dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008)  and non-parametric approaches  (Chao, 1987; Zelterman, 1988). Chao and Zelterman non-parametric estimators are convenient and easy to compute since they are based only on the frequency of units that have been detected exactly once or twice. This characteristic makes them quite robust to overdispersion  (Wilson and Collins, 1992)  but has led to controversy since these estimators only use a fraction of the available data. A number of methodological developments of the non-parametric approach have been suggested to account for overdispersion  (Chao and Bunge, 2002) , include covariate information  (Böhning and Del Rio Vilas, 2008) , account for small sample sizes  (Böhning et al. , 2011)  or handle clustered data  (Navaratna et al. , 2008). The freely available SPADE software 3  allows non-parametric estimation of zero-truncated count distribution parameters  (Chao and Shen, 2006).
, 2005; Böhning and Kuhnert, 2006)  or a zero-truncated negative binomial (ZTNB) model in which the mean and the variance of the count distribution can be fitted separately  (Cameron and Trivedi, 1998). The packages gamlss (Stasinopoulos and Rigby, 2007) and gamlss. tr (Stasinopoulos and Rigby, 2010)  for the R software (R-Development-Core-Team, 2008) allow fitting ZTP and ZTNB models to data. The freely available CAMCR software 4  also fits mixtures of zero-truncated count distributions  (Kuhnert and Bohning, 2009). In animal health, applications of zero-truncated CR analyses can be found in Del Rio Vilas and  Böhning (2008)  and  Vergne et al. (2012a)  who used non-parametric and parametric approaches respectively, to estimate the total number of scrapie-infected holdings in the United Kingdom and France.
Adapting ecological applications  (Agarwal et al. , 2002; Rathbun and Fei, 2006) ,  Vergne et al. (2014)  extended the ZIP model by incorporating a spatial autocorrelation term in the Bernoulli process to account for the contagious aspect of highly pathogenic avian influenza H5N1 in Thailand. ZIP and ZINB models can be run in R using the pscl package  (Jackman, 2008).
The sequential version of this problem (i. e. , with p = 1 processor) has already been studied. Liu  [17]  discusses how to find a memory-minimizing traversal when the traversal is required to correspond to a postorder traversal of the tree. A follow-up study  [18]  presents an optimal algorithm to solve the general problem, without the postorder constraint on the traversal. Postorder traversals are known to be arbitrarily worse than optimal traversals for memory minimization  [12]. However, they are very natural and straightforward solutions to this problem, as they allow to fully process one subtree before starting a new one. Therefore, they are thus widely used in sparse matrix software like MUMPS  [2, 3] , and in practice, they achieve close to optimal performance on actual elimination trees  [12].
We first order the matrices using MeTiS  [13]  (through the MeshPart toolbox  [7] ) and amd (available in Matlab), and then build the corresponding elimination trees using the symbfact routine of Matlab. We also perform a relaxed node amalgamation  [16]  on these elimination trees to create assembly trees. We have created a large set of instances by allowing 1, 2, 4, and 16 (if more than 1. 6 × 10 5 nodes) relaxed amalgamations per node. At the end we compute memory weights and processing times to accurately simulate the matrix factorization: we compute the memory weight n i of a node as η 2 +2η(µ-1), where η is the number of nodes amalgamated, and µ is the number of nonzeros in the column of the Cholesky factor of the matrix which is associated with the highest node (in the starting elimination tree); the processing time w i of a node is defined as 2/3η 3 + η 2 (µ -1) + η(µ -1) 2 (these terms corresponds to one gaussian elimination, two multiplications of a triangular η × η matrix with a η × (µ -1) matrix, and one multiplication of a (µ -1) × η matrix with a η × (µ -1) matrix).
III. DATA AND MONTE CARLO SAMPLES The data sample used in this measurement consists of proton-proton collision events at a center-of-mass energy √ s = 7 TeV recorded by ATLAS in 2011. Only data collected during stable beam conditions and with the relevant ATLAS sub-systems operational are used. In particular, the inner detector, the electromagnetic and hadronic calorimeters, and the muon spectrometer must deliver data of high quality to ensure that electrons, muons, jets, and missing transverse momentum are measured accurately. The data selected for this study were collected using single-lepton triggers (e or µ). In the case of the electron trigger, a threshold is applied to the transverse energy (E T ) of the electron while for the muon trigger a threshold is applied to the transverse momentum (p T ) of the muon.
The simulation includes modeling of additional pp interactions in the same and neighboring bunch crossings, referred to as pileup. These events are subsequently reweighted such that the distribution of the number of interactions per bunch crossing in simulation matches that of data. Corrections to the selection efficiency of electrons and muons are applied to simulated events, and the detector simulation is tuned to reproduce the energy and momentum measurements and resolution observed in data. Unless otherwise specified, common attributes between the Monte Carlo samples are the generation of the underlying event (UE), which is performed by pythia v. 6. 425  [14]  or jimmy v. 4. 31  [15]  (included as part of the herwig v. 6. 520  [16]  software package), and the choice of PDFs, which is the NLO CT10 set  [17].
Fitted event yields are used to extract fiducial and full cross-sections for the signal processes. The former is desirable because it is a quantity that is closer to what is measured by the detector and does not suffer from theoretical extrapolation errors. The two cross-sections are calculated as: σ fid X = N fid X C • L , (4) σ tot X = N tot X A • C • B(X → eµ + Y ) • L (5) respectively, where L corresponds to the integrated luminosity of the data sample; A is the kinematic and geometric acceptance of the fiducial region as a fraction of the complete phase space; C is the ratio of the number of events fulfilling the offline selection criteria to the number of events produced in the fiducial region estimated from simulation; N tot X (N fid X ) is the number of events attributed to the specified process by the fit using systematic uncertainties that affect A • C (C only); and B(X → eµ + Y ) is the branching fraction to inclusive eµ final states for the decay channel under consideration taking into account the branching fractions of tau-lepton decays to electrons and muons.
The full PDF set uncertainty combines the inter-and intra-PDF uncertainties by taking the envelope of the minimum and maximum of these values. Uncertainties associated with the parton distribution functions are not profiled in the fit. Shape uncertainties are measured by fitting the varied templates to data while variations between calculated A and C values are used to assign acceptance uncertainties. Fitting the templates with different PDF sets to data results in yield uncertainties, the envelope of which is taken as the PDF shape uncertainty. The PDF set uncertainties, shown in Table  II , are computed in this way to avoid the complexity that would otherwise be introduced into the fit if they were to be profiled.
In the case of Z/γ * → τ τ , the theoretical uncertainties are calculated by comparing sherpa to the appropriate alpgen sample interfaced to herwig. The evaluation of the uncertainty due to the choice of PDF has been described Sec. VI D. The second group of entries in Table  II  correspond to the experimental uncertainties. The uncertainties associated with Monte Carlo modeling of the lepton trigger, reconstruction and identification efficiencies are evaluated by studying Z → ee/Z → µµ and W → eν/W → µν events selected from data as well as Z → ee/Z → µµ, W → eν/W → µν, and t t events from simulation  [43]. The dominant experimental uncertainties on template normalization stem from electron reconstruction, identification, and isolation. These uncertainties are large due to the difference in efficiency of the isolation cut between the Z+jets region where the efficiency is measured and the rest of the signal region.
Although larger acceptance uncertainties clearly reduce the separation power with respect to the fiducial measurements, here the full theoretical calculations at NNLO in QCD can be used for Z/γ * → τ τ versus t t, as shown in Fig. 7(d). As described in Sec. III, the software packages FEWZ and top++ were used to calculate the cross-sections to NNLO. Figure  7 (d) (NNLO case) in contrast to Fig. 7(c ) (NLO case) shows good overlap between the experimental measurement and most of the NNLO theoretical predictions and corresponding PDF sets for Z/γ * → τ τ versus t t where they are available. Also notable is the difference in the uncertainties in theoretical predictions: in the NLO case scale uncertainties are dominant, while in the NNLO case the PDF model provides the dominant uncertainty. Theory contours using ABM11 and JR09 PDFs, however, do not overlap with the measurements.
VIII. CONCLUSION Simultaneous measurements of the t t, W W and Z/γ * → τ τ fiducial and total production cross-sections using 4. 6 fb -1 of data collected with the ATLAS detector from pp collisions at √ s = 7 TeV at the LHC are presented. Exactly two high transverse momentum isolated leptons are selected, and are required to be one electron and one muon of opposite charge. The number of signal events is extracted using a template fit to the distribution of missing transverse momentum and jet multiplicity observed in the data. The measurements are consistent with the previously published dedicated ATLAS cross-section measurements and with the predicted theoretical crosssections within their uncertainties. This simultaneous extraction of the cross-sections for these processes at the LHC provides a broader test of the SM predictions than individual measurements by unifying the fiducial region, object and event requirements, and background estimations.
The contours obtained from the data (full circle) represent the 68% CL (full line) and 90% CL (dashed line) areas accounting for the full set of systematic uncertainties described in Table  II. The fiducial cross-sections for W W and t t exclude contributions from tau-lepton decays. The theoretical W W cross-section does not include contributions from gg → H → W W. The theoretical fiducial cross-section predictions are shown at next-toleading-order (NLO) in QCD for different PDF sets (open symbols) with the ellipse contours corresponding to the 68% CL uncertainties on each PDF set. Also shown as horizontal and vertical error bars around each prediction are the uncertainties due to the choice of QCD factorization and renormalization scales (see text).
ACKNOWLEDGMENTS We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
We will also assess the response rate and relapse at 15 months post-inclusion, hospitalization rate and adherence to treatment during the follow-up period, quality of life and global functioning upon inclusion and at 9 and 15 months post inclusion. Discussion: If the proposed trial shows the effectiveness of the intervention, but also an increased remission rate in depressed outpatients at 15-months post-inclusion, in addition to improved treatment adherence in patients, it will further promotes arguments in favor of a wide dissemination of psychoeducational programs for depression. Trial registration: This trial is registered under number 2015-A00249-40 (PURE clinical trial: NCT02501226) (June 30th, 2015).
An independent researcher, not otherwise involved in the study, will perform the allocation. Each patient will be followed during 15 months with 5 visits: at baseline (V1), 3 months (V2), 6 months (V3), 9 months (V4) and 15 months (V5) after baseline. Blinded and trained evaluators will assess patients during the follow-up. This research, involving human subjects, has been performed in accordance with the Declaration of Helsinki. The study protocol has been approved by an appropriate ethics committee (CPP Sud Méditerranée IV) (covering all 11 participating centres) and is registered under number 2015-A00249-40 (PURE clinical trial: NCT02501226) (June 30th, 2015).
General statistical considerations All statistical analyses will be performed using SAS® (SAS Institute, Cary, NC, USA). For efficacy variables, we will use a Full Analysis Set, which will include all randomized subjects with a valid primary efficacy baseline measurement and at least one valid primary efficacy measurement. A robustness evaluation using the Completer Set (CS) will also be performed for the primary efficacy variable. The CS is a subset of the FAS and will include all subjects with a valid measurement at the end of the study Period (15 months) for the primary efficacy variable. A summary of demographic data and baseline characteristics, as well as medical and procedure history will be presented for the whole population (all treatment groups combined) and for each treatment group.
Safety analyses For the analysis of safety data (AEs, vital signs), RS will be used. Adverse events occurring during this study will be presented by system organ class (SOC), high level term (HLT), and preferred term  [65]  in a frequency table giving the number of events, number of subjects, and percentage of subjects who experienced the event by treatment group. Subjects with multiple AEs will be counted only once within each PT, HLT, and SOC. Coding of the AEs will be performed with the Medical Dictionary for Regulatory Activities. Vital signs variables and their changes from Baseline will be presented using descriptive statistics by visit and treatment group. Rate of suicidal behaviors (completed suicide and suicide attempts) at 15 months after inclusion, variation of severity and intensity of suicidal ideations subscores between inclusion and at 3, 6, 9 and 15 months after inclusion will be reported for each group.
Acknowledgements The authors thank  B. Clement  for the careful reading of the manuscript. The authors thank all investigators for their participation in this multicentric trial:  Dr Bendimerad Patrick ,  Dr Bennabi Djamila ,  Dr Briki Malick ,  Dr Charpeaud Thomas ,  Dr Giordana Bruno ,  Dr Johnson Peter Kwaku ,  Dr Lerond Jérôme ,  Dr Nicolas Alain ,  Dr Nieto Isabel ,  Dr Verger Guillaume. The author thank  Miss Emeline Massignan  for her material assistance. This project is funded by the  National Clinical Hospital Research Program  "PURE".
Introduction Genome annotation broadly refers to the set of meta-data associated with the coding regions in the genome, typically including the identification of the location of each gene as well as a determination of the functions related to the gene product (e. g. protein or RNA). In particular, gene annotations correspond to known functions related to the gene product, including molecular functions, biological pathways, or the cellular location of the gene products. A variety of well-known unified databases have been constructed with known functional annotations collected from bibliographic sources across species, including the Gene Ontology (GO)  (Ashburner et al. , 2000) , the Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa and Goto, 2000)  or the MSigDB (Molecular Signatures) databases  (Liberzon et al.
, 2011). Although such databases contain a rich source of functional information about the genome in a large variety of species (e. g. Arabidopsis thaliana, human, rat, mouse, fly), our knowledge of functional annotations is often far from complete  (Tipney and Hunter, 2010). In recent years, substantial improvements in high-throughput technologies, such as microarrays  (Schena et al. , 1995)  and more recently RNA sequencing (RNA-seq)  (Mortazavi et al. , 2008) , have enabled the simultaneous measurement of the expression levels of tens of thousands of genes. A rich body of work is now available on the appropriate statistical analyses of such gene expression data, including the analysis of differential expression  (Smyth, 2004; Anders and Huber, 2010)  and co-expression analyses to identify groups of genes with similar profiles across several experimental conditions or over the course of time  (Yeung et al.
, 2001; Rau et al. , 2015). The latter is of particular interest in this work, as identifying genes that share the same dynamic patterns of expression may help identify groups of genes that are involved in similar biological processes and generate hypotheses about the functional properties of poorly characterized genes  (Eisen et al. , 1998; Jiang et al. , 2004). Reviews and comparisons of different clustering methods for gene expression data may be found in  Datta (2003). In practice, annotation databases are often used to perform a posteriori validation and interpretation of co-expressed gene clusters through tests of functional enrichment  (Steuer et al. , 2006). Such functional annotation may instead be directly integrated into the clustering model itself. For example,  Tari et al. (2009)  incorporate GO annotations as prior knowledge in a fuzzy c-means clustering.
G g g g n n In general, ICAL tends to merge clusters to group genes annotated for the same function, reducing the number of optimal clusters K with respect to the optimal number of clusters selected by ICL. SICL tends to split clusters in order to obtain clusters made up only of annotated genes, increasing the number of optimal clusters with respect to the optimal number of clusters selected by ICL. In other words, SICL tends to select more complex models than ICL while ICAL tends to favor more parsimonious models than ICL. Note that this behavior of ICAL and SICL is a general trend, not a rule: ICAL does not always merge clusters and SICL does not always split them since clusters for different solutions are not necessarily nested in each other. Code to implement our method is available in the R package ICAL, which may be found at the following website: https://github.
Simulation settings To illustrate the behavior of the proposed ICAL criterion, we consider a numerical example. We simulate 200 observations from a mixture of four bivariate Gaussian distributions, 100 independent times (see parameters in Table  1 ). The first two components are close to one another while the third and fourth are clearly distinct from the first two and also distinct from each other. For a given model indexed by K, the estimation of parameters is performed with the R package Rmixmod  (Biernacki et al. , 2006; Lebret et al. , 2015)  for a Gaussian mixture model with diagonal variance matrix (that is, the p k L k B k model in the notation of the Rmixmod package, corresponding to clusters with variable proportions, variable volumes, variable shapes and vertical or horizontal orientation).
We estimate the parameters for models with the number of clusters K varying from 1 to 10 and perform model selection to select the most appropriate number of clusters. Over the 100 replicated datasets, the BIC most frequently selects four clusters (81 times). Indeed, we note that these clusters correspond to the simulated Gaussian components. The ICL criterion selects either three (54 times) or four clusters (46 times), as it tends to merge the two similar components (1 and 2 from Table  1 ). We illustrate the potential utility of accounting for external gene annotations in model selection by simulating such annotations and performing model selection with the corresponding SICL and the ICAL criteria. We simulate three types of functional annotations: u A , u B and u C (see Figure  1 ). The genes annotated for the first function u A are shared by the two closest mixture components (components 1 and 2 from Table  1 ).
In the following, we tested two levels of annotation densities = = annot annot : 0. 05 annot n d d n and d annot = 0. 25. Since the ICAL and SICL criteria can be used with more than one external annotation, we also illustrate the potential utility of including more than one annotation. For each set of annotations used in our simulation, the number of annotations varies from 1 to 12. Each annotation in the set is simulated independently. Table  1 : Parameters of simulated datasets: the first two components are close to one another while the third and fourth are clearly distinct from the first two and also distinct from each other.
Simulation results All penalized criteria (BIC, ICL, SICL and ICAL) versus the number of clusters for one simulated dataset with d annot = 0. 05 are displayed in Figure  2A. Over the 100 simulated datasets, ICAL selects three clusters 72 times, merging the two closest components 1 and 2 (Table  2 ). This three cluster solution is meaningful with respect to the information provided by u A , as all annotated genes are attributed to the same cluster. In this case, the external information provided by the associated annotation u A reinforces the model selection. Using the same pattern as u A (annotations shared by components 1 and 2 only), we simulate 12 independent associated annotations with density d annot = 0. 05. The evidence to merge clusters increases with the number of annotations (one versus 12 annotations).
The number of annotations in the set (12 annotations) is chosen so that the evidence to merge clusters was sufficiently strong: over the 100 simulated datasets, ICAL systematically selects a three cluster solution (Table  2 ). For this set of 12 external annotations, the peak of the ICAL displayed in Figure  2B  is much sharper than the peak of ICL. In contrast, SICL more frequently selects a four-or even five-cluster solution, as it leads to a preference of smaller clusters containing only annotated genes (i. e. a high specificity of annotation within each This demonstrates the utility of the ICAL criterion over the SICL, as it does not correctly take into account the specificity of gene annotation. For unassociated annotation u B with d annot = 0. 05, the behavior of the information criteria versus the number of clusters for one simulated dataset is displayed in Figure  3A.