dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 0 could not be run and we had to develop a custom python script ( junctionsCover2IRF. py) to do the quantification. For a given list of introns, a read length and BAM files, this script uses samtools view to quantify the number of aligned reads on the exon-exon junction and the two exon-intron junctions and creates a file formatted in the same way as a conventional IRFinder file, allowing us to merge them together (Supplemental Fig. S10 ). In the following analyses, the retention-supporting reads will either be reads aligned to the intron body, or reads aligned to the exon-intron junctions if the number of informative bases of the intron was smaller than the effective length of the exon-intron junction (66 nt). The list of introns we analyze corresponds to constitutively spliced introns, but also to alternatively spliced introns, some of which are spliced out only in specific tissues.
Vast-tools results concerning IR are not presented because >99% of the differential U12-type IR detected by vast-tools were also found by IRFinder, but only 35% or less of the differential U12-type IR detected by IRFinder were also found by vast-tools. All results are however available in the supporting shiny interface (http://lbbe-shiny. univ-lyon1. fr/TALS-RNAseq/). Briefly, vast-tools aligns the reads from each sample on different references (genome, exon-exon junction, …) using BOWTIE (v1. 1. 2. ), and then analyzes the alignment file to quantify the number of reads supporting the inclusion or exclusion of an exon for each of its 213,087 possible alternative splicing events annotated in its database. Some introns from the vast-tools' splicing event database were not annotated in Ensembl v75.
In order to determine their type, we ran T. Alioto's scripts on these introns if both of their splice sites were annotated in Ensembl v75. This resulted in 56 new U12type introns (of which 55 overlapped with a known U12-type intron but used a different acceptor site, Supplemental Table  S3 ). Because this method cannot detect alternative splicing events which are absent from its database, we also used an assembly-first and annotation-free method for alternative splicing events detection. Alternative splicing events detection without annotation (KisSplice v2. 4. 0-p1, assembly-first) Briefly, KisSplice  (Sacomoto et al. 2012)  assembles the reads in a de Bruijn graph and searches for so-called bubbles in this graph, which correspond to alternative splicing events. The two paths of the bubble are then mapped to a reference genome using STARlong v2.
Counts normalization, PSI/ΔPSI computation, and differential analysis (kissDE) IRFinder, vast-tools and KisSplice all output the number of reads supporting the inclusion (i. e. , a retained intron or exon) or exclusion (i. e. , a spliced intron or skipped exon) transcript in each sample and for each IR/alternative splicing event. The bioconductor R package kissDE (https://www. bioconductor. org/packages/devel/ bioc/html/kissDE. html) (Benoit-Pilven et al. 2018b) was then used for counts normalization, splicing event strength estimation (PSI and ΔPSI) and differential analysis between two conditions (FDR). Briefly, kissDE starts by normalizing the read counts for the library sizes using DESeq2, and by normalizing the inclusion-supporting reads by the length of the inclusion (that can be very large for IR events) compared to the exclusion.
Principal component analyses (PCA) We used the dudi. pca function from the R package ade4 v1. 7-11 (https://github. com/sdray/ade4) (Bougeard and Dray 2018) on either a table of TPM or PSI. For each PCA, the most variable values (up to 500) were used (as conventionally done in DESeq2) and the first (PC1) and second (PC2) most explanatory axes were plotted. We compared the percentage of the variance explained by each axis of these PCA (PCAvar) to the mean of the ones obtained after randomizing independently each row of the TPM or PSI table 100 times (randomVar). Axes with explained variance smaller or equal to our randomized data (PCAvar ≤ randomVar) are denoted with ns (not significant) and should not be interpreted.
GO terms enrichment analysis We searched for GO terms enriched in our set of genes with U12type differentially spliced introns to highlight potential biological processes specifically disrupted in patients and thus, possibly related to the phenotype. We used the topGO (Alexa and Rahnenfuhrer 2016) (v2. 30. 1) R package using the genes for which a U12-type intron alternative splicing event had been tested and a user provided quantitative score for each gene. We performed two different analyses using distinct scores. The first one defined the score based on the FDR of a gene's U12-type intron (minimum FDR in the special case where a gene harbors multiple U12-type introns or multiple splicing events for the same U12-type intron), which correspond to the classical use of TopGO. Genes gain more weight as their FDR value is close to 0.
The following describes the parameters used to create the topGOdata object in R: We searched for biological process (ontology = "BP") in the Gene Ontology DataBase version from October 2018 (mapping = "org. Hs. eg. db", annot = annFUN. org) using Ensembl ID (ID = "ensembl"). The enriched GO terms were mapped to a subset of more generic GO terms (GO slim) using the GSEABase R package v1. 44. 0 and the GO slim AGR subset (go_slim. agr) downloaded on the GeneOntology website (http://geneontology. org/docs/go-subset-guide/).
Features influencing U12-type IR In order to identify features that could have an impact on the level of U12-type IR, we used a linear model. We worked on all analyzed introns (using filters described in Materials and Methods) in the fibroblasts data set. We wanted to explain the U12-type introns' mean PSI (mPSI) of the patients with a set of 32 explicative variables (hereafter referred to as predictors), see Supplemental Table  S4. We used a log transformation of mPSI (Supplemental Fig. S11A ), since diagnostic plots (Supplemental Fig. S11B-E ) show that the assumptions of linear regression are much better satisfied with this transformation. Zero values were replaced by the minimum nonzero mPSI divided by two to guarantee that the transformed value for zero is still lower than all other values.
Motif sequences analysis In order to identify motifs enriched in differentially retained U12type introns compared to other analyzed U12-type introns, we used the MEME Suite 5. 0. 1. software  (Machanick and Bailey 2011; Bailey et al. 2015). Tested U12-type introns were separated into two groups: candidates (n = 49), for which a strong differential IR was detected in Patients (FDR ≤ 5% and ΔPSI ≥ 10%), and unchanged (n = 45), for which no differential IR was detected in Patients (FDR ≥ 20% and |ΔPSI| < 1%). In the case of overlapping U12-type introns, the largest one was conserved. Sequences of each intron, with 100 bp upstream and downstream the intron, were retrieved with bedtools' fastaFromBed (v2. 25. 0). Sequences of introns on the minus strand were reverse complemented. In order to have groups with comparable intron length, we calculated the minimum length ratio of each sequence from the candidates group with each sequence from the unchanged group (minRatioLength) and we selected the sequences with a minRatioLength ≥ 0.
ACKNOWLEDGMENTS This work was supported by  CNRS ,  Inserm ,  Université Paris  7 and Université Lyon 1 through recurrent funding, the  ANR Aster  (no. ANR-16-CE23-0001 ) and  U4ATAC-BRAIN  (no. ANR-18-CE12-0007-01 ) grants and an  Inserm/Hospices Civils de Lyon  grant to P. E. (Contrat d' Interface pour Hospitaliers). A. C. was supported by a grant from  Inria  ( Thése Inria-Inserm "Médecine Numérique" -2016 ) and C. B. P. by a grant from the  Fondation pour la Recherche Médicale  to P. E. ( Financement d'un ingénieur -ING20160435660 ). We thank the families for their participation in this study, the  CBC Biotec biobank  for sample management ( Emilie Chopin ,  Isabelle Rouvet ), the students for their contribution ( Clément Saccaro ,  Gabriel Sala ,  Nabil Sersoub ),  Integragen SA  for performing the RNA-seq experiments,  Daniele Merico  for critical reading of the manuscript and helpful suggestions,  Tyler Alioto  for providing us with the scripts for predicting U12 introns and  Cyril Bourgeois ,  Vincent Navratil  and  Marion Delous  for stimulating discussions.
SNP calling and filtering Raw sequencing data were demultiplexed with GBSX v1. 2  (Herten et al. 2015). Cutadapt v1. 9  (Martin 2011 ) was used to trim adapters (options: -a AGA TCG GAA GAG CG -O 10 -q 20,20 -m 30). SNP calling was done using the process_ reseq. 1. 0. py (python2) program followed by site pre-filtering using the VcfPreFilter. 1. 0. py (python2) program with the default parameters. Both programs are part of the Vcf-Hunter package  (Garsmeur et al. 2018 ; available at https :// githu b. com/South Green Platf orm/VcfHu nter/). As there was no complete D. alata reference genome, reads were aligned to the D. rotundata reference genome (pseudo-chromosomes BDMI0100001-21;  Tamiru et al. 2017). SNPs were thus named according to their position in this D. rotundata reference genome.
SNPs and progenies were filtered by population using the following filters: minimum depth 10, maximum depth 500, missing data per site < 20%, missing data per individual < 40%, minor allele frequencies per site > 10% and minimum count read for heterozygous genotype > 3. Sites with missing data on parents or segregation patterns in progenies that were not in agreement with the parental genotypes were also excluded. Dataset filtering and formatting were coded in R 3. 4. 4 (R core team 2017) and based on the vcfR 1. 5. 0 package  (Knaus and Grünwald 2017)  to import vcf files into the R environment.
Linkage analysis and parental map construction SNPs with 1:1 Mendelian segregation if segregating only in one parent and 1:2:1 if segregating in both parents were retained for linkage analysis. SNPs with a significant segregation deviation within families (χ 2 test; P < 0. 001) were eliminated. Finally, SNPs were thinned to maintain a minimum spacing of 100 bp between adjacent markers. Linkage analysis and map constructions were conducted separately for each parent by population according to the cross-pollinated (CP) model in JoinMap 4. 1 software (Van Ooijen 2012). In each family, the "hk × hk" segregation patterns were used for both parents, while "lm × ll" and "nn × np" segregation patterns were used for the female and male parental maps, respectively. Regarding the difference in dataset size, linkage groups were established using a grouping LOD threshold value of 7 for the parents of population A and 5 for those of population B.
Construction of the D. alata reference map and comparison with D. rotundata As the two mapping populations were derived from crosses involving the same female (74F), an integrated map of the female parent (74F) was computed using the JoinMap "combine groups for map integration" function. Three genetic maps of female 74F were thus generated: from the population A dataset (74F_A), the population B dataset (74F_B) and the integrated map (74F). The final D. alata consensus map was constructed using this same function and starting from the integrated female linkage groups (74F) and both male parent groups (Kabusa and 14 M). Homology between the four parental maps, the integrated female map and the final consensus map were visualized using the R package ggplot2 2. 1. 1 (Wickham 2016). The D. alata genome assembly v1 available as scaffolds accounting for roughly half of the genome and 80-90% of protein-coding loci (Water Yam Genome Project-ftp:// yamba se.
e. , depth and allele frequencies) resulted in the detection of 29,224 and 11,808 SNPs in populations A and B, respectively (Table  1 ). By excluding sites based on missing data or discrepancies between segregation patterns in progenies and parental genotypes, 17,446 and 5434 SNPs were conserved for populations A and B, respectively. The segregation distortion threshold discarded 33% of SNPs in population A and 59% in population B (Table  1 ). Keeping a maximum of one SNP every 100 bp reduced the SNP dataset by approximately half. Thus, 5373 SNPs and 1075 SNPs were used for linkage analysis in population A and population B, respectively. Because of the missing data threshold per progeny, the final dataset included 79 progenies for population A and 110 progenies for population B.
This was higher 1 3 than the D. alata base chromosome number (i. e. , 20) and may have been the result of the separation of linkage groups containing only a few markers. Nevertheless, the integrated female map (74F) contained 20 linkage groups built using information on both population datasets for each integrated linkage group. Maps of population B parents were smaller and less dense than those of population A parents. Parental map lengths ranged from 1227 cM for 14 M to 2348 cM for 74F_A, respectively. The map density ranged from one SNP every 2. 1 cM in the Kabusa map to one SNP every 3. 7 cM in the 74F_B map (Table  3 ).
Detection of sex-determining QTLs The phenotypic data analysis revealed no significant differences between the observed sex ratio and a 1:1 theoretical ratio within both populations (χ 2 test; population A, P value = 0. 55; population B, P value = 0. 13), indicating that sex determination may be controlled by one dominant allele. Combining genotypic and phenotypic datasets, QTL detection was performed on fewer progenies and conducted on parental maps with 42 (19 females + 23 males) and 60 (27 females and 33 males) progenies for populations A and B, respectively. Only one QTL per population was detected on  1 3 LG6 homologs and only in the male maps (Online Resource 6A). No QTLs were detected using the female maps. In agreement with the sex ratio observed within both populations, these findings suggest that only one locus may be involved in sex determination and may be inherited via a system of XX/XY sex chromosomes involving heterogametic males (XY).
A D. alata reference map The method used to build the reference genetic map was designed to be conservative. Indeed, integration of maps into the final consensus map was based on pairwise recombination frequencies in parental datasets and not on map projections. All SNPs mapped in parental maps were thus not necessarily included in the final reference map and vice versa. Although this procedure is more restrictive, it was applied to minimize the impact of errors due to the reversal of locations for short distance markers and/or of structural variations  (Mace et al. 2009; Khan et al. 2012 ). In the same conservative spirit, highly distorted markers were eliminated before linkage analysis  (Zhang et al. 2010). Our consensus map is the first SNP high-density genetic map for D. alata. It contains 20 linkage groups, as expected, and 1579 SNPs spread over 2613.
Segregation distortions have been widely reported in plant species including D. alata. In  Petro et al. (2011) , 19% of markers were tagged as distorted, while in  Bhattacharjee et al. (2018)  it was 39. 8%. However, comparisons are limited due to the unknown threshold used to test distortion. Regarding the decrease in the number of progenies from hybridization to mapping datasets, one major hypothesis could be proposed. Indeed, the main differences were the ratio of introduced embryos to the number of pollination between population A (140/250 = 56%) and population B (280/360 = 78%), which was related to gametophyte selection, and the proportion of rescued embryos successfully brought to the field (population A, 121/140 = 86%; population B, 193/280 = 69%), which was related to early-stage zygotic selection.
Acknowledgments This work was financially supported by the  Afri-caYam project  (Grant  OPP1052998-Bill  and  Melinda Gates Foundation ) and the  European Union and Guadeloupe Region (Programme Opérationnel FEDER-Guadeloupe-Conseil Régional   2014-2017 ). Floriane Lawac  received a grant from the  French Embassy in Vanuatu  and the  Vanuatu Agricultural Research and Technical Center (VARTC)  for her MSc research. The authors would like to thank  Ronan Rivallan ,  Louis Minfort  and  Guillaume Martin  for their help on genotyping, phenotyping and GBS analysis, respectively. A special thanks to  Brigitte Courtois  for her helpful discussions and manuscript improvement and to  David Manley  for English proofing.
However, for some models, especially the ones equipped with depth-wise/point-wise layers (e. g. , Mobilenet, Resnext, Xception) performing pruning and quantization operations on per-tensor level can lead to significant degradation of performance  [20]. Therefore, the higher granularity of the operations (channel or gate level) is essential for better convergence of pruning and quantization operations. As incurred latency is affected both by the number of required operations and their precision, the first stage of model mapping procedure should allow to quickly obtain a set of pruning and quantization parameters' combinations for further (and more time-consuming) tests. To this effect, we propose to apply a software pruning/quantization simulation and some form of multi-objective optimization.
For large datasets, the optimization can be performed using a small, but representative test dataset (e. g. , 5 % to 10 % of original), and final results should be validated on the full test dataset. The optimization objectives should include keeping the model quality as close to original as possible, ensuring the biggest overall model weights' sparsity, and bringing the precision down as much as possible. The analysis of the original model resources consumption can help to define the range of the optimization parameters, e. g. , it may turn out that to fit the model into the required hardware reaching a specific sparsity or quantization level is required. The generated parameters' combinations suitability for further tests may need to be further examined, e. g. , to remove all combinations that yield the model with quality drop above the acceptance threshold, usually ≈1 percentage point (p.
p. ). It may also be beneficial to run the inference with the full testing dataset and verify the model quality. Once the candidates were selected, the pruned and quantized models were once again mapped to FPGA for the examination of incurred latency. If the latency is satisfactory, the procedure is over; else flow B can be used. It may also turn out that the flow B better addresses the distribution of the compressed model across the IoT platforms comprised of multiple nodes with limited and different resources. In the flow A, the pruning and quantization parameters were chosen for the whole model (e. g. , the precision of N bits) and then applied per-tensor (e. g. , prune X% of each tensor, find the number of integral/fractional bits that will best represent this tensor). In scenario B, the pruning and quantization parameters are chosen and applied on the per-tensor basis, i.
For example, a two-layer LSTM network, with n =  [8, 4]  with k = 3 input features (data channels) and m = 2 output features (classes; e. g. , anomaly/non-anomaly) would yield following values: size(W LSTM 1 ) = 4 • k • n 1 = 96, size(U LSTM 1 ) = 4 • n 2 1 = 256, size(b LSTM 1 ) = 4 • n 1 = 32, size(W LSTM 2 ) = 4 • n 1 • n 2 = 128, size(U LSTM 2 ) = 4 • n 2 2 = 64, size(b LSTM 2 ) = 4 • n 2 = 16, size(W Dense ) = n 2 • m = 8, size(b Dense ) = m = 2, (9) summing up to 384 parameters in first LSTM layer, 208 in the second and 10 in the dense layer, with the total number of model parameters equal to 602. Applying the flow A of the presented methodology to the LSTM network means that the pruning and quantization parameters are selected globally for the model, and then applied for each of the W, U, and b tensors.
• MyHDL wrapper around the DL learning components was generated, • MyHDL test files were generated to be used for verification of the code this stage. The custom library of the DL components was created along with a tool which populates a DL model with the elements from the library. Currently, the library contains the following components: • a set of activation functions: relu, sigmoid, tanh and softmax • a set of layers: linear, conv1D, conv2D, • LSTM, • maxpool. All the layers were parameterizable, which enabled smooth integration to form a complete model. In addition to the layers, sample complete models based on recurrent neural network (RNN) and convolutional neural network (CNN) were available. Next, once the model was compiled HDL was generated using the to_HDL conversion tool based on MyHDL package  [33].
LSTM layers work on a series of data, but the linear layer needs a single sample, and for that purpose, the flow control block was introduced. It counted the sequence samples and passed only the last one to linear layer; other connection schemes can also be implemented. Inside the LSTM, apart from an LSTM cell, additional registers were placed to ensure synchronization of passing data in temporal dimension, additional sequence counter control clearing and setting these registers to zero input at the beginning of each new sequence. The LSTM cell was built according to the Equations (  2 )-  (6). First, the linear operations were performed, and gates were concatenated for input and hidden state so that only two linear modules were required. Then the data was split for separate activations, and all element-wise operations were performed.
For modern Xilinx devices, it is enough to insert registers every two to three levels to bring logic complexity close to the rest of the design; this parameter can be easily changed. Multiplication results were in fixed point format; summation result was also a fixed point, which introduces the risk of overflow for narrow representations. This effect was mitigated by the distribution of results concentrated on both the positive and negative side of zero; however, to ensure proper operation data width need to be verified during a simulation on training data set. Components inside the LSTM cell were also using AXI4-Stream interfaces to communicate. Summation in softmax function was implemented similarly as in the linear layer. Additionally, there is a choice between two exponent implementations: calculated approximation, and a look-up table.
Results To present and validate the methodology, we have examined three datasets using 'analysta', our time series analysis framework (see Supplementary Materials). The first one is the International Airline Passengers data from the Time Series Data Library (TSDL)  [35]. It was used in a toy example, depicting selected aspects of the methodology. The [IAP] tag was used in figures' and tables' captions for easier identification of the related results. The second dataset contains PM 2. 5 pollution data of the US Embassy in Beijing in conjunction with meteorological data from Beijing Capital International Airport  [36]. The model objective in this example was to predict the pollution value. The related results are marked with the [PM2. 5] tag. The third, most extensive use case was based on the experiments conducted in  [17] , which was focused on exploring the influence of history length and various data quantization schemes on the models' performance.
The data we used came from the post mortem (PM) database of European Organization for Nuclear Research (CERN) Large Hadron Collider (LHC), representing the state of the superconducting magnets. The figures and tables containing related results are marked with the [LHC] tag. It may be beneficial to estimate and compare the resulting model size with original, uncompressed one to interpret the compression results. For model with N weights w i ∈ W, its approximate size after pruning and linear quantization can be calculated as follows: compressed model size = N-1 ∑ i=0 bit-width (w i ) if w i <> 0. ( 14 ) This value can then be compared with original size, calculated as N • 32 bit: percentage of original model size = 100 % * compressed model size N • 32 bit. ( 15 ).