dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, speech technologists would be able to use the HomeBank recordings to improve the current automatic labeling algorithms. The transcriptions within HomeBank data could be used as labels for training supervised learning systems and for evaluation of automatic speech processing systems. Furthermore, the availability of the audio recordings to approved groups of listeners would enable anyone with the human resources necessary to contribute to providing additional labels that could be used for training their own systems and that would ideally be reshared back to the rest of the community using the recordings. These two examples illustrate the virtuous circle that could be established between the child language and the speech technology community, in essence providing the full set of daylong audio recordings for approved users would provide valuable data to speech processing engineers.
Acoustics of Child and Adult Vocalizations Several HomeBankCode extensions focus on acoustic analyses to get a more detailed understanding of the sounds the children and adults are producing. For example, Oller and colleagues analyzed child vocalizations by first segmenting them on the basis of amplitude contours into "vocal islands,"  20  which roughly corresponded to syllables, and then analyzed those vocal islands according to duration, spectral tilt, and various other acoustic features. Oller and colleagues then subjected those acoustic features to principal components analysis and used the principal components as inputs to classifiers of clinical group membership. They found that the approach could reliably discriminate the recordings of children with autism from those of nonautistic children with developmental delays and from those of typically developing children.
Other research teams have been combining the output of LENA with acoustic analysis tools available in Praat and custom routines in MATLAB, Python, and R to obtain acoustic information (pitch, vowel formants, spectral characteristics, amplitude, and so on) for child, female adult, and male adult vocalization segments. 57 One example of a current direction of this work is characterizing the acoustics of child-directed speech (CDS) and adult-directed speech during the naturalistic interactions represented in the home recordings. Characteristic CDS has increased pitch, extended syllable and word durations, exaggerated prosody, restricted syntax, and greater phonetic variability. 5, 8, 61, 62 Although CDS in general has had steady attention in the literature at least since the early 1970s, CDS is receiving renewed attention recently due in part to the availability of LENA.
This in turn has renewed discussions about ecological validity in this domain. 4] [65] [66] [67]  This possibility has been examined recently using a very large database of LENA recordings, showing that in daylong, ecologically valid samples, compared with mothers, fathers used fewer pitch fluctuations,  47, [68] [69] [70]  a greater variety of lexical items, and more complex syntactic forms. Another recent study used LENA recordings and LENA-generated annotated output as the input to doing acoustic analysis in Praat. 71 They found similarities in pitch between mothers and their children, associations between temporal contingencies in conversational exchanges between mothers and children, and acoustic convergence of pitch across conversational blocks of mothers and children.
Combined with computational modeling work,  21  the results provided support for the theory that there is a positive feedback loop between child behavior and reinforcing adult responses that helps support children's speech development, and that differences in the feedback loop can have cascading effects on the child's overall developmental trajectory. 74, 75 These results provide examples of how automatic speaker identification within daylong home audio recordings can provide the quantity of time series data needed to detect the presence of a two-part feedback loop. The code for these analyses was provided as supplemental material to the article and is now available via the HomeBankCode repository on GitHub.
Development of New Tools for Interacting with Data Other tools being developed include those that make human listening tasks more efficient. Researchers have developed tools for automatically extracting audio segments specified in the LENA output files and the associated WAV file, playing all the sounds from a given talker to the user, and allowing the user to provide feedback, for example on whether or not the sound was assigned the correct speaker label by the LENA algorithm. 49 Other research teams have used hybrid machine-human transcription techniques to test the automatic labeling procedures. 43 Some research groups have further developed custom software for audio extraction and playback, and human listener judgment collection, acoustic analysis, database management, and statistical analysis.
23, 27, 47 A major advantage to this type of specialized playback software is that it enables much more efficient human coding of the data. VanDam and Silbert reported on human judgments of over 90,000 exemplars of speech segments labeled by the automatic methods of the LENA system, with accuracy/validity compared for different label types. 49 They found that the automatic methods perform similarly to other state-of-the-art automatic speech recognition methods, but that performance varies for different labels (adult men are more accurately labeled than adult women, for example). Acoustic analyses showed that temporal and spectral qualities-but not amplitude-interact in complex ways in the automatic label determination process. This data extraction, playback, and acoustic analysis software has been explicitly developed in a format suitable for sharing, extensibility, documentation, and modification.
Acknowledgments This work was supported by a collaborative  National Science Foundation  grant awarded to A. S. W. (  1539129 ),  M. V. D. ( 1539133 ), and B. M. (1539010), and by  WSU Spokane Seed Grant Program  awarded to  M. V. D. It  was further supported by a  National Institutes of Health  grant to Bergelson ( DP5-OD019812-01 ).
The aim of this paper is to investigate the nature and the origin of the wind around the Herbig Be star HD 58647 by means of high spectral resolution interferometric observations combined with radiative transfer modellings. In Section 2, we describe our VLTI-AMBER observations and data reduction. A simple geometrical model to characterise the size of continuum emission is presented in Section 3. The description of the radiative transfer models and the results of model fits to the observations are given in Section 4. Brief discussions on our results are given in Section 5. Finally, the summary of our findings and conclusions are presented in Section 6.
OBSERVATIONS AND DATA REDUCTIONS The Herbig Be star HD58647 (B9 IV) was observed during the second half of the night of January 1, 2015 using the Very Large Telescope Interferometer (VLTI) with the AMBER beam combiner instrument  (Petrov et al. 2007) , as a part of the observing programme 094. C-0164 (PI: Kurosawa). The combination of UT2-UT3-UT4 telescopes along with the high spectral resolution mode (R=12000) of AM-BER were used. The log of the observations is summarised in Table  2. The interferograms were obtained using the fringe tracker FINITO for co-phasing with the detector integration time (DIT) of 0. 75 s for each interferogram. The total of 2400 interferograms were recorded, or equivalently about 30 min of total integration time was used. In addition to our target, HD 57939 (K0 III) was observed with the same DIT, as an interferometric calibrator.
The fringe tracking performance typically varies between the target and calibrator observations; therefore, lowresolution (LR, R ≈ 35) AMBER observations (ESO Prog. ID: 084. C-0170 and 086. C-0267, PI: Natta) are used to calibrate the continuum visibilities. A summary of the LR observations is shown in Table  3. The data were reduced with the AMBER data processing software amdlib (v3. 0. 5)  (Tatulli et al. 2007b; Chelli, Utrera, & Duvert 2009) , which is available at http://www. jmmc. fr/data_processing_amber. htm. Furthermore, for the reduction of the LR data, we applied a software that equalizes the optical path difference (OPD) histograms of the object and calibrator  (Kreplin et al. 2012 ). The end products of the data reduction process are the wavelength-dependent normalised flux (averaged over 3 telescopes), visibilities, differential phases, and closure phases.
RADIATIVE TRANSFER MODELS To model the interferometric quantities obtained by VLTI-AMBER in the high-spectral-resolution mode, i. e. the wavelength-dependent flux, visibility and differential and closure phases around Brγ presented in Section 2, we use the radiative transfer code torus (e. g. Harries 2000; Symington et al. 2005; Kurosawa et al. 2006; KU11). The numerical method used in the current work is essentially the same as in KU11. The model uses the tree-structured grid (octree/quadtree in 3-D and 2-D, respectively) in cartesian coordinate, but here we assume axisymmetry around the stellar rotation axis. The atomic model consists of hydrogen with 20 energy levels, and the non-local thermodynamic equilibrium (non-LTE) level populations are solved using the Sobolev approximation (e. g.
2 to a few days (e. g. Hubrig et al. 2009 , but see also  Hubrig et al. 2011). Until now, our model has been applied mainly to classical T Tauri stars which rotate relatively slowly with their rotation period 2-10 d (e. g. Herbst et al. 1987; Herbst et al. 1994 ) in which the effect of rotation is rather small (e. g. Muzerolle et al. 2001 ). The third modification is important because it will allow us to compute the interferometric quantities, such as visibility, differential and closure phases, as a function of wavelength. This enables us to directly compare our models with the spectro-interferometric data from VLTI-AMBER.
2011) , the Herbig Ae star MWC 275  (Garcia Lopez et al. 2015)  and the Herbig Be star HD 98922 (Caratti o  Garatti et al. 2015). These studies used similar kinematic disc wind models to probe the wind-launching region of these stars. Fig. 9  shows the inner radii of the disc wind launching regions plotted as a function of stellar luminosity (see also Table  8 ). 1 The figure shows that the inner radius of the wind launching region increases as the luminosity increases. By fitting the data with a power-law, we find the following relation: Rwi 1 au = a L * 1 L b (3) where a = 2. 0( +1. 1 -0. 7 ) × 10 -3 and b = 0. 59(±0. 07). As in Table  8 , the inner radius of the wind launching region is only a few times larger than the stellar radius (1. 7-3. 0 R * ) for MWC 275, HD 98922 and HD 58647, which have similar spectral types (A1 and B9).
ACKNOWLEDGEMENT We thank the anonymous referee who provided us insightful comments and suggestions which helped improving the manuscript. We thank the  ESO  support astronomer  Dr. W. J. M. de Wit  for his assistance during our observation. We also thank  Dr. Ignacio Mendigutía  for the comments on the optical spectra. We also thank  Prof. Makoto Kishimoto  and  Dr. Florentin Millour  for providing us their observing tools. A. K. acknowledges support from the  STFC Rutherford Grant  ( ST/K003445/1 ). S. K. acknowledges support from a  STFC Rutherford fellowship  ( ST/J004030/1 ). This publication makes use of VOSA, developed under the  Spanish Virtual Observatory  project supported from the  Spanish MICINN  through grant  AyA2011-24052. This research has made use of the  Jean-Marie Mariotti Center Aspro service.
Assuming that the coverages of a genomic k-mer by maximal exact regions of different lengths are independent, the variance of the coverage by exact regions of length at least k is σ 2 (C ℓ≥k ) = i≥k σ 2 (C ℓ=i ) = E(C ℓ≥k ). Figure  3  illustrates E(C ℓ≥k ) for various k and q ≈ p, with 100x original coverage of the target. Note that original coverage of the target genome by the read set is N/G. For the three datasets in  our experiments (see Table  1 ), with coverages 200x, 208x, and 129x, the expected coverage E(C ℓ≥k ) has values 9. 12, 9. 48, and 5. 89, respectively, for our initial k = 19 and for our assumed error rate p = 0. 15. Hence our adopted threshold h = 4 is from 0. 8 to 1. 8 standard deviations below the expected coverage meaning that most of the correct k-mers should be distinguishable from the erroneous ones.
Experimental results We ran experiments on three data sets that are detailed in Table  1. The simulated E. coli data set was generated with PBSIM  [18]  using the following parameters: mean accuracy 85%, average read length 10,000, and minimum read length 1,000. The other two data sets are real data. Although our method works solely on the PacBio reads, the table also includes statistics of complementary Illumina reads that were used to compare our method against hybrid methods that need also short reads. All experiments were run on 32 GB RAM machines equipped with 8 cores.
Parameters of our method We ran experiments on the real E. coli data set to test the effect of parameters on the performance of our method. First we tried several progressions of k in the first phase where LoRDEC * is run iteratively. We started all iterations with k = 19 because given the high error rate of the data k must be small for correct k-mers to occur in the read data. The results of these experiments are presented in Table  2. With more iterations the size of the corrected read set and the aligned proportion of reads decrease, but the aligned regions are more accurate. The decrease in the size of the corrected read set may be a result of better correction because PacBio reads have more insertions than deletions. However, the decrease in the aligned proportion of the reads may indicate some accumulation of false corrections.
The runtime of the method increases with the number of iterations but later iterations take less time as the reads have already been partially corrected during the previous rounds. To balance out these effects, we chose to use a moderate number of iterations, i. e. k = 19, 40, 61, by default which also optimises the error rate of the aligned regions. LoRMA also builds a DBG of the reads and thus we need to specify k. We investigated the effect of the value of k on the E. coli data set. Table  3  shows the effect of k on the performance of LoRMA. Because the DBG is only used to detect similar reads in LoRMA, the performance is not greatly affected by the choice of k. There is a slight decrease in the throughput of the method as k increases as well as a slight increase in runtime but these effects are very modest.
For the rest of the experiments we set k = 19. Another parameter of the method is the size of the set of friends of the current read (-friends parameter). We tested also the effect of this parameter on the E. coli data set. As the optimal value of this parameter might depend on the coverage of the data set, we created several subsets of this data set with different coverage to investigate this. Table  4  shows the results of these experiments. We can see that the accuracy of the correction increases as the size of the friends set increases. However, for the data set with the lowest coverage, 75x, the coverage of the genome by the corrected reads decreases when the size of the friends set is increased indicating that lower coverage areas are not well corrected. We can also see that increasing the size of the friends set increases the running time of the method.
All hybrid methods produce corrected reads that do not cover the whole E. coli reference, which could be a result of coverage bias in the Illumina data. On the yeast data proovread produced few corrected reads and thus the coverage of the corrected reads is very low. Table  6  shows that our method is slower and uses more memory than PBcR in selfcorrection mode but its disk usage is lower. On the E. coli data set our new method is faster than proovread and PBcR utilising short read data but slower than LoRDEC, Jabba or PBcR using only PacBio data. On the yeast data set we are faster than PBcR in hybrid mode but slower than the others. On the E. coli and yeast data sets, LoRDEC * +LoRMA uses 45% and 37%, respectively, of its running time on LoRDEC * iterations. On both data sets the error rate of the reads after LoRDEC * iterations and trimming was 0.
The effect of coverage Especially for larger genomes it is of interest to know how much coverage is needed for the error correction to succeed. We investigated this by creating random subsets of the E. coli data set with coverages 25x, 50x, 100x, and 150x. We then ran our method and PBcR  [10, 3]  on these subsets to investigate the effect of coverage on the error correction performance. Table  7  shows the results of   these experiments. The other tools, LoRDEC, Jabba and proovread, use also the complementary Illumina reads and the coverage of PacBio reads does not affect their performance. When the coverage is high, the new method retains a larger proportion of the reads than PBcR and is more accurate, whereas when the coverage is low, PBcR retains more of the data and a larger proportion of it can be aligned.
Funding This work was supported by the  Academy of Finland  (grant  267591  to L. S. ),  ANR Colib'read  (grant  ANR-12-BS02-0008 ),  IBC  ( ANR-11-BINF-0002 ), and  Défi MASTODONS  to  E. R. , and  EU   FP7  project  SYSCOL  (grant  UE7-SYSCOL-258236  to E. U. ).
2003)  to search for Neptunes and (super-)Earths  (Santos et al. 2014). The targets in this survey are bright, chromospherically quiet FGK dwarfs with metallicities between -2. 0 and -0. 4 dex. More details about this programme can be found in  Santos et al. (2014). To this date, no low-mass planets have been detected in this metal-poor sample, although there is a debate over one star, HD 41248, that shows clear signs of radial velocity variability. Jenkins et al. (2013)  reported on the existence of two planets orbiting this star, close to the 7:5 mean motion resonance. However, using the extended dataset coming from our large programme, these planets could not be confirmed  (Santos et al. 2014). One of the signals can clearly be seen in the activity indicators and is thought to be due to the stellar rotation and stellar spots on the surface of the star.
The other signal could not be detected any more in an extended dataset and may have shown up as a result of the time sampling of the data or as a signature of differential rotation (though  Jenkins & Tuomi (2014)  reported that the signals are coherent over time). This paper reports on the presence of at least one Neptune around one of the stars of the metal-poor HARPS survey, HD 175607. In Sect. 2 we describe the observations made. Section 3 presents the stellar properties. We analyse the stellar activity in Sect. 4 and the radial velocities in Sect. 5. We discuss our findings in Sect. 6. mous ftp to cdsarc. u-strasbg. fr (130. 79. 128. 5)  or via http://cdsweb. u-strasbg. fr/cgi-bin/qcat?J/A+A/.
Radial velocities (RVs) were homogeneously derived using the HARPS Data Reduction Software (DRS). This pipeline cross-correlates the observed spectra with a mask representing a G8 dwarf (the spectral type of HD 175607 is G6V). By fitting a Gaussian to the cross-correlation function (CCF), the value and uncertainty of the RV is determined (e. g. Baranne et al. 1996; Pepe et al. 2002). We end up with 110 precise RV measurements with a mean error bar of 0. 95 m s -1 , including photon, calibration, and instrumental noise. This mean error bar is slightly lower than the average error bar of all the stars in our sample. The data are taken over a time span of 3390 days (i. e. 9 years and 3 months). From the DRS, we also get measurements for different stellar activity indicators: full width at half maximum (FWHM) of the CCF, line bisector inverse slope (BIS), contrast from the CCF, chromospheric activity indicator log R ′ HK from the Ca ii H&K lines, Hα index 2.
Furthermore, we perform a coordinate transformation and use √ e cos(ω) and √ e sin(ω) instead of e and ω (see e. g. Ford 2006 ). This can be done easily since the Jacobian factor for this transformation is 1. To run the MCMC, we use emcee (Foreman-Mackey et al. 2013), a Python code that implements an affine invariant MCMC ensemble sampler  (Goodman & Weare 2010 ). An initial guess for the walkers is randomly chosen inside the final population of the genetic algorithm. We used 700 walkers with 2000 steps. We allow for a burn-in period, which is chosen to be ten times the maximum autocorrelation time of the resulting walkers. Afterwards, we additionally perform a declustering method to remove the walkers with significantly lower posterior probabilities (as in  Hou et al. 2012). This removes the walkers that got stuck inside local maxima.
Therefore, the fact that the simpler, less flexible Keplerian interpretation is favoured by the GP framework indicates that the 29 d signal must have a coherent phase over the entire dataset, strengthen-ing the planetary interpretation of the 29-d signal. The planet parameters we inferred when using the GP framework are presented in Table  3. The evolution timescale for the activity signal is found to be 67 d, slightly more than two rotation periods, as would be expected for this type of star. We used the sample size-adjusted Akaike information criterion (AIC;  Burnham & Anderson 2002)  to select between the one-planet vs. no-planet models. The AICc value for the no-planet model was -25. 44, and the corresponding value for the one-planet model -33. 06, indicating that the planetary explanation was favoured by about a factor of ten.
The lack thereof suggests the signal is planet related. -The Hα index shows significant periodicities around 24 days. It could thus be that the estimated rotational period, coming from the B-V colour and the mean log R ′ HK , is not accurate and the rotational period is closer to 24 days. In this case, the RV signal would not be at the same period of the stellar rotation. -We have data spanning over nine years with about 4. 5 years of intense datasets. This is of the order of 50 times the lifespan of a typical solar active region. Signals arising from activity are not expected to stay stable over this amount of time for this type of star. Since the period of the signal is still very well constrained, that hints that the signal is stable over time and thus not due to activity. -In the GP analysis, the red noise is modelled separately from the Keplerian, though both are at similar periods.
Acknowledgements. This work made use of the Simbad Database. The research leading to these results received funding from the  European Union Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement number  313014  (ETAEARTH). JPF acknowledges support from  FCT  through grant reference  SFRH/BD/93848/2013. This work was supported by  Fundação para a Ciência e a Tec-nologia (FCT)  through the research grant  UID/FIS/04434/2013. P. F. , N. C. S. , and S. G. S. also acknowledge the support from FCT through Investigador FCT contracts of reference  IF/01037/2013 ,  IF/00169/2012 , and  IF/00028/2014 , respectively, and  POPH/FSE (EC)  by  FEDER funding through the programme "Programa Operacional de Factores de Competitividade -COMPETE ". This work results within the collaboration of the  COST Action TD 1308.