dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Thank you to  Dr. Adrian Das  and  Dr. Nathan Stephenson  of the  US Geological Survey  for sharing the datasets that were used to parameterize this model. Dr Sam Davis , a former Moran lab postdoc, developed the allometric equations and began the process of updating SORTIE-ND behaviors. Thanks also to  Carmen Tubbesing ,  Dean Wu ,  Dr Mengjun Shu , and  Dr Jeffrey Lauder  for their comments on the manuscript.
Data availability Seedling and adult tree data used to parameterize the model are available on www. sciencebase. gov, as reported in  Moran et al. (2019)  ("Seedling and tree data from Sequoia National Park and Yosemite National Park" https://www. sciencebase. gov/catalog/item/ 5d114632e4b0941bde55077e). Seed data are not publicly available, as they are being used by for ongoing analyses, but are available upon reasonable request from corresponding author. Climate projections were obtained from the California Climate Commons (http://climate. calcommons. org/bcm). Code availability The parameterized model behaviors will be made available on the SORTIE-ND website upon publication of this manuscript http://sortie-nd. org/index. html. New behaviors currently under review by SORTIE-ND team include "ClimateSeedlingHeightGrowth", "ClimateSeedlingHeightSurvival", "ClimateNCIGrowth", and "ClimateNCIMoratality".
(Moran et al. 2019te pine (Pinus monticola Douglas ex D. Don; PIMO), ponderosa pine (Pinus ponderosa Douglas ex Lawson; PIPO), canyon live oak (Quercus chrysolepis; QUCH), and California black oak (Q. kelloggii Newb. ; QUKE). The analyses of adult growth (Aubry-Kientz and Moran 2017) and survival and growth of seedlings(Moran et al. 2019) have been published. Plot characteristics and analyses of fecundity and adult mortality are described in Appendix 2 and Appendix 3. Tree allometry relationships were calculated from FIA data, using the R package MakeMyForests (https://github. com/davisresearch/MakeMyForests). Demographic data come from 26 long-term forest plots maintained by the US Geological Survey (USGS) in Sequoia and Yosemite National Parks (Aubry-Kientz and Moran 2017; Moran et al. 2019).
In particular, supervised learning and reinforcement learning have proved potentially useful for quantum error correction on 2D stabilizer codes  [3]. The supervised approach is represented by Refs. [4] [5] [6] [7] [8] [9] , where a dataset of errors and valid corrections is used to extract what the most likely correction to be performed is. Compared to hard-coded decoding algorithms for stabilizer codes, these machine learning based decoders are more like maximum-likelihood decoders  [10, 11]  than, for instance, the minimum weight perfect matching (MWPM) algorithm that looks for the lowest energy correction  [12]. In the RL based approach, the decoding problem is formulated as a move-based single player game  [13] [14] [15] [16] : the player proposes a local correction, receives back the new state of the code and wins whenever the error-free state of the code is restored correctly.
Each connection is assigned a unique number (a "genome marker"), which enables a crossover procedure where two networks of distinct architectures can be meaningfully combined into a new network. See Appendix A for more details. toric code as a move-based single player game, so that an RL agent (the policy network) can be trained on the decoding task. Last up is a presentation of the results, and a discussion on some advantages and disadvantages of our approach compared to previous literature. Future enhancements of this approach are mentioned at the very end.
III. DECODING ON THE TORIC CODE AS A GAME In a nutshell, the toric code represents two logical qubits in the fourfold degenerate groundstates of a 2D periodic square lattice of physical qubits  [25]. The size of the lattice is referred to as the code distance d [26]. The system is governed by the Hamiltonian H = - plaquette P - star S, (1) where the stabilizers P (S) are products of 4 Pauli Z (X) operators around a plaquette (star), see Figure  2. The groundstate space is spanned by the states for which all plaquette and star operators have eigenvalue +1. If a single bitflip/phaseflip (Pauli X/Z) error occurs on a physical qubit, the two adjacent plaquette/star operators will measure -1 and will show a syndrome (indicated by the (orange) circles in Fig. 2 ). Further errors can move these syndrome endpoints around, forming an error string.
It could be, however, that (much) larger networks are required for further small improvements to the threshold. The NEAT algorithm is not designed to find large networks, though extensions (such as hyperNEAT  [30] ) and other genetic algorithms can optimize large-scale neural networks  [21]. In practice, for this work, we run NEAT separately for different code distances d. We point out that this makes the algorithmic error threshold somewhat ill-defined, in principle, since the decoders for different distances are not necessarily constrained to converge to the same decoding algorithm. The hyperparameters we chose for the mutation rates are reported in Table II in the Appendices. We are able to reach the same performance as previously reported with RL methods  [14] [15] [16]  (we note Ref. [15]  obtains higher error threshold and fidelity on depolarizing noise), though these results are obtained with considerably smaller neural-network decoders.
Indeed, as can be seen in Table  I , our policy neural networks have three to four orders of magnitude fewer parameters than the deep Q-networks used in Q-learning, though it should be noted that Ref. [13]  deals with faulty measurements, which is a considerably harder decoding task. We also remark that we did not investigate the number of required parameters for a policy network that is trained to mimics these Q-networks. Our results were obtained without the use of spatial information that comes with using convolutional neural networks as in Refs. [13] [14] [15] [16]. We remark that accessing larger code distances still becomes increasingly difficult due to slow convergence, and the genome transplantation procedure was crucial in particular for depolarizing noise. Bitflip ∼ 500000 ∼ 1200000  [15]  Depolarizing ∼ 900000 ∼ 9000000  [16]  Bitflip ∼ 640000 ∼ 1700000 ∼ 3200000 Table  I.
Our work shows that very shallow feed-forward networks are expressive enough to decode the toric code on bitflip noise though more depth might be needed to get better performance on depolarizing noise. It would be interesting to see how these performances translate to harder decoding scenarios such as faulttolerant computations. Allowing NEAT to evolve neu-ral networks other than feed-forward could be a possible direction for improvements. Extensions of NEAT include the evolution of convolutional  [31]  or deep  [21]  neural networks. Performance enhancements could also be expected from the use of policy gradient methods  [32] , which may be used to further improve the weights in a network topology that was found using NEAT. Preliminary work using the hyperNEAT algorithm of Ref. [30]  did not prove conclusive, although in principle hyper-NEAT would allow one to discover and exploit the symmetries of the problem in an automatic manner.
ACKNOWLEDGMENTS We acknowledge fruitful discussions about this project with Mats Granath. HT was supported by grants from the Fondation CFM pour la Recherche and from the Erasmus+ program of the European Union. This work also benefited from the support of the French Programme Investissements d'Avenir under the program ANR-11-IDEX-0002-02, reference ANR-10-LABX-0037-NEXT (projet AiQus). HT would like to thank the Niels Bohr Institute for hospitality during his stay. This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 847523 'INTERACTIONS', and the Marie Sklodowksa-Curie grant agreement No. 895439 'ConQuER'. The numerical calculations were performed on the local cluster of the Appendix A: Extra NEAT info This appendix is aimed at adding extra details to the NEAT algorithm description in the main text.
Data. Patients in COVIDOM filled out questionnaires, specifying characteristics (age, sex, weight, height), comorbidities (diabetes, hypertension, chronic obstructive pulmonary disease, asthma, heart failure, coronary heart disease, cancer under treatment, chronic kidney disease, other chronic disease), smoking status, symptoms since the beginning of the suspected Covid-19 disease (fatigue, myalgia, breathlessness, ageusia, anosmia, anorexia, chest pain, chest oppression, cough, fever, diarrhea, vomiting, shivers, rash, frostbites, conjunctivitis, other symptoms), hospitalisation history, investigations that were performed (RT-PCR, chest CT-scan, chest X-ray), and RT-PCR results. The questionnaire is available in Fig. S1. For each patient, we collected the information in a single copy. These patient-reported data are completed with RT-PCR results from the French Assistance Publique-Hôpitaux de Paris (AP-HP) data warehouse, also known as Entrepôt de Données de Santé (EDS).
Finally, in each group defined by the decision tree, we report the odds ratio of being RT-PCR+, and report RT-PCR+ proportion. Odds and proportions are weighted by propensity scores. We simplify the decision tree to propose actionable rules to prioritize RT-PCR access. We use the Python packages scikit-learn and stats models to perform statistical analyses  9, 10. The code for reproduction and reuse is available at the address http:// github. com/ arthu rmens ch/ covid om_ analy sis. Ethical approval. This study received the ethical approval of the ethical committee of AP-HP (IRB00011591).
We evaluate the decision tree on 20% held-out patients and illustrate how it splits this population. Each node is a splitting criterion (presence of the symptoms to the top, absence to the bottom). The colour of each node corresponds to the odds ratio of being RT-PCR+ at this stage of the decision path. For each leaf, the probability and odds ratio of being RT-PCR+ are reported (see Figs. S6, S8 and Table  S1  for details). (b) Permutation features importance on the evaluation set. The permutation importance is an indicator of the relevance of a feature at predicting RT-PCR positivity. It measures the decrease in the model score (here, average precision) when a single feature is randomly shuffled. We report the permutation importance on the left-out evaluation data (20% of the dataset) for each feature of the decision tree.
Most parameters analyzed were harbored from self-reported questionnaires, with an inevitable rate of mistakes. In particular, performance of the test partially relies on the patient's recollection, whereas RT-PCR results are verified by a medical database. In addition, our study falls short of considering that symptoms appear progressively. We analyze symptoms present during the course of the disease; those may not be present during the first medical evaluation. However, anosmia/ageusia are early signs, developing on average 4. 4 days after infection  16. They are likely to have developed when the patient seeks medical care (median 5 days, IQ2-8, after the beginning of the symptoms in the COVIDOM cohort). Less importantly, we only analyzed the symptoms provided by the forms submitted to the patients.
The colour of each node corresponds to the odds ratio of being RT-PCR+ at this stage of the decision path. For each leaf, the probability and odds ratio of being RT-PCR+ are reported (see Figs. S6, S8 and TableS1for details). (b) Permutation features importance on the evaluation set. The permutation importance is an indicator of the relevance of a feature at predicting RT-PCR positivity. It measures the decrease in the model score (here, average precision) when a single feature is randomly shuffled. We report the permutation importance on the left-out evaluation data (20% of the dataset) for each feature of the decision tree. Error bars are the standard deviations of the importance through 50 different permutations. (c) Performance of the decision tree on the test set. Precision-recall curve of the decision tree on the 20% held-out set of tested patients.
Acknowledgements Data used in preparation of this article were obtained from the  AP-HP Covid CDW Initiative (ACCI) database. A complete listing of the ACCI members can be found at: (https:// eds. aphp. fr/ covid-19). We thank  FALZON Alexandre ,  FAYOLLE Guillaume, LAPORTE Fanny ,  Amélie TORTEL  and all the Nouveal-e Santé team for their help in the web application and regional center surveillance interface development. We also thank  DEBASTARD Laurent ,  GRENIER Alexandre ,  HODY Julien ,  PENN Thomas  and the  Paris region URPS (Union régionale des professionnels de santé)  for their help in the development and spreading of the Covidom solution. We thank the  Polytechnique network  for helping with the volunteers recruitment.
(R)USLE-Based Actual Rainfall Erosivity Data Annual (R)USLE-based erosivity data were obtained for 1964-1990. This calibration period covers two climatic regimes, with a temperature decrease and weak winter westerlies until about 1976, and a strong temperature increase and winter westerlies after about 1976  (Mariani and Diodato, 2014). The actual erosivity data were obtained from  Bazzoffi and Pellegrini (1990)  at Vicarello di Volterra and  Cevasco et al. (2015)  at Levanto, and derived from  Borselli et al. (2004)  and  Angeli et al. (2007)  at Florence based on a power relationship between the R-factor and the monthly rainfall data (source: SCIA-National System for Climate Data Collection and Dissemination, http:// www. scia. isprambiente. it). With about 2300 (Levanto), 1718 (Florence) and 731 (Vicarello di Volterra) MJ mm hm -2 h -1 yr -1 on average, the three sites (Figure  1C , red circles) are representative of situations occurring in three sectors of the ARB.
Outside the basin, Levanto (station S 1 ) also maintains an upwind position with respect to the northern Apennine slopes, but thunderstorms are forced to move east and northeast, thus passing only over some areas of the river basin. Finally, Florence (station S 2 ) contributes the least as it is located far from the sea and is not much affected, on average, from the heaviest rainfall that discharges its energy before reaching the easternmost part of the basin. We used Eq. 2 to reconstruct a time-series of annual storm erosivity (MJ mm hm -2 h -1 yr -1 ) in the ARB from 1000 to 2019 CE. We also obtained erosivity density-Eq. 3-using the rainfall (mm) data provided for the period 1891-2016 (∼900 mm on average) by the GPCC (Global Precipitation Climatology Center) by 0. 25 °monthly land-surface precipitation dataset  Schneider et al.
Numerical and Categorical Inputs In addition to extensive research in the main Italian historical archives and libraries, literary sources were consulted by web search (https://books. google. com), which generated ∼100,000 bibliographical documents. We collected a massive bibliographic dataset but only ∼500 records have met the criteria of including the keywords abundant rainfall, storm, downpour, diluvial, flood and alluvial (piogge abbondanti, tempesta, nubifragio, diluvio, inondazione, alluvione), as well as some Latin locutions (e. g. , magnae pluviae, aqua maxima, diluvium, excrescentia fluminum, inundatio) that were chosen for careful reading. Useful data were extracted from documentary sources by converting the information contained in historical accounts  (Morozzi, 1766; Aiazzi, 1845; Giorgini, 1854; Rossini, 1855; Becchi and Paris, 1989; Salvestrini, 2005; Menduni, 2017; Pelucani et al.
The study was based on the systematic and critical analysis of data on the above phenomena provided by Italian documentary sources. For most of the information, it was possible to carry out an "event check" considering more than one documentary source on the same event. It was also possible to contextualize the storms with other types of historical events (e. g. , social, agricultural, religious). In this way, the reliability of information was assessed going beyond quantitative data and looking for other sources of information such as diaries, newspapers, chronicles and local stories.
Completeness of the Reconstructed Extreme Hydrological Events We have extracted 369 extreme hydrological events, occurring in the ARB from 1000 to 2019 CE. The classification of these events by severity led to 213 stormy events, 98 very stormy events, 41 great stormy events, and 17 extraordinary stormy events. However, our historical hydrological database is based on several types of heterogeneous sources, including reports that may be an exaggeration of what was an uncritical reference to previous sources, printing errors in the documentation, and natural records of environmental factors ("proxy" data). All of them contain different sources of uncertainty in the "cataloguing" of storm records  (Pavese et al. , 1994). It is well ascertained, for instance, that small storms tend to be underestimated, especially those far from inhabited centers, while more localized ones can be frequent.
In order to resolve some of these uncertainties in our database, we have established a reasonable standard for Monthly Storm-Severity Index (MSSI) recorded events. This was facilitated by defining a partition of the time-series in three equally-long sub-periods following  Diodato et al. (2019b) ,-1000-1340 (Figure  5A ), 1341-1680 (Figure  5B ), and 1681-2019 (Figure  5C )-and verifying, for each sub-period and for the entire dataset (Figure  5D ), the scale-invariance in the relationship between the number of events larger than the strength of a given storm event and the same strength event. The completeness analysis was formalized with the relationship between the cumulative number of events (CEN) and the MSSI values in the range 1 ≤ MSSI ≤4, as follows: log 10 CEN i,j a + b • MSSI i,j with i 1,.
, 4 and j 1,. , 4 (4) where MSSI is the Monthly Storm-Severity Index by severity class (i) and sub-period (j). In total, 369 events within the range 1 ≤ MSSI ≤4 are described in qualitative terms as stormy, very stormy, great stormy and extraordinary stormy. The negative slopes in the three sub-periods, and for the entire dataset, reflect the principle of a progression toward a lower frequency as storms become more severe. With determination coefficients 0. 97 ≤ R 2 ≤ 1. 00, it can be assumed that storms in the period 1000-2019 CE are scale-invariant and that ancient sources from different periods are not too fragmentarily available to facilitate a quantitative reconstruction  (Mazzarella and Diodato, 2002).
DATA AVAILABILITY STATEMENT All data used in this study are freely available. Spatial patterns of mean annual rainfall erosivity over the European region (Figure  1C ) are freely available from the ESDAC (European Soil Data Centre) dataset at https://esdac. jrc. ec. europa. eu/ content/global-rainfall-erosivity. The AMV (Atlantic Multidecadal Variation) reconstruction (Figure  7D ) was extracted from Climate Explorer of the Dutch Royal Netherlands Meteorological Institute (KNMI) at http:// climexp. knmi. nl. The proxy-based NAO dataset (Figure  7E ) is available at https://doi. pangaea. de/10. 1594/ PANGAEA. 921916. Areal mean of annual precipitation for the Arno River Basin was retrieved from the Global Precipitation Climatology Centre (GPCC) through http:// climexp. knmi. nl.
, 2017). Resolving phylogenomic relationships and disentangling the contribution of different genomic processes through time typically requires large-scale genomic datasets and thorough taxon sampling efforts (E. M. Lemmon & Lemmon, 2013; Linder, 2008; Straub et al. , 2012). Here, we present Bromeliad1776, a new bait set for targeted sequencing, designed to address a wide range of evolutionary hypotheses in Bromeliaceae: from producing robust phylogenies to studying the interplay of genomic processes during speciation and the genetic basis of trait shifts, such as photosynthetic and pollination syndrome. This highly diverse Neotropical radiation provides an excellent research system for studying the drivers and constraints of rapid adaptive radiation  (Benzing, 2000; Givnish et al. , 2011; Loiseau et al.
Leroy, Carrias, Céréghino, & Corbara, 2016). Bromeliads present a diversity of repeatedly evolving adaptive traits, which allowed them to occupy versatile habitats and ecological niches  (Benzing, 2000). CAM photosynthesis, water-absorbing trichomes, formation of tank habit, extensive rates of epiphytism and a diversity of pollination syndromes are some of the adaptations correlated with high rates of diversification within the family  (Benzing, 2000; Crayn, Winter, & Smith, 2004; Givnish et al. , 2014; Kessler, Abrahamczyk, & Krömer, 2020; Quezada & Gianoli, 2011). To assess the utility of the Bromeliad1776 kit, we performed a comparison between our taxonspecific kit and the universal Angiosperms353 kit using several methods across different evolutionary time-scales. We present Bromeliad1776 in the light of methodological considerations on bait design, data handling, analyses and other practical considerations.
Custom bait design Whole-genome sequences and gene models from Ananas comosus v. 3  (Ming et al. , 2015)  were used to design a bait set aiming to target i) single-copy protein coding genes distributed across the whole genome, ii) genes previously described as associated with key innovation traits in Bromeliaceae (see below), iii) markers previously used for phylogenomic inference in Bromeliaceae and iv) genes orthologous to those in the Angiosperms353 bait set. The 1776 selected genes are detailed in Supporting information Table  S1. Genes in subset i were selected based on genetic diversity parameters calculated using wholegenome sequence and RNAseq data previously published by  de La Harpe et al. , (2020;  data publicly available online at SRA Bioproject PRJNA649109) with the PopGenome R package v.
In addition, targets including exons smaller than 80 bp were completed with regions flanking the exons according to the A. comosus reference genome. The final kit included 1776 genes: 801 genes in subset i, 681 genes associated with key innovative traits, 13 genes representing phylogenetic markers and 281 genes orthologous to the Angiosperms353 set. Probes were designed with 57,445 80-mer baits tiling across targets in 2x coverage, targeting approximately 2. 3Mbp. The kit is subsequently referred to as the Bromeliad1776 bait set. Further specifications can be found in Supporting information Tables  S1  and S2  and in the github repository: https://github. com/giyany/Bromeliad1776/tree/ main/MS_2021_scripts.
Data processing The raw sequence data in BAM format was demultiplexed using deML v. 1. 1. 3 (Renaud, Stenzel, Maricic, Wiebe, & Kelso, 2015)  and samtools view v. 1. 7  (Li et al. , 2009) , converted to fastq using bamtools v. 2. 4. 0  (Barnett, Garrison, Quinlan, Strömberg, & Marth, 2011 ) and quality checked using FastQC v. 0. 11. 7  (Andrews, 2010). Reads were then trimmed for adapter content and quality using TrimGalore v. 0. 6. 5  (Krueger, 2019) , a wrapper tool around FastQC and Cutadapt, using settings -fastqc -retain unpaired. Sequence quality and adapter removal was confirmed with FastQC reports. Quality and adapter-trimmed reads were aligned to A. comosus reference genome v. 3  (Ming et al. , 2015)  using bowtie2  (Langmead & Salzberg, 2012)  with the -very-sensitive-local option to increase sensitivity and accuracy.