dataesr/scientific-paragraphs-categorization huggingface dataset:
The vocal tract model We implemented a realistic 3D model of the vocal tract based on a modified version of Peter Birkholz's VocalTractLab version 2. 1  (Birkholz, 2005 (Birkholz, , 2013b (Birkholz, , 2013a)). VocalTractLab 2. 1 is a 3D geometric model of the vocal tract where a number of articulatory parameters (such as tongue tip position or lip rounding, among others) can be manipulated, and which, for a given set of parameter values, estimates the vocal tract's area function and produces the corresponding acoustic output, resulting in naturally-sounding and intelligible speech  (Birkholz, 2005 (Birkholz, , 2013b (Birkholz, , 2013a)). We obtained VocalTractLab 2. 1's source code 1  and the permission to modify it from its author, Peter Birkholz (license agreement dated 21 st of March, 2014).
1 default). This vertical position of the glottis represents the lower point from where we compute the length of the vertical part of the vocal tract (the upper part is also fixed to Vocal-TractLab 2. 1 defaults) and which we denote as SVT v base length or LEN (see Fig. 2 ; SVT is an abbreviation of the Supralarygngeal Vocal Tract, and the v and h subscripts denote its vertical and horizontal parts, respectively). In practice, we implemented changes in LEN by adjusting glottis height relative to the hyoid and scaling the epilaryngeal tube and laryngopharynx 2  (Fig. 3 ). HY is an actual VocalTractLab 2. 1 articulatory parameter that can be dynamically adjusted for a given LEN value, and which moves the entire larynx vertically. For a given LEN, we compute the vertical position of the glottis, g, as g ¼ HY þ g def À ðLEN max À LENÞ þ LEN max À LEN min 2 ð1Þ where g def is the default VocalTractLab 2.
Learning to articulate target vowels For any of the seven conditions (LEN 2 fÀ9:45; À8:85; À8:25; À7:95; À7:65; À7:05; À6:45g), there are 11 parameters (HX ; HY ; JA; LP; LD; TCX ; TCY ; TTX ; TTY ; TBX and TBY) that can be directly controlled by setting them to 11 real number values, prompting our modified vocal tract model to produce the corresponding vowel sound, or to fail to produce any sound at all if the configuration is impossible or results in a completely closed vocal tract. Our goal here is to implement a learning mechanism that, given a target vowel sound, is able to discover, without human intervention, a set of 11 free parameter values that allow the model to produce the same (or a very similar) vowel sound. Essentially, this models the real-world problems encountered by a child acquiring their native language(s) in the sense that, in principle we do not know a priori what the actual values of the 11 parameters that produced the target sound are, so that supervised learning methods cannot be applied, having to use instead reinforcement techniques.
2 library  (Heaton, 2015)  for the neural network and the Watchmaker Framework version 0. 7. 1 (https://watchmaker. uncommons. org/) for the genetic algorithm, compiled into an executable JAR file with the Java Development Kit 1. 7; and (c) the various conditions and replications were controlled from a custom Python (version 2. 7. 6) script. All our source code is freely available under an open source license (GPL) in a dedicated GitHub repository (https://github. com/ddediu/larynx-height), which also contains pre-compiled binaries for Microsoft Windows with installation instructions, the configuration files needed to reproduce the results reported here, and the R and Rmarkdown scripts used to analyze and plot them.
8 As a note, because we learn here individual vowels using separate neural networks, it could be possible to disregard entirely the neural network and use instead the Genetic Algorithm to discover directly the 11 articulatory parameters themselves. While this would greatly increase the efficiency of the search and it would probably not alter the results, we decided to design our code as "future proof" as possible by allowing the simultaneous learning of sets of vowels. 9 We consider [ə] as a "control" in the sense that we wanted to have a "reference point" relatively unaffected by the anatomical manipulations we study here to which we can compare the other vowels.
e. , the acoustics does not directly affect articulation) and the input variables (as these are predefined), and (e) no back influences from the articulatory parameters to the input variables (again, as these are predefined). In summary, we modeled a uni-directional flow from the predefined input to the realized acoustic ouput strictly mediated by the articulatory parameters. While such a model is missing potentially important variables and fails to properly capture the complex feedback loops between articulators during learning, it does represent a testable hypothesis that arguably approximates reality to an acceptable degree for our purposes here  (Pearl & Mackenzie, 2018). We then searched automatically for the DAGs that meet these constraints and fit our z-scored data using two methods implemented by the bnlearn R package  (Scutari, 2010) : the constraints-based Incremental Association Markov Blanket algorithm (denoted in the following as iamb) and the score-based Tabu Search (denoted tabu; we focus here on iamb, as it tends to produce sparser networks, but the full details are available in the Supplementary materials in Appendix).
Acknowledgements We wish to thank  Peter Birkholz  for sharing the source code of VocalTactLab 2. 1, for allowing us to modify it and for answering our questions, and to three anonymous reviewers whose comments and suggestions greatly improved the paper. This work was Funded by the  Netherlands Organisation for Scientific Research (NWO) VIDI  grant  276-70-022  to DD. During the writing of this paper, DD was supported by an  European Institutes for Advanced Study (EURIAS)  Fellowship (2017Fellowship ( -2018) ) and an  IDEXLyon Fellowship ,  Université de Lyon  ( 2018-2021 ).
Introduction This paper, one of a set associated with the 2013 release of data from the Planck 1  mission (Planck Collaboration I 2013), describes the first release of the Planck Catalogue of Compact Sources (PCCS). The main goal of the Planck mission is to measure anisotropies in the cosmic microwave background (CMB), the relic radiation of the big bang; this radiation is "contaminated" by foreground emission arising from cosmic structures of all scales located between the CMB and us -galaxies, galaxy clusters, and gas and dust distributed on small as well as large scales within the Milky Way. In order to reveal the rich cosmological information concealed in the CMB such foreground emission must be characterized and separated  (Planck Collaboration XII 2013). As a by-product, the study of foregrounds delivers an extensive catalogue of discrete compact sources as well as a series of maps of the Galactic diffuse emission; both of these are valuable resources for a variety of studies in the fields of  Galactic and extragalactic astrophysics (e.
g. Planck Collaboration XIX 2011; Planck Collaboration Int. XIV 2013; Planck Collaboration VII 2011; Planck Collaboration XXIII 2011; Planck Collaboration XXIX 2013). In 1983 the IRAS FIR survey  (Beichman et al. 1988 ) revolutionized astronomy with the discovery of ULIRGS and debris disks, etc and is still relevant to active astrophysical research 30 years after its completion. Planck is at longer wavelengths but at comparable depths and promises to provide the community with an invaluable data set from the radio to sub-mm for many years to come. The Planck Early Release Compact Source Catalogue (ERCSC; Planck Collaboration VII 2011) presented catalogues of discrete sources detected during Planck's first 1. 6 all-sky surveys. It has already been exploited for follow-up observations (e. g.
Data The data obtained from the Planck nominal mission between 2009 August 12 and 2010 November 27 have been processed into full-sky maps by the Low Frequency Instrument (LFI; 30-70 GHz) and High Frequency Instrument (HFI;100-857 GHz) Data Processing Centres (DPCs) (see Planck  Collaboration II 2013; Planck Collaboration VI 2013). The data consist of two complete sky surveys and 60 % of the third survey. This implies that the flux densities of sources obtained from the nominal mission maps are the average of at least two observations separated by roughly six months. The nine Planck frequency channel maps were used as input to the source detection pipelines. The CMB dipole is removed during the map making stage. For the highest-frequency channels, 353, 545, and 857 GHz, a model of the zodiacal emission (Planck Collaboration XIV 2013) was the only foreground emission subtracted from the maps before detecting the sources.
Source detection pipelines Compact sources were detected in each frequency map by looking for peaks after convolving with a linear filter that preserves the amplitude of the source while reducing both large scale structure (e. g. , diffuse Galactic emission) and small scale fluctuations (e. g. , instrumental noise) in the vicinity of the sources. Although the matched filter is optimal for uniform Gaussian noise, the real data present additional challenges. For example, the power spectrum is needed to construct the matched filter and this quantity is not known a priori and has to be determined directly from the data. We have explored the performance of different filters using realistic Planck simulations, among them our implementation of a matched filter and the first and second members of the Mexican Hat Wavelet family, MHW and MHW2  (González-Nuevo et al.
2006; López-Caniego et al. 2006) , and for these particular data we have chosen the last of these, MHW2, which performs better than the MHW and similarly to the matched filter. The MHW2 has only one free parameter, the scale R, to be locally optimized, and is less sensitive to artefacts (e. g. , missing pixels) or very bright structures in the image, like those found in the Galactic plane. These bright structures introduce instabilities in the determination of the power spectrum needed to construct the matched filter and reduce its performance. The MHW2 is robust and gives good performance at all Galactic latitudes. Besides, it has previously been used to detect compact sources in astronomical images, including realistic simulations of Planck  (López-Caniego et al. 2006; González-Nuevo et al.
2006; Leach et al. 2008 ) and data from WMAP  (López-Caniego et al. 2007; Massardi et al. 2009). The MHW2 filter in Fourier space is given by ψ (kR) ∝ (kR) 4 τ(kR), ( ) 1 where k is the wavenumber; τ is the beam profile or point spread function, approximated by a Gaussian τ( x) = (1/2πσ b 2 ) exp[-1 2 (x/σ b ) 2 ] ; and σ b is the Gaussian beam dispersion. Two independent implementations of the MHW2 algorithm have been used, one by the LFI DPC and another by the HFI DPC. The outputs of the two implementations have been compared and the results are compatible at the level of statistical uncertainty (see Sect. 2. 5). An additional algorithm, the matrix filter  (Herranz & Sanz 2008; Herranz et al. 2009) , has been used to validate the catalogue; this is a multifrequency method that is also being used for the production of a multifrequency catalogue of non-thermal sources that will be published in a future paper.
LFI: The compact source detection pipeline used in the LFI is part of the IFCAMEX software package 2. It can be used to detect sources with no prior information on their position (blind 2 http://max. ifca. unican. es/IFCAMEX mode), or at the position of known objects (non-blind mode). For this analysis we blindly search for objects over the full sky with S/N ≥ 2 to produce a preliminary catalogue of potential sources with positions, flux densities. and uncertainties. In the second step, IFCAMEX is run in non-blind mode, using as input the coordinates of the objects detected in the first step and keeping those with S/N ≥ 4. In this case the patch is centred on the position of the source. The goal of this second iteration is to minimize the already small border and projection effects and to refine the estimation of the position and S/N of each detection, keeping only those objects that still have a S/N above the threshold, thus improving the reliability of the catalogue.
As described in Sect. 2. 2, the detection pipelines assume that sources are pointlike. The amplitude of the detected source is converted to flux density using the area of the beam and the conversion from map units into intensity units. If a source is resolved its flux density will be underestimated. In this case it may be better to use the GAUFLUX estimation. Aperture photometry (APERFLUX). The flux density is estimated by integrating the data in a circular aperture centred at the position of the source. An annulus around the aperture is used to evaluate the level of the background. The annulus is also used to make a local estimate of the noise to calculate the uncertainty in the estimate of the flux density. The flux density is corrected for the fraction of the beam solid angle falling outside the aperture and for the fraction of the beam solid angle falling in the annulus.
Low frequencies: 30, 44, and 70 GHz At the three lowest Planck frequencies, it is possible to validate the PCCS source identifications, completeness, reliability, positional accuracy, and in some case even flux density accuracy using external data sets, particularly large-area radio surveys. This external validation was undertaken using the following catalogues and surveys: (1) the full-sky NEWPS catalogue, based on WMAP maps  (López-Caniego et al. 2007; Massardi et al. 2009 ); (2) in the southern hemisphere, the Australia  Telescope 20 GHz survey (AT20G;  Murphy et al. 2010 ); (3) in the northern hemisphere, where no large-area survey at similar frequencies to AT20G is available, the 8. 4 GHz Combined Radio All-sky Targeted Eight GHz Survey (CRATES;  Healey et al. 2007 ). These catalogues have comparable frequency coverage and source density to the PCCS.
We also compared the PCCS with the Planck ERCSC: this provides a useful check on the PCCS pipelines, although the ERCSC is based on a subset of the data used for the PCCS and is not an independent catalogue. As discussed in Planck Collaboration VII (2011), more than 95 % of the ERCSC sources had a clear counterpart in external catalogues. For this comparison, a PCCS source is considered reliably identified if it falls within a circle of radius 0. 65 times the Planck effective beam FWHM (see Table  1 ) that is centred on a source at the corresponding frequency in one of the above catalogues. Of the four reference catalogues, only the ERCSC covers the Galactic plane and therefore for |b| < 2 • (the AT20G Galactic cut) the external validation relies on the previous identification effort performed for the ERCSC (Planck Collaboration XIV 2011).
Comparison with the Planck ERCSC The Early Release Compact Source Catalogue is a catalogue of high-reliability sources, both Galactic and extragalactic, detected over the full sky, in the first Planck all-sky survey. One of the primary goals of the ERCSC was to provide an early catalogue of sources for follow-up observations with existing facilities, in particular Herschel, while they were still in their cryogenic operational phase. The PCCS differs from the ERCSC both in the data and the philosophy. The data used to build the ERCSC consisted of one complete survey and 60 % of the second survey included in the maps. The data used for the PCCS consists of two complete surveys and 60 % of the third survey. Moreover, our knowledge of the in-    2010 ) and  Rigby et al. (2011) , respectively. The other wide area surveys shown as a comparison are: GB6  (Gregory et al.
A small fraction of them vary even on time scales of a few hours based on observed changes in the flux density as a source drifts through the different Planck horns (Planck Collaboration II 2013; Planck Collaboration VI 2013). Follow-up observations of these sources might show significant differences in flux density compared to the values in the data products. Although the maps used for the PCCS are based on 2. 6 sky coverages, the PCCS provides only a single average flux density estimate over all Planck data samples that were included in the maps and does not contain any measure of the variability of the sources from survey to survey. Contamination from CO: At infrared/submillimetre frequencies (100 GHz and above), the Planck bandpasses straddle energetically significant CO lines. The effect is the greatest at 100 GHz, where the line contributes strongly to the flux densities of Galactic sources.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project=planck&page=Planck_Collaboration. This research has made use of Aladin. We are deeply grateful to  Rick Perley  and the  U. S. National Radio Astronomy Observatory  for participating in the joint Planck-VLA observations.
We provide APASS photometry in the Landolt BV and Sloan g ′ r ′ i ′ bands for all the 425,743 stars included in the latest 4th RAVE Data Release. The internal accuracy of the APASS photometry of RAVE stars, expressed as error of the mean of data obtained and separately calibrated over a median of 4 distinct observing epochs and distributed between 2009 and 2013, is 0. 013, 0. 012, 0. 012, 0. 014 and 0. 021 mag for B,V ,g ′ ,r ′ and i ′ band, respectively. The equally high external accuracy of APASS photometry has been verified on secondary Landolt and Sloan photometric standard stars not involved in the APASS calibration process, and on a large body of literature data on field and cluster stars, confirming the absence of offsets and trends. Compared with the Carlsberg Meridian Catalog (CMC-15), APASS astrometry of RAVE stars is accurate to a median value of 0.
098 arcsec. Brightness distribution functions for the RAVE stars have been derived in all bands. APASS photometry of RAVE stars, augmented by 2MASS JHK infrared data, has been χ 2 fitted to a densely populated synthetic photometric library designed to widely explore in temperature, surface gravity, metallicity and reddening. Resulting T eff and E B-V , computed over a range of options, are provided and discussed, and will be kept updated in response to future APASS and RAVE data releases. In the process it is found that the reddening caused by an homogeneous slab of dust, extending for 140 pc on either side of the Galactic plane and responsible for E poles B-V =0. 036±0. 002 at the galactic poles, is a suitable approximation of the actual reddening encountered at Galactic latitudes |b|≥25 •.
The survey data releases  (Steinmetz et al. 2006; Zwitter et al. 2008; Siebert et al. 2011 ) have followed an incremental scheme, with the last Data Release N. 4 (DR4  Kordopatis et al. 2013 ) covering 482 194 spectra for 425 743 individual stars. RAVE data have so far directly supported about fifty refereed papers. Accurate photometry, based on bands spanning the optical and near infrared wavelength ranges, is an essential support to spectroscopic surveys like RAVE. Such photometry provides, among other things: (i) the brightness distribution functions of the survey targets, (ii) the effective temperature, useful per se or as a prior to the spectroscopic determination of atmospheric parameters, (iii) the reddening, (iv) the observed brightness against which to derive the distance modulus (m -M ) to the survey targets, (v) the color discriminant to segregate single normal stars from binaries and peculiar stars, and (vi) when multi-epoch photometry is available, information on the target intrinsic variability.
So far, only limited photometric information in the optical has been available for RAVE stars, as reviewed below. The goal of this paper is to provide accurate multi-band optical photometry for all RAVE stars and to combine it with near-IR 2MASS photometry. A total of 251 626 RAVE DR4 stars have a match in the Tycho-2 B T ,V T catalog. Unfortunately, the Tycho-2 photometric errors rapidly degrade toward the faint survey limit, and over the range of brightness of the bulk of RAVE stars, Tycho-2 data (especially the B T band) are virtually useless. The median value of quoted Tycho-2 errors for RAVE stars is σ(B T ) = 0. 15 and σ(V T ) = 0. 09 mag, for a combined σ(B -V ) T ∼ 0. 18 which is far larger than the reddening affecting the majority of RAVE stars, or larger than the color spread corresponding to the combined uncertainty in the RAVE atmospheric parameters (T eff , log g, [M/H]).
The original CMC observations did not include a second photometric band, necessary for the transformation from the local to the standard system, which was achieved by importing in the process the bluer color (B T -V T ) from Tycho-2 catalog. The median value of the CMC-15 reported internal error on r ′ band data for RAVE DR4 stars is 0. 026 mag. The CMC-15 r ′ band data will be further considered in sect. 2. 5. On the near-IR side, 2MASS J, H, and K magnitudes are available for all RAVE DR4 stars. The median value of the quoted errors for RAVE stars is σ(J)=0. 024, σ(H)=0. 025, σ(K)=0. 023 mag, much better than for the corresponding DENIS bands. However, even if 2MASS J,H,K data are available for all RAVE stars, and they are of reasonable accuracy, nevertheless they map the Rayleigh-Jeans tail of the energy distributions of typical RAVE stars (cf.
The APASS survey The BV g ′ r ′ i ′ photometric data of RAVE stars discussed in this paper have been obtained as part of the ongoing APASS survey  (Henden & Munari 2014; Henden et al. 2014). The all-sky APASS survey is carried out to fill the gap existing between the completeness limit of the all-sky Tycho-2 B T V T survey (V T ∼ 11) and the saturation limit (V ∼ 15) of planned or already operating near-IR and optical photometric surveys like SDSS, PanSTARRS, LSST, SkyMapper, VISTA, VST, etc. APASS results are already used as the photo-metric source catalog for the US Naval Observatory CCD Astrograph Catalog (UCAC-4), and for the Harvard College DASCH program (which is scanning the Harvard plate stacks and measuring the magnitude of all detected stars). The APASS photometric survey covers the whole sky, from Pole to Pole, with ongoing observations from CTIO (Chile), for the southern hemisphere, and New Mexico for the northern counterpart.
Internal photometric accuracy Each APASS observing night is independently calibrated in all-sky mode against the Landolt and Sloan standards observed that same night over a large range of airmass. The photometric quality of a given night can be evaluated only a posteriori during the data reduction process, and only data obtained during photometric nights are used to build the APASS catalog. The difference in magnitude for data on the same stars obtained in separate nights (usually months apart), is therefore a reliable estimate of the total internal errors affecting APASS photometry. Their distribution for RAVE stars is presented in Figure  3 , and the distribution of RAVE stars as function of the number of epoch APASS observations is given in Figure  4  (on average, a RAVE star has been observed on 4 independent epochs by APASS, and none at less than 2 epochs).