dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, instead of using Gaussian kernels as in rbfns, f (x) is as a set of E cosine functions f (x) = E e=1 w e • φ(x, ω e , ψ e ) (86) = E e=1 w e • cos(ω e x + ψ e ). ( 87 ) The basis functions are defined by randomly drawing their multidimensional frequency ω and phase ψ using ω ∼ N (0, 2γI) and ψ ∼ U(0, 2π). As in all other rbfn-like methods, the main meta-parameter in irfrls is the number of features E, that is easy to tune considering a trade-off between the model accuracy and the time complexity. The other two meta-parameters of irfrls are λ, the regularization parameter and γ, the variance over the period of the random cosine features. Experiments on several databases confirm that rfrls and irfrls can be set close to krr with a small enough processing time. Indeed, irfrls shows interesting practical performances  (Droniou et al.
Summary All the algorithms listed in this section use the same function model f (x) = E e=1 φ(x, θ e )• w e , (99) which is a special case of the unified model in (  41 ) with a = 0. However, different algorithms use different instantiations of (99), as listed in Table  2. In krr and gpr, the number of basis functions is taken to be N whereas in all other algorithms this is an arbitrary parameter E. Additionally, in gpr and krr, the basis functions are centered on the data points whereas in rbfns, they can be either regularly placed to pave the input space or drawn randomly. In all models but anns, irfrls and i-ssgpr, it is standard to use Gaussian basis functions (that combine the kernel and radial basis function properties), whereas irfrls and i-ssgpr use cosine basis functions and anns more often use sigmoids or hyperbolic functions.
, 2012; Marin et al. , 2011). A very pragmatic advantage of a unified model arose whilst implementing several of the algorithms presented here. The classes representing the models of the different algorithms became so similar that it was an obvious step to implement a routine for casting them all to a unified model, which greatly simplified the code, and made it easier to exchange algorithms. This is not merely a low-level implementation advantage though. On a more abstract level, such modularity is an important pre-condition for realizing the vision of model-based machine learning  (Bishop, 2013). research and innovation programme under grant agreement No 640891. We thank Vincent Padois, Nicolas Perrin, Ryan Lober and Mathieu Lefort for proofreading earlier versions of this article. Thank you Ajaya Kumar Pani for pointing out an error in the first paragraph (we switched "dependent" and "independent"), which we have corrected in this version.
We used this criterion to compare and benchmark YOC with five recent genome aligners on large bacterial genome datasets, and showed it is suitable for identifying the specificities and the potential flaws of their underlying strategies. Conclusions: The YOC prototype is available at https://github. com/ruricaru/YOC. It has several advantages over existing genome aligners: (1) it is based on a simplified two phase alignment strategy, (2) it is easy to parameterize, (3) it produces reliable genome alignments, which are easier to analyze and to use.
Background The huge number of genomes sequenced every day makes the development of effective comparison and alignment tools ever more urgent. Indeed, many microbiological applications rely directly on genome alignments, for instance micro-diversity and phylogenomic analysis of bacterial strains  [1] , assembly and annotation procedures for datasets of closely-related genomes  [2]  or prediction of maintenance motifs in non-model species  [3]. Despite many efforts in this field and the availability of numerous genome aligners, some of which were specially designed for bacterial genomes (e. g. , MGA  [4] , MAUVE  [5] , ProgressiveMAUVE  [6] , MUGSY  [7] , MAGIC  [8] ) and others that target more complex genomes (e. g. , MUMmer  [9] , GRIMM-Synteny  [10] , CHAINNET  [11] , PipMaker  [12] ), none is yet completely satisfactory.
Given that here we specifically address whole genome alignment in bacteria, such methods are beyond the scope of the present paper. The application of several whole genome aligners to large bacterial genome datasets  [15, 16]  leads to three major observations: The tuning of parameters is a critical and complex step with many whole genome aligners. Two sets of parameters, even if they only differ slightly, can produce considerably different genome alignments. The choice of ad-hoc parameter settings is complicated and time consuming and depends on both the scientific question and the genomes under consideration (their number and sizes, their The anchor-based strategy includes 4 phases, which are usually followed by a post-processing step to filter the erroneous parts of the produced alignments (mainly related to the P4 "last chance alignment phase").
We implemented this strategy in a new whole genome aligner named YOC, designed for robust pairwise alignment of collinear bacterial genomes. YOC provides several improvements: the strategy is simplified compared to other anchor-based tools and little parameter tuning is needed. Moreover, its sensitivity makes it possible to align more distantly related bacterial genomes. We also analyzed the quality and the reliability of the resulting alignments, which were extensively evaluated on several bacterial datasets. To this end, we introduce a quantitative criterion, GRA-FIL, based on the GRAPe software  [25] , and applied it to benchmark several tools. We show that this criterion measures efficiently the unreliable parts of the alignments, thus enabling rapid comparison of the performances of different genome aligners.
Genome datasets Dataset 1 -174 collinear pairs of bacterial genomes We considered all collinear pairs of bacterial strains of the same species (based on the species name), with complete genomes like in release 5 of the MOSAIC database (  [16] , http://genome. jouy. inra. fr/mosaic). This dataset includes 174 pairs of genomes (see Additional file 1 for a complete list) that are considered to be collinear as (according to the criteria described in  [16] ), they do not include either inversions or translocations exceeding 20 kb in length.
Dataset 2-69 pairs of genomes in the Lactobacillus genus and in the Bacillus cereus species We performed detailed analysis of genome alignments for 14 pairs of genomes of the Lactobacillus genus and 55 pairs of genomes of the Bacillus cereus species. These species were chosen because they mainly include collinear genomes (without rearrangements like inversions and translocations, according to the same criterion as in dataset 1) but in some cases, are nevertheless difficult to align due to high levels of divergence, even at the intra-species level. Nineteen complete genomes of the Lactobacillus genus were extracted from Genome Reviews release 128 (2011), which included eight species with at least two complete genomes of two different strains. Fourteen intra-species pairwise genome alignments were produced and analyzed in detail in this study.
Dataset 3-21 collinear pairs exhibiting increasing genomic divergence To examine the performance of WGA alignment tools with respect to the divergence rate, we selected 21 collinear pairs of genomes from the datasets used in a publication that introduced a measure of genome divergence called MUMi  [40]  (see Supplementary files 1 and 2 (http://jb. asm. org/content/191/1/91/suppl/DC1) of  [40] ). From the original datasets, only unique pairs without major rearrangements were used (pairs that do not include either inversions or translocations, according to the criteria described in  [16] ). Dataset 3 was composed of 21 genome pairs from 10 different bacterial species, exhibiting MUMi genomic distances ranging from 0. 01 (very close pairs) to 0. 97 (highly divergent pairs). See Additional file 4 for a complete list of these pairs.
Dataset 4 -Lactococcus lactis case study Lactococcus lactis is a gram-positive bacterium extensively used in the production of buttermilk and cheese. It includes two sub-species: L. lactis subsp. lactis and L. lactis subsp. cremoris. As a case study, we analyzed the results obtained with several genome aligners on the pair composed of L. lactis subsp. lactis, IL1403 strain genome (AE005176_GR) and L. lactis subsp. cremoris, SK11 strain genome (CP000425_GR), which is also part of Dataset 3. To facilitate interpretation, we used the MOSAIC database to analyze and visualize the aligned regions  [16]  paying particular attention to their biological relevance.
Genome aligners Six genome aligners, all based on the anchoring strategy, were compared on the previously described genome datasets; two state-of-the-art aligners, MGA  [4]  and LAGAN  [14] , clearly target collinear genomes, three others, MAUVE  [5] , ProgressiveMAUVE  [6]  and MUMmer  [9]  are able to align either rearranged or collinear genomes whatever their type of rearrangements, while our new method, YOC, aligns pairs of collinear genomes (possibly including locally inversed segments but no translocated segments). To facilitate the comparison of the results, we used the XMFA alignment format produced by MAUVE and ProgressiveMAUVE aligners (for description, see http://darlinglab. org/mauve/userguide/files. html) and wrote dedicated Perl scripts to transform the output of genome aligners that do not produce results in this format.
An original quality criterion, named GRA-FIL, was defined based on a filtration procedure consisting in post-processing raw alignments with the GRAPe  [25]  software. GRAPe is a probabilistic genome aligner capable of quantifying the uncertainty of each position of the alignment with a posterior probability. GRAPe was applied on each pairwise genome alignment obtained by each aligner with the aim of filtering the parts of the alignments that are suspected to be spurious and incorrectly aligned. In order to cope with the lengths of the sequences (as GRAPe is too slow to be systematically applied at large scale), we partitioned the alignments in adjacent, 500 position length blocks, and used GRAPe to realign every such short region. The procedure consists in eliminating (filtering) blocks that have at least half of their positions with a posterior probability of being incorrectly aligned greater than 0.
95 (i. e. regions that are predicted by GRAPe to be unalignable or to be part of insertions and gaps). Using this procedure, for each alignment, we computed the length of the regions filtered with GRAPe (as the number of aligned positions or as the percentage of the alignment length), a criterion we named GRA-FIL, which is a precise indicator of the proportion of low-quality regions in a genome alignment. The GRA-FIL procedure is very similar to the one used in the Alignathon competition, which is based on another probabilistic aligner, PSAR  [41]. Finally, we defined a criterion of biological relevance based on the analysis of orthologous gene positions in the aligned regions. The orthologous genes were extracted from the OMA database  [42]. We measured the number of known orthologous genes entirely included in the same aligned segment, the number of orthologous genes entirely included in unaligned regions, and the number of these genes that overlap the two types of segments.
Results Below we summarize and discuss the results we obtained with our two-phase anchor based strategy, YOC (described in Section "The YOC alignment method" and Figure  1 ), compared to five classical anchor based tools on the three datasets described above. The comparisons were conducted based on the criteria defined in Section "Quality criteria of genome alignments". To this we add a comparison of YOC results with MAGIC results on a dataset extracted from  [8]  (see subsection "Magic dataset case study"). On Dataset 1, we observed high variability of the overall quantitative results obtained with the different tools, e. g. the difference between the mean coverage obtained with MGA and that obtained with MAUVE ranged from -24% to 2% (meaning that there is at least one pair of genomes for which MGA's mean coverage is 24% below that of MAUVE, and at least one pair for which MGA's coverage exceeds that of MAUVE by 2%).
Global quality of genome aligners (Dataset 1) The results obtained with four tools: MGA, LAGAN, MAUVE and YOC, in terms of mean coverage before and after filtering with the GRA-FIL criterion are listed in Table  2  for the 174 pairs of genomes in Dataset 1 (see Additional file 1 for more details). As can be seen in Table  2 , the mean coverages ranged from 74% to 100% for MGA, MAUVE and YOC, while LAGAN achieved 100% mean coverage each time. However, when we look at the mean coverages after filtering with the GRA-FIL criterion, we observe that, taking MAUVE for example, in some cases coverage drops to 58%. Indeed, if we analyze the mean coverage that was lost by filtering, we deduce that MGA, LAGAN and MAUVE can produce important quantities of spurious alignments: up to 13% of the alignments were filtered for MGA, respectively 21% for LAGAN and 35% for MAUVE.
Assessment of the reliability of intra-species pairwise genome alignments (Dataset 2) Based on the preliminary results listed in Table  2 , we decided to compare five genome aligners, MGA, MUMmer (NUCmer), MAUVE, ProgressiveMAUVE and YOC, using the intra-species pairs of genomes from Dataset 2. Given that MUMmer (NUCmer) uses exact matches (unique or not) and that it does not include the 'last chance alignment phase' , the length of its alignment can serve as a lower bound of the number of alignable positions. Clearly, the GRAPe filtration should only affect its results marginally. Two sub-datasets of the Dataset 2 were extensively analyzed, the results are listed in Tables  3  and 4  (see Additional files 2 and 3 for more details). Table  3  presents the results regarding the alignment quality before and after filtration of alignments with the GRAPe procedure.
Aligner performances with respect to the genome divergence (Dataset 3) The way of life of a bacterium may affect the rapidity at which its genome diverges within a species. This raises an important question: how does the divergence level of ) before and after the GRAPe filtration procedure described in Section "Quality criteria". Full results are given in Additional files 2 and 3. a genome pair impact the performance of WGA? Using a dataset for which the divergence level was previously measured at the intra-species level  [40] , we applied four WGA tools and compared them using four criteria. Results are shown in Figure  3  (see Additional file 4 for more details): coverage (Figure  3a ), percentage of identities (Figure  3b ), percentage of gaps (Figure  3c ), and percentage of mismatches (Figure  3d ) of the alignments.
Lactococcus lactis case study (Dataset 4) Table  5  and Figure  5  show the results of a detailed analysis of three genome alignments produced by MAUVE, ProgressiveMAUVE and YOC on two Lactococcus lactis genomes, which were post-processed by filtering the low quality alignment regions (with more than 20 consecutive gaps) and the weakly conserved regions (with less than 76% of identity). The filtered alignments, The values in the table correspond to the average, the maximum and the minimum results using the GRA-FIL criterion, i. e. the number and the percentage of the likely erroneous alignment positions that were filtered by the GRAPe filtration procedure described in Section "Quality criteria". Full results are given in Additional files 2 and 3. i. e. backbone alignments, for each of the three tools were incorporated in the Mosaic database  [16]  to facilitate the comparison.
MAGIC dataset case study As we were unable to run MAGIC, we applied YOC on a bacterial set used to assess MAGIC's performance in  [8]  (i. e. , the 12 pairs of genomes listed in Tables three and six of MAGIC paper  [8] ). MAGIC's raw results on this dataset were extracted as such from  [8]  and correspond to the number and the coverage of Reordered Free (RF) segments obtained from curated pairs of orthologs refined in a multi-step pre-processing phase and iteratively post-processed in a clustering phase. We compared these values to YOC raw fragment number and coverage. Our results showed lower performances by YOC for 11 out of the 12 pairs (YOC coverage: 15 to 84%, MAGIC/RF coverage: 34 to 99%). But interestingly, for one of the 12 pairs (Buchnera aphidicola), YOC's performance was better according to the alignment coverage (YOC: 98-99%, MAGIC/RF: 93%).
Even though the results are not entirely comparable between the two tools and the dataset clearly does not fit YOC's application area (11 among the 12 pairs are highly rearranged), they confirm YOC's ability to align highly divergent genomes. Indeed, MAGIC is a versatile and sophisticated tool that, unlike YOC, appears to be perfectly adapted to dealing with rearrangements (as we observed in 11 out of the 12 pairs). Nonetheless, on the Buchnera aphidicola pair, which is highly divergent but rearrangement free, YOC showed a clear advantage over MAGIC with respect to coverage. For complete results see the Additional file 5.
Discussion In this paper, we present a new tool for pairwise alignment of collinear genomes, called YOC, which includes only two phases of the classical four phase anchor-based strategy: the first for detecting local alignments as potential anchors and the second to chain the similarities that will form the alignment. This simplified algorithm leaves out recursion and avoids the "last chance alignment" phase. We compared and benchmarked YOC with several well-known whole genome aligners on a priori easy cases: pairwise alignments of bacterial genomes of the same species. To evaluate the impact of the "last chance , the mean coverage of the backbone (Mean coverage), the percentage of identity of the backbone (Backbone identity%), the number of orthologs included in the backbone (#included orthologs), overlapping the backbone (#overlapping orthologs) and excluded from the backbone (#excluded orthologs), and the orthologous cumulative length coverage in the backbone (Orthologs coverage) The orthologous genes were extracted from the OMA database  [42].
cereus pairs, 20% of ProgressiveMAUVE's alignment was filtered out. After filtration of these regions, the percentages of identity of the original and final alignments were almost the same, strongly suggesting that regions filtered by GRAPe are of poor quality and should be removed. It also turns out that after filtration, these alignments exhibited the same coverages as those output by YOC. In contrast, alignments computed with YOC were much less altered by filtration, e. g. only 4. 5% on average over all B. cereus cases. This conclusion was corroborated on Dataset 3, which revealed MAUVE's and ProgressiveMAUVE's tendency to include an increasing number of mismatches and gaps for higher divergence levels, compared to YOC, which offers a good compromise between coverage and percentage of identities.
summarizes the amount of alignment filtered by GRAPe with each aligner and all genome pairs of both datasets and confirms these results. It turns out that the average amount of positions filtered by the GRAPe procedure (GRA-FIL) is very high in both MAUVE (449. 655 positions = 8. 31% for Lactobacillus and 1. 664. 114 positions = 22. 45% for Bacillus cereus) and ProgressiveMAUVE.
Bioinformatics and statistical analysis The quality of the FASTQ files was controlled with FASTQC  [20]. Pindel analyses were done after a standard BWA alignment  [21, 22]. Raw results from Pindel were exported to VCF format and then manually reviewed. For our analysis method called doMreps, the demultiplexed paired-end FASTQ files were imported from the MiSeq and merged as single-end reads using the PEAR software  [23]. A slightly modified version of mReps (only output format was modified for an easier parsing) was used to identify the FLT3 ITD sequence. [24]  doMreps is available online for Linux and Mac OSX/Darwin: https://github. com/mafouille/doMreps.
• SoA. In this alternative data structure, the discrete distribution functions f i for a particular velocity direction are stored sequentially in the same array (see Fig. 5(b) ). Since each GPU thread handles the update of a single lattice node, consecutive GPU threads access adjacent memory locations with the SoA layout  [7, 8, 32]. This way, they can be combined (coalesced) into a single memory transaction, which is not possible with the AoS counterpart. • SoAoS. We have also explored a hybrid data structure, denoted as SoAoS in  [36]. As SoA, it also allows coalesced memory transactions on GPUs. However, instead of storing the discrete distribution functions f i for a particular velocity direction in a single sequential array, it distributes them across different blocks of a certain block size (see Fig.
Data layout Once again, another key aspect affecting performance in this correction step is the data structure (data layout) that is used to store the information about the Lagrangian points and their supports (coordinates, velocities and forces). Using the locality principle, it is natural to use an array of structures (AoS) to place all this information at a given Lagrangian point in nearby memory locations. This is the data structure that we have used on multi-core processors, since it optimizes cache performance. In contrast, on GPUs it is more natural to use a SoA data structure that distributes the information of all Lagrangian points in a set of one-dimensional arrays. With SoA, consecutive threads access to contiguous memory locations and memory accesses are combined (coalesced) in a single memory transaction.
Standalone Lattice-Boltzmann update Before discussing the performance of our LBM-IB framework, it is important to determine the maximum performance that we can attain. We can estimate an upper limit omitting the IB correction. Fig. 10. Hybrid multicore-Phi implementation. The Lx × Ly lattice is split into two 2D sub-domains so that the IB correction is only needed on one of the domains. The multi-core processor updates the LxIB × Ly subdomain, which fully includes the immersed solid (marked as a circle). The Xeon Phi updates the rest of the lattice nodes (the LxLBM × Ly subdomain). The grey area highlights the ghost lattice nodes at the boundary between both sub-domains. In our target simulations, LxLBM ≫ LxIB. Fig. 11  shows the performance of this benchmark on a Kepler GPU. Recall from Section 3 that our implementation is based on the pull scheme and uses two lattice with the SoA data layout.
As a reference we also show the performance of the Sailfish software package  [32] , which includes a LBM solver based on the push scheme, and the following estimation of the ideal MFLUPS  [35] : MFLUPS ideal = B × 10 9 10 6 × n × 6 × 8 (7) where B × 10 9 is the memory bandwidth (GB/s), n depends on LBM model (DxQn), for our framework n = 9, D2Q9. The factor 6 is for the memory accesses, three read and write operations in the spreading step and three read and write operations in the collision step, and the factor 8 is for double precision (8 bytes). Fig. 12 (a) and (b) shows the performance on an Intel Xeon server. Although on multi-core processor it is natural to use the AoS data layout, SoA and SoAoS (with a block size of 32 elements) turn to be the most efficient data layouts. The main reason behind these unexpected results lies on the vector capabilities of modern processors.