dataesr/scientific-paragraphs-categorization huggingface dataset:
This dataset contains analogies extracted from Sigmorphon2016 Task 1 training data by aligning features in a similar manner as what we do. However, for each analogy A : B :: C : D, the dataset also contains the following permutations: We notice that not all the permutations we use (detailed in Section III-C) are considered. Table  III  presents the portion of the analogies appearing in our dataset that is also appearing in Lepage's dataset (that we call coverage by Lepage's dataset of ours), as well as the converse coverage by our dataset of Lepage's. For our computations, we considered that if any permutation (among the 8 mentioned in Section III-C) of an analogy is present in the other dataset, then the dataset covers this analogy. Surprisingly, neither version completely covers the other, which means that even though our version tends to cover most of Lepage's version, the latter somehow contains some analogies that were not extracted using our process.
On all languages our model outperforms the baselines, except for Finnish where the performance is comparable. Moreover, we could expect the performance for the raw positive examples ("Base" column) to match the one reported in  [8] , even if both versions of the dataset do not overlap exactly (see Section IV-A). A closer look at the suggested answers shows that this difference is due to a different preprocessing applied to the Sigmorphon2016: while we use all the analogies we can build using our simple alignment of features, Murena et al. [8]  use a variant where all the analogies that cannot be inferred by relying on only the source terms have been removed (for example in Finnish, the correct answer to the analogy "mäyrä:mäyrässä::kolo:kolossa" is dictated by the rules of vowel harmony). This means that some of the analogies in our version of the dataset are impossible to solve without additional data nor knowledge of the language (neither by an algorithm nor by a human).
For Russian and Finnish, our model has an accuracy lower than the baseline by at least 13% for negative examples. However, those baselines are much worse on the positive examples (by at least a 58%). Overall, our model performs worse on negative than on positive examples, which is coherent with the training process, as we use less than half the number of negative than positive examples during training. Additionally, while our model performs slightly worse than the baselines on most negative examples, it strongly outperforms them for the positive examples. V. TOWARDS A TRANSFERABLE NEURAL ANALOGY MODEL? We applied the model trained on each language to the other languages of the dataset. The objective is to explore the generalization capabilities of the CNN model, and to test its dependence on the training language.
A : B :: C : D (base form); • C : D :: A : B (symmetry); • A : C :: B : D (central permutation); • B : A :: D : C (inside pair reversing); • D : B :: C : A (extreme permutation); • D : C :: B : A (inside pair reversing followed by symmetry); • C : A :: D : B (central permutation followed by inside pair reversing); • B : D :: A : C (extreme permutation followed by inside pair reversing). In addition to those equivalent forms, we have 3 analogical forms per valid analogy which are considered invalid analogies as they cannot be deduced from the base form A : B :: C : D without contradicting at least one of the postulates mentioned in Section II-A: • B : A :: C : D; • C : B :: A : D; • A : A :: C : D. Using this process we obtain a total of 8 positive and 3 × 8 = 24 negative examples per analogy in the dataset, as each negative form has 8 equivalent forms.
• A : B :: C : D (base form); • C : D :: A : B (symmetry); • A : C :: B : D (central permutation); • B : A :: D : C (inside pair reversing); • D : B :: C : A (extreme permutation); • D : C :: B : A (inside pair reversing followed by symmetry); • C : A :: D : B (central permutation followed by inside pair reversing); • B : D :: A : C (extreme permutation followed by inside pair reversing). In addition to those equivalent forms, we have 3 analogical forms per valid analogy which are considered invalid analogies as they cannot be deduced from the base form A : B :: C : D without contradicting at least one of the postulates mentioned in Section II-A: • B : A :: C : D; • C : B :: A : D; • A : A :: C : D. Using this process we obtain a total of 8 positive and 3 × 8 = 24 negative examples per analogy in the dataset, as each negative form has 8 equivalent forms.
g. Teutschbein and Seibert, 2012; Chen et al. , 2013). Indeed, most impact models are calibrated on observational data and the use of biased climate data as input could result in inappropriate or even false impact projections. To overcome those spatial or inherent biases, various statistical post-processing methods have been developed for downscaling and/or bias correction of the climate simulations. The common idea is to statistically transform the numerical simulations -over a historical periodin such a way that some properties become equal or close to those of a chosen reference dataset (e. g. Gudmundsson et al. , 2012). The statistical downscaling or correction transformation is then usually supposed valid under other climate conditions and applied to changed (e. g. future) climate. The obtained downscaled/bias corrected climate data can then serve as input into impact models (e.
g. inter-site and inter-variable), and (iii) if this TSMBC is able to work for more than lag-1. To do so, the rest of the paper is organized as follows. Section 2 describes the climate and synthetic data used in this study and provides detailed explanations about the proposed TSMBC approach. Section 3 investigates the method based on artificially simulated multivariate dataset obtained from a vector auto-regressive (VAR) model. Then, TSMBC is applied to correct daily temperature and precipitation simulated from a climate model, and the results are given in Sect. 4. Finally, conclusions are reiterated and perspectives are discussed in Sect. 5.
11 • × 0. 11 • spatial resolution. Note that the extracted region of interest is small in comparison to the initial EURO-CORDEX domain  (Jacob et al. , 2014)  over which the RCM simulations were performed. This kind of "perfect model experiment", i. e. considering simulations as "pseudo-observations", is now a common approach to assess downscaling and bias correction methods (see for example  Charles et al. , 2004; Vrac et al. , 2007; Frost et al. , 2011; Bürger et al. , 2012; Grouillet et al. , 2016). The GCM data are then interpolated with a nearestneighbour method to the 0. 11 • × 0. 11 • spatial resolution of the RCM to apply the TSMBC approaches. The (GCM and reference) data over the period 1951-1980 will serve to calibrate the proposed TSMBC method, while projections and evaluations will be performed over 1981-2010.
x d s+2. x d N -s x d N -s+1. x d N          . ( 5 ) Such a transformation can be made to create M Y (s) and M X (s), the lagged matrices obtained from the reference dataset and the biased one to be corrected, respectively. An MBC method can thus be applied to correct the multivariate properties of M X (s) with respect to M Y (s) as reference. The result of the MBC is therefore a matrix M Z (s) of the same size as M X (s). The question to answer now is, from this resulting MBC matrix, how can a N × d matrix Z be extracted or reconstructed that corresponds to proper multivariate (time included) corrected data? There are indeed various possibilities for that, and they may not be equivalent. In this study, we propose a method based on a reconstruction by rows. For illustration, let us take this example of a bivariate dataset X and its associated matrix M X (1), i.
e. including a lag-1 shift:  1 10 2 20  2 20 3 30  3 30 4 40  4 40 5 50  5 50 6 60 X =         1 10 2 20 3 30 4 40 5 50 6 60         , M X (1) =            . In this example, the bold rows of the matrix M X (1) (its rows 1, 3 and 5) can be used to reconstruct the original dataset X. More generally, a row s of M X (1) is a vector corresponding to a portion of X, which continues at row s +2, and which continues at row 2s +3, etc. Concatenating these rows, we can re-construct the dataset X. Applying this method to the matrix M Z (s), we can reconstruct a corrected dataset Z. Because the same operation can be applied by starting at the second row (and any row between 1 and s + 1), the reconstruction depends on the choice of the starting row. However, even if the values are repeated in the lagged matrix, no repeated values can appear in the final reconstruction (i.
e. multivariate corrections). It is also worth noting the initial mapping (i. e. going from X to M X ) is injective, and that, in general, an inverse mapping is not. However, with the suggested reconstruction (i. e. inverse mapping), when a starting row is chosen, the inverse mapping gives a unique time series and is thus injective. Moreover, the choice of a starting row r > 1 omits the first r -1 values in the final reconstruction. This leads us to wonder about the influence of the choice of the starting row. These points will be investigated with the VAR processes. Our approach is summarized in the Algorithm 1. Note that a reconstruction "by column" could also be performed: each column of M X (1) being a sub-column of X, it could then be used to reconstruct the original dataset. However, preliminary analyses showed that this approach does not allow auto-correlations to be corrected, as the temporal dependence structure in each row is not corrected and mostly corresponds to that of the model to be adjusted (not shown).
In practice, values from Y are resam-pled. Note that this naive method corresponds to dOTC with a transport plan given by the product probability distribution between the biased and reference datasets; see Appendix A. We denote this multivariate method a "random bias correction" (RBC), and TSMBC is thus available in two versions for this evaluation: TSMBC(dOTC) and TSMBC(RBC). For these two methods, we test in the first part the influence of the choice of the starting row, and in the second part the influence of the choice of the lag.
S1  of the Supplement. We can see for TSMBC(dOTC) that all corrections are highly correlated -with values close to 1 -whereas for TSMBC(RBC) no significant correlation appears. This indicates that, whatever the chosen starting row for TSMBC, the results are very close to each other. Note that for TSMBC(dOTC) the corrections stay highly correlated with X. This is an effect of the dOTC method, which tries to preserve as much as possible the temporal properties of the model simulations to be corrected  (Robin et al. , 2019; François et al. , 2020). On the other hand, no correlation appears with Y. This was expected. Indeed, there is no reason for the bias corrected data to be correlated to the reference. If the BC procedure is efficient, corrected and reference time series can be seen as generated based on the same statisti- cal distributions and/or properties but independently.
7c ) and with TSMBC-5 (S2V, Fig. 7d ). In each panel, the black line is the es-timated mean cross-auto-correlogram. It is clear that the cross-auto-correlograms -as mean conditional correlations given distances -are not quite representative of the DCP scatter-plot structures. Indeed, the DCP structures show large spreads that cannot be visible on simple lines. Moreover, those structures are different from one dataset to another, for example between the GCM data to be corrected and from the reference RCM data, while their associated cross-autocorrelograms (i. e. mean black lines) can appear relatively close to each other. However, the DCP scatter plots of the https://doi. org/10. 5194/esd-12-1253-2021 Earth Syst. Dynam. , 12, 1253-1273, 2021 column gives the maps of auto-correlations of the RCM to be corrected (the GCM, the correction dOTC, the correction TSMBC-5 and the correction TSMBC-10 with method L2V, respectively).
A value of BR W close to 1 indicates that the chosen dependencies (correlation, auto-correlation or cross-auto-correlation) of the correction is close to the dependence structure of the reference RCM. We call the set of this indicator a W-crossauto-correlogram. As the Wasserstein metric is sensitive to the scale of the multivariate data (here, the DCP sets) it is applied to, two normalizations of the DCP sets are proposed before the com-Earth Syst. Dynam. , 12, 1253 Dynam. , 12, -1273 Dynam. , 12, , 2021  https://doi. org/10. 5194/esd-12-1253-2021 putation of the BR W values. First, we normalize the correlation values of the DCP sets between -1 and 1, independently for each method and lag. This allows us to compare only the pattern of the DPC sets, by removing mean and scale biases. The results are shown in Fig.
e. Wasserstein distances) of the raw GCM data for pr/tas and pr/pr are larger than those from tas/tas and tas/pr. This is visible in Fig. S7 , showing the values of the Wasserstein distances (based on the second normalization of the DCP sets) used to compute BR W. In addition, some negative BR W values in Fig. 9  are related to very small differences between Wasserstein distances very close to zero. Indeed, if the model simulations to be corrected have a DCP set already close to the reference, its Wasserstein distance will be near zero. If a corrected dataset also has a distance to the reference close to zero, the resulting ratio can be quite different from 1 (and thus induce a strongly negative BR W value) while the two W values are only slightly different. For example this is the case for, among others, TSMBC-5 (S1V) at lag 1 for the tas/tas matrix under a projection context in Fig.
dOTC was then applied to correct the IPSL dataset where various lagged versions of those simulations have been added. This approach -performing an MBC on lagged time series in addition to the initial ones -has been called time-shifted multivariate bias correction (TSMBC). Furthermore, it has been tested based on four configurations: only one variable is corrected at a time, either for a given location (L1V) or for all locations at the same https://doi. org/10. 5194/esd-12-1253-2021 Earth Syst. Dynam. , 12, 1253-1273, 2021 time (S1V); the two variables are jointly corrected, for a single location (L2V) or for all locations (S2V). From the synthetic data experiment, a comparison with a "reasonably naive" multivariate bias correction method, RBC, based on random sampling, has been proposed. The results showed the following: -TSMBC(dOTC) provides a clear improvement compared to TSMBC(RBC).
Code and data availability. The CMIP5 and CORDEX databases are freely available. Source codes of TSMBC are freely available in the R/Python package SBCK under the GNU-GPL3 license (https://doi. org/10. 5281/zenodo. 5483134; Robin, 2021). https://doi. org/10. 5194/esd-12-1253-2021 Earth Syst. Dynam. , 12, 1253-1273, 2021.
DNA extraction and polymerase chain reaction (PCR) confirmation of the isolates was done according to previously described methods  [6]. PCR products of ompW (outer membrane protein) andctxA (cholera enterotoxin sub-unit A) were separated by electrophoresis through 1. 5% agarose gels, stained with ethidium bromide and visualized with UV light. Upon confirmation, DNA from V. cholerae O1 isolates was amplified by PCR at each of five previously identified variable number of tandem repeat loci (VC0147, VC0437, VC1650, VCA0171 and VCA0283) using fluorescently labeled primers  [7]. The labeled fragments were separated using a 3730xl ABI Automatic Sequencer and sized using internal lane standards (LIZ600; ABI, Foster City, CA) with the Gene Mapper v4. 0 program (ABI). Alleles were identified by the number of repeats at the locus.
EBURST (http://eburst. mlst. net) was used to define the genetic relatedness between genotypes. Genotypes within a clonal complex (CC) were related by a series of single or double locus variants  [4]. The MLVA datasets are available from the corresponding author on request. WGS was performed on representatives from each outbreak, year and MLVA CC. Libraries for Illumina sequencing were prepared, enriched, barcoded in ten cycles of PCR amplification with primers containing an index sequence and sequenced using 150 basepaired-end runs on either an Illumina HiSeq2500 or Novaseq6000 (Illumina, San Diego, CA). The quality of the 150 base paired-end reads was assessed with FastQC v0. 11. 7  [8]  and reads were quality trimmed using Fastp (v 0. 21. 0 with the following flags: --detect_adapter_for_pe --trim_ poly_g --thread 12 --average_qual 20 -f 15)  [9]  and remaining contaminant sequences coming from the phiX genome (NC_001422.
1) were filtered out using bowtie2 v2. 3. 0  [10] , samtools v1. 5  [11]  and bedtools v2. 26. 0  [12]. High quality reads were assembled with Spades software v. 3. 14. 0 with the --isolate option  [13]  and the assembled genomes were checked using QUAST v5. 0. 0. dev0, b385864  [14]. All variants detected within one kb of the end of the contigs were removed. Information about each genome is in Supplemental Table  1. The assembled sequences were submitted to Genbank (https://www. ncbi. nlm. nih. gov/) accession number PRJNA701309. Parsnp (v1. 2) was used to align the variable nucleotides to generate variant and alignment description files. Gingr (v1. 2) was used to visualize the alignment file and export a multiple alignment file  [15]. To understand the genetic relatedness of the Zambian strains, 99 African isolates with known WGS from transmission events T10, T11 and T13 were selected (Supplemental Table  1 ), analyzed in FastTree2 (v2.
1. 9) [16]  and the maximum likelihood tree was visualized using Interactive Tree of Life (iTOL)  [17]. Clustering of variant nucleotides was performed manually using the vcf file from Parsnp (v1. 2)  [15]. To determine whether the variant nucleotide was in a gene, a 20 nt region surrounding the SNV was submitted to blastn to identify the 59_Lua-saka_2010 associated protein and compared by BLAST  [18]  to the V. cholerae MS6 proteins (https://www. ncbi. nlm. nih. gov/genome/?term=cholerae+MS6), which were functionally annotated previously by NCBI. The scripts for the analyses are available from the corresponding author on reasonable request.
Acknowledgments We thank the staff at the  Ministry of Health and University Teaching Hospital Departments of Pathology and Microbiology  for their support. Authors' contributions J. M. , methodology, data collection, conceptualization, writing-original draft, analyzed data,writing-review and editing,  A. K. D. , project administration, methodology, data collection, writing -original draft, analyzed data, writing-review and editing, K. N. M. , data collection, analyzed data, writing-review and editing, P. S. , M. S. , data collection, writing-review and editing, N. L. , K. K. , C. C. , writingreview and editing, S. L. , data collection, writing and reviewing, M. A. , J. S. M. , analyzed data, writing-review and editing, A. S. , T. K. , V. M. , reviewing, G. K. , writing-review and editing,  D.
, 1996). In this work our goal was to assess the diversity of mtDNA genomes interspecies among different Lactuca species, and intraspecies among different accessions of the same species. The final objective is to better understand the contribution of the different wild-type Lactuca species to the domestication and diversification of L. sativa. Here we determined the complete mtDNA sequences of nine accessions from four related species in the genus Lactuca. The nuclear data confirmed previous reports showing that L. sativa and L. serriola are closely related species and distinct from L. saligna and L. virosa  (Koopman et al. , 1998; Wei et al. , 2017; Zhang et al. , 2017). The phylogeny of the complete chloroplastic genome sequences was in line with the nuclear one. However, we found that from the sequence and structural points of view the mtDNA of L.
Plant Material and Growth Conditions A collection of nine accessions (Supplementary Table  1 ) belonging to four different Lactuca species (L. sativa, L. saligna, L. serriola, and L. virosa) originating from diverse geographical origins were used in this study and seeds are available upon request. Seeds of all accessions were provided by the company Enza Zaden (Enkhuizen, Netherlands), and propagated to obtain sufficient seeds for mitochondria purification. All accessions are recorded in the company Genbank and were isolated either by Enza Zaden (L. sativa accessions LACWENDEL and LAC004500, L. saligna accession LAC008020, L. serriola accession LAC005780, L. virosa accession LAC006941) or originate from other collections (L. serriola accession CGN004799, L. virosa accessions CGN013357 and CGN019045 are from the Centre for Genetic Resources, Wageningen University and Research and L.
DNA Sequencing For Illumina sequencing, library preparation and sequencing reactions were performed by the IBMP "gene expression analysis" platform. Libraries were prepared with the Nextera XT DNA Library Prep kit (Illumina, San Diego, CA, United States) and their size checked on a 2100 Bioanalyzer (Agilent, Santa Clara, CA, United States). The libraries were then sequenced with paired-end 2 × 151 reads on a MiSeq System (Illumina). The raw Illumina reads were deposited in GenBank under accession numbers ERS5266978 and ERS5267070 to ERS5267078. 1  Pacbio sequencing of the mtDNA of L. sativa var. capitata L. nidus tenerrima (accession LACWendel) was performed by Keygene (Wageningen, Netherlands) on a Pacbio Sequel II instrument (Pacific Biosciences, Menlo Park, CA, United States). The sequencing generated 19.
Sequences Alignments and Phylogenetic Analysis From GenBank we obtained 88 Lactuca ITS1-5. 8S-ITS2 sequences from eight species: L. sativa, Lactuca dregeana, Lactuca indica, L. saligna, Lactuca scandens, L. serriola, Lactuca sibirica, and Lactuca virosa (Supplementary Table  2 ). For the ITS1-5. 8S-ITS2 sequences of accessions described in Supplementary Table  1  and from  Kozik et al. (2019)  the Illumina reads were mapped on L. sativa references (KT249801. 1) with the Burrows-Wheeler Aligner bwa-mem (v07. 7. 17-r1188)  (Li and Durbin, 2009)  and mapped reads were assembled with SPAdes (v3. 13. 1)  (Bankevich et al. , 2012). Finally, assembled sequences were manually trimmed to match starts and ends of reference sequences. Sequence alignments were performed on MEGA X (v10. 1. 7) (Kumar et al.
, 2018; Stecher et al. , 2020)  using MUSCLE  (Edgar, 2004)  with default options. Maximum likelihood trees were also built on MEGA X, using the bootstrap method (1,000 replications) with default options and final trees were computed on the Interactive Tree Of Life (v6)  (Letunic and Bork, 2019). The plastid genomes assembly and comparison was performed using MacVector (v18. 1. 5) (MacVector INC. , Apex, NC, United States) Assembler plugin's. Illumina reads were mapped on the L. sativa cultivar Salinas cpDNA reference (AP007232. 1) with the aligner Bowtie v1. 3. 0  (Langmead et al. , 2009). Sequences were aligned with ClustalW 1. 83 implemented in the MacVector package using default options. Regions of ambiguity that are duplicated in the mitochondrial genome were removed from the alignment.
The code used for the assembly is available at github. com/ARNTET/Pacbio_Illumina_mtDNA_assembly. The assembly of the L. saligna, L. sativa, L. serriola, and L. virosa mitochondrial genomes was from Illumina reads. We built our pipeline (Supplementary Figure  1B ) based on a recent work from  Garcia et al. (2019). Illumina reads from Lactuca species were trimmed with Trimmomatic (v0. 39)  (Bolger et al. , 2014)  to remove adapter sequences and low quality ends and joined with Fastq-join (v1. 3. 1) 2 when paired reads mapped to each other. Then those reads were assembled into contigs with a first assembler: Velvet (v1. 2. 10)  (Zerbino and Birney, 2008). Those contigs were mapped on the L. sativa var. capitata L. nidus tenerrima mtDNA sequence and mapped Velvet contigs were used as trusted contigs for a second round of assembly with SPAdes (v13.