dataesr/scientific-paragraphs-categorization huggingface dataset:
PYHRF The installation of pyhrf relies on the setuptools python package and requires the following dependencies: numpy 7 and scipy 8 for core algorithms, nibabel for nifti or gifti input/outputs, nipy for the GLM implementation and parcellation tools, matplotlib for plots and PyQT4 for GUIs. Optional dependencies comprise joblib, scikit-learn and soma-workflow. pyhrf is mainly intended for linuxbased distributions as it has especially been developed under Ubuntu. Installation notes and documentation can be found online at http://www. pyhrf. org. Within the package, the following data files 9 are shipped: r two volume-based fMRI data sets (paradigm as CSV files, anatomical and BOLD data files). One serves quick testing while the other is intended for validation/demonstration purpose, which is used to generate results in section 4.
3, r one surface-based fMRI data set mainly intended for testing, r several simulation resources in the form of png images to provide 2D maps of various activation labels and HRF territories. The rest of this section is organized as follows. First, the overall workflow of how to use pyhrf is presented, which mainly resorts to command lines and some dedicated GUI tools. Second, to go further into the package architecture and also to address some features available when scripting, the design of pyhrf is introduced. Third, distributed computation is explained in terms of resource handling. Finally, the pyhrf viewer is presented with a focus on ergonomics.
DESIGN An overview of the static design of the main package components of the package is shown in Figure  3. The class FmriData is the within-subject fMRI data representation, for any spatial support: on the cortical surface, in the volume, or from a simulation. This data representation comprises spatially flat data (fMRI time series and parcellation) and a connectivity matrix which holds the data topology. At the center of the analysis component is the Analyzer class that handles parcelwise data splitting which is done according to the input data parcellation by default, and also takes care of merging parcelspecific outputs at the end of the analysis. This Analyzer class is then specialized into various method-specific analyzers: GLM, RFIR and JDE. Note that the analyzer component is decoupled from the data component, as is classically done in scientific programming because they do not have the same life-cycles (e.
To handle these two merging operations, stack and merge functions are provided. The reverse process is also available via the method explode which allows an array to be split according to a mask composed of integers, i. e. , a parcellation. It returns the dictionary of "flat" parcel-specific data arrays associated with each integer label present in the mask. In terms of data life cycle, xndarray objects are used to prepare data before analysis and to pack results after analysis. During the analysis process, it is more convenient to work with numpy arrays directly. The following code snippet illustrates the use of xndarray objects: functional and parcellation data are loaded, within-parcel means are computed and the result is saved to nifti:.
VIEWER pyhrf_view is a dedicated viewer built on PyQt4 which embeds a matplotlib view. The purpose of pyhrf_view is to provide convenient browsing of volume-based data 13. However, it does not provide advanced overlaying features such as the display of functional over anatomical data. Instead, to plot the final "publication-ready" maps after having selected the results of interest with pyhrf_view, one can resort to the command pyhrf_plot_slice to directly generate a slice image of functional rendering along with anatomical overlay. One can also use a third party viewer such as Anatomist 14 , FSL_view 15 or xjview 16. pyhrf_view offers n-dimensional browsing while most viewers in neuro-imaging software handle up to 4D volumes. In fact, there is a limit to the number of dimensions inherent to the nifti format which permits 7 axes at maximum.
ARTIFICIAL DATA GENERATOR Simulations in pyhrf mainly consist of building a script that defines a pipeline of versatile simulation bricks presented in Table  3. Writing a simulation script as a sequence of functions makes things difficult to read and to reuse. Instead, all simulation bricks are gathered inside a python dictionary that maps a simulation label to its corresponding value. This value can be directly defined as a python object or as a function which can depend on other simulation items and which is called when the simulation The "localizer" paradigm is described in  Pinel et al. (2007). Hand-drawn maps for activation labels are in the form of png images. Gaussian smooth generation of HRFs stands for the regularized prior used in the JDE model. pipeline is evaluated. The pipeline structure arises from the link between simulation labels and function arguments.
PACKAGE PERSPECTIVES In addition to improving the documentation and usability of the current package version, additional developments will be first motivated by the above-mentioned methodological perspectives, namely re-factoring part of the data design to integrate the grouplevel and multi-session data components. This will mainly involve the modification of the FmriData class and the addition of a new FmriGroupData class. The handling of data input will have to be extended to exploit a hierarchy of subject-specific files. We also plan to enrich the parcellation component by handling classical atlases such as the Automated Anatomical Labeling (AAL) atlas built by  Tzourio-Mazoyer et al. (2002) , the Brodmann regions  (Brodmann, 1909)  and the Harvard-Oxford atlas  (Desikan et al. , 2006)  available in FSL 18.
We rather think of a simple textured mesh viewer associated with a picking feature in order to synchronize other views. The main usage is to make the selection of a mesh node and the corresponding HRF estimate feasible. For making this surface-based rendering available, mayavi 19 is an appealing candidate since it has been already intensively used in the python community. Finally, we plan on incorporating GPU parallel computing features. This technology is becoming more and more available and powerful and may also appear cheaper than CPU computing systems (see  Owens et al. , 2008  for a review). Specifically, the NVIDIA chipsets are easily accessible for general purpose computing through the python package pyCUDA 20. A simple test on matrix products with a complexity similar to that of our models showed a gain of one order of magnitude in favor of GPU computations 21 (NVIDIA GeForce 435M graphics card) compared to CPU-based computations (Intel Core M480 @ 2.
CONCLUSION The pyhrf package provides tools to detect evoked brain activity and estimate the underlying dynamics from fMRI data in the context of event-related designs. Several "reference" methods are available: the GLM, FIR and RFIR approaches, and also more flexible models as provided by the JDE framework. The choice of the analysis tools depends on the experimenter's goal: if simple mappings are required, the GLM is appropriate provided that the HRF is expected to be close to its canonical version, but for finer dynamic estimation, the JDE procedure is more suitable. The design of pyhrf allows the handling of volume-based and surface-based data formats and also the utilization of several distributed computing resources. The main user interface is done by shell commands where the analysis setup is stored in an XML configuration file.
17 +0. 08  -0. 11 dex 1 , M V (RC) = 0. 88 ± 0. 07, M I (RC) = -0. 17 ± 0. 07, and (m -M) 0 = 15. 9 ± 0. 07. Combining these data with the ones derived using different methods (isochrone fitting, RGB bump luminosity) led to the following results: Dist=15. 1 ± 0. 5 kpc, E(V-I) = 0. 796, [Fe/H] = -0. 1 ± 0. 1 dex, age= 7 Gyr. The authors suggested that BH176 is an open, or transitional cluster. We simply use the term "star cluster" throughout this paper. Indeed, BH176 has properties that are distinct from both OCs and GCs, but the term "transitional cluster" has not been clearly defined. In this study we present and analyse data from the Gemini South Multi-Object Spectrograph for RGB and RC stars in BH176, selected using the above photometric study (D11). The pre-imaging run and spectroscopic observations are described in Sec.
2. The reduction method and spectroscopic results are summarised in Sec. 3. A short Sec. 4 is devoted to the proper-motion 1 [Fe/H] = log(Fe/H) * -log(Fe/H) ⊙ , where log(Fe) = log(n(Fe)/n(H)) + 12 and n(Fe) and n(H) are the numerical densities (cm -3 ) of iron and hydrogen atoms, respectively. issues. In Sec. 5 isochrones are fit to the VLT photometric data to improve the extinction, distance, and age estimates. The results are discussed in Sec. 6 and summarised in Sec. 7. The spectroscopic observations were carried out with the Gemini South telescope using the Gemini Multi Object Spectrograph (GMOS) as part of Gemini programme GS-2011A-Q-22 (PI: Donzelli). The targets selected within the boundaries of BH176 generally follow the ridge line outlined by the VLT stellar photometry of D11. These are bright stars falling on the possible positions of the slits with magnitudes and colours roughly corresponding to the RC and RGB of the SC.
Spectra Using the B600-G5323 grating blazed at 5000 Å, a slit width of 1 arcsec and a minimum slit length of 4 arcsec, we were able to allocate 41 and 39 slits in masks 1 and 2, respectively. In each case, a total of 3x1200 sec exposures were obtained at three different central wavelengths (4970, 5020, and 5070 Å) in order to correct for the gaps between CCDs. A binning of 2x2 was used, yielding a scale of 0. 146 arcsec pixel -1 , a theoretical dispersion of ∼ 0. 9 Å per pixel, and a resolution of FWHM ∼ 5 Å. The spectroscopic data were acquired in queue mode under very good seeing conditions (see Table  1  for details). The spectra cover the range 3600-6300 Å, but the exact range depends on the slit position on the mask. Bias frames, dome, and sky flat fields and Copper-Argon (CuAr) arcs were taken for calibration.
Wavelength calibration left residuals of 0. 2 Å. Flux calibration was performed using the spectra of the standard star LT T 9239, which was obtained with the same instrument configuration as for the science objects. All science and calibration files were retrieved from the Gemini Science Archive hosted by the Canadian Astronomy Data Centre. The data were reduced with the Gemini/GMOS package within IRAF following the standard procedure. We normalised the spectroscopic flat fields with gsflat, and then mosaicked the three GMOS CCDs into one image with gmosaic. We subtracted bias frames and flat-fielded the mosaic object frames using the task gsreduce. In the case of the arc frames, we only did the bias subtraction. The procedure gswavelength was used to determine the wavelength calibration from the CuAr spectra.
Analysis of the spectroscopic data To identify stars belonging to BH176 among our sample, we inspected theoretical and empirical stellar libraries for possible analogues. The main disadvantage of empirical stellar libraries (and stellar population models based on them) is that the abundance patterns are biased towards the solar neighbourhood and low-mass stars. Theoretical spectra are not limited in atmospheric parameters, wavelength range, spectral resolution, or abundance patterns. However, their computation is based on incomplete lists of atomic and molecular lines and inaccurate atomic constants (see e. g. Coelho 2014 , Husser et al. 2013  for a thorough discussion). Preliminary effective temperatures for the stars were estimated using the (V -K s ) 0 colour indices and the temperature calibrations by  Alonso et al.
Full spectrum fitting in ULySS To derive the properties of the stars observed in BH176 we used the ULySS program 2  (see e. g. Koleva et al. 2009  for a detailed explanation of the software) with the interpolating arrow built on the ELODIE 3. 2 stellar library 3. The ULySS program performs a non-linear least-squares minimisation of the difference between the model and observed spectra, together with a normalisation of the pseudo-continuum and taking the line-spread function of the spectrograph into account. Multiplicative and additive polynomials were applied to the observed spectrum to bring it in agreement with the model spectrum. Prugniel et al. (2011)  and  Wu et al. (2011)  determined the atmospheric parameters T e f f , log(g) and [Fe/H] for stars in the MILES and Coudé-feed Indo-US spectral libraries with this method.
1 ÷ 0. 2 dex, ∆T e f f = 30 ÷ 50 K, ∆log(g) = 0. 1 ÷ 0. 2 dex. The mean values for the 31 RC member stars within ∼ 3 ′ form the centre of BH176 (Table  A. 1) are the following: V h = 0±15 km/s, [Fe/H] = 0. 1±0. 1 dex, T e f f = 4500±150 K, log g = 2. 5 ± 0. 25 cm/s 2. The following parameters were obtained by fitting the co-added RC spectrum (Fig. 2 , upper panel) with the ULySS program and the ELODIE 3. 2 stellar library: V h = 4. 4 ± 0. 5 km/s, [Fe/H] = 0. 1 ± 0. 01 dex, T e f f = 4494 ± 6 K, and log g = 2. 56 ± 0. 02 cm/s 2. The location of all the observed stars and the member ones on the CMD is given in Sec. 5.
, 2010)  and for the development of theoretical stellar libraries with α-element enhanced mixtures (see Coelho 2014 and references therein). We found only one star that is similar to our object in all parameters. This is the variable star HD 184406 (19:34:05. 354 +07:22:44. 18 (J2000. 0)) with T e f f = 4450 K, log g = 2. 47, [Fe/H] = -0. 13 dex, and ξ turb = 1. 8  (McWilliam, 1990). The comparison of its spectrum with the composite spectrum of RC stars is shown in the lower panel of Fig. 2. We used a highresolution high S /N ∼ 90 spectrum of HD 184406 from the MILES database 7. The comparison was done in ULySS. Figure  2  shows that the difference between our spectrum and the reference spectrum does not exceed 5%. The abundances of chemical elements for HD 184406 determined using the high-resolution spectra by  Luck & Challener (1995)  and  McWilliam (1990)  are listed in Table  2  for comparison with the approximate data for our representative medium-resolution spectrum.
McWilliam (1990)  calibrated T e f f using the Johnson filters and estimated the surface gravities from the stellar luminosities and temperatures. We used [C/Fe] and [N/Fe] estimated for physical gravities. We calculated [El/Fe] from [M/H] values in  Luck & Challener (1995)  (from Tables  11  and 6  in that paper). If there were abundances determined for the same element from different lines, we averaged these estimates. These authors derived [Fe/H] = -0. 05, T e f f = 4375 K and log g = 2. 65 for HD 184406, where T e f f was determined using DDO, Geneva, and Johnson filters. McWilliam (1990)  provided [Fe/H] relative to the Sun and logarithmic numerical densities of atoms. We calculated [El/Fe] abundances from these data taking the atomic data for the Sun from Grevesse (1984) into account as done by that author.
Three M-type giants The knowledge of the radial velocities and physical properties for the three brightest and reddest stars located within 1. 5 ′ from the centre of the SC (slits 12a, 11b, and 9b in Table A. 1) is critical for understanding the physical parameters and the nature of BH176. Unfortunately, we did not manage to estimate these Table  3. Colours and heliocentric radial velocities for the three M-type stars and literature data for their analogues. 3. Slit V -K J -K V h Lit. analogue V -K J -K T e f f log(g) [Fe/H] V ampl. Period Source (mag) (mag) (km s -1 ) (mag) (mag) (K) (cms -2 ) (dex) (mag) (d) 12a 5 data exhaustively using the ELODIE3. 2 interpolating arrow 8 be-8 http://www. obs. u-bordeaux1. fr/m2a/soubiran/elodie -library. html cause the spectral library contains very few such objects.
1) demonstrates that only ∼ 0. 1% stars with the same colours in this area may be giants at the same distance as BH176. It appears that there are indeed only a few dozen M5III -M8III stars in the SIMBAD database 9  for which all the characteristics (colours, effective temperatures, surface gravities, and metallicities) are known. Spectra of these stars are available in the ELODIE and MILES stellar libraries. We found only one analogue for each of the stars 11b and 9b (Table  3 ). For slit 12a we found five stars with similar colours and a full set of data from the literature. The parameters for these five objects are in the range [Fe/H] ∼ -0. 02 ÷ -0. 34, T e f f ∼ 3181 ÷ 3452 K and log(g) ∼ 0. 47 ÷ 0. 8. The data for three Galactic field M giants are given in Table  3 , together with the velocities and colours of our three M stars.
Proper motions The proper motions (PMs) in right ascension µ α cos(δ) and declination µ δ extracted for the program stars from the PPMXL catalogue  (Roeser et al. 2010)    yr -1. To identify the stars we used the on-line Aladin sky atlas 10 and coordinate transformations from the positions of the slits in the Gemini image (Sec. 2. 1) in equatorial coordinates for epoch 2011. 1 to the coordinate system of the PPMXL catalogue. The accuracy of the latter is ∼80 mas relative to the Hipparcos reference frame for stars with K < 14  (Roeser et al. 2010 ). To derive the transformation equations, we applied the ccmap and cctran routines in IRAF to five bright relatively isolated stars with K < 12 mag. The solution was obtained with accuracies of 0. 002 and 0. 037 ′′ for the right ascension and declination, respectively.
005% of the continuum flux. We modelled the line profiles HI, MgI, MgII, AlI, and CaII taking deviations from local thermodynamic equilibrium (LTE) into account. For each model atmosphere, the non-LTE populations were computed using the complete linearisation method of  Auer & Heasley (1976)  with the package NONLTE3 of  Sakhibullin (1983). Summing the radiation from all the aforementioned sectors of the stellar atmosphere considering their areas and local radial velocities due to stellar rotation and radial and tangential microturbulence yielded the integrated radiation from the atmosphere. The resulting stellar spectra were broadened in accordance with the instrumental function of the spectrograph.
in the Appendix summarises the data for the resulting sixteen objects. The successive columns list the following information: (1) OC name; (2) absolute visual magnitude, corrected for Galactic extinction; (3) colour excess; (4) age; (5) [Fe/H]; (6) [Mg/Fe]; (7) Galactocentric radius; (8) heliocentric distance towards the north Galactic pole; (9) distance from the Sun. The data in columns 1 and 2 were calculated by us using the SIMBAD and NASA/IPAC Extragalactic Database (NED). All other data are from Gozha et al. (2012).
Ramírez & Prieto (2011)ved Arcturus spectrum from the ELODIE database and our calculated synthetic spectrum for a wavelength region including iron peak elements and for three wavelength regions corresponding to several prominent spectroscopic features (CH and MgH molecules, CaII H, K lines, CaI 4227Å). The adopted parameters are fromRamírez & Prieto (2011). Illustration of fitting the abundances of different chemical elements (Section 3. 2). The derived abundances are listed in Table2.
Acknowledgements. The Gemini data for this paper were obtained under programme  GS-2011A-Q-22  of the Argentinian time share. This research made use of the  NASA Astrophysics Data System Bibliographic services (ADS) , the  NASA/IPAC Extragalactic Database (NED)   11  , the SIMBAD database operated at the  CDS (Strasbourg, France) , and  Google. SME acknowledges a grant  RFBR14-02-96501 -r-ug-a. SVV acknowledges a grant  RFBR13-02-00351. We thank  Vladimir Marsakov  for useful discussions, which helped us to improve the paper, and an anonymous referee for comments.
Ramírez & Prieto (2011)ved Arcturus spectrum from the ELODIE database and our calculated synthetic spectrum for a wavelength region including iron peak elements and for three wavelength regions corresponding to several prominent spectroscopic features (CH and MgH molecules, CaII H, K lines, CaI 4227Å). The adopted parameters are fromRamírez & Prieto (2011). Illustration of fitting the abundances of different chemical elements (Section 3. 2). The derived abundances are listed in Table2. 1. 0 1. 0 Co I Cr I ξ turb = 1. 6 km/sec Cr I Fe I MnI Ti I Co I Observ ξ turb = 1. 8 km/sec Sc I Co I Ti I Co I Fe I Ni I 0,8 1. 0 MgI 5167. 32 Observ MgI 5172. 68 [Fe/H] = -0. 15 [Fe/H] = -0. 05 MgI 5183. 60 0. 8 0. 9 0,6 Intensity 0. 4 0. 6 0. 8 0,4 0. 8 Intensity 0,2 0. 2 0. 6 Observed Model 0,0 0. 0 0. 7 Obs.
Design of the component To design the integrated inductor structure, we have used the electromagnetic simulation software HFSS marketed by ANSOFT. This software uses the finite element method. The studied structure is a thin film of copper having the shape of a spiral ribbon. The geometrical characteristics are defined as follows: the number of turns (N), the ribbon width (R), the distance between two consecutive turns (D), the thickness of YIG film (E YIG ), the thickness of copper ribbon (E Co ) and the substrate thickness (E sub ) (Fig. 1  and Fig. 2 ). After studying different spiral shapes of the coil (square, circular and octagonal) and different number of turns, the design of figure  1  has given the better results  [17]. Two types of structures have been studied: a square spiral inductor without magnetic material (Fig.
Acknowledgements. This work was supported by  DFG  grant  RO 528/10-1 , and  RFBR  grant  10-02-91338 , and by  Sonderforschungsbereich SFB 881 "The MilkyWay System " (subproject  B5 ) of the  German Research Foundation (DFG). Funding for RAVE has been provided by: the  Australian Astronomical Observatory ; the  Leibniz-Institut für Astrophysik Potsdam (AIP) ; the  Australian National University ; the  Australian Research Council ; the  French National Research Agency ; the  German Research Foundation ; the  European Research Council  ( ERC-StG 240271 Galactica ); the  Istituto Nazionale di Astrofisica at Padova ; The  Johns Hopkins University ; the  National Science Foundation of the USA  ( AST-0908326 ); the W. M. Keck foundation; the  Macquarie University ; the  Netherlands Research School  for Astronomy; the  Natural Sciences and Engineering Research Council of Canada ; the  Slovenian Research Agency ; the  Swiss National Science Foundation ; the  Science & Technology Facilities Council  of the UK; Opticon; Strasbourg Observatory; and the  Universities of Groningen, Heidelberg and Sydney.
Optical imaging is the only technique that allows to record the activity of a neuronal population at the mesoscopic scale. A large region of the cortex (10-20 mm diameter) is directly imaged with a CCD camera while the animal performs a behavioral task, producing spatio-temporal data with an unprecedented combination of spatial and temporal resolutions (respectively, tens of micrometers and milliseconds). However, researchers who have developed and used this technique have relied on heterogeneous software and methods to analyze their data. In this paper, we introduce Vobi One, a software package entirely dedicated to the processing of functional optical imaging data. It has been designed to facilitate the processing of data and the comparison of different analysis methods. Moreover, it should help bring good analysis practices to the community because it relies on a database and a standard format for data handling and it provides tools that allow producing reproducible research.
VOBI ONE : A SOFTWARE PERSPECTIVE The vast heterogeneousity of methods dedicated to the processing of functional optical imaging datasets and the lack of a common platform to make these methods available and comparable led us to design such a software tool. In details, we had three main objectives when we designed Vobi One: • To facilitate the data processing itself, for instance by offering standardized workflows and by allowing for easy comparisons between different methods. • To help bringing good analysis practices to the users, i. e. , by using a standard data format, a database to store and retrieve files. • To provide a package that could be used both through a graphical user interface (GUI) and by writing scripts in order to fit the needs of all kinds of users, independently of their computing skills.