dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, in this configuration Sz compression is very slow. As for the height variable, Digit Rounding provides the highest compression ratio with compression/decompression speeds similar to the one obtained with Bit Grooming. The lowest errors are obtained with Bit Grooming but with a compression ratio lower than Digit Rounding. Table  12  provides the results of the compression of the full simulated SWOT L2_HR_PIXC pixel cloud product. The maximum absolute error and the mean absolute error are not provided because this dataset contains several variables compressed with different parameters. Compared to the SWOT baseline compression, Zstandard increases the compression speed by over 5 times, while offering a similar compression ratio. Sz compression was not applied because it does not allow achieving the high precision required on some variables.
Conclusions This study evaluated lossless and lossy compression algorithms both on synthetic datasets and on realistic simulated datasets of future science satellites. The compression methods were applied using netCDF-4 and HDF5 tools. It has been shown that the impact of the compression level options of Zstandard or Deflate on the compression ratio achieved is not significant compared to the impact of the Shuffle or Bitshuffle preprocessing. However, high compression levels can significantly reduce the compression speed. Deflate and Zstandard with low compression levels are both reasonable options to consider for the compression of scientific datasets, but must always follow a Shuffle or Bitshuffle preprocessing step. It has been shown that Zstandard can speed-up the compression of CFOSAT and SWOT datasets compared to the baseline solution based on Deflate.
The lossy compression of scientific datasets can be achieved in two different error-bounded modes: absolute and relative error-bounded. Four algorithms have been studied: Sz, Decimal Rounding, Bit Grooming and Digit Rounding. One useful feature of the last three is that the accuracy of the compressed data can easily be interpreted: rather than defining an absolute or a relative error bound, they define the number of significant decimal digits or the number of significant digits. In absolute error-bounded mode, Sz provides higher compression ratios than Decimal Rounding on most datasets. However for the compression of netCDF/HDF5 datasets composed of several variables, its usability is reduced by the fact that only one absolute error bound can be set for all the variables. It cannot be easily configured to achieve the precision required variable per variable.
This is why we rather recommend the Decimal Rounding algorithm to achieve fast and effective compression of the CFOSAT dataset. In relative error-bounded mode, the Digit Rounding algorithm introduced in this work provides higher compression ratios than the Bit Grooming algorithm from which it derives, but with lower compression speed. Sz can provide even higher compression ratios but fails to achieve the high precision required for some variables. This is why we rather recommend the Digit Rounding algorithm to achieve relative error bounded compression of SWOT datasets with a compression ratio 30 % higher than the baseline solution for SWOT compression.
Acknowledgements. This work was funded by  CNES  and carried out at Thales Services. We would like to thank  Hélène Vadon ,  Damien Desroches ,  Claire Pottier  and  Delphine Libby-Claybrough  for their contributions to the SWOT section and for their help in proofreading. We also thank  Charles S. Zender  and the anonymous reviewers their comments, which helped improving the quality of this paper. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales (CNES)  (grant no. 170850/00 ). Review statement. This paper was edited by  Steve Easterbrook  and reviewed by  Charles Zender ,  Dingwen Tao , and one anonymous referee.
Code and data availability. The Digit Rounding software source code is available from CNES GitHub at https://github. com/CNES/Digit_Rounding (last access: 26 August 2019). The datasets are available upon request to Xavier Delaunay (xavier. delaunay@thalesgroup. com) or to Flavien Gouillon (flavien. gouillon@cnes. fr). The Supplement details the datasets and provides the command lines used for running the compression tools. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-12-4099-2019-supplement. Author contributions. XD designed and implemented the Digit Rounding software and wrote most of the manuscript. AC performed most of the compression experiments and generated the analytical datasets. FG provided the scientific datasets used in the experiments, supervised the study, and contributed both to its design and to the writing of the manuscript.
Lattice Boltzmann Method (LBM) has been applied for the simulation of a mixed convection heat transfer of two dimensional Newtonian fluid in a square cavity driven by a periodically oscillating lid. The top wall is maintained at higher variable temperature. The developed LBM code in Matlab, is applied for the oscillating lid to simulate the fluid flow and heat transfer in cavity, was validated using various Reynolds and Grashof numbers. Nusselt number was calculated for various pertinent dimensionless groups. Fluid flow and heat transfer characteristics were examined in the domain of Reynolds number, Grashof number, the dimensionless lid oscillation frequency and different values of temperature. Such that: 10 2 ≤ Re ≤ 10 3 , 10 2 ≤ Gr ≤ 10 6 , 0. 1 ≤ ω ≤ 5, the Prandtl number was fixed as 0.
In view of previous studies, it is clear that no research has been conducted in the field of mixed convection heat transfer in cavities driven by periodic wall movement using LBM. Recently, the potential to use oscillatory flows to increase the rate of heat transfer for example: Stirling machines, Cryocoolers and electronic components has renewed interest in the field. Progress in under-standing heat transfer in oscillatory flows (alternative flow) is incomplete  [6]. In the present study, we applied TLBM to simulate a mixed convection heat transfer of flow two-dimensional inside in a cavity driven by a periodic oscillation using the Matlab program. We first validated the Moment Lattice Boltzmann Model (MLBM) simulation for different Reynolds and Grashof numbers compared to reference  [1] ,  [3]  solutions in the literature, then we studied the phenomena observed for different Reynolds and Grashof numbers and oscillation frequencies.
40 Ghz 8-core Intel Core i7-3770 processor and 16Gb RAM, running a 64-bit Linux operating system. The source codes were compiled with gcc 4. 8. 4 and -O3 optimizations. We used CPLEX 12. 7. 0 and the CALLABLE LIBRARIES framework to implement our branch-and-cut algorithm. CPLEX was run in single-threaded mode and all CPLEX parameters were set to their default values.
Test-bed of instances In order to asses the performance of the newly proposed exact algorithm CLIQUE-INTER, we consider three sets of benchmarks instances, called Set A, Set B and Set C : Set C -Real-world networks. These instances have been taken from the  [1]  and SNAP database. We consider instances with up to ≈ 100, 000 vertices and ≈ 3, 200, 000 edges. The reported 30 networks all fall into one of the following categories: • Social Networks (with vertices representing individuals) such as: Interaction networks, where the edges refer to interaction via message posts, for example e-mail, as in iaemail-EU ; Recommendation networks, where the edges indicate communication in terms of a recommendation or an opinion (e. g. , rec-eachmovie related to movie opinions); Collaboration networks, where edges represent scientific or other type of collaborations (e.
Acknowledgment Valuable comments of four anonymous reviewers are highly appreciated. Ivana Ljubić  is partially funded by the  Vienna Science and Technology Fund (WWTF)  through project  ICT15-014. Pablo San Segundo is funded by the  Spanish Ministry of Economy and Competitiveness  (grants  DPI 2014-53525-C3-1-R ,  DPI2017-86915-C3-3-R ).
2019 ) at low redshift (0. 15 < z < 0. 70). In this work, we rely on the wealth of photometric data from the COSMOS field to perform the filament extraction around intermediate-mass groups at higher redshift, and we make use of a robust group catalogue which has been extracted from X-Ray photometry  (Finoguenov et al. 2007; Gozaliasl et al. 2019). The correlation between group connectivity, group mass and the BGG properties is first quantified both in the observations and the corresponding mocks. In the second step, the HORIZON suite  (Dubois et al. 2014 ) is used to interpret the observational results. The paper is organised as follows. Section 2 describes the observed and simulated datasets and the tools used to extract the skeleton. Section 3 presents the measurements both from the observed and simulated catalogues, and an extensive assessment of the robustness of the results.
The COSMOS dataset The analysis presented here is based on the COSMOS dataset  (Scoville et al. 2007). The cosmic web is extracted using the photometric redshift of the COSMOS2015 catalogue  (Laigle et al. 2016) , which is also used for deriving galaxy masses. Groups are extracted from the X-Ray photometry as described in  Finoguenov et al. (2007)  and  Gozaliasl et al. (2019). The observed catalogue of groups, their associated BGG and filaments in COSMOS is called Ccosmos in the following analysis.
COSMOS2015 redshifts and stellar masses The COSMOS2015 catalogue provides apparent magnitudes in 30 bands from ultra-violet (UV) to infra-red (IR). The photometric data include the optical COSMOS-20 subaru survey  (Capak et al. 2007; Ilbert et al. 2009) , Subaru Suprime-Cam data  (Taniguchi et al. 2007 (Taniguchi et al. , 2015)) , the u * -band data from the Canada-Hawaii-France Telescope (CFHT/MegaCam), NIR photometry from the UltraVISTA survey (DR2,  McCracken et al. 2012) , the Y band from the Hyper Suprime-Cam at Subaru telescope  (Miyazaki et al. 2012 ) and mid-IR data in the four IRAC channels (i. e. in a wavelength range between ∼ 3 and 8 µm) from the SPLASH program (PI: Capak). Galaxy photometry in optical and NIR has been extracted using SEXTRACTOR  (Bertin & Arnouts 1996)  in dual image mode, using as the detection image a χ 2 sum of the four NIR images of UltraVISTA DR2 and the z ++ -band (taken with Subaru Suprime-Cam).
The X-Ray group catalogue The initial catalogues of the X-Ray galaxy groups in COS-MOS were presented in  Finoguenov et al. (2007)  and  George et al. (2011). These catalogues combined the available Chandra and XMM-Newton data (with improvements in the photometric datasets) used for identification of galaxy groups, with confident identification reaching out to z ∼ 1. 0. The COSMOS galaxy group catalogue we rely on is a combination of an updated version of the initial group catalogues and a new catalogue of 73 groups described in  Gozaliasl et al. (2019)  and  Gozaliasl et al. in preparation, which  combines data of all X-ray observations from Chandra and XMM-Newton in the 0. 5-2 keV band, with robust group identification up 1 A consequence of the shift of the Balmer break in NIR broad bands.
to z ∼ 1. 53. However, for the purpose of this study and for the reason explained above, we limit our selection to z ∼ 0. 5 -1. 2 (highlighted area in Fig. 1 ). Group halo mass is the total mass (commonly called M200, but we call it Mgroup in the rest of this paper), determined using the scaling relation LX -M200 with weak lensing mass calibration as presented by  Leauthaud et al. (2010). The radius of the group R200 is defined as the radius enclosing M200 with a mean overdensity of ∆ ∼ 200 times the critical background density. Fig. 1  presents the group mass log(M200/M ) as function of the redshift and cosmic time. Gozaliasl et al. (2019)  discussed the mass completeness of the group sample given the surface brightness limitation of the X-Ray dataset. Over the redshift range 0. 5 < z < 1.
Method to measure the connectivity In the skeleton produced by DISPERSE, two filaments joining the same node can become extremely close to one another, but still counted separately, as they both are topologically robust. However, physically they represent a single filament. Therefore, in order to avoid double counting filaments, they are merged in the final skeleton and a bifurcation point is added at the merging location 7. The same ID is then attributed to all segments belonging to the same filaments. Connectivity C is subsequently defined as the number of segments with different IDs crossing the 1. 5× virial radius circle around the group center. The same definition is taken in 3D and 2D. In appendix A we confirm on the observational dataset that varying this radius does not significantly change the measurement of connectivity and the signal presented hereafter.
Mean connectivity and group mass dependency The mean group connectivity as a function of group mass can now be measured. In both COSMOS and the HORIZON-AGN datasets, the group mass Mgroup is defined as the total mass, i. e. the sum of DM and baryonic mass. The left panel of Fig. 5  displays this measurement in the mock (C phot Hzagn2D and C true Hzagn2D , green solid and dashed lines resp. ) and in COSMOS (Ccosmos, black line). Group mass bins are split to contain an approximately equivalent number of groups in each bin. Unsurprisingly and as discussed above, the measurement performed with true-z lies above the one with photo-z. The COSMOS and photo-z HORIZON-AGN datasets are however in very good agreement. As expected from theoretical predictions  (Codis et al. 2018) , more massive groups have, on average, a higher connectivity.
ACKNOWLEDGMENTS EDF was supported by the  Oxford physics summer student program  when completing this work. CL is supported by a  Beecroft Fellowship. JD acknowledges funding support from  Adrian Beecroft  and the  STFC. CL would like to thank  Oliver Hahn  for very relevant suggestions,  Yiqing Liu  and  Hyunmi Song  for useful discussions and advices, and the  Korean Astronomy and Space science Institute  for hospitality when most of this work was finalised. CP thanks  Sandrine Codis  for advices and the  Royal Observatory in Edinburgh  for hospitality when this work was initiated. This work relied on the  HPC resources of CINES (Jade)  under the allocation 2013047012 and c2014047012 made by  GENCI  and on the HORIZON and CANDIDE clusters hosted by  Institut d'Astrophysique de Paris. We warmly thank  S.
Rouberol  for running the cluster on which the simulation was post-processed. This research is also partly supported by the  Centre National d'Etudes Spatiales (CNES)  and by the  National Science Foundation  under Grant No. NSF   PHY-1748958. This work is based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under  ESO  programme  ID 179. A-2005 and on data products produced by TERAPIX and the  Cambridge Astronomy Survey Unit on behalf of the UltraVISTA consortium. We thank  D. Munro  for freely distributing his YORICK programming language and opengl interface (available at http:// yorick. sourceforge. net/).
idle non required BBUs End To achieve that, we evaluated our proposed resource allocation scheme (Bee-Ant-CRAN) by simulations on Matlab with the MOESK tool. Moreover, we compared our proposed Bee-Ant-CRAN with the Zhu and Lei  [39] , Chen et al. [38]  and the CDI-CRAN  [26]  in terms of active RRHs/BBUs, packet throughput, power consumption, spectrum e ciency, and packet loss. Detailed simulation parameters are summarized in Tab. 1 and the Bee-Ant-CRAN parameters are shown in Tab. 2. The ABC and ACO parameters proposed in the Tab. 2 are properly chosen. Indeed, the values of the parameters adopted for the modified version of the MMAS used to achieve the RRH-BBU mapping have been carefully taken from the study of Ari et al. [29]. Moreover, the values of the parameters adopted for the proposed modified ABC mechanism for UEs association have are properly chosen after benchmarking with eleven stressful functions.
Simulation Results In this section, we present the computational results, analysis as well as discussion, in order to show the effectiveness of our proposed UE-RRH and RRH-BBU mapping schemes. To achieve that, we evaluated our proposed resource allocation scheme (Bee-Ant-CRAN) by simulations on Matlab with the MOESK tool. Moreover, we compared our proposed Bee-Ant-CRAN with the Zhu and Lei  [39] , Chen et al. [38]  and the CDI-CRAN  [26]  in terms of active RRHs/BBUs, packet throughput, power consumption, spectrum efficiency, and packet loss. Detailed simulation parameters are summarized in Tab. For each ant do 7. Compute the pheromone matrix χ k,l (o) Eq. (  36 ) 8. Compute the heuristic information η k,l (o) Eq. (37) 9. Generate an array of RRH-BBU according to Eq. (  38 ) End for 10. Compute the flow function f k,l Eq.
Introduction MicroRNAs are small, highly conserved, non-coding RNAs ∼22 nt in length  (1)  that participate in the post-transcriptional regulation of genes through their impact on targeted messenger RNAs (mRNAs). This phenomenon can lead to translation repression or degradation of the Downloaded from https://academic. oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by INRA -Institut National de la Recherche Agronomique user on 07 November 2019 targeted mRNAs and depends on the base-pair binding of the microRNAs to their target via a recognition site, the 'seed' sequence  (2, 3). Many studies currently use next-generation sequencing (NGS) technology to explore the transcriptome, notably in a context of microRNA discovery. A significant amount of data is thus being generated, which can attain more than 1000 microRNA sequences in a single publication  (4, 5).
Regularly updated tools for data exploration are therefore required and essential. Animal, plant and virus microRNAs are already listed in several databases, such as miRNEST 2. 0 (covering more than 400 different species)  (6)  or miROrtho, which contains 46 animal genomes  (7). Some databases are restricted to human microRNAs in a disease context, such as EpimiRBase (microRNAs associated with epilepsy)  (8) , miRCancer (microRNAs and cancer)  (9)  or the MicroRNA SNP Disease Database (MSDD; genetic variants affecting microRNAs in a disease context)  (10). The recent miRCarta database  (11) , which lists microRNAs for 148 species, is solely based on the prediction of novel microRNAs. A database portal, miRTools-Gallery, which contains more than 1000 tools for studying, identifying or predicting the targets of microRNAs, has recently been set up  (12).
The database most widely used at present is miRBase, which was created in 2006 by Griffiths-Jones and collaborators at the University of Manchester  (13)  and lists microRNAs for 271 species  (14). Studies in cattle, goats and sheep have often focused on production traits such as dairy and meat products  (15) (16) (17) , health [mastitis resistance  (18, 19) ] or reproduction [fertility and fecundity  (20) (21) (22) ]. Due to genome conservation between the three species, the miRNomes (all microRNAs expressed in a tissue or cell type) are relatively similar. However, there are some differences, as well as those between breeds  (23). This explains the importance of generating a database that includes all three species, together with specific information on the breed and physiological status of the animals, as this is frequently lacking in the most commonly employed databases.
RumimiR provides a single access portal to an integrated database containing all the information currently published on ruminant microRNAs. RumimiR is freely available online at the following URL: http://rumimir. sigenae. org/. This database offers an exhaustive list of bovine, caprine and ovine microRNAs, collected from the literature. Pertinent information, notably in the context of dairy production, has also been added to the description of each mature microRNA, and a filtering option for all these data is included. The entire database (and data filtered by applying one or more filters) can be downloaded in different formats. Moreover, past database versions are traceable and can be downloaded.
A home-made Python script was applied, which compared the microRNA sequences Downloaded from https://academic. oup. com/database/article-abstract/doi/10. 1093/database/baz099/5585578 by INRA -Institut National de la Recherche Agronomique user on 07 November 2019 Figure  1. Procedure followed to include microRNAs and their associated features in the RumimiR database. From each publication (black rectangle), the microRNA sequences and their features (orange rectangles) were extracted and included in the RumimiR database. Several analyses were performed to expand the description of the microRNAs collected (blue rectangles): determination of chromosomal position in the latest reference genome of each species (using the NCBI Blastn tool), search for redundancy with microRNAs already present in the RumimiR database, search for identity with other small microRNAs to compare the sequences, search for identity with human or mouse microRNAs present in miRBase.
* , using a home-made Python script (Supplementary Figure  2 ). with those extracted from the above-mentioned database (Supplementary Figure  2 ). Finally, each microRNA was assigned a RumimiR identification number: 'Rum-species ID-XXXXX'. The species IDs for cattle, goats and sheep are BTA, CHI or OAR, respectively, and XXXXX is an incremental five-digit number. To add information about a microRNA listed in the RumimiR database, a search was done to determine its identity with human or mouse microRNA. If it had a 100% sequence match with a known microRNA over the full length of the shorter sequence, using the miRBase database (release 22), it was considered identical  (14). These identities were verified by applying a Python script to compare the sequences with other microRNA sequences and those extracted from miRBase (Supplementary Figure  2 ) and to indicate any 'strict' homology with human or mouse microRNA.
The RumimiR website was built using HTML5 technology (https://dev. w3. org/html5), the bootstrap frontend framework (https://getbootstrap. com--v4. 0. 0) with additional jQuery user interface elements (http://jquery. com--v3. 2. 1) and DataTables jQuery plug-in for the data table (https://datatables. net--v1. 10. 16), and the plots were implemented by using the Highcharts JavaScript library (http://www. highcharts. com--v6. were provided to the DataTables jQuery plug-in by setting the ajax option to the address of the JSON data source. All statistics, charts and drop-down lists have been built into the data-based fly to make any updates as easy as possible. The Blast tool was developed using Perl-CGI. The 'Release history' box on the website shows the history of the various RumimiR database versions and allows the user to download entire datasets from current and past versions.
The proportion differs if only those microRNAs with weak false-positive score (code containing 0 and 1 '1') are considered, but the percentage of microRNAs with lengths of 17-18 nt is still high (∼25%; Figure  4B ). Most of the ovine sequences with lengths of 17 and 18 nt were obtained from a single paper (4). The authors explained, in their discussion, that they had discovered a large number of new microRNAs because they had not applied the commonly used restricting prescreening. All sequences detected at a low-count level or with a low-sample frequency were considered. Most of the ovine sequences with lengths of 17 and 18 nt correspond to new microRNAs. The majority of microRNAs in the database were taken from studies based on differences between breeds, Downloaded from https://academic.