dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall Results Both MLR and SVR were applied using the reduced feature set both for all raters and for the optimized group. We used 10-fold cross-validation with 10 random repetitions using the whole dataset. The overall results in terms of the explained variation R 2 are shown in Table  6. R 2 is defined as the squared correlation between the mean ratings and the prediction of the model across all notes. We see that we get rather modest overall results, reaching 66% for the best case. The SVR method performs better than MLR, as expected. Notice that there is a relatively large improvement for the optimized group in comparison to all raters. An example of the model results using the SVR method without cross-validation (thus trained on the whole dataset) is shown in Figure  2  for music example no.
Feature Analysis Using MLR In order to investigate the influence of each feature, the detailed results of the MLR applied on the whole dataset without crossvalidation are shown in Table  5. Since the R 2 for all raters were quite low, we show only the results for the optimized group. All features are listed along with the beta-weights, the semipartial correlation coefficient sr, and the corresponding p-values. The sr coefficient reflects the independent contribution of each feature. The highest contributions in terms of sr-value are shown in bold. It is not surprising that none of the pitch contour features gets a high sr-value since the system may combine the features in different ways if one feature is missing, thus "covering up" for the missing feature. This is natural, given the presumably high correlation between some features.
DISCUSSION Several topics emerged from the results of this study. They mainly concern its original methodology, the listening experiment, the music corpus, the music analysis, the computational model, and finally the relation between the music analysis and the model. In the listening experiment, where the participants marked the perceived accents, we collected a large set of empirical data about the perception of immanent accents. This database organizes the empirical data on the basis of vocal and instrumental music, and in three different historical styles. The overall agreement in terms of Cronbach's alpha (CA) was 0. 836 and the mean pairwise correlation was 0. 157. Given this relatively large variability among the raters, we made an automatic optimization of CA in which participants who lowered the final value were omitted.
Validating our implementation To validate our implementation, we applied our implementation of the coclustering algorithm to the continuous part alone and to the binary part alone while comparing the results with those of the blockcluster package (  [Bhatia et al. , 2014] ). Blockcluster is an R package for co-clustering binary, contingency, continuous and categorical data that implements the standard latent block models for co-clustering uni-type data. Figure  1  shows a comparison between the adjusted Rand index (of rows and columns) of the co-clustering obtained using blockcluster compared to our proposed approach. The comparison confirms that our implementation provides very comparable results, in terms of ARI and of parameter estimation, with respect to the blockcluster package in most of the cases.
As mentioned earlier, because of this focus on obtaining high quality results, our implementation takes at least ten times longer than the blockcluster package but provides more accurate row and column partitions and more accurate parameter estimation. Table  5  shows a comparative example of the means computation time for the rectangular matrix containing 100 rows and 2 × (2 + 2) co-clusters. µ or α J1 J2 I1 p1 p2 I2 p2 p1 µ or α J1 J2 J3 I1 p1 p1 p2 I2 p1 p2 p1 I3 p2 p1 p1 µ or α J1 J2 J3 J4 I1 p1 p1 p1 p2 I2 p1 p1 p2 p1 I3 p1 p2 p1 p1 I4 p2 p1 p1 p1.
Conclusion and future work In this article, we have proposed an extension of the latent block models to the co-clustering of mixed type data. The experiments show the capability of the approach to estimate the true model parameters, extract the true distributions from simulated data, and provide better quality results when the complete data set is used rather than separately co-clustering the continuous or binary parts. The proposed approach comes as a natural extension of the LBM based co-clustering and performs a co-clustering of mixed data in the same way that a standard LBM based co-clustering applies to uni-type data. On the course of our experiments, we have noticed that for the data sets with equal marginal parameters, both our algorithm and the state of the art algorithm implemented in the package blockcluster tend to fall in a local optimum.
The reference methods used for sickle cell disease (SCD) screening usually include two analytical steps: a first tier for differentiating haemoglobin S (HbS) heterozygotes, HbS homozygotes and β-thalassemia from other samples, and a confirmatory second tier. Here, we evaluated a first-tier approach based on a fully automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) platform with automated sample processing, a laboratory information management system and NeoSickle ® software for automatic data interpretation. A total of 6701 samples (with high proportions of phenotypes homozygous (FS) or heterozygous (FAS) for the inherited genes for sickle haemoglobin and samples from premature newborns) were screened. The NeoSickle ® software correctly classified 98.
In a multicentre pilot study, we evaluated a two-tier newborn SCD screening procedure in which the first tier is based on the fully automated MALDI-TOF MS classification of Hb profiles as FA, FS or FAS. To achieve this "plug-and-play" first tier, we maximised the performance levels of our previously described preanalytical and analytical procedures  [5]. Likewise, we improved the software used for automatic data interpretation. Lastly, a laboratory information management system was set up to ensure data traceability. We investigated the usability of this MALDI-TOF MS newborn SCD screening platform by assessing its analytical throughput, user-friendliness, ease of implementation, potential interfering factors, MS spectral quality and percentage of correctly classified samples. We established quality criteria for standard and nonstandard MS profiles as a function of the newborn's phenotype, determined our algorithm's ability to correctly classify MS profiles and identified interfering factors and their causes.
Sets of samples from our routine activity were selected each week, according to the following criteria: (i) samples presenting a β chain variant in CE and HPLC assays; (ii) samples from premature newborns (gestational age at delivery: 22-32 weeks); (iii) samples corresponding to late and very late screening; and (iv) and randomly selected samples of daily screening activity that had an interpretable CE profile. We analyzed 6701 samples with full datasets (i. e. , with clinical data as follows: N° of sample, sex, term, weight, name of maternity, transfusion, maternity location and date of screening, birth date plus validated results using CE in the first line and HPLC in the second line; Table  1 ). The percentage of FAS and FS phenotypes was higher for the premature newborns than for a standard population of newborns.
Data Processing Mass spectrometry acquisitions were analyzed by the algorithm if (i) the whole spectrum and the region of interest were sufficiently intense, (ii) the baseline was not too noisy and (iii) at least three of the four profiles per sample could be interpreted automatically. After evaluation of the quality of each spectrum, the spectra were normalized, smoothed and underwent baseline subtraction. The four spectra obtained of the same sample were then averaged to obtain a mean profile, which was submitted to the algorithm.
Another strong point is that all of the data from an analytical run were published and aggregated into a 96-well plate format. Abnormal profiles were differentiated from normal profiles by a colour code. Three different icons can be showed within a given coloured square: a cross indicating that the profile could not be interpreted, an exclamation mark indicating that the profile's initial automatic classification has been modified manually or a question mark indicating that the profile did not match the phenotype and must be checked by the operator. The spectrum's region of interest can be displayed in a mode in which β, βS and γ chains are seen or in a zoom mode in which only the β and βS chains are seen (Figure  1 ). The abnormal MS profiles were assessed visually by the operator. When applicable, two decision-support warnings could be displayed by the algorithm: "low HbA concentration" or "newborn having received a blood transfusion".
Spectral Quality of MALDI-TOF Data Taken as a whole, our results show NeoSickle ® 's ability to correctly classify 97% of the samples tested in Lille and 98. 8% of the samples tested in Dijon. The sensitivity of the NeoSickle ® approaches to detect the FA samples is 99. 2% and 97. 2% with a specificity of 99% and 99. 3% on the Lille and the Dijon MS facility, respectively. The detection of an HbS chain in samples is obtained on the Lille and the Dijon MS facility with a sensitivity of 98. 4% and 97% and with a specificity of 99. 8% and 99%, respectively. The positive predictive value of FS profiles detection was 96. 6% and 97. 7% with the results obtained by the Lille and the Dijon MS facility, respectively. The negative predictive value of FS profiles detection obtained at Lille and the Dijon MS facility was 100% and 99.
Criteria Used to Define a Standard MALDI-TOF Profile In 85. 8% of the analyses in Dijon and 92. 9% of the analyses in Lille, the median FA, FAS, FS and S-β + MS profiles (Figure  2a ,c,e,g, respectively) were considered to be standard and were automatically interpreted by the NeoSickle ® software. Limits not to be exceeded were defined: an FA profile with a low-noise ascending or convex baseline was considered to be normal (Figure  2b ); an FAS profile was considered to be standard as long as a plateau separated the β S and β A chain peaks (Figure  2d ); and, lastly, an FS profile with a low-noise ascending or convex baseline to the right of the β S chain peak was considered to be interpretable (Figure  2f ). The profile of a newborn with S-β + -thalassemia was characterized by a much more intense β S peak, relative to the β A peak (Figure  2g, h ).
Regular peaks at 18,850 and 1880 m/z were observed; however, the error was induced by the low intensity of the peak at 15,880 m/z. A profile with asymmetry for the β A and β S chains (a more intense β S peak) prompted its classification as FS by the e-NeoSickle software. The 16 samples misclassified in Dijon gave good results in Lille, indicating that the errors were probably due to a temporary lack of spectrum quality and not a sample or detection problem. The quality of the Dijon data was not as high as in Lille because the number of nonstandard spectra was higher; this finding agrees with the higher misclassification rate observed in Dijon.
Therefore, making comparisons and drawing general conclusions remains difficult and establishing a general numerical framework with capacity to jointly answer the scientific, regulatory and operational issues appears both necessary and innovative. In this context, the present article introduces a generic approach for modeling flexible rockfall barriers and discusses the potential use and capabilities of that approach. The principles of that generic approach are first defined and the implementation of the specifically developed GENER-OCK software is detailed. General assumptions regarding barrier modeling are given and the specific barrier component models implemented and used for the present work are recalled. Validation of the assembled barrier models against several experimental full-scale impact tests on different technologies of rockfall barriers is then presented.
Numerical Implementation The numerical implementation of the previously defined generic approach is now exposed. Impact calculations are performed numerically using the GENEROCK software. This software is dedicated to rockfall barriers' modeling and simulation and uses the generic representation defined in the previous paragraph. It is based on the work of  Chanut et al. (2012)  and  Coulibaly (2017)  and is being developed by Cerema (part of the French Department of Transportation and Infrastructures). The C++ code makes use of objectoriented programming features such as classes and polymorphism to define a data structure that replicates the physical generic representation defined in Fig. 1a. Such implementation provides a unique kernel and calculation process, independent of a barrier specificity, and can easily be completed with new components and technologies without altering the fundamental code structure.
Barriers' Behavior The developed generic approach along with the components' models developed makes the numerical modeling of various technologies of rockfall barriers possible and efficient. The validation carried out in the previous section highlights the validity of the individual components' models and of the two assembled barrier models introduced in the present study. On top of strong versatility, developments of the ring model  (Coulibaly et al. 2017a ) and sliding cable model  (Coulibaly et al. 2018 ) have been carried out with a clear focus to finely model the curtain effect and the irreversible mechanisms undergone by the structure with a low computational cost. In this section, the benefits and potential of the barrier models and the GENEROCK software are displayed through an exploratory numerical investigation.
Conclusion In this article, the GENEROCK rockfall barrier modeling software is introduced. Its underlying principles and capabilities are presented and discussed. Validation and exploratory simulations are performed to highlight the capacity of the generic approach to answer scientific, regulatory and operational research problems. In particular, the mechanical Fig. 15  Cumulative distributions obtained by polynomial expansion metamodels models of two barriers of distinct technology are introduced and validated against experimental data from full-scale impact tests. Specific advances in mechanical modeling, namely accounting for the irreversible behavior of ring nets and for the curtain effect, as well as low computational cost, make the present developed models well designed and their numerical implementation effective in addressing the complex nonlinear behavior of rockfall barriers.
The results of the exploratory investigations presented are mostly obtained from numerical simulations on one barrier model. It should be noted that some of the results may be specific to that given technology of barriers. However, implementation of additional models describing different barrier technologies in the GENEROCK software is required and the main conclusions drawn in this manuscript are expected to remain valid regardless of the investigated barrier technological details. The computational approach presented is already effective in addressing a range of research problem for the currently available barrier models. Future work will pursue the implementation of component models posts, chain-link nets. ) and the developments presented in this article to offer additional numerical insight into the scientific, regulatory and operational research problems identified.
In contrast to uncertainties in Se lifetime, the uncertainty in deposition flux maps are governed by Se emission factors, with all four Se sources (volcanic, marine biosphere, terrestrial biosphere, and anthropogenic emissions) contributing equally to the uncertainty in deposition over agricultural areas. We evaluated the simulated Se wet deposition fluxes from SOCOL-AER with a compiled database of rainwater Se measurements, since wet deposition contributes around 80% of total Se deposition. Despite difficulties in comparing a global, coarse resolution model with local measurements from a range of time periods, past Se wet deposition measurements are within the range of the model's 2 nd -98 th percentile at 79% of background sites. This agreement validates the application of the SOCOL-AER model to identifying regions which are at risk of low atmospheric Se inputs.
Outline This study focuses on the construction of the first global atmospheric Se model and the insights that this model reveals into which Se cycle uncertainties would be the most important to constrain. Section 2 describes the SOCOL-AER model and the implementation of Se chemistry in the SOCOL-AER model. The SOCOL-AER model is a suitable tool to model the Se cycle, since it successfully describes the major properties of the atmospheric S cycle  (Sheng et al. , 2015; Feinberg et al. , 2019b). The statistical methods that we use to conduct the sensitivity analysis are discussed in Sect. 3. Section 4 details the methods used to compile a database of measured wet Se deposition fluxes, which we use to evaluate the model. The results of the sensitivity analyses are presented in Sect. 5. 1 for the atmospheric Se lifetime and Sect.
The wet removal of tracers is coupled to the grid cell simulated properties of clouds and precipitation. The dry deposition scheme is based on  Kerkweg et al. (2006 Kerkweg et al. ( , 2009)) , which uses the surface resistance approach of  Wesely (1989). In addition to surface type and meteorology, the calculated dry deposition velocities depend on reactivity and solubility for gas-phase compounds and size for aerosol species. SOCOL-AER uses an operator splitting approach, wherein the model time step is 2 hours for chemistry and radiation and 15 minutes for dynamics and deposition. Aerosol microphysical routines use a sub-time step of 6 minutes. For the simulations in this study we use boundary conditions for the year 2000. Sea ice coverage and sea surface temperatures are prescribed from the Hadley Centre dataset  (Rayner et al.
, 2003). Year 2000 concentrations of the most relevant greenhouse gases (CO 2 , CH 4 , and N 2 O) derive from NOAA observations  (Eyring et al. , 2008). Anthropogenic CO and NO x emissions are based on the RETRO dataset  (Schultz and Rast, 2007) , while natural emissions are taken from  Horowitz et al. (2003). Sulfur dioxide (SO 2 ) emissions from anthropogenic sources follow the year 2000 inventory from  Lamarque et al. (2010); Smith et al. (2011). Volcanic degassing SO 2 emissions are assigned to surface grid boxes where volcanoes are located  (Andres and Kasgnoc, 1998; Dentener et al. , 2006). Atmospheric emissions of dimethyl sulfide (DMS) are calculated using a wind-based parametrization  (Nightingale et al. , 2000)  and a marine DMS climatology  (Lana et al. , 2011). To represent mean conditions for photolysis, the look-up table for photolysis rates is averaged over two solar cycles.
Due to the lack of detailed information available in literature about the parameter distributions, we chose loguniform or uniform distributions for all but one of the parameters. This follows the conservative approach recommended by the Maximum Entropy Principle, as the uniform and loguniform distributions maximize entropy while fulfilling the data constraints  (Kapur, 1989). The uncertainty distributions of all input parameters are listed in Table  3. 3. 1. 1 Measured rate constants (k 1 -k 12 ) The Se reactions studied in the literature have each only been measured by one laboratory group (Table  2 ). Since only one measurement technique has been applied, the reported measurement uncertainties may underestimate the true uncertainties of these rate constants. To approximate an uncertainty distribution for these rate constants, we reviewed S compound reactions that have been studied by multiple research groups.
75. PCE coefficients are generally calculated by least-square regression  (Berveiller et al. , 2006)  using an experimental design which consists of uniformly sampled realizations of the input variables X = {x (1). x (N ) } and corresponding model evalua-tions {M(x (1) ). M(x (N ) )}, i. e. : y α = arg min yα∈R cardA 1 N N i=1 M(x (i) ) - α∈A y α Ψ α (x (i) ) 2 (6) When the dimension is large, regression techniques that allow for sparsity, i. e. , by forcing some coefficients to be zero, are favored. In this work, we consider least-angle regression as proposed in  Blatman and Sudret (2011a)  and follow the implementation in the UQLAB PCE module  (Marelli et al. , 2019). The accuracy of the PCE in representing the full SOCOL-AER model is evaluated with a cross-validation metric named the leave-one-out (LOO) error, LOO  (Blatman and Sudret, 2010a).
The cross-validation approach removes the need for an independent validation dataset, saving computational expense. The leave-one-out procedure consists of creating a PCE using all but one of the training simulations, M PC\i. This PCE is then used to predict the output value of the model at the removed training simulation point, x (i). The process is repeated for all N training points so that a residual sum of squares can be calculated. This residual is then divided by the output variance in the training dataset, yielding the LOO error: LOO = N i=1 (M(x (i) ) -M PC\i (x (i) )) 2 N i=1 (M(x (i) ) -μY ) 2 (7) where μY is the sample mean of the model output in the training dataset. In practice, the LOO error is estimated using a single PCE model considering the entire experimental design  (Marelli and Sudret, 2014; Blatman and Sudret, 2010b) , to avoid the procedure of creating N PCE models.
Compilation of Se wet deposition flux data We decided to compare SOCOL-AER results with measurements of Se wet deposition, since wet deposition contributes an estimated 80% of total deposition  (Wen and Carignan, 2007). We conducted a systematic literature review to assemble a dataset of measured wet Se deposition fluxes, extending an earlier review from  Conde and Sanz Alaejos (1997). We searched in Web of Science (Clarivate Analytics) for combinations of the following criteria: "Selenium", "Se", "rain", "precipitation", "wet deposition", "trace element". The last search was completed in July 2019, yielding a total of 672 search results. We screened these search results for studies that measured Se concentrations in rainwater for at least one month, neglecting studies that measured bulk (wet and dry) deposition or that extrapolated wet deposition fluxes from aerosol measurements.
The compiled dataset, which is available in the Supplement, consists of 29 papers and data from two measurement networks, the European Monitoring and Evaluation Programme (EMEP) and Canadian National Atmospheric Chemistry (NAtChem) database, for a total of 73 measurement sites (Table  5 ). From these studies, we extracted the annual mean Se deposition fluxes, if available, or we used rainwater volume-mean weighted Se concentrations combined with the annual precipitation depths to calculate the deposition flux. If the paper did not provide the annual precipitation depth, we calculated the mean annual precipitation depth for the time period and location of the study from the historical Multi-Source Weighted-Ensemble Precipitation (MSWEP) 20 dataset  (Beck et al. , 2019). The calculated annual deposition fluxes for 9 sites are extrapolated from less than 12 months of rainwater Se measurements; the majority of sites (64) were measured for longer than a year.
For studies that spanned multiple years, a multi-annual mean deposition flux was calculated to compare with the model. Additional metadata was extracted from the papers, including geographic coordinates, the time period, collection methods, sampling frequency, and analytical methods. Despite the fact that the model is based on year 2000 conditions for emission maps and meteorology, we compare the model with data from all measurement years, since the dataset is relatively small. We created surrogate models of the Se wet deposition flux in the grid boxes where measurements were made (as in Sect. 3. 3 for the total deposition fluxes). We can then compare the resampled distribution of Se wet deposition in these model grid boxes with the corresponding observation.