dataesr/scientific-paragraphs-categorization huggingface dataset:
As an example, recent imaging applications in benthic ecology have included the 2D analysis of sediment reworking, which requires the establishment of vertical profiles of luminophores concentrations. The first step of this procedure consists in defining the water-sediment interface, which can either be achieved manually or  (Romero-Ramirez et al. , 2013). Its second step consists in flattening this interface  (Maire et al. , 2006) , which consists in transferring its position to the first pixel row of each pixel column. At present, none of the corresponding procedures are included in the AVIExplore -ScriptEdit module. Here again, and depending on interactions with future users, we may integrate them within a later version of AVIExplore.
Acknowledgements This work was carried out within the  JERICO Integrated Infrastructure Intiative  project (grant agreement  n°262584 ). It was also supported by the  BIOMIN  project ( LEFE CYBER  and  EC2CO-PNEC ). We would like to thank  Ifremer  for providing access to the video of the MEDECO cruise used during the 'Coral mapping' case study. Ifremer is not responsible for the use of this video. All rights reserved. Alicia Romero  was financially supported by the  French Muséum d'Histoire Naturelle. Ludovic Pascal was supported by a doctoral grant of the  French Ministère de l'Enseignement Supérieur et de la Recherche  (ED -304 Science et Environnement).
Spatial data Three types of spatial data were involved in the proposed approach. (i) A stereo-photogrammetric 5 m resolution Digital Elevation Model (DEM) provided by the local government. Preprocessing was applied to this DEM to remove sinks and ensure continuous water flow paths  (Wang and Liu, 2006). (ii) The lattices of the field boundaries that were digitised from an airborne image of the study area were acquired in 2007. (iii) A 5 m resolution land use map with a simple classification (agriculture, natural area, track, or road) was obtained by visually interpreting the image evoked above.
Many of these scenarios are difficult to constrain using current LHC data, despite some having small sparticle masses. To assess how the data obtained by CMS impact SUSY in the context of the pMSSM, we use a representative subset of the results based on data corresponding to integrated luminosities of 5. 0 fb -1 at 7 TeV and 19. 5 fb -1 at 8 TeV. We use results from hadronic searches, both general searches and those targeting top squark production; also included are searches with leptonic final states, both general and EW-targeted. For a selected set of pMSSM parameter points, event samples were simulated using the CMS fast detector simulation  [31]  and analyzed. Since the fast detector simulation does not accurately model the detector response to massive long-lived charged particles, and since it was not feasible to use the CMS full simulation  [32]  given the large number of model points, we work within a subspace of the pMSSM in which the chargino proper decay lifetime cτ( χ ± ) is less than 10 mm.
MICROMEGAS is also used to compute the DM relic density, and the spin-dependent and spin-independent DM-nucleon scattering cross sections; these observables are not used in the construction of the prior, but we study how they are impacted by the CMS searches. The program SDECAY 1. 3  [59]  is used to generate sparticle decay tables and HDECAY 5. 11  [60]  to generate Higgs boson decay tables. For evaluating the Higgs boson signal likelihood based on the latest ATLAS  [61]  and CMS  [62]  measurements, we use LILITH 1. 01  [51, 52] , following the approach explained in Section 2. 3 of Ref. [63]. Y values. Additional information on signal strengths (and invisible decays) in one dimension is included analogously, using the published likelihood function when available or else the Gaussian approximation.
Although use of the binary likelihood entails a loss of information, it is a convenient approach in cases of non-disjoint data, where a proper likelihood calculation is not feasible without more information. In this study, we use binary likelihoods for monojet searches, which have overlapping search regions, and for combining the 7 TeV, and 7+8 TeV results, where the considered analyses use nondisjoint data. To compute likelihoods and Z-significances, expected signal counts for the search regions of each analysis are computed for the 7200 pMSSM points. The simulated events for each model point, which were generated using PYTHIA 6. 4  [68]  and processed with the CMS fast detector simulation program  [31] , are passed through the analysis procedures in order to determine the counts. For each pMSSM point, 10,000 events have been simulated.
The curvature of the lines is added to help distinguish between similar pMSSM points, but the trajectories of the lines between the axes do not carry physical information. A number of distinct scenarios are seen to have survived the CMS analyses. A minimum threshold of 20 fb has been applied to the 8 TeV signal cross sections to limit the scope to those points that could potentially still be probed with the Run 1 data set using an expanded set of analyses and techniques. )- 0 1 χ ∼ q → q ( q q ~)- 0 1 χ ∼ ± W → 1 ± χ ∼ ( 0 1 χ ∼ 1 ± χ ∼ )- 0 1 χ ∼ V/h → χ ∼ ( 2 0 χ ∼ 1 ± χ ∼ )- 0 1 χ ∼ b → b ( b b ~- - )*- 1 ± χ ∼ q → q ( q q ~- - - 0 1 χ ∼ 0 1 χ ∼ Figure  14:  A parallel coordinates plot showing several hundred selected nonexcluded model points for the six most common principal processes, with seven key properties.
It requires the availability of genomic sequences and relies on a specific digestion enzyme (e. g. , Cell), which is able to cleave heteroduplexes formed by the association of wildtype and mutated PCR products at the site of mismatch  (Till et al. , 2004). Two websites are associated to this population: the REVGENUK web-server (http://revgenuk. jic. ac. uk/order. htm) to request a reverse screen of your candidate gene and the phenotypic database (http://www. inra. fr/legumbase) to facilitate the identification of mutant line phenotypes and to request specific mutated lines.
, 2014). Tnt1 mutant population has been used in forward genetic screens using inverse-PCR or thermal asymmetric interlaced-PCR (TAIL-PCR) to recover the flanking sequences of the insertion  (Benlloch et al. , 2006; Cheng et al. , 2014). The use of this population in forward genetic screens was recently facilitated by the development of an algorithm, ITIS, which retrieves Tnt1 insertion sites from mutant line genomes using low coverage genome sequencing data  (Jiang et al. , 2015). Meanwhile reverse genetic screening has been developed, including PCR-based DNA pool screening for candidate genes, and more recently using a BLAST database containing flanking sequence tags (FST; i. e. , flanking regions of the Tnt1 insertions)  (Cheng et al. , 2014). All the information regarding the Tnt1 population such as request of mutated lines, description of mutant line phenotypes and a BLAST-able database of FSTs are available at http://medicago-mutant.
PHOJET PHOJET is a MC event generator  [8, 9]  designed for simulating soft and semi-hard hadronic interactions, suited for describing accelerator events selected with minimum bias triggers. Special care is taken to have a self-consistent model for all partial cross sections, including the interplay of soft, hard, as well as diffractive and non-diffractive interactions  [8]. Each inelastic interaction configuration is related through unitarity to a contribution to the elastic amplitude. While PHOJET was originally developed for hadron-hadron, photon-hadron, and photonphoton interactions (hadron = p/π/K)  [9] , it has later been extended and included as a building block in the DPMJET III MC package  [10]  to also apply it to hadron-nucleus  [11] , nucleusnucleus  [12] , and photon-nucleus interactions  [13, 14].
The measurements of the azimuthal decorrelations are shown at Fig. 8. 13. The measurements extend to ∆y = 8. The value of the average cosine obtained by ATLAS is bigger than the CMS result ( 0. 8 for ATLAS and 0. 6 for CMS in 7 < ∆y < 8 bin) which can be attributed to p T ordering of jets and higher p T of jets in case of ATLAS. POWHEG interfaced to PYTHIA as well as HERWIG overestimate the de-correlation. HEJ+ARIADNE package also overestimates the de-correlations. The ratio C 2 /C 1 is well-described by HEJ+ARIADNE package while POWHEG + parton shower underestimates the ratio. To summarize, measurements of forward-backward dijet production performed by different experiments do not allow to make clear conclusions concerning observation of BFKL signal. Although a good description of the azimuthal de-correlation by the NLL BFKL calculations is obtained, the DGLAP MC parton shower calculations give a surprisingly good description of the measurement over the full acceptance region.
RunII expectations BFKL resummation is performed for "pQCD high energy limit" asymptotic region or when the collision energy is much larger than the momentum transfer: s Q 2. Obviously the validity of a given relation will increase for jets measured at the same p T 's and increased collision energy (we imply here transition from 7 to 13 TeV). For further investigations, a dataset of ∼100 pb -1 is needed to perform careful measurements of dijet production at large rapidity intervals at pp collision energy of 13 TeV. An average pileup of 1 can be tolerated by analysis techniques used in LHC RunI. The ability to measure jets with low p T is important for approaching the high energy limit and revealing BFKL contributions into production of jets. There are two obstacles making this task rather challenging.
Within the limited statistical precision consistency is seen with both FEWZ and Pythia predictions (Fig. 8. 5 ). An analysis of the full Run 1 dataset of 3fb-1 will allow significant improvements to the measurement precision and could allow discrimination between theoretical models that include higher order twist or saturation effects. From the theoretical point of view, everything that was done for pp collisions can be done as well for heavy ion collisions. In particular, for pA collisions there is a good possibility of analyzing nuclear parton distributions when one can disregard final state effects. One could study the same problems as before, except that saturation, multiple parton scattering and higher twist effects are much more important in the pA setup. In this case, backward and forward rapidities are different, but both are interesting.
TOTEM Experiment TOTEM  [8, 9]  is a LHC experiment with two detectors embedded in the CMS experiment (T1 and T2 telescopes) and Roman Pot units integrated in the LHC beam lines on both sides of the interaction point IP5/CMS. Since the start of LHC in 2010 the T1, T2 detectors and the 24 Roman Pot installed at ±147 m and at ±220 m from IP5 were operated successfully to perform the physics program of TOTEM during Run-I. The detectors of TOTEM, designed and optimized for special runs of low luminosity, were used for measurements at low-and highβ * optics and fundamental results like the total p-p cross section at the LHC energy of √ s = In a common effort of CMS and TOTEM the spectrometer was installed during the LS1 period, while the development of the detectors (timing and tracking) is ongoing.
Acknowledgments Pieces of this chapter has been supported in part by  Polish Nation Science Centre  grants number:  2013/08/M/ST2/00320 ,  DEC-2011/01/B/ST2/04535  and  2012/05/B/ST2/02480. Some of the results presented here come from a fruitful collaboration with  Emilien Chapon ,  Sylvain Fichet ,  Gero von Gersdorff ,  Oldřich Kepka ,  Bruno Lenzi. Acknowledgements M. S. 's research was supported by the  US Department of Energy Office of Science, Office of Nuclear Physics  under Award No. DE-FG02-93ER40771. channels for the detection of high-x F very forward Higgs events.
The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: -How does the Earth system respond to forcing? -What are the origins and consequences of systematic model biases? -How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? 1 Introduction The Coupled Model Intercomparison Project (CMIP) organized under the auspices of the World Climate Research Programme's (WCRP) Working Group on Coupled Modelling (WGCM) started 20 years ago as a comparison of a handful of early global coupled climate models performing experiments using atmosphere models coupled to a dynamic ocean, a simple land surface, and thermodynamic sea ice  (Meehl et al.
, 1997). It has since evolved over five phases into a major international multi-model research activity  (Meehl et al. , 2000 (Meehl et al. , , 2007;; Taylor et al. , 2012)  that has not only introduced a new era to climate science research but has also become a central element of national and international assessments of climate change (e. g. IPCC, 2013). An important part of CMIP is to make the multi-model output publicly available in a standardized format for analysis by the wider climate community and users. The standardization of the model output in a specified format, and the collection, archival, and access of the model output through the Earth System Grid Federation (ESGF) data replication centres have facilitated multi-model analyses. The objective of CMIP is to better understand past, present, and future climate change arising from natural, unforced variability or in response to changes in radiative forcings in a multi-model context.
Such approaches, however, require an appropriate experimental design to facilitate the identification of an ensemble of models with particular properties drawn from different phases of CMIP (e. g. Rauser et al. , 2014). At the same time, it was recognized that an increasing number of Model Intercomparison Projects (MIPs) were being organized independent of CMIP, the data structure and output requirements were often inconsistent, and the relationship between the models used in the various MIPs was often difficult to determine, in which context measures to help establish continuity across MIPs or phases of CMIP would also be welcome. Second, the scope of CMIP was taxing the resources of modelling centres making it impossible for many to consider contributing to all the proposed experiments. By providing a better basis to help modelling centres decide exactly which subset of experiments to perform, it was thought that it might be possible to minimize fragmented participation in CMIP6.
, 2015; Teixeira et al. , 2014 ) and ana4MIPs projects. Examples of available tools that target routine evaluation in CMIP include the PCMDI metrics software  (Gleckler et al. , 2016)  and the Earth System Model Evaluation Tool (ESMValTool,  Eyring et al. , 2016) , which brings together established diagnostics such as those used in the evaluation chapter of IPCC AR5  (Flato et al. , 2013). The ESMValTool also integrates other packages, such as the NCAR Climate Variability Diagnostics Package  (Phillips et al. , 2014) , or diagnostics such as the cloud regime metric  (Williams and Webb, 2009)  developed by the Cloud Feedback MIP (CFMIP) community. These tools can be used to broadly and comprehensively characterize the performance of the wide variety of models and model versions that will contribute to CMIP6.
www. geosci-model-dev. net/9/1937/2016/ Geosci. Model Dev. ,  9, 2016  -A background volcanic aerosol should be specified that results in radiative forcing matching, as closely as possible, that experienced, on average, during the historical simulation (i. e. 1850-2014 mean). -Models without interactive ozone chemistry should specify the pre-industrial ozone fields from a data set produced from a pre-industrial control simulation that uses 1850 emissions and a mean solar forcing averaged over solar cycles 8-10, representative of the mean mid-19th century solar forcing. -For models with interactive chemistry and/or aerosols, the CMIP6 pre-industrial emissions dataset of reactive gases and aerosol precursors should be used. For models without internally calculated aerosol concentrations, a monthly climatological dataset of aerosol physical and optical properties should be used.
Acknowledgements. We thank the scientific community for their engagement in the definition of  CMIP6  and for the broad participation in the CMIP5 survey in 2013. We thank the co-chairs and steering committee members of the  CMIP6-Endorsed MIPs  for their continuous engagement in defining  CMIP6 , and the modelling groups and wider community for reviewing the CMIP6 design and organization. We thank the  WGCM Infrastructure Panel (WIP)  for overseeing the CMIP6 infrastructure,  Martin Juckes  for taking the lead in preparing the CMIP6 data request, and the group of scientists who are producing forcing datasets for CMIP6. Thanks to  Jonathan Gregory  for raising awareness about the treatment of volcanic forcing in the pre-industrial control experiment and its consequence for sea level changes, and to  Pierre Friedlingstein ,  George Hurtt ,  Chris Jones , and  David Lawrence  for help in defining carbon cycle and land-use specifications in the DECK experiments and CMIP6 historical simulations.
Methods Climex CLIMEX  [30, 31]  was used to fit a Compare Locations model to the distribution records for E. crassipes. In the CLIMEX Compare Locations module, selected climate response functions for population growth and stress are usually manually adjusted until a satisfactory agreement is reached between the modelled and known distribution of the study organism in the native range, and any introduced ranges besides those for which the model is needed  [48, 49]. This latter consideration is an attempt to address issues of climatic range expansion that are sometimes noted when species are introduced to locations outside of their native range  [36, 48, 50] , and when exploring issues of climate change  [51]. CLIMEX uses a set of fitted growth and stress functions to assess the potential for a species to persist and grow at each location for which relevant climatic data are available.
Climate data Two sets of climate data were used in this project. The E. crassipes model was fitted using the 1961-1990 climate normals derived from the CRU 0. 5 degree gridded dataset CL1. 0  [53]. The reformatting and derivation of temperature and relative humidity variables from this dataset for use in CLIMEX is described in  [37]. This dataset was used to project the potential distribution of E. crassipes under current climate conditions. The second set of climate data were derived from Global Climate Model's (GCM's) projecting future climatic conditions. The GCM data employed in this project were drawn from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset  [54]. Three models were selected because they satisfied the following criteria: (i) monthly means of daily minimum and maximum temperature, monthly rainfall total, and monthly surface level specific humidity for the A1B and A2 emissions scenarios were available, (ii) the models had a relatively small horizontal grid spacing, and (iii) the models had to have a high skill score  [55]  in representing the observed climate between 1980 and 1999  [56].
In recognition of the observations by Rahmstorf et al. [57]  and Manning et al. [58]  that recent global historical temperature and emissions observations indicate that global temperatures are tracking "hot", only the higher emissions scenarios (the "A" family) are included in this project, rather than the more conservative "B" family of scenarios. The three GCMs that best met the selection criteria are: (i) CSIRO Mark 3. 0 (CSIRO, Australia), (ii) NCAR-CCSM (National Centre for Atmospheric Research, USA), and (iii) MIROC-H (Centre for Climate Research, Japan). Data from these GCMs were pattern-scaled to develop individual change scenarios for the year 2080 relative to the base climatology  [59]. The three GCMs also cover a range of climate sensitivity i. e. , the amount of global warming for a doubling of the atmospheric CO 2 concentration compared with 1990 levels (CSIRO Mark 3.
Present distribution of Eichhornia crassipes The global distribution of E. crassipes was assembled from a large number of sources in the framework of the EPPO PRA on the species (  [28] ;  Fig 1). Eichhornia crassipes is distributed throughout the world, flourishing in tropical and subtropical regions, extending into Mediterranean climatic areas. The latitudinal limits of its distribution were considered to be 40°  [27] , until it expanded its northern distribution in Spain (Fig 1 ). A worldwide updated distribution of E. crassipes is maintained by EPPO in the EPPO Plant Quarantine Data Retrieval system (see www. eppo. int/DATABASES/pqr/pqr. hrm. According to  [14] , E. crassipes is native to Argentina. Whilst it is present in La Rioja but not in Formosa or Salta, the CLIMEX projections indicate that the climate appears favourable for E.
Acknowledgments This work was supported by the  Cooperative Research Centre for National Plant Biosecurity  project  10071 , a non-commercial Government-funded research funder. The funder played no part in study design, data collection or analysis, decision to publish, or preparation of the manuscript. The support does not alter our adherence to PLOS ONE Editorial policies and criteria. We thank the people and organisations that provided distribution and climate data:  Pam Fuller  and  Amy Benson  of  USGS ,  Michael Schmidt  from the  Department of Natural Resources  &.
The robots are controlled using R. O. S. (Quigley et al. , 2009) , the 3D Mapping and localization is carried out using RTABMap  (Labbe and Michaud, 2014)  and we use the navigation stack for the movements. Beginning with a 2D occupancy grid, we successfully mapped the dozen rooms we considered using two robots. In 9 we have the mapping done by each robot, Rooms R0, R1, R2, R5 and R10 for the first, Rooms R3, R4, R11 for the second, and the merging of both mapping in 10. Finally, we show a 3D mapping of all rooms and of the corridor in 11. We also created some Rtabmap databases for each action (movement and mapping) to show the possible outcomes using simulated data. These are available at http://tinyurl. com/zauoap8. We found that both policies (with and without groups) were really close in terms of the number of steps necessary to perform the experiment and their results.
PSSweb is based on PSS (2), a previously published command-line program, and is intended for facilitating its use by a wider audience. In the original PSS article, we illustrated the interest of the program through several examples of application to pertinent biological problems. A search of other programs able to perform the same analysis showed that, to date, there are none that can carry out equivalent tasks (see the PSS article (2) for more details). In the next sections, the improvements to the PSS program are first presented, followed by a description of the PSSweb server and by an example of application. A detailed description of the inner functioning of PSS, the stand-alone program behind PSSweb, is available from the PSS web page (http://thomasgaillard. fr/pss) and paper  (2).