dataesr/scientific-paragraphs-categorization huggingface dataset:
The set of equations can be interpreted directly as a Constraint Logic Program in Prolog, with predicates cons(N,S). Prolog will compute the dependency tree for s ∈ Cons n , by backtracking over all choices for X. Along the way, all encountered LC ϕ predicates are asserted as constraints to the CLP solver. This has the advantage that locally inconsistent branches will be pruned directly, without ever generating their successors. By enumerating all feasible solutions, we obtain the complete parameter space as a disjunction of conjunctive constraints. However, the computation tree has a lot of duplicates, and the number of returned results is very high, since we start out with a deeply nested and-or-expression. We provide a more efficient version in Alg. 3. Here recomputations are avoided by caching all intermediate results in a Table (see l.
Long-read sequencing currently provides sequences of several thousand base pairs. It is therefore possible to obtain complete transcripts, offering an unprecedented vision of the cellular transcriptome. However the literature lacks tools for de novo clustering of such data, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads. Our goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution both proposes a new algorithm adapted to clustering of reads by gene and a practical and free access tool that allows to scale the complete processing of eukaryotic transcriptomes.
INTRODUCTION Massively parallel cDNA sequencing by Next Generation Sequencing (NGS) technologies (RNA-seq) has made it possible to take a big step forward in understanding the transcriptome of cells, by providing access to observations as diverse as the measurement of gene expression, the identification of alternative transcript isoforms, or the composition of different RNA populations  (1). The main drawback of RNA-seq is that the reads are usually shorter than a full-length RNA transcript. There has been a recent explosion in databases accession records for transcripts obtained with short reads (2) but a laborious curation is needed to filter out false positive reconstructed variants that do not have enough support. Long read sequencing technologies such as Pacific Biosciences  (3)  and Oxford Nanopore Technologies (4) are referred to as Third Generation Sequencing and make it possible to sequence full-length RNA molecules.
In doing so, they remove the need for transcript reconstruction before studying complete RNA transcripts  (5). The size of short reads is certainly a major limitation in the process of whole transcript reconstitution, because they may not carry enough information to enable the recovery of the full sequence. In addition, tools for de novo assembly of transcripts from short reads  (5, 6)  use heuristic approaches that cannot guarantee the retrieval of exact original transcripts. On the contrary long reads tend to cover full-length cDNA or RNA molecules, and can therefore provide information about the comprehensive exon combinations present in a dataset. This gain in length comes at the cost of a computationally challenging error rate (which varies significantly between protocols, up to over 15%, although RNA reads generally show lower rates, at ∼9% or less  (7, 8) ).
Over the last few years, increasing number of studies have been focusing on the treatment of long read data generated via the Oxford Nanopore MinION, GridION or PromethION platforms, for transcriptome and fulllength cDNA analysis  (4, (9) (10) (11). International projects have been launched and the WGS nanopore consortium (https://github. com/nanopore-wgs-consortium/NA12878/ blob/master/RNA. md) has for example sequenced the complete human transcriptome using the MinION and GridION nanopores. Besides Human and microbial sequencing, this technology has also proved useful for the de novo assembly of a wide variety of species including nematodes  (12)  and plants  (13)  or fishes  (14). It seems clear that the reduced cost of sequencing and the portable and real-time nature of the equipment compared to the PacBio technology will encourage a wide dissemination of this technology the laboratories (see Schalamun et al.
Problem statement Within a long-read dataset, our goal is to identify the associated subset of Third Generation Sequencing reads for each expressed gene without mapping them onto a reference. In order to group RNA transcripts from a given gene using these long and spurious reads, we propose a novel clustering approach. The application context of this paper is non-trivial and specific for at least three reasons: (i) in eukaryotes, it is common that alternative spliced and transcriptional variants with varying exon content (isoforms) occur for a given gene  (22). The challenge is to automatically group alternative transcripts in the same cluster (Figure  1 ); (ii) long reads currently suffer from a high rate of difficult indel errors  (7, 8) ; (iii) all genes are not expressed at the same level in the cell  (23) (24) (25).
This leads to a heterogeneous abundance of reads for the different transcripts present. Clusters of different sizes including small ones are expected, which is a hurdle for most algorithms, including the prevalent methods based on community detection  (26). Our method starts from a set of long reads and a graph of similarities between them. It performs an efficient and accurate clustering of the graph nodes to retrieve each group of a gene's expressed transcripts (detailed in Materials and Methods). A second contribution of our work is an implementation of the clustering algorithm via a tool dubbed CARNAC-LR (Clustering coefficient-based Acquisition of RNA Communities in Long Reads) inserted into a pipeline (see Results section). The input of this pipeline is a whole dataset of raw reads, with no prior filter or correction needed.
Background Early attempts to solve this problem can be traced back to before the age of NGS: in the NCBI UniGene database  (27)  Expressed Sequence Tags (ESTs) are partitioned into clusters that are very likely to represent distinct genes. In fact, clustering has been the basis for gene indexing in major gene catalogues like UniGene, HGI, STACK or the TIGR Gene Indices  (28, 29). Moreover this problem has come up in many disciplines, taking different forms according to the application domain. Many studies on sequence clustering worked to find the most efficient way to compute similarity but remained quite basic in their clustering scheme (e. g. CD-HIT  (30) , SEED  (31) , Uclust  (32) , DNACLUST  (33) ). They essentially used simple schemes to try to avoid all-versus-all pairwise comparison of sequences, which became a major issue with the advent of NGS and meta-transcriptomics.
Then, it is compared to the third best community that intersected if appropriate, and so on until all intersections are solved. This way, we start the comparison with the most promising communities that combine reliability (well-connected subgraphs) with a high potential for resolution (they likely to be the biggest communities, thereby solving intersections for many nodes). On the contrary, communities associated with small subgraphs and relatively low ClCo are only resolved afterwards. Complexity and Implementation choices. Our algorithm has a quadratic component in order to compare sets to generate clusters. In addition, it explores the whole space of clustering coefficients with fixed cutoffs. This results in a time complexity that could theoretically be cubic in the number of reads at worst, which is incompatible with processing large datasets.
RESULTS All experiments were run on Linux distribution with 24 Intel Xeon 2. 5 GHz processors, 40 threads and 200GB RAM available. First we present the tool we have developed and made available for large scale long-reads clustering. We demonstrate it performs well on a canonical example on which other clustering approaches were assessed. We compare our approach to well established community detection methods and demonstrate its relevance to long read application. Then we validate our method's results by comparing them with independent clusters obtained by mapping a real size dataset. In these two parts (sections 'Comparison to state of the art clustering algorithms' and 'Biological relevance'), reads from the mouse brain transcriptome were used in order to access a 'ground truth' via a reference.
CARNAC-LR, a software for long read clustering Input/Output. We implemented our novel algorithm presented in section 'Materials and Methods', integrated into   57 ) and then produces the clusters. The pipeline's input is a FASTA file of reads. The output is a text file with one line per cluster, each cluster containing the read indexes. Each read is represented by its index in the original FASTA file during CARNAC-LR computation. Then using indexes, each cluster can easily be converted to a FASTA file where each read's sequence is retrieved from the original file (a script is provided for this task). First step: computing similarity between long reads. We chose the Minimap tool for its efficiency and its very high level of precision on ONT and PB  (58) , with regard to other recent methods that can compute similarity or overlaps between long reads despite their error rate (59-62).
Method validation Input graph. In order to compare different clustering methods, we generated an input graph from the mouse dataset. We ran all methods on the same input graph, preprocessed using the procedure described in the 'Complexity and Implementation choices' section. For scaling purpose, we chose to perform the benchmark on a subset of 10K reads (9609 mouse reads within 527 reference clusters determined by mapping, section 'Production of validation material'). This sampling shows the effect of high gene expression on clustering. We also checked on a second 10K sample from the whole dataset that further accentuates the low expression effect. Directly after Minimap, the graph has 701 connected components, then pre-processing is applied to obtain an input graph that has 1109 connected components.
Comparison to other nucleic acid sequence clustering tools. We have simply situated the CARNAC-LR algorithm in relation to existing general cluster detection methods, but we still have to compare our pipeline to other tools dedicated to the comparison of nucleotide sequences that have been de-   7 ), (  5 ) and (  6 )) to assess for the goodness of communities detection. CPM3 denotes the CPM algorithm using k = 3. 'Clusters' column shows the number of output clusters of size >1 veloped for the same clustering task. We started with one of the most powerful tools currently available, Starcode  (66) , which was designed for reads correction and offers a benchmark for the most widely used clustering tools, which we have adopted. This includes CD-HIT  (30) , SEED  (31)  and Rainbow  (67). It should be noted that none of these tools have been designed to work with ONT reads.
They were created before the full development of long reads technology, they have proven not surprisingly completely ill-suited to clustering these long reads. For this test, we used the same mouse dataset as in the previous section. The methods stumble over two features of the data: the error rate and the length of the sequences. SEED for instance is designed to create clusters with sequences that show a maximum of three mismatches, and so finds no clusters. Starcode is not adapted to the size of ONT sequences and terminates with an error message. We tried to increase the maximum size allowed for sequences (initially set at 1024) but the memory consumed continued to grow rapidly and reasonable capacities (200GB) were quickly exceeded. We then tried to perform the calculation by rejecting the longest reads but like SEEDS, Starcode authorizes a limited distance between pairs of sequences (a maximum Levenshtein distance of 8) which is far too small for ONT reads, resulting in singleton clusters.
Complementarity of de novo and reference-based approaches. Intersection and difference with the set of mapping clusters. Since it does not rely on any reference information, our approach putatively yields different results than classical mapping approaches. In this section, we investigate the differences between the two approaches and demonstrate the usefulness of CARNAC-LR even if a reference is available. We ran it on the full mouse brain transcriptome dataset (1 256 967 reads). We compared the intersections and differences of the results of our approach and mapping. The CARNAC-LR+Minimap pipeline took less than three hours (using 40 threads). In comparison, the 'ground truth' clusters took 15 days to be computed (using up to 40 threads). Our approach was able to place 67 422 additional reads that were absent in the mapping procedure, resulting in 39 662 clusters.
We have demonstrated a straightforward use case for our pipeline as a good proxy for accessing expression levels by gene. ONT sequences have been shown to qualify for transcript quantification in Oikonomopoulos et al. (9). In a long read sequencing experiment, it is likely that some reads contain too many errors to be mapped onto a genome. CARNAC-LR can help identifying the origin gene of such reads, if they are clustered with other mapped reads. Moreover CARNAC-LR provides structured information that can be a sound input for other applications. For instance, a read correction step can be performed on each cluster instead of processing all the data, in order to obtain high quality reference transcripts.
CONCLUSION We propose a method for clustering long reads obtained from transcriptome sequencing in groups of expressed genes. New algorithmic challenge arises with the combination of a high error rate in the data  (7, 8) , a high heterogeneity of coverage typical of expression data and a large volume of data. In this, our issue differs from EST clustering problems for instance. We demonstrated our method's relevance for this application, in comparison to literature approaches. It takes reads early after their generation, without correction or filtering. The expressed variants of each gene are obtained from the clusters, and related transcripts are identified, even when no reference is available. To make our solution practical for users, we provide an implementation called CARNAC-LR that, combined with Minimap, scales and is able to quickly process real data instances, as demonstrated by the processing of the whole mouse brain transcriptome.
For a given fissile isotope and by identification of equation (1) and equation (  2 ), the effective yield    is defined as:    =       (,)             (,)           (3) Where: -The incident γ-spectrum    is calculated with MCNPX  [3]  and depends on the electron/photon conversion target and on the maximum energy of the LINAC (see figure  1 ). The maximum spectrum energy    will be fixed to 15 MeV for the whole paper but can be modified by the user as an input data of the developed tool DEGAS. -The photofission cross sections  (,)    are taken from the ENDF/B-VII. 1 library  [4]  for major actinides ( 235,238 U, 238,239,240,241 Pu, 241 Am) and from the TENDL-2015 library [5] for 242 Pu and 242m Am: -The independent fission product yield for a given fissioning nucleus at a given   incident energy,     , is determined from a Monte Carlo code called GEF (acronym for GEneral description of Fission observables)  [6].
1 for all fissioning nuclei investigated in the present work. The second factor   () of equation (  2 ) is a time averaged fission rate while the waste package is rotating in front of the electron/photon beam. With a simple hypothesis of the flux attenuation as a function of the square of the distance, one can write:   () =   ()   -         (5) Where the user's input data ,   ,   stand for the distance of the fissile material from the center of the waste package, the distance between the conversion target and the package surface and finally, the package radius, respectively.
2) Solving the Bateman equations For a given fissile nucleus amount, we will solve the previously described equation 2. The user has to fix a given mass   and the location  in the waste package for this particular isotope. The boundary conditions impose that no fission product are present before the irradiation in the waste package,   ( = ) = . We decide to use the DARWIN code package  [8]  to solve such equations, because of its robustness but also because it contains already preprocessed nuclear data libraries. This code package is able to solve non homogeneous differential equation but the source term has to be associated to neutron induced production and not to photon induced reactions. One can analytically demonstrate that solving the following homogeneous and time-iterative system (see lower case for "homogeneous" number densities   ):     = -    + ∑          (.
) =   .   ().  .  (6) leads to the following actual number densities which solve equation (  2 ):   (  ) = ∑   (  -. )  (7) Our developed DEGAS tool is using a MATLAB®  [9]  script which reads first the output file from the GEF simulation (   ), produces and submits a DARWIN input file (owing to the user specifications -  ,   -) in a second step, and then calculates the atomic number densities at the end of the irradiation phase:   (  ) from equation  (6).
B. Acquisition Phase Once the irradiation beam is stopped, one has to calculate the integrated activity for each fission product during the cooling/acquisition phase. To do so, a similar equation as equation  (6)  has to be solved, where the number densities obtained at the end of the previous phase are taken as initial conditions:     = -    + ∑          ( = ) =   (  ) (8) The DEGAS script generates a second input file for the DARWIN code package and calculates the delayed photon integrated source, starting after the HPGe deployment (  EPJ Web of Conferences 170, 06001 (2018) https://doi. org/10. 1051/epjconf/201817006001 ANIMMA 2017 around 1 to 5 minutes) up to the acquisition time (  ) as follows:      = ∑         ()        (9) where the continuous sum is approximated by a discrete sum with the same number of time steps as the previous irradiation phase.
IV. VERIFICATION OF THE WHOLE CALCULATION PROCEDURE A first simple verification case consists in producing one atom of   per second during   =1h and calculating the emitted γ-spectrum during   =1h after the HPGe deployment   =1s. The analytical solution of equation (  2 ) gives 3466. 88 nuclei at the end of irradiation phase. The DEGAS script gives the following results according to numerous times steps number (Tab. II). We estimate that 100 time steps for solving equations (  6 ) and (  7 ) are a good compromise versus the calculation time consuming. The analytical value by solving equations (  8 ) and (  9 ) leads to 227. 87 emitted γ for the 250 keV emission. This exact value is obtained with the iterative calculation scheme. See figure  4  for the full γ-spectrum : This validation is also done for two coupled differential equations (  2 ) and (  8 ) and does not show any discrepancies by using 100 time steps.
More of that we compared the results obtained for 10 g of uranium (235 and 238 with various isotopic vectors) to the results of MCNPX/CINDER  [10]    The goal of the automated script is to distinguish the amount of each isotope in the fissile material. Taking profit that independent fission yields vary from a fissile system to another one, we developed a fully automated procedure to help the user to select specific γ emissions. In order to avoid efficiency corrections in the final HPGe spectrum analyses, we decided to look for γ-line couples labeled 1 and 2 in a rather small energy window. The user has to specify two energy bands limits, for example 5 keV<      <60 keV. The script will then look for all the γ line couples that can distinguish uranium from plutonium sliding this energy window on the whole spectra.
We studied the particular case involving a fissile material made of 1g of depleted uranium mixed with 10 g of plutonium (isotopic vector calculated at 40 GWd-PWR/t HM ). The automated script DEGAS found more than 100 γ-lines couples allowing distinguishing uranium from plutonium in the waste package. The figure  6  shows a zoom in the 1590-1670 keV region. The bar histogram represents the plutonium and uranium emissions and the dashed lines symbolize the calculated and selected ratios of intensities for uranium and plutonium: Fig. 6. Superimposed uranium (1g) and plutonium (10g) delayed γ-spectra after 2h for irradiation (15MeV-W conversion target) and 3h for the acquisition time. In figure  6 , the first γ lines allowing to distinguish the uranium to the plutonium materials in this configuration are the following: -  =1596.
VI. CONCLUSION We proposed a simplified but robust and verified tool DEGAS to help the fine conception and the development of a nuclear material characterization facility by using photon interrogation techniques. It is based on the delayed γ-spectra analysis following fission events. More of that, we proposed an automated procedure to estimate the amount and to give insights on the isotopic vector of fissile materials in a waste package. Results showed more than 100 discriminant γ-ray couples within few minutes of computer calculation time. This script can also be applied to delayed neutron detection in coincidence with the γ lines. All those calculation results suffer from nuclear data uncertainties (photofission cross sections, independent fission yields, decay data) that can be propagated in a further work, if needed.
The evaluation of text simplification (TS)   systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics require high quality reference data, which is rarely available for TS. TS has the advantage over MT of being a monolingual task, which allows for direct comparisons to be made between the simplified text and its original version. In this paper, we compare multiple approaches to reference-less quality estimation of sentence-level text simplification systems, based on the dataset used for the QATS 2016 shared task. We distinguish three different dimensions: grammaticality, meaning preservation and simplicity. We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.
TS, seen as a sentence-level problem, is often viewed as a monolingual variant of (sentencelevel) MT. The standard approach to automatic TS evaluation is therefore to view the task as a translation problem and to use machine translation  (MT)  evaluation metrics such as BLEU  (Papineni et al. , 2002). However, MT evaluation metrics rely on the existence of parallel corpora of source sentences and manually produced reference translations, which are available on a large scale for many language pairs  (Tiedemann, 2012). TS datasets are less numerous and smaller. Moreover, they are often automatically extracted from comparable corpora rather than strictly parallel corpora, which results in noisier reference data. For example, the PWKP dataset  (Zhu et al. , 2010)  consists of 100,000 sentences from the English Wikipedia automatically aligned with sentences from the Simple English Wikipedia based on term-based similarity metrics.