dataesr/scientific-paragraphs-categorization huggingface dataset:
At the beginning of training, network biases are initialized as zero and weights are initialized with draws from an uniform distribution in the interval [-1, 1] and scaled with the factor 6 ni+ni-1  [41]. The dropout rate is fixed at r d = 0. 05 for all models. Although this is a much lower rate than the one adopted for instance in  [26] , it is found to provide sufficient regularization for the network and dataset sizes treated in this study. For the SGD solver, the default values recommended in  [38]  are used for all hyperparameters. All models are trained for a total of 200 000 epochs and the final model parameters are the ones associated with the lowest historical validation error. The only hyper-parameter to be studied is therefore the width n 1 of the hidden dense layer.
Pure stress states First, reduced models are trained to reproduce the material behavior of a single unidirectional composite layer under isolated stress components, i. e. uniaxial cases in the parameter space. Here the training dataset consists of twelve stress-strain curves, two for each of the n ε = 6 mesoscopic strain components (positive and negative directions). From this point on, strain and stress components are expressed in the local mesoscale coordinate systemi. e. {ε 11 , ε 22 , ε 33 , γ 12 , γ 13 , γ 23 }, where the 1-axis is the fiber direction and the superscript Ω is dropped for compactness. Hyper-reduced models are trained with different values of the inelastic SVD tolerance sv (Eq. (  49 )). The resultant model predictions for the transverse stress σ 22 are shown in Fig. 3.
For high values of svi. e. with a small number of inelastic modes -the plasticity response is not correctly captured, with predictions improving as the tolerance is lowered and more modes are added. Note that the snapshot decomposition of Section 5. 3 effectively guarantees an exact response during the elastic regime. A similar response is observed for the remaining five strain components. Using the surrogate models to reproduce stresses at the same strain values used for training, an average error over the complete dataset comparing the training targets σ with the surrogate responses σ can be defined: E = 1 n ε nε i   1 n t i n t i j |σ i (t j ) -σ i (t j )|   (54) with n t i being the number of load steps comprising the stress-strain curve associated with each strain component i. Errors are computed for different values of sv , with results being shown in Fig.
4. As with Fig. 3 , the error starts at a high value when only elastic modes are used and decreases to values as low as 0. 4 MPa for sv = 0. 01. Fig. 4  also includes the average error of predictions made with the mesoscopic model of Section 3. Since that model explicitly ensures no plasticity occurs in the fiber direction while the actual microscopic response in that direction is slightly nonlinear, the average absolute error over the dataset appears to be high  4  even though all the other directions are very well captured. For this reason, Fig. 4  shows two accuracy levels for the mesomodel, with and without including σ 11. Since controlling the tolerance only influences the number of modes n indirectly, the error tends to decrease in discrete steps. This can also be observed in Fig. 5 , which shows how the number of modes n and integration points m increases as sv is reduced.
Since the reduction in the number of integration points is made possible by the POD reduction, maintaining a low ECM integration error for higher values of n requires a larger set of cubature points. In any case, the reduction remains relatively efficient even for the lowest sv considered here -with compression factors N/n ≈ 1284 and M/m ≈ 65. The same dataset is used to train neural networks with a number of hidden units n 1 ranging from 10 to 1000. In order to track the training process, the evolution of the average absolute error over the validation set (20 % of the complete dataset) is plotted in Fig. 6. The monotonic error decrease observed for all curves suggests that no overfitting to the data is occurring. Increasing the size of the hidden layer improves the obtained predictions but with diminishing returns for n 1 larger than 100.
Indeed, doubling the size of the hidden layer from 500 to 1000 leads to a negligible decrease in the error. The same trend can be observed in Fig. 7 , where online predictions are computed from a one-element model loaded in the 2-direction (transverse direction). Although accurate predictions of the perfect plasticity plateau can be obtained by using sufficiently large networks, both the initial stiffness and the response leading up to the plasticity plateau are still slightly inaccurate even for n 1 = 1000. The important observation to be made here is   that even though neural networks are regarded as universal function approximators, the regularization brought by the dropout layer has the adverse effect of making an exact fit with the training data very difficult to achieve. The average absolute error for the complete dataset obtained with networks of different sizes is plotted in Fig.
8. Although showing a similar trend as Fig. 6 , two important differences between the errors in these two cases should be noted. Firstly, errors in Fig. 8  take into account the whole dataset, while Fig. 6  only shows errors computed for samples in the validation set. Secondly, while errors in Fig. 6  are computed by feeding the network with the exact strain vectors coming from micromodels, Fig. 8  is obtained by using the trained network online in a one-element model that includes numerical noise intrinsic to the Newton-Raphson procedure used to solve it. The presence of numerical noise combined with the fact that data-driven models lack any sort of physical constraint to their behavior can lead to substantial error accumulation as the analysis progresses: wrong stress predictions lead to wrong solutions for the displacements which in turn become wrong strains to be fed to the network.
ε 33 [-] σ 33 [MPa] Micro θ lim = 0°(90°) θ lim = 10°(80°) θ lim = 20°(70°) θ lim = 30°(60°) θ lim = 40°(50°) Plotting the ensemble response together with predictions obtained with the mesomodel of Section 3 in Fig. 16 , it can be seen that both give predictions with roughly the same level of accuracy, with errors of up to 10 MPa. The advantage of the network model over the mesomodel lies in the possibility of retraining. Fig. 16  also shows the prediction of a single network trained with all values of θ used to construct the envelope. Even though this network is now trained on two complete datasets (pure stress states and biaxial transverse tension), the size n 1 = 500 of the network is kept unchanged. Nevertheless, the same level of accuracy shown in Fig. 8  is achieved. Finally, an analogous study is performed with the hyper-reduced model.
Models trained with pure stress cases plus two combined stress casesθ = 45°for σ tt = σ 22 and σ tt = σ 33 -are used to predict the complete stress envelopes for σ 22 -τ 12 and σ 33 -τ 12. The bootstrap strategy is once again employed in order to obtain the average and variance of a combination of 50 different network models. Results are shown in Fig. 21 , with each envelope point corresponding to predictions at a strain level ε 2 tt + γ 2 12 = 0. 04. It is interesting to note that the network ensemble gives more accurate and more confident predictions for the region of the envelope dominated by shear than for the one dominated by transverse stresses. The average response is compared with the one obtained from a single network trained on the complete dataset as well as.
If the spectacular actions of a famous writer such as Feng Jicai did not save the site, they helped to form public opinion. This was the case with the 'Let's restore our Tianjin' movement, created in 2004 by the Tianjin Volunteer Cultural Heritage Conservation Team, created by retirees, civil servants, students, and journalists on the occasion of the First Cultural Heritage Day in 2006, and the Tianjin Memory Network in 2008. They were all very active in alerting the public to the issues, as well as collecting data on the lived history of Tianjin, and engaging in heritage restitution through exhibitions and publications. In 2001, Feng Jicai set up the Institute of Arts and Letters within the University of Tianjin, which includes a museum and a research centre on intangible heritage. Another writer, Hang Ying, created a museum of modern history in Tianjin in 2002.
The role of this famous writer, defender, and promoter of the popular arts undoubtedly has something to do with this perspective. While a folklore museum has existed since 1989, it is noteworthy that an intangible heritage museum has recently opened in Minyuan Plaza. knowledge produced by the many Chinese academic studies, and the no less numerous data collected by Tianjin's heritage associations, has a hard time being disseminated and interpreted. In particular, the approach of academics and intellectuals to protecting and rehabilitating the old centre of Tianjin, including the former concessions and the Chinese city, and their analysis of the fusion of Chinese and Western components have remained marginal, not to mention the modernisation of the city by Chinese elites as in the Hebei New District.
CT-MQC has been employed to simulate nonadiabatic dynamics in model systems  [38, 57, 60, 61, 63] , and implemented in a developer version of the electronic-structure package CPMD  [96] , based on (time-dependent) density functional theory (TDDFT), to simulate the photo-induced dynamics in Oxirane  [34, 56, 62]. We will summarize in Section IV A the approximations that have led to the derivation of the algorithm, and we will present the results for Oxirane in Section IV B. The focus will be mainly put on two phenomena occurring in nonadiabatic events, the amplitude transfer between electronic states and quantum decoherence. The former is mainly induced by the nonadiabatic coupling vectors, thus occurring in regions of configuration space where the adiabatic states are close in energy. The latter  [55,  is usually defined as the decay of the off-diagonal elements of the reduced density matrix of the electronic subsystem, consequence of the coupling with the nuclear environment.
C. CT-MQC: The algorithm Currently, for on-the-fly ab initio calculations, the CT-MQC algorithm is only implemented in a developer version of the CPMD electronic-structure software  [96] , thus it is not yet released. A code for model-systems calculations based on CT-MQC is available as a module of the E-CAM Software library  [125]. A flowchart describing the actions to be performed in the actual implementation of CT-MQC are given in Fig. 11. One of the advantages of the CT-MQC algorithm if compared, for instance, to the surface-hopping algorithm  [12]  is that it is not stochastic. The electronic equation  (44) , for instance, yields the actual populations of the adiabatic states, including decoherence effects. It follows that even a small number of trajectories is able to provide reliable and accurate results.
Missing data were imputed using k-nearest neighbor imputation. Raw signal intensity of all metabolites detected are presented in Supplementary Data 2. RNA quality was assed using the Bioanalyzer instrument (Agilent Technologies) and RNA sequencing libraries were prepared using the Illumina TruSeq Stranded Total RNA with Ribo-Zero Gold protocol (Illumina). Libraries were sequenced on a NextSeq500 instrument (Illumina) with 38-bp paired end. Reads were mapped to ENSEMBL hg38 release 92 using STAR aligner  49  , and transcripts were counted with FeatureCounts 50 using gencode release 27. Sequencing depth ranged from 52. 9 to 10. 4 million with an average of 20. 5 million reads. Reads were filtered using filterbyExpression  51  , with minimum count of 10. Transcriptomic data is deposited under accession number GSE129843.
Acknowledgements This research was supported by a  Novo Nordisk Foundation   Challenge Grant  ( NNF14OC0011493 ) to P. S-C. , J. R. Z. and J. A. H. , a  Novo Nordisk Foundation Basic Metabolic Research Center Grant  ( NNF18CC003490 ) to J. R. Z. , a  Swedish Research Council ,  Distinguished Professor Award  ( 2015-00165 ) to J. R. Z. and an  ACURF grant  ( ACURF 2016000353 ) to  J. A. H. Metabolon Inc. generated the metabolic analysis, and L. S. L. , L. R. I, and A. A. performed the data analysis. We gratefully acknowledge the technical assistance from  Dr. Andrew Garnham , as well as  Ms. Bridget Radford ,  Mr. Marcus Callahan ,  Mr. Samuel Pinto  and  MS Marylee Warburton  ( ACU ) for assistance completing the data collection. Most importantly, we thank the participants for their time and commitment to completing the difficult study protocols.
Material and methods During a scientific and technical internship, samples were collected in autumn 2019 from several caves (Fig. 2 ) from the mountains of the Moroccan High Atlas (Agadir region, Fig. 3a ), including: Ifri N'Telmate, Ifri N'Taghrat and the two distinct areas of Imi Ougoug: Ifri Ouado ("Araignées" cave) and "Chauves-souris" cave (Fig. 3b ). Specimens were collected by hand and preserved in situ in 96% ethanol. Epigynes were dissected and cleaned using lactic acid. Photographs of genitalia were taken under the microscope. For measurements, an ocular micrometer was used; all measurements are in millimetres (mm). Geographical coordinates are presented in the WGS 84 system. For identification we relied on several bibliographical and data sources:  Levy & Amitai (1982) ,  Georgescu (1989) ,  Wunderlich (1992) ,  Weiss & Sárbu (1994) ,  Levy (1998) ,  Van Keer & Bosmans (2010) , Le Péru (2011),  Nentwig et al.
The samples were illuminated in an oblique manner from the top of the probe, with τ = 5 ns laser pulses (λ = 680 nm, fluence ≈ 3 mJ/cm 2 , pulse repetition frequency (PRF) = 100 Hz) by a frequency-doubled Nd:YAG laser (Spitlight DPSS 250, Innolas Laser GmbH, Krailling, Germany). At each laser shot, PA signals were recorded with a capacitive micromachined ultrasonic (CMUT) array (L22-8v, Verasonics, USA: 128 elements, pitch ∼ 100µm, center frequency f c ∼ 15 MHz, bandwidth 80%) connected to multichannel acquisition electronics (High Frequency Vantage 256, Verasonics, USA). For each PA acquisition, the raw data was available as a radio-frequency (RF) frame containing the signals received by each transducer element of the CMUT array measured with a sampling frequency f s = 62. 5 MHz. The total number of acquired RF frames was M 1 = 1,000 (C-shaped capillary), M2 = 1,000 (glass tube, parallel orientation) and M3 = 10,000 (glass tube, perpendicular orientation).
Warm Phosphate-Buffered Saline (PBS) solution was poured into the shell to ensure acoustic contact with the latex membrane. The sample was held by a hammock made of food wrap. The hammock was attached to a bowl containing water maintained at 36 • C using a hot plate. The sample was illuminated with λ = 730nm light via a custom fiber bundle (Ceramoptec, Germany) going through the transducer, resulting in a fluence at the top surface of the sample of approximately 3 mJ/cm 2. M = 768 frames were acquired at 100 Hz and saved to the computer for off-line data processing. The associated average power density, 300 mW/cm 2 , was of the order of the ANSI limit of 230 mW/cm 2. Offline processing was performed exactly as in the 2D phantom experiments, with delay-and-sum beamforming, SVD filtering and statistical computations.
Methods Global geographic distribution of insular endemic mammals. We used occurrences of 873 mammal species, endemic to one (i. e. single-island endemics) or many islands (i. e. multi-island endemics) based on distribution information taken from the IUCN Red List  54. By overlapping mammal with island data from Weigelt et al. 55  , 340 islands from 14 archipelagos (Supplementary Table  2 ) with climatic information (see next section) were considered: each island harbours >5 species (mean ± s. d. : 15 ± 13 species; Supplementary Fig. 9 ). Calculation of vulnerability components. To measure vulnerability, which is a function of three components including exposure, sensitivity, and adaptive capacity (Fig. 1 ), we proposed to adapt trait-based assessment frameworks  11, 14  by combining them with a quantitative framework  23.
For a given island, we characterized the sensitivity of climate change of all its inhabiting endemic mammals based on four species attributes: habitat specialization, dietary specialization, generation length, and ecological redundancy (Table  1  and Supplementary Table  3 ), which we calculated as an average value for each island. First, habitat and dietary specializations were defined as the number of habitats and diets for each endemic mammal species. Habitat information was extracted from the IUCN Habitat Classification Scheme  54  and dietary data from the EltonTraits database  62. Habitat and dietary specialization among mammals has been shown to be an important response factor to climate change, since a more specialized species has a greater probability of negative responses  39. Then generation length is defined as the average age of parents of the current cohort, reflecting the turnover rate of breeding individuals in a population.
Such data were extracted from the database of Pacifici et al. 63. Long generation lengths have been shown to be associated with a heightened extinction risk under climate change  21  , especially among mammals, in which low reproductive rates (linked to long generation lengths) showed a greater probability of negative responses to climate change  7, 39. Last, ecological redundancy was calculated as the number of species that shared similar combinations of ecological trait values  64  : here, main diet, foraging niche, foraging period, habitat niche breadth, and body mass. Habitat information was extracted from the IUCN Habitat Classification Scheme  54  and the other four variables data from the EltonTraits database  62. All trait values were transformed into categorical nature to identify ecological trait combinations and thus grouped species sharing the same trait values into these entities  64.
To estimate protection status, PA spatial information was collected from the World Database on Protected Areas (available at http:// protectedplanet. net/; see Supplementary Methods for more details). Furthermore, phylogenetic distinctiveness was defined as the average evolutionary isolation of endemic mammal species within each island, which is based on the fair proportion index that quantifies the number of relatives of each species and their phylogenetic distance  67. We used an updated phylogeny for mammals  68  that was restricted to the species considered in this study. We analysed species phylogenetic distinctiveness based on the 1000 trees available  68  with randomly resolved polytomies to account for phylogenetic uncertainty. Such an index can be informative about the evolutionary potential of species pools, and so adaptive capacity in the context of climate change  69.
Acknowledgements We are thankful to  Boris Leroy  for his valuable comments on the methodology, as well as to  Victoria Grace  for her corrections of the English. This work was supported by grants from the  ANR  ( 14-CE02-0021-01 ), the  AXA Research Fund Chair for Invasion Biology , and the  Biodiversa Eranet AlienScenarios. More generally, this work has been funded by our salaries as French agents and by the  CNRS  funding for young researcher.
Data availability All data that support the analyses and findings of this study are public and listed hereunder. Current and future global climate data are available at http://www. worldclim. com/version2. Data about island geographic isolation and spatial distribution are available at https://datadryad. org/stash/dataset/doi:10. 5061/dryad. fv94v. Protected area data are available at http://protectedplanet. net/. Species phylogeny is available at https:// megapast2future. github. io/PHYLACINE_1. 2/. Species generation lengths are available at https://datadryad. org/stash/dataset/doi:10. 5061/dryad. gd0m3. Data about species habitat, extinction status, and spatial distribution are available at https://www. iucnredlist. org/. Species diet data are available at http://www. esapubs. org/archive/ecol/E095/178/ metadata.
d. /min-max: 11. 33 ± 8. 35/0. 86-69. 78 [RCP 6. 0]; 13. 89 ± 9. 74/0. 75-67. 74 [RCP Current climate data were averaged from 1970 to 2000 at 30-s resolution (~1 km 2 ) from the Worldclim database 59. Future climate projections for 2041-2060 (hereafter 2050) were downloaded for five general circulation models from the Fifth Assessment Report of the IPCC 60 : CCSM4 (Community Climate System Model, version 4), HadGEM2-ES (Hadley Global Environment Model 2-Earth System), MIROC5 (Model for Interdisciplinary Research on Climate, version 5), MRI-CGCM3 (Meteorological Research Institute Coupled Global Climate Model, version 3), and NorESM1-M (Norwegian Earth System Model 1-medium resolution), using two RCPs: RCP 6. 0 (assumed global average increase of 2. 85 ± 0. 62 °C in mean annual temperatures) and RCP 8.
1 ). This polymorphism has previously been described in three other HLA-DRB1 alleles. This nucleotide change was confirmed using DNA sequencing reagents provided by Protrans (Protrans S4 monoallelic SBT kits, Hockenheim, Germany) run on the ABI 3730xl (Applied Biosystems, Foster City, California). Data were analyzed with SeqPilot software (version 4. 2. 2). Sequencing was performed in both directions (forward and reverse) for exon 2. The nucleotide sequence of the new allele is available at European Nucleotide Archive accession number LR131830 and to the IPD-IMGT/HLA Database (Submission No. HWS10054433). The name DRB1*04:275 has been officially assigned by the World Health Organization (WHO) Nomenclature Committee in April 2019. This follows the agreed policy that, subject to the conditions stated in the most recent Nomenclature Report, 2 names will be assigned to new sequences as they are identified.
Phylogenetic Analyses DNA sequences were aligned by using the MEGA 5 computer program  [18]  for further phylogenetic calculations and calculating the cytochrome b DNA distances and cytochrome b protein distances. MEGA 5 has also been used to compare differences in the codons between C. x. stejnegeri, C. atrata and C. xanthogastra. The final length of the sequences used was 924 nucleotides. Chaffinch, Fringilla coelebs (family Fringillidae, subfamily Fringillinae), was chosen as an outgroup to root the phylogenetic dendrograms. Phylogenetic dendrograms were obtained using the Maximum Likelihood (ML) methodology  [19]  with PAUP* v. 4. 0b10 program [20]  and Bayesian Inference (BI) methodology using Mr. Bayes program  [21, 22]. Model test v. 3. 7 [23]  was used to find out a DNA substitution model that fits the data best.
Results DNA methylation prediction models. Using FHS data, we were able to build DNA methylation prediction models for 223,959 CpG sites, of which 81,432 showed a prediction performance (R 2 ) of at least 0. 01 (≥10% correlation between predicted and measured DNA methylation levels). For 77,243 of those CpG sites, there were no SNPs within the binding site. Interestingly, there tended to be positive weak correlations between methylation prediction model performance and number of input variants within the 2-MB window of each CpG site (Pearson correlation coefficient 0. 03, P = 1. 60 × 10 -13 ; Spearman correlation coefficient 0. 02, P = 1. 43 × 10 -6 ). We further applied these 77,243 models to the genetic data in WHI and evaluated their performance by comparing predicted methylation levels with measured levels.