dataesr/scientific-paragraphs-categorization huggingface dataset:
The virtual machine model used in this article has been done based on the Google Traces workload  [4]. The analyzes done on a previous work on this dataset have allowed to extract few relevant properties in order to set-up a quite accurate virtual machine model based on real data. Moreover, three metrics have been proposed to evaluate different kind of scheduling behaviour. The GEC metric has been adapted to the global electrical context of the proposed architecture while taking into account both energy purchase and sell from the supplier. The two others is more focused on the ability of having a suitable number of computing resources to schedule virtual machines. We have adapted a set of algorithms to meet the following objectives: -Create a scheduling based on green energy prediction -Be as little dependent as possible on the electricity supplier -Be able to evaluate scheduling using metrics We chose greedy algorithms because the use of green energy is not sustainable in time and it allows us to get a quick decision making.
A C C E P T E D M A N U S C R I P T 14 been simulated, revealing that both membranes curve in a manner dependent on the size of the embedded proteins  [223]  (Figure  12D ). Similar approaches enabled the simulation of enveloped viruses whose shells encompass complex compositions  [224] [225] [226]. A current challenge to further optimize coarse-grained membrane models is to remove the explicit solvent  [227] [228] [229] [230] , which calls for some treatment of hydrodynamics  [231]. We have recently set up an implicit water membrane model capable of describing for instance hydrodynamics within a lipid vesicle nanoreactor by extending previous work with the MUPHY software in line with the concepts described in  [232]. An example of a vesicle nano-reactor in its flowfield is illustrated in Figure  12F.
This representation is particularly useful with large systems, eventually containing several lipid bilayers  [256]. To recapitulate the dynamics of lipid flow within the membrane, a combination of path line, vector field and streamline techniques can be used  [257]  as illustrated in Figure  14C. Many membrane simulations are carried out at coarse-grained resolution, hence an effortless way of displaying such systems facilitates the visualization task. Molecular visualization software such as UnityMol  [258]  start to incorporate native import filters for coarse grained topologies. This visualization capacity was used to visualize carbon nanotube perturbations of a lipid bilayer  [259]  (Figure  14D ). Zooming into the fine details of glycolipids, specificpossibly abstractedrepresentations for the sugar moieties  [260]  (Figure  14E ) are yet to be generalized, which applies to e.
Hence, performance and visualization efficiency are important aspects to be considered. The Wang tiling concept has been extended to membranes for precisely this purpose  [173]  as shown in Figure  14G. Visualization for analysis. Such visualizations are typically closely connected to the analysis of simulation trajectories, a vast domain. Often the 2-dimensional nature of the bilayer is exploited in these analyses, which requires defining membrane leaflets in case of deformed membranes. Voronoi diagrams and Delaunay triangulation can be used for such analysis  [263]  (Figure  15A ), as well as grid-based approaches  [264]. Some solutions are extensions to visualization software such as VMD  [265] , others are intended for command-line use, such as FATSLiM  [266]. The latter is particularly apt to handle curved membranes, as it relies on a local calculation of membrane normals illustrated in Figure  15B.
Acknowledgments I would like to express my gratitude for many insightful remarks and stimulating discussions leading to this review, with support from  B. Bechinger ,  A. Brandner ,  M. Chavent ,  A. Duncan ,  M. Edwardson ,  J. Jenkinson ,  J. Hénin ,  S. Khalid ,  B. Kozlíková ,  G. McGill ,  D. Picot ,  A. Rose ,  F. Samsudin, M. S. P. Sansom ,  T. Steck ,  F. Sterpone ,  J. Sturgis ,  S. Timr ,  J. Tyrwhitt-Drake ,  D. Warschawski  and  D. Yuen. This work was.
spectral information in addition to the topographical data. The considerable use of digital photogrammetry this last decade is also due to the rise of many affordable software packages producing DOMs (e. g. , Agisoft Photoscan, MicMac, VisualSFM; Niederheiser et    al. , 2016)  as well as the democratization of personal computers with high RAM memory capacity and ultra-fast central and graphic processor units. Cave systems stand out as exceptional environments for studying sedimentary and tectonic structures because of their generally better preservation from atmospheric erosion processes than for surface outcrops. However, mapping and modelling their complex geometries may be a challenging task due to limited accessibility and light conditions. Although geological structures are numerous and easy to identify, quantifying their orientation may be complicated or impractical due to their inaccessibility.
Such novel kind of datasets represent significant inputs for new quantitative structural modelling and may bring new clues with respect to traditional sectional interpretations  (Bistacchi et al. , 2011; Chalke et al. , 2012; Franceschi et al. 2015). Three main methods are commonly used for acquiring 3D datasets: (i) tacheometric surveying instruments (i. e. traverse via distance meters, compasses, tilt meters or total stations), (ii) terrestrial laser/light-based scans (LIDAR) and (iii) digital photogrammetry technology using photographs captured from the ground using field digital camera and/or from an Unmanned Aerial Vehicle (UAV)  (Lerma et al. , 2010; Westoby et al. , 2012). The first method requires intensive acquisition works and complex data processing resulting in low spatial-resolution data sets  (Gallay et al.
5 x 15. 6 mm CMOS sensor; photographs size of 6000 x 4000 pixels) with a fixed 18mm lens (2. 8 of focal ratio) and the UAV default action camera (50. 8 x 25. 4 mm CMOS sensor; photographs size of 4000 x 3000 pixels) with a fixed 35mm lens (2. 8 of focal ratio). For more post-production flexibility, all pictures from each digital camera were captured as RAW data files (equivalent of 'digital negatives' recording all the data captured by the sensor without white balance set, etc. ). During field operations, we ensured, as far as possible, an image overlap of 60% -85% (as recommended by  Agisoft LLC, 2015). Overall, using these two cameras reduces the acquisition time and tends to enhance the resolution of the produced 3D model. Data sets of 1090 and 215 photographs were taken with the Nikon and the DJI digital camera in total.
Pre-processing of individual photographs. The outcrops in the Val d'Enfer cave chamber is made of complex and irregular concave/convex shapes. Such geometry can lead to physical occlusion of some portions of the outcrop, shadows and over-exposure caused by directional artificial lightings (Fig. 3 ). The presence of shadows/overexposed zones may however form poorly reconstructed parts of the final model (low density of tie points) and such variation of exposure can also be problematic for further model-based interpretations of the lithology. The resulting dark or light colours may indeed not be representative of the lithology at the surface. This could lead to misinterpretations between dark rock surface and/or in shadowed zones. Blurry photographs were removed using a routine python script and a home-made Photoshop script was used to correct over-exposure.
This consists of correcting each RAW photograph by darkening the overexposed zones and by lightening the shadowed portions of the photographs as well as adjusting the white balance to correct halogen 3000K yellowish colours (Fig. 3 ). After this treatment, each picture was automatically converted and saved as a JPG image file type. 4. 2 Building the 3D dense point cloud and mesh using SfM photogrammetry. In order to build the digital outcrop model of the Val d'Enfer chamber, enhanced photographs are imported and processed using Agisoft Photoscan Pro software v. 1. 3. 4 (see Appendix B1 for used parameters in Agisoft Photoscan software and desktop computer specifications). The EXIF metadata of each image (EXIF, 2012) including the focal length and the pixel size are automatically read by the software when importing the photographs.
Based on this first dataset, a high resolution dense point cloud is computed, which results in 395. 4 million points, providing a point density of approximately 1. 65 million points per square meters (Fig. 4b ). Some small and localized holes occur in the point cloud, especially in small complex-geometry areas (e. g. , narrow corridors) which obstruct the lighting system or the camera view. Building a mesh model from such a huge dataset requires massive computation resources in terms of RAM-and GPU-memory  (Agisoft LLC, 2015). Therefore, the point cloud has been splitted in 11 chunks to facilitate the processing workflow. Individual meshes were successfully computed for each chunk and then merged into a single high-resolution 3D mesh, made of 40. 2 million of polygons (ca. 18 600 polygons/m², Fig.
4 c-d ). Compared to the point cloud model, the mesh consists of a digital surface model of the outcrop allowing to provide volumetric estimation and topological information of the cave. This is also a crucial step before creating a model texture. These further steps allow to minimize the size of the digital model and computation resources needed to render and visualize it, which is mandatory in order to share it via 3D platforms and/or virtual reality technologies  (Remondino, 2011; Triantafyllou et al. , 2018)  but those are beyond the scope of this paper. In this study, we focus on the dense point cloud which is finally exported in an ASCII text file for further processing in CloudCompare software (see next section). Every point is characterized by 9 attributes: the relative location (x, y, z coordinates), the colour calculated from the original pictures (red, green, blue channels ranging from 0 to 255) and orientations of the normal vectors (nx, ny, nz ranging from -1 to 1).
The latter are computed along with the dense point cloud in Agisoft software. They provide a geometrical information for each point and consist of the normal vector of a local surface delimited by the nearest neighbours of each specific point  (Agisoft LLC, 2015). A textured low resolution version of the 3D model of the main chamber of the Lorette cave has been uploaded on the sketchfab open repository and can be accessed online through this link: https://skfb. ly/6FQNw.
Georeferencing the Digital Outcrop Model The resulting high-resolution 3D points cloud needs to be levelled, oriented according to the north, scaled and translated to geographical coordinates for facilitating regional geology interpretations (Fig. 4 e-f ). We used the location and orientation of GCP and GCT references along with CloudCompare software version 2. 9 (Girardeau-Montaut, 2015) combined with our own Python routines specifically developed for this study. Firstly, the DOM, arbitrarily oriented when outputting from Agisoft software, is levelled. Points colored in flashy green and yellow (forming the Ground Control Triaxes; GCT, see section 3. 2) are detected and extracted from the point cloud. The polar median of their normal vectors is calculated and compared to an expected upward vertical vector [0, 0, 1].
Sedimentary bedding planes. We measured the sedimentary bedding planes (n = 191 in total) in five subsets from the base (northward, A-bedding) to the top (southward, E-bedding) of lithostratigraphic pile in order to validate the monoclinic geometry of the sedimentary pile and/or track a potential variation of strata orientation due to high amplitude folded structures affecting the sedimentary units in the studied area. Detailed results are given in Table  A3  and in the Fig. 5c. The median strikedirection value of the bedding planes is N065 with 48° dipping to the SE. The limited spread of the data supports that there is no significant variation of the orientation of the stratigraphic bedding through the sedimentary pile of the Val d'Enfer chamber (Fig. 5c ). Two surfaces are marked by a stylolithic bedding surface.
Using these field photographs and the Agisoft Photoscan software, a high resolution digital outcrop model containing more than 395 million points was generated, then georeferenced in the CloudCompare software. This study leads to the 3D mapping and characterization of sedimentary structures. We developed a specific workflow to extract the orientation and spatial distribution of sedimentary beddings and brittle geological structures (faults, slickenlines, joints, tension gashes) exposed on the roof of one of the main chamber of the Lorette cave system which are inaccessible to the fieldworker. Such an approach aims at triggering future applications and new research directions in subsurface environments. The workflow presented in this study could be used in routine for speleological investigations by providing detailed and accurate morphological mappings and volumetric calculations of cave systems.
Cave Laboratory (RCL in white) overlain on a digital elevation model with a XY resolution of 1m (based on LiDAR data of the Public Service of Wallonia). In the area of the doline giving access to the cave, the DEM is replaced by a high-resolution DEM obtained from a photoscan  (Watlet, 2017). orientations measured through a virtual survey. Great circles are for fault planes (n_p) and points for slickenlines (n_l). Stereonets (h) and (i) display show respectively the orientation of individual calcite-filled planar veins as well as the orientation of planar structures containing the en-echelon calcite-filled veins. All stereonets are drawn on a lower hemisphere Schmidt net using Geolokit software  (Triantafyllou et al. , 2017). The stress tensor was calculated using the Right Dihedra Method, described in  Delvaux and Sperner (2003)  derived from e.
While model trends have traditionally been given greater consideration than the absolute values, it is recognized that this is changing as model resolution increases. Forecasts of RI and rapid weakening (RW) are typically confined to landfall scenarios (weakening) and for the first 24-48 hours as it is difficult to pick the timing of such changes. Forecasters appreciate tools that make it easy to visualize and interpret the range of guidance. It is an ongoing frustration that multiple sources have to be viewed to enable guidance to be compared. This has led to the development of the intensity tool in the TCModule software package (figure  3 ). Web displays such as the CIRA multi-model display (figure  4 ) are also well used as it includes displays of wind shear, SST and RH, but doesn't have the full range of guidance.
acknowledgements Preparation of this report for IWTC-9 greatly benefited from discussions at the workshop. The mix of formal and informal discussions makes the IWTC a great initiative in the sharing of ideas and activities. The co-authorship order  after the first two authors is determined by the surnames and not by the degree of author contribution The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U. S. Government position, policy, or decision. acronyms used in the report:.
Context. Small dust grains are essential ingredients of star, disk and planet formation. Aims. We present an Eulerian numerical approach to study small dust grain dynamics in the context of star and protoplanetary disk formation. It is designed for finite volume codes. We use it to investigate dust dynamics during the protostellar collapse. Methods. We present a method to solve the monofluid equations of gas and dust mixtures with several dust species in the diffusion approximation implemented in the adaptive-mesh-refinement code RAMSES. It uses a finite volume second-order Godunov method with a predictor-corrector MUSCL scheme to estimate the fluxes between the grid cells. Results. We benchmark our method against six distinct tests, dustyadvect, dustydiffuse, dustyshock, dustywave, settling and dustycollapse.
Lin & Youdin (2017)  and  Chen & Lin (2018)  have applied the monofluid formalism on a cylindrical grid adapted to protoplanetary disk simulations with an Eulerian approach. Adaptive Mesh Refinement (AMR,  Berger & Oliger 1984 ) codes are a complementary approach to SPH in astrophysical or cosmological simulations. Commercon et al. (2008)  show in the context of protostellar collapse that the two methods lead to converged results, but AMR codes capture details more efficiently thanks to the versatility of the refinement criteria. The adaptive mesh is a high performance tool for star formation simulations where a wide range of physical and dynamical scales are considered (e. g. ,  Hennebelle 2018; Vaytet et al. 2018). In this paper, we present a numerical Eulerian approach of dust dynamics in the diffusion approximation on AMR grids and its implementation in the RAMSES code  (Teyssier 2002) , as well as an application to the first collapse  (Larson 1969).
Basic features Before we present our method, we recall the main features of the RAMSES code  (Teyssier 2002)  in order to facilitate the understanding of our implementation of dust dynamics in the diffusion approximation. RAMSES is a finite volume Eulerian code that integrates the equation of hydrodynamics in their conservative form on an AMR grid  (Berger & Oliger 1984). Among others applications, it has been extended to magnetohydrodynamics  (Teyssier et al. 2006; Fromang et al. 2006; Masson et al. 2012; Marchand et al. 2018) , radiation hydrodynamics  (Commercon et al. 2011; Rosdahl et al. 2013; Commercon et al. 2014; Rosdahl & Teyssier 2015; González et al. 2015) , and cosmic ray and anisotropic heat conduction  (Dubois & Commercon 2016). For simplicity, the hydrodynamical version of RAMSES is presented here.
Acknowledgements. First, we sincerely thank the referee for providing extremely useful remarks and ideas which helped to significantly improve the quality of our work. We acknowledge financial support from the  Programme National de Physique Stellaire (PNPS)  of  CNRS/INSU ,  CEA , and  CNES, France. This work was granted access to the  HPC resources of CINES (Occigen)  under the allocation DARI A0020407247 made by  GENCI. Computations were also performed at the  Common Computing Facility (CCF)  of the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ). This work took part under the programs ISM3D and Core2disk of the  PSI2  project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. This project was partly supported by the  IDEXLyon  project (contract nANR- 16-IDEX-0005 ) under the auspices  University of Lyon.
1. Viruses, such as dengue, Zika, yellow fever and chikungunya, depend on mosquitoes for transmission. Their epidemics typically present periodic patterns, linked to the underlying mosquito population dynamics, which are known to be driven by natural climate fluctuations. Understanding how climate dictates the timing and potential of viral transmission is essential for preparedness of public health systems and design of control strategies. While various alternative approaches have been proposed to estimate local transmission potential of such viruses, few open-source, ready to use and freely available software tools exist. 2. We developed the Mosquito-borne Viral Suitability Estimator (MVSE) software package for the R programming environment. MVSE estimates the index P, a novel suitability index based on a climate-driven mathematical expression for the basic reproductive number of mosquito-borne viruses.
Following a similar rationale to previous work based on vectorial capacity measures  (Brady et al. , 2014; Gething et al. , 2011; Hamlet et al. , 2018a; Mordecai et al. , 2017) , we have recently shown that a mosquito-borne viral suitability measure called the index P can be derived from the R0′s expression of our modelling framework  (Perez-Guzman et al. , 2018). In theory, this index is a proxy for timing and scale of mosquito-borne viral transmission potential, without the need to fit epidemic curves. In the context of Myanmar, we have recently shown that the index P is highly correlated with both city-level mosquito infestation and dengue notifications  (Perez-Guzman et al. , 2018). In this article, we translate our experience with this modelling framework and index P into a novel R-package named the Mosquito-borne Viral Suitability Estimator (MVSE).
| MVSE implementation, availability and requirements The R-package MVSE is available under a GNU GPL 3. 0 license at a SourceForge repository (sourceforge. net/projects/mvse/), where both a source-based package file and PDF reference manual can be found. MVSE is platform-independent, requiring R ( >= 3. 4) and Rpackages pbapply (for user-console feedback), scales (for plotting), genlasso (for time-series smoothing). The version of the package used in this manuscript is v0. 3 (cryptonym: spectral). Newer versions and related materials will be deposited in this repository and we refer the reader to it for further information and future changes. Sabino et al. , 2016). In contrast, São Paulo is yet to experience a sustained ZIKV epidemic, DENV incidence is known to be low for the national average  (Teixeira, Siqueira, Ferreira, Bricks, & Joint, 2013)  and transmission of mosquito-borne viruses is unstable, with phylogenetic studies showing that epidemics are seeded by new lineages almost every year  (Faria et al.
| Index P spatiotemporal characterization across Brazil In the previous figures, we demonstrated how the index P can be used to compare transmission potential and timing between two Brazilian cities. In this section, we demonstrate the usefulness of index P to characterize the transmission potential across vast geographical ranges, using Brazil as a case study. We use the WorldClim V2 dataset, which holds mean world-wide climatic data per month, during 1970-2000 (see Data accessibility3 for details). Our analysis is performed over a regular grid of Brazil, in which each pixel represents an area of ≈ 340km 2. For each pixel, we use 12 time points (months) of humidity and temperature to run MVSE and estimate the dynamics of index P over 1 year. Although computationally intensive, due to the size of the grid, this exercise only employs functions available in MVSE.
, 2014; Kraemer et al. , 2015b; Messina et al. , 2016). AaS includes a myriad of variables that are known to affect entomological and viral factors but which are not included in the formulation of the index Pfor instance, precipitation, vegetation levels, urbanization levels and geo-occurrence (reports of) adult or aquatic forms of Ae. aegypti and Aedes albopictus  (Kraemer et al. , 2015b). The implementation of AaS is explicitly spatial, with temporal and spatial resolutions restricted by the underlying WorldClim dataset used (maximum resolution of 12 months and spatial resolution from 1 to 340 km 2 per geopixel). To follow the 12 month time-scale of AaS, we transformed the three weekly incidence time series into months by adding up each month's weeks. At the country level, we estimated index P in time and space using the same approach as we used for Brazil (Figure  3 ) and South America (S2 Text).