dataesr/scientific-paragraphs-categorization huggingface dataset:
The WCRT increases after five cores because of the increasing scheduling overheads and cost of accessing global memory. These costs reduce the benefit of multi-core execution. We perform additional experiments to compare the observed worst-case execution times (WCETs) of ForeC and Esterel versions of Life, Lzss, Mandelbrot, and MatrixMultiply on embedded multi-cores. For our experiments, the WCET of a program is the total time that it takes for the entire program to execute from start to finish. 2  We limit the Life program to simulate 10,000 iterations of the game. The same input vector is given to the ForeC and Esterel versions of a benchmark program to ensure that the same computations are performed. The Esterel programs are compiled using Yuan et al. 's approach  [151]. For each benchmark program in Figure  36 , the WCETs for ForeC and Esterel are plotted.
ForeC's main innovation revolves around its shared variable semantics that provides thread isolation and deterministic communication. The behavior of a shared variable can be tailored to the application at hand by specifying a suitable combine function and policy. All ForeC programs are correct by construction (no race conditions, no deadlocks) because mutual exclusion constructs are not needed. The formal semantics greatly simplifies the understanding and debugging of parallel programs. Section 5 presented a compilation approach that used non-preemptive static thread scheduling. The key strategy was to preserve the ForeC threads and to use light-weight context-switching and simple scheduling routines to preserve the ForeC semantics. For future work, the ForeC compiler could be improved to generate more efficient code that remains amenable to static timing analysis.
A. 2 Combining More Than Two Copies The ForeC program shown in Figure  38a  is used to explain how multiple copies of a shared variable are combined. The program's control-flow graph is shown in Figure  38b. The program has a shared variable called s that uses the combine function plus. The initial value of s is 3 for the program's first tick. Figure  39a  shows the copies of s at the end of the first tick, organized by the thread genealogy. Each node represents a thread and the current value of its local copy, e. g. , main: 3 means that the main thread has a local copy of s with the value 3. Copies that were assigned a value during the tick have the • symbol, e. g. , tA: 1• means that thread tA's copy has been assigned the value 1. Arrows are drawn from the child threads to their parents to show the thread genealogy.
Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells.
The Cytonaut Rover module enables interactive data visualization. Additional details in "Methods". of the reversible hydrogel that contains cells linked to barcoded beads. Sequencing outputs are evaluated and displayed using Cytonaut (https:// cyton aut-scipio. bio), a cloud-based data analysis platform that includes preand post-processing, as well as interactive data visualization (see Fig. 1B ). Being a cloud solution, Cytonaut can be used via a simple web interface, bypassing the need to install software packages or use computational resources. Overall, results presented establish RevGel-seq performance levels, in comparison to the predominant microfluidics technology, and illustrate a clinically relevant application.
Results Mixed species experiment. The performance of RevGel-seq was assessed using a cell barcode purity criterion (see "Methods") by Cytonaut (v1. 2) in the sequencing data for each of the three technical replicates. Figure  2A  represents the Barnyard plot for one of the technical replicates, where cell barcodes are annotated based on the percentage of detected transcripts from each species. The "human" cells (in orange) and "mouse" cells (in teal) are cell barcodes for which cell barcode purity is higher than 95%. The transcripts from the two types of cells were readily separated, with a hetero-species cell multiplet rate of 3%, implying an equal rate of non-detectable homo-species cell multiplets. A hetero-species cell multiplet is defined as a barcode for cell barcode purity is lower than 2/3.
Characterization of pancreatic islet cells. An application of RevGel-seq to a current preclinical research project is illustrated by studies to phenotype pancreatic islet cells in relation to insulin resistance and islet cell adaptation  29. After sequencing data analysis with Cytonaut (v1. 2), the automated classification of pancreatic cell types (Fig. 3A ) and associated gene counts (Fig. 3B ) are in accord with previous studies  30. Expression profiles of known marker genes  30  are presented in Fig. S2 , revealing the predominant gene expression for each cell-type, Figure  2. Benchmarking and applications. (A) Barnyard plot showing for each cell-associated barcode the number of detected mouse NIH3T3 transcripts and the number of detected human HEK293 transcripts, from 10,000 input cells prepared with RevGel-seq, with sequencing data downsampled by raw read subsampling at a depth of 50,000 raw reads per cell on average.
The hetero-species cell multiplet rate is 3% (for details on criteria see "Methods"). (B) Cell classification and 2D embedding from a PBMC sample of 10,000 input cells prepared with RevGel-seq were downsampled by raw read subsampling at a depth of 20,000 raw reads per cell on average. Post-processing was performed using Seurat 21 and automated cell classification was performed using the SingleR  22  algorithm based on the reference dataset MonacoImmuneData  23. Unassigned cells had classification uncertainties that were considered too high according to the pruneScores method with default parameters. All relevant PBMC sub-types were identified. (C) Percentage of automatically classified cells for each cell type identified in the same PBMC sample prepared with a reference microfluidic technology (10x Chromium 3′ v3.
1) and prepared with RevGel-seq, with 10,000 input cells and a sequencing depth of 20,000 raw reads per cell on average. For both methods, RevGel-seq and 10x Chromium 3′ v3. 1, the same original sample was used and the same data analysis software (Cytonaut v1. 2) was applied. The relative proportions of cell types are highly similar between RevGel-seq and the reference microfluidic technology. notably for the three principal cell-types alpha, beta, and delta: glucagon (GCG), insulin (INS), somatostatin (SST), respectively. Establishment of large cell compatibility. We next tested RevGel-seq compatibility with large cells, using cardiomyocyte suspensions. Bead-cell complexes were subsampled and examined under brightfield microscope. A cardiomyocyte cell suspension prior to coupling to barcoded beads is illustrated in Fig.
For the current study, barcoded cDNAs from two PCR tubes were converted to the sequencing library separately (each represents 1250 input cells). For both subsamples #1 and #2, the number of cells found in the output data  (F) Estimated cell quantities obtained from numbers of cell-bead complexes (for all 10,000 input cells), observed bead recovery rate (for each PCR tube, max 12. 5% for each of 8 PCR tubes), and transcript/ gene counts per analyzed cell at average 50,000 raw reads per cell. Sequencing data was processed according to Methods, section "RevGel-seq sample preparation workflow" by the pre-processing analysis pipeline of Cytonaut, which automatically detects the analyzed cells. (530 and 523, respectively) was consistent with the number of bead-cell complexes estimated by microscope observations (595 and 527, respectively), as shown in Fig.
4F , demonstrating that microscopic assessment of cellbarcoded bead complexes enables a useful estimation of the number of cells in sequencing data. The raw sequencing data of the two subsamples were analyzed by Cytonaut (v1. 2) which performed initial data pre-processing to generate count matrices and quality indicators, then data post-processing with dimension reduction and projection in 2D. The UMAP graphs presented in Fig. 4C  show that cells from both subsamples are homogeneously distributed, indicating the absence of batch effects of split cDNA amplification and subsequent library preparation. Results in Fig. 4F  demonstrate that the median number of transcripts, the median number of genes, and the median mitochondrial transcript rate per cell are all comparable between the two libraries.
Discussion In this study, we present results obtained using the RevGel-seq approach, a single-cell RNA instrument-free sample preparation technology that relies on formation of cell-barcoded bead complexes. The complexes are suspended in a gelation tube containing a reversible hydrogel in the liquid state. Upon transition to the gel state by incubating on ice, the complexes are separated by the gel matrix. For typical conditions of experiments presented here (for 10,000 cells in 2 mL of gel) the average center-to-center distance between cells is ~ 500 μm, sufficient to minimize crosstalk (i. e. , mRNA from one complex diffusing after cell lysis to the barcoded bead of another complex in the vicinity). Following cell lysis and degelation, barcoded beads are recovered and the information combining mRNA 3′ end and barcode sequence is obtained through a series of biomolecular steps Figure  5.
Freezing samples at an early stopping point during RevGel-seq protocol does not modify scRNA preparation performances. (A) Schematic representation of the timing differences between the "Control" and "Early stop" samples (see "Methods" for specific steps). "Control" samples were obtained by freezing the RT reaction mixture at -20 °C overnight, while "Early stop 80 °C" and "Early stop dry ice" samples were obtained by freezing (either at -80 °C or in dry ice, respectively) immediately after applying lysis buffer to the hydrogel. The RevGel-seq protocol was resumed on a subsequent day for all samples. (B) Comparison of performance indicators between the tested conditions (average of 3 PCR tubes for each condition). Gene and transcript counts per analyzed cell at 50,000 raw reads per cell with raw read downsampling.
(C,D) Projection of scRNAseq data on 2D (UMAP) of the pooled samples (pool of data from 1 PCR tube for all 3 conditions), shown per condition (C) and per determined cell species (D). ("undefined" corresponds to cells with a cell barcode purity lower than 95%; see Methods section "Pre-processing pipeline", iii). With respect to cell capture yields, obtained cell species proportions were as follows: human 45. 8%, mouse 49. 9%, undefined 4. 3% (Control sample); human 48. 6%, mouse 52. 5%, undefined 3. 9% (Early stop dry ice); and human 46. 2%, mouse 48. 4%, undefined 5. 4% (Early stop -80 °C). (reverse transcription, ExoI digestion, second strand synthesis and PCR amplification), leading to single cell cDNAs ready for library preparation and subsequent sequencing. RevGel-seq performances have been assayed using mixed-species cell suspensions, human peripheral blood mononuclear cells, and pancreatic islet cells.
Data analysis with Cytonaut, the Scipio bioscience cloud-based bioinformatics platform. The end-to-end data analysis from raw sequencing data to differential gene expression is performed by Cytonaut (v1. 2) cloud software (http:// www. cyton aut-scipio. bio), which integrates data pre-processing (v6. 7) as well as data post-processing (v1. 5) and interactive data visualization (v1. 2). The same end-to-end data analysis methodology based on Cytonaut was applied to all samples except for RevGel-seq and 10 × PBMCs samples and for RevGel-seq Pancreatic islet cells samples for which Cytonaut was used for pre-processing analysis but a separate script was used for post-processing analysis based on Seurat (v4. 1. 0) and automated cell annotation based on SingleR (v1. 8. 1). The workflow of the end-to-end data processing pipeline integrated in the Cytonaut platform is summarized in Fig.
1B , with details on individual steps presented below: Pre-processing pipeline. The data pre-processing pipeline of Cytonaut takes as input the two FASTQ files of the sample (R1 and R2), leverages the specific barcode pattern of RevGel-seq capture beads, and successively performs read quality control, detection of cell-associated barcodes, read alignment based on exons only, read deduplication, and read assignment. Detection of cell-associated barcodes was performed for each sample by applying the distance-based knee method of UMI-tools v1. 1. 2. The reference genome sequences and annotations are based on Ensembl release v99 for human species (GRCh38; https:// ftp. ensem bl. org// pub/ relea se-99/ fasta/ homo_ sapie ns/ dna/; https:// ftp. ensem bl. org// pub/ relea se-99/ gtf/ homo_ sapie ns/) and mouse species (GRCm382; https:// ftp.
Introduction Access to spatially and temporally consistent climate data at high spatial and temporal resolutions have progressively turned into a growing need in the 21st century, for being a paramount to numerous fields of study that investigate ecological, hydrological, and climate change processes, among others  [1] [2] [3] [4] [5] [6] [7]. Using numerical weather models and data assimilation techniques to produce model-based reanalysis products is one viable strategy for generating climate datasets in light of this need  [8] [9] [10] [11]. Several international and local meteorological centers and data assimilation offices have collaborated over the past few decades to make numerous reanalysis products available to the public  [12]. Examples of the most popular reanalysis products are: the ERA5 and ERA5-Land from European Centre for Medium Range Weather Forecasts (ECMWF); the second version of Modern-Era Retrospective Analysis for Research and Applications (MERRA2)  [13]  produced by NASA's Global Modeling and Assimilation Office (GMAO); the second version of Climate Forecast System Reanalysis (CFSv2) from the National Centers for Environment Prediction and National Center for Atmospheric Research (NCEP/NCAR)  [14] ; NCEP/NCAR Global Reanalysis Products from NCEP and NCAR  [15, 16] ; and the Japanese 55-year Reanalysis (JRA-55)  [17]  from the Japanese Meteorological Agency (JMA )  [12, 18].
The common key strength of the numerous existing reanalysis products resides in providing global data sets devoid of gaps, at high temporal resolution and over long time periods (generally over 3 or more decades). Still, reanalysis data frequently fail to simulate many of the processes that drive regional and local climate variability. Their limitations lie in their incapability of accurately depicting sub-km scale climate variables at the needed timescales and do not allow for proper representations of the local topography and sub-grid scale features that are essential in areas with complex terrain, microclimates or narrow mountain valleys, as highlighted by Holden et al. [19] , Zhang et al. [20] , Le Roux et al. [21] , Alessi & DeGaetano  [22] , Zhang et al. [23]. When evaluated in contrast to observational data, the raw output data are regularly found to have systematic biases  [24, 25]  limiting their usefulness for local applications  [26].
Reanalysis data For this study, the most advanced global reanalysis data produced in Europe, specifically optimized for land surface applications, was used. The dataset used is the ERA5-Land enhanced global dataset for the land component of the fifth generation of European Reanalysis, which is freely available on the website https://cds. climate. copernicus. eu  [57]. In comparison to ERA5 and older ERA-Interim, the ERA5-Land dataset has the advantage of enhanced horizontal resolution of 9-km (released on a regular 0. 1°x 0. 1°grid) compared to 31-km (ERA5) and 80-km (ERA-Interim), while maintaining the same hourly temporal resolution as ERA5  [18]. The high temporal and spatial resolutions of ERA5-Land, and the consistency of the fields produced, make it a valuable dataset for diverse applications related to water resources, land and environmental management.
The variable of interest in this study is the air temperature at 2-m above the surface of land, sea or in-land waters, which is calculated through interpolation between the lowest model level and the Earth's surface, considering atmospheric conditions. Hourly ERA5-Land temperature data was downloaded and processed to be consistent with the measured data screening. Additionally, ERA5-Land temperature's daily mean, minimum, maximum and standard deviation were computed as ancillary data for the entire study period.
The "RobustScaler" method was used as it can handle outliers. The performance of the machine learning models heavily depends on the hyperparameter values, therefore, a significant step was determining the optimal values for the model through hyperparameter tuning. This was done using the "Scikit-learn" library's Grid Search function, which considers multiple hyperparameter combinations and chooses the one that returns the lowest error score. Since MLR model does not have any hyperparameters to tune, only SVR's and Xgboost's hyperparameters were tuned. The Grid search function also includes a pre-defined k-fold cross validation method  [70] [71] [72] [73] [74] , where each fold serves as a single hold-out test fold, and the model is built using the remaining k-1 folds. Grid search methodology with 5-fold cross-validation was applied to get the optimal model parameters for SVR and Xgboost, meaning that during the cross-validation process, 4 years of data were used for calibration and 1 year of data for validation.
This suggests that the reanalysis Ta has higher amplitude of Ta variations than what it should be over the study area. The corrections to be applied to the Ta_5 are then to adjust for the bias that may present in the reanalysis dataset. This bias can be caused by errors in the input data, topographical effects, the modeling approach or in the assimilation of observations. The bias can also be caused by the lack of representation of the complex topography or urbanization of the study area in the reanalysis dataset. At first, we attempted to debias/correct the Ta_5 using simple linear regression, modeling daily temperature changes as a sinusoidal function, and constant (positive or negative) bias correction prior to downscaling. However, these methods did not yield significant improvement and were not practical for the study area, hence the choice of the machine learning approaches.
20 °C, R² = 0. 97, and MBE=0 °C using the Python package scikit-learn and the rules of "Lesser is better" for the RMSE and MBE and "Greater is better" for R². The best parameters found were C = 1, γ = 'scale' and ϵ= 0. 02. The grid search methodology was also applied to the Xgboost algorithm to find the best evaluation metrics (lowest RMSE and MBE and highest R2). An analysis of Aarshay's (2016) work was used as a reference to determine typical values of learning rate, maximum depth, minimum child weight, gamma, subsample, and colsample by tree, such as: [0. 01,0. 2],  [3, 10] ,  [1, 6] , [0. 1,0. 2], [0. 5,0. 9], and [0. 5,0. 9]. The best fit was found when the following settings were used: learning rate= 0. 4, maximum depth= 6, minimum child weight= 1, subsample= 1, colsample by tree= 1, and a "number of estimators" of 2000.
Discussion The correction of ERA5-Land Ta_5 data through the application of machine learning techniques resulted in an enhanced spatial distribution of downscaled Ta estimates. The improvement was demonstrated through comparison with two classical downscaling methods: the annual average and the MicroMet model. To summarize, the process began with the creation of Ta_5_ref temperatures calculated for each ERA5-Land grid points elevation to match the observed local temperature-elevation relationship. In simpler terms, Ta_5_ref is a 9-km adjusted version of the ERA5-Land Ta_5 and a more accurate representation of the actual measurements of Ta_st and ELR_st. Hence, Ta_5_ref served as the desired outcome for the correction of Ta_5. The gap between Ta_5 and Ta_5_ref values was filled through machine learning.
Three different machine learning techniques, MLR (simple), SVR (relatively complex), and Xgboost (recent), were selected to make the prediction of Ta_5_ref. A correlation analysis was performed to determine the input variables that could be correlated to Ta_5_ref. These candidate input variables were all derived from the ERA5-Land data, meaning that once the models were calibrated, the Ta_5 temperature was corrected using its own data to align with the observed local temperature-elevation relationship before downscaling. The results of the correlation analysis showed that the set of input variables is includes in addition to hourly Ta_5: hourly ELR_E5, the mean and standard deviation of daily Ta_5, and the elevation of the ERA5-Land grid points. The predicted/corrected values at 9-km spatial resolution, referred to as Ta_5_corr, were validated against Ta_5_ref and showed significant improvement.
Conclusions The ERA5-Land Ta_5 data has been improved through the use of machine learning techniques in downscaling. The correction process started with the creation of Ta_5_ref, which is a 9-km adjusted version of the ERA5-Land Ta_5, better representing the actual temperature measurements. The gap between Ta_5 and Ta_5_ref was filled through machine learning using three models: MLR, SVR, and Xgboost. The results showed that the Xgboost model performed the best, surpassing the SVR and MLR models. The downscaled product showed significant improvement compared to the one obtained through classic downscaling approachs (constant ELR and MicroMet model). The primary benefit of this method is that it can accurately downscale to an hourly time-step, is applicable throughout all seasons, and captures diurnal and regional temperature fluctuations.
However, the models must be calibrated for a specific area before use. Overall, this method presents a promising solution for improving the accuracy of temperature data downscaling and can be used for other climate studies. In perspective, assessment of the added value of this novel machine learning based method for hydrological applications is considered (e. g. , reference evapotranspiration over mountains). Another avenue would be the extension of the use of machine learning models to downscale other meteorological variables (e. g. , wind speed, relative humidity. ). Finally, although the time-window would be more restrained, we can also consider the use of machine learning based methods on ERA5-Land's Land Surface Temperature (LST) to reproduce high resolution satellite product such as thermal based Landsat-8 LST.
Funding: This project has been financially supported by the  OCP group foundation  and the  Mohammed VI Polytechnic University  (UM6P). The lead author received financial support from the  UM6P. The  Horizon 2020 ACCWA project  (grant agreement #  823965 ) in the context of  Marie Sklodowska-Curie research  and the innovation staff exchange ( RISE) program , and the project  PRIMA-S2-ALTOS-2018  "Managing water resources within Mediterranean agrosystems by Accounting for spatiaL sTructures and cOnnectivitieS are also acknowledged.