dataesr/scientific-paragraphs-categorization huggingface dataset:
We re-orientated LG5 and not LG10 because LG5 has been reported in less genetic studies on QTL and candidate genes identification in apple (Supplementary File S5). Due to the limited number of cases where putative QTLs and genes have been reported on LG5, we expect the benefits for future studies to offset the initial inconvenience generated by comparing our iGLMap with those of previous papers. In pear, the naming and orientation of linkage groups was based on apple 68 having made use of the high synteny between these two crops. 68, 69 In the few QTL reports on pear, LG5 has been mentioned more frequently than LG10 (Supplementary File S5). The HB strategy, genotyping by sequencing and single families The HB strategy contributed considerably to the quality of the IGLMap. This strategy is applicable to a wide range of genetic data and population types.
The presented strategy permits the almost complete exploitation of the segregation information available (losing only some information from the rare recombination events within a HB) while considerably reducing the amount of missing information: in this example, from 76% for the initial CP data sets of the two HBs to 28% in the final unique BC population. For the single SNP, the amount of missing data did not change throughout the process by definition and was 66%. This approach of data aggregation and mating type was implemented in the software Haplotype Aggregator (HapAg-http://www. wageningenur. nl/en/show/HaploblockAg gregator. htm), whose manual describes the process in more detail.
ACKNOWLEDGEMENTS We thank  Yolanda Noordijk  for the isolation of DNA from all samples at  Wageningen-UR  and  Elisa Banchi  for her work on the genotyping of these samples with the 20 K Infinium SNP array at the  Fondazione Edmund Mach. This work has been co-funded by the  EU seventh Framework Programme  by the  FruitBreedomics  project  N°. 265582 :  Integrated Approach  for increasing breeding efficiency in fruit tree crops (www. FruitBreedomics. com). The views expressed in this work are the sole responsibility of the authors and do not necessary reflect the views of the  European Commission.
The reason for the time-variation is that the stations have no moving parts. Sources are tracked by adjusting the delays between the dipole elements when the sources move across the sky. In addition, small differences in station beam models and the actual station beam shapes cause errors that need to be corrected for in order to produce high-quality images, in particular for bright sources. In this work we present a new calibration scheme, facet calibration, that has enabled us to make images that reach the LOFAR HBA Tier-1 survey depth and resolution. Facet calibration was developed to process a LOFAR HBA observation of the "Toothbrush" galaxy cluster and we will use this particular dataset to lay out the method. The scientific results on this galaxy cluster are presented in  van Weeren et al.
(2016). {Facet calibration builds upon the "peeling" technique (e. g. ,  Noordam 2004) , where calibration solutions in a discrete number of directions are obtained, similar to other low-frequency calibration schemes (e. g. , SPAM, Sagecal, and MeqTrees;  Intema et al. 2009; Kazemi et al. 2011; Smirnov 2011a; Noordam & Smirnov 2010). The layout of this paper is as follows. We start with a description of the observations and general characteristics of a typical LOFAR dataset in Section 2. We provide an overview of the direction independent calibration and processing in Section 4. The corrections for direction dependent effects (DDE) are described in Section 5. We end with a discussion and conclusions in Sections 6 and 7.
LOFAR HBA OBSERVATIONS Below we describe the setup of the Cycle 0 Toothbrush cluster observations which will serve as a reference dataset to demonstrate the facet calibration scheme. The Toothbrush cluster was observed on Feb 24, 2013, mostly during nighttime with the LOFAR High Band Antenna (HBA) stations. Two station beams were formed: one on the target and one on the nearby (8. 3 • separation) calibrator 3C147. By default, all four correlation products were recorded and the frequency band was divided into subbands, each 195. 3125 kHz wide. Each subband was further divided into 64 channels. The integration time was set to 1 s to facilitate the removal of radio frequency interference (RFI). Complete frequency coverage between 112-181 MHz was obtained on the Toothbrush field, while the calibrator 3C147 was covered with 121 subbands 3 , randomly spread between 112-181 MHz.
Removal of sources in the far sidelobes Cas A and Cyg A are sufficiently bright that they can contribute flux through the sidelobes of the station beam. We therefore computed the contribution of Cas A and Cyg A using the station beam model  (Hamaker 2011; van Haarlem et al. 2013). Whenever the apparent flux density of these sources exceeded 0. 5 Jy for the core-core baselines, we obtained gain solutions towards these two sources using the BlackBoard Selfcal (BBS) software  (Pandey et al. 2009 ). In addition, simultaneous gain solutions were obtained towards 3C147 or the Toothbrush Field. We solved for the gains using all four correlations (XX, XY, YX, YY). For 3C147 we took a point source model. For the Toothbrush Field the model was derived from a GMRT 150 MHz image (presented in van Weeren et al.
Self-calibration of the target field data We combined the clock and amplitude corrected individual subbands of the target field into groups of 10. The resulting combined datasets thus have about 2 MHz bandwidth and 20 channels. It is assumed that the phase change across 2 MHz bandwidth is small enough to be neglected during the calibration. As can be judged from Figure  4  (green lines) this a reasonable assumption since the phase change due to the TEC is much smaller than  1 rad across a 2 MHz band. The concatenation of 10 subbands also increases the S/N for the calibration by a factor of about √ 10. We performed another round of RFI removal with AOFlagger on the combined 10 subband datasets as this allowed the detection of lower-level RFI. Typically, 95% of the target field data remained after this second round of RFI excision.
We discarded the data between 112 and 120 MHz, as signal to noise was relatively poor there compared to the data above 120 MHz. We phase-calibrated these 10 subband datasets against the GMRT 150 MHz model described in Sect. 4. 2 which has a resolution of 26 × 22 and a r. m. s. noise that varies mostly between 1 and 2 mJy beam -1. The phase calibration was carried out on a 10 s timescale to avoid phase decorrelation by the ionosphere. A spectral index of -0. 8 was assumed to scale the GMRT model to the different frequencies. The S/N in the 10 subband datasets was high enough to obtain good quality phase solutions for all stations, i. e. , we could easily track the phase solutions over time. The station beam model was used when computing the sky model. We then imaged these 10 subband datasets with CASA and corrected for the effect of the station beams in the phase center.
4. 7. Subtracting the sources from the data As a final step, we subtracted the sources from the 10 subband datasets. This was done using a two step approach. First we subtracted the clean components from the medium resolution images (with the corresponding gain solutions). We then re-imaged this clean component subtracted data at a lower ∼ 1. 5 resolution using an outer uv-range cut of 2 kλ, but now with a FoV of ≈ 33 •. This allowed us to detect sources in the first and second sidelobes as well as extended low-surface brightness emission in the field that was not cleaned in the medium resolution imaging. The clean components found in the low-resolution image were again subtracted with the direction independent self-calibration solutions. The reason for imaging this far out is to (i) remove the contribution of sources in the far sidelobes, so they do not influence the direction dependent calibration and (ii) search for the presence of "off-axis" sources that are bright enough that they need to be included in the direction dependent calibration (often 3C sources).
After we obtain the starting DDE calibration model the direction independent selfcalibration solutions are discarded. The first two self-calibration cycles consist of Stokes I phase and TEC calibration on the visibility data using BBS. At this point, instead of having 29 sky models for each of the 10 subband blocks, we now go down to a single clean component model that is valid over the entire 120-181 MHz band (each clean component having a flux and a spectral index, i. e. , corresponding to nterms=2 in the CASA clean task). Imaging the complete bandwidth at once has the additional advantage of an improved point spread function (PSF) as the large frequency almost completely fills the uv-plane; in addition it allows cleaning fainter sources/emission that can then be included in the sky model.
This "slow gain" calibration is carried out to correct for the slowly vary-   37, 2. 0, 8. 8, 6. 4, 27. 1, 51. 8, 14. 3, 20. 8, and 55. 7   Fig. 10. -Images showing the incremental improvements during the DDE calibration, see Sect. 5. 3. For reference, the first and second row of images show direction s2 and s21, respectively (Figure  6 ). All images are made using the full dataset (120-181 MHz, robust=-0. 25) and have a resolution of 8 × 6. 5. Note that at this resolution many of the bright DDE calibrator sources are resolved. The first column displays the images made with the (direction independent) self-calibration solutions, see Sect 4. 6. The blue contours show the clean mask that was created with PyBDSM for the imaging. The clean mask is updated at each imaging step during the DDE calibration (not shown).
After subtraction, we then proceed with the next direction, and start again with the process described in Sect. 5. 2. This whole process is repeated for all directions. We thus gradually build up a DDE corrected view of the sky, see Figure  7  for the schematic overview of this process. After each direction, the residual visibility dataset becomes "emptier" as more and more sources are subtracted with the DDE calibration solutions, instead of the direction independent self-calibration solutions. We note that the order in which the facets are treated goes roughly with the brightness of the source (group) that defines a facet. Thus the "worst" facets (with the largest calibration errors in the medium resolution images) are treated first so that at a later point they do not influence the DDE calibration for the facets that have a fainter source (group).
Comparison with SPAM and Sagecal Besides facet calibration, other direction dependent calibration schemes have been developed to deal with low-frequency radio data. We discuss some of the similarities and differences with the SPAM  (Intema et al. 2009 ) and Sagecal  (Yatawatta et al. 2008; Kazemi et al. 2011)  packages. SPAM has successfully been applied to a large number of GMRT and VLA datasets. Sagecal has mainly been used for LOFAR HBA observation taken for the Epoch of Reionisation (EoR) KSP. In Table  2  we provide a general comparison between these three different calibration schemes. The main difference between facet calibration and Sagecal are the calibration parameters that are solved for, the underlying solver, and the application of the calibration solutions. Sagecal obtains solutions for all directions at once, while facet calibration obtains solutions for a single direction at each step.
However, Sagecal can subtract interfering bright sources that would otherwise decrease the image quality of the main science target in the field, for example. The facet calibration scheme is more similar to SPAM than Sagecal, as SPAM's main goal is also to produce corrected images of the radio sky. SPAM also attempts to solve for the ionosphere by obtaining phase solutions in a dozen or more directions on short timescales. SPAM does currently not make use of the frequency dependence of the ionosphere, mainly because the GMRT and VLA bandwidths were too narrow to make this practical. A key difference with SPAM is that the facet calibration scheme currently does not obtain a global phase screen from the phase solutions. Attempts to do this with using LOFAR data have only been partially successful till now.
Acknowledgments: We would like to thank the anonymous referee for useful comments. R. J. W. was supported by  NASA  through the  Einstein Postdoctoral  grant number  PF2-130104  awarded by the Chandra X-ray Center, which is operated by the  Smithsonian Astrophysical Observatory  for  NASA  under contract  NAS8-03060. Support for this work was provided by the  National Aeronau-tics and Space Administration  through  Chandra Award Number   GO3-14138X  issued by the Chandra X-ray Observatory Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of the  National Aeronautics Space Administration  under contract  NAS8-03060. G. A. O. acknowledges support by  NASA  through a  Hubble Fellowship  grant  HST-HF2-51345. 001-A  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555.
G. B. acknowledges support from the  Alexander von Humboldt Foundation. G. B. and R. C. acknowledge support from  PRIN-INAF 2014. W. R. F. , C. J. , and F. A-S. acknowledge support from the  Smithsonian Institution. F. A. -S. acknowledges support from  Chandra  grant  G03-14131X. C. F. acknowledges support by the  Agence Nationale pour la Recherche ,  MAGEL - LAN  project,  ANR-14-CE23-0004-01. Partial support for L. R. is provided by  NSF  Grant  AST-1211595  to the  University of Minnesota. Part of this work performed under the auspices of the U. S. DOE by LLNL under Contract  DE-AC52-07NA27344. LOFAR, the  Low Frequency Array designed  and constructed by  ASTRON , 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 LO-FAR Telescope (ILT)  foundation under a joint scientific policy.
The communication devices have produced digital traces for their users either voluntarily or not. This type of collective data can give powerful indications that are affecting the urban systems design and development. In this study mobile phone data during Armada event is investigated. Analyzing mobile phone traces gives conceptual views about individuals densities and their mobility patterns in the urban city. The geo-visualization and statistical techniques have been used for understanding human mobility collectively and individually. The undertaken substantial parameters are inter-event times, travel distances (displacements) and radius of gyration. They have been analyzed and simulated using computing platform by integrating various applications for huge database management, visualization, analysis, and simulation.
The research  [69]  suggested the activity-aware map, using the user mobile phone to uncover the dynamic of inhabitants for urban planning, and transportation purposes. The researchers in  [70]  deal with large telecommunication database for Great Britain to explore human interactions, and emphasized on the highly interactions correlation with administrative regions. And in  [71]  they invented the co-location perspective to outline the behavior of the interacted telecom network users, who frequently call each other and share same spatio-temporal in a city. Whereas, the research  [72]  is presented the digital footprints (left traces), that are put through individuals during their mobility in urban space. This is performed using CDRs and georeferenced images of Flickr in order to investigate the tourists presence and mobility in Rome city.
Another problem with the CDRs is the privacy problem, since the information of the mobile network subscribers will be vulnerable, so be announced for unauthorized parties; therefore it is anonymized (hidden) to protect the subscribers privacy. As well as, the graphical or geographical representation of these datasets is difficult, because of the hardware/software limitations that could be faced during processing. The CDRs datasets overcome the acquisition problems (financially, time consuming), but their projection and trajectories tracing still time and resource consuming. The researchers try to solve this limitation by selecting the samples randomly from the CDRs, and obtain the highly frequent records  [60, 62, 30, 42, 63, 64, 32, 10, 33, 20, 12, 28]. This research captures the case study data in two phases; the analysis phase, and the modeling and simulation phase with regards to spatio-temporal features.
The perspectives are elaborated in both static and dynamic views. The analysis and representation of the individuals concentration in the observed region are achieved in order to give a static view perspective for the investigated data, as will be shown in section 3. Thereafter, the statistical techniques are applied on the investigated data to model and simulate the human mobility, which gives the dynamic view perspective. The modeling and simulating of the Armada data event is performed at two levels: 1. Collective level: The data of aggregated space, aggregated time intervals, and both of them, which are pointing to aggregated data items. They are manipulated using statistical probability distributions. As will be elaborated in section 4. 2. Individual level: The data of space and time, which are pointing to individual items, they are used to reconstruct the individuals trajectories via a mathematical model of physical mobility characteristics.
DATA SET OF ARMADA CASE STUDY AND MOBILE NETWORKS DATA The analysis of mobile phone traces allows describing human mobility with accuracy as never done before. These data are spatio-temporal data could be registered as CDRs (Call Detail Records), which have both time and spatial properties. Their unique features enable them to reflect human mobility in real time. Also, they have complex nature and a very rich environment to obtain human life pattern elaboration from multi points of view  [19, 20, 4]. The main objective of this contribution is modeling and simulating individuals mobility during Armada event. The essential material used in this study is database generated by the given mobile phone operator (Orange Company in France) in form of CDRs. Armada is the name of the famous marine festival takes ten days period, it takes place every 4 years periodically  [21, 22]  coordinates.
So, they would be considered to estimate individual transitions from position to another (from tower to tower), where each two consecutive activities between two towers could be considered as the displacement (mobility from coverage region to another), it is the main point to capture the transition from one location to another  [25, 4]. These positions would be estimated approximately with regard to tower coverage (signal strength). Seeing as these data have non-deterministic and discrete nature, therefore the collective mode would be the effective approach to be analyzed and simulated. As well as, since each individual could be disappeared for a while from the DB records which makes individual tracing is unworthy, and without significant indications on the individuals' mobility in the city.
DATA ANALYSIS AND VISUALIZATION The GIS influences several scientific fields (urban planning, traffic management, sustainable infrastructure establishment, tourism… etc. ). So, the combination of social sensor data with data mining techniques, and GIS could give deep total perception to urban dynamics towards sociotechnical conceptualization for the city by elaborating the implicit daily patterns  [29, 30, 10, 33, 34, 35, 5, 36, 37, 38, 11]. Specific platform has been developed to integrate and analyze the initial raw database; PHP server and SQL query language are used. Data visualization is accomplished using graphs, representing regions densities corresponding to human activities. The combination of these tools allows lining up the spatio-temporal data (CDRs) on the city map. ArcGIS platform is used for geographic representation, and to extract the aggregated data of five classified sectors, which are corresponding to the observed spatial sub-areas.
which refers to the center of mass of the individual trajectory. ‫ݎ‬ = ඩ 1 ݊ ሺ‫ݐ‬ሻ ሺ ೌ ሱ ሮ - ೌ ሱሮሻ ଶ ೌ ୀଵ … … ሺ5ሻ Where ೌ ሱ ሮ refers to i=1. ݊ ሺ‫ݐ‬ሻ positions recorded for individual a, and ೌ ሱሮ = ଵ ೌ ሺ௧ሻ ∑ ೌ ሱ ሮ ೌ ୀଵ , The distribution P(‫ݎ‬ ) produces power law investigated in the aggregated traveled distance (displacements) distribution P(‫ݎ‬ ) as in equation  (6). ‫ݎ‪൫‬‬ ൯ = ൫‫ݎ‬ ‫ݔ݁‪൯‬‬ ି൫ ൯ … … ሺ6ሻ The developed algorithm to compute radius of gyration distribution has ܱሺ݊ ଶ + 4݊ሻ complexity, and it is implemented with Matlab2015 platform. Whereas, the ‫ݎ‪൫‬‬ ൯ is computed for total population along observed period, in order to classify the ‫ݎ‬ evolution along the time series, as figure  5. a shows the ‫ݎ‬ ௦ variance along time, they are approximately similar in their patterns, also the highest ‫ݎ‬ ௦ are the small once ranged ሺ5 -20ሻ, while the range ሺ25 -30ሻ are the lowest once to indicate that individuals who have the tendency to mobile in small ‫ݎ‬ ௦ and in stable patterns along observed period.
Finally, the information or data about Bob's activities is distributed geographically and temporarily. In order to address these challenges, multi-agent systems stand as a promising way to understand, manage and use distributed, large-scale, dynamic, and heterogeneous information. The idea is to develop recommender systems to help users confronted with situations in which they have too many options to choose from with the aim of assisting them to explore and to filter out their preferences from a number of different possibilities. Based on this real-world application scenario, we propose in this paper a multiagent-based recommender system where agents are described using the BDI model as a multi-context system. The system's goal is to recommend a list of activities according to user preferences.
CIB In the 2013 papers, the CIB anisotropies were modelled as a power law: D ν 1 ×ν 2 = A CIB ν 1 ×ν 2 3000 γ CIB. ( 1 ) Planck data alone provide a constraint on A CIB 217×217 and very weak constraints on the CIB amplitudes at lower frequencies. PCP13 reported typical values of A CIB 217×217 = (29 ± 6) µK 2 and γ CIB = 0. 40 ± 0. 15, fitted over the range 500 ≤ ≤ 2500. The addition of the ACT and SPT data ("highL") led to solutions with steeper values of γ CIB , closer to 0. 8, suggesting that the CIB component was not well fit by a power law. Planck results on the CIB, using H i as a tracer of Galactic dust, are discussed in detail in Planck Collaboration XXX  (2014). In that paper, a model with 1-halo and 2-halo contributions was developed that provides an accurate description of the Planckand IRAS CIB spectra from 217 GHz through to 3000 GHz.
4) measured by SPT. In the 2015 Planck analysis, we impose a conservative Gaussian prior for A SZ , as defined in Eq. (  5 ), with a mean of 9. 5 µK 2 and a dispersion 3µK 2 (i. e. , somewhat broader than the dispersion measured by  Reichardt et al. 2012). The purpose of imposing this prior on A SZ is to prevent the parameters A kSZ and A tSZ from wandering into unphysical regions of parameter space when using Planck data alone. We retain the uniform prior of [0,1] for ξ tSZ×CIB. As this paper was being written, results from the complete 2540 deg 2 SPT-SZ survey area appeared  (George et al. 2015). These are consistent with Eq. (  5 ) and in addition constrain the correlation parameter to low values, ξ tSZ×CIB = 0. 113 +0. 057 -0. 054. The looser priors on these parameters adopted in this paper are, however, sufficient to eliminate any significant sensitivity of cosmological parameters derived from Planck to the modelling of the SZ components.