dataesr/scientific-paragraphs-categorization huggingface dataset:
We suggest that this effect was encouraged by individual therapy, which helped introduce a metacognitive dimension to the sessions with the therapist, and adapt exercises to the motivations and clinical profile of participants during the transfer sessions. Furthermore, the structure of the GAÏA s-face program, offering computer exercises adjusted to the participant's everyday environment, may have facilitated the transfer and generalization of benefits to everyday life. This study did not show any improvement in social functioning for RECOS participants, probably because transfer between neurocognition and social functioning is less straightforward than between social cognition and social functioning. However, the program is open to incorporate a metacognitive dimension and individualization, to facilitate the transfer of strategies to daily tasks.
This study also highlighted the benefits of cognitive remediation with the RECOS program. The RECOS group underwent a training program focusing on selective attention (in its usual version, the program provides targeted cognitive remediation, adapted to the participant's cognitive profile) and results at T2 showed a significant improvement in selective ing and visuospatial attention tasks. The GAÏA group also showed positive effects on neuropsychological assessment at T2. Besides visuospatial functions, improved working memory was observed after the therapy. Several hypotheses may explain this unexpected effect. Some neuropsychological tests measuring working memory skills used visual modality and, therefore, also required visuospatial memory skills. Improvement in memory and visuospatial attention for GAÏA participants may underlie improvement in working memory.
COnClUSiOn Although the understanding of the cognitive mechanisms improved by the GAÏA program is still incomplete, the results of this study show the applicability and effectiveness of the program to improve facial emotion recognition skills, reduce positive and negative symptoms in schizophrenia, and improve social functioning. Positive results were obtained for both programs in the main criteria. Facial emotion recognition skills improved in both arms, selective attention was significantly improved in the RECOS group, which received specific training targeting these processes, and the GAÏA program showed positive effects on symptoms and social functioning. These results were obtained with cognitive remediation provided by nurses after a 3-day training session. This is an interesting prospect for the spread of cognitive remediation and its potential inclusion into the routine care of schizophrenia alongside drug treatment, psychoeducation, social skills training, and psychotherapy.
Facial emotion processing is also impaired in other disorders, so it would be interesting for future studies to explore whether the GAÏA s-face program is effective in people with diseases, such as autism spectrum disorders or 22q11. 2 deletion syndrome. aUTHOr COnTriBUTiOnS BG: nurse; GAÏA s-face program conceptor; contibution in study design; and first author. JV: neuropsychologist; cognitive assessment; contibution in study design; DATA analysis; substantial contribution. SG: nurse; symptoms, social functioning and subjective assessment; contribution in study design; substantial contribution. The GAÏA/RECOS study team: Dupuis M. , Hochard C. , Josserand AC. , Koubichkine A. , Lambert T. , Perez M. , Rouyre B. : nurses; therapists; critical review. Scherding P. : occupational therapist; therapist; critical review.
aCKnOWlEDGMEnTS The authors wish to thank the CSR (scientific research board) of Le Vinatier Hospital for financially supporting this study, D. Gros (Tutoractiv') for his work on the GAÏA s-face software, P. Vianin for his agreement in using the RECOS program and his help in designing the GAÏA s-face program, JY. Baudouin for his help in designing the GAÏA s-face program, G. Collot and F. Apruzzese for editing the manuscript, I. Amado for her final review. rEFErEnCES.
, 2008). The green line in the LTT plot represents the reconstructed "sampled" phylogeny in which only 50% of the original extant taxa have been sampled (N = 10); the latter was done with a sample algorithm script that randomly removed tips from the reconstructed tree using the drop. tip function from the R package ape  (Paradis et al. , 2004). 1  for more details on simulations). Rec: statistics estimated for the "reconstructed" phylogenies with all extant species included; Samp: statistics estimated for the "sampled" phylogenies, in which only 50% of extant species were sampled. Gamma Stat: median and 95% confidence interval around median  (Chambers et al. , 1983)  of the gamma statistic estimated over 10 trees simulated under each birth-death model (see Supplementary Figure  1  for histograms representing the variation in these values).
Distinguishing between the ME and SRD models often requires the use of additional information, such as biogeographic and paleontological (fossil record) data  (Antonelli and Sanmartín, 2011; Stadler, 2011b). Both the HE and ME models represent scenarios in which a large number of lineages have been removed in relation to the number that ever existed, but while this is a discrete time event in the ME model, extinction rates are maintained high and constant over time in the HE model. The apparent result in our simulated phylogenies (Figure  1 ) is a higher loss of diversity in the HE model than in the punctual ME model. Complete phylogenies under the HE model are on average older than those under the ME model (Figures  1  and 3 ), and the mean number of extinct taxa in the complete phylogeny is also higher in the HE than in the ME model (see Table  1 ).
Recently,  May et al. (2016)  proposed a new Bayesian approach, CoMET implemented in the R package TESS  (Höhna et al. , 2015) , in which temporal rate heterogeneity (diversification rate shifts) and ME events are modeled through Compound Poisson Processes, with the first considered as nuisance parameters in the Bayesian inference. The timing and intensity of the ME can also be assigned informative priors based on paleontological knowledge to avoid overparameterization. An additional limitation of early birth-death models was the assumption of equal diversification rates across clades. Given that speciation and extinction rates are likely dependent on speciesspecific biological traits  (Ezard et al. , 2011) , rate homogeneity across clades seems unrealistic. One of the most important advancements in this field came from the work performed by  Rabosky et al.
Beaulieu and O'Meara (2015)  investigated the performance of likelihoodbased diversification methods to estimate extinction rates from reconstructed phylogenies, and concluded that even under cases of rate heterogeneity, extinction rates could be estimated from phylogenies of moderate size (N ≥ 50), though with large confidence intervals. Laurent et al. (2015)  examined the ability of  Stadler's (2011b)  episodic birth-death model, implemented in the R package TreePar, to detect ME events, and showed that the statistical power of TreePar increased with the size of the phylogeny (N > 200-300) and the intensity of the ME event (ρ > 0. 2). Table  2  illustrates the ability of TreePar to estimate the timing of the ME event in our very small (20-taxa) reconstructed phylogenies. As found by  Laurent et al.
Expansion of these models to allow temporal variation (FitzJohn, 2012) truly integrates rate heterogeneity over time and across clades. However, these models have proven even more data-demanding than time-dependent rate models  (Davis et al. , 2013). One potential solution is to increase the number of data points (branching times) by using phylogenetic data from multiple clades. Since a ME event presumably acts clade-wide, its phylogenetic signature should be imprinted across a diverse range of organismal phylogenies. A hierarchical Bayesian approach in which the time and intensity of the ME event is estimated across clades, while allowing the extinction fraction -dependent on intrinsic biological traits  (Jablonski, 2008; Purvis, 2008)  -to differ among clades, could be used to detect these large-scale extinction events affecting a region's biota  (May et al.
, 2011). In recent years, expansions of the DEC model have been developed that include direct movement between single areas (jump dispersal or "founder-event speciation";  Matzke, 2014). The expanded DEC model implemented in the R package BioGeoBEARS  (Matzke, 2013)  incorporates a free parameter "j" that weights for the probability of jump dispersal versus range expansion at points in the phylogeny. Reconstructed scenarios with DEC + J usually include very low or null extinction estimates compared to DECinferred scenarios. One explanation is that extinction in DEC is often estimated along long branches, where loss of areas within widespread distributions, acquired by range expansion, cannot be countered off by cladogenesis (i. e. , via vicariance or peripheral isolate speciation). In DEC + J, these extinction events are not inferred because a jump dispersal at the next cladogenetic event is modeled instead.
3, a = 0. 15) interrupted by a ME event at time t = 1 that removes 90% of lineages, and followed by a recovery period with slower growth (λ = 2, µ = 1, a = 0. 50). Ten phylogenies were simulated for each model using the function sim. bd. taxa in the R package TreeSim (Stadler, 2011c)  conditional on a number of 20 extant taxa (N = 20). For the ME model, we used the command sim. rateshift. taxa conditioning on the number of extant taxa and the time of the ME event using as sampling fraction ρ = 0. 1. The phylogeny to the left (A,C,E,G) represents one of the 10 reconstructed phylogenies simulated under each model. The black line in the LTT plot represents the average LTT plot over the 10 simulated phylogenies for the "complete" birth-death process, i. e. , including extant and extinct taxa (thus, the varying number of initial taxa), and using the option COMPLETE = TRUE; the red line shows the average LTT plot for the corresponding "reconstructed" phylogeny after removing the extinct lineages using the function drop.
extinct in the R package geiger (Harmon et al. , 2008). The green line in the LTT plot represents the reconstructed "sampled" phylogeny in which only 50% of the original extant taxa have been sampled (N = 10); the latter was done with a sample algorithm script that randomly removed tips from the reconstructed tree using the drop. tip function from the R package ape (Paradis et al. , 2004).
Most of the applications have shown that large amount of money that can be saved by accurate and efficient assessment based on probabilistic approaches  [23]. In this paper, Monte Carlo simulations are coupled with the finite element method and implemented in a software package (named ArcProg Z), in order to propose a consistent methodology for probabilistic assessment of the load bearing capacity of the vault. This methodology aims at minimizing the number of measurements and consequently the diagnosis cost. The first part of this paper presents the model assumptions (loads, materials, etc) and the second part is devoted to the study of masonry vault inspection of localized defect. Finally, the probabilistic modeling of inspection is carried out and the results are discussed with respect to the vault assessment.
Comparison with previous studies In order to validate the numerical model, the comparison is carried out with published theoretical and experimental results. Schwarzwasserbrücke bridge (Switzerland) has been modeled with RING2 software  [25]. With a span of 16. 75 m and a rise of 2. 84 m, this bridge is composed of 40 segments. The finite element analysis is performed by the program ArcProg Z using bridge input data and the comparison is considered regarding the two main results: hinge locations and critical load. As a results, the four hinges were found to be exactly at the same positions and the critical load given by RING2. 0 was 678. 4 kN, which is very close to the one given by ArcProg Z equal to 679. 3 kN (i. e. , the difference is around 0. 1%). The experimental results provide an interesting case-study for comparison.
However, in spite of the high success rate for selected targets, CONSRANK was ranked 9 th overall, due to failure for the more complicated cases. In comparison, the Bonvin's group-the firstranked scorer group-listed 18 successful targets, 14 of which with high or medium quality solutions. Apart from the three targets (T68, T77, T88) for which no correct solution was identified in the ensemble of the experiment, CONSRANK also failed on five other targets, for which other Scorers could identify few correct solutions. Of these targets, two (T70 and T71) were predicted homotetramers, which our contact-based approach at present cannot handle. Three (T72, T79 and T86) were predicted dimers. We would like to stress that the oligomeric state assignment for some of these targets is ambiguous and mostly unconfirmed; for instance, T70, assumed tetrameric at the time of the experiment, was finally listed as a dimer in the corresponding PDB entry (PDB ID: 4PWU).
Dataset Thirteen decoy sets for old CAPRI scoring models corresponding to fourteen interfaces and relative CAPRI classification in incorrect, acceptable, medium and high-quality models, were downloaded from the SCORE_SET site: http://cb. iri. univ-lille1. fr/Users/lensink/Score_set [25]. 3D models for the three CAPRI Round 30 targets (T72, T79 and T86) were also analysed. A classification in incorrect, acceptable, medium and high-quality models according to the CAPRI criteria  [13]  was obtained for all their six interfaces. A total of 20721 3D models were analysed.
Models scoring and selection For the original CONSRANK function, the whole set of models per each target was submitted to the CONSRANK code  [17]  and the 10 top ranked models were selected as predicted positive. In the clustering approaches (see below), after selecting the 10 top (most populated) clusters, the CONSRANK code was run on models belonging to each cluster and the model top ranked by CONSRANK for each cluster, for a total of 10 models, was selected. All the complex 3D representations were prepared with PyMol  [27]. Contact maps of the X-ray interfaces were obtained by COCOMAPS  [28]. Consensus maps for the model ensembles were obtained by the CONSRANK server  [18].
Clustering Two residues are considered in contact if they have any pair of heavy atoms within a distance of 5 Å. Then, a Hamming distance between the models is calculated based on the above defined contacts. For instance, a Hamming distance of 20 between two models means that they differ by 20 inter-residue contacts. Therefore, the absolute number of different contacts is used here as a distance measure between pairs of models, instead of the fraction of common contacts normalized over the number of contacts of either model, used as a similarity measure by Bonvin and colleagues  [23]. Python libraries ScyPy  [29]  and fastcluster  [30]  have been used in the following steps. Based on the above calculated metric, a distance n(n-1)/2 sized vector, where n is the number of models, has been obtained by the cluster.
The darker the dot, the more conserved the contact in the ensemble of analysed models/structures. T50. The T50 target is a SCORE_SET target corresponding to the de novo designed binding protein HB36. 3 in complex with influenza virus hemagglutinin (HA), cleaved into its two subunits, HA1, a large globular domain, and HA2, a long, helical domain anchoring the protein to the membrane (PDB ID: 3R2X,  [34] ). The HM36. 3 protein was successfully designed to bind a conserved surface patch on the stem of HA (HA2 subunit). As we have previously discussed  [17] , having many models pointing to the same false consensus is not the most probable event, as incorrect contacts are usually wrong in a different way, thus giving destructive interference and indeed we rarely observed this to happen  [17, 24].
However, models selected from other MC /10 clusters do explore other interfaces, with four of them correctly pointing to the stem region on the HA2 subunit, where the binding of the HB36. 3 designed protein is directed, and including two medium quality models, from the 3 rd and 6 th clusters (containing 75 and 43 models, respectively). T86. T86, the polyketide Cyclase from Sinorhizobium meliloti, was predicted to be homodimeric by both PISA and the structure authors (PDB ID: 4UI3). However, it features very small subunit interface areas, with the largest one being around 470 Å 2. The two largest interfaces according to PISA  [35]  were assessed in CAPRI, named interface 1 and 2. This is one of the three dimeric CAPRI Round 30 targets for which we were unable to submit any correct solution for either interface by the classical CONSRANK approach  [12].
This paper presents studies of the performance of several jet-substructure techniques, which are used to identify hadronically decaying top quarks with high transverse momentum contained in large-radius jets. The efficiency of identifying top quarks is measured using a sample of top-quark pairs and the rate of wrongly identifying jets from other quarks or gluons as top quarks is measured using multijet events collected with the ATLAS experiment in 20. 3 fb -1 of 8 TeV proton-proton collisions at the Large Hadron Collider. Predictions from Monte Carlo simulations are found to provide an accurate description of the performance. The techniques are compared in terms of signal efficiency and background rejection using simulations, covering a larger range in jet transverse momenta than accessible in the dataset.
0 jets. Beyond this p T , the statistical and systematic uncertainties become larger than 30% and 65%, respectively. The efficiency predicted by the POWHEG+PYTHIA simulation agrees with the measurement within the uncertainties. The ratio f data / f MC is consistent with unity, within uncertainties of ≈ 30% in the lowest and highest measured p T intervals and ≈ 15% between 250 and 450 GeV. The total systematic uncertainty of the efficiency measurements when integrating over the full p T range and the range 0 < |η| < 2 is given in Table  5. The total uncertainty is 12-20% for the substructurevariable-based taggers, 22% for SD, and 9. 9% for the HEPTopTagger. The largest uncertainty results from the choice of t t generator for the subtraction of the not-matched t t contribution, which introduces a normalization uncertainty in the acceptance region of the measurement (high top-quark p T ), because the p T -dependence of the cross section is different between POWHEG and MC@NLO.
Mistag rate Large-R jets identified in the background selection are used to measure the top-tagging misidentification rate (mistag rate). In each large-R jet p T bin i, the mistag rate is defined as f mistag data,i =        N tag data N data        i , (8) with N (tag) data the number of measured (tagged) large-R jets. The contamination from t t events is negligible before requiring a tagged top candidate. After requiring a HEPTopTagger-tagged top candidate, the average contamination is ≈ 3% (200 < p T < 700 GeV). It is smaller than 3% for p T < 350 GeV. For larger values of p T , however, the contamination from t t increases, as the large-R jet p T spectrum falls more steeply for multijet production than for t t events, leading to a contamination of up to ≈ 5% for 350 < p T < 600 GeV and ≈ 11% for 600 < p T < 700 GeV.
Summary and conclusions Jet substructure techniques are used to identify high-transverse-momentum top quarks produced in protonproton collisions at √ s = 8 TeV at the LHC. The 2012 ATLAS dataset is used, corresponding to an integrated luminosity of 20. 3 ± 0. 6 fb -1. Jets with a large radius parameter R are reconstructed and their substructure is analysed using a range of techniques that are sensitive to differences between hadronic top-quark decay and background processes. Jets are tagged as top jets by requirements imposed on the jet mass, splitting scales, and N-subjettiness, and by using the more elaborated algorithms of Shower Deconstruction (SD) and the original (not multivariate) HEPTopTagger. Six different combinations of requirements on substructure variables are investigated, five combinations denoted by taggers I-V and the W top tagger.
Acknowledgements 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 ;  BMWFW  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  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ;.
New processing capabilities are under development at the NEA Data Bank (DB) that aim to provide enlarged and enhanced nuclear data services to member countries in the framework of processing, verification and benchmarking of evaluated nuclear data. Within the context of the Joint Evaluated Fission and Fusion Nuclear Data Library Project (JEFF), we undertake to generate, with the latest version of AMPX processing code, a JEFF-3. 2 incident-neutron nuclear data application library for the SCALE tool suite. In this paper we describe the requirements, in terms of nuclear data content, for new data to be considered and used in the SCALE tool suite. An overview of how to process and prepare JEFF-3. 2 incident-neutron data with AMPX for its application in SCALE neutron transport codes is given. The resulting library is verified and tested at differential and integral level by comparing the performance of JEFF-3.
Introduction The OECD Nuclear Energy Agency (NEA) Data Bank (DB) is host to the Joint Evaluated Fission and Fusion (JEFF) Nuclear Data Library project since its creation in 1981. JEFF official releases of nuclear data are distributed in ENDF-6 format  [1]. Historically, the DB has also made available processed versions of neutron cross section libraries in ACE (A Compact ENDF) format for use with Continuous Energy (CE) Monte Carlo codes such as MCNP5  [2]. These processed libraries are available upon request from NEA or freely accessible from the web, e. g. for the latest release of JEFF: JEFF-3. 2 library  [3]. In order to expand JEFF's user-base to include other widely used neutron transport codes, the DB has undertaken to process neutron data of JEFF-3. 2, into application libraries for the SCALE tool suite  [4] , for criticality and shielding calculations.
This task is possible thanks to the AMPX processing code release since SCALE6. 2β3. In this paper, we describe how JEFF-3. 2 neutron data files are processed with AMPX into CE libraries. Problems encountered during processing are reported and, whenever possible, their implemented solutions. Verification tests and numerical benchmarks are performed to check correct usage of the resulting processed data. Finally, the performance of these CE libraries is assessed with a selection of criticality benchmarks used in MCNP6 validation  [5]. 2 Processing evaluated cross section data.
The JEFF-3. 2 neutron library The JEFF nuclear data library is a compilation of evaluated nuclear data files produced by a collaboration of Data Bank member countries which form the JEFF Project. The JEFF library consists in its diverse releases over the year of neutron and proton interaction data libraries, radioactive decay data libraries, fission yields, and thermal scattering law data libraries. This work only addresses the treatment of JEFF-3. 2 neutron data, which is the latest JEFF release of March 2014. The JEFF-3. 2 neutron data library provides cross section data for 472 nuclides and thermal scattering libraries (TSL) for 9 different compounds in ENDF-6 formatted files.