dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary An extended set of five event-shape variables (the transverse thrust τ ⊥ , the jet broadening B tot , the total jet mass ρ tot , the total transverse jet mass ρ T tot , and the third-jet resolution parameter Y 23 ) have been studied in multijet final states measured in pp collisions at √ s = 7 TeV. Such observables are sensitive to perturbative and nonperturbative aspects of QCD, and allow the validation of hadronic event generators. The experimental distributions have been measured in five different ranges of leading jet transverse momenta from 110 < p T < 170 GeV up to p T > 390 GeV, and compared to the predictions of six different event generators. For the transverse thrust, all generators show an overall agreement with the data within 10%, with PYTHIA 8 and HERWIG++ exhibiting a better agreement than the others.
A 20% level of agreement is also found for the total transverse jet mass distributions. However, event-shape variables that are more sensitive to the longitudinal energy flow (such as the total jet mass) or to hard parton emissions (such as the jet broadening) show a larger discrepancy between data and parton shower MC simulations. The predictions of PYTHIA 6-D6T show better agreement with data for the event-shape variables that make use only of the jet p T , but have worse agreement for Y 23 compared to other PYTHIA 6 models. The modelling of colour connection between the soft scatters and beam remnants, and initial-and final-state radiations are the major sources of differences between the various QCD event generators. The generator that consistently reproduces all distributions within the uncertainties is the MADGRAPH matrix-element calculator combined with PYTHIA 6-tuneZ2 for multiparton interactions and parton showering and hadronization.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP ,.
EXPERIMENTAL CONDITIONS FOR CALIBRATION In  Baumgartner et al. (2013) , the model was calibrated to the actual performance of a pool of listeners for the so-called baseline condition, for which actual data (DTFs and localization responses) were collected in two studies, namely in Goupell et al. (  2010 ) and  Majdak et al. (2013). In both studies, localization responses were collected using virtual stimuli presented via headphones. While localization performance seems to be better when using free-field stimuli presented via loudspeakers  (Middlebrooks, 1999b) , we used virtual stimuli in order to better control for cues like head movements, loudspeaker equalization, or room reflections. In this section, we summarize the methods used to obtain the baseline conditions in those two studies.
HRTFs and DTFs In both  Goupell et al. (2010)  and  Majdak et al. (2013) , HRTFs were measured individually for each listener. The DTFs were then calculated from the HRTFs. Both HRTFs and DTFs are part of the ARI HRTF database  (Majdak et al. , 2010). Twenty-two loudspeakers (custom-made boxes with VIFA 10 BGS as drivers) were mounted on a vertical circular arc at fixed elevations from -30 • to 80 • , with a 10 • spacing between 70 • and 80 • and 5 • spacing elsewhere. The listener was seated in the center point of the circular arc on a computer-controlled rotating chair. The distance between the center point and each speaker was 1. 2 m. Microphones (Sennheiser KE-4-211-2) were inserted into the listener's ear canals and their output signals were directly recorded via amplifiers (FP-MP1, RDL) by the digital audio interface.
Apparatus In both studies, Goupell et al. (  2010 ) and  Majdak et al. (2013) , the virtual acoustic stimuli were presented via headphones (HD 580, Sennheiser) in a semi-anechoic room. Stimuli were generated using a computer and output via a digital audio interface (ADI-8, RME) with a 48-kHz sampling rate. A virtual visual environment was presented via a head-mounted display (3-Scope, Trivisio). It provided two screens with a field of view of 32 • x 24 • (horizontal x vertical dimension). The virtual visual environment was presented binocularly with the same picture for both eyes. A tracking sensor (Flock of Birds, Ascension), mounted on the top of the listener's head, captured the position and orientation of the head in real time. A second tracking sensor was mounted on a manual pointer. The tracking data were used for the 3-D graphic rendering and response acquisition.
Procedure For the calibration, the data were collected in two studies using the same procedure. In  Goupell et al. (2010) , the data were the last 300 trials collected within the acoustic training, see their Sec. II. D. In  Majdak et al. (2013) , the data were the 300 trials collected within the acoustic test performed at the beginning of the pre-training experiments, see their Sec. II. D. In the following, we summarize the procedure used in the two studies. In both studies, the listeners were immersed in a spherical virtual visual environment (for more details see  Majdak et al. , 2010). They were standing on a platform and held a pointer in their right hand. The projection of the pointer direction on the sphere's surface, calculated based on the position and orientation of the tracker sensors, was visualized and recorded as the perceived target position.
The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world. Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit. Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-data provided consistency checks within (self-tests) and among (cross-tests) assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models.
Simulations could be conducted for a range of specific stocks (i. e. based on real data and model fits to the data) covering a breadth of life history, data availability, and fishery type. The simulation exercise would then produce results tuned to the data for each stock, but patterns of results among stocks would also allow for generic advice and conclusions  (ICES, 2012a). Results from the SISAM exercise were reported and discussed during a 2-day workshop and 3-day symposium as part of the World Conference on Stock Assessment Methods (WCSAM) in Boston, MA, USA, in July 2013. WCSAM brought together stock assessment scientists from around the world and was uniquely suited for conducting, presenting, and discussing the proposed simulation exercise. The global level of participation broadened the applicability of the results.
Methods The SISAM Steering Committee with support of the ICES WGMG selected 14 datasets from stocks intended to cover a range of life histories, data availabilities, and assessment challenges for consideration in the SISAM exercise (Table  1 ; ICES, 2012a). Throughout this article, the term "real" data refers to the actual data used for conducting a stock assessment and providing management advice for a given stock, while the term "pseudo-data" refers to computergenerated observations with the characteristics detailed below. The models currently used to assess the stocks and to provide management advice include delay-difference models, age-based models, length-based models, as well as age-and length-based models  (ICES, 2012a). The details of the assessment models used in the SISAM exercise were not provided here in the interest of brevity and because these details were not necessary for understanding the results or conclusions.
The research covered in this article is a portion of that conducted for the SISAM exercise and was presented here as two steps (ICES, 2012a). For Step 1, the 14 real datasets were made available to stock assessment scientists throughout the world and the assessment model of their choice was fit using these real data. At this step, the assessment model was not necessarily optimized and may have contained issues of fit that in practice might require additional refinement. Most participants, however, suggested that assessment model fits were optimized (e. g. considered residuals, used measures of statistical fit), as might be done in an actual assessment. In Step 2, characteristics of the fit of a model to real data from Step 1 were used to generate pseudo-data with error. The pseudo-data were then made available to the participants and assessment models were fit to each simulated pseudo-dataset.
Pseudo-data generation with error For each fit of a stock assessment model to real data, 100 sets of pseudodata with error were generated using the age-based Population Simulator (PopSim; NOAA, 2013). An age-and length-based variant of PopSim has been used previously to evaluate the performance of stock assessments when natural mortality is misspecified  (Deroba and Schueller, 2013). The version used for SISAM, however, was strictly age-based. Owing to the age-based restriction of PopSim, pseudo-data were able to be generated only for assessment models that were also strictly age-based. Several other simulation exercises not using PopSim were carried out for the SISAM workshop, but are not covered in this article. PopSim required several inputs that were held constant among each of the pseudo-datasets: annual fully recruited fishing mortality rate, annual fishery selectivity at age, population size at age in the first year of the simulation, annual recruitment, annual mean weights-at-age associated with harvested and spawning fish, annual maturity at age, annual natural mortality at age, annual survey catchability, and annual survey selectivity at age.
Output metrics For assessment model fits to real and pseudo-data, time-series estimates of stock biomass (spawning stock in most cases, total biomass in others) and fishing mortality (fully selected in most cases, an average among ages in others) were recorded. For assessment model fits to real data from each stock, the time-series estimates were plotted and qualitatively examined for variation in scale and trend. Because the measures of biomass and fishing mortality were not standardized among all assessment model fits, each time series was also rescaled to have a mean of 1. 0 by dividing each time series by its average. A plot of the rescaled time series partially accounted for the issue of inconsistent measures and was easier to compare for variation in temporal trends. The fits of assessment models to pseudo-data with error allowed for two general types of comparisons, which were termed self-tests and cross-tests.
In self-tests, an assessment model was fit to the 100 pseudo-datasets generated in a manner consistent with the fit of the same assessment model (i. e. same modelling platform with the same structural assumptions and model settings) to real data for a given stock. Summary statistics (i. e. median, 10th percentile, 90th percentile) of the fits to the 100 pseudo-datasets were then plotted with results of the fit to the real data. The time-series estimates from the fit to real data provided a basis of comparison for the fits from the 100 pseudo-datasets. The term self-test was used because the assessment model fit to the real data and to the pseudodata were identical (i. e. same assessment platform, structural assumptions, and settings). In cross-tests, an assessment model was fit to 100 pseudo-datasets generated in a manner consistent with the fit of a different assessment model (possibly same assessment platform with different structural assumptions or model settings) to the real data for a given stock.
Summary statistics (i. e. median, 10th percentile, 90th percentile) of the fits to the 100 pseudo-datasets from a given assessment were then plotted together with the fit of the alternative assessment model to the real data. The time-series estimates of the alternative assessment model fit to real data provided a basis of comparison for the fits from the 100 pseudodatasets. The term cross-test was used because the assessment model fit to the real data and to the pseudo-data were different (i. e. different assessment platforms, different structural assumptions, or different model settings). Plots were qualitatively examined for consistency between the time series based on the fit to real data and the summary statistics from the fits to 100 pseudo-datasets. Conditional on a given assessment model fit and the errors added (e.
Results Thirty different assessment models were applied at some step of the SISAM exercise, either using different modelling frameworks or different structural assumptions within the same modelling framework (Table  2 ). Most assessment models were statistical catch at age, while the remainder were virtual population analysis, surplus production, or delay difference models (Table  2 ). Assessment models were fit to real data in 60 unique combinations covering 12 of the 14 datasets selected by the WGMG (Table  3 ). The extent of variation among assessment model runs depended upon the models applied. Greater variation in scale was generally evident when surplus production models that produced time series of fishable biomass were applied to the real data along with other assessment models that could broadly be classified as agebased assessment models that provided spawning-stock biomass.
A first step would be to audit code, and model settings to ensure the behaviour of the data-generating model (commonly called the "operating model") and -assessment model (commonly called the "estimation model") are consistent with expectations. In cases where the estimation model is not entirely consistent with the data-generating model (e. g. estimation model is a simplification of the data-generating model), as was common in this manuscript, another follow-up exercise would be to apply the assessment model to pseudo-data generated without errors. Continued divergence would suggest that the cause is structural uncertainty, while consistent results would suggest that the errors themselves are the cause. These types of follow-up exercises have not yet been conducted for results of the SISAM workshop and symposium due to the scale of the undertaking, but are planned for the future.
Divergence in cross-tests was also common. The assessment models with divergent results in cross-tests during SISAM also had divergent results in self-tests. Divergence in self-tests had generally been unexpected, and understanding the reasons for such lack of robustness in self-tests should take priority over understanding reasons for divergence in cross-tests. Thus, explanations for divergence in cross-tests of the SISAM exercise have not yet been explored. A possible way to conduct such explorations would be to fit the assessment model to the pseudo-data, but to impose a penalty on the likelihood for deviations from the time series based on the fit to real data. A comparison of the likelihood component values for the assessment model with and without the penalty should reveal the data source or parameters largely responsible for inducing the divergence.
Furthermore, initially conducting cross-tests Figure  6. Results of self-tests for Georges Bank yellowtail flounder for a statistical catch-at-age model with a random walk in catchability (a and b) and a virtual population analysis with a random walk in natural mortality (c and d). The same assessment model with identical settings was used in the fit to real data as in the fits to the pseudo-datasets summarized as percentiles in the graphs. using the same modelling framework but with two different sets of structural assumptions or model settings might be informative and more tractable because the chances for divergence induced by unforeseen structural uncertainties would be diminished and allow the analyst greater control. Divergence in self-and cross-tests was especially common for models that featured temporal random walks for some parameters (e.
Future research, however, could expand the general SISAM simulation methodology to examine a range of specific topics, including evaluations of sampling programme investments, causes and consequences of retrospective patterns, the extent of complexity of the data-generating model (e. g. sexual  dimorphism, ecosystem models, stock structure, spatial heterogeneity, fleet dynamics), data availability, and degree of data aggregation (e. g. error in age/length composition, tagging data, predatory consumption). Exploring specific topics, however, will likely require continued international collaborations to achieve enough treatments among stocks so that broad conclusions can be drawn, as initiated by SISAM. SISAM utilized PopSim as a centralized pseudo-data-generating model (i. e. operating model).
g. underlying statistical distributions) for some self-and cross-tests. Nonetheless, divergence caused by differences between the estimation model and data-generating model still suggests a lack of robustness. Continued simulation research like that of SISAM should carefully weigh the competing objectives of centralized control and equitable treatment against consistency between the pseudo-data-generating and assessment models. Data from the 14 stocks used in SISAM were made available to all participants, and questions raised by the participants when applying an assessment model to data (real or pseudo-data) from a given stock were resolved by an individual expert, often with help from a single assessment scientist who acted as liaison between participants and stock experts. This approach created an unbalanced workload for the stock experts and liaison, and this often led to delays in resolving questions.
Furthermore, in practice a model is often applied to a real dataset for the first time in the context of an extensive review process that includes time for dialogue and repeated model fits to ensure optimized tuning for the given situation. Time constraints prevented a thorough review of model fits during the SISAM exercise so that some results might be driven by suboptimal assessment model settings. While controlling for this issue might be preferred, the possibility of results being driven by suboptimal settings does illustrate the importance of practitioner expert in the assessment process. An alternative organizational scheme that might alleviate some of the problems above would be to stratify participants by stock, objective to be addressed, or some other feature of interest (e. g. assessment model type).
Stock experts could then respond to questions from a subset of participants only, ensure proper interpretation of data (e. g. units), and could be more directly involved with each application of an assessment model to a given dataset. This stratification scheme would have the benefits of more efficient communication and would be less error prone because participants would have better knowledge of a single stock in contrast to limited knowledge of multiple stocks. Some drawbacks, however, would be that participants may not get to research all stocks or questions that interest them, and distilling results into broad conclusions would require coordination among the stratified groups. In conclusion, the simulation exercise highlighted the following issues: (i) Different models were consistent in regard to estimating trends, but did not consistently estimate the scale of absolute biomass.
Acknowledgements We are grateful to the participants and support staff of the WCSAM workshop and symposium. We also thank many agencies and stock assessment scientists who were willing to dedicate their data and time to these efforts. Likewise, this research would not have been possible without the work of those responsible for fishery data collection and storage. We also offer this research and associated future endeavours in memoriam of  Alberto Murta , who passed away during preparation of this article. This research addresses the good practices in stock assessment modelling programme of the  Center for the Advancement of Population Assessment Methodology (CAPAM).
PBS-rinsed sections were incubated in avidin-biotin-peroxydase complex (ABC reagent; Vector Laboratories, 1:1000) for 1 h. Signal was revealed using the peroxidase-substrate-kit-DAB, as recommended by Vector Laboratories. Quantification of c-Fos immunopositive cells was done semi-automatically using Mercator Explora-Nova software (La-Rochelle, France). CPU and NAc regions were delineated according to Paxino's mouse brain atlas. For, drug-induced c-Fos expression, mice received an acute challenge of saline or amphetamine, and were perfused 1 h later.
MICRO-IONTOPHORESIS AND IN VIVO RECORDINGS Electrophysiological recordings of NAc medium spiny neurons were performed during the diurnal phase. The experimenter was blind to the genotype during recordings. Mice were anesthetized with chloral hydrate (5. 0 mg/kg, i. p. ) and mounted in a stereotaxic apparatus. The lateral tail vein was catheterized to administer additional anesthetic or drugs. Body temperature was monitored and maintained with a heating pad at 36. 5-37. 0 • C. Standard electrophysiological procedures were employed. The electrode signal was amplified 2000 times with an AC high impedance amplifier, band pass filtered at 0. 4-1 kHz (Dagan 2400A, Minneapolis, MN), and digitized with an interface board at 10 kHz (Digidata 1440A, Axon Instruments Inc. , Foster City, CA) and fed to a computer for offline analysis.
These effects seem mediated by alterations of integration of glutamate signaling within the striatum and NAc. On the other side, behavioral responses to food remained unchanged in the absence of GR within dopaminoceptive neurons. These results could be interesting in the context of the development of new medications able to decrease sensitivity to abused drugs while sparing general motivation for natural reinforcers. the Mission Interministérielle de Lutte contre la Dépendance et la Toxicomanie. François Tronche's group is member of the LabEx BioPsy and the DHU PePSY, Sébastien Parnaudeau and Jacques Barik have been supported by the UPMC and the Collège de France (Sébastien Parnaudeau) and the Fondation pour la Recherche Médicale (Jacques Barik).