dataesr/scientific-paragraphs-categorization huggingface dataset:
Keeping in mind the preliminary examination of the data accuracy, the large number of historical data items collected in this study should enable deeper analyses of channel shifting, island dynamics, and landscape changes over a longer time period   2009)  to date a series of channel disconnections and study terrestrialization processes. They showed that terrestrialization of naturally cut-off channels was much quicker than that observed on artificially cut-off channels, probably because of much higher bedload transport, which promoted active sediment infilling. The historical dataset is also valuable to explore longerterm ecological succession, such as the work of  Janssen et al. (2020) , who showed that the critical changes undergone by the riparian forests of the Rhône River strongly diverged from natural systems under persistent multiple stressors during the past century.
Historical data on fish communities along the Rhône River are available in documents of various types (e. g. Gobin, 1868, the numerous papers and maps from L. Léger and his collaborators, anonymous reports), and comparisons with present datasets allow assessments of changes in fish community assemblages to be made. Combining physical data describing habitat heterogeneity (e. g. channel width, river bank length, diversity and accessibility of different types of floodplain channels; see  Hohensinner et al. 2004; 2011  for examples on the Danube River) at different periods with data on fish habitat preferences  (Lamouroux et al. , 1999; Rifflart et al. , 2009; Sukhodolov et al. , 2009 ) may be useful to achieve a better understanding of the influence of fluvial landscape transformation over the last few centuries on fish abundance and spatial patterns.
Historical maps and associated physical data can thus be used to assess abiotic conditions over time in the framework of biomonitoring methods. For example, historical analysis (from the end of the 19 th century) of fish community structure in the Seine River catchment highlighted the major role of river continuity alteration in the decline of migratory species, the importance of introductions of non-native species, and changes in the proportion of rheophilic/eurytopic species in relation to the increase of human population densities and water pollution  (Belliard et al. , 2018). Finally, another potential use of the dataset is the reconstruction of extreme flood events based on historical channel geometries. The Beaucaire-Tarascon station (KP 269. 5) records flows of the whole basin, and is the oldest hydrometric station on the Rhône.
Daily water level measurements started in 1816, and daily estimates of discharge started in 1920  (Billy et al. , 2018). These chronicles are part of the HISTRHONE database, which contains a large volume of archives related to hydroclimatic events in the lower Rhône over a period between the 14 th and 20 th centuries  (Pichard and Roucaute, 2014). Further work by hydrologists will involve exploiting historical topographic data prior to river training to reduce uncertainties in the extrapolation of rating curves of hydraulic modelling based on original channel geometries. The topo-bathymetric data highlighted in this study will be of particular interest (Fig. 7  and 11 ).
Data availability The dataset is freely available in Pangaea at doi 10. 1594/PANGAEA. 922437 (Arnaud et al. , 2020a). It contains the iconographic database (12. 7 GB), the. csv table describing the properties of the 350 data items (109 KB), the. csv table listing the 281 archive box references (13 KB), and the GIS layer of the longitudinal coverage of the 350 data items (10. 8 MB). The dataset is available under the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC) license, which permits use, distribution and reproduction in any medium, provided that the use is not for commercial purposes and that the original work is properly cited. This license was chosen to meet the data reuse policies of the 14 French archive services investigated in this study, 4 of which have a reuse policy equivalent to the CC BY license (National Archives, AD Ain, AD Vaucluse, DREAL, Univ.
Grenoble), and 5 have a reuse policy equivalent to the CC BY-NC license (BnF, AD Rhône, AD Drôme, AD Ardèche, AD Gard). Data from AD Isère, AD Savoie, CNR, and IGN, i. e. 10 items among the 350 items in the dataset, are not furnished in the iconographic database. They are indicated as "available on request", because of specific reuse policies of these institutions. The URLs of each institution's reuse policy are given in Pangaea metadata. Extracts of the 350 data items are available in the public photo gallery of the Rhône Valley Human-Environment Observatory, to enhance data visibility. This photo gallery uses the open source Piwigo software, with images being geolocated on an interactive map. Map extracts have been oriented according to the flow direction of the Rhône, as long as this did not disturb the toponym reading.
In addition to Pangaea, technical information on the dataset is available in the public metadata catalogue of the Rhône Valley Human-Environment Observatory. Based on the open source GeoNetwork software, this metadata catalogue is harvested by the LabEx DRIIHM (international consortium of Human-Environment Observatories) geocatalogue, which is then harvested by the French geocatalogue and the European INSPIRE Geoportal, so that data discovery is possible at several hierarchical levels  (Arnaud et al. , 2020b).
Conclusions This study presents a historical database of 350 maps, topo-bathymetric cross-sections, and long profiles compiled on the French Rhône River over the period 1640-1950. Such an inventory of historical data over such a long time period and long river course (530 km) has never been created before for the Rhône, and has rarely been created for other large rivers. This database will enable further comparisons with other multi-impacted large rivers in Europe, such as the Danube, the Rhine, the Ebro, and the Po, or even from one continent to another when data are available. The densification of archived time series is useful to detail and extend past trajectories of geomorphic, hydraulic, and ecological river features, and for conducting comparative analyses to detect differences and similarities between reaches and between rivers located in different geographical settings.
article, notably the  National Archives  and AD Rhône, whereas additional investigations could be conducted in CNR or other departmental archives in coming years. Possible updates or extensions to the dataset might therefore happen in the future. Acknowledgements This research was funded by the  OHM Vallée du Rhône in the LabEx DRIIHM French program  " Investissements d'Avenir"  ( ANR-11-LABX-0010 ), managed by the  French National Research Agency (ANR). The work was performed in the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon. Data were collected in  Archives Nationales (France) ,  Bibliothèque nationale de France ,  Archives départementales du Rhône et de la métropole de Lyon, Archives départementales de l'Ain, Archives départementales du Gard, Archives départementales de la Savoie, Archives départementales de l'Isère, Archives départementales du Vaucluse, Archives départementales de l'Ardèche ,  Compagnie Nationale du Rhône ,  Direction Régionale de l'Environnement, de l'Aménagement et du Logement Auvergne-Rhône-Alpes ,  Institut National de l'information Géographique et forestière, Université de Grenoble.
and Olivier, J. M. : Cartographic https://doi. org/10. 5194/essd-2020-274 Preprint. Discussion started: 27 November 2020 c Author(s) 2020. CC BY 4. 0 License. Open Access Earth System Science Data Discussions and topo-bathymetric archive dataset on the French Rhône River (17th-20th centuries). Sector Upstr. KP Downstr. KP Length (km) Comment Nb of images Image acquisition Storage file Sub-file Seyssel to Virignin -151 -117 34 This map may have been surveyed after the Sardinian Maps of 1738 and before 1760 because 'lateral lines' from 1760 are not visible everywhere 20 Table 1730-1875_Plans_UpperR hone 1750_NA_Se yssel-Virignin Pont-St-Esprit 191 193 2 Names of the bridge piers. 3 cross sections 8 Table 1756-1822_Grangent_Plan s PontStEsprit_ 1812 St Rambert to Pont-St-Esprit 61. 5 191. 5 130 Map of 12 m long.
Summary We used NIKA2 on the IRAM 30m telescope to map over 2 square degrees of the Galactic plane at 1. 2 mm and 2. 0 mm. While the observing campaign is still ongoing we performed a first analysis of the 1. 2 mm image. The high sensitivity of these GASTON images allowed us to identify a brand new population of low-brightness compact sources, which, being amongst the coldest sources, have a similar mass distribution as those which were already known. This new sample might well represent a missing building-block in our understanding of the stellar mass determination process. Using infrared darkness as a possible proxy of their evolutionary stage, we also suggest that there might be some evidence for a time evolution of the clump mass distribution. However, further detailed analysis of the data is required to evaluate the robustness of such interpretation.
Acknowledgements N. P. and A. R. would like to thank  STFC  for support under the consolidated grant number  ST/N000706/1. We thank the  Royal Society  for providing computing resources under Research Grant number  RG150741. We would also like to thank the  IRAM  staff for their support during the campaigns. The NIKA dilution cryostat has been designed and built at the  Institut Néel. In particular, we acknowledge the crucial contribution of the Cryogenics Group, and in particular  Gregory Garde ,  Henri Rodenas ,  Jean Paul Leggeri ,  Philippe Camus. This work has been partially funded by the  Foundation Nanoscience Grenoble  and the  LabEx FOCUS   ANR-11-LABX-0013. This work is supported by the  French National Research Agency  under the contracts "MKIDS", " NIKA " and  ANR-15-CE31-0017  and in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
This work has benefited from the support of the  European Research Council Advanced Grant ORISTARS  under the European Union's  Seventh Framework Programme  (Grant Agreement no. 291294 ). We acknowledge fundings from the  ENIGMASS French LabEx (R. A. and F. R. ), the  CNES post-doctoral fellowship program (R. A. ) , the  CNES doctoral fellowship program  (A. R. ) and the  FOCUS   French LabEx doctoral fellowship program  (A. R. ). R. A. acknowledges support from  Spanish Ministerio de Economía and Competitividad (MINECO)  through grant number  AYA2015-66211-C2-2.
C. vdW-D3 Octopus also supports the DFT-D3 van der Waals correction. 66 This correction depends only on the atomic positions and does not depend on the atomic density. It is implemented in Octopus by linking to the DFT-D3 library provided by the authors. As we have validated Octopus results with the reference data provided with the library, the results are guaranteed to be consistent with other codes that implement this correction. The only modification we did to the library was to move the very large set of coefficients from a source file to a stand alone text file that is parsed only when necessary. This speeds up compilation and reduces the size of the binaries. The modified library is distributed with Octopus, so users need not compile it separately.
Using the packed storage format described above, Octopus is able to store the states fully in the GPU memory, provided the memory is large enough, thus reducing memory transfers to a minimum. Recently, this was improved further by removing frequent allocations and deallocations of temporary variables on the GPU, now using a custom memory management for those. This proved very effective in improving the scaling to several GPUs, also to several nodes with GPUs. We show two examples of time-dependent runs in Fig. 16. For the first example (left panel), β-cyclodextrine was used as an input system and the simulation was run on the GPU island of the COBRA supercomputer at the Max Planck Computing and Data Facility. Each node in this island consists of two 20-core Intel Xeon 6148 Gold sockets (Skylake architecture) with two Nvidia Volta 100 GPUs (PCIe); the nodes are connected with an Omnipath fabric (100 Gbit/s).
XVII. CONCLUSIONS It has been almost 20 years since the development of Octopus started. During this period the code has matured and expanded to cover an ever-growing range of methods, theories, applications, and systems. It has also continuously adapted to the available computer architectures and computing paradigms, allowing researchers to tackle increasingly challenging problems in electronic structure theory. Although many of the code capabilities have been routinely used by many groups around the world, mainly to study electronic excited states properties and dynamics, which still remain to a large extent the core of Octopus, we believe the main reason for the code's success lies elsewhere. From the beginning, the code has been designed to take full advantage of the flexibility and versatility offered by the use of real-space grids and provide developers with a framework to easily implement and test new ideas and methods that can be later on adopted by other codes (as it has already been the case with quite a few features previously developed within Octopus).
ACKNOWLEDGMENTS The authors would like to thank all the people that have contributed to the development of Octopus over the last two decades. They would also like to thank  Lin Lin  for useful and interesting discussions and acknowledge the open discussions about real space methods with the group of  Prof. Chelikowsky. This work was supported by the  European Research Council  ( ERC-2015-AdG694097 ), the  Cluster of Excellence "Advanced Imaging of Matter"(AIM) ,  Grupos Consolidados  ( IT1249-19 ) and  SFB925. The  Flatiron Institute  is a division of the  Simons Foundation. XA, AW and AC acknowledge that part of this work was performed under the auspices of the  U. S. Department of Energy at Lawrence Livermore National Laboratory  under Contract  DE-AC52-07A27344. JJS gratefully acknowledges the funding from the  European Union   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No.
g. , a particular architecture, or is it simply a larger numerical variation of the results? Moreover, it facilitates updating the tests and improving the testsuite itself. The application has been implemented using the web framework Django coupled to a Postgres database. The testsuite results are automatically uploaded by the Buildbot service as soon as they are available. The application allows to analyze single toolchain runs and single comparison matches and to compare all toolchain runs for a single commit in git. It provides histograms to judge if there are outliers or if it is a broad distribution; moreover this allows to determine, e. g. , if there is a difference between MPI and non-MPI toolchains.
To prevent such out-of-range situation, some solutions already exist. For instance, it is possible to change the actuator technology, or to tune the temperature of the femtosecond oscillator chiller  [15] , having a constant environement maintening the slow loop in its range  [16] , among others, or by changing the optical design  [17] [18] [19] [20]. In this paper, a Labview-based software is proposed, which automatically locks the CEP and prevents problems due to the limited range of the slow feedback loop actuator. The software drives all the relevant parameters to reproduce the manual actions required to lock and optimize the CEP of a Ti:Sa femtosecond oscillator. Also, when the actuator gets close to its range limit, the program unlocks the CEP, locates the other locking point (on the other side of its range), relocks the CEP and optimizes its performance.
Conclusion A software solution to automatically lock and maintain the long term CEP stabilization is proposed for Ti:Sa oscillators under the standard configuration that eventually has a limited slow-loop compensation range. The algorithm mimics the manual CEP-locking and optimization procedure. The CEP can be stabilized without time limitations with only short (few-second) interruptions close to the range limit. The interruption time can further be reduced by improving the communication protocol. This solution is successfully used in the day-to-day basis without any manual alignment. More recent Vitara versions provide greater than 2π tuning range  [24] , making the range limit events less frequent. Hence, at least three locking points would be available. The software solution would be well suited to automatically lock on the most relevant locking point (in the middle of the range with the desired polarity) and, when it arrives close to the range limit, it can switch to another locking point on the other side of the range that has the same polarity.
Parameters for this linear model were set as follows. For each simulated dataset we sampled from the available SNPs a set of n QTL QTLs and a set of n poly polygenic SNPs, as well as their corresponding genetic variances for each selected marker. To reduce the impact of extreme MAFs on genomic prediction  [17]  and QTL detection, we focused on frequent QTLs by drawing the n QTL and n poly SNPs from those with a MAF ≥ 0. 15. In all simulations, we selected a total of n QTL = 5 large QTLs, varying the corresponding proportion k of total genetic additive variance σ 2 g as described below. The phenotypic variance and mean were respectively set to σ 2 y = 100 and µ = 0, and SNP heritability h 2 = σ 2 g σ 2 y was varied across simulation settings. We constructed 13 scenarios with different proportions k of genetic variance attributed to the QTLs, with 10 independent datasets created for each (Table  1 ).
As the same 5 QTLs were simulated across scenarios for each of the 10 independent datasets, a total of 50 QTLs was considered. Finally, each scenario was run for four different levels of heritability h 2 = {0. 1, 0. 3, 0. 5, 0. 8}, and we evaluated the performance of BayesR for two alternatives: (1) using genotype data that excludes the 5 known QTLs, resembling a classic 50k genotyping array ("50k data"); and (2) using genotype data that includes the 5 known QTLs, which mimics a custom 50k genotyping array ("50k custom data"). In total, this corresponds to 13×10×4×2 = 1040 simulated datasets.
Here, the Gibbs sampler was run for a total of 50,000 iterations, including 20,000 as a burn-in and a thinning rate of 10. In this work, we used the open source Fortran 90 code described in  [15]  and available at https://github. com/syntheke/bayesR. We made a few modifications to this code, notably adding the posterior variance of estimated SNP effects at each iteration to the output; our modified BayesR code may be found at https: //github. com/fmollandin/BayesR_Simulations. Prediction accuracy for BayesR was quantified using the Pearson correlation between the true phenotypic values (y) and those estimated using BayesR (ŷ) in the validation set.
To assess the impact of the prior specification on per-SNP effect estimates, we calculated the Pearson correlation between the estimated posterior means β i across SNPs, simulated scenarios and datasets. Among the three prior specifications, the correlation of estimated SNP effects was between 97. 4% and 98. 6% for all SNPs. Based on these results, we consider that the prior specification appears to have little practical impact on the performance of BayesR, whether for its predictive performance or for per-SNP effect estimates. For the remainder, we therefore use the default prior specification for proportion of genetic variance in each effect class.
Predictive power of BayesR in varied simulation settings We next sought to investigate the predictive power of BayesR across simulation scenarios, varying the contribution of QTLs to the additive genetic variance (which we refer to as scenarios below), heritability, and use of 50k or 50k custom genotype data. The mean validation correlation (over the ten independent datasets simulated for each) for each simulation scenario illustrates the expected drop in prediction quality for decreasing heritabilities, whether 50k or 50k custom data are used (Figure  1 ). For the former, the mean (± sd) validation correlation across scenarios is 0. 125 (±0. 048), 0. 301 (±0. 057), 0. 447 (±0. 058) and 0. 650 (±0. 049) for h 2 = {0. 1, 0. 3, 0. 5, 0. 8}. For the latter, the inclusion of the true QTLs among the genotypes unsurprisingly leads to higher validation correlations, with mean (± sd) values across scenarios equal to 0.
Similar results are observed at this level of heritability regardless of the simulated effect size of the QTLs. However, for h 2 = {0. 3, 0. 5, 0. 8}, 50k custom data led to a nearly systematic gain in performance: the average increase in validation correlation was 0. 011 (±0. 014), 0. 019 (±0. 020) and 0. 031 (±0. 030) across QTL effect size scenarios, and attained maximum values of 0. 076, 0. 112, and 0. 160 respectively. For a given heritability, Figure  2  also shows marked improvements in prediction when including QTLs simulated with large shares of additive genetic variance. [FIGURE  1  here] [FIGURE  2  here] QTL mapping using BayesR A natural first tool to investigate for QTL mapping is the neighborhood PIP defined in Equation  (6). We focus on the behavior of the neighborhood PIPs for the true QTLs across scenarios (Figure  3 ), averaging over the 50 QTLs available for each (5 QTLs × 10 independent datasets); note that as this is a window-based measure, this measure can be computed for the true QTLs whether the 50k or 50k custom data are used.
When h 2 = {0. 3, 0. 5, 0. 8}, as the simulated share of genetic variances for QTLs increases for both the 50k and 50k custom data, the null neighborhood PIP decreases and the large-effect neighborhood PIP increases. Across all simulated datasets and scenarios, the average (± sd) small-and medium-effect neighborhood PIPs are 0. 117 (±0. 053) and 0. 058 (±0. 040) respectively, illustrating that these two classes appear to be less often filled compared to the null and large classes (although all four classes do appear to be used outside of the lowest heritability setting). [FIGURE  3  here] The neighborhood PIP results provide a preview of how QTLs are grouped into non-null effect classes according to the neighborhood MAP rule (Equation (  8 ); Figure  4 ). In all simulation settings, no QTL neighborhoods were assigned to the small effect class using this criterion.
Similar to the 50k data, there is an effect size threshold at about k = 1. 8% at which QTL windows are more frequently detected. Because the same five QTLs are simulated in each independent dataset across effect size scenarios, Figure  5  also allows for their specific detection to be followed across configurations. Thus, it can be seen that some QTLs windows are not detected in any of the scenarios, while others are more easily detected, even for lower shares of the genetic variance. That said, there are occasionally discontinuities in detection observed for increasing shares of the variance (i. e. , a QTL window correctly identified for k = 0. 02 but not 0. 025). With the exception of h 2 = 0. 1, which had very weak detection in all scenarios and datasets, we found similar conclusions for h 2 = 0.
Abbrevations Figure Legends • Figure  1 : BayesR predictive performance across simulation settings. For each setting (h 2 and percentage of genetic variance assigned to each QTL), points represent mean validation correlations across 10 independent datasets. Heritability values are represented by dark to light blue (h 2 = 0. 8 to 0. 1), and solid and dotted lines represent results for the 50k and 50k custom datasets, respectively. • Figure  2 : Difference in BayesR predictive performance for the 50k versus 50k custom genotypes across simulation settings. Each panel from top to bottom represents a given heritability (h 2 = 0. 1 to 0. 8), and boxplots represent the distribution of differences in validation correlation between the 50k and 50k custom datasets for each independent dataset (i. e.