dataesr/scientific-paragraphs-categorization huggingface dataset:
Stellar mass gradients towards filaments We have shown that the 2D skeleton is a good tracer of the projected 3D structures. However, to use the 2D skeleton in real data to infer filament properties we need to confirm that we can trace signals that actually exist in 3D from our reconstruction. Here, we consider the stellar mass gradient towards filaments observed in 3D by  Malavasi et al. (2017)  and  Kraljic et al. (2018). Laigle et al. (2018)  showed that this gradient is recovered by the 2D reconstruction given the COSMOS precision. Here, we study whether it is recovered in our CFHTLS-like mock or not. As in  Laigle et al. (2018)  and  Kraljic et al. (2018)  we remove the contribution of nodes by removing galaxies too close to nodes, in order to compute the effect of filaments alone. Indeed, there is a known stellar mass gradient towards nodes (i.
(2018)  showed, in addition to the stellar mass gradient towards filaments, there is also a stellar mass gradient towards walls as well, that we pick up in our 2D analysis. Despite this limitation, we reach a quite high purity in our 2D filament detection at the CFHTLS accuracy (∼ 90% in the global skeleton reconstruction and ∼ 70% for filaments connected to clusters). We can thus use our filament reconstruction on the CFHTLS T0007 data to study how filaments impact galaxy properties.
15 < z clus < 0. 70 4. 50 ± 0. 05 4. 98 ± 0. 05 7. 6σ 0. 15 < z clus ≤ 0. 40 4. 22 ± 0. 08 4. 71 ± 0. 07 4. 9σ 0. 40 < z clus < 0. 70 5. 00 ± 0. 13 4. 98 ± 0. 10 NS --------∆z low -z high 5. 0σ 2. 3σ Table  5 : Median values of the PDF of the filament galaxy distances to AMASCFI clusters along filaments for passive galaxies and star-forming galaxies in different redshift bins. NS stands for 'not significant' and ∆ X-Y is defined in eq. 7 scaling was also found in lower mass groups in the COSMOS survey by  Darragh Ford et al. (in prep). In this figure, the error bars are standard errors on the mean. The standard deviation of the distribution in each bin is actually much larger, due to a large intrinsic scatter in the κ -log M 200 scaling relation. We note that the mean connectivity at a given mass is higher in the CFHTLS data when compared to the value obtained in the lightcone.
Conclusions We presented a method to detect the cosmic web filaments based on photometric redshifts. We showed the ability of the method, already applied to the COSMOS field by  Laigle et al. (2018) , to statistically reconstruct the filament distribution. In particular, we focused here on the infall regions around clusters, where we showed that the scaling of connectivity with cluster mass is recovered, and found a completeness of ∼ 70% and a purity of ∼ 66% for the connected filament reconstruction. We then applied the method to the CFHTLS T0007 data to study filaments of galaxies around AMASCFI clusters  (Sarron et al. 2018 ). For each cluster, we analysed the cosmic filaments connected to the clusters. Studying galaxy properties in these filaments connected to clusters, we find that galaxies are located closer to clusters in the redshift range 0.
INTRODUCTION Xylella fastidiosa (Xf) is a worldwide insect-transmitted plantpathogenic bacterium that presents a very large host range. Altogether, 563 plant species grouped into 82 botanical families have been reported as Xf hosts (EFSA, 2018a). Plants with a major socio-economic interest such as grapevine, citrus, coffee, and olive trees are hosts of Xf (EFSA, 2018a). Forest trees, shade trees, ornamentals, and landscape species are included in the host plant database making this pathogen a potential worldwide threat  (EFSA, 2018a). Disease management of Xf is impeded by its asymptomatic period that can last several years  (EFSA, 2018b). This bacterial species is genetically diverse as five subspecies including fastidiosa, morus, multiplex, pauca, and sandyi are currently described  (EFSA, 2018b).
Then, the individual primer and probe combinations and the six sets of four combinations were tested using Amplify to check the absence of dimer and cross-amplification  (Engels, 1993). The specificity of all primers and probes was tested in silico using PrimerSearch (Val Curwen, Human Genome Mapping Project, Cambridge, UK) on the initial set of 58 genomic sequences of Xylella and on the 154,478 bacterial Whole Genome Shotgun (WGS) sequences available in the NCBI database (as on August 22, 2018). BLASTn of the amplicons were run on the NCBI WGS database to evidence their specificity. Four other primer and probe combinations previously published were used in this study. The first targets the rimM gene of Xf  (Harper et al. , 2010)  and was used as reference protocol. The second targets the eukaryotic rRNA18S gene  (Ioos et al.
e. set n°1: XF-XFFSL-XFM-XFP, set n°2: XF-XFF-XFM-XFP, set n°3: XF-XFF-XFM-XMO, set n°4: XFFSL-XFM-XFP, set n°5: Harper-XFFSL-XFM-XFP, and set n°6: 18S-XFFSL-XFM-XFP. The optimized final reaction conditions were performed in a final volume of 10 µl containing 1X of SsoAdvanced™ Universal Probes Supermix (Bio-Rad), 575 nM of primers, 200 nM of probes and 600 ng. µl -1 of BSA (ThermoFisher), and 1 µl of extracted DNA. The optimal thermocycling conditions selected were: 3 min at 95°C, followed by 40 cycles of 15 s at 95°C and 30 s at 60°C. The qPCR assays results were analyzed, with expert verification, using Bio-Rad CFX Manager 3. 1 software and its regression mode. The reaction efficiency was calculated using serial dilutions with the formula: E = 10 (-1/slope). qPCR Assay Specificity, Efficiency, and.
Design of Primers and Probes and In Silico Analysis Species-specific and subspecies-specific long-mers were identified with SkIf  (Briand et al. , 2016; Denancé et al. , 2019)  on genomic sequences. For the Xf species and the subspecies fastidiosa, morus, multiplex, and pauca, one of the two longest long-mers identified by  Denancé et al. (2019)  was selected for this study (Table  2 ). For the subspecies fastidiosa sl specific longmers were searched for on our 58 genome sequences of Xf, using the subspecies fastidiosa, morus, and sandyi genomes as ingroups and the multiplex and pauca genomes as outgroups. In total, 3,345 long-mers were identified, ranging from 22 to 235 bp (Supplemental Data 1). Primers and probes were designed within specific long-mers (Table  3 ). Specific amplifications were obtained in silico on XF genome sequences and WGS bacterial sequences from NCBI at the expected amplification size, without any mismatch for the five primer and probe combinations (XFF, XFFSL, XFM, XFMO, and XFP).
The efficiency of each combination was evaluated on serial dilutions of calibrated DNA solutions. The XF, XFM, XFMO, XFP, and XFFSL primers and probes allowed detection of Xf up to 10 pg. ml -1 (4 copies/reaction). XFF primers were slightly less sensitive with a threshold up to 100 pg. ml -1 (40 copies/reaction). On the same DNA solutions,  Harper et al. (2010)  qPCR assay allowed the detection of strains CFBP 8402 (Xfp) and CFBP 8084 (Xfmo) up to 10 pg. ml -1 , and CFBP 7970 (Xff) and CFBP 8416 (Xfm) strain up to 100 pg. ml -1. This makes our new primer qPCR assays good alternatives to Harper's qPCR assay. The three tetraplex qPCR assays (set n°1: XF-XFFSL-XFM-XFP, set n°2: XF-XFF-XFM-XFP, and set n°3: XF-XFF-XFM-XFMO) allowed both detection and identification of Xf and its subspecies (Supplemental Data 2).
In silico, Xfm primers potentially amplified eight strains of subsp. pauca (CFBP 8072, CoDiRo, COF0407, De Donno, OLS0478, OLS0479, Salento-1, Salento-2) with three mismatches on the forward primer, two mismatches on the reverse primer, and one mismatch on the probe, and amplicons had the expected size. We double-checked the specificity of these two sets in vitro on bacterial suspensions (Supplemental Data 4). Xff primers amplified the three tested strains of subsp. sandyi (CFBP 8356, CFBP 8419, and CFBP 8077) and six of the seven tested strains of subsp. pauca (CFBP 8074, CFBP 8402, CFBP 8429, CFBP 8477, CFBP 8495, and CFBP 8498). The sequencing of all amplicons confirmed the results of the qPCR assays. Xfm primers amplified five of the seven tested strains of Xf subsp. pauca (CFBP 8072, CFBP 8074, CFBP 8402, CFBP 8495, and CFBP 8498).
ACKNOWLEDGMENTS We thank  Muriel Bahut  ( ANAN technical facility, SFR QUASAV, Angers, FR ) for DNA extraction automatization,  CIRM-CFBP (Beaucouzé, INRA, France ; http://www6. inra. fr/cirm_eng/ CFBP-Plant-Associated-Bacteria ) for strain preservation and supply,  Leonardo de la Fuente  ( Auburn University, AL, USA ) and  LSV-ANSES  for sharing strains, and colleagues from  CNBC  for sampling plants in Corsica, France. We acknowledge  Nicolas Denancé  for preliminary experiments to design specific PCR tests. We thank  Charles Manceau  ( Anses, Angers, FR ) for his contribution while applying for funding, and  Armelle Darrasse  and  Matthieu Barret  for fruitful discussions and critical reading of the manuscript. This manuscript has been released as a pre-print at bioRxiv (Dupas et al. , 2019).
Purely Mechanical Test. As the first test case, we consider an elastic medium with dimension L � 1. 0 m of width and H � 1. 25 m in height which is intersected by a fracture with 1 mm of width as shown in Figure  4  Different numerical simulations can be conducted to study this problem. In the first step, the commercial code Flac3D (Itasca Consulting Group Inc. ) is used in which the fracture is explicitly modeled as the elastic interface elements (Figure  5(a) ). e results obtained from this simulation will serve as the referent results for the comparison purpose of the results calculated from the EFC approach. In addition, for this last approach, two meshing configurations of the fractured medium are considered. e first configuration uses a uniform mesh, resulting from the global refinement technique as shown in Figure  5(b).
For this configuration, the commercial code Flac3D is also chosen in which we implemented the EFC approach. is simulation demonstrates the easy use of the EFC approach presented in this work in an existence commercial code when only a simple algorithmic to search the mesh cells intersected by the fracture is required. e other configuration corresponding to the case uses the nonuniform meshing based on the local refinement technique (Figure  5(d) ) to describe the fracture in the EFC approach. e numerical simulation of this second configuration is performed using open source library deal. II. e reasons for the choice of this code are not only on its capacity to deal with different problems, as demonstrated in numerous works (  [44] [45] [46] [47]  and different references cited therein) but also on its capacity for local refinement of meshes using the hanging nodes technique  [27, 30, [34] [35] [36].
Application of the EFC Approach to Investigate the Effective Permeability and Elastic Compliance Tensor of Fractured Rock Masses in Real Field e capacity of the EFC approach will be also demonstrated in this section through a numerical investigation of the effective permeability and elastic compliance tensor of the real rock mass taken from the Sellafield site. e abundancy of information about this site, thanks to the investigation program performed by United Kingdom Nirex Limited  [28, 29]  as part of the second Bench Mark Test (BMT2) of the international collaborative program DECOVALEX III, is the main reason of this choice. Additionally, the information of the site is also well-synthetized in various works  [4] [5] [6] [7] [49] [50] [51]. e necessary information about the spatial distribution of fractures is summarized in Table  1  and is sufficient to generate without difficulty the fracture network in the rock mass.
Introduction MegaM@Rt2 is a three-years project, funded by European Components and Systems for European Leadership Joint Undertaking (ECSEL JU) under the H2020 European program, that started in April 2017  [1]. The main goal is to create an integrated framework incorporating methods and tools for continuous system engineering and runtime V&V  [2, 8]. The underlying objective is to develop and apply scalable model-based methods and tools in order to provide improved productivity, quality, and predictability of large and complex industrial systems  [9]. One of the main challenges is to cover the needs coming from diverse and heterogeneous industrial domains, going from transportation  [3]  and telecommunications to logistics. Among the partners providing use cases in the project, we can cite Thales, Volvo Construction Equipment, Bombardier Transportation and Nokia.
These organizations have different product management and engineering practices, as well as regulations and legal constraints. This results in a large and complex catalog of requirements to be realized by the architecture building blocks at different levels of abstraction. Thus, the development of the MegaM@Rt2 framework is based on a feature-intensive architecture and a related implementation roadmap. The MegaM@Rt2 framework plans to integrate more than 30 tools implementing the above mentioned methods and satisfying requirements of the case studies. The tools features are grouped in three conceptual tool sets: 1. MegaM@Rt Systems Engineering Tool Set regroups a variety of current engineering tools featuring AADL, EAST-ADL, Matlab/Simulink, AUTOSAR, Method B or Modelica, SysML and UML in order to precisely specify both functional and non-functional properties.
Moreover, system level V&V and testing practices will also be supported by this tool set. 2. MegaM@Rt Runtime Analysis Tool Set seeks to extensively exploit system data obtained at runtime. Different methods for model-based V&V and model-based testing (MBT) will be rethought and/or extended for runtime analysis. Model-based monitoring will allow to observe executions of a system (in its environment) and to compare it against the executions of corresponding model(s). Monitoring will also allow a particular system to be observed under controlled conditions, in order to better understand its performance. 3. MegaM@Rt Model & Traceability Management Tool Set is a key part of the framework as it is dedicated to support traceability between models across all layers of the system design and execution (runtime).
Architecture specification Modelio is an MDE workbench supporting standard modeling languages such as UML, SysML among others. All the modeling notations can be used in the same global model repository which is important for model traceability and management. Requirements modeling. In our approach, requirements originated from different sources, i. e. from 9 case study providers and 22 tool providers. In order to have an uniform approach for requirement specification that would facilitate gap analysis and roadmap identification, we defined requirement templates that were used to define the expected properties to be collected, such as criticality for the case study requirements and planned release date for tool purposes. Modelio allowed us to edit requirements in both diagram view and tabular view (see Fig.
2 ). The requirements were manually edited or automatically imported from other documents, e. g. MS Excel. Architecture modeling. On the architecture level, we used Class and Deployment diagrams. We limited modeling to a subset of UML to enforce the common understanding of the architecture and simplify editing. In particular, we choose to use UML Components, Interfaces, Associations, Generalizations and Dependencies. For tool components, we set a template for the architecture specification that included class diagram to specify functional interfaces, tool components subordinates and the relation to the conceptual tool set in the framework, and deployment diagrams to identify the execution environment of the tool component. In addition, Package diagrams have been used to define the high level structure of the MegaM@Rt2 framework architecture.
In addition, we splitted the model in several parts corresponding to each work package and we provided clear guidelines on how the work is organized. A third challenge came from the limitations of the selected tools. For instance, there were different restrictions on how the styling of the documents generated from the models could be configured and how the information could be visualized using different types of diagrams. Manual effort is also required to create document templates and configure the document generators. However, once the generators were created they could be reused easily and effectively. In addition to the above challenges, some industrial partners were already using an existing company-wide tool chain that is not part of our project consortium. In this case, they still gave their requirements, which were mapped to MegaM@Rt tool set capabilities.
However, manually developing frame semantic databases and annotating corpus-derived illustrative examples to support analyses of frames are resource-intensive tasks. The most well-known frame semantic (lexical) resource is FrameNet  (Ruppenhofer et al. , 2016) , which only covers a (relatively) small set of the vocabulary of contemporary English. While NLP research has integrated FrameNet data into semantic parsing, e. g. ,  Swayamdipta et al. (2018) , these methods cannot extend beyond previously seen training labels, tagging out-of-domain semantics as unknown at best. This limitation does not hinder unsupervised methods, which will port and extend the coverage of semantic parsers, a common challenge in semantic parsing  (Hartmann et al. , 2017). Unsupervised frame induction methods can serve as an assistive semantic analytic tool, to build language resources and facilitate linguistic studies.
Evaluation Dataset The dataset consists of manual annotations for verb-headed frame structures anchored in tokenized sentences. These frame structures were manually annotated using the guidelines for this task (Q. Zadeh and Petruck, 2019). For example, as already illustrated, the verb come from. v is annotated in terms of FN's ORIGIN frame and its core FEs, as Example 1 shows. (1) Criticism of futures COMES FROM Wall Street.
Data Sampling We sampled data from the Wall Street Journal (WSJ) corpus of the Penn Treebank. Kallmeyer et al. (2018)  provided frame annotations similar to those in this task for a portion of WSJ sentences, using SemLink  (Bonial et al. , 2013)  and EngVallex  (Cinková et al. , 2014)  to generate frame semantic annotations semi-automatically. That work was based on FrameNet and the Prague Dependency Treebank (PSD)  (Hajič et al. , 2012)  from the Broad-coverage Semantic Dependency resource  (Oepen et al. , 2016). We started by annotating a portion of the records in  Kallmeyer et al. (2018) , and later deviated from this subset to create a more representative sample of the overall diversity and distribution of verbs in the WSJ corpus using a stratified random sampling method.
Annotation Procedure We annotated the data in a modular manner and in a semi-controlled environment using an annotation system developed for this purpose. The procedure consisted of four steps: 1) Reading and Comprehension; 2) Choosing a Frame; 3) Annotating Arguments; and 4) Rating, Commenting, or Revising. We tracked and logged all changes in the data as well as annotator interaction with the annotation system upon starting to annotate. The tool measured the time that annotators spent on each record and each annotation step, as well as how annotators moved between steps. In Step 1, annotators viewed a sentence with one highlighted verb, as in Example 2. (2) Criticism of futures COMES from Wall Street. The goal of this step was understanding the meaning of the verb and its semantic function, and identifying semantic heads of arguments and their associated words or MWUs.
Quality Control At least two annotators verified all annotation used in the evaluation. A main annotator annotated all records in the dataset; two other annotators verified or disputed those annotations. If annotators could not reach an agreement, we removed the record from the SemEval dataset. A full analysis of annotator disagreement goes beyond the scope of this work. While the source of annotator disagreement may seem trivial and simple (e. g. , only one annotator understood the sentence correctly), we believe that some sentences may have more than one interpretation, all of which are plausible. Like the disagreement resulting from incorrect frame assignment, deciding what frame a verb evokes may be challenging; and resolving the dilemma is not always simple. Choosing between two related frames (e.
This result is not surprising since that work did not effectively handle MWUs in the test, where only the head of the MWU was kept. However, the output of Open-SESAME, and its low BCF was indeed surprising. We fed Open-SESAME the sentences from the test set; it identified approximately 5k frames. However, the overlap with the test set was only 1,216 records (identification problem in Open-SESAME). These 1,216 records exhibit a mismatch between 536 of the arguments and their respective target verbs. We ignored the system's extra or incorrectly generated arguments, and replaced the missing items with those of the 1CPHG baseline records. We then used the resulting records for evaluation against the task's gold data as did the task's participants. As Table  3  shows, the unsupervised method outperforms the supervised system for all tasks by a wide margin, i.
Table4shows the amount of effort to develop the SemEval dataset in terms of time and moves that the annotation system recorded. 4. 4). Annotator Activity Time Moves Reading and Comprehension 78 4,795 Choosing a Frame 177 9,737 Annotating Arguments 81 19,510 Rating, Revising, Commenting 115 25,793 Multi-word Unit Annotation 89 8,949 Total 539 68,784.
To visualize the results, we cluster the neighboring nodes associated with the same states and use graph visualization tools. The method is implemented in the PastML program and web server. The results on simulated data demonstrate the accuracy and robustness of the approach. PastML was applied to the phylogeography of Dengue serotype 2 (DENV2), and the evolution of drug resistances in a large HIV data set. These analyses took a few minutes and provided convincing results. PastML retrieved the main transmission routes of human DENV2 and showed the uncertainty of the human-sylvatic DENV2 geographic origin. With HIV, the results show that resistance mutations mostly emerge independently under treatment pressure, but resistance clusters are found, corresponding to transmissions among untreated patients.
2014; Leventhal et al. 2014; Ku ¨hnert et al. 2014 Ku ¨hnert et al. , 2016)). The Bayesian approach is very popular because of this wealth of options and flexibility, via famous software programs like BEAST  (Drummond et al. 2012 ). However, Markov chain Monte Carlo based methods have a high computational cost, and the joint inference of all the tree, parameter, and character distributions cannot be achieved for large data sets. Even the stepwise approach where we first infer the tree distribution, and then the distribution of the studied character along the most likely trees is hardly applicable to medium data sets (500-1,000 tips), requiring high performance computing units (Graphics Processing Units  [GPUs] ), sophisticated parallel implementations  (Ayres et al. 2012 (Ayres et al. , 2019)) , and days to weeks of computation.
[2019]  for a closely related method), but we restrict ourselves here to unique, discrete characters, for example geographical or morphological. To summarize and visualize the resulting scenarios in this framework, we cluster the neighboring nodes with identical predictions and reuse some of the ideas we developed in parsimony-based PhyloType software  (Chevenet et al. 2013). This way we obtain a compact, tree-shaped and easily interpretable graphical representation of the most likely ancestral scenarios, which is robust to phylogenetic uncertainties and sampling rate variations. In the following, we first describe the different components of the method, then the results with simulated data along with comparisons with other ACR methods, and lastly two real data analyses on the phylogeography of Dengue and the evolution of drug resistances in a large HIV data set.