dataesr/scientific-paragraphs-categorization huggingface dataset:
Open an Excel file containing various dose-response data sets. Open a floating window to manage all sheets of current workbook. user to think about the data and the u nderlying mechanisms. This software is intricately linked to Microsoft Excel. Proper operation is not guaranteed with future versions of Excel because Microsoft is not bound to offer downward compatibility with previous versions. Currently, the software works only on computers running the Windows operating system. It would require significant modifications to be compatible with Apple computers. Values obtained with Origin 8 were identical. Fits of various sets of data were performed with eeFit and Origin and are detailed as supplementary information. This table summarized the differences observed for the best-fit values of half-maximal concentrations K (and K2 for biphasic effects) and Hill coefficients h (and h2 for biphasic effects), as well as the reported standard errors of these parameters, and the overall RMSE between experimental and predicted data.
In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative.
Each pixel of the SLM is addressable with an 8-bit value (256 levels). The SLM is not capable of independently modulating the magnitude and phase of the optical field. In the Optalysys processing system, the SLMs are configured to modulate both the amplitude and phase in a coupled manner such that optimal correlation performance is achieved. The optical Fourier transform and all of the functions are inherently two-dimensional. The propagating light beam can be thought of as a 2-D function propagating and transforming along a third direction. The system is most naturally applied to 2-D datasets, and many problems can be mapped to an appropriate representation. A critical aspect to realising the potential of optical processing systems is the interface to a traditional computing platform. Bridging this gap has been a significant undertaking for Optalysys and has resulted in the development of a custom PCIe drive board.
These algorithms will utilise DSLs for performance portability. Code availability. The data structure framework Atlas is available at https://github. com/ecmwf/atlas (last access: 27 September 2019) under an Apache License 2. 0. The GridTools framework used as a domain-specific language approach for MPDATA is available at https://github. com/GridTools/gridtools (last access: 27 September 2019) under a BSD-3-Clause licence. The CLAW DSL used for the cloud microphysics dwarf is available at https://github. com/ claw-project/claw-compiler (last access: 27 September 2019) under a BSD-2-Clause licence. Model codes developed at ECMWF are the intellectual property of ECMWF and its member states, and therefore the IFS code and the IFS-FVM code are not publicly available. Access to a reduced version of the IFS code may be obtained from ECMWF under an OpenIFS licence (see http://www.
10, CUDA 9. 0. 176, and OpenMPI 2. 1. 3. For the GPU optimisation of the MPDATA dwarf we used the PGI compiler version 17. 10, CUDA 9. 2. 88, and OpenMPI 2. 1. 3. CUDA includes the compiler nvcc used to compile the library function wrappers, the libraries themselves, and the profiling tool nvprof, which we used for profiling on the GPUs. For the CPU optimisation we used Intel compilers and libraries version 2018. 1. 163. This includes the compilers icc and ifort and the libraries MKL and MPI. The work on the optical processor Optalysys used the MATLAB software version 2017b. Data availability. The data used to create the figures can be downloaded from https://doi. org/10. 5281/zenodo. 3462374 (Müller et al. , 2019). Author contributions. AnM performed energy measurements for the spectral transform dwarf, implemented the optimised spectral transform for post-processing, performed the simulations on Summit, worked on the implementation of the MPDATA dwarf, supported maintenance for all dwarf prototypes, and assembled the paper.
Acknowledgements. The authors thank three anonymous referees for their thorough reviews and many detailed suggestions that helped to improve the presentation. This research used resources of the  Oak Ridge Leadership Computing Facility , which is a  DOE Office of Science User Facility  supported under contract  DE-AC05-00OR22725. Financial support. This research has been supported by the  European Commission ,  H2020 Future and Emerging Technologies (ES-CAPE  (grant no. 671627 )). This work has also been supported by  HPC  resources provided by the  Poznan Supercomputing and Networking Center  and corresponding research activities under MAE-STRO grant number  DEC-2013/08/A/ST6/00296  in the  National Science Centre (NCN). An award of computer time was provided by the  INCITE programme. Review statement.
Results In order to include both participants and items (i. e. , magic tricks) as random factors in all analyses and to avoid any fixed effect fallacies by separating by-participant and by-item analyses  (Clark, 1973; Brysbaert, 2007) , data were analysed by fitting linear mixed-effects models using the R software (R Development Core Team, 2010, version 3. 1. 2). Models were tested using the lmer() function of the lmer4 package of R, and model comparisons were assessed using the anova() function which calculates the Chi-square value of the loglikelihood in order to evaluate the difference between models, following  Baayen's (2008)  procedure. Models were compared using a forward-testing approach, from the simplest model to more complex ones, as advocated by  Field (2014) , and as commonly done in psycholinguistics research (e.
Results Again, as in Experiment 1 and as justified by the design and convergence, we included Sex of Magician as a random slope for items. As in Experiment 1, although participants did not notice the change of magician within each session, the quality of the magic tricks was not completely homogeneous, as shown by an overall slightly better evaluationyet non-significantof our male magician (male magician: M = 4. 42, SD = 0. 75; female magician: M = 4. 19, SD = 1. 01, t (13) = 1. 40, p =. 185, 95% CIdifference [-0. 59; 0. 13]). Finally, again, to obtain p-values for our final model, we used the summary() function from the lmerTest package  (Kuznetsova, Bruun Brockhoff & Haubo Bojesen Christensen, 2014).
Here, we propose to apply a similar strategy on ISU and ESV data, in order to limit the bias associated to OTU clustering and to improve the reproducibility and standardization of biomonitoring indices. In our study, we compare different strategies to use biological data and provide quality assessment indices. Four diatom indices were developed, based on the ecological optimum and tolerance of morphospecies/OTUs/ISUs/ESVs calculated along an integrated environmental gradient, using the classical  Zelinka-Marvan equation (1961). The first index is based on taxa inventory obtained via the microscopic identification of morphospecies and their relative abundance. The second index is based on OTU inventory without taxonomic assignment, obtained via DNA metabarcoding and hierarchical de novo clustering with 95% sequence similarity.
Physical and Chemical Parameters For each site, environmental data were extracted from the French national database "Naïades" (http://www. naiades. eaufrance. fr/) over a period of 70 days (60 days preceding and 10 days following the biological sampling). For each environmental variable, we computed the mean value of all the records available during this time window. It resulted in a table of 76 rows (samples) and 15 columns (variables) without missing values. The environmental parameters kept are dissolved oxygen (O 2 ), oxygen saturation (O 2 sat), pH, Conductivity, nitrate (NO - 3 ), nitrite (NO - 2 ), ammonium (NH + 4 ), total Kjeldahl nitrogen (TKN), total phosphorus (TP), phosphate (PO 3- 4 ), temperature (T), total suspended solids (TSS), total organic carbon (TOC), biological oxygen demand (BOD 5 ), and turbidity (Turb) (Table  S1 ).
Bioinformatics Initial Bioinformatic Steps for ISUs and OTUs The GeT-PlaGe sequencing platform assembled the MiSeq paired-end reads into full-length DNA sequences (paired sequences overlap >140 bp and mismatches <0. 1%) and performed the demultiplexing of the 76 samples, providing 1 fastq file per sample. All the bioinformatics treatments were performed using Mothur software v1. 39. 5  (Schloss et al. , 2009). Initial bioinformatic steps were applied to keep good quality DNA reads using the trim. seqs() command and the following parameters: a sequence length of 263 ± 10 bp (rbcL barcode length without primers), a Phred quality score ≥23 over a moving window of 25 bp, 0 ambiguities ("N"), a maximum homopolymer length of 8 bp, a maximum of 1 mismatch in the primer sequence. Remaining DNA reads were dereplicated into ISUs with the unique.
Preparation of ISU Table Even if the rbcL primers used for metabarcoding were designed to be diatom specific, the presence of degenerated bases in the primer sequence may introduced non-target organism amplification  (Linhart and Shamir, 2002)  In order to perform the most objective comparison between diatom morphospecies and ISUs water quality indices developed in this study, "nondiatom" ISUs must be removed as they can interfere, positively or negatively, on the predictive power of the ISU index. Thus, we used the classify. seqs() command (default parameters, cutoff = 75%) with the "diat. barcode" reference database (version v7: 23-02-2018, https://doi. org/10. 15454/HYRVUH) to provide a taxonomy to each ISU and we applied the remove. lineage() command to remove the non-Bacillariophyta (phylum) ISUs ("Bacillaryophyta_unclassified" ISUs were also discarded).
Preparation of ESV Table The software package DADA2 was used to infer ESVs from demultiplexed MiSeq reads (one R1 and one R2 fastq file per sample) following the methods described by  Callahan et al. (2016). The DADA2 pipeline adapted to diatom metabarcoding data and applied in this study is available on Github (https:// github. com/fkeck/DADA2_diatoms_pipeline) and includes : (i) for each sample, primers sequences are removed from R1 and R2 reads using cutadapt  (Martin, 2011) ; (ii) the R1 and R2 reads are truncated to 200 and 170 nucleotides, respectively in order to remove last poor quality nucleotides; (iii) R1 and R2 reads with 0 ambiguities ("N") and a maximum of expected errors (maxEE) of 2 are conserved; (iv) after dereplication of R1 and R2 reads into ISUs, ESVs are selected based on the error rates model determined by the DADA2 denoising algorithm and paired reads merged into one sequence; (v) chimeric ESVs are removed; (vi) ESVs are taxonomically assigned using the DADA2 default parameters with an adapted version of the "diat.
Correlation Between Community Data Tables Prior to indices development, the correlation between morphospecies, OTU, ISU and ESV tables was assessed using the Procrustes superimposition method (Peres-Neto and Jackson, 2001). Non-metric multidimensional scaling (NMDS) on Bray-Curtis distances was used to derive a three-dimensional configuration of each table. The pairwise matching between NMDS ordinations was then measured using Procrustes correlation and tested by permutations (999 repetitions). Analyses were conducted with the metaMDS and protest functions of the R package "vegan" (R Development Core  Team, 2008; Oksanen et al. , 2016).
Calculation of Diatoms Indices (Idx_morph, Idx_OTU, Idx_ISU, Idx_ESV) The development of the four diatom indices followed the methodology described in  Tapolczai et al. (2019). Both morphospecies inventory obtained via microscopic identification and sequence reads inventory obtained via HTS were transformed into relative abundances in order to ensure a comparable quantification among samples. The four datasets according to the biological inventories (morphospecies, OTU, ISU and ESV lists) were randomly divided into: (i) a training datasets containing the randomly selected 75% of the samples, including their position along PC1 and their associated morphospecies (and OTUs, ISUs, ESVs) relative abundances; (ii) a test dataset containing the remaining 25% of the samples. Therefore, the indices could be tested on an independent dataset that was not included in the index development.
This cross validation approach to randomly select training and test datasets was executed 100 times to measure the average and standard deviation of the values of the four indices at each sample instead of a single measure that could bias the results. This resulted in 100 indices tested for each of the four index types (Idx_morph, Idx_OTU, Idx_ISU, Idx_ESV) (400 indices in total). Ecological profiles of the morphospecies, OTUs, ISUs, and ESVs in the training datasets were defined by modeling their relative abundances in the samples along PC1. Rare morphospecies, OTUs, ISUs, and ESVs were removed from the data tables and only those present in more than 5% of the samples in the training dataset were kept. This arbitrary limit, well-established in previous studies  (Stenger-Kovács et al. , 2007; Bere et al.
, 2014; Tapolczai et al. , 2019) , was necessary to keep a minimum number of samples based on which robust ecological profiles are ensured. Weighted averages and standard deviations of the profiles were calculated to estimate the ecological optimum (s) and the tolerance (v) values. The Zelinka-Marvan equation  (Zelinka and Marvan, 1961)  was adapted to our data to define the four indices: Idx_morph/OTU/ISU/ESV = n j = 1 a j s j v j n j = 1 a j s j where Idx_morph/OTU/ISU/ESV are the indices based on morphospecies, OTUs, ISUs, and ESVs, respectively; a j is the relative abundance of morphospecies/OTU/ISU/ESV j; s j is the sensitivity or optimum of morphospecies/OTU/ISU/ESV j; and v j is the indicator value or tolerance of morphospecies/OTU/ISU/ESV j in the sample. Sensitivity and indicator values for each morphospecies, OTUs and ISUs were calculated using their abundance values plotted as functions of the samples' PC1 values.
The two ecological values (sensitivity and indicator) comprised a database that was used together with the relative abundance values of the morphospecies, OTUs, ISUs, and ESVs in the samples for which the indices were calculated. Data of the training dataset was used to define these profiles. Idx_morph, Idx_OTU, Idx_ISU, and Idx_ESV for each sample in the test dataset were calculated and correlated with their corresponding PC1 values. The R script used for the index development and data analysis is uploaded and freely available on Zenodo repository (https:// doi. org/10. 5281/zenodo. 3463043).
Index Comparison In order to assess and compare the performance of the four quality indices developed, several metrics were used. Correlation coefficients of the linear models fitted on mean data of quality values per sites were compared and significance tests were performed using the "cocor" package in R (R Development Core  Team, 2008; Diedenhofen and Musch, 2015). The residuals of the four regression models were compared with Wilcoxon signed rank test and Bonferroni correction  (Hollander and Wolfe, 1973)  in order to measure the prediction performance of the models. Stability of the indices were estimated by comparing the standard deviation of index values per sites originated from the 100 iterations to select training and test datasets. It was tested with Wilcoxon signed rank test with Bonferroni correction.
Reference Gradient The first principal component of the PCA (Figure  2A ), explaining the 47. 01% of the total variation in the dataset was used as the reference gradient for the indices. Poor quality is associated with those parameters indicating higher nutrient concentration, organic matter concentration, turbidity. Good quality is represented by well-oxygenated waters. pH, oxygen concentration, oxygen saturation and conductivity are the main factors responsible for the distribution of sites on the second principal component, explaining the 14. 84% of the total variation. All environmental variables correlated significantly with PC1 (p < 0. 05) with Pearson's correlation coefficients (r) presented on Figure  2B.
Morphological Identification A total of 355 diatom taxa were identified via microscopic analysis from which 321 at species level. The average number of taxa identified per sample was 28 (SD = 10) with a minimum of 4 and with a maximum of 56. Based on our criteria to remove rare taxa, the number of taxa kept in the training datasets and used for index development varied between 110 and 141 depending on the random selection of training and test datasets, with a mean taxa number of 122 (Table  S3 ).
HTS Results The 76 samples selected for this study were part of a MiSeq (2 × 250 bp) sequencing run composed of 284 rbcL diatoms libraries from freshwater biofilm samples and were analyzed in a joined analysis of 464 samples. In order to allow the bioinformatic reproducibility of our study, the global dataset corresponding to the 464 samples fastq files used for the Mothur and DADA2 bioinformatics analysis are available on the Zenodo repository system (https://doi. org/10. 5281/zenodo. 3244156). We will only present the results obtained for 76 samples studied here. The sequencing platform performed the demultiplexing and the contig steps, providing one fastq file per sample which generated a total of 3,071,693 DNA reads for the 76 samples with an average of 40,417 reads per sample (min = 23,140; max = 67,292).
Distribution of Ecological Values Ecological values (sensitivity and indicator) derived from the abundance distribution of the four kinds of biological units were defined (Tables  S8-S11 ) and their distribution is presented in Figure  3. The general pattern for the four data types is similar to each other with a quasi-normal distribution of sensitivity values and a right skewed distribution pattern of the indicator values. Morphospecies inventory consists of the fewest data points while ISU database contains the most. Consequently, morphospecies inventory involves higher relative abundances of taxa than the abundances of unique sequences. Both the relative abundance per OTUs and the number of OTUs are between the morphospecies and ISU inventories. ISU composition and abundance within OTUs were further analyzed in order to reveal hidden ecological information and the results are presented on Figure  4  in the case of the ten most abundant OTUs.
De novo Construction of Morphological and Molecular Diatom Indices In this study we developed, tested, and compared diatom indices based on morphospecies identified with microscopy and molecular taxonomic units based on metabarcoding. Similar studies aiming to develop quality indices using such approaches have been already conducted but their number is quite few  (Apothéloz-Perret-Gentil et al. , 2017; Cordier et al. , 2017 Cordier et al. , , 2018)). Significant correlations in our study between the reference gradient and the predicted quality notes proved the validity of our approach the model developed on the training dataset using cross validation method could successfully be used on the test dataset. From a further aspect, we carried out a comparison of index performances based on molecular and microscopical inventories but also studied the differences within molecular methods, between OTU-, ISU-, and ESV-based indices.
Thus, the construction of de novo indices enabled a fair comparison of different approaches for the improvement in bioassessment. Superscript letters indicate significant pairwise differences detected by Wilcoxon tests. Currently used diatom indices, as the Trophic Diatom Index (TDI;  Kelly and Whitton, 1995) , the Biological Diatom Index (Jean  Prygiel, 2002)  or the Specific Pollution sensitivity Index  (Coste, 1982) , were developed using the ecological profile of species along particular physical and chemical parameters related to eutrophication, organic pollution, etc. Following the strategy of previous studies of the authors  (Tapolczai et al. , 2017 (Tapolczai et al. , , 2019)) , this study used another approach by applying the first principal component of a PCA, carried out on our dataset, as the reference gradient.
Finally, even if the Idx_ISU and Idx_ESV outperformed the Idx_OTU, it provided similar predictive power than the Idx_morph with a higher correlation slope between the expected gradient and the estimated index values, but appeared to be significantly less stable. The highest prediction instability was observed for sites corresponding to the extreme situation on the physico-chemical reference gradient characterized by few sites (particularly on polluted sites). It was already described that the instability of the index development is related to the cross validation process, used for defining training and test datasets, which is sensitive to the size of the dataset and the presence of outliers  (Tapolczai et al. , 2019). Even if this bias also occurred in the Idx_morph, the highest instability was observed for the Idx_ISU certainly due to the high number of ISUs obtained which fragmented the ecological signal.
[8]  who developed an agent-based process chain model in order to evaluate the impact of the manufacturing process parameters on the battery performance. [11]  AI concept was introduced by Turing in his famous paper "Can machines think?"  [12]  and it is nowadays one of the most active areas for fundamental  [13] [14] [15]  and applied  [16] [17] [18] [19] [20] [21] [22] [23]  research. Overall, AI algorithms offer powerful capabilities to automatically identify correlations in very large datasets (big data) containing many variables. [26]  UMs aim to find some intrinsic clusters within the data to define a consistent classification. Afterwards, users must explain the sense of the different formed classes. In SMs, the user predefines the class label for each data in the dataset and the model tries to find the best classification in order to group together all the data withthe same label.
The aim of this article is to propose a methodology that uses ML algorithms in order to predict how manufacturing parameters affect the properties of the electrode, as schematized in Figure  1. In this work, we propose a computational strategy based on AI, to predict the impact of the manufacturing parameters on the final electrode characteristics: active material mass loading and porosity. Three different ML algorithms were tested: decision tree, deep neural network and support vector machine. The effect of the training dataset size on the accuracy of the predictions is discussed, along with the main features and disadvantages of each algorithm. The demonstration is carried out for the prediction of the slurry manufacturing parameters/properties impact on the final characteristics of NMC cathodes before calendering.