dataesr/scientific-paragraphs-categorization huggingface dataset:
2019 , Ruppert et al. 2019). The training of CNN allows an efficient adjustment to the reference database, avoiding the need to explore a large number of parameters and arbitrary thresholds as in classical bioinformatic pipelines. Existing bioinformatic pipelines contain a variety of modules (i. e. QIIME2, DADA2, Vsearch), each with its own set of parameters  (Bolyen et al. 2019 , Callahan et al. 2016 , Rognes et al. 2016). Selecting the appropriate modules and parameters requires advanced knowledge of the functioning of the program since changes in those parameters can considerably modify the outputs  (Flynn et al. 2015 , Bonder et al. 2012 , Brown et al. 2015). The absence of an appropriate and automated method for parameter optimization  (Alberdi et al. 2018)  often challenges the use of those pipelines by non-specialists.
C, overlap of species detections between the CNN applied on clean reads (red), OBITools (yellow) and historical records (grey). D, number of species per family, detected with only one method (CNN on clean reads, OBITools or historical records) by the user, the binarization threshold and the minimum number of reads per sample to be considered. We expect that those steps can be near automated within a user-friendly software as developed in other machine learning application  (Thuiller et al. 2009). Given the relative ease in the training and application of CNN, it could be transformed into an application with a user-friendly interface and only a minimum of actions required from the user. Hence, CNNs could make eDNA metabarcoding data processing accessible even to less trained users. And they could, for example, be combined with the minion sequencer to provide an instantaneous view of the biodiversity.
A CNN trained on a complete reference database produced species composition congruent with the outputs of a popular bioinformatic pipeline, but showed a tendency to predict more species than those of OBITools and historical records. Compositional differences in the outputs of pipelines have already been highlighted (e. g. Brandt et al. 2021 ) and mainly resulted from the detection of several false positives and false negatives  (Mathon et al. 2021). With a binarization threshold of 0. 9 optimized during the training, we found congruent but slightly diverging results between OBITools and the CNN when applied to both the raw and clean reads. While the CNN and OBITools shared most of their recovered species, each method detected a few species not detected by the other (Fig. 5 ). However, the CNN showed a general tendency for overprediction compared to OBITools and historical records, especially when applying it directly on the raw sequencing data.
Using the historical records as a baseline, the CNN on clean reads reduces the detection of species only found with the CNN, without decreasing the number of species shared with OBITools or historical records, suggesting false positives result from noisy inputs. Specifically, the CNN on raw reads detected a higher number of species from Loricariidae, Cichlidae and Characidae families that are not found with OBITools, which can come from sequencing errors that are not denoised by the CNN. In the case of the Cichlidae family, the short barcode we use is known to be poorly resolutive  (Taberlet et al. 2018) , with a lot of species sharing the same sequence (Polanco et al. submitted), and our CNN cannot perform well in this situation like all other pipelines. Moreover, Loricaridae and Characidae are the two most speciose families from the Guianese fish fauna, with more than 50 species per family  (Le Bail et al.
Our study proposes a first application of CNN to eDNA metabarcoding data, but several improvements are required before broad scale future applications to eDNA big data. The current CNN is learning from species class and is forced to assign the sequences to that taxonomic level. Thus, when presented with conflicting sequences, the network might assign all of them to a single species, or may split the probabilities across several species, which could be then discarded given the use of the 0. 9 binarisation threshold. In contrast, in case of conflict, OBITools can assign sequences to higher taxonomic levels, allowing to keep information related to these species with identical sequences. In this case study, we were in the ideal situation where the reference database is almost complete for the territory and the CNN can be improved to be able to handle incomplete reference database and be able to assign the read to other taxonomic levels or to an unknown class, rather that forcing a species-level identification and relying on the binarisation threshold to reject unknown sequences.
We could also improve the CNN by implementing more stringent filters that would reduce the number of false detections and prediction errors. For instance, some filters for tag jump handling included in previous pipelines for eDNA matabracoding for fish (e. g. Cilleros et al. 2019)  are not included here. The CNN we present in this study is a first proof of concept for the application of machine learning on eDNA metabarcoding data, but further improvements are possible, especially in regards to dealing with inconsistent reference databases, and implementation as a user-friendly interface.
Conclusion and perspectives We demonstrate that we can use machine learning to increase the speed and decrease the energy consumption for processing eDNA metabarcoding data with good accuracy on clean reads and slightly lower accuracy on raw reads. The highest computation time for the CNN is on the training phase, but once trained, the CNN can be used as a computationally efficient tool for the application in the cloud facilitating the analyses of the mass of eDNA data collected in future biodiversity surveys. eDNA data are now produced at an exponentially increasing rate. By its easy application due to the reduced number of processing steps and the automated learning of best suited parameters, a CNN approach contrasts with other widely-used bioinpipelines. Our work paves the way towards computationally efficient and user-friendly online processing pipelines that will allow the democratisation of bioinformatic analyses of eDNA samples.
Our work is a major complement to the recent development and standardisation of eDNA in the laboratory, which together allow for extending the use of eDNA in community ecology and biogeography even for poorly known ecosystems or lineages  (Juhel et al. 2020) , and install eDNA as a standard monitoring tool  (Jarman et al. 2018). It also reinforces its initial goal of quick and efficient application. We expect that the results from this study will be scaled up to become a major toolkit for ecological analyses of eDNA data possibly associated with a cloud infrastructure and parallel computation on GPU. Figure  7 : Species detections with the CNN, OBITools and in historical records, in the Maroni river. A, overlap of species detections between the CNN applied on raw reads (blue), OBITools (yellow) and historical records (grey).
All the source codes for verification are publicly available at https://github. com/raghavrohit/ascon_cube_ distinguishers. Outline. The rest of the paper is organized as follows. Section 2 provides an overview of useful techniques in the theory of Boolean functions, cube attacks, and division properties. The specification of Ascon and our attack models are described in Section 3. We introduce the notion of partial polynomial multiplication in Section 4 and give our key recovery attacks in Sections 5 and 6. Our MILP modeling for the division properties and the obtained distinguishers are discussed in Section 7. Finally, we conclude in Section 8 with some open problems.
Verification. For verifying the correctness of our model, we computed the ANF of each state bit and matched with the output of Sage. The source codes are available at https://github. com/raghavrohit/ascon_cube_distinguishers in case reader wants to verify the models. degree in cube variables may be less than 4, 8, 16, 32 and 64 after 3, 4, 5, 6 and 7 rounds, respectively. Upper Bounds of Degree. To compute the upper bounds of algebraic degree in cube variables, we set N 0 = N 1 and model the 3SBDP of Ascon following Algorithm 1. We then compute the degree upper bound of each state bit using Algorithm 2. In Table  3 , we list the obtained upper bounds for each state words till 7 rounds. For 7-round, the upper bound is 59 which means the superpoly of any 60 dimensional cube (with N 0 = N 1 ) is constant zero.
There exists 64  60 ≈ 2 19. 27 such cubes. Further, we can distinguish 4, 5 and 6-round with data 2 8 , 2 16 and 2 31 , respectively, which improves the existing results. Distinguisher for 4-round with complexity 2 5. Our previous distinguisher for 4-round requires 2 8 data. However, we find that there exists a set of six cube variables {v i , v i+8 , v i+16 , v i+17 , v i+34 , v i+63 } 5 which do not multiply with each other after round 2. Choosing any 5 out of 6 gives a cube distinguisher with 32 nonces. Experimental Verification. We have experimentally verified all our distinguishers for 4, 5 and 6 rounds using the Ascon reference C code. The codes are also publicly available at https://github. com/raghavrohit/ascon_cube_distinguishers.
Acknowledgments The authors would like to thank the reviewers of  ToSC 2021  for providing us with insightful comments to improve the quality of the paper. Raghvendra Rohit  was partially supported by the  French Agence Nationale de la Recherche  through the  CryptAudit  project under Contract  ANR-17-CE39-0003. Siwei Sun  and  Kai Hu  are supported by the  National Key Research and Development Program of China  (No. 2018YFA0704702 ,  2018YFA0704704 ), the  Major Scientific and Technological Innovation Project of Shandong Province, China  (No. 2019JZZY010133 ), the  Chinese Major Program of National Cryptography Development Foundation  ( MMJJ20180102 ), and the  National Natural Science Foundation of China  ( 62032014 ,  61772519 ).
Chemical analysis of AMBRE: HARPS, UVES, FEROS spectra Our data consists of a collection of about 100,000 ESO archived spectra from the FEROS, HARPS, and UVES spectrographs, already parametrised within the AMBRE Project (see  Worley et al. (2012 ), De Pascale et al. (2014)  and  Worley et al. (2016) , respectively). The number of selected spectra having a S/N higher than 20 and a quality flag for their AMBRE parametrisation equal to 0 or 1 (i. e. good or very good parametrisation; see the above AM-BRE papers), together with their atmospheric parameter ranges and their mean signal-to-noise ratio is summarised in Table  2. We note that two UVES setups cover the selected Si multiplet 8 lines, and are thus identified as two separate spectrographs. In order to be analysed with our GAUGUIN pipeline in a homogeneous way, the observed spectra were first corrected by their radial velocity.
Then, the spectral resolutions of the HARPS and FEROS spectra were degraded to the UVES value (R ∼ 40, 000). We adopted a sampling wavelength step of 0. 005 nm for the whole dataset in order to fulfil the Nyquist-Shannon criteria. The reference grid spectra was convolved and re-sampled accordingly. Then, the prepared 99,271 spectra were ingested into our chemical analysis pipeline. The spectra are first automatically normalised by comparing a synthetic and an observed spectrum over a ∼7 nm domain, centred on each sulfur line. Then, the normalisation is refined over a ∼0. 4 nm interval around each line (for more details on the normalisation procedure, see  Santos-Peral et al. 2020). For each of these spectra, the three components of the Si multiplet 8 in Table  1  are then analysed independently by comparing the observed and reference grid line profiles over domains covering 0.
Construction of the AMBRE-sulfur catalogue The AMBRE-sulfur catalogue that is presented and discussed in the next sections was built as follows. We first note that our dataset contains, for some stars, a large number of repeated spectra (hereafter called 'repeats'). We thus describe below how we derived sulfur abundances from the analysis of several repeats of the same star from which up to three distinct lines can be measured. 2. 3. 1. Cross-match with the Gaia DR2 and adopted ID The AMBRE spectra were collected with quite different instruments. The available spectra may therefore contain heterogeneous names of targets and accuracy of coordinates. The first step was thus to identify the corresponding observed stars, and in particular the identification of the spectra belonging to the same star.
For this purpose we made use of the Gaia DR2 catalogue  (Gaia Collaboration et al. 2018 ) and adopted, when found, the Gaia DR2 ID. We refer to a forthcoming article for the detailed presentation of this cross-match between the Gaia/DR2 and the AMBRE catalogues. Briefly, this cross-match was performed using the stellar spectra coordinates and different checks between the derived AMBRE atmospheric parameters, T eff estimated from ground based photometry (2MASS and APASS,  Skrutskie et al. 2006; Henden et al. 2018) , and Gaia data (e. g. Gmagnitude, B P -R P colours, T eff , radial velocities, but for more details, see also  Santos-Peral, 2021, submitted). We were able to identify 5,076 distinct stars from the 99,271 AMBRE spectra. There is also a significant fraction (∼ 20%) of our spectra for which no Gaia DR2 ID were found.
Acknowledgements. We are grateful to  Robin Bonannini  who starts a preliminary study of sulfur abundances with AMBRE data several years ago. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the  Gaia Multilateral Agreement. This research has also made use of the SIMBAD database, operated at  CDS, Strasbourg, France. The authors acknowledge financial support from the  ANR   14-CE33-014-01. This work was also supported by the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  co-funded by  CEA  and  CNES.
During the last few years, a considerable effort has been made to provide robust and efficient data structures and algorithms for tda that are now implemented and available and easy to use through standard libraries such as the Gudhi library 1 (C++ and Python)  Maria et al. (2014)  and its R software interface  Fasy et al. (2014a) , Dionysus 2 , PHAT 3 , DIPHA 4 , or Giotto 5. Although it is still rapidly evolving, tda now provides a set of mature and efficient tools that can be used in combination or complementary to other data sciences tools. The tdapipeline. tda has recently known developments in various directions and application fields. There now exist a large variety of methods inspired by topological and geometric approaches. Providing a complete overview of all these existing approaches is beyond the scope of this introductory survey.
Simplicial complexes can be seen as higher dimensional generalizations of neighboring graphs that are classically built on top of data in many standard data analysis or learning algorithms. The challenge here is to define such structures that are proven to reflect relevant information about the structure of data and that can be effectively constructed and manipulated in practice. 3. Topological or geometric information is extracted from the structures built on top of the data. This may either results in a full reconstruction, typically a triangulation, of the shape underlying the data from which topological/geometric features can be easily extracted or, in crude summaries or approximations from which the extraction of relevant information requires specific methods, such as e. g. persistent homology.
The overall objective of this survey paper is two-fold. First, it intends to provide data scientists with a brief and comprehensive introduction to the mathematical and statistical foundations of tda. For that purpose, the focus is put on a few selected, but fundamental, tools and topics: simplicial complexes (Section 2) and their use for exploratory topological data analysis (Section 3), geometric inference (Section 4) and persistent homology theory (Section 5) that play a central role in tda. Second, this paper also aims at demonstrating how, thanks to the recent progress of software, tda tools can be easily applied in data science. In particular, we show how the Python version of the Gudhi library allows to easily implement and use the tda tools presented in this paper (Section 7). Our goal is to quickly provide the data scientist with a few basic keys -and relevant references -to get a clear understanding of the basics of tda to be able to start to use tda methods and software for his own problems and data.
The Nerve Theorem plays a fundamental role in tda: it provide a way to encode the topology of continuous spaces into abstract combinatorial structures that are well-suited for the design of effective data structures and algorithms. 3 Using covers and nerves for exploratory data analysis and visualization: the Mapper algorithm Using the nerve of covers as a way to summarize, visualize and explore data is a natural idea that was first proposed for tda in  Singh et al. (2007) , giving rise to the so-called Mapper algorithm. Definition 2. Let f : X → R d , d ≥ 1, be a continuous real valued function and let U = (U i ) i∈I be a cover of R d. The pull back cover of X induced by (f, U) is the collection of open sets (f -1 (U i )) i∈I. The refined pull back is the collection of connected components of the open sets f -1 (U i ), i ∈ I.
There are two strategies to compute the clusters. A first strategy consists in applying, for each U ∈ U, a cluster algorithm, chosen by the user, to the premimage f -1 (U ). A second, more global, strategy consists in building a neighboring graph on top of the data set X, e. g. k-NN graph or ε-graph, and, for each U ∈ U, taking the connected components of the subgraph with vertex set f -1 (U ). Theoretical and statistical aspects of Mapper. Based on the results on stability and the structure of Mapper proposed in  Carrière and Oudot (2015) , advances towards a statistically wellfounded version of Mapper have been obtained recently in  Carriere et al. (2018). Unsurprisingly, the convergence of Mapper depends on both the sampling of the data and the regularity of the filter function. Moreover, subsampling strategies can be proposed to select a complex in a Rips filtration at a convenient scale, as well as the resolution and the gain for defining the Mapper graph.
Homology inference The above results provide a mathematically well-founded framework to infer the topology of shapes from a simplicial complex built on top of an approximating finite sample. However, from a more practical perspective it raises raise two issues. First, the Reconstruction Theorem requires a regularity assumption through the α-reach condition that may not always be satisfied and, the choice of a radius r for the ball used to build the Čech complex Cech r (X n ). Second, Cech r (X n ) provides a topologically faithfull summary of the data, through a simplicial complex that is usually not well-suited for further data processing. One often needs easier to handle topological descriptors, in particular numerical ones, that can be easily computed from the complex. This second issue is addressed by considering the homology of the considered simplicial complexes in the next paragraph, while the first issue will be addressed in the next section with the introduction of persistent homology.
g. Carlsson and Gabrielsson (2020) ;  Chen et al. (2019) ;  Gabrielsson and Carlsson (2019); Hofer et al. (2019a) ;  Moor et al. (2020) ;  Ramamurthy et al. (2019) ;  Rieck et al. (2019). Many of these tools rely on the introduction of loss or regularization functions depending on persistent homology features, raising the problem of their optimization. Building on the powerful tools provided by software libraries such as PyTorch or TensorFlow, practical methods allowing to encode and optimize a large family of persistence-based functions have been proposed and experimented Brüel-  Gabrielsson et al. (2019) ;  Poulenard et al. (2018). A general framework for persistence-based function optimization based on stochastic subgradient descent algorithms with convergence guarantees has been recently proposed and implemented in a easy-to-use software tool  Carriere et al.
Classification for sensor data In this experiment, the 3d acceleration of 3 walkers (A, B and C) have been recorded from the sensor of a smart phone 14. Persistence homology is not sensitive to the choice of axes and so no preprocessing is necessary to align the 3 times series according to the same axis. From these three times series, we have picked at random sequences of 8 seconds in the complete time series, that is 200 consecutive points of acceleration in R 3. For each walker, we extract 100 time series in this way. The next block of statements computes the persistence for the alpha complex filtration for data_A_sample, one of the 100 times series of acceleration of Walker A. 1 alpha_complex_sample = gd. AlphaComplex ( p o i n t s = data_A_sample ) 2 simplex_tree_sample = alpha_complex_sample.
Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecules are among the frequent targets of such investigation, since they contain multiple modified residues. However, the major challenge in tRNA examination is related to a large number of duplicated and point-mutated genes encoding those RNA molecules. Moreover, the existence of multiple isoacceptors/isodecoders complicates both the analysis and read mapping. Existing databases for tRNA sequencing provide near exhaustive listings of tRNA genes, but the use of such highly redundant reference sequences in RNA-seq analyses leads to a large number of ambiguously mapped sequencing reads.
Multiple approaches were proposed as alignment strategies for tRNAs. Existing tRNA-oriented databases (the most popular are gtRNAdb  [23]  and tRNAdb-CE 2011  [24] ), collect already published information and also propose prediction of tRNA genes in multiple species, generally using tRNAScan-SE  [25, 26]. These sources of information are extremely helpful, but, as a reference sequence, they propose either full genomic datasets of all tRNA genes or non-redundant collection of all possible tRNA species found (and predicted) in the genomic DNA. These reference sequences are certainly complete, but their direct use in bioinformatic analyses leads to numerous ambiguously (multiply) aligned reads. On the other hand, databases collecting experimental data on already sequenced tRNAs (MODOMICS  [27] , tRNAdb  [28]  and also T-psi-C database  [29] ) propose only validated sequences of existing and presumably most abundant tRNA species, however, for many reference/model organisms this information is scarce or simply not available (e.
Furthermore, they are not suitable for routine analysis of tRNA pools in biomedical projects or for analysis of tRNA modification profiles when multiple species are involved. We propose a simple and reproducible algorithm as a tool for semi-automatic analysis of tRNA datasets starting from full and redundant genomic references identified by tRNA-ScanSE. The dataset, downloaded as fasta file (*. fa) from gtRNAdb or tRNAdb-CE 2011 was first collapsed in full automatic mode, to obtain a non-redundant tRNA reference. This reference may be used to extract all tRNA-related reads from experimental tRNA sequencing data. Full non-redundant tRNA reference was further analyzed in a semiautomatic way, to collapse closely related tRNA sequences in a single tRNA reference. The distance for collapsing into a unique entry is proposed by the algorithm and modified or validated by the user after visual inspection of the pairwise Levenshtein distance heatmap.