dataesr/scientific-paragraphs-categorization huggingface dataset:
The advantage of this measure is that it does not depend on the size of the anonymity set but on the distribution of probabilities. We are quantifying the quality of our dataset with respect to an attribute uniqueness independently from the number of fingerprints in our database. This way, we can qualitatively compare the datasets despite their different sizes. Table  3  lists the Shannon's entropy for all attributes from both the Panopticlick and AmIUnique studies, and our dataset. Column 'Entropy' shows the bits of entropy and column 'Norm. ' shows the normalized Shannon's entropy. The last two rows of Table  3  show the worst case scenario where the entropy is maximum (i. e. all the values are unique) and the total number of fingerprints. In the novel dataset analyzed here, the most distinctive attributes are the List of plugins, the Canvas, the User-agent and the Available fonts.
The second significant difference is 0. 214 for the Available fonts. Installing fonts on mobile devices is much more restrained than on personal computers. Even if we test a very limited set of fonts through JavaScript compared to what could be collected through Flash, we can see that there is clearly more diversity on personal computers. The last significant difference is for the User-agent attribute, with a difference of 0. 182. On mobile devices, the user-agent has the highest entropy value. This is because phone manufacturers include the model of their phone and even sometimes the version of firmware directly in the user-agent as revealed by Laperdrix et al. [22]. Attributes Use of an ad blocker and Use of local/session storage have very low entropy values because their values are either "yes" or "no".
Comparison with Panopticlick and AmIunique In the data collected by Panopticlick, Eckersley observed that 83% of visitors had instantaneously recognizable fingerprints. This number reached 94% for devices with Flash or Java installed. With the AmIUnique website, Laperdrix and colleagues observed that 89. 4% of fingerprints from their dataset were unique. Thanks to the high percentages of unique browser fingerprints, browser fingerprinting established itself as an effective stateless tracking technique on the web. However, with our study, we provide an additional layer of understanding in the fingerprinting domain. By having 33. 6% of unique fingerprints compared to the 80+% of the other two studies, we show that browser fingerprinting may not be effective at a very large scale and that the targeted audience plays an important role in its effectiveness.
Comparing data size. When analyzing the percentages of unique fingerprints, the amount of fingerprints is an important element that influences the results. As discussed by Eckersley in  [15] , the probability of any fingerprint to be unique in a sample of size N is 1/N. It is clear that probabilities of being unique in our dataset are much lower than the probabilities of being unique in the AmIUnique one. With the aim of establishing a more equitable comparison, we took some samples with the same number of fingerprints as the AmIUnique data and we then calculated the percentage of unique fingerprints. We perform a comparison with the AmIUnique data because the amount of fingerprints is four times smaller than the one collected by Panopticlick. Because our dataset spans a six month period, we divided the data into six parts, each part containing data for one month.
Comparing entropy values. Comparison with Panopticlick can be established by taking into account only six attributes. We observe that entropy values for our dataset and Panopticlick differ significantly for all attributes, except for the Screen resolution. Entropy values for the Screen resolution attribute hardly change for the three datasets. Regarding Timezone and Cookies enabled, drops occur in entropy values due to the characteristics of our dataset. As we explained in Section 2, we analyze fingerprints from users who accepted cookies, and most of them live in the same geographic region. The difference in the entropy value for Content language is due to the fact that most users are located in the same geographic region, which implies that most of them share the same language. In fact, 98% of users present the same value for timezone, which corresponds to Central European Time Zone UTC+01:00 and as a direct consequence of this, 97.
From AmIUnique to our dataset, the difference is 0. 126 resulting in a decrease of 0. 365 from Panopticlick to our data. This gradual decrease in the entropy value for the List of plugins is explained by the absence of plugins on mobile devices and by the removal of plugins from modern browsers. Over time, features have been added in HTML5 to replace plugins as they were considered a source of many security problems. Chrome stopped supporting the old NPAPI plugin architecture on Chrome in 2015 (topic discussed in  [22] ). Mozilla dropped support in version 52 of the Firefox browser released in March 2017. Safari has never supported plugins, Flash is long discontinued for Android, and MS Edge for Windows 10 does not support most plugins. Anything else reliant on the Netscape Plugin API (NPAPI) is now dropped which means Silverlight, Java and Acrobat are gone  [27].
The difference in the entropy value for Available fonts between Panopticlick and AmIUnique is explained by Laperdrix et al. [22]. Half of the fingerprints in the AmIUnique dataset were collected on browsers that do not have the Flash plugin installed or activated. Between AmIUnique and our data, the difference for the entropy value of fonts is 0. 117. Even if collecting fonts through JavaScript is not as effective as with Flash, we observe that the entropy of fonts is still high, keeping its place as one of the top distinctive attributes. For the other attributes, we observe that the entropy values for both our dataset and AmIUnique are similar.
The impact of different demographics In Section 3, we compared our dataset with the two available sets of fingerprint statistics. There are two key elements that can influence the results of this analysis: the targeted audience and the evolution of web technologies. Previous datasets were collected through websites dedicated to browser fingerprint collection. Both websites amiunique. org and panopticlick. eff. org inform users about online fingerprint tracking, so users who visit these websites are aware of online privacy, interested in the topic or might be more cautious than the average web user. Our dataset is much different as it was collected by targeting a general audience through a commercial website. We believe that this difference in the fingerprint collection process is key to explain the differences between datasets, giving rise to RQ 3.
Towards a potential privacy-aware fingerprinting An arms race is currently developing between users and thirdparties. As people are getting educated on the questions of tracking and privacy on the web, more and more users are installing browser extensions to protect their daily browsing activities. At the end of 2016, 11% of the global Internet population is blocking ads on the web  [8]. This represents 615 million devices with an observed 30% growth in a single year. With regards to browser fingerprinting, several browsers already include protection to defend against it. Pale Moon  [1] , Brave  [3]  and the Tor Browser  [7]  were the very first ones to add barriers against techniques like Canvas or WebGL fingerprinting. Mozilla is also currently adding its own fingerprinting protection in Firefox  [2]  as part of the Tor Uplift program  [9].
With our study, we show that we do not know yet the full extent of what is possible with browser fingerprinting and as so, modern browsers are getting equipped with mitigation techniques that require a lot of development to integrate and maintain. But, Does the evolution of web technologies limit the effectiveness of browser fingerprinting? In order to answer the RQ 4, we follow the idea proposed by Laperdrix et al. [22]. The authors simulated the effectiveness of browser fingerprinting against possible technical evolutions. We recreated some of their scenarios on our dataset. Scenario n°1 -The end of browser plugins. Web browsers are evolving to an architecture not based on plugins. Despite the progressive disappearance of plugins, the list of plugins is still the most distinctive attribute for personal computers in our data.
A glimpse of the impact of this scenario is observed on mobile devices, although some plugins still remain. To estimate the impact of the disappearance of plugins, we simulate the fact that they are all the same in our dataset, but only on personal computers and thus taking mobile devices as reference. The improvement is significant with a decrease of exactly 19. 2% from 35. 7% to 16. 5%, taking slightly lower value than on mobile devices, which is 18. 5%. Disappearance of plugins for personal computers reduces significantly the effectiveness of browser fingerprinting at uniquely identifying users, as we observed first on mobile web browsers. Scenario n°2 -Adherence to the standard HTTP headers. Laperdrix et al. [22]  simulated this scenario assuming that the HTTP header fields had the same value for all fingerprints.
CONCLUSION In this work, we analyzed 2,067,942 browser fingerprints collected through a script that was launched on one of the top 15 French websites. Our work focuses on determining if fingerprinting is still possible at a large scale. Our findings show that current fingerprinting techniques do not provide effective mechanisms to uniquely identify users belonging to a specific demographic region as 33. 6% of collected fingerprints were unique in our dataset. Compared to other large scale studies on browser fingerprinting, this number is two to three times lower. This difference is even larger when only considering mobile devices as 18. 5% of mobile fingerprints are unique compared to the 81% from  [22]. The other key elements from our study are as follows. Personal computers and mobile devices have unique fingerprints that are composed differently.
While desktop/laptop fingerprints are unique mostly because of their unique combinations of attributes, mobile devices present attributes that have unique values across our whole dataset. We show that by changing some features of the fingerprint, such as Content language or Timezone, it is very probable that the fingerprint will become unique. We also show that User-agent and HTML5 canvas fingerprinting play an essential role in identifying browsers on mobile devices, meanwhile the List of plugins is the most distinctive elements on personal computers, followed by the HTML5 canvas element. Furthermore, in the absence of the Flash plugin to provide the list of fonts, we used an alternative for collecting fonts through JavaScript. Even if the list of tested fonts is much smaller compared to what could be captured through Flash, collecting fonts through JavaScript still presents some good results to distinguish two devices from each other.
Removal of contaminants The newly assembled contigs were compared with NCBI nt database (01/08/2016) (blastn -task dc-megablast -template_length 18 - template_type coding_and_optimal -max_target_seqs 2 -e-value 1eÀ5) using BLAST+  (Camacho et al. , 2009)  v2. 2. 31. Contigs, which had best hits (pid > 90 and (alignment length/query length) ≥ 50) against nongreen plants, mitochondrial and chloroplast sequences were considered to be contamination and excluded from further analysis. The assembled contigs were compared with a collection of Illumina adapters obtained from the adapter database distributed with Trimmomatic, and the adapter database obtained from https://github. com/csf-ngs/fastqc/blob/master/Contaminants/ contaminant_list. txt. Adapters at the end of contigs were trimmed while those within contigs were masked.
Gene presence/absence variation Gene PAV discovery was performed using the SGSGeneLoss package  (Golicz et al. , 2015)  (lostCutoff = 0. 05 and minCov = 2). Reads were mapped to the pangenome using Bowtie2 (-end-toend -sensitive), and reads mapping in proper pairs were retained using SAMtools  (Li et al. , 2009)  v 1. 2 (samtools view -bS -f 2). Gene PAV was also determined in the 280 accessions from the ERANET-ASSYST B. napus diversity set. The analysis performed on the ERANET-ASSYST diversity set in this pangenome study is distinct from the analysis performed by  Schiessl et al. (2017b). Different thresholds (minCov = 300 and lostCutoff = 0. 70) were applied due to the high read depth coverage of the accessions in the diversity set. To estimate the relationship between the accessions used in the pangenome study, a dendrogram was constructed based on the gene presence/absence results, which were converted into a binary matrix ('1' representing present genes and '0' representing absent genes).
Gene ontology enrichment analysis Functional annotation of the pangenome was carried out using Blast2GO  (Conesa et al. , 2005)  command line v2. 5. The pangenome genes were compared with the proteins in the Viridiplantae database and preformatted to comply with Blas-t2GO naming requirements. Comparisons were made using BLAST. Gene ontology (GO) enrichment analysis of the variable genes (absent and present) was performed in R using the topGO package (Alexa and Rahnenfuhrer, 2010) using Fisher's exact test with method 'elim' used to adjust for multiple comparisons.
Pangenome modelling Curves corresponding to pangenome size and core genome size with respect to genes and gene families were fitted in R (R Development Core Team, 2011) using the nlsLM function (nonlinear least squares based on a modified version of the Levenberg-Marquardt algorithm) from the package minpack. lm. The combinations of genomes were obtained according to the following formula: 50!/(n!(50Àn)!), n = [1,50]. The pangenome size was modelled using the power law regression: y = Ax B + C  (Tettelin et al. , 2005; Zhao et al. , 2014)  while the core genome size was modelled using the exponential regression y = Ae Bx + C. Due to the large number of genomes used in this study, an alternative to calculating all possible combinations of genomes was used; in this case, only 100 000 combinations of genomes (where possible) were calculated for each genome.
The model was fitted using all 100 000 points for each genome. Identification of candidate R-genes R-genes in the pangenome were identified using InterProScan based on the domains from Pfam and Coils databases. The domain names are listed in Table  S18. All possible amino acid sequences of LepR3, Rlm2, lepr3/rlm2 CRa and crr1a were downloaded from NCBI. A BLAST search (blastp -e-value 1eÀ50) was first performed on the protein sequences of these genes against the pangenome proteins. In the case of CRa and crr1a where the query protein sequences were only partially covered by their candidate B. napus orthologs, a BLAST search (tblastn -e-value 1eÀ50 and pid > 90%) was performed on the genes against the pangenome assembly to identify the target regions while making sure that the candidate orthologs were present in those regions.
Similarly, to delimit the exact location of lepr3/rlm2 in the pangenome, a BLAST search was performed on the B. napus homologue of lepr3 (BnaA10g20720D) from the Darmor-bzh v4. 0 reference genome assembly against the pangenome (blastp -e-value 1eÀ50 and pid > 90%). To identify whether the accessions used to build the pangenome carried the susceptible or resistant alleles of the blackleg genes, the read mappings from the SNP discovery step were used. For each accession, the nucleotide sequence corresponding to the candidate region was extracted from the BAM file using SAMtools (samtools mpileup -uAIf) and BCFtools  (Li et al. , 2009)  v 1. 2 (bcftools call -m -o). To obtain the protein sequence corresponding to the extracted region, TransDecoder v 3. 0 (https://transdecoder. github. io/) was used to predict the longest open-reading frame (ORF).
Targeted gene family analysis Genes for lipid biosynthesis genes, GSL metabolism and FLC genes were retrieved from the Darmor-bzh v4. 0 reference genome. Their A. thaliana orthologs were obtained from the A. thaliana metabolic pathway database (ftp://ftp. plantcyc. org/Pa thways/Data_dumps/PMN11_June2016/aracyc_pathways. 20160601, version downloaded on 28. 07. 2016). A BLAST comparison (e-value 1eÀ5) was performed on the Darmor-bzh genes with the B. napus pangenome, and the best hits were selected for targeted gene families analysed. The A. thaliana orthologs were compared with the orthologous gene clusters, and the pangenome target genes were identified as follows: if a cluster contained A. thaliana and B. napus genes, and the A. thaliana genes belonged to a pathway, then all the associated B.
SNP discovery and annotation Mappings used for gene PAV discovery were also used for SNP discovery. Additionally, duplicates were marked using Picard tools (https://sourceforge. net/projects/picard) v2. 2. 1 MarkDuplicates. SNPs were discovered using BCFtools 'call' command (-v -m) using the output from SAMtools mpileup (-q 30 -Q 20 -g -I). The resulting files were filtered using vcflib (https://github. com/vcflib/ vcflib) (vcffilter -f 'DP > 10 & QUAL > 30'). Heterozygous SNPs were considered potential artefacts and removed. SNPs were categorized as coding, synonymous, nonsynonymous and nonsense using the R package VariantAnnotation  (Obenchain et al. , 2014)  v 1. 13. 46. Only genes with at least one coding SNP were considered in the analysis.
EST evidence was based on the 95k ESTs (http://brassica. nbi. ac. uk/array_info. html), and B. rapa, B. oleracea and B. napus unigenes downloaded from UniGene (ftp://ftp. ncbi. nlm. nih. gov/repository/UniGene). Protein evidence was based on Brassicaceae proteins downloaded from RefSeq. Publicly available RNA-Seq data (SRA accession numbers PRJEB5461.
Acknowledgements BH was supported by a  University of Queensland Research Scholarship (UQRS)  award granted by  The University of Queensland, Australia. This work was funded by the  Australian Research Council (ARC)  (Projects  LP110100200 ,  FT130100604 ,  LP130100925 ,  LP140100537  and  DP160104497 ). This work was supported by resources provided by the  Pawsey Supercomputing Centre  with funding from the  Australian Government and the Government of Western Australia , the  Australian Genome Research Facility (AGRF) , the  Queensland Cyber Infrastructure Foundation (QCIF)  and the  Australian Partnership for Advanced Computing (APAC).
Data availability All the data used in this study are available on SRA through accessions numbers PRJEB5974, PRJEB6069, PRJNA342383, PRJEB5841, ERA036824 and SRP087610. All results stemming from this study are available at http://brassicagenome. net/. All other data supporting the findings of this study are available in this article and its Supplementary Information or available from the corresponding author on request.
The coupling between an implicit finite elements (FE) code and an explicit spectral elements (SE) code has been explored for solving the elastic wave propagation in the case of soil/structure interaction problem. The coupling approach is based on domain decomposition methods in transient dynamics. The spatial coupling at the interface is managed by a standard coupling mortar approach, whereas the time integration is dealt with an hybrid asynchronous time integrator. An external coupling software, handling the interface problem, has been set up in order to couple the FE software Code_Aster with the SE software EFISPEC3D.
SEM/FEM co-simulation The purpose of this section is to briefly present the adopted codes and how to handle the co-simulation. The goal is to couple the implicit FE code, Code_Aster  [14] , developed by EDF, with the SE code, EFISPEC3D developed by BRGM. To handle the interface problem given in Eq. (  23 ), an external coupling software has been developed in C language. In order to minimize the modifications of the sources of the different codes, an external coupling software has been set up rather than solving the interface problem in the SE or FE code. Besides solving the interface problem, the coupling software is also in charge of data exchange with the FE and SE codes. The data exchange requires communication between several software running on the same computer. For this purpose, pipes in C language, also called FIFO (First In First Out), are efficient tools.
Then an hybrid (different time integrators) and asynchronous (different time steps) time integrator is adopted in order to couple the explicit SEM with an implicit FEM, while keeping the appropriate time step size in the different partitions. Finally, the explicit/implicit multi time step co-simulation, coupling EFISPEC3D code with Code_Aster, has been validated in comparison to full-SEM approach for simple 3D soil interaction problems. As a conclusion, the proposed methodology is very promising for modeling soil/structure interaction with a wide earth medium, including details of an earthquake source and details of the site of study (fine meshed sedimentary basin and structure with its foundation), thanks to the good accuracy of the spectral element method for wave propagation from the source to the site and the flexibility offered by the finite element method for building simulation with its surrounding soil.
High-dimensional analyses are critical for disentangling the numerous cellular and molecular events induced by HIV infection. Our mass cytometry panel included typical and atypical T-cell markers that may be associated with the uncommon expression of CD32a on CD4 + T cells during HIV infection. Moreover, all previous studies carried out flow-cytometry analysis using a pan anti-CD32 antibody that binds both CD32a and CD32b receptors, possibly leading to misinterpretation of the data  (7, 10). We thus included antibodies that make it possible to clearly distinguish CD32a from CD32b isoforms (Figure  S1  in Supplementary Material). Hence, our data unambiguously reflect CD4 + T cells expressing CD32a, but not CD32b, protein on their surface (Figure  2A ). Our study showed that CD32a + CD4 + T cells can be found not only in HIV + patients but also in healthy donors and encompass heterogeneous populations.
Data normalization Raw cytometry profiles generated by CyTOF were normalized with the MatLab Compiler software normalizer  (40) , using the signal from the normalization beads, as recommended by the software developers. Manual gating of cD45 + cD4 + T cells Normalized events were gated to exclude doublets and triplets from the analysis by selecting cells with only one Iridium level. Then, beads added for normalization and dead cells were removed by selecting cells negative for Cerium (Ce140) and Rhodium, respectively. Finally, leukocytes were selected based on the positive expression of CD45. CD4 + T cells were then selected based on the expression of CD3, CD19, CD4, and CD8 (Figure  S2  in Supplementary Material). After this gating strategy, an average of 28,305 ± 3,375 cells remained per sample.
acKnOWleDgMenTs We thank the ANRS (French National Agency for AIDS research) CO6 PRIMO cohort for helpful collaboration and all the medical professionals involved in blood sampling at the Hôpital Kremlin-Bicêtre. We also thank Bruno Iannascoli (Institut Pasteur) for the in vitro production of anti-CD32b antibodies. This work was supported by French government "Programme d'Investissements d' Avenir" (PIA) under Grant ANR-11-INBS-0008 that fund the Infectious Disease Models and Innovative Therapies (IDMIT, Fontenay-aux-Roses, France) infrastructure and PIA grant ANR-10-EQPX-02-01 that funds the FlowCyTech facility.