dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , f air ) has to also be estimated. The fitting process consists of feeding a trace of processed data in the form T ROOM (t n ), P server (t n ), T server (t n ) → P Server (t n + T) for each QRad server CPU to an optimizer that can compute the model parameters using gradient descent-based algorithms  [71]. The processed data samples were split into 80% training data for model fitting, while 20% was used to validate the model. The fitting process is performed iteratively, at each step a sample of training data is read, and the variables C server , ε server and f air were varied from a set of initial values until they fit best the data. The sequential fitting was done using a custom script from the SciPy library, namely the scipy. optimize method  [75] , applied on each data sample, recording values of C server , ε server and f air from the fit process and using them as initial guesses for the next sample's fit.
2. A set of several machine learning algorithms were implemented to model the QRad server heat generation. The Linear and Polynomial Regression, Random Forest Regression, Support Vector Regression, and K Neighbors Regression were implemented using Python's SciKitLearn  [76]  library. The Multi-Layer Perceptron was implemented using Keras  [77]  and Tensorflow  [78]  while for the Gradient Boosting Regression, the XGBoost's  [79]  was used. The dataset processed using the defined pipeline of operations can be split into train and test subsets, with the proportions of 0. 8 and 0. 2 of the initial data. The models were validated using 5-fold cross-validation and were evaluated by computing the Root Mean Square Error (RMSE), Root Mean Percentage Square Error (RMSPE), the coefficient of determination R2 (R Squared), the error mean, the error standard deviation, and MAPE.
The average results obtained are listed in Table  4. As Table  4  shows, considering the MAPE and the RMSPE, the best results are obtained by the Gradient Boosting Regression (GBR). These are the most relevant metrics for prediction accuracy, considering the percentages of the errors. They are showing that the GBR model can predict the heater power demand with less than 5% error, corresponding to less than 10 W of power. However, by analyzing the mean and the standard deviation on the error besides the RMSPE and the MAPE, the Random Forrest Regression (RFR) gives better results, showing a small error distribution considering the mean error. Finally, the R2 metric for the two models is closest to 1, with values of 0. 94 for GBR and 0. 92 for RFR. Looking at all calculated metrics, we considered that the GBR gives the best results considering the datasets collected from the QRad heaters and is most suitable for being used in a thermal aware workload scheduling algorithm.
Funding: This work has been conducted within the  CATALYST  project Grant number  768739 , co-funded by the  European Commission  as part of the  H2020 Framework Programme  ( H2020-EE-2016-2017 ) and it was partially supported by a grant of the  Romanian Ministry of Education and Research, CNCS-UEFISCDI , project number  PN-III-P1-1. 1-PD-2019-0154 ,within  PNCDI III. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Population There are 2 populations for the proposed project. The first population consists of veterans (n=10) who have been diagnosed with an acute ischemic or hemorrhagic stroke and are admitted to JAHVH inpatient rehabilitation after a stroke. Inclusion criteria are as follows: (1) age 18-80 years and (2) stroke diagnosis verified by brain imaging. Exclusion criteria are as follows: (1) unable to follow instructions or participate in immersive VR therapy due to significant cognitive impairment and (2) history of seizures. The second population consists of occupational therapists and rehabilitation nurses (clinician champions) working in the Comprehensive Interdisciplinary Inpatient Rehabilitation Program, who will provide data on the feasibility of using VR in an inpatient environment (RQ 1.
Aim 1 Feasibility is the degree to which the VR treatment can be successfully integrated within the flow of usual care  [13]. Feasibility will be measured with a 6-item survey based on the Consolidated Implementation Framework  [44]  that will be administered to 10 clinical staff using Research Electronic Data Capture (REDCap). Tolerability refers to the prevalence of patient-reported physical (eg, vertigo, nausea, cybersickness) and emotional (eg, fear, anxiety) adverse effects of the VR treatment, along with any discomfort or inconvenience related to the VR equipment (eg, ill-fitting headset, facial discomfort, inability to explore the 3D environment fully due to limited mobility)  [32, 38]. Tolerability data (complaints and adverse events frequencies) will be extracted from detailed meeting minutes where such events are reported and discussed.
Analyses A data set was used during the first month of the study by using Microsoft Excel software as Excel is easily imported into the statistical analysis system for analysis. We have chosen to use Excel on our local research server rather than VINCI because this is a prospective cohort of new admissions and a relatively small sample. Data will be collected and entered into the database by the project manager. Data entry will be verified by the principal investigator. Data will be stored on the secure JAHVH Research Service R-drive. With the proposed pilot study design, the overall analytic goals are to (1) determine the feasibility and tolerability of using a therapeutic VR platform in an inpatient comprehensive stroke rehabilitation program and to (2) estimate, with reasonable precision, the effect sizes of upper extremity neurologic recovery, hand dexterity, and pain reduction outcomes.
Acknowledgments We would like to thank  Dr. Timothy Brindle  for his input following the first submission of our grant proposal. This material is the result of work supported with resources and the use of facilities at the  James A Haley Veterans' Hospital. The contents of this manuscript do not reflect the views of the Department of VA of the  United States Government. Funding was received from the  Department of VA Office of Research and Development, Rehabilitation Research and   Development Small Projects in Rehabilitation Research grant  ( RR&D SPiRE )  RX-20-009 ,  2020-2022. The final deidentified data sets from this study (qualitative and quantitative) and the VR user manual will be made available by the corresponding author upon reasonable request.
We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.
Introduction The European Union (EU) is now composed from 27 countries and is considered as a major world leading power  [1]. January 2021 has seen Brexit officially taking place, triggering the withdrawal of the United Kingdom (UK) from EU  [2]. This event has important political, economical and social effects. Here we project and study its consequences from the view point of international trade between world countries. Our analysis is based on the UN COMTRADE database  [3]  for the multiproduct trade between world countries in recent years. From this database we construct the world trade network (WTN) and evaluate the influence and trade power of specific countries using the Google matrix analysis of the WTN. We consider 27 EU countries as a single trade player having the trade exchange between EU and other countries.
(1 + δ), renormalizing column to unity and computing all rank probabilities and the derivatives dB c /dδ. We also use the REGOMAX algorithm  [9, 6]  to construct the reduced Google matrix G R for a selected subset of WTN nodes N r N. This algorithm takes into accounts all transitions of direct and indirect pathways happening in the full Google matrix G between N r nodes of interest. We use this G R matrix to construct a reduced network of most strong transitions ("network of friends") between a selection of nodes representing countries and products. Even if Brexit enter into play in 2021, we use UN COMTRADE data of previous years to make a projecting analysis of present and future power of EU composed of 27 countries. Finally we note that GMA allows to obtain interesting results for various types of directed networks including Wikipedia  [13, 14]  and protein-protein interaction  [15, 16]  networks.
Discussion We presented the Google matrix analysis of multiproduct WTN obtained from UN COMTRADE database in recent years. In contrast to the legacy Import- Export characterization of trade, this new approach captures multiplicity of trade transactions between world countries and highlights in a better way the global significance and influence of trade relations between specific countries and products. The Google matrix analysis clearly shows that the dominant position in WTN is taken by the EU of 27 countries despite the leave of UK after Brexit. This result demonstrates the robust structure of worldwide EU trade. It is in contrast with the usual Import-Export analysis in which USA and China are considered as main players. We also see that machinery and mineral fuels products play a dominant role in the international trade.
Acknowledgments: We thank  Leonardo Ermann  for useful discussions. This research has been partially supported through the grant  NANOX N • ANR-17-EURE-0009  (project  MTDINA ) in the frame of the  Programme des Investissements d'Avenir, France  and in part by APR 2019 call of University of Toulouse and by  Region Occitanie  (project  GoIA ). We thank  UN COMTRADE  for providing us a friendly access to their detailed database.
In this study, all analytical solutions were obtained by using the software DISPERSE  [40]  and the conventional SAFE solutions by implementing the weak formulations (Section 2. 3) into the software COMSOL Multiphysics (COMSOL AB, Stockholm, Sweden), in which, the isoparametric elements were used for the discretization. In all examples, the order of basis functions in the direction e 1 and e 2 are assumed to be the same and denoted by p. The convergence analysis was performed for two cases: free hollow cylinder and fluid-filled cylinder. In order to carry out a convergence analysis of the proposed method at a given frequency, we introduced a function err which is estimated as the summation of the relative errors of the numerical solutions of the first m modes: err =    √ m ∑ i=1 (C i,num ph -C i,r e f ph ) 2 (C i,r e f ph ) 2 , ( 25 ) where C i,num ph ( f ) is the phase velocity of ith mode at a frequency f calculated by using SAFE or SAIGA, C i,r e f ph is the corresponding reference solution.
For the SAFE modeling, both fluid and solid domains are meshed by using quadrilateral elements. We first validate the proposed FE formulations for calculating C ph 's dispersion curves. Thanks to the simple cylindrical geometry, the considered fluid-filled hollow cylinder can also be solved by using the semi-analytical method  [41]  which was implemented in the Disperse software  [40]. Note that when analyzing a coupled fluid-solid system, solving the dispersion equation may sometimes have numerical issues due to singularities when establishing transfer matrices. For this example, DISPERSE software starts to have difficulties to compute analytical solutions at frequencies higher than 1 MHz, and consequently, some modes are missed. On the contrary, using the finite element method, which requires higher computational costs, allows to avoid this kind of difficulty.
Moreover, using higher-order basis functions in SAIGA allows to manifestly reduce the numerical errors. For the SAFE analysis, while the results using p = 4 clearly have better convergence than the ones obtained with p = 2, the advantage of using p = 6 is not clearly found in this example. Note that the asymptotic behavior of the errors when the becomes smaller than 10 -6 is due to inaccurate reference solution, which was calculated using the Disperse software. In fact, although the Disperse's solutions were computed by using an analytical method, the results were exported in a format with limited digits after decimal point. Fig. 9  presents the displacement shapes of the mode (L(0,4)) at high frequency f = 2 MHz. As the eigenvectors of the fluid domain derived from (Eq. (  23 )) are defined in terms of pressures, the fluid displacements shape modes may be determined using Euler's equation: u f = -1 ω 2 ∇ p.
Moreover, it has been shown that waveguides immersed in an infinite fluid may efficiently be modeled by introducing perfectly matched layers (PML) and then employing SAIGA procedure. In terms of computational time, our numerical experiences showed that using SAGA allows to reduce significantly the computational time to archive a similar precision. It was also observed that using the same number of DOFs, SAFE and SAIGA required similar computational times. However, a rigorous comparison of computational times requires that both methods should be implemented in a same environment. Note that in this work, while the SAFE simulation was performed in Comsol Multiphysics, the SAIGA method was implemented in an in-house Matlab code. For wave propagation simulation using FEM, it is well-known that the discretization needs to ensure a number of elements which is sufficient not only for simulating interested wavelengths, but also for correctly describing the curved boundaries.
Acknowledgments: The authors really thank Araceli Menduíña and Ana Durán for their great technical support during hydrolysis experiments. We also thank Ana Riaza, Ramón Ojeda, and Carlos Tavares (Prodemar, Stolt Sea Farm S. A. ) for the kind supply of the turbot skins. The authors would like to acknowledge the European Union for their financial support under the GAIN project (EU, Horizon 2020 Framework Research and Innovation Programme under GA n. 773330). Funding from Xunta de Galicia (Grupos de Potencial Crecimiento, IN607B 2021/11) is also acknowledged. The authors also thank CACTI (University of Vigo) for technical assistance.
Acknowledgements This work was performed within the framework of the  Centre of Excellence of Multifunctional Architectured Materials (CEMAM)  no. ANR-10-LABX-44-01. Agence Nationale de la Recherche (ANR)  is acknowledged for nancial support under contracts  ANR-18-CE09-0040  ( MEANING ) and  ANR-18-CE09-0036  ( PAN-ASSE ). NC thanks  Csaba Forró  and  Olivier Caudrelier  for fruitful discussions.
Genetic diversity and population structure Analyses were first conducted on individual populations and subsequently on data pooled for each species pair (HYB, ANG, and BER). Genetic diversity summary statistics and analysis of molecular variance (AMOVA) were calculated using GENALEX v. 6. 5  (Peakall and Smouse, 2006, 2012). Principal component analysis (PCA) was conducted in R v. 3. 6. 2 using the adegenet package; results were plotted using GGPLOT2  (Wickham, 2009; R Core Team, 2020). We used thermodynamic integration using the R package rmaverick to detect admixture between the four parental species  (Verity and Nichols, 2016). Preliminary analyses were run to identify appropriate parameters. In our final analyses of the hybrid zones, we ran five repetitions of 5000 burn-in steps, 150,000 Markov chain Monte Carlo (MCMC) iterations and 10 rungs using the admixture model.
Admixture analysis and calculation of hybrid indices Nuclear admixture proportions were estimated using a Bayesian analysis implemented in the program NewHybrids v. 1. 0  (Anderson and Thompson, 2002)  for individual populations and for pooled data. This model works under the assumption that all admixed samples are earlygeneration hybrids, the genomes of which are contributed to by two parental gene pools. NewHybrids uses allopatric parental populations to estimate allele frequencies, then calculates the posterior probability that sympatric individuals belong to one of six hybrid classes (parent A, parent B, F1, F2, backcross to parent A and backcross to parent B). After running preliminary tests to establish appropriate parameters, we performed 10 independent repeats of 10,000 burn-in steps and 100,000 MCMC iterations using Jeffrey's priors.
Hybrid simulations To evaluate the power of the developed markers to detect the different hybrid classes we used the hybridize function in the R package adegenet to simulate two generations of hybrids (F1s, F2s and backcrosses; N = 100 for each class) in each of the three species combinations, using real allopatric individuals as the parental populations (OA1, OM1, OP1 and OS1; Table  1 ). We calculated hybrid indices and interspecific heterozygosity for all simulated hybrids for comparison with our observed data. The calc. intersp. het function was used to calculate interspecific heterozygosity for all individuals in the simulated hybrid data set and in the real hybrid zones  (Gompert and Buerkle, 2010).
Assigning parental and hybrid status using NewHybrids We used NewHybrids software to assign individuals in the hybrid zones to different hybrid classes (Figure  4D-F ). Using a threshold q value of 0. 8, we found that 349 plants (94. 8%) in the HYB populations could be unequivocally placed in one of four classes (O. militaris, O. purpurea, F1, and F2). Of these, there were 107 O. purpurea individuals, 100 O. militaris, 126 F1, and 16 F2 (Figure  4D ). No backcrosses to either O. militaris or O. purpurea were detected. All four HYB populations include parental taxa and F1 and F2 hybrids. In ANG and BER populations, the number of individuals that NewHybrids could categorize were similar. Using a threshold q value of 0. 8, we found that 89 plants in ANG populations (95. 7%) were successfully placed into one of three classes, with 24 O.
ACKNOWLEDGMENTS We thank  László Csiba ,  Penny Malakasi , and  Dion Devey  (all  RBG, Kew ) for help with sequencing and technical support. This work was supported by  NERC , the  Bentham-Moxon Trust , the  Systematics Research Fund , the  Botanical Research Fund , and the  OSU-OREME. We are also very grateful to the reviewers and editors whose comments made a big difference to the quality of this study.
Foraminiferal DNA was amplified with a hot start (2 min at 95°C) PCR in a volume of 25 μl with 40 cycles of 30 s at 95°C, 30 s at 50°C, and 2 min at 72°C, followed by 10 min at 72°C for final extension with primers s14F3 and J2 for the first PCR and 30 cycles and an annealing temperature of 52°C (other parameters unchanged) for the nested PCR with primers s14F1 and N6  (Darling et al. , 2016; Pawlowski, 2000). Positive amplifications were Sanger-sequenced directly at Eurofins Genomics (Cologne, Germany). DNA sequences were automatically aligned with available sequences of nonionids from GenBank with MUSCLE implemented in SeaView 4  (Gouy et al. , 2010)  and corrected manually to obtain an alignment of 512 sites. Among these sites, 316 did not show any polymorphism (61. 7%). A maximum likelihood analysis was performed by PhyML implemented in Seaview under the GTR + G (General Time Reversible) evolutionary model with non-parametric bootstrapping (1,000 replicates).
The detection limit of [Mn] per pixel was in the ppm range for these high-resolution maps. The information depth was several tens of μm. The PyMca software was used to fit the XRF sum-spectra to identify the elements present in the sample and possible spectral overlapping  (Solé et al. , 2007). The "zoom-in" maps of specimens A and C were acquired by three successive scans and rebound into single maps after dedicated image realignment. The elemental distribution maps were reconstructed by a homemade Matlab-code including dead-time correction,.
The analytical session (Table  S1  for details) was setup to run automatically with standard-sample-standard bracketing. Each analysis was done with 300 shots at 10 Hz and a fluence of 3 J/cm 2 for the NIST glass and 2 J/cm 2 for the carbonate matrix materials (Table  S1  for details). Baseline compositions were measured for 30 s before each measurement. U. S. National Institute of Standards and Technology SRM NIST610 was used as external calibration material (composition values as in GeoReM  [Jochum et al. , 2005]  via http://georem. mpch-mainz. gwdg. de). Data reduction was done using Iolite software build within the Wavemetrics IgorPro using the X_Trace_Elements_IS data reduction scheme  (Paton et al. , 2011; Woodhead et al. , 2007). For each specimen, locations for three spots (if all areas of interest were exposed for analysis), one each on the final (n), penultimate (n-1), and antepenultimate (n-2) chamber walls, were preselected based on SEM images.
To exclude potentially contaminated spots, the Mn/Ca data was paired with measurements of Mg/Ca and Sr/Ca and their correlations tested (Spearman's rank correlation coefficient). As the Mn/Ca data was nonnormally distributed (Shapiro-Wilk's test), and in parts comprised variable specimen or sample numbers (heteroscedastic data), statistical analyses suitable for such datasets were used: Welch ANOVA or two-way PERMANOVA  (Anderson, 2017; McDonald, 2014)  for testing differences between chambers, sediment intervals and sites and Kruskal-Wallis and associated Mann-Whitney post-hoc tests for pairwise comparisons. Relationships between two variables were tested with the Pearson correlation coefficient (e. g. , Mn/Ca foram and BWO concentration). All statistical analyses were performed with PAST 3.
Two new DNA sequences were obtained (GenBank accession numbers MT591587-MT591588). Sequences were identical to each other and 99. 8% similar to a sequence from GenBank identified as the morphospecies N. stella (AY818727;  Bernhard, Habura, Bowser, 2006) , also from SBB. The new sequences were aligned with a selection of other nonionid sequences retrieved from GenBank. Neighbor Joining and Maximum Likelihood gave the same phylogenetic tree topology (Figure  3c  and S3 ). The nonionid sequences were grouped in six clades interpreted as different phylotypes (T1-T6). T1-T5 have been previously described in  Deldicq et al. (2019) , whereas T6 originated from the split of T4 into two clades and accordingly is here described as a new phylotype. The variation between phylotypes was mainly concentrated in three variable regions and 38.
stella needs to be reconsidered. Among the other clades presented in  Deldicq et al. (2019) , the phylotype T4 harbors a specimen from the SBB morphologically identified as N. stella  (Bernhard, Habura, et al. , 2006)  and other genetically closely related specimens sampled in Namibia (accession numbers FM999902-3) and the Adriatic Sea  (Holzmann & Pawlowski, 2017) , unfortunately with no published image of the test. As previously suspected  (Deldicq et al. , 2019) , T4 is now split into two phylotypes, T4 with the unidentified nonionids from Namibia and the Adriatic Sea, and T6, newly defined in this article, for the SBB individuals. The morphospecies N. stella is defined after specimens collected in the San Pedro Basin, off California  (Cushman & Moyer, 1930). As the SBB individuals (Nonionella sp.