dataesr/scientific-paragraphs-categorization huggingface dataset:
The second fundamental visualization technique for scalar fields is volume rendering. Volume rendering is a method of rendering three-dimensional volumetric scalar data in twodimensional images without the need to calculate intermediate geometries. The individual values in the dataset are made visible by selecting a transfer function that maps the data to optical properties such as color and opacity. These are then projected and blended together to form an image. For a meaningful visualization, the correct transfer function must be found that highlights interesting regions and characteristics of the data. Finding a good transfer function is crucial for creating an informative image. Multidimensional transfer functions enable more precise delimitation from the important to the unimportant. Therefore, they are widely used in volume rendering for medical imaging and the scientific visualization of complex threedimensional scalar fields (Figure  14 ).
Visualization of vector fields. The visualization of vector field data is challenging because no existing natural representation can convey a visually large amount of three-dimensional directional information. Visualization methods for three-dimensional vector fields must therefore bring together the opposing goals of an informative and clear representation of a large number of directional information. The techniques relevant for the visual analysis of vector fields can be categorized as follows. The simplest representations of the discrete vector information are oriented glyphs. Glyphs are graphical symbols that range from simple arrows to complex graphical icons, directional information, and additional derived variables such as rotation. Streamlines provide a natural way to follow a vector dataset.
The key aspect of real-time processing is that data are used for visualization/analysis while still in memory. This type of visualization/analysis can extract and preserve important information from the simulation that would be lost as a result of aggressive data reduction. Various interfaces for the coupling of simulation and analysis tools have been developed in recent years-for the scientific visualization of CFD data, ParaView/Catalyst  [73]  and VisIt/ libSim  [74]  are to be mentioned in particular. These interfaces allow a fixed coupling between the simulation and the visualization and integrate large parts of the visualization libraries into the program code of the simulation. Recent developments  [75, 76]  favor methods for loose coupling as tight coupling proves to be inflexible and susceptible to faults.
Acknowledgements Part of the research developments and results presented in this chapter were funded by: the  European Union 's  Horizon 2020 Programme  ( 2014-2020 ) and from  Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP)  under the  HPC4E  Project, grant agreement  689772 ;  EoCoE , a project funded by the  European Union  Contract  H2020-EINFRA-2015-1-676629 ;  PRACE Type C and Type A  projects.
To find them, we designed REGULOUT, a program which automatically identifies genes with unique profiles among their group of orthologs (i. e. , orthogroups). We applied REGULOUT to comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer. From these data, REGULOUT identified 38 ROs in C. glabrata. Using transcriptome analyses of the C. glabrata response to five different stresses, we pointed out five C. glabrata ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence  (Nevitt and Thiele, 2011; Srivastava et al. , 2014). Global chromatin Immunoprecipitation (ChIP-seq) and gene profiling analyses showed that these five genes were under the control of the iron starvation responsive transcription factor Aft2 and that four of them were actually C.
Multispecies Transcriptome Analyses: Microarray Design, Microarray Experiments, Data Analyses Agilent 8x60k custom microarrays were designed for each species (array express accession numbers: A-MEXP-2402, A-MEXP-2365, A-MTAB-642 to 647). For probes design, we used the Teolenn software version 2. 0. 1  (Jourdren et al. , 2010). The genome sequences and ORFs positions used to create probe sequences were downloaded from the Genolevures website. Only the coding sequences were used for probe design, introns and intergenic regions were not considered. The masked genomes (i. e. , without repeated sequences) required for Teolenn were created using RepeatMasker version open-3. 2. 8 with the "-pa 4 -species fungi -xsmall" arguments. The main features of the design are summarized in Supplementary File S2.
75 (less than -0. 75) and if its expression variation was considered as being statistically significant using the LIMMA package with a cut-off p-value of 0. 01  (Ritchie et al. , 2015)  for at least two consecutive time points. The complete microarray data are available at Array express database under the accession numbers: E-MTAB-7022, E-MTAB-7023, E-MTAB-7044 to 7047, E-MTAB-7049 and E-MTAB-7053. The processed dataset is available in Supplementary Table  T1.
REGULOUT REGULOUT is a python program which takes as an input a multispecies expression matrix and the repartition of the genes in orthogroups. There are two parameters which need to be set up by the user: the minimal size of the orthogroups to be used and the Euclidian distance cut-off to be applied. Then REGULOUT calculates, for each orthogroup reaching the defined minimal size, the pairwise distances between the expression profiles of the genes belonging to the orthogroup. In a next step, REGULOUT defines as ROs all the genes which minimal pairwise distance in their orthogroup is equal to or higher than the minimal distance cut-off. As an output, REGULOUT provides a text file with the name of the ROs, their minimal distance in their orthogroup, the ID number and the size of their orthogroup.
It also generates automatically PNG files with the expression profiles of the orthogroups in which at least one RO has been identified. REGULOUT can be freely downloaded from www. lcqb. upmc. fr/REGULOUT/, together with a tutorial and the input data sets used in this study. More precisely, the input files used in this article were the expression matrix for all species and the composition of yeast orthogroups taken from the PhylomeDB database  (Huerta-Cepas et al. , 2014). The distribution of the sizes of the orthogroups is available in Supplementary File S3. The minimal size of the orthogroups to be used was set up to 8 to work mostly with genes which were conserved in all the eight yeast species that were considered. The impact of this filter on the number of expression profiles analyzed by REGULOUT is indicated in Supplementary File S2.
The minimal distance cutoff was set up at 3, which corresponded to the 75 percentile of all the possible pairwise distances in the dataset. From the raw output of REGULOUT (440 genes), we filtered out the genes which were defined as ROs based on expression variations that were poorly reproducible (p-value > 0. 01). Because the Euclidian distance that we used is very sensitive to stochastic variations for genes with a large amplitude in their expression changes, we also removed from the RO list the genes belonging to orthogroups in which all the members had the same directionality in their expression variations (see Supplementary File S4 for an illustration). The filtered REGULOUT output contained 289 ROs which are listed in Supplementary Table  T2.
Multi-Stress Transcriptome Analyses in C. glabrata All cultures were conducted in a rotating shaker at 30 • C in YPD (glucose 2%, yeast extract 1%, bacto peptone 1%). Stress conditions used were: 1 mM for sodium selenite, 1 M for NaCl, 2 mM for cadmium chloride, 5 mM for iron sulfate or 0. 5 mM for bathophenanthroline disulfonate (BPS). Stressed and mocktreated cells were collected as described above, 20 min and 40 min after treatment. RNA extractions, microarray hybridization and data normalization were performed as described above. For the aft2∆ versus wild type comparisons, the stress concentrations were the same as above but the cells were collected only 30 min after treatment. The complete microarray data are available at Array express database under the accession numbers: E-MTAB-7023, E-MTAB-7042, and E-MTAB-7043.
Chromatin Immunoprecipitation and High-Throughput Sequencing (ChIP-Seq) For Aft2 ChIP experiments, myc-tagged strains were grown in YPD until exponential phase (OD = 0. 8), they were then treated by 0. 5 mM of BPS for 60 min. Cross-linking of the cells and ChIP were performed as described previously  (Merhej et al. , 2016). The parental HTL (untagged strain) was grown and processed the same way to provide the mock-IP samples. Sequencing of the IPs, Input DNAs and mock IPs samples and primary data analyses (quality controls and mapping of the reads) were performed as described previously  (Merhej et al. , 2016). Peak calling was performed with the bpeaks software, using both the Input DNA and the mock IP as references  (Merhej et al. , 2014). For peak calling using the Input DNA as reference, the bpeaks parameters were T1 = 1, T2 = 6, T3 = 1, T4 = 0.
These genomic sequences were used as inputs for the peak-motif tool to search for regulatory motifs  (Thomas-Chollier et al. , 2012 ). An additional filtering step was added to the standard peak motif procedure to discard low complexity motifs (e. g. , CCCCCCC). The list of Aft targets from S. cerevisiae was obtained from Yeastract  (Teixeira et al. , 2018)  and completed by the "regulation" pages of the SGD 1  , taking all the documented regulatory interactions using DNA binding or expression evidences and keeping only the targets which were found in relevant growth conditions (i. e. , metal stress conditions). The raw ChIP-seq data can be downloaded from the GEO database with the accession number GSE116077. Bioinformatics Analyses: Gene Ontology, Hierarchical Clustering, Promoter Sequence Analyses Gene ontology (GO) analyses were performed using the GO term finder tool at the Candida Genome Database 2  for C.
albicans, C. glabrata, and D. hansenii (with the names of the C. albicans orthologs), at the Saccharomyces Genome Database for S. cerevisiae, the two Lachancea species, K. lactis and Y. lipolytica (using the names of the S. cerevisiae orthologs). The hierarchical clustering of Figure  2  was performed using Mev with Euclidian distance, optimization of gene leaf order and average linkage. For phylogenetic analyses of the promoter sequences, we downloaded the promoter sequences (i. e. , 500 base pairs upstream of the ATG) from the Genome Resources for Yeast Chromosomes database (GRYC 3  ), except for the Saccharomyces sequences which were taken from the www. saccharomycessensustricto. org website  (Scannell et al. , 2011)  and for the Kazachstania Africana sequences which were downloaded from the NCBI website.
As a posteriori validation of our approach, we examined the expression patterns of genes from the general Environmental Stress Response (ESR), which were shown to be highly conserved among the species we studied  (Gasch et al. , 2000 (Gasch et al. , , 2004;; Gasch, 2007; Roy et al. , 2013; Thompson et al. , 2013). We observed that the induction of genes encoding proteasomal subunits and the repression of genes from the Ribosome Biogenesis (Ribi) regulon were consistent between the different species (Supplementary File S9). This was a good indication that the eight species were encountering physiologically similar stress conditions. To identify ROs from these 44,723 expression profiles, we designed a program called REGULOUT (Figure  1 , left panel). REGULOUT takes as input files the expression profiles obtained in the different species and a table describing the orthology relationships between genes (i.
For the present work, we only extracted the data for the C. glabrata ROs (Supplementary Table  T4 ). We then clustered them according to their expression profiles in these five stress conditions (Figure  2 ). Doing so, we could point out a group of five ROs which showed early and strong induction in response to the two iron starvation conditions (namely BPS and selenite treatments) and unchanged or slightly increased expression in response to osmotic and iron excess stresses. This group included the C. glabrata orthologs of the S. cerevisiae genes encoding the Grx3 glutaredoxin (CAGL0L11990g), the Mmt2 mitochondrial iron transporter (CAGL0E06006g), the Mak16 ribosome biogenesis factor encoding (CAGL0F00715g) and the Dom34 and Hbs1 translation surveillance factors (CAGL0B04675g and CAGL0G02255g).
DISCUSSION In this study, we designed a program, called REGULOUT, for the identification of conserved genes with divergent expression profiles from comparative transcriptomics datasets. The inputs of REGULOUT are multispecies gene expression profiles on one hand and the composition of orthogroups on the other. Hence, REGULOUT can be applied to any group of species for which this information is available. The output of REGULOUT is strongly influenced by the distance cut-off chosen and the type of distance used but also by the way the orthogroups were defined and by the stress conditions which were tested. Hence, the label of "ROs" that we introduced in this article is more a technical concept than a straightforward biological feature and REGULOUT should be used as a fast way to sort out lists of potentially interesting cases and not as a tool for gene annotation.
ACKNOWLEDGMENTS We are grateful to  Marc Lemaire ,  Dominique Sanglard , Christophe D'enfert,  Cecile Neuveglise ,  Cecile Fairhead , and  Gilles Fischer  for providing strains. We thank  Gilles Fischer ,  Cécile Neuveglise , and  Julianna Silva Bernardes  for useful discussions. We thank  Mathilde Garcia  for critical reading of the manuscript. We are grateful to the  Array express database  staff for their valuable help in the submission of the transcriptome data.
Stimuli were 500 per 500 pixels (width and height) and were presented on a white background in an unbroken sequential manner. Each stimulus was presented 10 times for 3 s, followed by a jittered intertrial interval, ranging from 1. 5 to 3 s, during which a fixation point was shown. In each trial  participants were to decide, as rapidly and accurately as possible, whether the presented stimulus was a right or a left foot, pressing the corresponding button on a keypad. The experiment was implemented in Matlab, using Cogent 2000 (Wellcome Laboratory of Neurobiology, UCL, London, http:// http://www. vislab. ucl. ac. uk/cogent. php). Accuracy of each participant was calculated as the proportion of correct responses. This value was submitted to arcsine transformation and used for the statistical analysis  [16].
The system is controlled by a credit card-sized computer board powered by an ARM processor. The operating system is a custom-made Linux system, built from scratch with the Buildroot framework. The computer runs advanced software algorithms  (Landau (1993) ) to achieve tight control of the flow rate (+/-0. 02 l/min) and of the temperatures (+/-0. 1°C). The B3010 features a TSI compatible serial port command set, a BNC pulse output connector, data recording capability, and a 3. 8" touchscreen. An Ethernet port allows remote access while the software is running, due to the multitasking nature of Linux. The software is written in C++, with the Qt opensource library. 6 Two different types of aerosols are used in this study to test the response of the B3010 in the sub 3 nm range. Mobility standard ions generated with an electrospray source for organics, and metal oxides produced with a glowing wire generator for hydrophobic particles, are used sequentially as the sources of polydisperse aerosols in front of the high resolution Hermann type DMA  (Kangasluoma et al.
Acknowledgements. This work was funded by the  ClerVolc  project  -Programme 1 "Detection and characterization of volcanic plumes and ash clouds " funded by the  French government "Laboratory of Excellence" initiative. 16 Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2018-243Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 14 September 2018 c Author(s) 2018. CC BY 4. 0 License.
In this paper, we propose a secure and trusted channel protocol that satisfies the stated security and operational requirements for a UAV-to-UAV communication protocol. This protocol supports three main objectives: 1) it provides the assurance that all communicating entities can trust each other and can trust their internal (secure) software and hardware states; 2) it establishes a fair key exchange process between all communicating entities so as to provide a secure channel; 3) it is efficient for both the initial start-up of the network and when resuming a session after a cold and/or warm restart of a UAV. The proposed protocol is formally verified using CasperFDR and AVISPA.
Protocol Verification by CasperFDR and AVISPA We selected the CasperFDR approach for formal analysis of the proposed protocol. The Casper compiler  [28]  takes as input a high-level description of the protocol, together with its security requirements along with the definition of an attacker and of its capabilities. The compiler then translates the description into the process algebra of Communicating Sequential Processes (CSP)  [29]. The CSP description of the protocol can be machine-verified using the Failures-Divergence Refinement (FDR) model checker  [30]. The intruder's capabilities modeled in the Casper script for the proposed protocol are as follows: an intruder can masquerade any entity in the network, an intruder can read the messages transmitted in the network, and an intruder cannot influence the internal process of an entity in the network.
The security specification for which CasperFDR evaluates the network is shown below. The listed specifications are defined in the #Specification section of the Casper script: the protocol run is fresh and both applications are alive, the key generated by the entity A is known only to the entity B (A and B are communication partners/devices), entities mutually authenticate each other and have mutual key assurance at the conclusion of the protocol, long-term keys of communicating entities are not compromised, and an intruder is unable to deduce the identities from observing the protocol messages. The CasperFDR tool evaluated the protocol and did not find any feasible attack(s). The script is provided in appendix A. Similarly, we scripted the proposed protocol in to High-Level Protocol Specification Language (HLPSL), as protocol description language for Automated Validation of Internet Security Protocols and Applications (AVISPA)  [31].
Acknowledgments The authors from XLIM acknowledge the support of: the  SFD (Security of Fleets of Drones)  project funded by  Région Limousin ; the  TRUSTED (TRUSted TEstbed for Drones)  project funded by the  CNRS INS2I  institute through the call 2016 PEPS (" Projet Exploratoire Premier Soutien") SISC ("Sécurité Informatique et des Systèmes Cyberphysiques "); the  SUITED (Suited secUrIty TEstbed for Drones) ,  SUITED2  and  UNITED (United NetworkIng TEstbed for Drones) ,  UNITED2  projects funded by the  MIRES  ( Mathématiques et leurs Interactions, Images et information numérique, Réseaux et Sécurité ) CNRS research federation. The authors from LaBRI acknowledge the support of: the  TRUSTED (TRUSted TEstbed for Drones)  project funded by the  CNRS INS2I  institute through the call 2016 PEPS (" Projet Exploratoire Premier Soutien") SISC ("Sécurité Informatique et des Systèmes Cyberphysiques "); the  SUITED-BX ,  SUITED2-BX  and  UNITED-BX ,  UNITED2-BX  projects funded by  LaBRI  and its MUSe team.
Simulation and Experimental Results In this section, the authors analyse an implementation scenario for the proposed collision-free manipulation algorithm on an ROV equipped with two manipulators. One such robotic vehicle is the MRE ROV (Figure  5 ), a University of Limerick owned ROV which is mainly used for research purposes. This ROV is equipped with two seven function Schilling ORION manipulators, both of which have position sensors in each joint. The information provided by these sensors along with the known CAD model of the whole ROV, including the relative pose between the robotic manipulator bases, are sufficient to implement the proposed collision detection algorithm. The collision detection algorithm is developed in C++ in the form of an independent Dynamic-Link Library (DLL) which is encapsulated in the robotic manipulation control software, developed previously using the Matlab Robotics Toolbox and LabView  [22].
The simulation scenario for the validation of the developed software, including the proposed collision avoidance algorithm, consists of a mathematical model of the real MRE ROV and the two accompanying manipulators. Two cases are addressed in simulations: the collision between each manipulator and the base vehicle, and the collision between the two manipulators. Additionally, the developed collision-free manipulation algorithm has been tested in a real-world experimental setup in dry laboratory conditions, where a single Schilling Titan 2 manipulator was kept from colliding with the floor using the discussed method. Voxels of different sizes ranging from 10 mm to 100 mm were used for the modelling of all the rigid objects that are part of the ROV, including the manipulators (Figure  6 ). Such variation enabled analysing how the algorithm behaves with the increase in the computational load due to the voxel map size, that is, how the voxel size affects the algorithm execution time.
This would reduce their fatigue and eventually provide cost savings in subsea IRM operations in oil and gas, the MRE sector, and other fields of application. Ongoing work is integrating the developed algorithm into the MRE ROV control software and testing it in offshore trials. Subsea experiments will include physical simulation of intervention operations with various mock-up test panels and tool skids. Specific manipulation tasks are to be repeated multiple times with and without the implementation of the collision detection algorithm, where an ROV manipulator operator will focus on executing tasks with increased speed. Measuring the time required to complete the task and number of collisions during the process, and comparing them to the traditional method, will reveal the actual performance of the proposed collision detection.
Acknowledgments: This material is based upon works supported by  Science Foundation Ireland (SFI)  under the  Research Centres Award 2012 ,  SFI Centre for Marine & Renewable Energy Research  ( 12/RC/2302  and  14/SP/2740 ). The  MaREI  project is also supported by the following industrial partners:  Resolve Marine Group ,  Shannon Foynes Port Company ,  Teledyne BlueView ,  Teledyne Reson  and The Commissioners of Irish Lights.
Recent research that focused on art-directed filter-based approaches in 3D-space has shown promising results  [Harvill 2007; Montesdeoca et al. 2017a,b]. However, these existing approaches have only been applied to a single style and present only a narrow interaction spectrum, limiting and slowing down their adoption in the industry. There is still extensive work to be done for expressive non-photorealistic rendering to address and enable the range of stylized looks that artists envision. The research presented in this paper is motivated by the artistic need for an intuitive and generalized approach towards expressive NPR in 3D computer graphics. For this purpose, we have developed MNPR, an expressive rendering framework in a real production software which focuses on enabling art-direction throughout the interaction spectrum-to maximize its adoption by any skill level and optimize the workflow for each use case.