dataesr/scientific-paragraphs-categorization huggingface dataset:
IV. ADAPTIVE GEOMETRY-DRIVEN NEAREST NEIGHBOR SEARCH In this section, we present the Adaptive Geometry-driven Nearest Neighbor search (AGNN) strategy for selecting the nearest neighbors of each test data point within the training data points with respect to an intrinsic manifold structure. Our subset selection method builds on the RGC algorithm  [18] , which targets the clustering of data with respect to the underlying manifold. The RGC method seeks a globally consistent affinity matrix that is the same as its diffused version with respect to the underlying graph topology. However, the RGC method focuses only on the initially available training samples and does not provide a means of handling initially unavailable test samples. We thus present an out-of-sample generalization of RGC and propose a strategy to compute and diffuse the affinities between the test sample and all training samples in a way that is consistent with the data manifold.
V. GEOMETRY-DRIVEN OVERLAPPING CLUSTERS As we will see in Section VI, the AGNN method presented in Section IV is efficient in terms of image reconstruction performance. However, it may have a high computational complexity and considerable memory requirements in settings with a large training set D, as the size of the affinity matrix grows quadratically with the number of training samples and the subset selection is adaptive (repeated for each test sample). For this reason, we propose in this section the Geometrydriven Overlapping Clusters (GOC) method, which provides a computationally less complex solution for obtaining the nearest neighbors of test samples. The GOC algorithm computes a collection {S k } C k=1 of subsets S k ⊂ D of the training data set, which are to be used in local basis computation.
In Sections VI-B1 and VI-B2, the results for the algorithms in comparison are obtained by using the software packages made publicly available by the corresponding authors  1. 1) Performance Evaluation of AGNN and GOC: We compare the proposed AGNN and GOC methods with 4 different clustering algorithms; namely, the K-means algorithm (Kmeans), Fuzzy C-means clustering algorithm (FCM)  [32] , Spectral Clustering (SC)  [20] , Replicator Graph Clustering (RGC)  [18] ; and also with K-NN search using geodesic distance (GeoD). Among the clustering methods, Kmeans and FCM employ the Euclidean distance as a dissimilarity measure, while SC and RGC are graph-based methods that consider the manifold structure of data. When testing these four methods, we cluster the training patches and compute a PCA basis for each cluster.
The methods are ordered according to the average PSNR values (from the lowest to the highest). image, the clusters used in learning a basis for reconstructing the plain regions on the cap contain also patches extracted from the wall, which have a similar intensity with the cap. This reproduces the shadowy patterns of the wall also on the cap. On the other hand, the AGNN method groups together patches that have a connection on the data graph. As the patches are extracted with overlapping windows shifting by one pixel, AGNN and GOC may have a stronger tendency than K-means for favoring patches from nearby or similar regions on the image that all share a common structure, which is also confirmed by the experiment in Section VI-A. The proposed methods yield local bases better fitted to the characteristics of patches, therefore, less artifacts are observed.
Images In Table  I  the performance of the compared clustering methods are measured with the PSNR and the SSIM metrics. Graph-based methods are generally seen to yield a better performance than methods based on Euclidean distance. This confirms the intuition that motivates our study; when selecting neighborhoods for learning local models, the geometry of the data should be respected. As far as the average performance is concerned, the AGNN method gives the highest reconstruction quality and is followed by the GOC method. The performance difference between AGNN and GOC can be justified with the fact that the training subset selection is adaptive to the test patches in AGNN, while GOC is a nonadaptive method that offers a less complex solution. In particular, with a nonoptimized implementation of our algorithms, we have observed that GOC has roughly the same computation time as K-means, while the computation time of AGNN is around three times K-means and GOC in the tested images on an Intel Core i5 2.
6GHz under the Matlab R2015a programming environment, as shown in Table  III. K-means and GOC in the tested images. After the proposed AGNN and GOC methods, GeoD gives the best average performance. While this adaptive method ensures a good reconstruction quality, it requires the computation of the geodesic distance between each test patch and all training patches. Therefore, it is computationally very complex. Although several works such as  [27]  and  [28]  provide solutions for fast approximations of the geodesic distance, we observe that in terms of reconstruction quality AGNN performs better than GeoD in most images. This suggests that using a globally consistent affinity measure optimized with respect to the entire graph topology provides a more refined and precise similarity metric than the geodesic distance, which only takes into account the shortest paths between samples.
e. , allopatric divergence followed by secondary contacts) hence no general conclusion about the ubiquity of either mechanism during speciation could be drawn yet. Recent advances in coalescent theory  (Wakeley, 2008)  and Bayesian methods  (Tavare et al. , 1997; Beaumont, Zhang & Balding, 2002; Beaumont, 2010; Csilléry et al. , 2010)  now allow for explicit tests of alternative and complex models of divergence. In particular, approximate Bayesian computation (ABC) bypasses the need to compute full likelihoods, as this is not possible or computationally too intensive for complex models with many parameters and large datasets  (Beaumont, Zhang & Balding, 2002). ABC has been used with success to test alternative models of divergence in various taxa and provided useful information on the level of interspecific introgression and complexity of demographic history underlying population divergence  (Fagundes et al.
Summary statistics Given the lack of genetic differentiation between samples collected in different years in the same river, they were merged together  (Rougemont et al. , 2015). Similarly, we pooled brook lamprey individuals sampled in upstream and downstream areas on the Aa and Hem river as these two groups were not significantly differentiated (Table  S1 ). We also pooled river lamprey individuals from the Aa and Hem rivers given the lack of differentiation between these populations  (Rougemont et al. , 2015). Summary statistics were then computed for each pooled sample. As summary statistics used for comparison between simulated and observed datasets, we computed the average and standard deviation values of: the number of alleles (A), Allelic richness (Ar), observed and expected heterozygosity (Ho and He), allele size in base pairs, the Garza-Williamson index (GW,  Garza & Williamson, 2001) , G ST (Nei1, 973) and δµ 2  (Goldstein et al.
, 1995). The GW index (M ratio) aims at assessing reductions in effective population size and is calculated as M = A R+1 with   2  and F st values are given for each population pair. A is the number of alleles for a given loci in each population and R is the allelic range. Following  (Excoffier, Estoup & Cornuet, 2005)  we add 1 to the denominator to avoided dividing by zero in case of a monomorphic sample. Goldstein et al. δµ 2 is a measure of genetic distance developed for microsatellite data defined as δµ2 = (µ 1 -µ 2 ) 2 where µ 1 and µ 2 represents the average number of allelic size differences within populations 1 and populations 2 respectively. Each statistics was computed within populations as well as globally except for the Gst and δµ 2 which are pairwise statistics. All statistics were computed using R scripts (R Development Core  Team, 2011)  available on github at https://github.
The IM model assumes continuous gene-flow between daughter populations after the initial split at constant rate over generations. The AM model assumes gene-flow between the two diverging populations during the first generations following the split of the ancestral population. The SC model describes the split of an ancestral population in two isolated daughter populations, the two evolving lineages then experience gene flow through a secondary contact starting T SC generations ago. For IM, AM and SC models, the number of migrants was scaled by M = 4. N 0. m, with M 1 the number of migrants from L. fluviatilis to L. planeri and M 2 the number of migrants from L. planeri to L. fluviatilis and m the fraction of the population made of migrants from the other population at each generation. Coalescent simulations were performed using the ms software  (Hudson, 2002)  assuming an infinite-site model of mutation, in which most parameters are scaled by the effective population size of an arbitrarily chosen reference population (N ref ) with impact on conclusions drawn by the ABC analysis.
ABC approach We evaluated the posterior probabilities of each demographic model using an ABC framework implemented in the abc package in R  (Csilléry, François & Blum, 2012). We compared all models at a time by computing posterior probabilities using a feed forward neural network based on a nonlinear conditional heteroscedastic regression in which the model is considered as an additional parameter to be inferred. This procedure allows taking into account correlations of summary statistics and distortion hence reducing the problem of curse of dimensionality  (Blum & François, 2010). In the rejection step, we retained the 0. 02% simulations closest to the observed summary statistics, which were subsequently weighted by an Epanechnikov kernel that peaks when S obs = S sim. The regression step was performed using 50 neural networks and 15 hidden layers.
ABC cross-validation We performed model checking to compute the classification error rate of the inferred model using pseudo-observed simulated datasets (PODS). We randomly selected 1,000 PODS from one million of simulations computed under each simulated model. We used the same ABC selection procedure as above to estimate the probability of the PODS. Knowing the true model we then computed the type I error rate that corresponds to the risk of excluding the previously inferred scenario when it is the true and the type II error rate that corresponds to the risk of selecting the previously inferred scenario when it is false following  Cornuet, Ravigné & Estoup (2010). The most important here was the type II error that, with regards to the selected dataset, corresponds to the risks of erroneously selecting the focal scenario.
We first constructed 6 random forests (one by river) using the randomForestSRC package in R  (Ishwaran et al. , 2008; Ishwaran & Kogalur, 2015)  allowing for parallelization and fast computations. We grew 1,000 trees on subsets of 50,000 simulated datasets (5%) that were used as a training set. Prior analysis using different numbers of trees and training set sizes indicated that the OOB errors reached stationarity using between 500-1,000 trees (see also Fig. 3 ), so we did not grow bigger forests that would have required extensive computations. All summary statistics were included to get an estimation of the importance of each variable. This allowed us to estimate the OOB error rate for each comparison, which is similar to a prior error rate in ABC inference  (Pudlo et al. , 2014). Ultimately our forest was used as a prediction tool to compute the probability that our observed data belongs to one of the 5 alternatives models.
Parameter estimation and cross-validation Parameter estimation was performed for the best models using nonlinear regressions. We first used a logit transformation of the parameters on the 2,000 best replicate simulations providing the smallest Euclidian distance δ  (Csilléry, François & Blum, 2012). We then jointly estimated parameters' posterior probability using the neural network procedure implemented in the abc package. We obtained the best model by weighted nonlinear regressions of the parameters on the summary statistics using 50 feed-forward neural networks and 15 hidden layers. We performed posterior predictive checks for crossvalidation in an attempt to check the ability of our parameter estimates to generate data summary statistics close to the observed summary statistics. For each best model, we selected 10,000 samples drawn from the posterior distribution obtained after parameter estimation (from the abc package) and simulated 10,000 new datasets by using again ms and custom R scripts.
Posterior predictive checks We performed posterior predictive checks in order to assess the ability of the models to accurately reproduce summary statistics close to our observed statistics based on 10,000 simulated datasets drawn from the posterior distribution and computed the robustness of our inference. Under both the IM and SC models we consistently found some statistics that differed significantly from our observed data, indicating that the best proposed models could not reproduce exactly the observed datasets. Genomic variances in heterozygosity and in allelic number are respectively underestimated and overestimated by the best estimated models. In some cases the mean Garza-Williamson index was not accurately reproduced and underestimated (Table  S5 ). Similarly, in three cases, the variance in F ST did not yield accurate results (Table  S5  and Fig.
Methods Molecular Dynamics Simulations. Four different systems were simulated: full-length complex, PPARγ -RXRα LBD heterodimer, PPARγ -LBD as a monomer and apo-HRE. The initial coordinates were taken from crystallographic structures deposited in the Protein Data Bank with the corresponding entries: 3ZDY (fulllength complex)  44  and 1FM6 (heterodimer and monomer)  32. The initial structure of apo-HRE was also taken from PDB 3ZDY after removal of the non-nucleic parts. The Ω -loops (residues 242 to 265 in PPARγ and 260 to 275 in RXRα ) -which were missing in the full-length structure -were taken from 1FM6 after an alignment of the structures had been performed with LovoAlign  62. Hydrogen atoms were added and protonation states of ionizable groups were estimated using the server H+ + 63 at pH 7.
Complete simulation boxes were built with VMD  64. Structures were solvated with TIP3P water molecules 65 within rectangular boxes such that the solute molecules (protein, DNA and ligands) were at least 20 Å from the box boundaries. Sodium and chloride ions were added in order to make the systems electrically neutral at physiological concentration (0. 154 mol/L). The ions were placed at favorable positions as determined by electrostatic potential calculations with MeadIonize VMD plugin. Complete systems consisted of approximately 160,000 atoms (full-length complex), 85,000 atoms (LBD heterodimer), 60,000 atoms (PPARγ -LBD monomer) and 40,000 (apo-HRE). Simulations were performed with NAMD by applying periodic boundary conditions and an integration time-step of 2. 0 fs. All bonds involving non-polar hydrogens were constrained at their equilibrium length using the SETTLE algorithm, as implemented in the NAMD code  66.
Acknowledgements This work was supported by the  São Paulo Research Foundation FAPESP  (Grant # 2013/08293-7 ). We would like to thank  Prof. Richard Lavery  for kindly adapting the  Curves + program  to handle PDB trajectories. VSB acknowledges supercomputer time from the  National Energy Research Scientific Computing Center (NERSC)  and support from the  National Institute of Health (NIH)  grant  1R01GM10621-01A1.
Methods Participants Patients were recruited at the Nice Research Memory Center & CoBTeK research unit (CMRR), located at the Institut Claude Pompidou. Thirty participants with MCI and 30 participants with dementia volunteered to take part in the study. Three participants (two participants with MCI and one with dementia) were excluded from the data analysis because they did not meet all the inclusion criteria (two participants because the MMSE score was not in the correct range, one because she presented major perceptual impairments; see below), leaving the final sample to 28 participants with MCI (13 female and 15 male; mean age = 75. 0 years; SD = 6. 8; age range = 62-89) and 29 patients with dementia (12 female, 17 male; mean age = 76. 3 years; SD = 7. 2; age range = 65-90). Patients with dementia included 15 patients with AD, 10 patients with mixed dementia, one patient with vascular dementia, two patients with primary progressive aphasia (PPA) and one patient with organic brain syndrome (OBS) according to the International Classification of Diseases 10 th revision (ICD 10).
Statistical analysis Statistical analysis was computed using SPSS 20. 0. In order to verify the acceptability of the intervention, we computed: a) the mean scores for the self-report questionnaires, separately for the VR and the paper condition; b) the number and percentage of participants who reported to have preferred each of the two conditions, and, at the exploratory level, c) the number of participants who continued playing after the experiment, along with the additional time played for each condition. Task performance in each condition was assessed by means of the number of targets correctly found in five minutes, and by the number of errors (non-target stimuli incorrectly selected). Group comparisons were performed with repeated-measures ANOVAs with Condition (VR vs. paper) as within-subject factor and Diagnosis (MCI vs.
Cognitive and behavioral assessment Demographic, cognitive and behavioral characteristics of the patients are presented in Table  1  (see also S1 Dataset). Compared to MCI participants, participants with dementia had significantly lower MMSE scores (F (1,55) = 47. 51, p <. 001, partial η 2 =. 46) and significantly higher CDR-SOB scores (t (1,53) = 24. 60, p <. 001, partial η 2 =. 32), confirming the presence of a significant level of cognitive impairment, and a significant impairment in the activities of daily living. Also, participants with dementia had a higher Apathy Inventory compared to MCI participants (t (1,55) = 34. 64, p <. 001, partial η 2 =. 39), and, compared to MCI participants, a higher proportion of participants met the diagnostic criteria for the presence of apathy (χ 2 = 20.
Intervention acceptability Self-report questionnaire. Results of the self-report questionnaires for the VR and the paper conditions are presented in Fig  3  (see also S1 Dataset). For both the VR and the paper conditions, participants reported to be highly satisfied concerning the experience (VR, M = 8. 6/10, SD = 1. 7; paper: M = 8. 2/10, SD = 2. 3) and highly interested (VR, M = 8. 0/10, SD = 2. 3; paper: M = 7. 9/10, SD = 2. 2). They reported high feelings of security in both conditions (VR, M = 9. 4/10, SD = 1. 3; paper: M = 9. 7/10, SD = 1. 1). Furthermore, participants reported low levels of discomfort (VR, M = 1. 4/10, SD = 2. 5; paper: M = 1. 0/ 10, SD = 1. 9), anxiety (VR, M = 1. 7/10, SD = 2. 9; paper: M = 1. 7/10, SD = 3. 2), and fatigue (VR, M =. 9/10, SD = 2. 0; paper: M =. 7/10, SD = 1.
and S1 Dataset). A repeated-measures ANOVA with Condition (VR vs. paper) as within-subject factor and Diagnosis (dementia vs. MCI) as between subject factor revealed a main effect of Condition (F (1,55) = 36. 67, p <. 001, partial η 2 =. 40) on the number of targets found, with participants (as a group) identifying significantly more targets in the paper condition (M = 28. 5, SD = 10. 9) compared to the VR condition (M = 20. 6, SD = 10. 6). In our view, this was mostly due to the low familiarity of many participants with the experimental setup, specifically the mouse, which resulted in a slow target selection. Furthermore a main effect of Diagnosis was found (F (1,55) = 7. 58, p =. 008, partial η 2 =. 12), with MCI participants finding significantly more targets compared to participants with dementia.
This study was supported by a grant from the  FP7 European Commission Seventh Framework Programme VERVE  project (Vanquishing fear and apathy through e-inclusion: http://www. verveconsortium. com/),by the  Innovation Alzheimer Association , and by the  Lions district Provence-Alpes Côte  d'azur. The study has been realized in the ecological room of the Edmond et  Lily Safra center at the Institut Claude Pompidou. Disney Research Los Angeles  provided support in the form of salaries for authors [JO], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
Acknowledgments Funding for this work was provided by the  UK Ministry of Defence  under contract  DSTLX-1000097390. In addition, R. E. would like to acknowledge an  Engineering and Physical Sciences Research Council (UK)   First Grant  ( EP/K033689/1 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Introduction A ReadMe file is a simple and short written document that is commonly distributed along with a computer software, forming part of its documentation. It is generally written by the developer and is supposed to contain basic and crucial information that the user reads before installing and running the software. Existing NLP software may range from unstable prototypes to industrial applications. Many of them are developed by researchers, in the framework of temporary projects (training, PhD theses, funded projects). As their use is often restricted to their developers, they do not always meet Information technology (IT) requirements in terms of documentation and reusability. This is especially the case for underresourced languages, which are often developed by researchers and released without standard documentation, or written fully or partly in the developer's native language.
Providing a clear ReadMe file is essential for effective software distribution and use: a confusing one could prevent the user from using the software. However, there is no well established guidelines or good practices for writing a ReadMe. In this paper we propose an ontology-based approach for the generation of ordered and structured ReadMe files for NLP tools. The ontology defines a meta-data model built based on a joint study of NLP tool documentation practices and existing meta-data model for language resources (cf. section 2). Translation functions (TFs) for different languages (currently eight) are associated to ontology properties characterising NLP tools. These TFs are defined within the Simple Knowledge Organization System (SKOS) (cf. section 2. 2). The ontology is filled via an on-line platform by NLP experts speaking different languages.
NLP tools ontology This work takes place in the framework of the project MultiTal which aims at making NLP tool descriptions available through an online platform, containing factual information and verbose descriptions that should ease installation and use of considered NLP tools. This project involves numerous NLP experts in diverse languages, currently Arabic, English, French, Hindi, Japanese, Mandarin Chinese, Russian, Ukrainian and Tibetan. Our objective is to take advantage of the NLP experts knowledge both to retrieve NLP tools in their languages and to generate multilingual ReadMe files for the retrieved NLP tools. A first step to reach this goal is to propose a conceptual model whose elements are as much independent as possible from the language. Then, associate to each conceptual element, a lexicalisation for each targeted language.