dataesr/scientific-paragraphs-categorization huggingface dataset:
A. The L6 case We will use Pillar n°L6 (Meaning the one in the sixth position from the entrance on the left) as our first example because of its iconographic and recording challenges and enormous potential. The purpose here is not to describe the precise significance of signs and patterns; but to depict the means that led to better analyses. The first idea was to mimic an analogical process with software. Using oblique lighting is highly efficient in revealing details, but it takes time, requires being on location and is sometimes uncomfortable, because of the size and shape of the the spaces being recorded. A CG process on a dense geometry would give the same results. The first tests were made with Meshlab, but despite of the free software's benefits, the processing took too long: every step took dozens of minutes to complete.
VrMesh was also tested, but the processing was still long. This software now specializes in Lidar data recognition. In the end, Geomagic was chosen after several tests, both because it finished its calculations faster than the competing programs and because it offered a lot of tools for building perfect geometries, which was our goal for further real-time use. A mesh of 2,501,332 triangles was formed out of the point cloud and virtual lighting was used to simulate oblique lighting. The flexibility offered by this approach proved to have added value over the analogous method. A further refinement was added to complement these results. The geometry was copied and then smoothed to a high degree. This new geometry is intercalated with the original one, in a new color, filling in the lowest parts of the engravings and giving the whole a flatter look, which allows the engravings to be more easily read in relationship with each other.
Visual SFM and 123Dcatch were then compared for the reconstruction and both of the results to the laser recording. 123D Catch is a free online application offered by Autodesk. It is a "black box", meaning that it only has a few tweaking solutions, but the reconstruction is usually good. In this case, the deviation of 123DCatch was only 1. 78mm. Visual SFM is software that aggregates the photo-matching process, point cloud generation, and meshing. A lot of finetuning possibilities make it possible to produce a good survey. VSfm only deviated in this case by 0. 98mm. The deviance between the laser scan and photomodeling was not uniform. Depending to the light and number of photos used, the differences were between one and two centimeters, which provided satisfactory results for this kind of subject.
The retopology stage can be performed by many software programs, but it must often be carried out by hand and can be very time-consuming. The latest version of Mudbox provides another nice and easy tool for adjusting the topological parameters and the learning curve is acceptable. Using the model for real-time applications becomes easy. Gavrinis is a complex subject and more than one tool was needed to achieve our goals, which were to record, interpret, speculate and communicate. The overall digital reconstruction of Gavrinis is still in progress. Our work raised new problems, techniques and questions as it progressed. Neolithic funeral architecture is intractable: hidden parts often cannot be deduced from what can be seen, each part is slightly different from all the others, and yet the monuments are laid out precisely.
ACKNOWLEDGMENT The authors wish to thank the  Ministère de la Culture et de la Communication, la CIRA (Commission interrégionale de la recherche archéologique ) le  CG56  ( Conseil Général ), la  SDA  ( Sous Direction de l'Archéologie ). We are also very grateful to  Y. Bellenfant ,  SAGEMOR (Société Anonyme de Gestion du Morbihan ), the  CNRS  ( Centre national de la recherche scientifique ) and  ENSANantes (Ecole Nationale Supérieure d'Architecture de Nantes ). The authors also wish to warmly thank  Duncan Caldwell  for revising the text.
The X-ray absorption spectroscopy (XAS) pump-probe studies of photoexcited transition metal complexes, with pulsed laser pump and pulsed X-ray probe were performed using a scanning monochromator and a 100 ps time resolution arising from the electron bunch width of the synchrotron source (Advanced Photon Source). The technique is of great current interest, with major current developments being carried out at the APS  24  and SLS. 25  Each of these facilities utilizes a scanning monochromator maintained at fixed energy with a time series built up by repeat acquisitions at each point in a time and energy 2-dimensional grid, giving experimental acquisition times of up to 40 h (with a 1 kHz laser), for a good signal-to-noise spectrum. 21, 26 Here, we present the first results obtained using an energy dispersive data acquisition approach.
To perform the X-ray experiments, first the [Cu I (dmp) 2 ] + ground state system was measured using normal ED-XAS. The spectrum is shown in Figure  2a  and is identical to the Cu Kedge XANES as previously reported;  23  Figure  2b  shows the acquired Cu K-edge XANES difference spectrum. Before excitation (-5 ns) and 5 ns after excitation, there is no difference spectrum observed confirming the lifetime to be a maximum of 5 ns. At the laser excitation, however, a significant difference spectrum is observed, indicating the difference in species between ground state and after activation (excited state species). By comparison of the obtained data with the data as presented by L. X. Chen,  23  reported then to require 40 h using the scanning approach, similar data quality has been derived and the same difference spectrum recorded.
Cloud computing allows IT resources to be provisioned in a matter of minutes, rather than days or weeks. This allows load testing solutions to be developed on-demand as a service on the cloud. This type of Benchmark-as-a-Service platform (BaaSP) provides significant benefits in terms of cost and resources as hardware, software and tools are charged for on a per-use basis. The platform setup for the tests is also greatly simplified, allowing testers to focus on analyzing test campaign results. This paper describes a BaaSP, Benchmark-as-a-Service platform, that: returns the maximum number of virtual users a system under test (SUT) can serve, while satisfying operational constraints (e. g. avoid CPU or memory saturation) as well as quality of service constraints (e. g. acceptable response time).
) and external data provisioning to enable variability in request parameters. As shown by figure  1 , a CLIF test consists of a number of distributed components: load injectors, to generate traffic, and probes, to monitor resources usage. Each injector contributes to the global workload by executing a scenario, and measures the response time of each generated request. Probes measure how given resources (CPU, memory, network adapter or equipment, database, middleware, etc. ) are used. Injectors and probes are bound to a central test control and monitoring component, the Supervisor, and a central Storage component which gathers all measurements upon test completion. The next section describes how we adapted CLIF to design the BaaSP platform.
One of the advantages of Expertus is that it generates code to automate the execution of a set of tests. BlazeMeter  [21]  is an evolution of JMeter  [22] , it allows dynamic injector allocation and de-allocation in the cloud to reduce the cost of tests. As this tool is proprietary, no technical or scientific description is available, making comparisons difficult. NeoLoad  [23]  is similar to BlazeMeter, it allows deployment of injectors in a cloud environment to benchmark an application. New injectors can be integrated throughout the benchmarking process. However, this integration must be initiated by the administrator through planning. Unlike BaaSP, Ne-oLoad does not include an automated injector saturation detection component.
1 ), radiative forcing changes and large-scale climate responses in extratropical regions on orbital timescales. Utilisation of tropical Pacific SST proxy records requires careful consideration of potential seasonal biases of proxy data  (Schneider et al. , 2010) , orbital effects  (Timmermann et al. , 2007)  and of remote signals from the Northern Hemisphere icesheets  (Timmermann et al. , 2004). Extracting these effects to derive the greenhouse gas contribution in past SST records is challenging, but can be best accomplished by the analysis of long-term SST proxy datasets spanning at least several orbital cycles. Hitherto only few such datasets are available to date (Fig. S1 ). Here we set out to generate and analyse a ∼ 400 kyr-long high-resolution SST reconstruction using Mg/Ca ratios of the surface-dwelling planktonic foraminifera Globigerinoides ruber from a sediment core (MD05-2920) in the WPWP, near Papua New Guinea (Fig.
Acknowledgements. Competing interests statement: The authors declare that they have no competing financial interests. We acknowledge support from  INSU  and the  French Polar Institute IPEV , who provided the RV  Marion Dufresne  and CALYPSO coring system used during the MD148 IMAGES XIII PECTEN cruise. A. T. and O. T. were supported by grant  1010869  from the  US National Science Foundation. We thank  Luc Beaufort  ( Chief Scientist ),  Yvon Balut  and the crew of the PECTEN cruise.
Data The main aim of our work is to build a galaxy sample with high quality fluxes from the UV to the FIR using the ADF-S sample. Redshift information is also needed to obtain physical parameters from the SEDs. Our sample is drawn from the AKARI ADF-S catalog presented by  Małek et al. (2010) , and published at the Centre de Données astronomiques de Strasbourg, Strasbourg astronomical Data Center (http://cdsarc. u-strasbg. fr/viz-bin/qcat?J/A+A/514/A11). The sample consists of the 545 ADF-S sources from the so-called 6σ catalog (S 90 µm >0. 0301Jy, which corresponds to the 6σ detection level) measured by the AKARI FIS detector, for which the optical counterparts were found in public catalogues  (Małek et al. , 2010). Additional measurements, mostly from WISE  (Wright et al. , 2010)  and GALEX  (Dale et al.
Spectroscopic redshifts distribution In total (data coming from  Małek et al. , 2010 ; the dedicated spectroscopic measurements of selected ADF-S sources performed by  Sedgwick et al. , 2011 ; new data from the NED and SIMBAD databases) we have 173 galaxies with known spectroscopic redshifts (z spectro ). The mean value of the redshift in this sample is equal to 0. 08 ± 0. 01, with a median value of 0. 06. This implies, together with AKARI FIR measurements, that our sample mainly consists of nearby FIR-bright galaxies. Unfortunately, the majority of our sample with spectroscopic redshift information does not have enough photometric measurements to fit SED models with a high confidence. After a visual inspection of all the spectra, we decided to apply a more stricter selection criterion, and for the next procedure we have chosen  only galaxies with at least six photometric measurements in the whole spectral range.
Since our sample selection was based on the WIDE-S 90 µm AKARI band, each galaxy has at least one measurement in the FIR, at 90 µm. Additionally, 95% of sources are detected in the MIR, all galaxies have optical information, and half of them were also detected in the UV. The typical spectral coverage of a source from our sample is shown in Fig. 1. After this selection, 95 galaxies from the initial sample of 173 galaxies with a known spectroscopic redshift remained for the subsequent analysis. All the available measurements from the ADF-S database were used for the SED fitting. The redshift distribution N (z) of the selected sample with spectroscopic redshifts is presented in Fig. 2.
The best results were obtained for the Rieke library, which includes eleven SEDs of luminous star forming galaxies constructed by  Rieke et al. (2009). This is not very surprising, since it is expected that most of our galaxies belong to this class of galaxies. -CIGALE: Considering that a significant part of our data is in the FIR, which is not well covered by the templates included in the Le PHARE distribution, we decided to test a different approach, i. e. to use the Code Investigating GALaxy Emission 2 (CIGALE;  Noll et al. , 2009)  SED fitting program as a tool for the estimation of photometric redshifts. CIGALE was not developed as a tool for the estimation of z phot but since it uses a large number of models covering the wide spectral range, including IR and FIR, it may be expected to provide a better z phot for our FIR-selected sample than the software that mainly uses optical to NIR data.
SED fitting To study the physical parameters of the ADF-S sources, we selected galaxies with a known z spectro or z CIGALE , and with the highest quality photometry available. The main selection criteria were to have redshift information, together with at least six measurements in the whole spectral range. As a result, we use 222 galaxies: 95 sources with z spectro and 127 galaxies with z CIGALE. All available photometric measurements for these galaxies were used for the SED fitting with CIGALE. CIGALE uses models describing the emission from a galaxy in the wavelength range from the rest-frame far-UV to the restframe far-IR  (Noll et al. , 2011). The code derives physical parameters of galaxies by fitting their spectral energy distributions (SEDs) to SEDs based on models and templates. CIGALE takes into account both the dust UV attenuation and IR emission.
75 ± 0. 39   Giovannoli et al. 2011 (LIRGs sample, with   2010 ) is characterized by a mean stellar mass logM star =11. 24 ± 0. 25 [M ⊙ ]. We conclude that the (U)LIRGs in our ADF-S sample are slightly more massive than those in the samples used in previous works, but still all the samples are statistically consistent. [M ⊙ ]), Figure  7  shows the relation between the SFRs and stellar masses for (U)LIRGs in our ADF-S catalogue. The mean redshift of the (U)LIRG sample is equal to 0. 34 (minimum and maximum redshifts are equal to 0. 09 and 0. 98, respectively -then, the redshift distribution is quite broad). We compared our results with observations at redshifts 0 and 1  (Elbaz et al. , 2007) , and 2  (Daddi, et al. , 2007) , and also with other data mentioned above: a sample of nearby (z<0.
Conclusions • For our analysis we used an AKARI ADF-S catalogue  (Małek et al. , 2010) , with additional information about spectroscopic redshifts  (Małek et al. , 2013)  • The CIGALE  (Noll et al. , 2009) , program for fitting SEDs, was used for the first time to estimate photometric redshifts for galaxies without known spectroscopic redshifts (127 galaxies), and then to fit Spectral Energy Distribution models to our whole sample. We conclude that although CIGALE was not designed as a tool for the estimation of z phot , in the case of the ADF-S galaxies it is more efficient than the standard code (Le PHARE), based on the UV and optical data. This observation might be worth taking into account for IR-selected galaxies in the future analysis. • We used CIGALE for the redshift estimation for 127 galaxies with at least 6 photometric measurements in the whole spectral range, from UV to FIR.
Acknowledgments. We would like to thank both anonymous Reviewers for their very constructive comments and suggestions which helped to improve the quality of this paper. We thank  Olivier Ilbert  for useful discussions and kind help in using Le PHARE. This work is based on observations with AKARI a JAXA project with the participation of  ESA. This research has made use of the SIMBAD and NED databases. KM, AP and AK were financed by the research grant of the  Polish Ministry of Science N   N203 512938. The collaboration between French and Polish participants was partially supported by the  European Associated Laboratory Astrophysics Poland-France HECOLS. This research was partially supported by the project  POLISH-SWISS ASTRO PROJECT  co-financed by a grant from  Switzerland  through the  Swiss Contribution  to the enlarged European Union.
Results: In this study, we have developed three different work flows dedicated to ISs and MITEs detection: the first two use de novo methods detecting either repeated sequences or presence of Inverted Repeats; the third one use 28 in-house transposase alignment profiles with HMM search methods. We have compared the respective performances of each method using a reference dataset of 30 archaeal and 30 bacterial genomes in addition to simulated and real metagenomes. Compared to a BLAST-based method using ISFinder as library, de novo methods significantly improve ISs and MITEs detection. For example, in the 30 archaeal genomes, we discovered 30 new elements (+20%) in addition to the 141 multi-copies elements already detected by the BLAST approach. Many of the new elements correspond to ISs belonging to unknown or highly divergent families.
In addition, metagenomic analyses revealed that transposases are the most abundant and ubiquitous genes in nature  [6]. The quantitative importance of ISs is coupled with a large diversity of families and mechanisms of transposition. Based on transposase sequence similarities, ISs have been classified in 25 different families that belong to three main classes of enzymes: the DDE transposase, the Serine Recombinase and the Tyrosine Recombinase  [4]. Consequently, ISs are considered as major players of genome evolution and plasticity, mediating gene transfers and promoting genome duplication, deletion and rearrangement  [7]. Due to their abundance and diversity, ISs and MITEs identification and annotation have represented a longstanding challenge, partially solved with the availability of a reference database that compile a large body of ISs (ISFinder at https://www-is.
biotoul. fr/). Thus, several studies have used the referenced sequences in the ISFinder database to mine various collections of genomic data using BLAST softwares (see for example  [2, 5, [8] [9] [10] ). However, with the development of highthroughput sequencing techniques leading to the availability of thousands complete genomes and metagenomes, ISs and MITEs identification and annotation require more sophisticated and integrated approaches. Recently, ISSaga have been then developed to automate IS annotation in complete genomes  [11]. ISSaga used a relatively simple library-based methods using BLAST seeded with the ISFinder sequences and classify them into families. Although ISSaga have represented a significant progress in the field, the efficiency of library-based approaches in identifying transposable elements is questionable for two reasons.
First, the efficiency of library based method is critically dependent on the quality and the exhaustiveness of the database used. Several families such as IS4 for example display extremely elevated levels of divergence, with many emerging clusters that show very weak level of sequence conservation with the other members of the family  [12]. Other families such as IS91 for example show a very low sampling effort with only twenty sequences present in ISFinder, mainly in a single bacterial clade (alpha Proteobacteria). Second, librarybased methods are unable to identify new families that display no similarities with existing families. This limit is especially problematic with MITEs that do not encode for a transposase and that display low level of similarities with autonomous ISs  [13]. For this reason, in Eukaryotes more than 50 different methods have been developed to identify and annotate transposable elements  [14].
We tested these different pipelines against different datasets: -A genomic dataset of 30 archaeal genomes previously annotated by us for ISs and MITEs with a BLAST-based method  [5]. Our results demonstrate that de novo methods increased significantly ISs (+10%) and MITEs (+50%) identification compared to library based methods. The gain was important with highly divergent families such as IS4 or IS200/ 605 and for MITEs that display only very weak similarities with previously identified ISs. In addition to be fast, reliable for non-specialist users and generating low level of false positive, these methods offer the advantage to produce outputs with complete IS sequences (transposase, accessory genes, IRs…) directly usable for databases as ISFinders. Similar results were obtained using 30 additional bacterial genomes annotated in the ISFinder database.
-We also tested some marine metagenomic datasets including samples composed of 1 kb reads and 250 bp reads. Our results show that HMM searches outclass a BLAST-based approach, finding many more transposase genes (up to +50%) and generating a considerable lower level of false positive compared to BLAST. Thus, we suggested that BLAST based methods should be avoided to detect transposase on metagenomic data and should be reserved to the final annotation step in order to class the elements by IS families. Moreover future studies on ISs and MITEs may combine at least one de novo method of detection in addition to a library based approach.
De novo pipelines Our de novo pipelines used two different approaches to detect ISs and MITEs (Figures  1  and 2 ). The first one called "Repeats search" (Figure  1 ) used the RepeatScout algorithm  [15]. RepeatScout detects repeated sequences in a given genome and generates a consensus of these sequences (with default parameter and word size l = 9). The second path called "IRs search" (Figure  2 ) used the Palindrome software of the EMBOSS package  [16]  which identifies the IRs in the input sequences (with the following parameters: minpallen = 10, maxpallen = 50, gaplimit = 2000, nummismatches = 2). All sequences delimited by IR pairs are then extracted. In order to avoid doubleton, sequences bordered with IRs but present less than 2 times were removed. The consensus sequences generated by RepeatScout and the sequences delimited by IR following Palindrome were then clustered using UCLUST with default parameters  [17].
Profile HMM pipeline The "profile HMM search" pipeline used in entry files either the complete genomes (nucleotides) or the list of ORFs (amino acids). In the first case, as profile search methods proceed with protein sequences only, the genome is then cut in 5 kb segment and translated using a six frames window. Transposase sequences are searched using the hmmpfam software of the HMMER2  [19]  package using 28 transposase sequence profiles (with default parameters and E value = 10e-5). These profiles were built as follow: for each family, all the sequences present in the ISFinder database have been retrieved according to their family name. For underrepresented families in ISFinder as IS91 for example, additional homologous sequences were retrieved using BLASTP (E value = 10e-5) searches against a NR database maintained locally and regularly updated (minimum number of sequences per profiles = 50).
For large families with little similarities between subgroups we have generated several different profiles (example: IS4). These sets of sequences are aligned with MUSCLE  [20] , alignments are refined manually using the Jalview sequence editor  [21]  and the profiles are then constructed using the hmmbuild software  [19]. Sensitivity of the profiles has been improved using the calibration software hmmcalibrate. The sequences encoding a putative transposase identified using these profiles are extracted and clustered using the UCLUST  [17]  softwares and the sequence clusters are finally aligned with MUSCLE  [20]. At the end of the pipeline we will have a list of files containing the different clusters of related sequences encoding putative transposase. Test with prokaryotic genomes. The benchmarking of our different methods has been tested against a set of 30 Archaeal genomes previously annotated by us for ISs and MITEs  [5].