. Specifically, hospitals located in the peripheral or central positions may be most vulnerable to diminished quality outcomes. While it is intuitively obvious that patients often use healthcare from different hospitals or settings of care, this study further demonstrates that this interconnectivity is associated with patient outcomes. With further validation with deep clinical data, network properties should be considered in the accurate assessment of hospital performance. Future network studies with clinical datasets may be able to elucidate areas of weakness in the network which may be targets for quality improvement interventions.
Methods We carried out an input-output analysis of 49 health-care systems from 1995 to 2015. We harmonised the EXIOBASE v3. 8. 2 database-providing data for 49 world regions-to the World Health Organization Health Expenditures Database. We then performed a panel data analysis to understand the relationship between Healthcare Access and Quality index and energy footprint per capita of health-care systems. EXIOBASE3 does not provide measurement errors so it was not possible to propagate the uncertainties as can be done with other input-output databases. Findings Health-care systems' footprint increased over the past two decades, reaching 7% of global non-metallic minerals footprint, 4% of global metal ores footprint, and 5% of global fossil fuels footprint in 2013. This increase was mostly due to China, rising from 7% of the non-metallic minerals footprint in 1995 to 45% in 2013.
Study design We carried out an input-output analysis of regional healthcare systems using EXIOBASE3 for 44 countries and five rest of the world regions from 1995 to 2015. The rest of the world regions correspond to all other countries in Asia and the Pacific, America, Europe, Africa, and the Middle East (see appendix 1 pp 27-36 for a correspondence table between UN and EXIOBASE regions). EXIOBASE3 is an open-access multiregional input-output database. 24 XIOBASE3 does not provide measurement errors so it was not possible to propagate the uncertainties as can be.