dataesr/scientific-paragraphs-categorization huggingface dataset:
X-ray analysis We used the HEASoft package 2 version 6. 19 for data reduction and analysis. The data are processed through the Suzaku standard pipeline in which standard event screening criteria are applied to data obtained with all the XIS charge-coupled devices (CCDs, XIS 0-3). We then extract X-ray spectra from the analysis regions in Pointings 0, 1, and 2 (see Figure  1 ). The instrument response is calculated using the xisrmfgen and xissimarfgen tools. The instrument response is generated assuming uniform emission that extends to a circular region with a radius of 20 , which is sufficiently larger than the field of view and a standard setting of xissimarfgen for data analysis of diffuse emission. We evaluated instrumental background based on night Earth's observations by using xisnxbgen.
The gamma-ray emission is generated through IC scattering on CMB and FIR photon fields. For the latter we use a recent model by  Popescu et al. (2017)  at the position of Vela X. The relevance of FIR radiation, which was often overlooked in past studies, and the possible uncertainties imposed by the model for the FIR field as seed for the IC scattering in Vela X are discussed in Appendix B (for a general discussion of the contribution of different photon fields to IC scattering in extended gamma-ray sources see also  Aharonian et al. 1997). The computation of the SED models and subsequent fit to the multi-wavelength SEDs are performed using the naima Python package  (Zabalza 2015). Specifically, synchrotron emission is computed based on the formalism in  Aharonian et al. (2010) , and IC emission on the formalism in  Khangulyan et al.
(2014) ;  Aharonian & Atoyan (1981). The naima package enables us to derive the best-fit values and posterior probability distributions of the model parameters given the SED points from the χ 2 , calculated assuming that the SED point uncertainties are Gaussian and uncorrelated. Owing to the presence of systematic uncertainties, that, to this end, we combine in quadrature with statistical ones, and to the instruments' energy dispersion, this is only an approximate assumption, that should be overcome in future works through multi-mission full-forward analyses (e. g. Vianello et al. 2015). The model parameters are scanned using the Markov chain Monte Carlo (MCMC) method, as implemented in the emcee software package  (Foreman-Mackey et al. 2013 ). For all model parameters we assume a flat prior probability distribution, within physical constraints on the parameters values (e.
3 pc), which corresponds to the 95% containment radius of the Suzaku XIS point spread function (PSF). The final spectral extraction region in Pointing 0 is therefore defined as a rectangle centred at R. A. = 128 •. 81, Dec. = -45 •. 286, with major edge of 17. 4 , minor edge of 4. 2 , and tilted by 157 • with respect to the R. A. axis. From 2004 to 2012 H. E. S. S. was operated as an array of four telescopes with 12 m diameter. In 2012 a fifth telescope with 28 m diameter was added, improving the performance at low energies. We used a dataset spanning from 2004 to 2016, comprising observations taken as part of the survey of the Galactic plane (H. E. S. S. Collaboration et al. 2018b), from studies of nearby sources such as the Vela pulsar (H. E. S. S. Collaboration et al. 2018c), Puppis A (H.
Acknowledgements. The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  Alexander von Humboldt Foundation , the  Deutsche Forschungsgemeinschaft , the  French Ministry for Research , the  CNRS-IN2P3  and the  Astroparticle Interdisciplinary Programme of the CNRS , the  U. K. Science and Technology Facilities Council (STFC) , the  IPNP of the Charles University , the  Czech Science Foundation , the  Polish National Science Centre , the  South African Department of Science and Technology and National Research Foundation , the  University of Namibia , the  National Commission on Research, Science & Technology of Namibia (NCRST) , the  Innsbruck University , the  Austrian Science Fund (FWF) , and the  Austrian Federal Ministry for Science, Research and Economy , the  University of Adelaide  and the  Australian Research Council , the  Japan Society for the Promotion of Science  and by the  University of Amsterdam.
During the learning sessions, the first five putts were made to each target distance and were recorded as retrieval trials (i. e. , performed without any sensory display (sound or vision) being made available). A further fifteen putts were also recorded as learning trials where the sound or visual display was made available to the GS and GV respectively, with no display for the GC. The order of putting distances was randomized in each session using custom made software (Docometre). Participants performed 120 practice shots to each putt length (360 in total across three lengths) with 40 retrieval trials (120 in total across three lengths) over eight learning sessions (4 weeks). The breakdown of each session is available in Table  1. Baseline measurements were conducted 2 weeks prior to the start of the training and the retention measures were taken 2 weeks after the end of the training.
Apparatus A 2 × 0. 03 × 15 m (W × H × L) artificial golf green was positioned on wooden planks with a 10. 8 cm hole cut out 1. 5 m from the wall end in a dedicated golf putting lab. Five black painted marks on the artificial green were made to determine five distances to the hole (3, 4. 5, 6, 7. 5, 9 m). Although we chose three distances (3, 6, 9 m) to manipulate the difficulty of the task, we are aware that the typical putt in the game of golf does not normally exceed 7. 5 m  (Burchfield and Venkatesan, 2010). A Logitech Camera (HD Video Camera-Pro Webcam C930e) was mounted on an extended mechanical arm parallel to the green and overlooked the putting hole (2. 5 m above the putting green) and allowed us to measure the accuracy of each putt (Figure  4 ). The camera was controlled using custom-made software that recorded ball movement at 30 Hz.
The recording was triggered at the start of each trial and was stopped by the researcher when the ball was stationary near the hole. An Oddysey White Ice putter for right handers was used for the task, along with a set of Titleist balls PROV1X (60 balls for each session). All putting movements were recorded using the CodaMotion system. One CX1 camera was placed parallel to the starting position of the putt on the putting green, with infra-red active markers being placed near the top of the putter shaft and on the club head of the putter. Positional data of the movement of the putter were exported to Matlab for processing. The launch of trials and all the devices connected were controlled using the Adwin Gold system (©JAGER GmbH) piloted via our inhouse Docometre software. Sound was delivered by a Raspberry Pi and custom-made program based on the ALSA software.
Auditory Guidance for GS Many studies select a sonification method a priori without considering what is important for the design of the sound stimuli  (Sigrist et al. , 2013). In fact, there is a need for research to map properties of sound, such as amplitude, brightness, or loudness, onto movement parameters. To convey the motion in sound in the best possible way, we ran two pre-tests to decide on the best sound design to use (see  O'Brien et al. , 2018). The sounds implemented in this study (Figure  2 ) were synthesized using a tailor-made script as white noise with the center of a band-pass filter mapped to velocity ("whoosh" sound designed to resemble the aural consequence of metal club cutting through the air). We used a psychometric conversion to the Mel scale incorporating a linear mapping of the velocity signal.
Visual Guidance for GV To depict motion visually, we used a LED guide consisting of a series of 400 linearly aligned LEDs (1. 2 m long) fully programmable and mounted in a portable, rectangular unit, with a PIC board inside (Figure  3 , top panel). The connection was set up via a PCB USB adapter to the external computer, which allowed us to trigger the display using a User Datagram Protocol (UDP) predesignated signal (Unicode character). The custom software made in C++ meant we could load any artificial, biological motion profile allowing us to control the number of LEDs involved in the display and the time each was lit for. Using a custom-made script in Matlab, we translated the position on the x axis into the LED display scaling the amplitude of the movement to the amplitude of the display (see Table  2  for information on speed and amplitude of movements across different putt distances).
The congruency between the display and the original kinematic was previously validated using video tracking method in a prototype of the used LED guide in a study by  Bienkiewicz et al. (2013). The original motion capture profile of the expert golfer was translated into the LED display using a custom MATLAB script that translated positional data into the amplitude and time that each LED was lit up for. This programme has full functionality to determine the direction and the timing of the LED display. This way, the visual motion of the expert player was depicted as a point of light moving in a linear fashion on a predesignated path conveyed  the movement of a club head in a golf putting movement (See Video 1). We validated the span of the display with the actual physical measurements of the swing from the motion capture and observed differences of ± 5 mm due to the small gaps between blocks of LEDs.
Calibration Method for Video Acquisition For each participant, and each trial, camera images from the experimental sessions were captured at a frequency of 30 Hz and saved in a separate folder. Post session, all images were processed using the automatic custom-made ball trajectory recognition software Eclipse RCP and OpenCV technologies. Algorithms were able to detect the contrast between the background putting green and the ball, tracking the point that corresponded to the center of the ball. The coordinates of the ball in each frame were extracted and saved as a text file. Each trial was visually inspected to verify that the automatic tracking was correct. If there was too much light or an alien object was present in the camera view distorting the recognition, relevant masks were applied and the trial was reprocessed.
The video calibration was applied to the post-processed text files to translate the pixel coordinates into the physical metric coordinates of the experimental space. This was done using a custom written Python script that incorporated static and dynamic calibration using an A3 print-out of a chessboard panel (calibration image). Firstly, the camera's intrinsic parameters and distortion coefficients were computed using 32 images taken at different perspectives. This allowed us to transform the image obtained using coefficients that could account for the light modification due to hardware properties. Secondly, perspective projection was computed using homography of a pixel position mapping onto the experimental metric space in a reference calibration image. The origin was placed in the center pixel of the putting hole.
Authors would like to thank Mr. Rudy Lucas (professional golf trainer) for his expertise and guidelines, and time spent coming to the lab on the numerous occasions for motion capture recordings; Dr. Jorge Ibáñez-Gijón for his help and providing the video calibration protocols in Python for analysis of the spatial accuracy of the shots and assistance with various MATLAB problems; Dr. Stuart Ferguson for providing his help regarding the custom designed visual display-LED guide and help with adapting it to work in the apparatus used in this study. Dr. Benjamin O'Brien for his helpful suggestions on the draft versions of this manuscript and providing an expertise in acoustics. Dr. Mario LaFortune for his insights and expert knowledge in golf shared with us. We also would like to acknowledge our project partners at PRISM CRNS Marseille in providing input, equipment and guidance regarding the sound design: Richard Kronland-Martinet, Solvi Ystad, Mitsuko Aramaki, Gaëton Parseihian for their contributions for the sound design.
We would like to thank student assistants for the help with data collection: Pascaline Lantoine, Benjamin Mathieu, Tom Auclair, Nicolas Leclere. We would like to acknowledge the contribution of Dr. Stuart Ferguson and the know-how and hardware from the TEMPUS-G project, funded by the European Research Council under the European Union's Seventh Framework Programme, (FP7/2007-2013) ERC grant agreement n • 21 0007-2. Furthermore, we are grateful to the Editor and to the two referees for their valuable comments that helped us to improve the quality and the clarity of our work. The manuscript is part of the SONIMOVE project, which was fully funded and supported by ANR agency, project ID ANR-14-CE24-0018.
Genome-wide chromosomal contact maps are widely used to uncover the 3D organisation of genomes. They rely on the collection of millions of contacting pairs of genomic loci. Contact frequencies at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts. We propose to use the sparse information contained in raw contact maps to determine high-confidence contact frequency between all pairs of loci. Our algorithmic procedure, Boost-HiC, enables the detection of Hi-C patterns such as chromosomal compartments at a resolution that would be otherwise only attainable by sequencing a hundred times deeper the experimental Hi-C library. Boost-HiC can also be used to compare contact maps at an improved resolution. Boost-HiC is available at https://github.
Introduction Chromosomal conformation capture (3C) has originally been developed to identify sets of DNA segments in close spatial proximity within a cell nucleus, and thus get an experimental in vivo access to the genome 3D organisation. It relies on the chemical fixation of chromosomal contacts, digestion with a restriction enzyme and a subsequent re-ligation of the cross-linked fragments  [3]. Next-generation sequencing techniques brought this protocol to the whole-genome scale (Hi-C) in cell populations  [14]  and an everyday increasing number of data-sets are now available. These datasets are usually difficult to compare since they can largely vary in terms of quality and sequencing depth. The highest resolution that can be achieved is in theory the size of the restriction fragments but very few datasets reach this resolution in practice.
As a stunning example of pushing the experiments to its limits, Rao and colleagues provided the first 1kb-resolution contact map of the human genome, obtained by sequencing 4 billions of read pairs  [9]. This dataset as been used in several studies since its release and many other data-sets have been produced in other human cell types and other organisms, but generally at a lower resolution  [11]. In mouse, another very high resolution data-set has recently been produced, covering the in vitro 1/10 differentiation of mouse neurons  [1]. These maps are often interpreted by determining the position along genomes of 3D structural features such as Topologically Associated Domains (TADs) boundaries, loops and compartments (e. g. in  [9] ). Many algorithms have been developed to delineate these features of the 3D genome organisation but their power is always hindered by the limited resolution of the dataset itself.
Hi-C sequence alignment We used previously published dataset  [1]  from mouse Embryonic Stem (ES) cells and Cortical Neurons (CN) available as GSE96107. Hi-C reads were processed using the mm9 reference genome with HiC-Pro  [12]. Bowtie2 was used with default pipeline parameter -very-sensitive -L 30 -score-min L,-0. 6,-0. 2 -end-to-end -reorder. We analysed replicates for each dataset at a resolution of 10kb separately before merging them at the end of the HiC-Pro pipeline. This merged array defines the raw contact map, M.
Hi-C Filtering Hi-C maps were filtered in two steps. First, empty bins are removed. In a second step, the distribution of the sum of contacts for each bin is fitted using a Gaussian Kernel Density function  [8]  with parameter kernel='gaussian', bandwidth=2000. Since Gaussian Kernel Density function returns the logarithm of the distribution, we took the exponential of the output. The resulting distribution is then used to identify bins for which the sum of contacts has a value below 5 % and above 95 % of the mean contact number. These bins are removed from the contact map. Filtered datasets were finally normalised using the Sequential Component Normalization  [2]  so that the L1-norm of each column/line is equal to 1.
01 and computed nr. When nr started to increase, we stopped the procedure and kept this value for the exponent α. We adjusted the contact probability curve associated with F o so that it matches exactly the one computed for C. Each contact F o ij for sites i and j separated by a genomic distance s = |i -j| is adjusted by the ratio between the raw probability against the improved one : F ′ ij = F o ij P C (|i -j|)/P F o (|i -j|) The code recapitulating Boost-HiC steps for a given value of alpha is available at https://github. com/LeopoldC/Boost-HiC.
Boost-HiC partially restores the resolution of downsampled contact maps Hi-C datasets are generally processed in the form of contact maps, in which the contact frequency between two loci is determined by normalising the number of times they have been found together in the Hi-C library. At fine resolution (i. e. when 4/10 contacts are aggregated over small genomic bins), this number is frequently equal to zero due to the finite sequencing depth. The issue is currently circumvented by working at a lower resolution, by binning the restriction fragments into larger and less numerous regions. We propose a computational alternative, Boost-HiC, to infer the missing fine-resolution contact frequencies from the knowledge of the measured contacts, and get a complete fine-resolution contact map. The procedure is based on the computation of shortest-paths between any pair of genomic loci on the contact network, a method that we introduced previously for 3D reconstruction  [6, 7].
Figure2: A) Barplot of the first eigenvector of mouse ES-cell correlation map (for a region on chromosome 16 located between 100kb and 29,8Mb) at different downsampling rates (in order from top to bottom) : 100 %, 10 %, 1 %, 0. 1 %. B) Similar barplot computed after application of the Boost-HiC algorithm. C) Similar barplot computed after application of HiCPlus algorithm [18]  D) Fraction of regions attributed to the correct compartment from downsampled maps using different enrichment methods for 10 %, 1 % and 0. 1 % downsampling. E) TAD border signal along the genome coordinate (from 100kb to 29,8Mb of chr16). In all plots the blue line shows the score for the raw normalised dataset at 100%. In the top plot red and yellow line correspond to the dataset downsampled at 10% enhanced respectively with Boost-HiC and HiCPlus methods.
Funding This work has been funded by the  programme InFIniTI 2018 of the Mission for Interdisciplinarity  of the  French Centre National de la Recherche Scientifique (CNRS) , grant  238301  (to A. L. ) and by the  Agence Nationale pour la Recherche (HiResBac   ANR-15-CE11-0023-03  to J. M. ),.
FFigure 2 : 2 Figure2: A) Barplot of the first eigenvector of mouse ES-cell correlation map (for a region on chromosome 16 located between 100kb and 29,8Mb) at different downsampling rates (in order from top to bottom) : 100 %, 10 %, 1 %, 0. 1 %. B) Similar barplot computed after application of the Boost-HiC algorithm. C) Similar barplot computed after application of HiCPlus algorithm [18]  D) Fraction of regions attributed to the correct compartment from downsampled maps using different enrichment methods for 10 %, 1 % and 0. 1 % downsampling. E) TAD border signal along the genome coordinate (from 100kb to 29,8Mb of chr16). In all plots the blue line shows the score for the raw normalised dataset at 100%. In the top plot red and yellow line correspond to the dataset downsampled at 10% enhanced respectively with Boost-HiC and HiCPlus methods.
Model Evaluation WRF-CHIMERE model including the aerosol indirect feedback is evaluated in cloud resolving domain by using aerosol measurements issued in May 2008 during the Intensive Cloud Aerosol Measurement Campaign (IMPACT) campaign in the frame of European Integrated project on Aerosol Cloud Climate and Air quality interaction (EUCAARI) project  [35]. Model results are also evaluated through cloud products of MODIS satellite. EUCAARI dataset is a state-of-the-art collection of aerosol measurements available in Europe, it is a unique and complete dataset available for model evaluation of ACI and in the past was already used in many modelling studies (e. g. ,  [71] [72] [73] [74] [75] [76] ).
Cloud Optical Properties Model skill in reproducing cloud optical properties was assessed by comparing WRF-CHIMERE output with MODIS L2 cloud products. Model output was sampled at the time of MODIS overpass on the high resolution domain, D3. MODIS and modelled data were regridded and averaged on a same regular grid with horizontal resolution of 5 km. The "Cloud_Phase_Optical_Properties" MODIS product has been used to distinguish liquid and ice cloud [80].
Metric for Model Evaluation All ground-based and aircraft measurements described in the previous section have been used to evaluate WRF-CHIMERE performances within the high resolution domain D3. The model skill in reproducing the observations was quantified through the use of some statistical indices as the trimean (TM), the interquantile range (IQR), correlation coefficient (r), mean absolute bias (MB), normalized mean bias (NMB), and normalized mean gross error (NMGE). The indices are defined in Appendix A. The use of TM and IQR instead of the classic mean and standard deviation prevents the model evaluation from being affected by outliers  [81]. IQR quantifies the spread of the data around the central tendency given by TM. Three different periods (P1, P2, and P3) may be identified as a function of the synoptic meteorological situation.
It is underestimated by a factor 1. 3 (-19%) but exhibits larger variability (IQR) than the observations. As we would expect, the bias in simulation of cloud optical properties depend on the particular aerosol mechanism adopted in the models. For example, Tuccella et al. [76] , using WRF/Chem with a modal aerosol model on the same region and period of our study, found an opposite behavior with respect to our results. Indeed, Tuccella et al. [76]  reported an overestimation of CCN that produces smaller Re and larger LCOD relative to the observations. However, it should be noted that the magnitude of bias found here is comparable to WRF/Chem performances shown by Tuccella et al. [76] , for example LCOD is overestimated by a few percent up to 48%. As described in Section 2, WRF-CHIMERE includes also the glaciation indirect effect through the processes of heterogeneous and homogeneous ice nucleation starting from aerosol predicted by chemical modules.
[76] , for example LCOD is overestimated by a few percent up to 48%. As described in Section 2, WRF-CHIMERE includes also the glaciation indirect effect through the processes of heterogeneous and homogeneous ice nucleation starting from aerosol predicted by chemical modules. Unfortunately, no available in situ measurements allow the evaluation of the ice particle concentration. The simulated ice cloud optical depth (ICOD) was compared to that observed by MODIS, including the precipitating hydrometeors and suspended ice cloud. Figure  10c ,d shows the TMs of observed and simulated ICOD. WRF-CHIMERE captures qualitatively the maximum values displayed in the observed pattern in a band between 52-53 • N and on the border between Belgium and Germany. WRF-CHIMERE tends to underpredict the minimum values of ICOD observed over the sea and Northwestern Germany.