dataesr/scientific-paragraphs-categorization huggingface dataset:
Amplicon-Based Protocol For each sample, the ProtoScript II First Strand cDNA synthesis kit (New England Biolabs, NEB, United States) was used to obtain single-strand cDNA from RNA extracts using random hexamers. RT was performed at 48 • C for 15 min and 80 • C for 5 min. Pools of specific primer sets were used to generate 36 amplicons from the cDNA using the Q5 Hot Start High-Fidelity DNA Polymerase (NEB, United States) (Supplementary Table ). These multiplex primer sets (divided into two separate pools) were designed using the Primal scheme software 1 on the SARS-CoV-2 reference sequence (Genbank accession number NC_045512) to sequentially amplify 1,000 bp using the following PCR conditions: 98 • C for 30 s, 40 cycles of 98 • C for 15 s, 65 • C for 5 min, and ended by 65 • C for 5 min. Clean-up of PCR products was performed with AMPure XP magnetic beads (Beckman Coulter, United States).
4 flow cell (FLO-MIN106) and sequenced on a MinION Mk1B device within 4 h (Figure  1 ). ONT MinKNOW software (version 19. 12. 2) was used to collect raw data and perform basecalling (Guppy v3. 4). The RAMPART tool (Read Assignment, Mapping, and Phylogenetic Analysis in Real Time) developed by the ARTIC network has been used to visualize genome coverage in real time and reference matching for each barcode.
MinION Bioinformatics Workflow From the fastq files, only reads with a minimum Q score of 7 were selected for the analysis. Data were demultiplexed using the guppy_barcoder with the option require_barcodes_both_ends to ensure barcodes are present at each end of the fragment. The reads were filtered on the expected length of amplicons. Reads between 900 and 1,200 base pairs were kept; thus, potential chimeric reads were removed. Selected reads were mapped against SARS-CoV-2 reference (NC_045512) using Minimap2 (v2. 9)  (Li, 2018). SAMtools (v1. 9) were used to sort the aligned BAM files, to obtain coverage data and a consensus sequence. Alignment statistics were also calculated with SAMtools. Data were manually inspected using Tablet (v1. 19)  (Milne et al. , 2013).
Illumina iSeq100 TM Sequencing The appropriate volume of each pool of amplicons was adjusted to have a total quantity of 100 ng of amplicon input (50 ng of each) per sample. The sequencing-ready libraries were prepared using the Nextera DNA Flex Prep kit (Illumina, United States). The libraries were qualified on an Agilent Technologies 2100 Bioanalyzer using a high-sensitivity DNA chip following the manufacturer's instructions and quantified with the Qubit 2. 0 fluorometer using the dsDNA HS Assay Kit (Thermo Fisher Scientific, United States). The resulting libraries were sequenced using the iSeq100 TM system (Illumina, United States) and in two multiplexed runs were performed generating 2 × 151 bp read length data during a 17 h run time (Figure  1 ).
Illumina Bioinformatics Workflow To remove low quality reads, trim off low-quality and contaminant residues, and filter out duplicated reads, fqCleaner v. 0. 5. 0 was used, with Phred quality score of 28. Filtered reads were mapped against SARS-CoV-2 reference (NC_045512) using Burrows-Wheeler Aligner MEM algorithm (BWA-MEM) (v0. 7. 7). SAMtools (v1. 9) was used to sort BAM files and to generate alignment statistics, coverage data, and a consensus sequence. Data were manually inspected using Tablet (v1. 19)  (Milne et al. , 2013).
Amplicon-Based Protocol As soon as the first SARS-CoV-2 reference genome was released in Genbank (NC_045512), we designed two sets of primers to generate a tiling path along the genome using the Primal Scheme tool. The full-length genome was amplified directly from the RNA extracts from the clinical samples and the corresponding isolates, to obtain PCR products overlapping by 120 bp and covering the entire 29,903 bp viral genomic sequence. Thirtysix amplicons, split into two pools, were successfully generated for each specimen with a size expected range between 900 and 1,098 bp (with an average length of 972 bp) and controlled by gel electrophoresis.
However, due to the high sequencing error rate around 8%, we observed that a reliable consensus sequence should be analyzed at a minimum depth coverage at 30X, or even 100X for some nucleotides. Conversely, the genome coverage for the isolates was quite similar, with at 30X 94. 9% and 95. 3%, respectively (Table  1 ). For the clinical sample with the lower viral load CIBU-200107, the genome coverage was significantly improved by the isolation step, less so for the second clinical sample with a very low Ct value (Figure  2 ). Concerning the bioinformatics analysis, the RAMPART tool (Read Assignment, Mapping and Phylogenetic Analysis in Real Time) developed by the ARTIC network was helpful and time-saving to visualize genome coverage in real time and allowed to stop the run when sufficient data were generated.
Mobile Next-Generation Sequencing Platforms Comparison for SARS-CoV-2 Genome Procurement Viral consensus genomic sequences were rapidly and easily obtained for the two SARS-CoV-2 clinical specimens and their respective isolates, by using the two different sequencing platforms, MinION and iSeq100 TM system. We noted that the sequences obtained for the same specimen are identical, regardless of the sequencer used. Sequence analysis of the SARS-CoV-2 consensus genomes showed that they belong to the proposed phylogenetic clade A2 2  , which is consistent with the phylogenetic analysis of the other human French SARS-CoV-2 sequences  (Gámbaro et al. , 2020). Compared to the Wuhan reference sequence (NC_045512), the viral genomic sequences CIBU-200107 and CIBU-200132 (GISAID Accession numbers EPI_ISL_437689 and EPI_ISL_437690) display seven and six nucleotide mutations, respectively.
In this regard, we therefore evaluated two space-saving and easily portable next-generation sequencers, MinION and iSeq100 TM system, through the current pandemic of COVID-19. Although the initial investment gives an advantage to the MinION, the cost of reagents on the other hand is similar, and both approaches require the identical minimum of laboratory equipment to generate the libraries. By using the Primal scheme software on the first released SARS-CoV-2 reference sequence, we immediately developed an amplicon-based approach to sequence around 1,000 bp amplicons on our small portable sequencers. It has been reported that the ampliseq panel proposed by ARTIC network, based on 109 amplicons, may lead to coverage bias due to dimer formation between primers  (Itokawa et al. , 2020). Our choice to produce 36 longer amplicons, with lower number of primers, could be more efficient.
The MinION retains the advantage of greater versatility, with the possibility of analyzing data in true real time, particularly with tools such as RAMPART, and thus being able to stop the run after a few hours as soon as the amount of data is sufficient. Concerning the further generation of the alignment of amplicon reads against the reference and the variant calling, both devices usually offer tools in the cloud, such as Local Run Manager (DNA amplicon analysis module) for iSeq100 TM system, and EPI2ME for MinION. In outbreak situations, these two sequencers therefore make rapid sequencing accessible to a very large number of laboratories, requiring a minimum of molecular biology skills. Nevertheless, the improvement of the dedicated bioinformatics user-friendly resources should still be developed to take into account the low network and informatics resources in the field.
Available data The precipitation measurements available on the Agly catchment come from two different observational networks. -PLU: the operational hourly rain-gauge network for flood-monitoring purposes and data provided by the re-gional flood forecasting service, the Service de Prévision des Crues Méditerranée Ouest (SPCMO); -JP1: 1 km 2 quantitative hourly precipitation estimates ANTILOPE J +1 (ANalyse par spaTIaLisation hOraire des PrEcipitations) that come from a merging of radar data and rain-gauge measurements  (Laurantin, 2008; Champeaux et al. , 2009). The hydrometric data were derived from the network of operational measurements at variable time steps (Hy-droFrance databank, http://www. hydro. eaufrance. fr/, last access: 20 November 2019). The stream gauges are located in five upstream stations not influenced by the dam (Table 1 and Fig.
Rainfall-runoff model The MARINE model is a distributed mechanistic hydrological model specially developed for flash-flood simulations. It models the main physical processes in flash flooding: infiltration, overland flow, and lateral flows in soil and channel routing. Conversely, it does not incorporate low-rate flow processes such as evapotranspiration or base flow. MARINE is structured into three main modules that are run for each catchment grid cell (Fig. 3 ). The first module allows the separation of surface runoff and infiltration using the Green-Ampt model  (Green and Ampt, 1911). The second module represents subsurface downhill flow, based on the generalized Darcy law used in the TOPMODEL hydrological model  (Beven and Kirkby, 1979). Lastly, the third module represents overland and channel flows.
Financial support. This work was carried out in the framework of the PGRI-EPM project (Prévision et gestion du risque d'inondation en Eurorégion Pyrénées Méditerranée) funded by the call for projects "Développement durable, Ressource en eau -Gestion des risques" of the Eurorégion Pyrénées-Méditerranée. This work has also been sponsored by several Spanish research projects (PCIN-2015-221 (METEOforSIM) and CGL2017-82868-R (COASTEPS), which are partially supported with FEDER funds) and by the French Central Service for Flood Forecasting (SCHAPI). Review statement. This paper was edited by Christian Barthlott and reviewed by two anonymous referees.
Introduction Big Data processing  [1]  is a resource-intensive operation that uses specific hardware and software. Due to the intense Input/Output (I/O) nature of the processing, the hardware architecture is different from the traditional High-Performance Computing (HPC) clusters or supercomputers, particularly, local disks are required for all data nodes. Moreover, the data processing application stack is also significantly different from traditional approaches. For instance, the data volume is substantially larger than in other operations, and the data sets are poorly structured, and various data types are available. The traditional relational database management systems, like SQL queries, are incapable of tackling semi-structured or unstructured Big Data processing. Thus, the MapReduce model has been introduced, a critical technology for processing and generating extensive data sets.
In the meantime, the splittable compressing algorithms split and merge back the data while using the MapReduce development model. The suggested system is based on Apache Hadoop and Apache Spark general-purpose Big Data computing frameworks. If the Apache Hadoop is a model for reading and writing data processing based on disk, the Apache Spark performs in-memory calculations with the resilient distributed data sets. Apache Hadoop is an open-source Java-based distributed computing framework built for applications implemented using MapReduce parallel data processing paradigm  [7]  and Hadoop Distributed File System (HDFS)  [8]. As a distributed file system, HDFS provides a reliable, scalable, and faulttolerant distributed data storage. The data is stored as blocks for handling the hardware failures.
Methodology The suggested system allows studying the tradeoff, with compression between saving CPU and saving I/O, to evaluate the efficiency of Big Data applications using Hadoop and Spark frameworks based on compression tools and tuning the compression factor. The performance optimization methodology allows users to explore and optimize Big Data applications (Fig. 1 ). A decision-making service sends the application type and the complexity to the service trading module through the REST API to select an optimal configuration. Several MapReduce type benchmarks, tools, and applications have been studied and implemented in the simulation module. As a distributed I/O benchmark tool, the TestDFSIO benchmark is used to stress test HDFS and determine cluster I/O speeds  [22]. TestDFSIO is also essential to identify bottlenecks in networks and stress the hardware, OS, and Spark/Hadoop configuration on cluster nodes.
TestDFSIO performs (see Table  2  for the list of the options) parallel reading and writing bulk data using separate Map tasks (or Spark jobs). The statistics are collected in the Reduce task to get a summary of HDFS throughput and average I/O. There are many MapReduce applications used to test both layers of HDFS and MapReduce. The Terasort package is used to check the HDFS and MapReduce layers, consisting of TeraGen designed to generate data, Terasort to sort data, and TeraValidate to verify data sorting. TeraGen is designed to generate a large amount of data, which is the input to TeraSort. The size of the generated data and the output are the input arguments. Terasort sorts the data generated by TeraGen. TeraValidate checks the sorted TeraSort output. The input and output paths are the TeraSort and TeraValidate benchmarks arguments.
The WordCount and LogAnalyzer are studied, as MapReduce applications  [23]. The WordCount workload reads text files and counts how often words are found. The LogAnalyzer workload reads log file as an input, detects lines that match the entered regular expression, and outputs a report that informs if the keyword is present or not and if present how many times. The clustering data analysis technique divides the entire data into groups according to a similarity measure. k-Means clustering is one of the simplest, powerful, and popular unsupervised machine learning algorithms in Data Science  [24]. Parallel k-Means MapReduce application has been used, allowing to manage large datasets finding distances between objects  [25]. 1, 2, and 4 centroids have been identified for the experiments to allocate every data point to the nearest cluster.
For firefly swarms, this even enables the recording of collective male displays from the perspective of a stationary female on the ground. Here, we present a general framework for 3D reconstruction using a pair of 360-cameras, and demonstrate its implementation for the analysis of firefly swarms' internal dynamics, in particular flight patterns and spatiotemporal correlations. This paper consists of three main parts. The next section presents the theory behind the 3D reconstruction technique and its practical implementation, and may be useful to experimentalists for the study of fireflies and other insect swarms. Our Matlab code is provided in the Electronic Supplemental Material (ESM 1. 1). The Results section reports findings relative to the behaviour of P. carolinus fireflies, and may be of interest to biologists, entomologists, physicists, and even the general public.
Finally, the problem reads: min {tx,ty,tz,ψx,ψy,ψz} t 2 x +t 2 y +t 2 z =1 i |r 1,i α 1,i -( t + r 2,i R -1 α 2,i )|, (2. 7) and can be solved using Matlab's fmincon function. Method 2: fundamental matrix. The camera pose can also be determined from the fundamental matrix, which can be computed using the Matlab built-in function estimateFundamentalMatrix. (Note that this function is designed for planar calibration, and therefore it only takes 2D vectors as arguments, assuming that the third coordinate is 1. Consequently, using this function requires renormalizing the projection vectors α by their third coordinate α = α/αz. ) From the estimation of F = T × • R -1 , t is simply a unitary vector in the null space of F -1 , which leaves an ambiguity of factor ±1. Since det(T × ) = 0, estimating R relies on singular-value decomposition, for example using the method described in Ref.
Matching. Points extracted from pairs of frames are originally not matched, so a pairing algorithm has to be employed to determine point correspondences. Given t, R, a set of n 1 points { α 1,i } in C 1 , and n 2 points { α 2,j } in C 2 , an optimal pairing can be made by applying the Hungarian algorithm on an appropriate cost matrix [c i,j ]. We chose the cost of pairing α 1,i with α 2,j as equal to the smallest possible distance: c i,j = min r1,r2>0 |r 1 α 1,i -( t + r 2 R -1 α 2,j )|. Triangulation: implementation. Given the camera pose ( t, R) and two matched points α 1 , α 2 corresponding to projections of world point X, the distances r 1 , r 2 ≥ 0 from C 1 , C 2 respectively can be calculated by minimizing the distance |r 1 α 1 -( t + r 2 R -1 α 2 )| using a linear solver such as Matlab's lsqnonneg.
(a) Equipment We used two GoPro Fusion cameras as our 360-cameras. Temperature was recorded using two Kestrel Temperature Data Loggers (one reading every 5min). The cameras were positioned on small tripods (0. 6m above ground), and aligned manually as precisely as possible to have the same side facing the same direction (so that R is close to identity). The spacing between the two cameras was always set to 3ft (0. 91 m) using a wooden yard stick (figure  2b ). We recorded either at 30 or 60 frames-per-second (fps), and the ISO was manually set to 1600. We applied black electrical tape on the screens and LEDs of the cameras so as not to perturb fireflies with artificial light signals. The recorded footage was then processed using the software provided with the cameras, GoPro Fusion Studio, in order to create equirectangular movies in high-resolution (4K) MPEG format which could later be processed in Matlab (figure  1c, d ).
(c) Data collection in controlled environment In addition to recording firefly displays in the natural, unperturbed habitat, we performed a series of controlled experiments, in which a specific number of P. carolinus male fireflies were placed in a large tent. For these experiments, fireflies were gently captured during peak flashing hour using insect nets, and delicately placed into petri dishes for up to a few minutes before being introduced into the tent, where they were visually inspected to confirm their sex and species (see ESM 3. 1). They were then placed by increasing numbers into a cuboid black fabric tent of dimensions 2m×1. 5m×1. 5m (xy-z, figure  5b ). An additional black plastic tarp was added on top in order to ensure visual insulation from fireflies on the outside. Outside temperature decreased from 19 • C to 17 • C over the course of the experiments, while the temperature inside the tent was 18 • C.
Results (a) Natural habitat Spatial distribution of flash occurrences. The 3D reconstruction of flash occurrences in its natural habitat (5min-interval starting at 10pm) shows a P. carolinus swarm that closely follows the slope of the surrounding terrain, and notably flashes almost exclusively in a layer of about 2m above ground (figure 2d-g and Movie S2). Viewed from above, the reconstructed swarm reveals the limits of the imaging technique: flashes farther than about 10m are not captured, and visual occlusion creates significant "blind zones". However, triangulated positions show clear streaks of lights (figure  2d ). This dataset is available for visualization with FireflyNavigator (ESM 1. 2). Temporal pattern of flash occurrences. In every frame of the movie, zero, one, or several flashes are captured.
Although the confining conditions of the tent plausibly perturbed natural behaviour to some extent, the accessible volume was large enough (∼ 4m 3 ) that fireflies were able to move and fly freely, as seen in the trajectories in figure  6c1-4. This dataset is available for visualization with FireflyNavigator (ESM 1. 2). 1 firefly. When a single male firefly was introduced in the tent, it emitted flashes continuously over the 15min of the experiment (figure  6a1 ), even in the absence of a responding female. Flashes lasted typically between 0. 10s and 0. 15s (5 to 10 frames), although shorter and longer flashes were also recorded (figure  7a ). Flashes occurred by bursts of typically 4 consecutive flashes, and overall between 1 and 6 (figure  7b ), and independently of the pattern, the time interval between two successive flashes was sharply distributed around 0.
Artifacts in 360-degree movies. First, recording over an entire sphere necessarily requires stitching different FOVs together. While this is usually performed by commercial software, small stitching discrepancies are known to be largely unavoidable, creating localization "jumps" along FOV edges in rectangular frames. 360-cameras built around more than 2 lenses might provide better stitching. Second, projecting a sphere onto a plane generates stereographic distortions, so that objects near the poles are stretched out and localized with less accuracy. The GoPro Fusion Studio software, however, enables changing the orientation of the projection, so that if important dynamics occur near the pole of a movie, the axes' origin can be modified to place these events at the equator. Triangulation resolution.
(b) Firefly density estimations While firefly activity shows variability between successive years due to a variety of factors, most notably temperature and humidity conditions, it is widely suspected that firefly populations are generally declining  [29]. Climate change, habitat loss, increasing light pollution, and degrading environment are some of the most probable causes. Therefore, estimating firefly densities is fundamental to understanding firefly resilience and promoting conservation efforts  [29]. The use of stereoscopic 360-camera setups to record flashing displays is accurate, simple, and inexpensive, and therefore may be appropriate for large-scale monitoring programs. Here, we briefly discuss how 3D-reconstructed data could be used to estimate firefly density. The goal is to estimate the number N (d) of flashes recorded within a certain distance d from the midpoint between the two cameras ( t/2).
Data accessibility calibration, matching, and triangulation code for pairs of 360-cameras is made available at http://www. github. com/rapsar/stereo360. We are providing two datasets of reconstructed swarms, one in the wild (5min recorded at 30fps on June 5 th , 2019), and one in the tent with 40 fireflies (60fps). They can be inputted into the FireflyNavigator tool available at https://www. github. com/elie-s/FireflyNavigator.
• The GRU-MF recommender models a high level of non-linearities and exhibits interactions between users and items in latent embeddings. Its Gated Recurrent Units adjusts embeddings for better collaborative filtering. Also, it reduces user biases towards items that are rated frequently by users. • The GloVe-CBF exploits the strengths of embedding representations and the stacking ensemble learning for modeling the content. Our motivation while suggesting this recommender is to extract effective features from the item-based side information, which would ameliorate the analysis of user-item interactions and thus resulting in more accurate predictions. • EnsVAE framework is a very powerful design and can be considered as a successful guideline for hybridizing various sub-recommenders. The empirical evaluation demonstrates that the EnsGG instance significantly outperforms state-of-the-art baselines on several real-world datasets, including two recently proposed neural-network approaches.
It is worth mentioning that GloVe-CBF on its own managed outperforms the stateof-the-art recommendation methods, according to our evaluations. The rest of this article is structured as follows: Section 2 reviews related works. Section 3 introduces the proposed EnsVAE framework for designing and building highperformance hybrid recommender systems. We create an experimental EnsVAE-compliant hybrid based on a two innovative sub-recommenders: collaborative GRU-MF model and an item-based content-based model. Section 4 is devoted to data and method presentation. Section 5 reports the results of the experimentations with EnsGG over real-world datasets. Finally, conclusions are provided in Section 6.