dataesr/scientific-paragraphs-categorization huggingface dataset:
3. Design rules information is stored in data silos, each representing a specific design context. For example, all rules for milling compliance will be in the silo "design for milling". Design is a multi-domain process and many design rules do not fit in a single pre-defined context. [Semantic Network] In  [8] , Sowa describes Semantic Networks (SN) as "a graph structure for representing knowledge in patterns of interconnected nodes and arcs". Languages for developing ontology (RDF, RDFS, OWL) and graph-databases (Neo4j, Grakn, Trinity RDF, Cayley) support the implementation of semantic networks. Such knowledge graphs break silos and focus on the interactions. SN are effective at modeling complex and unstructured knowledge, such as common  [9]  or specific design information  [10]. Several strategies can be used to model design information.
Survey instrument The aims and objectives of the survey, and initial content were developed during a focus group meeting at the Dizzynet meeting in Starnberg near Munich in October 2018 (with 7 of the authors in attendance)  [25]. Dizzynet is a European network initiative for vertigo and balance research (www. klini kum. uni-muenc hen. de/Europ ean-Dizzy net). Subsequently, the survey instrument was designed using Google Surveys. It was initially piloted by the authors, further refined and once consensus was reached on content and format, it was sent to ten therapists from eight countries for feedback. It was also translated to German. The survey instrument (Appendix 1) was available in the English and German languages and had 39 questions pertaining to four sections. The majority of questions were closed questions requiring either quantitative data (e.
Acknowledgements The authors wish to thank  Susan Whitney ,  University of Pittsburgh , for feedback on the pen-ultimate draft. Deirdre Murray ,  TCD School of Medicine , served as study gatekeeper. Balz Winteler ,  University of Bern , helped contacting therapists in Switzerland. Silke Heller  ( Bad Aibling ),  Silvy Kellerer  (Munich), and  Firat Kesgin (Hamburg)  supported the distribution in the network of German therapists.
Advanced Virgo+ is a major upgrade of the Advanced Virgo gravitational-wave detector aiming to increase sensitivity in terms of binary neutron star (BNS) range by a factor 3-5 in the next few years. In this work, we present an optimization of the mirror transmittances for the second phase of the project (to be implemented for the O5 observation run) using a random walk algorithm implemented with the advGWINC software. In addition to BNS range, a post merger (PM) SNR is also used as a figure of merit to identify configurations that fine-tune the sensitivity curve, as a function of arm-cavity round trip losses.
Once the optimization is completed, points drawn in the initial 'burn in' period, where the algorithm is locating the global maximum, are removed from the chain. The first of the two sensitivity parameters used in this study is BNS inspiral-merger-ringdown (IMR) range R. It corresponds to an averaged maximum distance at which a BNS IMR event can be measured  [14]  with SNR greater than or equal to some pre-defined threshold, assuming a detector with strain noise power spectral density S n ( f ). It was one of the primary quantifiers of detector sensitivity during the first three observation runs. The advGWINC software approximates this number using an optimally aligned inspiral-merger-ringdown (IMR) waveform produced by IMRPhenomD  [15]  scaled to a range of redshifts z to find the cutoff distance where the SNR falls below a threshold ρ 0 = 8.
Computation of h( f ) was done by a specialized module added to advGWINC that follows the numerical approximations reported by  Breschi et al. [20]  and assumes co-aligned spins and ideal binary orientation to the line of sight. Characteristic frequencies and times are calculated with polynomial fits to numerical data for several neutron star equations of state. In this work, the SLy4 equation of state  [21]  is used for a source with total mass (1. 364 + 1. 364) M. Figure  4  shows the reproduced waveform from the green curves of Figure  5  in  [20]. Figure  4a  shows the amplitude, angular frequency, and real component of the waveform in geometric units scaled to the total mass. Figure  4b  shows the Fourier transform of a time domain waveform (red) assuming a redshift of 0. 008, roughly matching the luminosity distance used in the reference, the equivalent IMR waveform with the same redshift (yellow), and the nominal sensitivity curve (blue).
Acknowledgments: The authors acknowledge the useful feedback and discussions with  Eleonora Capocasa ,  Jérôme Degallaix ,  Sebastian Steinlechner ,  Gianluca Gemme ,  Alessio Rocchi ,  Eric Chassande-Mottin ,  Simone Mastrogiovanni  and the developers of the advGWINC code. The authors also acknowledge the useful work by  Breschi  et al. and the helpful communications in developing a dedicated PM waveform module within advGWINC.
The Numerical Models 3. 1. RANS: OpenFOAM 3. 1. 1. Description of the Solver OpenFOAM (Open source Field Operation and Manipulation) is an extensive software package for solving continuous mechanics problems. It was initially developed in the late 1980s at Imperial College, London and it was later rewritten in C++, incorporating the advantages of object oriented programming  [36, 37]. OpenFOAM releases are open-source and freely available under GNU General Public Licence and, nowadays, it is widely used for industrial and academic CFD applications  [38]. The version used in the present study is 2. 1. x. Regarding free surface fluid flows, OpenFOAM can solve the 3-Dimensional (3D) NSE multiphase flows including turbulence, using the Finite Volume Method (FVM) discretization with the Volume of Fluid Method (VoF)  [39].
This allows for employing the model for nonlinear problems with highly distorted free surface, such as wave breaking and interaction of waves and structures. The code has been applied for coastal and ocean engineering problems after the development of appropriate boundary conditions for wave generation and absorption, mainly through the libraries IHFOAM  [40]  and waves2Foam  [41]. Here, the former is used, thanks to its higher computational efficiency, as discussed in  [5]. Both these integrated libraries employ the "interFoam" solver for two-phase incompressible NSE. In the present study, the Reynolds Averaged Navier-Stokes (RANS) approach is employed as a common and relatively efficient way to solve the NSE  [42]. The continuity (Equation (  6 )) and momentum (Equation (  7 )) equations are solved simultaneously for the two Newtonian and immiscible fluids (air and water).
Here, it is shown that the overall increase of the crest amplitude for the coarser resolutions reaches 20%. Coarser resolutions than the R10-C0. 2 cause local distortions of the crest, which can be associated with premature breaking. The total number of cells in the computational domain is 2. 48 millions for the high resolution NWT that was finally chosen (R2. 5-C0. 1), resulting to a computational time of approximately 50 h on a 16-core Intel Xeon E5-2650 @ 2. 6 GHz. [17]  following precious works  [47] [48] [49] [50]. The code is written in FORTRAN and its structure resembles the well-established spectral model SWAN. SWASH is considered an operational model for coastal engineering problems at regional scale, but thanks to its flexibility, it can be applied to different space and time scales ranging from field to laboratory studies.
This will set the basis for further studying the loading on structures with a design wave that will be representative of a steep extreme wave event. Already however, the present findings open the possibility for coupling of the models, for example combination of PFT for the far field propagation and CFD near the examined structure  [16, 71] , or for using SWASH and HOS-NWT for preliminary investigations and a computationally efficient correction of the phases and amplitudes of the wave group using the focusing methodology. Funding: The authors would like to thank the School of Engineering of the University of Plymouth, Ifremer, LabEx Mer and the EPSRC SuperGen project SMARTY (grant number EP/J010316/1) for the financial support.
Molecular Mechanics All molecular mechanics simulations have been carried out with CP2K 5. 1, 78-81 using the FIST module under periodic boundary conditions. Long-range Coulombic interactions were evaluated through the smooth particle-mesh Ewald summation. 82 Molecular dynamics were run in the NVT ensemble at 300 K, using the default settings for the Nose-Hoover thermostat. 83, 84 Water molecules interactions were simulated according to the TIP3P model  66  and inter-atomic distances were accordingly constraint. All metallic atoms were kept frozen. The water/metal interactions were described by our implementation of the GAL19 force-field, which will be released to the public in a future version of the CP2K software. The interfacial systems contained 192 metal atoms (c(4×6), 4 layers) and 250 water molecules, yielding a water layer of about 20 Å.
Low-coverage water adsorption Energy Minima Water adsorption at 1/9 monolayer (ML) on the (111) and (100) facets of Cu, Pd, Ag, Pt and Au has been studied by DFT and used for fitting GAL19 on these ten metallic surfaces. Water adsorbing flat on top sites (θ ≈ 90 • , φ = 0 • ) is the most stable conformation adsorption according to previous DFT optimization and experimental observations. 64 erefore, Fig. 4  reports the distance dependence of this adsorption modes for the 10 facets at the PBE-dDsC level of theory, used herein to fit GAL19. This level of theory has been validated against experimental data on Pt(111) in a previous study. 72 Furthermore, the comparison of the minimum adsorption energy on the (111) surface with other vdWinclusive functionals  86  shows a good agreement, with deviations of about 0.
The mono-metallic/water interface Molecular dynamics simulations of 1 ns were carried out with GAL19 to evaluate the structure of the metal/water interface for Pt, Pd, Au, Ag, Cu. Due to the slow diffusion of water at the interface, such an extensive phase-space sampling is necessary for obtaining equilibrated results. 18, 21 Fig. S6  reports the results of a 5 ns trajectory for Pt(111), which shows that 1 ns simulations are well converged. Experimentally, the evidence on the neutral metal/water interface is very scarce: Toney et al. have shown that the density in the first layer over Ag(111) is at least 30% higher compared to the bulk and that two to three layers can be distinguished. 8 The IR data on the Au(111) and Pt(111) is debated  95  but tends to suggest either flat  96  or locally ice-like arrangements  97  for the first layer.
Acknowledgement The authors thank the  SYSPROD  project and  AXELERA Pôle de Compétitivité  for financial support (PSMN Data Center). This work benefited from the support of the project MuSiC  ANR-14-CE06-0030  of the  French National Research Agency (ANR). We are also grateful to the  GdR SolvATE  for a travel grant. Hatim Lagriai  and  Sana Abbassi  have performed preliminary tests for the establishment of the DFT reference data. We thank  Jinggang Lan  for sharing the Pt/water AIMD trajectory from which we extracted snapshots.
Background: Long-read sequencing can be applied to generate very long contigs and even completely assembled genomes at relatively low cost and with minimal sample preparation. As a result, long-read sequencing platforms are becoming more popular. In this respect, the Oxford Nanopore Technologies-based long-read sequencing "nanopore" platform is becoming a widely used tool with a broad range of applications and end-users. However, the need to explore and manipulate the complex data generated by long-read sequencing platforms necessitates accompanying specialized bioinformatics platforms and tools to process the long-read data correctly. Importantly, such tools should additionally help democratize bioinformatics analysis by enabling easy access and ease-of-use solutions for researchers. Results: The Galaxy platform provides a user-friendly interface to computational command line-based tools, handles the software dependencies, and provides refined workflows.
The platform hosts a wide range of tools/software and is widely used for bioinformatics analysis within the biological science community  [12, 13]. Here we introduce the NanoGalaxy toolkit for analysing Nanopore long-read data. NanoGalaxy comprises a series of integrated Galaxy-based tools that enable researchers to generate powerful short-or long-sequence read assemblies for genomic and plasmid bioinformatics analyses. The NanoGalaxy toolkit is a userfriendly environment that can be utilized inside or outside of traditional research laboratories. • Canu  [15]  • Unicycler  [16]  • Wtdbg2  [17]  • Miniasm  [18]  • Racon  [19]  • Spades  [20]  • Medaka (2 tools) [21] Long-read mapping • Minimap2  [22]  • GraphMap (2 tools)  [23]  Polishing, quality control, and pre-processing • ont fast5 api (4 tools)  [24]  • Nanopolish (3 tools)  [25]  • Porechop  [26]  • Filtlong  [27]  • Poretools (13 tools)  [28]  • Pilon  [29]  Visualization • Nanoplot  [30]  • Bandage (2 tools)  [31]  • Circos  [32]  Taxonomy and metagenomics • Kraken2 [33] • PlasFlow [34] • Staramr [35] Methylation • Nanopolish (1 tool) [25] Variant calling • Medaka (2 tools) [21].
Metagenomics taxonomic classification The base quality of nanopore sequencing reads is constantly improving, making the actual assembly of reads more reliable. Furthermore, the long reads generated by nanopore sequencing can be used to provide valuable information from metagenomics data, including taxonomic classifications. Kraken2 is a k-mer-based classification technique that can efficiently assign the taxa of long reads that are resilient to the noisy nature of long-read data. The input reads for Kraken2 are compared to a database containing different classes and domains of life that are pre-indexed for algorithm effi-ciency. Within the NanoGalaxy toolkit we provide a workflow for taxonomic classification using Kraken2, including the postprocessing of data and visualization of the results as interactive pie charts using the Krona tool  [36].
Nanopolish tutorials Nanopolish includes an extensive set of software tools for analysing nanopore long-read information at the raw signal level. Furthermore, accompanying Nanopolish documentation provides intuitive tutorials on common scenarios, such as variation analysis and base methylation calling from the raw and mapped signals  [25]. We have integrated Nanopolish and its tutorials into NanoGalaxy in the form of workflows that can be used by researchers to analyse and interpret common quality values for their data.
De novo assembly of genome with highly repetitive repeats Compared to short reads, long-read data have the advantage of facilitating the assembly of large genomes that contain high numbers of repetitive elements. Schmid et al. utilized Flye and several other tools to generate a comprehensive assembly of the Pseudomonas koreensis genome, identifying that the genome has near identical repeat pairs up to 70 kb in length  [37]. These workflows have also been integrated in the NanoGalaxy toolkit.
Use case 1: Long-read sequencing analysis The NanoGalaxy toolkit incorporates a rapid long-read assembly workflow using minimap2  [22] , miniasm  [18] , and Racon  [44]. Tools for further analysis in the toolkit include Staramr  [35]  for resistance gene detection, PlasFlow  [34]  and Bandage  [31]  for microbial species/plasmid determination, and NanoPlot  [30]  for quality assessment. In this worked example, the outcome of the NanoGalaxy pipeline was compared to the plasmid sequences recovered by Li et al. [45]  (Supplementary Table  S1 ). The pipeline recovered 19 of 21 plasmids, with a mean identity of 97. 76%. The number of detected resistance genes was higher than that found by Li et al. [45] , which was expected because Staramr  [35]  includes the PointFinder (chromosomal point mutations) database  [46]  and current long-read sequencing may generate relatively high sequence error rates.
Use case 2: Combining short-and long-read sequencing The previously described long-read assembly workflow rapidly assembles genomes. Because short-read sequencing platforms tend to have a higher accuracy at single-nucleotide level, hybrid solutions to gain from both short-and long-read data are of special interest. The NanoGalaxy toolkit includes a workflow that processes both long-and short-read sequences. In this respect, Unicycler was integrated into the NanoGalaxy toolkit to combine the best features of long-and short-read sequencing technologies. The workflow recommended by the Unicycler developers  [16]  includes Trim Galore  [47] , Porechop  [26] , and Filtlong  [27]  for quality trimming; Unicycler  [16]  for de novo assembly; and Bandage  [31]  for plasmid visualization. These tools are available as stand-alone tools and combined in a NanoGalaxy workflow.
Conclusion In this work we covered some important aspects of long-read sequencing analysis with a special focus on ONT sequencing data. We aggregated commonly used tools into a single consistent interoperable interface and presented solutions for metagenomic analysis and genome assembly. Furthermore, other longread sequencing data analysis tools have been developed or are currently under development; however, we have focused on the most established and widely used tools. Nevertheless, we expect that the toolkit will be further extended by the community because NanoGalaxy is part of the open Galaxy platform and Galaxy community. Last, the majority of the integrated tools that support other technologies such as Pacific Biosciences should also work inside Galaxy. However, here we have performed intensive testing of the integrated tools for ONT data.
SPInS uses a Bayesian approach to find the probability distribution function of stellar parameters from a set of classical constraints. At the heart of the code is a Markov Chain Monte Carlo solver coupled with interpolation within a pre-computed stellar model grid. Priors can be considered, such as the initial mass function or stellar formation rate. SPInS can characterise single stars or coeval stars, such as members of binary systems or of stellar clusters. Results. We first illustrate the capabilities of SPInS by studying stars that are spread over the Hertzsprung-Russell diagram. We then validate the tool by inferring the ages and masses of stars in several catalogues and by comparing them with literature results. We show that in addition to the age and mass, SPInS can efficiently provide derived quantities, such as the radius, surface gravity, and seismic indices.
SPInS is a modified version of the AIMS (Asteroseismic Inference on a Massive Scale) pipeline 7. The AIMS code has been described and evaluated by  Lund & Reese (2018)  and  Rendle et al. (2019). AIMS is able to perform a full asteroseismic analysis and can estimate stellar parameters from two sets of observations: classical data and detailed asteroseismic constraints (individual oscillation frequencies or a combination thereof). While AIMS is essentially an asteroseismic tool, SPInS is not intended to handle detailed seismic data but rather focuses on classical or mean observed stellar data (these will be explained in the following sections). This greatly simplifies the procedure with a substantial gain in computational time and occupied disc space. In particular, SPInS only uses the standard outputs of stellar evolution models and does not need to be provided with the detailed calculations of the oscillation spectrum of the models.
). The advantage of this approach is that it provides a full probability distribution function (hereafter PDF) for any stellar parameter to be inferred, thereby accounting for multiple solutions when present. It also allows the user to incorporate in the calculation various priors (i. e. a priori assumptions), such as the initial mass function (IMF), the stellar formation rate (SFR), or the metallicity distribution function (MDF). SPInS can be used in two operational modes: characterisation of a single star or characterisation of coeval groups, including binaries and stellar clusters. SPInS is mostly written in Python with a modular structure to facilitate contributions from the community. Only a few computationally intensive parts have been written in Fortran in order to speed up calculations.
Overview SPInS uses a Bayesian approach to find the PDF of the stellar parameters from a set of observational constraints. At the heart of the code is a MCMC solver based on the Python EMCEE package  (Foreman-Mackey et al. 2013 ) coupled to interpolation Y. Lebreton and D. R. Reese: SPInS, a pipeline for massive stellar parameter inference within a pre-computed grid of stellar models. This allows SPInS to produce a sample of interpolated models representative of the underlying posterior probability distribution. We recall that the posterior probability distribution can be obtained from the priors and the likelihood via Bayes' theorem: P(θ|O) = P(O|θ)P(θ) P(O) , (1) where O are the observational constraints and θ the model parameters. In other words, this theorem provides a way of calculating the probability distribution for model parameters given a set of observational constraints, as represented by the likelihood function P(O|θ), and priors P(θ).
Grids of stellar models SPInS can easily include any set of evolutionary tracks or isochrones available in the literature or calculated by the user. In this work, we used the BaSTI stellar evolutionary tracks available at http://albione. oa-teramo. inaf. it/index. html and described in  Pietrinferni et al. (2004 Pietrinferni et al. ( , 2006)). We chose to use these data rather than more recent ones in order to make comparisons with previous works. In the BaSTI database, many sets of stellar tracks, all in the mass range M ∈ [0. 5M , 10M ], are available. These models are well-suited to age-dating stars of different kinds: they cover evolutionary stages running from the zero-age main sequence (we do not use here the additional pre-main sequence grid provided for a narrower range of mass) to advanced stages, including the red-giant and horizontal branches, and a metal abundance range Z ∈ [0.
As described in  Pietrinferni et al. (2004) , in these models, convective mixing is extended over 0. 2 pressure scale-heights above the Schwarzschild's core for a stellar mass higher than 1. 7 M , no overshooting is considered for a mass lower than 1. 1 M , and a linear variation is assumed in-between. -α-enhanced model grids (both canonical and non-canonical): their element mixture corresponds to a metal distribution typical of the Galactic halo and bulge stars, with [α/Fe] = +0. 4, where [α/Fe] is the decimal logarithm of the ratio of the number abundance of α-elements (i. e. formed by αcapture thermonuclear reactions) with respect to iron and referenced to the solar value ([α/Fe] = 0. 0). These grids are described in  Pietrinferni et al. (2006). For each evolutionary track in the BaSTI database, the variation of the luminosity, effective temperature, absolute M b magnitude, and colour indices are provided as a function of the age and mass of the model star for several photometric systems.
We here use the tracks given in the Johnson-Cousins system which provide M V , and (B -V), (U -B), (V -I), (V -R), (V -J), (V -K), (V -L), (H -K) colours, but other photometric systems are available in the BaSTI database. All these quantities can be used indifferently in SPInS. In addition, we considered four quantities that can be inferred from BaSTI models straightforwardly: the photospheric radius R calculated from Stefan Boltzmann's law, the surface gravity g = GM R 2 , (10) (or its decimal logarithm log g), and the frequency at maximum amplitude ν max,sc and the mean large frequency separation of pressure modes ∆ν sc expressed in asteroseismic scaling relations. The latter read, ν max,sc ν max, = M M T eff, T eff, -1/2 R R -2 , (11) and ∆ν sc ∆ν = M M 1/2 R R -3/2 , (12) and are explained in  Brown et al.