dataesr/scientific-paragraphs-categorization huggingface dataset:
( 13 ) For a given parameter p 1 , the marginal PDF of the solution is P {p 1 (i)} = L (i) dp 1 (i) i j L (i, j ) dp (i, j ) , (14) with L (i) = j L(i, j )dp 2 (j ). The value p 1 (i) in the database with the highest probability (maximum likelihood) corresponds to the sampling step that is closest to the maximum of the a posteriori PDF. The marginal PDF can be described by the mean, p 1 = i p 1 (i) L (i) dp 1 (i) i j L (i, j ) dp (i, j ) , (15) and the associated standard deviation, σ p 1 = i (p 1 (i) -p 1 ) 2 L (i) dp 1 (i) i j L (i, j ) dp (i, j ). ( 16 ) As for the roughness parameter, we give error bars on the results that correspond to two standard deviations, and thus a returned value for p 1 that is p 1r = p 1 ± 2σ p 1. (17).
Numerical validations of the inversion method In order to numerically validate the inversion method described above, two kinds of tests were conducted. First, we applied Gaussian noise and inverted every spectrum in the synthetic spectral database. We show that with a negligible noise, the parameters are always correctly retrieved with negligible uncertainties, and as the level of noise on the data increases, so do the uncertainties on the results. Secondly, we generated spectra for parameters that were not sampled in the database and tried to recover their characteristics successfully. On Fig. 4  each curve corresponds to a the normalized sum of 1000 a posteriori PDFs for the grain diameter of the underlying snow resulting from 1000 random noise draws of the same 2 % level. We name this normalized sum of PDF a "stack" in the remainder of this article.
424 • ± 0. 046 •. Unfortunately, we have no direct measurement of θ because the experimental determination of the roughness of a sample requires a digital terrain model (DTM) of its surface. These DTMs are measured with a laser beam, and are thus very difficult to obtain for ice samples, as multiple reflections create false measures. Still, we find a low value, which is consistent with the production of slabs of columnar ice in the laboratory that are very flat, but still imperfect as described in the dataset. The average slope is compatible with a long-wavelength slope at the scale of the sample, demonstrating that the microscale was not important in our case. Indeed, for a sample that has a length L, a 1σ standard deviation on the thickness h can be attributed to a general slope ϑ = arctan h L due to a small error in the parallelism of the two surfaces of the slab.
Full BRDF inversion Figure  13  shows the measure and the simulation corresponding to the maximum likelihood at the λ = 1. 0 µm wavelength when conducting the inversion on the whole BRDF dataset for each sample. The relatively flat behavior of the radiation with the phase angle is reasonably well reproduced. The quality of the geometrical match increases with the thickness of the sample. This is consistent with the fact that a thicker slab will permit a stronger isotropization of the radiation. It is also consistent with the disappearance of the geometrical dependence on the estimation of large thicknesses noted in Fig. 12. The values of thicknesses returned by the inversion are displayed in Fig. 14a. They are also compatible with the data, and the results are close to the one given by independent inversions of each geometry (see  Figs.
Discussion The two main goals of this work were (i) to develop and validate an inversion method that is adapted to the treatment of massive and complex datasets such as satellite hyperspectral datasets, and (ii) to partially validate a previously developed radiative transfer model. The first criterion is the speed of the whole method, including the direct computation of the LUT and the inversion. The lookup tables used for this project were computed in 150 s for the roughness study (1763 wavelengths sampled, 30 933 spectra) and ∼ 2. 5 h for the thickness and grain diameter study (33 186 wavelengths sampled, 666 315 spectra). The inversions themselves were performed in less than onetenth of a second for specular lobe and independent spec- tral inversions, and 2 s for BRDF-as-a-whole inversions.
Every calculation was computed on one Intel CPU with 4 GB RAM. It has to be noted that once the lookup table has been created, an unlimited number of inversions can be conducted. This means that this method satisfies the speed criterion for the study of massive and complex datasets. For inversions over very large databases, the code has been adapted to GPU parallelization. It is also possible to increase the speed of the calculation of the lookup tables by means of multi-CPU computing. This Bayesian method has been designed to deal with the particular case of a direct model for which the computation time per simulation decreases with the size of the database. In our case, the direct radiative transfer algorithm is slow to simulate only one spectrum (∼ 1 s), but becomes fast for the calculation of large spectral databases (75 spectra per second in the case of the grain diameter and thickness study in this work).
Conclusions The aim of this present work is to validate an approximate radiative transfer model developed in  Andrieu et al. (2015d)  using several assumptions. The most debated one is that the radiation becomes Lambertian when it reaches the substrate. We first qualitatively validated this assumption with snow and ice data. We then quantitatively tested and validated our method using a pure slab ice with various thicknesses and snow as a bottom condition. The thicknesses retrieved by the inversion are compatible with the measurements for every geometry, demonstrating the robustness of this method to re-  trieve the slab thickness from spectroscopy only. The result given by the inversion of the whole dataset is also compatible with the measurements. We also validate the angular response of such slabs in the specular lobe.
Acknowledgements. The authors would like to thank the four anonymous reviewers for their comments that helped to greatly improve this paper. This work was supported by  "Institut National des Sciences de l'Univers" (INSU) , the  "Centre National de la Recherche Scientifique" (CNRS)  and  "Centre National d'Etude Spatiale" (CNES) , through the  "Programme National de Planétologie " and  MEX/OMEGA Program. Edited by:  E. Hanna Reviewed  by: four anonymous referees.
We sequenced the whole genome of M. lentiflavum to exact the phylogenetic relationships within the M. simiae complex, design tools for its genotyping, and disclose any genotypic pattern possibly related to host-mycobacterium relationships. Genomic DNA was extracted from M. lentiflavum strain CSUR P1491 (isolated from the sputum of a cystic fibrosis patient) grown on Middlebrook 7H10 solid medium at 37°C in a 5% CO 2 atmosphere for 3 weeks. Genomic DNA was sequenced on Illumina MiSeq throughout three runs, including one paired-end Nextera and two mate pair libraries, in a 2 ϫ 250-bp run for each barcoded library. The whole set of reads was trimmed using Trimmomatic (8), then assembled through the assembler software SPAdes v3. 5  (9, 10). Contigs obtained were combined together by SSPACE v2  (11) , Opera v2  (12)  helped by GapFiller v1.
10 (13), and homemade tools to refine the set. The draft genome of M. lentiflavum consists of five contigs without gaps, containing 6,818,507 bp. The GϩC content of this genome is 65. 75%. Noncoding genes and miscellaneous features were predicted using RNAmmer  (14) , ARAGORN  (15) , Rfam  (16) , Pfam  (17) , and Infernal  (18). Coding DNA sequences (CDSs) were predicted using Prodigal  (19)  and functional annotation was achieved using BLASTϩ  (20)  and HMMER3  (21)  against the UniProtKB database (  22 ). This genome is predicted to encode at least 75 predicted RNAs, including 3 rRNAs, 52 tRNAs, 1 transfer-messenger RNA (tmRNA), and 19 miscellaneous RNAs. A total of 6,354 genes were identified, representing a coding capacity of 6,173,541 bp (coding percentage, 90. 54%). Whereas 4,369 genes matched a least one sequence in the Clusters of Orthologous Groups (COGs) database  (23, 24, 25)  with BLASTP default parameters, 1,150 (18.
1%) were assigned as hypothetical proteins, and 332 (5. 23%) genes were founded as encoding putative protein. No genes encoding antibiotic resistance were found, but bleomycin resistance is encoded. In silico DNA-DNA hybridization (DDH) was performed with the genome-to-genome distances between pairs of two closed genomes  (25). The genome of M. lentiflavum was locally aligned 2-by-2 using the BLAT algorithm  (26, 27)  against M. simiae and M. genavense. The DDH was estimated from a generalized linear model using a specific distance formula  (28) , resulting in following values: 27. 5% (Ϯ2. 43) with M. simiae and 34. 6% (Ϯ2. 47) with M. genavense. These data confirm M. lentiflavum as a unique species clearly distinct from M. simiae and M. genavense. Nucleotide sequence accession numbers. The M.
Background: Acute exacerbations represent a significant burden for patients with moderate-tosevere chronic obstructive pulmonary disease. Each exacerbation episode is frequently associated with a lengthy recovery and impaired quality of life. Prognostic factors for outpatients that may predict poor outcome after treatment with antibiotics recommended in the guidelines, are not fully understood. We aimed to identify pretherapy factors predictive of clinical failure in elderly ($60 years old) outpatients with acute Anthonisen type 1 exacerbations. Trial registration: NCT00656747. Methods: Based on the moxifloxacin in AECOPDs (acute exacerbations of chronic obstructive pulmonary disease) trial (MAESTRAL) database, this study evaluated pretherapy demographic, clinical, sputum bacteriological factors using multivariate logistic regression analysis, with internal validation by bootstrap replicates, to investigate their possible association with clinical failure at end of therapy (EOT) and 8 weeks posttherapy.
Materials and methods study design MAESTRAL was a Phase III, prospective, multinational, randomized, double-blind, double-dummy, noninferiority, controlled study, comparing the efficacy and safety of 5-day oral MXF 400 mg, once daily with 7-day oral AMC 875/125 mg, twice daily in elderly ($60 years old) outpatients with a history of AECOPD with an Anthonisen type 1 exacerbation (NCT00656747). 17, 19 Randomization was stratified based on concomitant administration of a 5-day course of systemic steroids (30-40 mg/d prednisolone or equivalent, according to physician preference) for the current exacerbation at the treating physician's discretion to ensure equal numbers of steroid treated patients per arm. The study protocol was approved by the ethics committees, and patients provided their written informed consent.
Treatment (MXF vs AMC) was included in the logistic regression model regardless of its significance level. 22 The goodness-of-fit of the model (χ 2 ) was evaluated by Hosmer and Lemeshow test with P. 0. 05 as threshold, with higher P-value indicating better fit. To estimate the performance and efficiency of the logistic regression model (or to check overfitting), bootstrap approach was implemented with 500 and 1,000 randomly selected replicates from the entire dataset. Discrimination of the model was determined by receiver operating characteristics curve analysis, and the area under the curve was determined. Variables were considered as either continuous or categorical. Parameters of individual patients were not analyzed if values were missing.
Discrimination and goodness-of-fit analyses Discrimination of the models at EOT and 8 weeks posttherapy was tested by receiver operating characteristics analysis to estimate the probability of true positive failures. The analyses provided an area under the curve of 0. 6639 for the EOT logistic regression model (weaker) and of 0. 7274 (relatively strong) for the 8 weeks posttherapy model. To test the predictive strength of the logistic regression models, Hosmer and Lemeshow test was performed. The predictive strength of the EOT and 8 weeks posttherapy logistic regression models is demonstrated by the goodness-of-fit P-values of 0. 53 and 0. 98, respectively (ie, P. 0. 05). Internal validation of the models was implemented in bootstrap analyses by using 500 and 1,000 random replicates selected from the entire dataset for both time points, confirming the results of the logistic regression analysis (data not shown).
Related work Document classification has been a long researched topic. Tools and algorithms have been developed to enable people to classify pre-labeled documents. The approach in this paper is single-label text classification using ADAGRAD  (Duchi et al. , 2011a). Later on, we explored Latent Dirichlet Modeling  (Blei et al. , 2003)  on the basis of induced subtrees, which are commonly used in data mining, but not frequently seen in Natural Language Processing. Frequent Subtree Mining is a common data mining topic. Related algorithms such as TreeMiner, FreeQT have been developed to find most frequent structure in a given tree bank  (Chi et al. , 2005). Similar approaches have been explored in Moschitti (2006)'s work on using subtrees as features for Support Vector Machine. We did not use his approach because were were not interested in the similarity between tree structures, but rather in the linguistic regularities implicit in the text.
Data We used data collected through Amazon Mechanical Turk (MTurk). Participants were asked to write down their mind wanderings as follows: Please think back to the last time you were thinking about something other than what you were currently doing. Please share with us what you were thinking about. If you found yourself thinking about many different things, please share with us as many of these things that you can remember. In addition to writing down their mind wanderings, participants (N = 795) also answered a series of behavioral survey questions related to anxiety, health, happiness, life and financial satisfaction. The task resulted in a total of 2007 sentences. Table  1  describes the distribution of our data. The sentences were rated by three human raters. For each sentence, raters indicated whether the expression referred to the future and their level of confidence of their decision.
Syntactic structural rules We used the constituency grammar rules generated by Wolff and Copley. Rules were generated on the basis of linguistic theory, and then later refined on the basis of analyses of the false positives and misses. The rules were instantiated in the Tregex pattern language  (Levy and Andrew, 2006) , which could then be used to find matching structures in the parsed sentences. There were 39 future-related rules, 16 past-related rules, and 3 present-related rules. The rules varied from the purely syntactic to the lexical, with a number of rules containing of mix of both. Syntactic information helped to disambiguate the senses of the modal verbs. Fourteen of the future-related rules emphasized the modal verbs. Rules are released online at https://github. com/clir/time-percep tion.
Adaptive sub-gradient descent To build statistical models, we used a stochastic adaptive subgradient algorithm called ADAGRAD that uses per-coordinate learning rates to exploit rarely seen features while remaining scalable  (Duchi et al. , 2011b). This is suitable for NLP tasks where rarely seen features often play an important role and training data consists of a large number of instances with high dimensional features. We use the implementation of ADAGRAD in ClearNLP (Choi, 2013) using the hinge-loss, and the default hyperparameters (learning rate: a = 0. 01, termination criterion: r = 0. 1).
Filtering SNPs from RADseq Data The libraries were demultiplexed into individual samples according to the respective barcode combinations by allowing for single errors at the barcodes using the RADpools module of the RADtools v. 1. 2. 4 package  (Baxter et al. 2011 ). The 84 individual files have been further imported in the CLC Genomic Workbench v. 6. 5 (Qiagen) and trimmed/filtered to retain only full-length (i. e. , 94 bp after barcode trimming) reads, free of any adaptor sequence and with all bases of a quality Phred score 30. The final highquality filtered and demultiplexed data set contained over 160 million pairs of reads. The forward reads were further used for running denovo_map. pl script of STACKS v. 1. 12  (Catchen et al. 2011). To find the best settings for STACKS, we first varied the value of the minimum number of identical reads required for a stack to be formed (i.
e. , the setting "m") from 5 to 15, by allowing one base pair difference between loci when processing one individual (i. e. , the setting "M = 1") and when building the catalog  (i. e. , setting "n = 1"). We have chosen the value of m = 13 as the best for our data because it delivered the most polymorphic stacks with less than 10 single nucleotide polymorphism (SNP) positions (in order to avoid any pooled paralogs in the same locus) that are covered by data in at least 90% of individuals (i. e. , with data present for at least 75 individuals, to avoid artificially splitting individual loci). Further, for the value of m = 13 we have run additional tests by varying the value of "M" from 1 to 4 and the value of "n" from 0 to 6 (Supplementary Table  S1 , available on Dryad at http://dx. doi. org/10.
5061/dryad. 62247). The final combination of settings was chosen based on the criteria above for m = 13, M = 1, and n = 1. The deleveraging algorithm of stacks has been left on in order to split loci merged incorrectly and remove highly repetitive sequences from further analyses. To avoid retention of any merged paralogs, the loci showing 10 or more SNPs have been blacklisted in further analyses by filtering them out using the export_sql. pl script from STACKS. The SNP data have been extracted by using the populations script of STACKS. By retaining SNPs from loci covered in at least 75 individuals with a maximum of 9 polymorphic nucleotide positions, we obtained a data matrix containing 8488 concatenated SNPs (hereafter data set D1), which has been further used for phylogenomic analyses. In an additional filtering step we retained only one SNP per locus (data set D2 hereafter, including 1506 SNPs) to minimize linkage in the data, and we finally filtered out all apomorphic SNPs (i.
Phylogenomic Analyses To study the phylogenetic relationships between the sampled Diospyros individuals, we used maximum parsimony (MP), maximum likelihood (ML) and Bayesian inference (BI) on data set D1. For BI and molecular dating, the program BEAST v1. 7. 5  (Drummond et al. 2012 ) was run on the CIPRES Science Gateway (http://www. phylo. org/portal2/; last accessed October 15, 2015;  Miller et al. 2010 ). Estimation of evolutionary models was conducted with jModelTest v2. 1. 4 (Darriba et al. 2012 ) that indicated the transversional model (TVMef;  Posada 2003)  with equal frequencies modeled with a gamma distribution and a proportion of invariable sites (TVMef+ +I) to be the best fit to our data. We used a relaxed uncorrelated lognormal clock model to keep the age estimations flexible  (Drummond et al.
Genetic Clustering and Patterns of Reticulation Within closely related groups, representation of relationships as networks rather than bifurcating trees appears to be more appropriate  (Huson and Scornavacca 2011 ) to account for the potential presence of hybridization. We used SplitsTree v. 4. 12. 6  (Huson and Bryant 2006)  to create a network based on Hamming distances  (Hamming 1950)  and data set D1. The simple calculation method of Hamming distance was considered appropriate for the RADseq-derived SNPs data set that lacks indels after processing with STACKS. To investigate the higher-level clustering of the included individuals and potential admixture between different groups we used Structure v2. 3. 3  (Pritchard et al. 2000; Hubisz et al. 2009 ) on data set D3. We ran the program separately for (i) all individuals analyzed and (ii) the individuals forming the larger cluster in the first analysis (i.
e. , the RR clade as defined in Results). Both analyses have been performed at the Lifeportal of the University of Oslo for K = 1-10, each with 10 replicates, and a model based on admixture and independent allelic frequencies. Each run had 1 million iterations with 10% additional burn in. The calculation of K  (Evanno et al. 2005 ; Supplementary Fig. S3 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247) was performed with Harvester  (Earl and vonHoldt 2012). To avoid any stochastic aspect of the process, we have produced a permuted matrix from the 10 replicates of the best K value with Clumpp v1. 1. 2 (Jakobsson and Rosenberg 2007)  in the greedy algorithm and 1000 repeats. The graphical display of Structure results was prepared with Distruct v1. 1  (Rosenberg 2004). Historical relationships inferred with a population graph analysis that allows both population splits and migration events were constructed with TreeMix 1.
The phylogenetic trees obtained pointed to several sister clades comprising individuals with divergent preference for ultramafic versus volcanic soils: Diospyros flavocarpa/Diospyros umbrosa; D. parviflora (L10)/D. parviflora (L22); Diospyros yahouensis + D. perplexa/Diospyros pancheri; Diospyros minimifolia (L17)/D. sp. Pic N'ga. We searched for RAD regions that contained SNPs with pairwise F ST values over 0. 5 at least for two pairs of sister species with divergent soil preferences. We then made use of the pairedend RADseq data and assembled mini-contigs of the candidate stacks by extracting a list of reads for each locus with Sort_read_pairs. pl from STACKS, sorting reads from FastQ files with Fastq. filter. pl (L. M. Rodriguez, unpublished data, available from http://enveomics. blogspot.
Phylogenomic Analyses After demultiplexing, trimming and filtering the raw 100 bp reads from two RADseq libraries, we retained on average 1. 9±0. 7 million high-quality pairs of reads per individual. After parameter optimization as described above, the de novo assembly pipeline of STACKS produced 37,336 loci (excluding any tags present in only one individual). The MP (Supplementary Fig. S1 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247), ML (Supplementary Fig. S2 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247), and BI (Fig. 2 ) analyses resulted in generally consistent topologies, pointing to two major groups of species: one older grade of more slowly radiating species (SR hereafter) that are more divergent, and a younger clade of more rapidly radiating species (RR hereafter).
4 Ma, with a wide 95% confidence interval of 2. 7 Ma. The next divergence (i. e. , D. flavocarpa/D. umbrosa from the rest of the species) took place around 6. 6 Ma. The lineage forming D. cherrieri and Diospyros veillonii separated from the rest around 5. 6 Ma. The RR group started to diversify around 4 Ma. The RRc clade is a young group, about 2. 7 myr old. Most speciation events seem to have taken place in this group between 3. 5 and 1. 5 Ma. The MP analysis resulted in 31 equally parsimonious trees (Supplementary Fig. S1 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247). The phylogenetic relationships between the earlier diverged lineages (group SR) are the same as in the BI genealogy. The relationships between the clades within group RR differ slightly between MP (Supplementary Fig.
S1 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247) and BI (Fig. 2 ). The general topology of the ML tree (Supplementary Fig. S2 , available on Dryad at for the subset of individuals from group RR, for K = 3; c) the preference for climate type, generally following a E-W separation-light gray for humid, dark gray for dry; d) the geography, generally following a N-S separation-black for north, hashed area for center, and white bars for south; and e) for substrate-type preference-lemon green for volcanic rock, dark purple for ultramafic rock, orange for schist, pink for limestone, and yellow for serpentine. L. refers to sampling location given in Fig. 1  and Table  1. http://dx. doi. org/10. 5061/dryad. 62247) inferred with RAxML is also similar to that of the BI. The SNAPP results (Fig.