dataesr/scientific-paragraphs-categorization huggingface dataset:
The explanation for this result lies in the Italian economy's cyclical trend that the algorithm is able to capture. The economic policies implemented in recent years are not suitable for a new economic growth phase after the previously mentioned financial and economic crises. Additionally, the negative trend of economic growth confirms the result obtained for CO 2 emissions. The lower economic growth has pushed CO 2 emissions, confirming the theory that sustainable production and renewable energy sources are not the main choices of policymakers in the phases of economic stagnation. To verify the goodness of the algorithm' results, we carry out the Sunburst ML test (Fig. 9 ). This test verifies the infinite combinations of prediction of the algorithm used concerning the set dataset. The test divides the combinations into numerous circular areas, representing the predictive phases from the beginning to the end of the elaboration.
To this end, in our algorithm, we have divided the time series into five eras. Subsequently, we elaborated on a process of analysis on two inputs' densities crossed with two outputs. In this way, we created a neural network on the infinite possibilities of predicting the correlation between per capita GDP per capita growth and CO 2 emissions. The scheme of the new process is shown in Fig. 10. Following the process depicted Fig. 10 , we transcribe the new commands to process the predictive correlation. Therefore, we add the #bigml package to the program language. As we can see from Fig. 11 , the algorithm detects the correlation between per capita GDP and CO 2 emissions predicted values. The values shown on the abscissa and ordinate axes (1-5) represent the algorithm's 5 eras. The areas of the predictive correlation quadrant show a color trend ranging from light green to dark blue.
29-1. 65, and with imaginary parts of the complex refractive index in the range of 0-0. 05. We test twelve wavelengths from 0. 355 to 2. 264 μm and find that the elements of the normalized scattering matrix as well as the asymmetry parameter, the aerosol absorption, backscatter, extinction, and scattering coefficients are precise to within 1% for 99. 99% of cases. The look-up table together with C++, Fortran, Matlab, and Python codes are freely available online.
PRINCIPLES OF LOOK-UP TABLE In order to reduce the size of the dataset that must be stored in computer memory, the LUT takes advantage of features that are universal and allow us to develop and apply a generalized approach for all the tabulated IOPs. One can see that Eqs. 2-4 share some commonality. The core structure of these equations can be generalized as where p {P ii′ (Θ, m, λ), β(m, λ), sca(m, λ), α(m, λ)}. The other common feature of Eqs. 2-4 is that it is optimal to perform the numerical integration on a logarithmic scale because it is often assumed in atmospheric sciences that the PSD n(r) dN(r)/dr has lognormal shape  (Seinfeld and Pandis, 2006). The cross sections C p (. , r) πr 2 Q p (. , r) are also often plotted and analyzed on a logarithmic scale because they exhibit smoother variability in equal relative steps Δr/r (i.
Structure of Look-Up Table File In Section 3 we described the theoretical background of the SIR LUT and in Section 4. 2 provided a justification for the selection of quadratures that formed the actual LUT. Starting from this section, we switch our focus on the matters related to the practical application of the SIR LUT. We computed the coefficients {C p } [see Eq. 16] at a reference wavelength λ r and stored them in a file on the computer's hard drive. The SIR LUT file is binary and consists of a header (3,536 bytes, see Table  5 ) that is followed by 31 • 75 2,325 data records (1,284,408 bytes per record, see Table  6 ) for each CRI separately (see Section 4. 2. 3). The total size of the file is equal to 3,536 + 2,325 • 1,284,408 2,986,252,136 bytes. We intentionally limited the size of the SIR LUT file to 3 GB because the majority of modern blade servers have at least 4 GB of RAM per core.
The SIR LUT file can then be uploaded into the memory of each core of the blade to further speed up the computations. In the future, we plan to refine the quadratures for the radii and CRIs when the progress in computational hardware will allow us to increase the size of LUT file. The header contains information defining the reference wavelength and the quadratures for the radii, scattering angles, and CRIs (see Table  5 ). Each data record of the SIR LUT contains the sets of values {C p } that were computed using Eq. 16 at the reference wavelength λ r 0. 355 μm (see Table  6 ). For the computations we used a reliable and accurate Lorenz-Mie scattering program  (Mishchenko et al. , 2002; Mishchenko, 2019). We slightly modified the program to make it   suitable for parallel computation of the coefficients C p,j,λ r [see Eq.
2. 2). The beginning position in the SIR LUT file for the data record that corresponds to a certain CRI can be computed using the sequential numbers of the real and imaginary parts (see Section 4. 2. 3) as 3,536 + 1,284,408 • (75 • (j R -1) + j I -1) bytes. For instance, if one needs the data record that corresponds to the CRI with real part m R 1. 362 (j R 7) and imaginary part m I 2. 6 × 10 -4 (j I 30) then the pointer inside of the SIR LUT file should be set at the position of 615,234,968 bytes from the beginning of file. We also share with the community codes written in C++, Fortran, Python and Matlab that can be used to efficiently compute all aerosol IOPs of interest [see Eqs. 2-6] from the SIR LUT.
, 2002b). Thus, the total number of CRI-PSD unit tests is equal to 1,818 • 8,568 15,576,624 at each wavelength of interest {λ}. Recall from Section 4. 2. 2, the SIR LUT uses a quadrature of 123 scattering angles that precisely represents the aerosol asymmetry parameter as well as equidistant quadratures with many more angles. Therefore, we use this scattering angle quadrature in our unit test dataset as well. Let us compute the simulated truth elements P ii′,t (Θ) of normalized scattering matrices [see Eq. 1] for the same angular quadrature. Putting it all together, we track 5 + 4 • 123 497 relative differences [see Eqs. 26, 27] of aerosol IOPs [see Eqs. 2-6] with a ± 1% precision target. For all wavelengths of interest let us compute the simulated truth {p t , P ii′,t (Θ)} by integrating over the radius range [r min 0.
Mitochondrial phylogenetic relationships We evaluated genetic relationships between mtDNA haplotypes using three different methods. First, we estimated phylogenetic relationships among unique haplotypes using the maximum-likelihood (ML) approach of PHYML  (Guindon et al. , 2010)  implemented in Geneious v. 8. 1. 9 (Kearse et al. , 2012). Prior to phylogenetic reconstruction, we used jModelTest v. 2. 1  (Darriba et al. , 2012)  to identify the model of nucleotide evolution best fitting our dataset. The tree was rooted with two sequences from Dall's porpoise. Node support was estimated using 1,000 bootstrap replicates. The resulting tree was visualized using ggtree v. 1. 4 (Yu et al. , 2018)  in the R statistical environment v. 3. 5. 1  (R Core Team, 2018). In addition to the phylogenetic trees, we reconstructed a median-joining haplotype network using PopART (http://popart.
otago. ac. nz), which allows displaying distances among haplotypes in a number of mutational steps and reticulations among them. Finally, we also summarized genetic relationships among haplotypes using a non-metric multidimensional scaling (nMDS) based on the Jukes-Cantor genetic distances between pairs of sequences. We used MEGA v. 7. 0  (Kumar et al. , 2016)  to calculate the genetic distance and the R package ecodist  (Goslee & Urban, 2007)  to compute the nMDS.
Population genetic structure and differentiation We investigated the genetic structure among porpoises from Northwest (NWA: US, CA, WGLD) and Northeast (NEA: IC, NN, NS) Atlantic at the microsatellite and mitochondrial loci. For the microsatellite dataset, we first explored patterns of genetic variation and structure using multivariate methods, including a principal components analysis (PCA)  (Patterson et al. , 2006) , a discriminant analysis of principal components (DAPC)  (Jombart et al. , 2010) , and a spatial PCA (sPCA)  (Jombart et al. , 2008). The three approaches were performed using the Adegenet v2. 1. 1 R package  (Jombart, 2008)  on centered genetic data (i. e. , set to a mean allele frequency of zero). The PCA was run with missing data replaced by the mean. The DAPC was performed without any missing data using the subspecies or geographical sub-groups as a priori groupings with the number of principal components set to 30 and 34, respectively, following alpha-score indication as recommended by the author.
We tested different numbers of plausible clusters (K) ranging from 1 to 5. Each run used 1x10 6 iterations after a burn-in of 1x10 5 iterations. To evaluate the convergence of the Monte Carlo Markov Chains (MCMC), we performed 10 independent replicates for each K value and checked the consistency of the results using CLUMPAK v. 1. 0 (Kopelman et al. , 2015). We determined the best K value using the loglikelihood of the data for each K value using STRUCTURE HARVESTER v. 0. 6  (Earl & vonHoldt, 2011)  and by visually inspecting newly created clusters with increasing K values  (Vercken et al. , 2010). We plotted the results as barplots using CLUMPAK. Finally, we investigated geographical variation in genetic ancestry coefficients (Q) of the predominant solution for the best K by computing the spatial interpolation of Q values among sampling locations using the script provided at http://membrestimc.
imag. fr/Olivier. Francois/TESS_Plot. html. We also ran STRUCTURE in a supervised way focusing only on P. p. phocoena individuals, as it has been suggested that rerunning STRUCTURE on identified clusters may reveal finer scale genetic structuring  (Evanno et al. , 2005). Pairwise genetic differentiation among geographical regions and sub-groups at microsatellite loci was assessed using the FST estimator of  Weir and Cockerham (1984)  in the R package diversity v. 1. 9 (Keenan et al. , 2013). The FST significance was tested using an exact test implemented in GENEPOP v. 1. 1. 3 (Rousset, 2008)  in R using 10,000 iterations. Geographical sub-groups with less than 10 individuals were excluded. We estimated the degree of mtDNA differentiation in terms of haplotype frequencies between pairwise geographical regions using the FST estimator of  Hudson et al.
While the estimations of Ar and pAr with ADZE already implement a standardization to account for differences in sample size among groups, calculations for the other statistics did not. Differences in sample size can significantly impact the values of genetic diversity estimators  (Goodall-Copestake et al. , 2012). Therefore, we also applied a rarefaction procedure  (Sanders, 1968 ) using a custom R script to calculate He, FIS, RD, π and θW in order to account for differences in sample size among geographic regions. However, while ADZE subsamples gene copies for the rarefaction procedure, here we subsampled individuals. For each geographical region and each statistic, we randomly subsampled 14 and 5 individuals 1,000 times-respectively for the statistics relative to microsatellite and mtDNA data, respectively.
We then estimated the mean and standard error for each statistic and for each geographical region. The rarefaction method was also performed for geographical sub-groups for He assuming a standardized sample size of 10 individuals. Sub-groups with less than 10 individuals were excluded (Table  S2 ). Genetic diversity is expected to decrease with distance away from glacial refugia  (Excoffier & Ray, 2008). Therefore, we investigated patterns of spatial variation in various genetic diversity estimators across the North Atlantic subspecies distribution range. We plotted the mean standardized values for He, Ar, pAr and π on geographical maps using MARMAP v1. 0. 2  (Pante & Simon-Bouhet, 2013). Spatial interpolation between sampling locations was calculated for each genetic diversity index to assess variation across geographical regions and sub-groups using the R script available at http://membrestimc.
To visualize potential fracture in the spatial distribution of the genetic distance between populations, we plotted genetic distance against marine geographic distances for all population pairs. Then, we focused only on North Atlantic populations (P. p. phocoena) to test specifically for IBD, excluding individuals south of the northern Bay of Biscay to avoid the effect of inter-subspecies comparison. We used a Mantel test to assess the correlation significance between genetic and geographic marine distances using 1×10 6 permutations in the R package ade4 v1. 7  (Dray & Dufour, 2007). We also assessed the relationship between average relatedness (between pairwise sub-groups) and marine genetic distances using the same approach. Average relatedness was assessed using the microsatellite data and the Wang's estimator of the coefficient of relatedness  (Wang, 2002)  in the R package related v.
Connectivity and gene flow across North Atlantic populations We further quantify the amount of dispersal, connectivity and contemporary gene flow between geographical regions and sub-groups that included at least 10 individuals using the 'divMigrate()' function of the R package diveRsity. This method relies on the detection of the direction of genetic differentiation, here the GST statistics  (Nei, 1973) , to infer the asymmetric pattern of migration rate (m) between groups  (Sundqvist et al. , 2016). We tested whether gene flow was significantly asymmetric between groups using 5,000 bootstrapped genotype resampling. The results were visualized as a network drawn using igraph v. 1. 2. 4. 1  (Csardi & Nepusz, 2006 ) and popgraph v. 1. 5. 1  (Dyer, 2017) in R. Nodes in the network represent populations and edges indicate migration rates from one population to another.
Acknowledgements We thank all the people involved in the sample collection, including  KA Tolley ,  M Ferreira ,  T Jauniaux ,  J Haelters ,  AA Llavona , B and  AA Ozturk ,  V Ridoux ,  F Caurant ,  W Dabin ,  E Rogan ,  M Sequeira ,  U Siebert ,  GA Vikingsson ,  A Borrell ,  AA Aguilar ,  D Palka ,  F Wenzel ,  M-P Heide-Jorgensen ,  AJ Read ,  G Stenson ,  V. Lesage ,  S. Barco  and the national stranding networks:  Réseau National d'Échouage and PELAGIS in France ;  Royal Belgian Institute of Natural Sciences-MUMM , and  Dept. Pathology  of the U.
The second component of the care pathway considered the costs and outcomes after initiation of VA-ECMO or Impella 5. 0. This component of the care pathway captured the likelihood of myocardial recovery, and for patients without myocardial recovery, palliative care, heart transplantation or placement of a long-term left-ventricular assist device (LVAD). The likelihood of mortality at each juncture was also captured (Figures  2  and 3 ). Event probabilities for both the Impella 5. 0 and VA-ECMO arms were based on clinical expert opinion, clinical databases, and published literature  14, 17  (Table  1 ). Specifically, clinical input was sought from a total of six clinical experts who participated in the data collection and were based at French hospitals, such that the modeled care pathway was an accurate representation of the current situation in clinical practice in France.
0, cost of circulatory support withdrawal strategy and non-diagnosis-related group (DRG) costs for device-related complications; this included the costs of daily supplements (daily supplementary cost for intensive care unit [ICU] stay, supplementary cost for a dialysis session), add-on list costs (LVADs) as well as the costs of complications over the 5-year time horizon (lower limb amputation, management of stroke), all of which represent costs incurred by the insurer (Table  3 ). As the payer perspective of the analysis was that of mandatory health insurance intra-DRG costs were not captured within the budget impact calculation. Hospital costs were 2019 values and add-on list prices were 2020 values, cost data relating to amputations were sourced from published literature and inflated to 2019 values and finally, costs associated with the management of stroke over the 5-year period were sourced from 2017 data and not adjusted.
As with any economic analysis, the analysis presented here is associated with a number of limitations. In particular, as noted, the clinical input data used in the model were sourced from a combination of expert opinion and observational studies rather than RCTs. However, as noted RCT data comparing Impella 5. 0 and VA-ECMO are currently lacking. The analysis was also performed from the perspective of the national health insurer and therefore does not capture all the cost components associated with the management of patients with CS, the omission of costs such as those associated with the use of additional IABPs or labile products, which was higher with ECMO than with Impella 5. 0, may therefore present a conservative picture in terms of the overall patient management costs. Further, a simplifying assumption used in the model was that all simulated patients were identical.
The production of a unique localisation result for all multi-spectral images is a common limitation to all cited works, which we address in this study with a multi-task network. We introduce MultiSpectral-MultiTask-CNN (MSMT-CNN), a multi-tasking DNN framework, as a robust solution for solar AR detection that takes into consideration the multi-spectral aspect of the data and the 3-dimensional spatial dependencies between image bands. This multi-spectral and multi-tasking concept may be applied to any CNN backbone. The 3D nature of our multi-spectral imaging scenario, which differs from previous multi-spectral applications, requires a new benchmark. We introduce two annotated datasets comprised of solar images from both ground and space, and which cover evenly all phases of solar activity, which follows an 11-year cycle.
The SOHO/EIT images are prepared by EIT routines. We eliminate any prominences or solar erup-tions by masking out all areas outside the solar disk. The contrast of SOHO/MDI Magnetograms is enhanced by intensity rescaling. Contrast enhancement was not used on SOHO/EIT and PM/SH images, as it was found to have minimal effect on our detection results. Both datasets are augmented using north-south flipping, east-west flipping, and a combination of the two. Augmentation with arbitrary rotations of the images is a popular way of augmenting astronomy datasets. However, such rotations are ruled out from our study because ARs tend to appear predominantly alongside the solar equator. Finally, a single-channel solar image was repeated along the depth axis resulting in a 3-channel image in order to match the pre-trained CNN's input depth.
Data We consider data from SOHO (space-based) and PM observatory (ground-based). We use the bands 171 Å and 195 Å (transition region), 284 Å (corona), and 304 Å (chromosphere and base of transition region) from SOHO/EIT, 3934 Å (chromosphere) from PM/SH, and line-of-sight magnetograms (photosphere) from SOHO/MDI, as illustrated in Fig. 1. To account for the regular solar cycle, we select images evenly from each of 3 periods of varying solar activity level, with years 2002-03, 2004-05, and 2008-10 for high, medium, and low activity respectively. We publish two new datasets with detection annotations (i. e bounding boxes): the Lower Atmosphere Dataset (LAD) and Upper Atmosphere Dataset (UAD). All annotations were validated by a solar physics expert. Localising ARs in multi-spectral images can be challenging due to inconsistent numbers of polarity centers, shapes, sizes, and activity levels.
To mitigate this issue, when manually annotating, we exploit neighboring image bands, magnetograms, as well as temporal information where we examine the evolution of a suspected AR to validate its detection and localisation. We designed a new multi-spectral labeling tool 2  which displays, side by side, images from an auxiliary modality and from a sequence of 3 previous and 3 subsequent time steps. ARs have a high spatial coherence in 3934 Å and magnetogram images due to the physical proximity of the two imaged regions. Hence, when annotating 3934 Å images, time-matched magnetograms were used as an extra support. Furthermore, the 3934 Å bounding boxes could be considered to be good approximations of magnetograms' annotations. Table  1  presents an overview of annotated images for both datasets.
We split the datasets into training and testing as indicated in the table. To compare against SPOCA, we consider a subset of the UAD testing set for which SPOCA detection results are available in HFC: the SPOCA subset. It consists of 26 testing images  (181, 168, 213, and 166  bounding boxes in the 304 Å, 171 Å, 195 Å and 284 Å images respectively).
A similar observation can be made in the LAD dataset when comparing the magnetogram results to 3934 Å, where magnetograms observe a lower altitude than 3934 Å. We also notice a strong contrast between a same detector's precision and recall on the different UAD bands. This further demonstrates that these bands are not equal in how easily they may be analysed, even though they were acquired at the same time with same size and resolution. Detections are visually verified to be poorer for small ARs and for spread and faint ones with more ambiguous boundaries. Visual inspection also confirms the different performances in various bands being caused by differing visual complexities of ARs. These observations suggest that detecting ARs using information provided by a single band may be an under-constrained problem.
Joint detection on multiple image bands We now present the results of our framework when detecting ARs over the LAD/UAD bands jointly. Joint detection results are summarised in Table  3. We first experimented with different types of featurefusion (image-level and feature-level, by addition and concatenation) over the LAD dataset. Overall, the three approaches show an enhanced performance in contrast to single band based detection. However, we find that late fusion with concatenation performs best. Hence, we pick this approach for all following experiments. We also evaluate the benefit of our MOO strategy using our 2-band architecture over the UAD. This approach generally improves the F1-scores in most bands comparing to the non-MOO architectures. This behaviour may indicate that the two feature extraction stages were indeed more effectively optimised for their different tasks at different epochs.
We retain this MOO approach for all other experiments. On the UAD dataset, with various combinations of 2 bands, we notice a general improvement over single band detections. In addition, the performance varies in correspondence to the bands being used. Combining bands that are difficult to analyse (304 Å or 195 Å that have lowest F1-scores in the single band analyses) with easier bands (171 Å and 284 Å) unsurprisingly enhances their respective performance. More interestingly, combining the difficult 304 Å and 195 Å bands together also improve on their individual per-formance. Similarly, when combining bands that are easier to analyse (171 Å and 284 Å), in contrast to using combinations of difficult and easy bands in the analysis, performances are also improved over their individual analyses.
Following these settings, our 2band based approach was able to record higher or similar F1-scores in contrast to the best performing single-band detector. This supports our hypothesis that joint detection may provide an increased robustness through learning the inter-dependencies between the image bands. Moreover, the most dramatic improvement in F1scores across both LAD and UAD datasets is for the analysis. This is in line with the current understanding of AR having strong magnetic signatures. Generally, in the UAD dataset, we find that using a combination of 2 bands produces the best results in comparison to using 3 or 4 bands. This may be caused by the fact that optimising the network for multiple tasks (2, 3, or 4 detection tasks) simultaneously increases the complexity of the problem.