dataesr/scientific-paragraphs-categorization huggingface dataset:
Such direct juxtaposition allowed identifying settings where model behavior extends from machine learning datasets to brain-imaging datasets. In addition, these analyses allow developing some first intuitions of the settings where extrapolation of effects should not be expected. UKBB imaging was a natural choice for the motivation behind our experiments. This resource is the largest existing biomedical dataset to date. Our data request of the UKBB brain-imaging initiative provided structural and fMRI data for ~10,000 subjects from the same scanning site (see this illustration to get a sense of the scale of the brain-imaging challenges: https://www. youtube. com/watch?v=DbPNscjIC6U, UKBB application number 25163, information on the consent procedure can be found at biobank. ctsu. ox. ac. uk/ crystal/field.
cgi?id=200). We centered our analysis on a complete set of UKBB individuals who simultaneously provided the data from both imaging modalities of brain structure (sMRI) and function (fMRI), which resulted in a total sample size of 9300 data points with brain images. From the UKBB release, we compiled a multifaceted set of brain-imaging data, representing different modalities and different views on a given modality. We derived four working datasets based on the brain images as follows: (a) intrinsic neural activity fluctuations measured by fMRI data, with pre-computed independent component analysis yielding 100 spatiotemporally coherent neural activity patterns, resulting in a feature space of 1485 connectivity strengths between cleaned "network" components estimated using partial correlation analysis.
(b) One hundred and sixty-four atlas-derived features describing structural (T1weighted MRI) MRI gray and white matter regions, as well as fiber tract summary statistics. For both region volume and fiber bundle microstructure estimates, the biologically meaningful features were provided directly by UKBB as image-derived phenotypes  4. (c) Approximately 70,000 raw T1 voxel intensities, after gray matter masking. (d) Finally, axial, sagittal, and coronal T1 slices at the origin with resolutions of 91 × 109, 91 × 91, and 91 × 109 voxels, respectively. Further details on data acquisition, data preprocessing, and IDPs are provided elsewhere  4. For the sake of comparability with the MNIST and Fashion datasets from machine learning, we generated a prediction target of 10 classes from all possible combinations of two sexes and five age quintiles.
That is, the UKBB subjects were divided into five male subgroups with four age cut-offs between 40 and 70 years, and five female subgroups with these four age cut-offs. As our brain-imaging dataset provided a total of 9300 individuals with multi-modal imaging information, at the time of our analysis, we randomly subsampled the much larger MNIST and Fashion datasets to the same sample size in our analyses. In addition, we used the subjectsʼ age at scanning time and the UKBB variables fluid intelligence (field 20016), number of people in household (field 709), and household income (field 738) as prediction targets for regression analyses. Although we have designed brain-imaging classification problems with a similar number of classes, features, and example observations as the MNIST and Fashion datasets, the brain-imaging classification tasks were still expected to be much more complex than the classification problems posed in MNIST and Fashion.
Our main reference datasets, MNIST and Fashion, provide classification tasks. To show that our results and conclusions transfer to the regression setting, we additionally used the "superconductivity" dataset 53 as a regression benchmark dataset (i. e. , with a continuous outcome prediction goal). Here, critical temperature needs to be predicted as a function of 82 physical properties such as thermal conductivity, atomic radius, and atomic mass. Preprocessing procedures. In all analysis scenarios, the input variables were appropriately standardized by scaling variance to one and centering to zero mean across observations. In the special case of convolutional DNN models (cf. below), the images were standardized on the aggregate pixel statistics to keep all pixels on the same scale  54. For sMRI images, we applied an additional dimensionality-reduction step for the purpose of comparability with MNIST and Fashion.
To keep the model complexity comparable between datasets (related to the input variables and thus the number of model parameters to be fitted when building a prediction model), sMRI data were reduced to 784 features, such as MNIST/Fashion, by three dimensionality-reduction methods. Each of these feature screening approaches followed a popular, but distinct approach to perform feature selection/engineering as follows  11, 55  : (a) univariate feature selection (F-tests) selected input variables under the assumption of statistical independence between variables. (b) Recursive feature elimination was used in four steps of logistic regression, each discarding the 25% least predictive input variables of the current active variable set in each step  56. This dimensionality-reduction strategy selects features under joint consideration of the current set of input variables.
(c) Gaussian random projections were used to reexpress the original input variables in a set of latent factors that rely on underlying low-rank structure across the whole set of input variables  57. Linear models. Conceptually, three classes of models-linear, kernel, and deepwere used to comprehensively evaluate the prediction performance on each dataset (i. e. , MNIST, Fashion, and UKBB). Each level of model complexity represents the state of the art of a key epoch in data analysis 9 : regularized linear models to handle large numbers of input variables (~1990-2000), kernel SVMs became go-to methods for many applications in bioinformatics and beyond from late 1990s (~2000-2010), and DNN algorithms in turn became an exponential technology very recently (~2010-2020). Within each of these distinct model classes, we used three commonly employed representative models, attempting to cover the range of approaches within each particular modeling class.
Block 7 included average pooling, 50% training dropout, fully connected layers, and softmax output layers. The unabridged technical details and full implementation of re-using this 3D convolutional neural network for our brain images are open to any reader at https://github. com/maschulz/deeperbrain/tree/master/subanalyses/3d. It is important to note that the purpose of our study was not to benchmark highly specialized DNN architectures from the recent neuroimaging literature. Instead, we decided to rely on established best practices to implement simple, straightforward architectures as representatives for commonly used deep learning approaches. Model selection and model evaluation. To estimate the prediction accuracy that we expect to obtain in new observations sampled from the population, crossvalidation was computed as a gold-standard to obtain out-of-sample accuracies  55.
We have repeatedly split the observations, sometimes called Monte-Carlo crossvalidation, into a training set, as well as a validation set used for model selection (i. e. , hyperparameter choice) and a test set used for model evaluation with 650 observations each in the sample complexity analyses (cf. below). In the UKBB, we considered 9300 subjects with the brain images of interest. This random splitting was repeated 20 times in each modeling scenario. Training, validation, and test set were drawn exactly once per training sample size and per splitting iteration, such that the different models operated on the exact same data splits. To carry out model selection, hyperparameters were handled in a datadependent way by grid search  11  , separately for each dataset, model, training set size, and splitting iteration.
For each model and dataset, we ran separate cross-validated prediction analyses for increasing steps of training set sizes: n = 100, 200, 500, 1000, 2000, 4,000, and 8000. The resulting prediction estimates provided the basis to build a so-called learning curve  11, 69. We will call the resulting relation between the available number of observations and prediction performance the empirical sample complexity of a given model on a particular dataset and a specific prediction goal. Such learning curves of pattern-recognition algorithms typically follow an inverse-power law. The accuracy often increases rapidly in the beginning and then slowly saturates  11. These diagnostic assessments are typically further characterized by a saturation point and a saturation velocity. The saturation point provides a sense of the maximal performance that the prediction algorithm can achieve as the sample size keeps growing infinite to always more observations.
The delineation of a model's scaling of predictive performance is directly tied to the signal-to-noise ratio of the given dataset and the expressive capacity of the given model  11. The saturation velocity indicates how many observations are necessary to approach maximal prediction performance and is further tied to the complexity of the target function to be approximated by the model in the dataset. Different scaling behavior of linear, kernel, and deep models can delineate the extent to which exploitable nonlinear structure is present in the data, and at what sample sizes such nonlinear information becomes practically relevant for realizing better phenotype predictions.
Acknowledgements We thank  Parashkev Nachev ,  Robert Gray ,  Sami Hamdan , and  Cembre Zor  for insightful comments on a previous version of this manuscript. We thank the UKBiobank participants for their voluntary commitment and the  UKBiobank team  for their work in collecting, processing, and disseminating these data for analysis. This research was conducted using the  UKBiobank Resource  under approved project  25163. D. B. was supported by the  Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund) , the  CIFAR Artificial Intelligence Chairs program  ( Canada Institute for Advanced Research) ,  Google  ( Research Award ), and by  NIH  grant  R01AG068563A. D. B. was also funded by the  Deutsche Forschungsgemeinschaft (DFG ,  BZ2/2-1 ,  BZ2/3-1 , and  BZ2/4-1 ;  International Research Training Group   IRTG2150 ),  Amazon   AWS Research Grant  ( 2016  and  2017 ), as well as the  START-Program  of the  Faculty of Medicine  ( 126/ 16 ) and  Exploratory Research Space  ( OPSF449 ),  RWTH Aachen.
Data availability MNIST and Fashion data are freely available at http://yann. lecun. com/exdb/mnist/ and https://github. com/zalandoresearch/fashion-mnist. The brain data used in this work was obtained from UKBiobank under Data Access Application 25163 and (as with all UKBiobank data) are available to any bona fide researcher upon data access application to UKBiobank (http://www. ukbiobank. ac. uk/register-apply/).
In 2012 CEA produced a entire new evaluation of sodium nuclear data for the release of the JEFF-3. 2 evaluated nuclear data library. During the evaluation process performed with the CONRAD code, several differential measurements (total and discrete inelastic cross-sections) have been used. However double differential data (elastic angular distribution) that were yet available in the EXFOR database were not incorporated in the analysis at that time. The experimental elastic angular distribution were discarded because of it was impossible to obtain a good agreement for both angle-integrated cross-sections and double differential ones. The underlying cause of this disagreement is expected to be due to the attribution of quantum numbers to resonance and related channel amplitudes. Indeed these numbers are imposed during the analysis but impact differently angular distributions and angle-integrated cross-sections.
An automated search for an accurate set of quantum numbers has been implemented in order to produce a reliable quantum numbers set. In this paper we present a new evaluation of Na-23 taking into account both differential and double differential measurements. The analysis performed with the CONRAD code reached the level of agreement with experimental data for the total and inelastic cross-sections but this time with a significant improvement for the elastic angular distributions. This new evaluation produced in the ENDF-6 format has then been tested and validated on critical facilities calculation (MASURCA and ZPPR) in different configurations (nominal and voided) in order to assess its performances.
Benchmarking the evaluation Having obtained a new set of resonance parameters, the new evaluated data have been tested on a set of integral benchmarks known to be highly sensitive to 23  data. These are the ZPPR-10A sodium void effect described in the IRPhEP database  [11]  (see Fig. 4 ) and the Racine and Pre-Racine (see Fig. 3 ) sodium void effect measurements performed at the MASURCA facility at CEA/Cadarache  [12, 13]. The C/E results for these benchmarks are shown in Tab. 1, it can be observed that the new evaluation of the sodium resonances improve significantly the global trend of JEFF-3. 2 that overestimates the void effect.
Example: multi-generational genetic connectivity in the striped red mullet across the Mediterranean Sea To illustrate our approach, we re-analysed a published genetic dataset of an economically important fish species with a pelagic larval stage, the striped red mullet Mullus surmuletus, in the Mediterranean Sea  (Dalongeville et al. 2018b ). We hypothesize that building a spatial graph of potential larval dispersal over multiple generations, can improve our understanding of the genetic structure across the Mediterranean Sea and of the temporal scale at which connectivity operates. We build the spatial graph of genetic connectivity to characterize the spatial pattern of genetic structure and to compare it to the graph of multi-generational larval dispersal.
2017 ). To maximise the detection of these various processes influencing population structure, M. surmuletus samples were collected along the entire Mediterranean coastal range including islands (47 sites, Fig. 2a ). Fin clips were taken from specimens obtained at local artisanal fisheries landing sites. DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's protocol, and individuals were genotyped using a Genotyping by Sequencing method  (Elshire et al. 2011). Individual genotypes were then pooled by their sampling locality to maximize sequencing coverage (see  Dalongeville et al. 2018b  for a complete description of sampling, molecular and bioinformatics procedure). This resulted in a dataset containing the allele frequencies of 1153 Single Nucleotide Polymorphism (SNP) loci for 47 pools of nine to 18 individuals, hereafter referred to as populations.
Genetic differentiation among these populations was calculated in the 'ade4' package in R v3. 2. 3 (Dray and Dufour 2007, R Core Team) using Cavalli-Sforza and Edwards' chord distance D c (Cavalli-Sforza and Edwards 1967), which is well-suited to distinguish genetically similar populations in high gene flow species  (Libiger et al. 2009).
Step 1. Single-generation propagule dispersal Larval dispersal was simulated using the biophysical model of  Andrello et al. (2013) , which was adapted to fit the life history parameters of M. surmuletus  (Dalongeville et al. 2018b ). In brief, three-dimensional sea current velocities were obtained from the hydrodynamics model NEMOMED12  (Beuvier et al. 2012 ) and used to simulate passive larval dispersion using the software Ichthyop 3. 1  (Lett et al. 2008). Larvae were released in 1/10th degree cells covering the Mediterranean continental shelf (7703 cells;  Andrello et al. 2015)  every three days during the species' spawning season (1-28 May) and allowed to passively disperse during 30 d. Pairwise probabilities of larval dispersal c(i,j) between cells i and j were calculated from the numbers of simulated larvae released in cell j that arrived in cell i after 30 d.
The 47 populations are connected through single-generation larval dispersal by 108 edges. Marine geographic distances (minimum distances constrained by water) were computed as least-cost path distances between all pairs of populations by assigning infinite resistance to land areas and constant zero resistance to water using the functions 'transition', 'geoCorrection' and 'costDistance' of the R package 'gdistance' v. 1. 1-1 (van Etten 2017). The nearest geographical neighbour was additionally determined for each population.
Network assortativity and clustering were also calculated for the single-generation propagule dispersal graph. The pruned genetic graph was constructed with the 'gengraph' function in the R package 'adegenet', and all subsequent graph-theoretic analyses were conducted with the R package 'igraph' (functions 'degree', 'betweenness', 'assor-tativity_degree' and 'transitivity')  (Csardi and Nepusz 2006, Jombart 2008 ). Random networks were generated using the 'sample_gnm' function. Correlations between genetic distances and marine geographic distances, single-generation larval dispersal probabilities as well as multi-generational larval dispersal distances were estimated using the Mantel coefficient along with 9999 permutations (R package 'vegan',  Oksanen et al. 2016). As the Mantel test is known to have an inflated type I error rate and low statistical power  (Legendre and Fortin 2010, Guillot and Rousset 2013) , we also computed maximum-likelihood population-effects (MLPE) mixed models  (Clarke et al.
2002)  which account for the non-independence of pairwise data and provide the greatest probability of identifying the true model from competing alternatives  (Shirk et al. 2018). We used the 'lmer' function of the R package 'lme4' to fit the MLPE mixed models with a random-effects term accounting for population-level influence  (Bates et al. 2015 , Row et al. 2017). Model fit was evaluated using the coefficient of determination R 2 calculated with the 'MuMIn' package  (Bartoń 2019).
Acknowledgements -We thank  Raquel Torres ,  Laure Velez ,  Jennifer Tournois ,  Eva Maire ,  Sebastien Villeger ,  Johanna Brun-Vitelli ,  Manuel Muntoni ,  Claire Seceh ,  Sofiène Ben Abdelhamid ,  Achraf Barbena ,  Itai Granot ,  Ori Frid  and  Shahar Malamud  for collecting M. surmuletus samples, as well as all the fishermen that provided the fishes. We are grateful to the SMGE platform, and especially  Veronique Arnal  and  Marie-Pierre Dubois  for technical support during molecular analyses. We also thank  Stéphane Lobreaux  for the bioinformatic analyses. We thank the anonymous referees as well as the editor for their insightful comments. Funding -This study was funded by the  'ANR'  through the  RESERVEBENEFIT  project,  PSL Environment  through the project  RESMOD , and EB was partially funded by the  Region Occitanie.
Development of SNP markers A total of ~750 million total unfiltered reads were generated from 309 inbred lines arranged in four GBS plates run twice independently on an Ion Proton Next-Generation Sequencer (ThermoFisher Scientific, Waltham, MA, US). All the raw sequence reads for all the accessions have been submitted to the National Center for Biotechnology Information (NCBI) sequence read archive and deposited under the accession ID "BioProject ID": PRJNA598172. Mapping the GBS reads to the pearl millet reference genome sequence initially detected 150, 977 unfiltered and non-imputed SNPs for the panel. Among these, 123,995 SNPs were distributed on chromosomes 1 to 7. A total of 26,982 SNPs were mostly from the unanchored genome sequences or from the part of the genome not covered in the reference genome sequence.
No cluster made exclusively of inbred lines from the same country was found. In general, most of the lines closely related in pedigree or derived from landraces/improved varieties with high Fe and Zn content were grouped together, except for subgroup 2 which was formed mainly of inbred lines from the accessions collected in WCA. To confirm the ADMIXTURE results, principal component analysis (PCA) using the snpgdsPCA function of the R package SNPRelate  [41]  and genetic relatedness among accessions was visualized using a neighbor joining (NJ) tree which revealed a clear separation between groups. Both the PCA and the NJ tree confirmed the five  subgroups found in the population structure analysis (Fig. 5a  and b ). PCA based on the results of ADMIX-TURE revealed that the total genetic variation explained by the first ten PCs was 11.
Discussion SNPs have become the markers of choice in genetics and evolutionary biology studies, as well as in applications for marker-assisted selection in plant breeding. High-density of markers on a large numbers of individuals is vital for precise quantitative trait locus (QTL) mapping and association analysis  [4]. GBS is a NGS based genotyping platform  [14]  that has the advantage of reduced genome representation to enable highthroughput genome-wide SNP genotyping with an affordable cost. In this study, 309 pearl millet inbred lines were genotyped using GBS. Using the pearl millet genome  [37]  as a reference and filtering the dataset resulted in the development of 54,770 high quality genome-wide SNPs. The level of heterozygosity of the SNPs ranged from of 0 to 20% with an average of 15%.
SNP calling and filtering The sequencing runs generated a total of 750. 2 million reads. The pearl millet reference genome (https://cegresources. icrisat. org/data_public/PearlMillet_Genome/v1. 1 )  [37]  was used to map GBS reads and identify SNPs using TASSEL 5. 0 GBSv2 pipeline, (www. maizegenetics. net)  [6]. The mapping of reads to the pearl millet reference genome was done using BWA version 0. 7. 17-r1188  [23]. An 80 bases poly-A tail was added to the 3′ end of all the sequencing reads to avoid discarding short reads. The alignment detected a total of 150,977 raw SNP data points with a minimum SNP locus coverage of 0. 19 and a minimum minor allele frequency of 0. 0097. Then, the SNPs were filtered to remove those with unknown locations, indels, a minor allele frequency (MAF) of < 5% and a maximum missing data of 20%.
Data analysis Genotype and taxa summaries (markers distribution, MAF, SNP heterozygosity, genetic distance, genetic purity, pairwise comparison and Kinship matrix) were calculated using TASSEL v. 5 software  [6]. Population structure of the SNP genotype datasets was analyzed using ADMIXTURE software  [1]  for K = 1 to 10. An accession was assigned to a subpopulation when the proportion of coefficient of membership to a subpopulation was greater than 60%. To confirm the admixture results, PCA was performed using the snpgdsPCA function of the R package SNPRelate  [41]. LD analysis was performed using TASSEL as a standardized disequilibrium coefficient (D')  [17]  and squared allele-frequency correlations (r 2 )  [40]  among pairs of loci. LD decay was fitted using a nonlinear ls function in R.
Validated auto-staging will be applied based on the ratios of the power spectral densities and auto-detection of cortical and microarousals, sleep spindles and ocular activity  [47, 48]. After data processing, an experimented sleep expert will review the recordings to confirm the accuracy of the auto-sleep staging. The Sleep Profiler-PSG2™ provides total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings lasting more than 30 s, arousal index, and time spent in non-rapid (NREM; Stage-1, Stage-2, and Stage-3) and rapid eye movement (REM) according to American Academy of Sleep Medicine (AASM) recommendations. Circadian phase and morning cortisol Five salivary samples will be collected in the participant's habitual sleep environment in dim light conditions (<20 lux in any direction of gaze) according to the method described by  Mandrell et al.