dataesr/scientific-paragraphs-categorization huggingface dataset:
A similar quasi-Newton approach is performed by Gerbeau and Vidrascu 28 for transient FSI problems in blood flows. They propose to neglect the expensive mesh motion and the nonlinear terms in the fluid model. In our work, however, the LoFi solver is a stand-alone module that is completely independent from the HiFi solver. The routines for the PQN solver are described in Table  2. Note that in this method the structural residual is only evaluated and not solved, in contrast to the NLBGS method. Table  2 : The SQN algorithm (0) Set initial values for u (0) , w (0) and x (0). Iterate n = 0,. , ∞ (1) Approximately solve the flow problem, R a (w (n+1) , x (n) ) ≈ 0, by n a flow subiterations. (2) Interpolate the aeroloads on the structural model: f (n+1) s = T f (n+1) a. (3) Evaluate the structural residual: R s = Ku (n)f (n+1) s.
(37) The assumption for this modification is that no panel method is available to precondition the fixed-point linear system. This modification results in a looser coupling and is distinguished with a subscript as PSC K. The original version (34) is therefore identified for this test case with an added subscript as PSC S. Note that the SQN method from the aeroelastic analysis is not applicable in the sensitivity analysis. The direct application of the LoFi Schur complement would only result in LoFi gradients. The LoFi Schur complement preconditioner indicates to have an impact on the convergence rate for both test cases, as shown in Table  6. Since we are solving a linear system, only one generation of the LoFi Schur complement is required. The CPU cost of this operation is negligible compared to the overall CPU cost.
It is the most robust method, in this regard, but not the most efficient in terms of CPU time. The sensitivity analysis version of the PSQN, the PSC, demonstrates much faster convergence when preconditioned with the LoFi Schur complement. The convergence rate is up to three times faster compared to the case when the PSC is preconditioned with the structural stiffness matrix. Moreover, the reduction of the problem in Schur complement form effectively reduces the number of mesh deformations to a minimum. The number of mesh deformations correspond to the number of fixed-point iterations which varied from 9 to 11 for the two test cases conducted in this paper. These values can be further reduced if the solution is allowed to converge to a higher tolerance. In conclusion, the panel code preconditioner is successfully demonstrated to reduce the computation time of high-fidelity sensitivity analysis for fluid-structure interaction problems.
VII. Acknowledgements The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7-PEOPLE-2012-ITN  under grant agreement n  316394 ,  Aerospace Multidisciplinarity-Enabling DEsign Optimization (AMEDEO) Marie Curie Initial Training Network. The authors would like to acknowledge  Philippe Girodroux-Lavigne  for his support on all actions that allowed us to use the flow solver elsA.
, 2008; Nõges et al. , 2016; Zeppilli et al. , 2016). In 2008, the European Commission enacted the Marine Strategy Framework Directive (MSFD; 2008/56/EC), which aims to manage the European seas by using an ecosystem-based approach in order to gain a healthy and productive state (so called good environmental status; GES; see Box 1 for the list of acronyms)  (Borja et al. , 2013). The MSFD particularly aims at investigating the functioning of ecosystems  (Cardoso et al. , 2010; Borja et al. , 2011) , making a shift from structural, site-specific approaches to a functional, whole-sea system of monitoring  (Borja and Elliott, 2013). An overarching aim is to promote regional harmonization of monitoring methods, used to assess marine environmental health and to obtain complete and long-term datasets from multiple ecosystem components, ranging from microbes to large marine mammals  (Caruso et al.
A recent study demonstrated that different PCR conditions could affect the final taxonomic assignment in metabarcoding studies. A constant low annealing temperature (46 or 50 • C) provides more accurate taxonomic inferences compared to the touch down profile  (Aylagas et al. , 2016). Conversely, increasing the number of PCR cycles leads to the increase in the number of spurious sequences and chimeras formed  (Haas et al. , 2011). Chimeras can inflate the overall biodiversity estimates and be eliminated by comparing the length of matched bases from the top hit in a MEGABLAST search to the length of the query sequence. As long as the database sequence is longer than the query sequence and a portion of the 3 ′ end does not match, it is likely that the query is a recombinant. Chimeras can be removed also by using other algorithms, including Perseus  (Quince et al.
An additional limitation of metabarcoding is that it does not differentiate between life stages, and thus juvenile stages and adults are pooled together. Further, species lists produced through metabarcoding currently are presence-absence based, and lack relative abundance data. Thus, traditional community analyses used for impact detection cannot be applied in the traditional manner, and instead the focus will be on overall species richness and presence of indicator species. Another issue is represented by the still limited availability of sequences in public databases  (Carugati et al. , 2015). In some cases, operational taxonomic units (OTUs) can not be taxonomically assigned to a species, or even to a genus, due to the paucity of data in reference databases and the lack of taxonomic resolution at the species level of the marker gene  (Dell'Anno et al.
, 2015; Leray and Knowlton, 2016). Thus, exploiting the data will require the continued refinement of database resources and bioinformatic pipelines  (Minster and Connolly, 2006; Hajibabaei et al. , 2011; Bik et al. , 2012; Radom et al. , 2012). Consequently, the collaboration between molecular ecologists and taxonomists is required for the accurate characterization of species and for the deposition of quality assured barcode sequences in public databases  (Jenner, 2004). The improvement of reference databases and thus the ability to assign OTUs to known species will enable metabarcoding techniques to be more reliably used in monitoring surveys, with high potential for the detection of non-indigenous species. It is also important to underline that relating sequences to taxonomically described species is not a necessity for many applications since in monitoring the focus is in pattern changes, not on taxonomic composition per se.
We suggest that, in order to apply metabarcoding for the purposes of the MSFD (e. g. , Descriptor 1), an attempt could be made using the overall species richness. For instance, significant changes in the species richness of the community can be a useful warning indicator and assessing such changes does not require that each molecular OTU is assigned to a precise taxon. The Biodiversity Descriptor of the MSFD does not explicitly require that species are all taxonomically identified. Furthermore, molecular barcodes of a species, even when the species is not in the reference database, generally allow identification at the genus or family level if other species of the same genus or family are present in the reference database. Case study 1. Microbes HTS approaches have been recently applied to study the biodiversity of marine viruses  (Tangherlini et al.
Sediment samples (from shallow to deep-sea environments) are collected and subsequently frozen (-20 • C or -80 • C). In the laboratory, meiofaunal organisms are recovered from the sediments and their DNA extracted and purified. Following the PCR amplification of marker genes (e. g. , 18S rRNA), high-throughput sequencing can be conducted on Roche 454 or Illumina platforms. Raw reads are processed and then clustered into operational taxonomic units (OTUs) under a range of pairwise identity cutoffs. After the BLAST-match of the obtained OTUs against public nucleotide databases, analysis of α-and β-diversity and phylogeography are performed. Image of Illumina MiSeq platform: Source: Wikipedia, Author: Konrad Förstner  (Carugati et al. , 2015). the results obtained by morphological techniques, but some OTU's remained unassigned  (Dell'Anno et al.
, 2015). Although metabarcoding is a useful tool to explore the diversity of marine meiofaunal organisms, it still presents some gaps. Indeed, not all species in a sample are detected and a certain percentage remains unidentified due to the limited coverage of public sequence repositories for meiofaunal taxa  (Carugati et al. , 2015). This applies particularly to the deep sea, where most of the taxa are still unknown  (Appeltans et al. , 2012). Thus, we suggest to continue combining morphological identification performed though light microscopy with molecular analyses, in order to feed or even create local database, at least for marine protected area or high priority areas. To more accurately delineate species in metabarcoding datasets major efforts should be devoted to understanding the actual variability of the 18S rRNA gene amongst individuals of the same species and amongst different species taking into account the contribution of potential biases due to PCR and sequencing steps in such variability.
, 2010; Roy et al. , 2011). Computational approaches, such as the statistical fitting tool CHEMTAX, have been used to determine phytoplankton biodiversity by estimating the relative contribution of different taxa to the total chlorophyll a (TChla) concentration in a sample  (Mackey et al. , 1996; Gibb et al. , 2001; Goela et al. , 2015). Although the software is fully developed, an a priori knowledge of the classes existent in the samples is required, as well as an appropriate choice of the ratios of pigment:Chla, considering the characteristics of the investigated geographical region (i. e. , light availability;  Higgins et al. , 2011). As the inferences of this technique are based on the chemical composition of a sample and not on the direct observation of the phytoplankton cells, it has an improved capability to differentiate among organisms in smaller size classes, which in traditional methods such as microscopy fall into the category of unidentified flagellates  (Goela et al.
g. ,  Örnólfsdóttir et al. , 2003; Smith et al. , 2010; Roy et al. , 2011). The versatility of the method, that is, the possibility to run the software with different combinations and values of pigment:Chla ratios is, in fact, one of the major advantages of the method, allowing easily to locally adapted pigment profile schemes. Recently, several studies have focused on the effective and successful use of CHEMTAX to detect HABs (e. g. ,  Örnólfsdóttir et al. , 2003) , although pigment profiling studies, such as  Liu et al. (2014) , in other areas of the globe would be beneficial to the fulfillment of this objective.
2 km resolution, 1999 -2008 data). mapped using modern methods. A direct consequence of such data deficiency is that 76% of seabed habitats are in unknown status  (EEA, 2015)  and there are no systematic habitat mapping programmes in place at national or pan-European scales. In the absence of adequate seabed data, the urgent need to define seabed habitats for management has resulted in the construction of modeled seabed data such as UKSeaMap  (Connor et al. , 2006). These existing broadscale maps will inevitably contain errors due to data deficiencies and generalizations. However, the alternative of using the scattering of existing high-resolution maps, does not address our needs to define biogeographical limits of species or overall habitat distribution at a regional scale. To overcome this difficulty (of high resolution data only existing as a localized patchwork) and make best use of existing resources, the novel strategy of continuously logging high-resolution multibeam data during existing monitoring cruises has been adopted on the RV Cefas Endeavor using the Olex software programme.
The use of a standard sampling unit and the application of homogeneous protocols for morphological and molecular identifications can produce comparable datasets over different geographical areas. Despite some limitations of the metabarcoding technique  (Carugati et al. , 2015 ; see metabarcoding section), such as the incompleteness of reference databases, the sequence inventory obtained is already valuable for biodiversity assessment that be further improved in the future without additional laboratory work by rerunning the bioinformatics analyses on updated reference databases. Over a deployment of 1-3 years, colonization and succession patterns could be affected by changes in environmental conditions, making ARMS proper tools for marine monitoring of coastal areas. ARMS can be also re-deployed in the same locations and used to assess biodiversity changes over time.
, 2005) , overall costs may still be high. Moreover, protocols for the assessment of biodiversity associated to ARMS rely upon the use of molecular approaches and thus the use of such devices present the same problems described above for metabarcoding. The ARMS protocol of  Leray and Knowlton (2015)  proposed the use of the mt COI gene. However, the database for this gene is highly biased toward metazoans and may thus be limited in the detection of other groups (such as algae and unicellular eukaryotes). Other genes have been targeted for ecological studies (e. g. , 18S rDNA,  Logares et al. , 2014a , 28S rDNA, Hirai et al. , 2015 , and the ITS region Tonge et al. , 2014 ) and a combination of these genes and COI may give a more comprehensive assessment of diversity. In the future, molecular studies using ARMS may also investigate the functional ability of the assemblage using shotgun metagenomic techniques.
All OSD data (i. e. , sequences and contextual data) are archived and immediately made openly accessible without an embargo period  (Ten Hoopen and Cochrane, 2014). OSD sampling sites are typically located in coastal regions within exclusive economic zones (EEZ) and thus the OSD data set provides a unique opportunity to test anthropogenic influences on microbial assemblages. The final aim is to create an OSD time-series indicators to assess environmental vulnerability and resilience of ecosystems and climatic impacts. In the long term such indicators may be incorporated into the Ocean Health Index (OHI)  (Halpern et al. , 2012) , which currently does not include microorganisms due to the lack of reliable data. OSD has the potential to close that gap expanding oceanic monitoring toward microbes.
Other tools, especially molecular ones, e. g. , metabarcoding, need further evaluation  (Bourlat et al. , 2013). In this context, such innovative approaches for marine monitoring need to be further implemented through: (i) defining standardized manuals and protocols for sampling and sample processing; (ii) developing new indicator metrics and indices fitting the new approaches and also useful for policy and decision-making; (iii) integrating, in monitoring surveys, information on biodiversity with other data sources (CTD, remote sensing, multibeam, taxonomy databases) for an holistic marine ecosystem assessment. Innovative methods can improve monitoring and contribute to the definition of criteria for better conservation of marine biodiversity. While the potential of these approaches to work exists, further studies are needed before their complete implementation application in routine marine monitoring programmes.
ACKNOWLEDGMENTS This manuscript is a result of  DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status ) project, funded by the  European Union  under the  7th Framework Programme, "The Ocean of Tomorrow" Theme  (grant agreement no. 308392 ) (http://www. devotes-project. eu). Further financial assistance was provided to VS and ER by the  European Union  under the  ENPI CBC Mediterranean Sea Basin Programme (Sustainability and Tourism in the Mediterranean-S&T Med Strategic Project ). The contents of this article are the sole responsibility of the authors and can under no circumstances be regarded as reflecting the position of the European Union or of the Programme's management structures.
Visual similarity In CBIR field, some works used visual similarity measures  (Deselaers and Ferrari, 2011) ,  (Cho et al. , 2011). Popular distance measures are used as metric distances like euclidean distance, mahalanobis distance and cosine distance  (Zhang and Lu, 2003)  and  (Cho et al. , 2011). In our context we used the euclidean distance to compute similarity between visual words. VisualSim = d(q, r i ) = n ∑ j=1 ( f j (q)) -f j (r i )) 2 (6) where • q : is the vector of query visual words; • r i : is a reference visual word i from visual words database; • f j : is the jth feature; • n : is the size of visual vocabulary.
After that, visual similarity measure is computed between each visual words of request image and those that are built from image dataset, according to their similarity, the top ranked images are outputs of the retrieval. In the strategy that based on integrating ontology, visual retrieval is combined to the semantic features based on concepts. During the offline phase, visual and semantic image features are built. This is done by two processes: building the visual vocabulary (Figure  2  Step (2)) and building ontology (Figure  2  Step (  5 )). In order to extract concepts the ontology is built based on the annotation files. After that, the concepts set associated to all of the image dataset is stored. In order to build our ontology, first, ontology must be initialized by adding concepts to it.
CONCLUSIONS In this paper, we introduced our image retrieval system based on building visual vocabulary and ontologies. Our proposed system is focused on two image aspects: visual and semantic features. During the offline phase, visual vocabulary is built in order to describe image database by their own visual content. Moreover, this phase can be performed by extracting concepts and relationships between them from annotation files in order to build ontologies that are then used in retrieval process. The ontologies are enriched by the concepts and relationships that are extracted from the BabelNet's lexical resource. The case study shows the feasibility of our proposal. In future works, we will evaluate our proposed system. In order to achieve this goal, we will compare retrieval results from our image retrieval strategies based on combining both visual and semantic similarities with the classical content based image retrieval based on visual vocabulary.
Study area and data collection The study area is 107 000 km 2 in extent, covering parts of Republic of Congo, Cameroon and Central African Republic. Most of the area is characterized by Guineo-Congolian semi-deciduous forest  (White, 1983). This region has only recently been subject to a rapid expansion of logging road networks into previously intact forests  (Laporte et al. , 2007). These logging operations mostly take place in concessions, large state-owned forest areas that are allocated to companies for timber harvesting according to a forest management plan  (Lescuyer et al. , 2015). We analysed a total of 67 concessions of varying sizes (range 217-10 270, median 660 km 2 ). These are operated by 17 different companies, mostly in groups of several bordering concessions (range 365-15 560, median 1970 km 2 ) to reduce infrastructure costs.
The drawback of this test is that if the null hypothesis H 0 is not rejected, this does not mean that FA and FB are equal. Therefore, our focus lies on those cases where H 0 is rejected. For all E[d] = 0 we compared d i between multiple categories using pairwise t test with pooled standard deviations and Holm-adjusted P values for all possible comparisons  (Holm, 1979). All analyses were carried out in R (R Core Team, 2014) using the "spatstat" package  (Baddeley and Turner, 2005). The R codes implementing the present methodology are available by simply sending an email to the first author of this paper.
Introduction R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. It comprises libraries of nearly all the statistical literature. Please consult the R project homepage 1 for further information. R is an object-oriented interpretated language, made of an ensemble of packages. Packages are installed through a compilation of codes  C++ and FORTRAN (gcc compiler). R is command-line driven, making it a wonderful tool for complex scripts and repeated procedures. R is used by most statisticians in the world mainly for research and development. There are more and more packages for astronomical purposes, with a package to manage FITS format: FITSio.
CRAN 2 (Comprehensive R Archive Network) is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use the CRAN mirror nearest to you to minimize network load. There are many and many help websites and blogs 3. Simply asking your question in your favorite internet search engine, you will surely find the corresponding help and even full scripts. To make a search on the web regarding R, type "r-cran" instead of "R"! For MATLAB users, there are tables of command equivalence 4. You can find a Short Reference Card 5  on the School website with the Codes and Data 6.
An illustration with simple classification tools As an illustration of both the graphical capabilities of R and some basic classification techniques using kmeans and DBSCAN, we use the built-in database iris. ? kmeans kmeans colnames ( i r i s ) i r i s 2 <-i r i s [ , -5 ] colnames ( i r i s 2 ) checkkmeans <-function ( n s t a r t =1000){ i r i s. kmeans <-kmeans ( i r i s 2 , 3 , n s t a r t=n s t a r t ) plot ( i r i s 2 [ c ( " S e p a l. Length " , " S e p a l. Width " ) ] , c o l= i r i s. kmeans$ c l u s t e r ) points ( i r i s. kmeans$ c e n t e r s [ , c ( " S e p a l. Length " , " S e p a l. Width" ) ] , c o l =1:3 , pch=" * " , cex =5) }.
2011a) , which are rarely known with great precision. An alternative strategy, which is the focus of this review, takes advantage of the information about the age of the sequenced samples themselves to calibrate the phy-logeny by assigning dates to the tips (sometimes also called terminal nodes) of the tree, hence the term tip dating. Tip dating is only possible when there is sufficient spread in the age of the samples analysed and is ideally suited for data sets of serially sampled fast-evolving taxa, or those including ancient DNA sequences  (Drummond et al. 2003b). The conceptual bases of tip dating were laid out in the late 1980s when sequence data from samples with associated dates of isolation started to accumulate in public databases  (Rambaut 2000). In order to obtain accurate divergence time estimates for fast-evolving genomes such as RNA viruses, the date of isolation must be accounted for.
To realistically express such uncertainty,  Rambaut (2000)  and  Drummond et al. (2001)  introduced new methods incorporating sequence dates into a maximum-likelihood (ML) tree reconstruction framework. Those approaches were subsequently embedded into a Bayesian statistical inference framework to jointly estimate substitution rates, divergence times and demography while accounting for the uncertainty in the genealogy by using Markov chain Monte Carlo (MCMC) integration  (Drummond et al. 2002). Such developments ultimately led to the release of BEAST  (Drummond & Rambaut 2007) , which has been the most popular tip-dating program developed so far (Table  1 ). Early implementations were assuming a constant rate of evolution throughout the tree. However, the analysis of numerous data sets showing considerable departures from clockwise evolution led to the development and incorporation of methods accounting for rate variation among lineages  (Bromham & Penny 2003; Welch & Bromham 2005; Ho & Duchêne 2014).