dataesr/scientific-paragraphs-categorization huggingface dataset:
Cosmic Dawn II (CoDa II) is a new, fully-coupled radiation-hydrodynamics simulation of cosmic reionization and galaxy formation and their mutual impact, to redshift z < 6. With 4096 3 particles and cells in a 94 Mpc box, it is large enough to model global reionization and its feedback on galaxy formation while resolving all haloes above 10 8 M. Using the same hybrid CPU-GPU code RAMSES-CUDATON as CoDa I in Ocvirk et al. (  2016 ), CoDa II modified and re-calibrated the subgrid star-formation algorithm, making reionization end earlier, at z 6, thereby better matching the observations of intergalactic Lyman-alpha opacity from quasar spectra and electron-scattering optical depth from cosmic microwave background fluctuations. CoDa II predicts a UV continuum luminosity function in good agreement with observations of high-z galaxies, especially at z = 6.
The first of the CoDa simulations (hereafter, referred to as "CoDa I"), described in  Ocvirk et al. (2016)   (hereafter, O16) , with further results presented in  Dawoodbhoy et al. (2018) , was based upon the massively-parallel, hybrid CPU-GPU code RAMSES-CUDATON and ran for 11 days on the Titan supercomputer at the Oak Ridge Leadership Computational Facility (OLCF), with 8092 GPUs and 8092 CPUs, utilizing 4096 3 particles and cells in a volume 94 comoving Mpc on a side. The second simulation, CoDa I-AMR, described in  Aubert et al. (2018) , was based upon the massively-parallel, hybrid CPU-GPU code EMMA, with Adaptive Mesh Refinement (AMR), which also ran on Titan, with 4096 GPUs and 32768 CPUs, utilizing 2048 3 particles, starting from a uniform grid of 2048 3 unrefined cells from which AMR increased the resolution locally, by up to a factor of 8, depending on the local overdensity, leading ultimately to 18 billion cells after refinement, in the same size box, starting from the same initial conditions (except coarsened to the initial, unrefined grid of CoDa I-AMR).
In Sec. 2, we describe the RAMSES-CUDATON code on which the CoDa II simulation is based, and the simulation set-up, including the new "constrained realization" initial conditions used here. Our results are presented in Sec. 3, including the global history of reionization compared with observational constraints, the star formation rates of galaxies and the net star formation rate density of the universe, and the UV continuum luminosity function of high-z galaxies during the EoR. We use the simulation to show how the star formation rate in low-mass haloes is affected by reionization and the environmental dependence of this process. Finally, we investigate the reionization history of the Local Group, and then finish with a short summary in Sec. 4.
RAMSES-CUDATON Since, the code is already described in detail in O16, we here only give a brief summary of it and highlight the differences in implementation / parameter choices between CoDa I and CoDa II. RAMSES-CUDATON results from the coupling of RAMSES  (Teyssier 2002)  and  ATON (Aubert & Teyssier 2008). RAMSES handles gravity, hydrodynamics and star formation and its mechanical feedback, while ATON handles photon propagation, hydrogen-photon thermochemistry, and computes the cooling terms. Finally, CUDATON results from the porting of ATON to CUDA  (Aubert & Teyssier 2010)  to enable it to take advantage of hybrid architectures featuring GPGPU (General Purpose Graphics Processing Unit). Thanks to this unique feature, we were able to perform CoDa II using the full speed of light, therefore circumventing potential artefacts arising with reduced speed.
This IPE is based on an assumed stellar IMF and, since star particles are assumed to form embedded in high-density molecular clouds which are unresolved by our grid cells, we also adopt a subgrid stellarbirthplace escape fraction. We define this IPE, therefore, as ξipe ≡ fesc, ξ ph,imf , where fesc, is the stellar-birthplace escape fraction and ξ ph,imf is the number of ionizing photons emitted per Myr per stellar baryon. The subgrid stellarbirthplace escape fraction fesc, should not be confused with the galactic escape fraction, which is not a subgrid quantity put in "by hand" but is rather the self-consistent outcome of our radiative transfer module CUDATON between grid cells, for all the cells associated with a given galactic halo. There is no need for us to adopt a galactic escape fraction, therefore.
The over-all galactic escape fraction will be even smaller, since galactic halos typically involve multiple cells, and one cell can absorb the photons released in another. Our radiative transfer module CUDATON explicitly accounts for this. As in Coda I, we neglect here the possible release of hard, ionizing UV radiation associated with a phase in the evolution of our simulated galaxies in which they host Active Galactic Nuclei (hereafter, AGN). Some AGN sources may arise during the epoch of reionization, e. g. in rare, massive proto-clusters  (Dubois et al. 2012). However, they are very rare and have long been thought to be minor contributors to the cosmic budget of ionizing photons responsible for the reionization of hydrogen (e. g. Shapiro & Giroux 1987; Giroux & Shapiro 1996; Haardt & Madau 2012; Haardt & Salvaterra 2015) , although they could be important for explaining the line of sight variations of the properties of the Ly α forest just after reionization  (Chardin et al.
For this purpose, we created 200 random realizations of the initial conditions with these higher-frequency modes added, and tried them out by dark-matter-only N-body simulations [using the GADGET-2 code  (Springel 2005) ] evolved to z = 0, in order to select that with the best match to the observed features of the Local Universe at z = 0, including the Local Group 3 (according to its mass, the separation between the Milky Way and M31, their mass ratio, and very nearby environment with M33 and Centaurus A counterparts). The production of other prominent features of the Local Universe, like the Virgo cluster (with close to the right mass at close to the right position), was also used as a selection criterion. The CoDa II initial conditions were thus chosen so as to reproduce familiar features of the observed Local Universe within a volume 94 cMpc on a side, centered on the LG, as closely as possible.
Comparison of the N-body halos with masses M > 2 × 10 11 M in CoDa II-DM2048 at z = 0 in the Supergalactic Plane, in a subregion 16 h -1 Mpc on a side and 4 h -1 Mpc thick (left panel) with the observed galaxies, groups and clusters of the nearby Local Universe for the same region, a thin slice centered on the supergalactic equator, 4 h -1 Mpc thick (right panel), based upon the observed galaxies as plotted in Fig. 4  of  Courtois et al. (2013) , taken from the Extragalactic Distance Database (http://edd. ifa. hawaii. edu). Circles of radius equal to the virial radii of the simulated halos are shown on the left panel. Features of the Local Universe and their analogs in the simulation are labelled. The correspondence between figure labels and Simbad (http://simbad. u-strasbg. fr/simbad/) names is given in Tab.
CONCLUSIONS CoDa II (Cosmic Dawn II) is a state-of-the-art, fullycoupled radiation-hydrodynamics simulation of reionization and galaxy formation, in a volume large enough to model global reionization and with a uniform spatial resolution high enough to resolve the formation of all the galactic halos in that volume above ∼ 10 8 M. Based upon the massively-parallel, hybrid CPU-GPU code RAMSES-CUDATON, CoDa II utilizes 4096 3 dark matter particles on a uniform cubic lattice of 4096 3 grid cells, which is also used for the gas and radiation field, in a comoving box 94 Mpc on a side, centered on the Local Group in a constrained realization of the Local Universe. CoDa II is a successor to our first such simulation, CoDa I, described in O16 and  Dawoodbhoy et al. (2018) , but with new initial conditions, updated parameters for the background universe, and a recalibration and modification of our subgrid model for star formation, chosen to ensure that CoDa II finished reionizing the universe before z ∼ 6.
For both CoDa I and II, our initial conditions were based upon a constrained realization of the Gaussian random density fluctuations for the ΛCDM model, chosen by "reverse-engineering" the structure of the presentday Local Universe, to yield initial conditions in the linear regime that, when evolved over cosmic time, will reproduce that observed structure. This makes CoDa I and II both suitable for modelling both global and local reionization and its impact on galaxy formation, including the progenitors of the Local Group. The galaxy data and background cosmology on which the constrained realization for CoDa II is based were updated and improved relative to those used for CoDa I. CoDa II was performed on the hybrid CPU-GPU supercomputer Titan at OLCF, using 16384 nodes and 16384 GPUs (one GPU per node, 4 cores per GPU), for a total of 65536 cores.
ACKNOWLEDGEMENTS This study was performed in the context of several  French ANR  ( Agence Nationale de la Recherche ) projects. PO acknowledges support from the  French ANR  funded project  ORAGE  ( ANR-14-CE33-0016 ). ND and DA acknowledge funding from the  French ANR  for project  ANR-12-JS05-0001  (EMMA). The CoDa II simulation was performed at  Oak Ridge National Laboratory / Oak Ridge Leadership Computing Facility  on the Titan supercomputer ( INCITE 2016  award  AST031 ). Processing was performed on the Eos and Rhea clusters. Resolution study simulations were performed on Piz Daint at the  Swiss National Supercomputing Center  ( PRACE Tier 0 award , project id pr37). The authors would like to acknowledge the  High Performance Computing center of the University of Strasbourg  for supporting this work by providing scientific support and access to computing resources.
Part of the computing resources were funded by the  Equipex EquipMeso project  ( Programme Investissements d'Avenir ) and the  CPER Alsacalcul/Big Data. The CoDaII-DM2048 / ESMDPL 2048 simulation has been performed at  LRZ Munich  within the project pr74no. ITI was supported by the  Science and Technology Facilities Council  [grant number  ST/L000652/1 ]. JS acknowledges support from the  "l'Oréal-UNESCO Pour les femmes et la Science"  and the  "Centre National d' Études Spatiales (CNES) " postdoctoral fellowship programs. KA was supported by  NRF-2016R1D1A1B04935414. YH has been partially supported by the  Israel Science Foundation  ( 1013/12 ). GY also acknowledges support from  MINECO-FEDER  under research grants  AYA2012-31101 ,  AYA2015-63810-P  and  AYA2015-63810-P , as well as  MICIU/FEDER  under grant  PGC2018-094975-C21.
Numerical simulation of an Ogee spillway in 2D using the commercial code of Flow3D  [2, 3]  based on the multiphase Volume of Fluid method shows a good agreement of water surface elevation and pressure compared with the experiment. Another study implemented the ADINA numerical model  [4]  , wherein the free surface dynamics are calculated using a mesh deformation method. Although it showed promising results, tracking the free surface using mesh deformation was difficult for a rapidly varying flow problem. In this paper, an open source CFD toolbox called OpenFOAM® is applied to an Ogee-shaped spillway model. The multiphase interFoam solver is applied to the model for free surface dynamics with a shape-fitted mesh to represent the spillway. The numerical simulation is compared with the rating curve from experimental measurements and the USBR calculation method in order to see its capabilities as a designing tool.
This makes the process unintuitive. As in,  35  this approach assists the user during the teaching process by modulating the robot compliance based on the given task requirements. However, the final goal is to teach a task that will be automatically reproduced by the robot, so using motion primitives makes more sense in this case. In our context, we want the user to be the master of the teaching process since our framework relies on a human's expert knowledge of the task. XSplines are therefore a more intuitive way for the teacher to encode exactly the desired trajectory. Moreover, our method can generate a guide with only one demonstration. If the task is complex, the user can teach the translation movements first, and then while being assisted by the robot, teach the orientations. The user can also refine the guide later without needing to perform full demonstrations of the trajectory.
These points were then interpolated via XSplines and used to create 6D virtual guides as explained in Section 6. Participants were asked to program the task with a good accuracy/speed ratio. They were reminded that in order to draw a line at least two points are needed, to draw an arc at least 3 points are needed and that there should be a good compromise between number of points (precision) and the time recording them (speed). A demonstration of the task can be visualized using the following links: r 6D virtual guides iterative programming. r 6D Virtual guides visualization using augmented reality. To analyze the impact of our Iterative Programming Mode, we compare it to a classic programming mode used in the industry that we refer here as One Shot. The One Shot programming mode allows only one demonstration with gravity compensation assistance.
Accuracy. To measure the accuracy of the programming task, two quantitative variables were taken into account: r The RMSE of the angle r The RMSE of the distance between the participants' inputted paths and the reference paths Participants' paths were saved after each programming task execution using MATLAB through a RPC communication protocol with the cobot. The interpolation algorithm we used gives us a 0. 5 mm spatial resolution. We defined a parametric model of the wood part based on its exact measures. Before the experiment, we took nine calibration points with the tooltip of the cobot as shown in Figure  9. Using these nine recorded points, we computed the affine transformation to fit the cobot reference frame to the MALTAB reference frame 1. We applied this unique transformation to every participant's path for both Programming Modes and both Repetitions.
Conclusion In this work we presented a kinesthetic teaching framework that uses virtual guiding assistance to program virtual guides in an intuitive and flexible way. We proposed a novel implementation of virtual guides using virtual mechanisms and XSplines to create 6D virtual guide constraints. Our approach enables non-robotics experts users to create virtual guides by demonstration. Users may also iteratively reprogram the constraints by modifying a portion of the guide through physical interaction with the cobot. We suggested using a scaled force control to escape the active guide in order to modify it. The experimental evaluation of the system with several users showed an application of our approach with an assistance cobot where the task is defined as a combination of position and orientation constraints, simulating the complexity of some manufacturing tasks such as: glue depositing and joint installation (automobile industry), grinding, and polishing.
Background: High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-byassociation approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type. Methods: To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition.
Co-expression values were calculated for each category to create the MyoMiner database. Results: Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.
The purpose of gene co-expression analysis is distinct from that of differential expression analysis, the purpose of which is to identify differences in individual gene transcript levels between conditions. Differential expression analysis may be combined with functional enrichment testing to detect changes across gene sets (e. g. representing functions, pathways, and cellular components), but it cannot tell us about mechanistic relationships between genes within a given gene set. Several organism-specific co-expression databases already exist such as the Arabidopsis Co-expression Tool (ACT)  [16, 17]  and ATTED-II  [18]  for Arabidopsis thaliana, and CoXPRESdb  [19] , STARNET  [20] , Genevestigator  [21]  and Human Gene Correlation Analysis (HGCA)  [22]  for mammals. They collect gene expression data and a Pearson correlation coefficient  [23]  is calculated for each pair of genes or probes, which can be used as a measure of expression correlation and for network construction from the highly-correlated genes.
However, these databases are not tissue-or cell-specific, because their expression matrices are derived from a mix of tissue types and in some cases from mixed conditions (e. g. treated and untreated cells). Since gene expression differs between types of tissues and cells  [24] , it is expected that gene co-expression will also vary. Experimentalists seeking to identify correlation patterns for a chosen gene of interest, usually focus on a specific tissue or cell model and thus the relevance of co-expression values is greatly enhanced by the specificity of the data used  [25]. ImmuCo  [26]  and Immuno-Navigator  [27]  gene coexpression databases are among the first to address immune cell-specific correlation, and the latter also corrects the expression matrices for batch effects. Many conditions, such as reagents, equipment, software and personnel could vary during the course of an experiment and may introduce batch effects, which is a common and strong source of variation on high-throughput data  [28, 29].
Batch effects are unrelated to biological or scientific variables, are not corrected by normalization  [29]  and must be removed before any further analysis. By combining studies, one extra layer of batch effects is introduced: experiments from different laboratories  [30]. If left uncorrected, this technical variation will introduce error into the results of correlation analysis. Another issue is that current co-expression databases include gene co-expression from healthy samples only or from a mix of healthy and diseased conditions. Studying the changes in co-expression between healthy and pathological states could lead to biomarker discovery and to improved understanding of disease mechanisms  [31]. Here, we introduce MyoMiner (https://www. sys-myo. com/myominer), the first striated muscle cell-and tissue-specific database that provides co-expression analyses in both normal and pathological tissues, addressing both issues of overall correlation and batch effects.
GEO provides a list of the alternative platforms into the original platform information sheet, but many were missing. An additional way to identify the alternative platforms is to search on ArrayExpress (which is manually curated) for alternative IDs. In the array browser of ArrayExpress (https://www. ebi. ac. uk/arrayexpress/arrays/browse. html), we searched for U133 Plus 2. 0, MG 430 2. 0 and retrieved all of the alternative GEO platforms and IDs to A-AFFY-44 [GEO: GPL570] for human and to A-AFFY-45 [GEO: GPL1261] for mouse (Additional file 1: Table  S1 ). Next, we developed a script to parse automatically their MIAME  [33]  metadata and confirm them manually, selecting only those pertinent to muscle research. We excluded all series that did not include the raw CEL files (Affymetrix fluorescence light intensity files) because we pre-processed them using a robust data analysis pipeline, described in detail below, so as to homogenize the data as much as possible.
BrainArray CDFs are annually updated and many microarray algorithms and tools now use them by default. The SCAN normalization algorithm has in-built parameters to download and use BrainArray CDFs. For MyoMiner we used Ensembl genome  [46]  (ENSG) version 20. 0. 0. We set the SCAN CDF specified parameter probeSummaryPackage to InstallBrainArray-Package("human_sample_name. CEL", "20. 0. 0", "hs", "ensg") and InstallBrainArrayPackage("mouse_sample_name. CEL", "20. 0. 0", "mm", "ensg") for human and mouse organisms respectively.
Combining datasets To define categories of similar samples based on organism, gender, age, anatomic part and condition criteria, we extracted all the available MIAME metadata for each organism (Additional file 2: Table  S7  and Additional file 3: Table  S8 ). Then, we filtered all possible metadata combinations into categories that had at least n = 12 samples. With this approach we created a large number of categories while maintaining a high level of power. For each category, we created a single expression matrix that includes all the samples from that category. Further analyses such as batch correction and gene coexpression were based on each category's expression matrix.
Batch effects evaluation For batch effect reduction we used the ComBat algorithm  [55]  from the "SVA" Bioconductor package  [56]. ComBat is a robust empirical Bayes method that adjusts for known batch covariates. By default, we considered each data series (i. e. study) to be a different batch for every category (gender, age, etc). However, it is also known that processing date/time can be a strong batch surrogate  [29]. From the text converted CEL files we retrieved the scan dates and also used these as batch surrogates for each series, assuming that microarray experiments performed on the same day belonged to the same experimental batch, thus subdividing the aforementioned default series batches to date and series batches. Using principal component analysis (PCA) 3D plots, by the "rgl" R package  [57] , for each category, we identified if the samples correlate with batch surrogates and proceeded with batch correction if necessary (Table  S4 ).
Data extraction for validation To validate the findings of MyoMiner we compared it to the existing databases MEM, SEEK and the GTEx RNA-Seq collection. Since the databases include generic coexpression data and not specific categories, we limited their studies to muscle relevant as follows: for MEM we selected the GPL570 (U133 Plus 2. 0) platform, the Pearson distance method, betaMEM as the ranking method and the dataset filter Stdev = 0 with "Skeletal Muscle" as the text field search. For SEEK we used the refined search option to Muscle (Non-cancer) datasets and the Pearson distance method. For GTEx v8 we downloaded the annotation data and extracted the gene TPMs for muscle relevant samples using the options SMTSD =" Muscle -Skeletal" and SMAFRZE =" RNASEQ". We then calculated Spearman ρ the same way as for MyoMiner.