dataesr/scientific-paragraphs-categorization huggingface dataset:
When a target position is provided it is the "locomotion pattern" that defines how the robot goes to this position. We provide two locomotion patterns: the constant linear speed pattern and the fish-like pattern. When applying the constant linear speed locomotion pattern a FishBot follows the target at a parametrised constant speed. The control is done using Fig. 3. Overview of the Control And Tracking Software (CATS), used to save videos of the experiments, to track in real-time the positions of the fish and of the robots, and to control robot behaviour. The video stream from the main camera fixed above the setup is compressed and saved on disk in high resolution (2040 × 2040 pixels) for further analysis. It is also converted to a lower resolution (500 × 500 pixels) and published on the Internet (streamyfish.
Several tracking methods are available, which methods to use is defined in the configuration file. The software provides a GUI that displays input video streams with the tracking information. Fig. 5. Overview of the robot control sub-system of CATS. Several kind of control modes (behaviours) and locomotion patterns are available, and can be selected by the user in the user interface. For the FishBots, the desired speed of each wheel is sent through a serial connection via the ASEBA network, in which all the FishBots are connected and can receive or emit events. For the RiBots, the communication is only one way, where the IR RC5 signal is broadcast to control stepper motors that drive the caudal peduncles. Fig. 6. The merge of the tracking results from (left) the camera above the setup that tracks all the agents, RiBots and fish, and (right) the camera below the setup that tracks only FishBots.
M32 does not appear in three of the panels due to its high central surface brightness (11) and velocity dispersion (92). are in the thin plane of co-rotating galaxies identified by  Ibata et al. (2013). 2. 2. Kinematic and Abundance Data from the Literature In Table  2 , we have assembled data from the literature for the kinematics and chemical abundances for the IS-LAndS sample galaxies. These data come from spectroscopic studies of individual RGB stars in these galaxies and represent a significant investment of ground-based (especially Keck) observing time. We have two reasons for the compilation in Table  2. First, we would like to present the original sources for reference for the masses and chemical abundances for discussion of the ISLAndS galaxies. Secondly, while there has been tremendous progress on this front, we would like to highlight that more work is still needed.
Data Reduction We analyzed images taken directly from the STScI pipeline (bias, flat-field, and image distortion corrected) working with the charge transfer efficiency corrected images (i. e. ,. flc images). Two PSF-fitting photometry packages, DAOPHOT/ALLFRAME  (Stetson 1994)  and DOLPHOT, an updated version of HSTPHOT with ACS and WFC3 specific modules  (Dolphin 2000) , were used independently to obtain the photometry of the resolved stars. See  Monelli et al. (2010b)  for more details about both photometry reduction procedures. Individual photometry catalogs were calibrated using equations provided by the STScI (e. g. ,  Sirianni et al. 2005)  with the most recent updates (e. g. , ACS ISR 12-01). The differences between the two sets of photometry are small and typical for obtaining HST photometry with different methods  (Hill et al.
1998; Holtzman et al. 2006) , and so, for simplicity, the rest of this paper is based on only the DOLPHOT photometry datasets. Signal-to-noise limitations, detector defects, and stellar crowding can all impact the quality of the photometry of resolved stars with the resulting loss of stars, changes in measured stellar colors and magnitudes, and systematic uncertainties. To characterize these observational effects, we injected ∼ 10 6 artificial stars in the observed images and obtained their photometry in an identical manner as for the real stars. Monelli et al. (2010b)  and  Hidalgo et al. (2011)  provide detailed descriptions of the procedures we adopt for the characterization and simulation of these observational effects.
In this regard, ground-based observations extending below the oMSTO (e. g. ,  Okamoto et al. 2008 Okamoto et al. , 2012) )  can be competitive for providing accurate SFHs. As part of the ISLAndS project, we are reanalyzing a large collection of ground-based observations of MW companion galaxies to produce a single, uniform analysis. For this reason, we consider the following comparisons preliminary, and will re-visit the exercise with well-defined uncertainties for all MW companion galaxies at a later date. Nonetheless, we feel that the patterns revealed in this study are quite robust, even if the individual data points may experience small shifts in the final analysis. Note also that these comparisons are all in the regime of small number statistics. A single addition or subtraction of a galaxy can make a significant impact on the impression of these comparisons.
Choi et al. (2016)  present a useful comparison of some of the differences between existing databases. Dramatic differences in the bin-to-bin SFRs are minimized in the cumulative SFHs plots, but the positions of steep changes in the SFHs can be seen to shift systematically with choice of stellar evolution library. Note that the differences are consistent between the galaxies, i. e. , the MIST and BASTI solutions form stars the fastest, the Padova and Dartmouth solutions are intermediate and the PARSEC solutions form the stars the slowest. Thus, when comparing SFHs between galaxies or between groups of galaxies, it is imperative to approach the data with as uniform a methodology as possible, and especially, to use the same stellar libraries. Ideally, comparisons should be conducted using a variety of libraries such that a sense of the systematic uncertainty associated with the choice of the library can be achieved.
Support for this work was provided by  NASA  through grants  GO-13028  and  GO-13739  from the  Space Telescope Science Institute , which is operated by  AURA, Inc. , under  NASA  contract  NAS5-26555. Support for DRW is provided by  NASA  through  Hubble Fellowship grant   HST-HF-51331. 01awarded by the  Space Telescope Science Institute. The computer network at  IAC  operated under the Condor software license has been used. Authors SH and AA are funded by the  IAC  (grant  P3/94 ) and, with SC, by the  Economy and Competitiveness Ministry of the Kingdom of Spain  (grant  AYA2013-42781P ). Authors CG, CEM-V, and MM are funded by the  IAC  grant  P/301204  and by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the grant (project reference  AYA2014-56795-P ). This research has made use of  NASA's Astrophysics Data System Bibliographic Services  and the  NASA/IPAC  Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
We introduce ScannerBit, the statistics and sampling module of the public, open-source global fitting framework GAMBIT. ScannerBit provides a standardised interface to different sampling algorithms, enabling the use and comparison of multiple computational methods for inferring profile likelihoods, Bayesian posteriors, and other statistical quantities. The current version offers random, grid, raster, nested sampling, differential evolution, Markov Chain Monte Carlo (MCMC) and ensemble Monte Carlo samplers. We also announce the release of a new standalone differential evolution sampler, Diver, and describe its design, usage and interface to ScannerBit. We subject Diver and three other samplers (the nested sampler MultiNest, the MCMC GreAT, and the native ScannerBit implementation of the ensemble Monte Carlo algorithm T-Walk) to a battery of statistical tests.
Introduction Science has entered an era of increasing computational complexity. Large data sets and burgeoning model complexity have necessitated the development of increasingly sophisticated and efficient analysis techniques. As datasets and theories in particle physics and cosmology have become more computationally expensive to work with, the problem of efficiently and comprehensively sampling model parameter spaces has become steadily more challenging. Simple random parameter sampling (e. g. [1, 2] ) has gradually proven more inadequate as time goes on, as it typically leads to incomplete and biased inferences when applied to all but the simplest problems. Workers in various fields have employed increasingly advanced numerical and statistical methods to deal with this challenge. Bayesian numerical techniques such as Markov Chain Monte Carlos (MCMCs) became particularly popular in cosmology, because of their theoretical near-linear scalability with parameter dimensionality.
In this paper we provide a detailed manual for Scan-nerBit, a package designed to provide a common interface to a range of different sampling algorithms, so that the performance of the different algorithms can be easily compared, and the most appropriate algorithm (or combination thereof) chosen for the problem at hand. We also carry out some such comparisons of sampling algorithms, and provide recommended settings for different samplers. ScannerBit is designed to be modular and expandable, allowing it to access a multitude of different samplers in a plug and play fashion. As the GAMBIT project grows, we will continually add scanners to the ScannerBit suite. Users can also easily implement various scanners to meet their personal needs. ScannerBit initially ships with four production-quality scanners: an adaptive MCMC (GreAT), an ensemble MCMC (T-Walk), a nested sampler (MultiNest) and a differential evolution sampler (Diver).
ScannerBit can either be used within its parent code GAMBIT  [29] , or as a standalone package, or simply interfaced directly to an external likelihood function. We begin by describing the ScannerBit package in Sec. 2, before giving the implementation details and the underlying statistical methods that we employ in Sec. 3. The user interface is covered in Sec. 4 , and the simple scanners in Sec. 5. Secs. 6-10 respectively describe the postprocessor, MCMC, ensemble MCMC, nested sampler and differential evolution samplers. In Sec. 11 we perform a detailed comparison of the different algorithms implemented in ScannerBit, and their available parameters and options. We summarise in Sec. 12, then provide an extensive set of appendices. These cover the sources, options and outputs of our differential evolution sampler Diver (Appendix A), ScannerBit options and outputs for all five major scanners (Appendix B), examples of how to implement new priors (Appendix C), examples of adding new scanners and objective functions (Appendix D), some supplementary comparisons of scanner performance (Appendix E), a minimal example input file (Appendix F), and a glossary of the most commonly-used GAMBIT terms (Appendix G).
The results of Step 3 are output in each iteration, in a format of the user's choice. Any output format supported by the GAMBIT printer system can be chosen, including ASCII and HDF5 database files. The GAM-BIT printers are described in detail in Sec. 9 of Ref. [29] , and ScannerBit's interface to them is described in Sec. D. 4. Both ASCII and HDF5 outputs can be parsed and plotted as profile likelihoods with pippi  [36] , which can be installed automatically from within GAMBIT (or ScannerBit) by typing make get-pippi from within the build directory. As the printer output is handled in Step 3, independent of the sampling algorithms responsible for Step 1, every parameter set tested is printed, along with every quantity derived from this set, regardless of whether the scanner accepts the tested point or not.
ScannerBit plugins ScannerBit is designed to be completely modular and expandable. It achieves this via a plugin interface, which allows various scanners and likelihood functions to be connected at will. Plugins are either scanner plugins, which each contain code implementing a single sampling algorithm, or objective plugins, also known as test function plugins, which contain specific objective functions to be scanned (such as simple test functions and likelihoods). Scanner plugins are responsible for efficiently navigating the unit cube in Step 1, whereas objective plugins provide the user-specified function in Step 3. Each plugin is compiled into an independent library with a common interface to ScannerBit, so that at runtime it can be passed necessary information like the dimensionality of the space being scanned and the user's preferred method of outputting the results.
Statistics and scanning To run a parameter scan in GAMBIT, the user writes an input YAML file specifying that they want to analyse a particular model. They indicate the parameter ranges and priors over which GAMBIT should sample that model, how that sampling should be done, and what quantities should be computed for each parameter combination. GAMBIT activates the model in its model database, along with all other models that the model in question is a subspace of. The dependency resolver uses the activated model hierarchy and the list of the user's requested quantities to activate and connect various module functions into a dependency graph (see Ref. [29] ). ScannerBit is then responsible for determining which parameter combinations to run through the dependency graph. Every quantity requested for calculation in a scan must be assigned a purpose in the input YAML file, using the eponymous option purpose.
ScannerBit standalone executable Like other GAMBIT modules, ScannerBit can be compiled into a standalone executable, and used independently of GAMBIT. This can be useful for sampling external objective functions that do not come from GAMBIT. The build command is simply make ScannerBit_standalone which creates the executable ScannerBit_standalone and places it in the main GAMBIT directory. The interface of the ScannerBit _standalone is similar to that of GAMBIT itself. Launching ScannerBit_standalone -f yaml_file runs a scan defined in the file yaml_file. To replace rather than resume from any existing files when beginning a scan, use the -r option. ScannerBit_standalone also provides a diagnostic list of recognised scanners and objective plugins à la GAMBIT, using the commands ScannerBit_standalone scanners and ScannerBit_standalone objectives (or simply ScannerBit_standalone plugins to see both together).
yaml or objective_locations. yaml files cannot be located. Finally, a status of excluded indicates that the plugin was -Ditched from the configuration of the code when CMake was invoked. The Header & Link Info section contains include and link paths of headers and libraries requested by the plugin, information about which of them were found, and a list of all input file options that the plugin requires to be defined in order to run. Finally, the Description section contains a short description of the plugin. This typically includes recognised input file options and a description of the algorithm or function that the plugin provides.
When calling these functions, the driver program must pass the address of another, user-supplied, likelihood/objective function, which Diver then minimises. The package includes example driver programs and objective functions in Fortran, C and C++; these can be respectively found in the example_f, example_c, and example_cpp subdirectories of the main Diver installation directory. Synopses of the different source files in Diver, the various run options it offers, and the format of its outputs can be found in Appendices A. 1, A. 2 and A. 3, respectively.
Diver Diver is a differential evolution optimisation package that is also highly effective at sampling parameter spaces. The size of the evolving population is determined by  Fig. 8 : Marginalised posterior probability density maps from a 15-dimensional scan of the scalar singlet parameter space, using the MultiNest scanner with a selection of difference tolerances (tol) and numbers of live points (nlive). Note that the colourbar strictly only applies to the rightmost panel, and that colours map to the same enclosed posterior mass on each plot, rather than to the same iso-posterior density level (i. e. the transition from red to purple is designed to occur at the edge of the 1σ credible region, and so on). The posterior mean is shown with a grey bullet point. the NP parameter, and the threshold for convergence is controlled by the convthresh parameter.
It can also post-process existing sets of samples previously computed and saved with GAMBIT. ScannerBit can be used from within GAMBIT, or as a standalone package independent of GAMBIT, allowing the user to connect to an arbitrary likelihood function and sample it using their desired algorithm. In addition to ScannerBit itself, we have presented a new standalone sampling package based on differential evolution: Diver. Diver features a full suite of differential evolution variants, from standard rand/1/bin to adaptive and discrete versions, and additional operation modes designed to provide approximate Bayesian results. We have also presented a new implementation of the T-Walk algorithm, implemented natively in ScannerBit. We compared the performance of the four main sampling algorithms interfaced to ScannerBit in GAMBIT 1.
permit_discard_old_likes[false]: When set to false, this option forbids the purpose chosen for like from clashing with any data label in the input samples. For example: if the original purpose was LogLike, a different purpose must be chosen for like, or an error will be thrown. If this option is set true, then clashes are permitted, and will be resolved in the new output by replacing the old data with the newly-computed data (as occurs automatically for all other clashes between old and new dataset names). This option also applies to likelihood components listed in add_to_like, subtract_from_like, and reweighted_like. If set to false then these names may not be recomputed during postprocessing. update_interval[1000]: Defines the number of iterations between messages reporting on the progress of the postprocessing.
reader: Options under this item specify the format of the old output file to be read, along with e. g. the path at which the file is located. The required options differ depending on which GAMBIT printer was used to save the results of the previous scan. The final option, reader, is used to inform the postprocessor of the format and location of the old data that needs to be reprocessed. In this first release of GAMBIT there are only two possible printer formats, ascii and hdf5, as described in  [29]. There are therefore at present only two sets of options that may be specified for the reader. For files created with the hdf5 printer: type: hdf5 file: Path to the HDF5 file containing the data to be parsed group: Group within the HDF5 file containing datasets to be parsed. For ascii output: type: ascii data_filename: Path to the ASCII file containing the data to be parsed info_filename: Path to the ASCII 'header' file that contains the labelling information for the columns of data_filename.
9999 times the value of model_invalid_for_lnlike_below in the likelihood node of the KeyValues section of the main YAML file verbose is instead referred to by the YAML option verbosity, and defaults to 0 instead of 1. Note that doBayesian is not available as a YAML option, and is hard-coded to false; there are multiple other scanners available in ScannerBit more efficient and accurate at scanning the Bayesian posterior than Diver. Correspondingly, maxNodePop and Ztolerance are not offered as YAML parameters either. Any user especially interested in obtaining posteriors from Diver running within ScannerBit should find this relatively easy to recode by comparison with e. g. the MultiNest or GreAT interface. can used to initialise, allocate, or deallocate variables needed by the plugin. The plugin_main block defines the function that will be run by the plugin.
The form of the arguments for plugin_main required by ScannerBit depends on whether the plugin is a scanner plugin or an objective (test function) plugin. For scanner plugins, plugin_main must take the form void plugin_main() { code } where code is the code that actually drives a statistical sampling algorithm. We give a full example of a minimal scanner plugin in Appendix D. 5. 1. Objective plugins can be further categorised into 'likelihood' plugins, which compute likelihoods, and 'prior' plugins, which provide the transformation function needed to implement a ScannerBit prior (see Sec. 3). For likelihood plugins, plugin_main must be of the form double plugin_main(const std::vector<double>&) whereas for prior plugins, the required form is void plugin_main(const std::vector<double>&, std::unordered_map<std::string, double>&) We give a worked example of a minimal likelihoodoriented objective plugin in Appendix D.
D. 6. 1: Objective plugin example Here we give a simple example of an objective plugin declaration contained in the ScannerBit source code (ScannerBit/src/objectives/examples. cpp). This example declares a scanner plugin EggBox, version 1. 0. 0. It returns a likelihood of the form: P(x, y) = 2 + cos π 2 x cos π 2 y 5. (D. 2) and fifteen. However, for clarity we only displayed twodimensional profile likelihoods for the 15-dimensional scans (Figs. 7-9, 11, 12, 14 and 15). In this Section we present the equivalent plots to these for the twodimensional scans. In some cases, where the optimal settings depends strongly on dimensionality, we have chosen different sampler settings in two than in fifteen dimensions, so as to allow a meaningful comparison. The quality of the profile likelihood is dramatically better in the two-dimensional scans than in the fifteendimensional equivalents.
Acknowledgements We thank the other members of the  GAMBIT Collaboration  for helpful discussions. We warmly thank the  Casa Matemáticas Oaxaca , affiliated with the  Banff International Research Station , for hospitality whilst part of this work was completed, and the staff at  Cyfronet , for their always helpful supercomputing support. GAM-BIT  has been supported by  STFC (UK ;  ST/K00414X/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ), Glasgow University (UK; Leadership Fellowship), the  Research Council of Norway  ( FRIPRO 230546/F20 ), NOTUR (Norway;  NN9284K ), the  Knut and Alice Wallenberg Foundation (Sweden; Wallenberg Academy Fellowship) , the  Swedish Research Council  ( 621-2014-5772 ), the  Australian Research Council  ( CE110001004 ,  FT130100018 ,  FT140100244 ,  FT160100274 ),  The University of Sydney (Australia ;  IRCA-G162448 ),  PLGrid Infrastructure (Poland) ,  Polish National Science Center (Sonata   UMO-2015/17/D/ST2/03532 ), the  Swiss National Science Foundation  ( PP00P2-144674 ), the  European Commission Horizon   2020 Marie Skłodowska-Curie actions  ( H2020-MSCA-RISE-2015-691164 ), the  ERA-CAN+ Twinning Program  ( EU & Canada ), the  Netherlands Organisation for Scientific Research  ( NWO-Vidi 680-47-532 ), the  National Science Foundation.
The database, semantic web, and linked data communities have proposed solutions that federate queries over multiple data sources using a single data model. Nowadays, the data retrieval requirements originating from versatile and broad domains like healthcare and life sciences (HCLS) are changing this conventional trend -of federating query over a single data model -primarily due to the simultaneous use of different data models (CSV, JSON, RDB, RDF, XML, etc. ) in a real-life scenario. It's now impractical to assume that the variety (graph, key-value, stream, text, table, tree, etc. ) of high volume data residing in specialised storage engines will first be converted to a common data model, stored in a general-purpose data storage engine, and finally be queried over the Web. Nevertheless, in this era where genomics datasets are growing from petascale to exascale, it is now important to exploit such vast domain resources in their native data models.