dataesr/scientific-paragraphs-categorization huggingface dataset:
Predicting and monitoring the locations and dynamics of future lakes will become increasingly important, especially in steep mountain regions that are difficult to access, but have growing populations and infrastructure downstream. International collaborations such as the United Nations Development Programme project 'GLOF-I/-II' in Pakistan aim to mitigate GLOF hazards, but more effort will be required to manage the episodic GLOF discharges as the variability in precipitation, temperature and river discharge rises  53, 54. Our dataset forms a solid base to help predict changes in GLOF hazard from the catchment to the regional scale to ensure more sustainable development for one of the most sensitive regions on Earth.
Developing a database of ice-dam failures We define an ice-dammed lake as any water body at the margin of and impounded by glacier ice. We exclude all other glacier-fed lakes, such as lakes dammed by moraines or landslides, internal water pockets, supraglacial ponds, lakes below glaciers and lakes formed by geothermal activity. Ice dams often trap runoff from catchments and cause loss and damage when the impounded water is suddenly released and reaches human settlements  59. We focus on six major glaciated mountain regions that support 99% of the contemporary ice volume outside polar regions  47. Our evidence for the 1,569 ice dam failures reported between 1900 and 2021 comes from a total of 446 different sources of information. We distinguish between primary sources, where we have direct observations or access to raw data, and secondary sources that describe, interpret or summarize previous findings on historical GLOFs.
Primary sources are available for 64% of all reported cases and include original scientific publications (36%), annals and reports published by local and regional authorities (18%), written communications in emails with eyewitnesses and experts (9%), and newspapers and internet resources such as video footage and posts on social media platforms (altogether 1%). Secondary sources (36% of all cases) include second-hand information derived from reviews and summaries of previous publications (19%) and entries in databases with no direct access to the primary source (17%). Half of all sources report only a single event, whereas a few studies include information on dozens of GLOFs. For example, ref. 60  identified more than 100 outbursts from lakes impounded by Kennicott Glacier during the twentieth century by studying stream gauge data and historical aerial photographs.
Still, decades may elapse between the date of a GLOF and the reporting, especially where researchers reprocessed and reanalysed historical data (Extended Data Fig. 1 ). In half of all cases, however, GLOFs were reported within a decade after they occurred. A few studies contributed significantly to improving the regional record of GLOFs in the first decades of the twentieth century, when the overall reporting rate was low  3, 7, 11, [60] [61] [62]. In a few cases, reporting may have changed due to resettlement in this period. For example, repeated flooding of an ice-dammed lake below the Folgefonni Ice Cap (Norway) forced farmers to abandon their homes in the mid-1960s  16. It is only since the 1970s that research activity on GLOFs has become more consistent  24  : 63% GLOFs in our databases were first documented in that period (Extended Data Fig.
1 ). In collecting historic GLOFs, we only considered cases with either a documented date (at least the year of occurrence) or a time interval of failure, centroidal coordinates of the lake and at least one reference. For each GLOF, we documented the mountain range, the country from which the flood originated, the name of the parent glacier (local name and the ID in the Randolph Glacier Inventory RGI V6. 0  63  ) and the name of the burst glacier lake. We extracted the surface elevation of each lake centroid from the Advanced Land Observing Satellite (ALOS) V3. 2 digital elevation model (DEM)  64. We collected the reported flood volume (n = 510, 33% of all cases), peak discharge (n = 506, 33%) and timing (n = 940, 61%). Finally, we documented any impacts, damage, and losses for events in our database.
Lake mapping We could verify the origin, timing and area of 53% of all reported lake outbursts that have occurred since the Landsat 5 mission began to continuously obtain satellite images in the late-1980s  65. Later generations of satellites, such as RapidEye and the Planet Cubesat constellation (launched in 2009 and 2015, respectively), have higher spatial resolution and thus capture GLOF features in greater detail. Ice-dammed lakes decrease in size, or even empty entirely, during GLOFs, exposing either partly or fully the lake floor, which is often covered with stranded icebergs (Fig. 5c ). Ice, clouds, and shadows compromise the automatic detection of water and lakes  66. Hence, we manually digitized the extents of the lakes from satellite images in QGIS V3. 16 software. To this end, we scanned the archive of satellite images to retrieve the last available image before and the first available image after the lake drained.
The lakes in these databases were either manually digitized or refined after automatic mapping. We chose a minimum mapping unit of 0. 01 km 2 to ensure comparability between inventories. No contemporary lake inventory is available for Iceland. Thus, we manually mapped ice-dammed Icelandic lakes from high-resolution Google Earth imagery obtained between May and September 2021. We added the elevations obtained from the ALOS V3. 2 DEM  64  to both the previous lake inventories and our manually mapped lakes.
We stacked the ASTER DEMs in temporal order and further added high-resolution data from the ArcticDEM 77 to the time series. Data from ArcticDEM cover glacier dams in Alaska, Iceland and Scandinavia (Supplementary Table  6 ). Each pixel in the DEM time series is then interpolated using a Gaussian process regression model to estimate the elevation at any date within this period. We extracted the elevation time series for the areas of the glacier dams, resulting in an area-weighted average of 45 independent DEMs between 2000 and 2019 for each dam. Individual glacier dams are covered by at least 95% of measured pixels, drawing on at least 13 independent DEMs per pixel. We calculated the associated elevation differences between successive time steps, interpolated spatial data gaps hypsometrically  78  and finally aggregated them to the cumulative mean elevation changes with respect to 1 January 2000.
GLOF reporting between 1900 and 2021. a, Histograms of all reported GLOFs (top), reported GLOFs with documented peak discharge Q p (middle), and reported GLOFs with documented flood volume V 0. Numbers are the total number of GLOFs every three decades. b, Plot of year of GLOF occurrence versus year of first available document. White circles are individual GLOFs; colours are aggregated cases in 5-year intervals. c, Histogram showing the year of first available documentation for all cases in our database. Extended Data Fig. 2 | Regional trends of GLOF peak discharge Q p with time t obtained by hierarchical quantile regression. a, Temporal trends of Q p for the periods 1900-2021 and 1990-2021. Colours distinguish between the median (blue) and 90th (orange) percentile of reported Q p , including the posterior 95% HDI (shades).
Acknowledgements We thank  Planet Labs Inc. for a basic license in the Education and Research programme that granted free access to high-resolution Planet and RapidEye images. We thank many experts for sharing regional data on ice-dammed lakes:  B. Einarsson ,  J. Carrivick  and  F. Tweed  (Iceland);  M. Huss  and  M. Werder  ( European Alps );  M. Jackson  (Scandinavia);  P. Iribarren Anacona  (Andes);  R. Johnston  (northwest North America);  J. Steiner  and  V. Round  ( High Mountain Asia ); and  V. Foord  and  J. Jacklin  for internal reviews.
Data availability The GLOF inventory V2. 0 is freely available at http://glofs. geoecology. uni-potsdam. de, and archived at https://doi. org/10. 5281/ zenodo. 7326571. The ALOS DEM V3. 2 is freely available at https://www. eorc. jaxa. jp/ALOS/en/dataset/aw3d30/aw3d30_e. htm. The RGI V6. 0 is freely available at https://nsidc. org/data/nsidc-0770. Planet and RapidEye images are available under a basic license from the Education and Research programme from Planet Labs Inc. through the Planet-Explorer (https://www. planet. com/). Landsat images were obtained from the EarthExplorer (https://earthexplorer. usgs. gov/). Data on glacier elevation change for the period 2000-2019 are freely available at https://doi. org/10. 6096/13.
Extended Data Fig. 1 | 1 GLOF reporting between 1900 and 2021. a, Histograms of all reported GLOFs (top), reported GLOFs with documented peak discharge Q p (middle), and reported GLOFs with documented flood volume V 0. Numbers are the total number of GLOFs every three decades. b, Plot of year of GLOF occurrence versus year of first available document. White circles are individual GLOFs; colours are aggregated cases in 5-year intervals. c, Histogram showing the year of first available documentation for all cases in our database. Extended Data Fig. 2 | Regional trends of GLOF peak discharge Q p with time t obtained by hierarchical quantile regression. a, Temporal trends of Q p for the periods 1900-2021 and 1990-2021. Colours distinguish between the median (blue) and 90th (orange) percentile of reported Q p , including the posterior 95% HDI (shades).
While this iridescence, temporality, and responsiveness emphasise Flavobacteria's potential as an attractive living medium, the complex combination of spatial and temporal qualities poses a challenge for understanding and predicting Flavobacteria's living aesthetics. Models in computer graphics have shown the possibilities of rendering various iridescent effects  [21, 10] , yet more complex effects such as those of Flavobacteria's photonic crystals require data-driven approaches as described in this work. Viewing angle: 45°Viewing angle: 80°2 When implemented in material rendering software such as Blender 3D, the colour can be accessed from the BRDF map as a function of the camera view and light directions.
of 11 In this paper, we describe our model for the production of literary sentences based on homosyntax, where semantically different sentences present the same syntactic structure. This approach is different than paraphrasing and seeks to generate a new sentence which preserves a given syntactic structure by strongly modifying the semantics. Our proposal is based on a combination of language models, grammatical analysis and semantic analysis based on an implementation of Word2vec  [7]. In Section 2, we present some current work regarding literary text production, with a focus on methods related to our own work. In Section 3, we present the corpus that we have constructed and used for training our algorithms. We explain the algorithms that we have developed for the production of literary sentences in Section 4.
( 5 ) We then take the first embedding in L that corresponds to the highest score, θ max , and the best word, w max , to replace o. Finally, a morphological analysis is performed with FreeLing, in order to transform the selected word, w max , to the correct form according to the inflection specified by its corresponding POS tag (e. g. , conjugations, gender, or number conversions). With this procedure, we thus select the word that will substitute o that is semantically closest to y, based on the analysis performed with Word2vec, while maintaining the coherence and consistency of the generated text by using the linguistic bigram analysis performed on the language model (the MEGALITE FR corpus). This process is repeated for each lexical word in f that is to be substituted, i. e. , for each POS tag in the PGS.
2. Il n'y a pas d'affection sans estime. (There is no affection without esteem. ) 3. Il n'y a ni aimante ni mélancolie en sérénité. (There is neither love nor melancholy in serenity. ) 4. Il n'y a ni fraternelle ni inquiétude en anxiété. (There is no brotherhood or concern in anxiety. ) In these examples, we can observe reasonably coherent sentences with words belonging to the same semantic field. There are some small syntax errors that occurred within the FreeLing tokenization module, which we expect may be solved by a fine-grained, a posteriori analysis, based on regular expressions. For Q = Sadness.
This work is funded by the  Consejo Nacional de Ciencia y Tecnología (Conacyt, Mexico) , grant number  661101 , and partially funded by the  Université d'Avignon ,  Laboratoire Informatique d'Avignon (LIA) ,  Agorantic programme (France). It has also received partial financial support from the  Brazilian science  funding agencies:  Conselho Nacional de Desenvolvimento Científico.
Reproduced from  [2]  by the ALICE collaboration under the terms of the Creative Commons License 4. 0 (http://creativecommons. org/licenses/by/4. 0/); no changes have been effected. (The red and green lines correspond to PYTHIA 6 with two Perugia tunes, and play no further role in our discussion) 1. 2 Common Use of the PYTHIA Software to Predict Correlations PYTHIA is a Monte Carlo event-generator software program, widely used to simulate possible outcomes of experiments (like collisions between two objects, be it fundamental particles or nuclei) and help understand detector responses. Through various physical model mechanisms, PYTHIA evolves the initial input state into a number of output particles. These are listed by their flavor, spin and momentum in its original format described in  [6].
Such simulation packages are popular because of their convenience to explore possible experimental outcomes when designing a measurement or during its later analysis. Moreover, PYTHIA allows one to quickly test theoretical ideas, for example, adding some Beyond the Standard Model feature, to start assessing its impact on the data, even to guess physics at energies that current colliders cannot reach. Finally, finding a way to simulate a process can help understand it, so the shortcomings of PYTHIA can sometimes reveal shortcomings in current knowledge. PYTHIA addresses those goals by quickly generating a great number of simulated events. The main requirements on the software are speed of execution and fidelity of the data produced at the end of the simulation compared to the data from experiment.
Recent Attempts: The Afterburners Method To correct the results of the simulation, a postprocessing procedure of the data has been proposed  [3]. In the following, it will be named the "afterburners" procedure (in analogy to motor technology). This correction to the PYTHIA output produces encouraging results for generic kinematics, but in this work we will only evaluate the very specific problem of proton-proton anticorrelation at ϕ = 0, only one of several physical situations  [3]  but a problematic one: the correction to the pp correlation (see Fig. 2  in that article) is insufficient to account for the anticorrelation. The idea behind this procedure is to write an additional code that from the output of PYTHIA associates to each pair of particles a weight. The same-event correlation function S is computed and binned as an histogram, which allows to weight each pair.
Simplified Working Method A wise initial focus to minimize intervention in a time-tested code is to try to solve the problem at one single step of the algorithm. Since the afterburner postprocessing in Sect. 1. 3 proved insufficient, this step would probably be at a previous level inside PYTHIA (the earlier a modification is applied, the larger its effect upon cascading over the rest of the simulation. ) On the one hand, unstable-hadron decays cannot be the source of the problem, for they are simple processes in the code mimicking experimentally measured decays, so a disagreement with experiment is hard to expect. On the other hand, computed meson-meson correlations broadly agree with experiment, hinting that we should modify only the baryon-production algorithm. Additionally, QCD amplitudes are also well understood so the perturbative matrix elements affecting the initial stages of the process are unlikely to be the culprit.
Lund String Model The PYTHIA level just before the formation of hadrons handles quarks and gluons organized into color singlets. Any interactions involving different such singlet clusters had been taken into account earlier during the parton level computations and can probably be ignored for hadronization. At the stage of hadron formation, the code takes as input a set of partons and returns hadrons, modelling low-energy QCD interactions. Due to its complexity and for speed of algorithm execution, PYTHIA uses a phenomenological model called the Lund string model. Being far from perfect, this is convenient to treat hadron formation with sufficient richness of detail while maintaining acceptable turnout. Summarizing the next few paragraphs, PYTHIA views the input color singlet made of quarks and gluons as a string that fragments into pieces (the hadrons).
Results That simplest procedure of imposing a one-baryon per string policy can be very time consuming. Combining it with the afterburners correction worsens this and additionally leads to technical difficulties that makes it unpractical to gather sufficient Monte Carlo statistics in a limited university departmental cluster. In the afterburners procedure, the lengthy code, together with the number of pairs increasing with the square of the number of events, can be a drawback. It brings about a rise of the whole time of execution,  increases the frequency of software crashes and produces random pairs that occasionally give nonsense results (like negative weights) and must be discarded. If the afterburners method is used alone this is not dramatic and can be handled. But the difficulties compound if this addition to PYTHIA is combined with another.
Conclusion and Perspectives We have examined the problem of baryon anticorrelations, a severe current disagreement between experimental data from the ALICE collaboration (lately also from the STAR collaboration) and theoretical Monte Carlo simulations of hadron collisions. Such anticorrelations are typical of lower-energy nuclear physics, for example in two-proton radiactivity (see  [12] , right panel of Fig. 18 , that shows a decrease at cos θ Y = -1 when the protons are emitted back to back, respect to cos θ Y > -1, an effect ascribed to Coulomb repulsion and the Pauli principle) but are now also routinely obtained in high-energy collisions, due to the improved particle identification; they should, and are not, reproduced by current event generators. We believe that the "fault" lies on the theory side and that we have made a contribution to the discussion by exposing that modification of the Monte Carlo event generator package PYTHIA 8 at the fragmentation level can qualitatively account for anticorrelation.
Acknowledgements  Noe Demazure  thanks the members of the  Theoretical Physics Department/IPARCOS institute at UCM  for hosting him during this investigation, as well as the  École Normale Supérieure Paris-Saclay  for its financial support. The authors thank  Claude Pruneau  from  Wayne State University  for providing the WAC software package to represent the raw PYTHIA output. They also thank  Torbjörn Sjöstrand  for useful comments on a first draft of the manuscript. Work partially supported by the  EU  under grant  824093  ( STRONG2020 ); by the  US DOE  under grant  DE-FG02-92ER40713 ; spanish  MICINN  under  PID2019-108655GB-I00 ,  PID2019-106080GB-C21 ;  Univ. Complutense de Madrid  under research group  910309  and the  IPARCOS institute.
The pivotal step in any pangenomic analysis is the construction of a panreference that captures the (almost) full diversity of a large set of genomes. However, the pangenome construction remains a cumbersome and challenging process, especially for Eukaryotes due to their large genome size and complexity (e. g. repeat content or polyploidy). Although long reads sequencing technologies are increasingly used to directly detect large structural variations, generally through reassembly of genomes  (12, (17) (18) (19) , short reads ones currently remain less expensive and is still widely used. In addition, the numerous short reads datasets already available on many organisms provide an important source of data to perform large-scale pangenomic analyses. Two approaches were mainly used for the pangenome construction from individuals sequencing with such short reads: Table  1.
List of Illumina sequencing data used from 248 African rice accessions. For each dataset, the species, the number of samples (#samples), the number of reads (#reads), the total number of gigabases (#Gb), the average depth sequencing (X) and the reference are provided (i) de novo genome assembly followed by genome comparison (here referred as the 'assemble-then-map' approach; 10,20), and (ii) the 'map-then-assemble' approach, based on the mapping of resequencing short reads followed by the de novo assembly of the unmapped reads  (6, 13, 21). Very few tools are publicly available to perform all the steps at once, being either developed for bacteria  (22) (23) (24) , or based on the de novo 'assemble-then-map' approach  (25).
Sample sequencing Short-read sequencing data. We used whole genome sequencing data (    (28). TOG5681 long reads sequencing and assembly. DNA was extracted following the protocol from Serret et al. (29)  using an adapted CTAB-lysis approach to ensure high molecular weight DNA. DNA quality and concentration were controlled using PFGE and Qubit, and subject to a LSK-109 library as recommended by suppliers (Oxford Nanopore Technology, Inc, Oxford, United Kingdom). The library was loaded on two 9. 4. 1 flowcells, raw FAST5 base-called using Guppy 4. 0. 5 (hac model) with a cut-off at PhredQ 7, and FASTQ data were controlled using NanoPlot 1. 38. 1  (30). FASTQ were then assembled using Flye 2. 8  (31)  with the -nano-raw mode and standard options. Initial polishing was ensured by three turns of Racon 1.
3  (32)  under standard conditions using the initial set of nanopore reads and mapping performed by Minimap2 v2. 10 (33) in -x mapont mode. Final polishing was performed using Medaka 1. 2 (https://Github. com/Nanoporetech/Medaka) with the standard model. Contamination was checked using Blobtools 1. 1  (34)  and remapping of short reads, as recommended, in the same way as described below. Final chromosomescale scaffolding was done using RagTag 2. 1 (35) using the CG14 OMAPv2 as reference sequence. BUSCO v5. 0  (36)  with Viridiplantae database v10 was used for computing the gene space completion, and all basic statistics on contigs and scaffolds were obtained using QUAST 5. 0  (37).
7. 15)  (38). Mapping results were sorted with Picardtools sortSam (version 2. 6. 0). Samtools view (version 1. 3. 1) (39) was used to extract unaligned reads (option -F 2). Unmapped reads assembly and filtering. For each sample, unmapped pair-ended short reads were assembled with Abyss-pe (version 2. 0. 2)  (40). We first optimized k-mer size for assembly in the AA accession (Supplementary notes and Supplementary Table  S1 ), and chose a k-mer size (option -k 64) maximizing N50 and minimizing both contigs number and L50. Contigs shorter than 300 bp were excluded. We screened contigs for vector sequences using Vecscreen (https://www. ncbi. nlm. nih. gov/Tools/Vecscreen/about/) and the NCBI UniVec core database (V. build 10. 0, http://www. ncbi. nlm. nih. gov/VecScreen/UniVec. html). Contigs were aligned with blastn against the NCBI NT nucleotide database (27 October 2019) and the rice organites genomes (mitochondrial and chloroplast).