dataesr/scientific-paragraphs-categorization huggingface dataset:
L Note. These contamination fractions are estimated based on the photometric redshift analysis (the first method in Section 3. 4). 3. 5. Source Selection at z ∼ 2-3 In addition to the z ∼ 4-7 catalogs, we also use catalogs of galaxy candidates at z ∼ 2-3 to study clustering properties. We use BM, BX, and U-dropout galaxy catalogs at z ∼ 1. 7, 2. 2, and 3 constructed in C. Liu et al. (2021, in preparation). Here we briefly describe our source selection at z ∼ 2-3. The z ∼ 2-3 galaxy candidates are selected from a combined CLAUDS +HSC-SSP catalog made by  Sawicki et al. (2019)  based on hscpipe. Note that the HSC-SSP data in the combined CLAUDS+HSC-SSP catalog is based on the S16A internal data release, which is different from the S18A data release product we use for the z ∼ 4-7 selection. As described in Section 2.
1 0. 01 h -around the halo mass of 10 11 -10 12 M e. However, the slopes of the relation at the high-mass and lowmass ends are different between these studies. These differences are possibly due to differences in used observational data sets, the halo mass functions, and details of modeling. We also note that there are several systematic uncertainties on the  M SFR h measurements. Assumptions on the IMF and dust attenuation have impacts on the SFR. The conversion factor between the SFR and UV luminosity (Equation (  55 )) depends on the stellar age and metallicity (e. g. , Madau & Dickinson 2014).
CLAUDS uses data obtained in part through the  Telescope Access Program (TAP) , which has been funded by the  National Astronomical Observatories, Chinese Academy of Sciences , and the  Special Fund for Astronomy  from the  Ministry of Finance of China. CLAUDS uses data products from TERAPIX and the  Canadian Astronomy Data Centre (CADC)  and was carried out using resources from  Compute Canada  and  Canadian Advanced Network For Astrophysical Research (CANFAR). CADC is operated by the  National Research Council of Canada  with the support of the  Canadian Space Agency. The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg , and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under grant No.
NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  grant No. AST-1238877 , the  University of Maryland, Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation. The Cosmic Dawn Center is funded by the  Danish National Research Foundation  under grant No. 140. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 847523  " INTERACTIONS. " Software: galaxev (Bruzual & Charlot 2003), GalSim (v1. 4;Rowe et al. 2015), hscpipe (Bosch et al. 2018), MIZUKI (Tanaka 2015;Tanaka et al. 2018), SynPipe (Huang et al. 2018).
A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are fontindependent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97. 7% for words segmentation and 97. 51% for characters segmentation.
Second, we present an indirect character segmentation approach that has the following characteristics: (i) the presented method is font-independent and can handle simple and complex font types, (ii) solves the problem of overlapping between adjacent characters and between subwords, (iii) optimizes the location of the segmentation points to maintains character shape, and (iv) reduces the number of ligatures as much as possible. Third, in the evaluation stage, experimental results on the APTI dataset prove that our method achieves better performance than the state-of-the-art methods. This paper organized as follows: Section 2 describes the state of art-related work. Section 3 introduces the proposed method. Sections 4 describes the dataset used in the experiments then introduce the results with a comprehensive comparison with other related methods.
Also, it will not get sufficient results if it is not supported or combined with other techniques. Timsari (1996)  employed a hit-or-miss morphological method to segment words into its characters where the word described in terms of predefined patterns. The approach searches the database which is holding the description of all characters, for possible matches of characters using the hit-or-miss method. Cowell (2001)  used an iterative based thinning method to generate the skeleton of isolated Arabic characters. Also, the authors discussed the problems of thinning Arabic characters for poor-quality images. Besides, he used a post-processing method to enhance the generated skeleton. Fitriyatul Qomariyah and Mahmudy (2017) proposed a segmentation method using an interesting point based on a set of rules to separate the connected Arabic character.
Dataset description Several datasets are available for Arabic character recognition  (Lawgali, 2015). The proposed algorithm was tested with Arabic Printed Text Image Database (APTI)  (Slimane et al. , 2009)   et al. , 2009), sizes (6, 7, 8, 9, 10, 12, 14, 16, 18, and 24 points) , and styles (plain, italic, bold and combination of italic and bold). The images in the dataset are 72 dpi resolution images. Besides, it includes very large open-vocabulary, various forms of ligatures, overlapping characters, low-resolution images, and variability of the height of each word image. Moreover, APTI covers all styles that are widely used in computer typing, newspapers, magazines, and books.
Comparison with related work Table  4  shows our method compared with previous related works in terms of the segmentation method, used Dataset, font types, styles, sizes, and accuracy. Indeed, it is fairer to compare with other related works using the same dataset (same font types, sizes and styles). However, most of the authors tested their work on their own collected data and didn't make it public. In addition, the implementation code of the proposed methods in the related work is not available and sometimes it is difficult to write the code since it may depend on parameters, hypothesis, tools and APIs that are not mentioned clearly in published paper. Therefore, we tested our method using APTI benchmark dataset in order to make some fair comparisons with other related works. As shown from the Table  4  and other methods summarized in section two this method has one or more of the following weakness: (i) some of these methods are font type, style, and size-dependent, (ii) parameterized method, (iii) handle simple font types and styles, and (iv) tested on a small and unpublished standard dataset.
On the other side, the proposed method has the following effectiveness: (i) Font type, style, and size-independent, (ii) Non-parametric method, (iii) handles simple and complex font types and styles, (iv) solves the problem of inter overlapping between sub-words, (v) optimizes the location of the cut point to save character shape, (vi) reduces the number of ligatures, and (vii) tested on large and publish dataset. Besides, we proposed a simple, efficient, font invariant and parameter-free word segmentation approach that can employed at the recognition stage as a post-processing method to enhance the recognition performance.
The algorithm was able to segment characters of complex Arabic fonts where the overlapping between characters exists. The algorithm experimented on the APTI dataset which has variability in the generation procedure of text images including different font types, font size, and styles. The experimental results showed the reliability of our algorithm in segmenting words and characters correctly with an average accuracy of 97. 7% and 97. 51% respectively. Compared with the previously-proposed approach, our algorithm has the following points of strength: (i) promising results with variability of font types, sizes, and styles, (ii) non-parametric method, (iii) facilitating the recognition stage by saving the shape of the extracted characters as much as possible, (iv) reducing number of ligatures, and (v) proposing an efficient word segmentation which can be used to enhance the accuracy of the recognition stage through incorporating language dictionaries and spell-checks method.
APTI dataset is a standard large-scale benchmark used in Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text held in the context of the 11th International Conference on Document Analysis and Recognition (ICDAR2011). It has a variability in the generation procedure of text images including different font types (Andalus, Arabic Transparent, Advertising Bold, Diwani Letter, DecoType Thuluth, Simplified Arabic, Tahoma, Traditional Arabic, DecoType Naskh, and M Unicode Sara) as shown in Fig. 10 (Slimane.
Observations and data processing The observing procedure, as well as the pipeline for the data processing, is described in more detail in  Jackson et al. (2016) , so we summarise it briefly here before describing the updates to the process. The multi-beaming capabilities of LOFAR were used to observe 30 sources at once using the High-Band Array   1. Considerable effort was made to observe at uniformly high elevation above the horizon, due to LOFAR's reduced sensitivity at lower elevations. 82% of the observations were made at elevations of ≥60 • , 3% of observations were made at elevations ≤40 • (mainly the most southerly sources) and no observations were made below 34 •. As in the preliminary observations, data were converted to FITS format  (Wells et al. 1981) , and all baselines to core stations (within about 4 km from the centre of the array) and the nearer remote stations were removed from the dataset 2.
The final averaged data product consisted of 64 × 48. 9 kHz channels in  A&A 658, A2 (2022)  the frequency direction, and 90 × 2 s time steps, for each baseline. Global fringe fitting was performed in AIPS  (Greisen 2003)   3  and implemented using the PARSELTONGUE Python interface  (Kettenis et al. 2006 ). The pipeline used standard settings of a 500 ns delay window and 5 mHz rate window  (Jackson et al. 2016 ) and a 6 s solution interval for the phases in the fringe fit. The scatter in the delay and phase solutions were then used to assess the strength of compact structure in the source; a strong compact source that gives a correlated signal on a particular baseline will give little scatter, whereas a weak signal gives essentially random noise in the signal. The scatter on each station's solutions was used to give designations of 'P' (clear detection of correlated flux indicative of compact structure), 'S' (some relatively low signal-to-noise detection of correlated flux), and 'X' (little or no correlated flux) for correlated signal strength, as described in Fig.
2  of  Jackson et al. (2016). For the full dataset, the previously described procedure was modified slightly. It was found that the quality of the solution could be noticeably affected by the data weights applied to the data prior to fringe fitting. Statistically, the weight should be proportional to σ -2 , where σ is the r. m. s. error. However, this rarely gives the best solution since it effectively downweights lower signal-to-noise baselines at the cost of decreasing the number of stations making effective contributions to the solution. Two other weighting schemes were used instead, a σ -1 weighting and a weighting independent of σ; both weightings were applied in the fringe fit and the result that was used corresponded to that which gave the lowest scatter in the fit. Fringe rate-and-delay maps were also produced for the full dataset, by performing a Fourier transform on a dynamic spectrum plot (as a function of frequency and time) of the complex visibility amplitude for each baseline to ST001, and then correcting the resulting geometric distortion using the correction matrix discussed in  Jackson et al.
Information in the catalogue The information in the results catalogue allows users to perform cone searches to find signal-to-noise data. These data products are available using a cone search 4. The repository also contains other metadata, which allows judgements to be made about the suitability of observed sources as phase calibrators. This information is used when selecting the best in-field calibrator as described in  Morabito et al. (2022). The first five columns give basic data: the index name ('L' followed by a six-digit number) corresponding to the number in the observing catalogue, the right ascension and declination (taken from the WENSS catalogue), and the date and time of the observation. The sixth column, as in the preliminary catalogue, is a string of 13 characters, each representing the correlated flux determined from the global fringe fit on each antenna.
The characters in the string are given in order of telescope name, the first being DE601 (Effelsberg) and the last being IE613 (Birr). The seventh column provides an additional quality flag based on the data itself: 'O' for a good observation; 'A' for an observation with out-of-range amplitudes, defined as high points above 15 times the median amplitude; 'M' and 'X' for failed observations with very few channels and time steps, respectively; and 'Z' for a dataset with more than half of the data flagged as bad. The eighth column, in the same format as the sixth, presents the FT signalto-noise statistic S in the form of numbers from 0 to 9 for each station. Each number n S is a code related to the signal-to-noise statistic S by int((S -132)/26. 4), with values below S = 132 represented as 0 and above S = 370 represented as 9.
Ionospheric coherence times as a function of baseline We repeat the analysis of ionospheric coherence times as a function of baseline length carried out by  Jackson et al. (2016) , but now with the full dataset. The coherence time is again calculated using the phase solutions from the global fringe fit, to which a second-order polynomial is fitted; the average gradient of this fit can be used to calculate a coherence time as the time needed for a phase slope of this gradient to reach one radian. The core superstation ST001 is used as the reference, and a cumulative fraction plot of coherence time for each station is shown in Fig. 5. For all stations the coherence time for a 50% chance of coherence, with respect to ST001, is between 100 and 150 s. Stations closer to the core generally have longer coherence times, although the difference is not large.
Signal-to-noise reproducibility in the LoTSS survey We investigated the signal-to-noise ratio of sources in the field of MG 1549+3047 using both LBCS data and data obtained separately as part of project LC9-012. The LBCS fluxes for each source are from a pointing in the direction of the source, and the LC9-012 fluxes are at one central pointing; therefore, the LBCS fluxes will not be affected by bandwidth and integration time smearing, whereas the LC9-012 fluxes will be, allowing estimates of the amplitude reduction by smearing as a function of distance from a pointing centre. There are six LBCS sources in this field, one in the centre and five others at distances of up to 2 • from the field centre. Data processed using the LOFAR-VLBI pipeline  (Morabito et al. 2022)  were compared with the LBCS data by degrading the LBCS resolution by a factor of 4 in integration time and 2 in frequency to the 97 kHz per channel and 8 s integrations of the post-Split-Directions stage of the pipeline.
Conclusion The LOFAR Long-Baseline Calibrator Survey (LBCS) has now concluded. It covers the whole of the northern sky, and provides a calibrator network suitable for calibration of long-baseline observations with low-frequency telescopes. The calibrator density decreases at declinations below 30 • N, due to relative difficulty in selecting flat-spectrum candidate sources in this area of the sky and to loss of sensitivity at lower elevations. Supplementary observations may be undertaken in the future using low-frequency data at more southern declinations (e. g. Hurley-Walker et al. 2017 ) and the main LoTSS survey in the north, to fill in particular gaps in the coverage. The LBCS catalogue is publicly available, and can be used to choose suitable calibrators for any observation. We used the source statistics to investigate the nature of the LBCS calibrators.
For unresolved sources in FIRST, we find that more than 50 percent lose a significant portion of correlated flux density between scales of 2 and 0. 3, suggesting that they are CSS sources. Comparison with 3CRR sources shows that the largest sources are generally less detected by LBCS on the smallest scales. Those which are detected tend to have flat spectra, consistent with the dominant sources of flux density being concentrated on small scales in either core-jet or hotspot emission. Dividing all LBCS sources into quasars and non-quasars using the milliquas database, we find that quasars are generally more compact than non-quasars, which is consistent with orientationbased unification theory. Finally, comparison with MWA allows a study of interplanetary scintillation (IPS). There is a good agreement between the flux density on IPS scales 0.
3 measured by LBCS and that calculated using data from the MWA IPS survey and GLEAM. Given this good agreement, it is very likely that IPS detections can be used to select candidate calibrators with very high reliability. In the future, development of the LOFAR-VLBI pipeline will allow for automatic generation of standardised images for calibrated LBCS sources. We hope to add these communitygenerated images of LBCS calibrators to the publicly available database of information.
Acknowledgements. We thank  Benito Marcote  for comments on the paper. Part of this work was supported by  LOFAR , the  Low Frequency Array designed  and constructed by  ASTRON , that has facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy. This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by  CSIRO. We acknowledge the  Wajarri Yamatji people  as the traditional owners of the Observatory site. Support for the operation of the MWA is provided by the  Australian Government (NCRIS) , under a contract to  Curtin University  administered by  Astronomy Australia Limited. We acknowledge the  Pawsey Supercomputing Centre  which is supported by the  Western Australian and Australian Governments.
A. D. acknowledges support by the  BMBF Verbundforschung  under the grant  052020. L. K. M. is grateful for support from the  UKRI   Future Leaders Fellowship (grant   MR/T042842/1 ). J. Moldón  acknowledges financial support from the  State Agency for Research of the Spanish MCIU  through the  "Center of Excellence Severo Ochoa" award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ) and from the grant  RTI2018-096228-B-C31  ( MICIU/FEDER, EU ). J. P. M. acknowledges support from the  Netherlands Organization for Scientific Research (NWO , project number  629. 001. 023 ) and the  Chinese Academy of Sciences  (CAS, project number  114A11KYSB20170054 ).
Stations are numbered from 1=DE601 (Effelsberg) to 13=IE613 (Birr). (HBA) in the DUAL-INNER mode, giving a primary beam FWHM of 3. 96 • at 144 MHz. Each observation lasted 3 min with a 3 MHz bandwidth beginning at 140. 16 MHz per source beam, divided into 64 channels. The core stations were phased up within the dataset after the observations, using calibrator observations, into a superstation (ST001), and into a second superstation (ST002) using only the central six stations. Obser-vations were conducted beginning in 2014 December and ending in 2019. The observation log is summarised in Table.
S1 ). Figure  2B  shows qualitatively how the neonatal tract delineations compare against the ones defined before in the adult brain and in the nonhuman primate brain. We tested the applicability and robustness of these tractography protocols using independent neonatal dMRI data from a different scanner and with different acquisition parameters. In addition to the dHCP dataset (high angular and spatial resolution, bespoke setup, and subset of neonates scanned at 37 to 42 weeks PMA), we used two further datasets (from neonates also scanned at 37 to 42 weeks PMA): (i) a dataset from a conventional clinical scanner (no specialized acquisition hardware/software, lower angular and spatial resolution than dHCP-"Oxford dataset") and (ii) a subsampled version of the Oxford dataset [keeping only b≤1000 s/mm 2 to mimic a standard diffusion tensor imaging (DTI) acquisition-"Oxfordsubsampled dataset"].
For acquisition parameters and data quality, see table  S1 , and for cohort details, see fig. S9. We compared the tract atlases and the intersubject variability across these three datasets (after age and sex matching). Strong similarity was observed between the average tract atlases, shown in Fig. 2C  (right and fig. S2 ), allowing good neonatal tract delineations even with a standard data acquisition protocol. Some differences can be observed between the datasets, for instance, reduced tract extent in the second branch of the superior longitudinal fasciculus (SLF2) and lower population coverage in the acoustic radiation (AR) in the Oxford-subsampled dataset. However, the average spatial correlation across all tracts in the group atlases was 0. 89 (SD, 0. 04) between the dHCP and Oxford datasets and 0.
86 (SD, 0. 08) between the dHCP and Oxford-subsampled datasets. Intersubject variability in the tractography results was assessed within and across the subject groups (relative to the dHCP dataset). As shown in Fig. 2C  (left), intersubject variability was relatively consistent across the datasets with, as expected, greater intersubject similarity within than across groups, albeit with more variance in the Oxford and Oxford-subsampled datasets. For the remaining analyses, we used the dHCP data, due to its better quality (table  S1 ) and larger cohort. We further investigated whether the developed neonatal tractography protocols could capture early developmental trends in tract maturation. Projection fibers (e. g. , thalamocortical and corticothalamic) are expected to mature more quickly over this early life period, followed by commissural fibers (e.
Reverse seeding is used in all cases for the macaque brain but not in all cases for the adult/neonate brains. (VOF), with connections also to the optic radiation (OR), inferior fronto-occipital fasciculus (IFO), middle longitudinal fasciculus (MDLF), and corpus callosum (FMA); while, for instance, vertex A x in the adult human superior frontal cortex connects more to association tracts. We performed comparisons across both the ontogeny and phylogeny dimensions using this common connectivity space. Connectivity blueprints were constructed for groups of adult [50 random Human Connectome Project (HCP) subjects], macaque (six highquality postmortem macaque datasets), and neonate brains, and KL divergence was used to assess similarities and divergences. To avoid confounding our analysis with the effects of neonatal development, incidental findings or prematurity, we used a subset of full-term neonates (33 neonates, born and scanned at 40 weeks PMA, with no incidental findings).