dataesr/scientific-paragraphs-categorization huggingface dataset:
First, we compute the consensus according to the results of Section 4. 1 where a unique training dataset is considered independently of surface or climatic zone. The four methods agree on identifying 33% of the scenes as Clear and 49% as Cloudy, resulting in a full concordance on 82% of the scenes. Then we consider the case when different separate classifications are performed for each type of surface (Section 2. 5), whose results are reported in Table  13. When additional information on surface type is given, global concordance of the methods is slightly higher (83%), with a slightly better concordance on Clear scenes at detriment of Cloudy ones. Values disaggregated by surface type and for each sky condition are reported in the same Table : the highest consensus is obtained for Vegetation (87%) and the lowest one on Water (78%).
Conclusions The paper shows a detailed analysis of the method presented by one the authors in a Round Robin exercise organized by ESA for detecting clouds from images taken by the PROBA-V sensor. Availability of a common high-quality dataset of scenes labelled as Clear or Cloudy by experts (gold standard) is a unique benchmark for comparing different methods for detecting clouds and investigating on questions still open. We considered some prototypes of methods and compared them under different frameworks but using a common training dataset for all of them. We demonstrated that CDA, chosen for participating in the Round Robin, was adequate yielding good accuracy. However, ANN and, particularly, KNN can both improve accuracy and better detect scenes with semi-transparent clouds. In addition, a silver standard training dataset, semi-automatically obtained by algorithms developed for other sensors, was proved to be effective in detecting clouds, yielding high accuracy.
We stress that the silver standard dataset considered in this paper covers only a portion of the globe and that the majority of gold standard pixels are outside the region covered by the silver standard. Indeed a silver standard dataset is the only feasible solution to get very large training datasets needed to train Artificial Intelligence methods (see, e. g. ,  [7] ). Then, even though it was not possible to give a conclusive quantitative answer whether separate classifications based on ancillary information as surface types and/or climatic zones improve accuracy, a qualitative analysis shows that introducing such information reduces probability to misinterpret Clear/Cloudy condition on Water. Finally, we performed a consensus analysis aimed at estimating the degree of mutual agreement among classification methods in detecting Clear or Cloudy sky.
b) Balancing of cloud/Clear conditions: this largely depends on the training dataset and on the fraction of Clear/Cloudy scenes included. Then the classification methods will generally and naturally favor the most populated class when Clear and Cloudy features overlap, so to improve global accuracy. However commonly remotely sensed images can refer to conditions that are prevalently Clear or prevalently Cloudy that could benefit from a training dataset or a classification method that weights scenes according to the different proportion of Clear/Cloudy conditions in the region. CDA is an attempt into this direction, even though in a global way, and Discriminant Analysis is naturally prone to include such weights, simulating a balancing of Clear and Sky conditions different from the training dataset.
Western blot analysis Proteins were separated on 10% or 12% SDSpolyacrylamide gels, transferred to nitrocellulose membranes (Ambion), and western blot analyses were performed with specific primary antibodies and peroxidasecoupled secondary antibodies (Sigma). We used the Clarity chemiluminescent substrate (Biorad) and Biorad Chemidoc XRS camera to visualize the blot. Quantification was done using Image lab software (Biorad). We used anti-Mtq2-Trm112 and eRF1 antibodies raised in rabbit against purified recombinant yeast Mtq2-Trm112 complex and recombinant eRF1, respectively. Mouse monoclonal anti-PGK1 was purchased from Invitrogen. Antibodies against methylated eRF1 were raised in rabbit against the peptide HGRGGQSALRFA present in yeast eRF1, where the glutamine residue is N5-monomethylated (Eurogentec).
RNA extraction, northern blot, quantitation RNA extraction from yeast cells and northern blots were performed as described previously  (42). Oligonucleotides used in the hybridizations are listed in Supplementary Materials and Methods Supplementary Table  S3. For the analysis of high molecular weight species, 10 g of total RNA was separated on 1. 2% agarose -6% formaldehyde gels; for low molecular weight species, 5 g of total RNA was separated on 8% urea-acrylamide gels. Phosphor imager quantification used a Fuji FLA-7000 and the native Multi Gauge software (v3. 1).
Mass spectrometry experiments and data analysis These different steps are described in details in Supplementary data. Briefly, proteins were concentrated by SDS-PAGE gel and then digested with trypsin while in the gel. Peptides were analyzed by NanoLC-MS/MS. Data were processed using MaxQuant including Andromeda search engine. Label Free Quantification intensities were calculated. The dataset was deposited on the ProteomeXchange Consortium severe via the PRIDE partner repository with the dataset identifier PXD018733  (46).
Statistical data analysis Statistical analyses were performed using ProStaR software suite  (47) (48) (49). Proteins identified in the reverse and contaminant databases, proteins only identified by site, and proteins for which less than two LFQ values were available in a single condition were removed from the list. After log 2 transformation, imputation of missing values was performed. For each sample, the slsa algorithm was used for the POV (Partially Observed Values) imputation, and missing values were replaced by the 2. 5 percentile value for the MEC (Missing on the Entire Condition); statistical testing was performed using Welch's t-test. Slim (sliding linear model) (50) method was used to adjust p-values for multiple testing and differentially expressed proteins were sorted out using a p-value threshold that guarantees a FDR below 5%.
ACKNOWLEDGEMENTS We thank  Kyle Tanner  ( IBPC, Paris ) for critical reading of the manuscript. We are indebted to  E. Hurt  ( University of Heidelberg ) for plasmids expressing uS5-GFP and uL23-GFP and  J. Banroques  ( IBPC, Paris ) for the derivative of p415 plasmid carrying the ADH promoter and a polylinker upstream of the gene encoding eGFP, and  J. R. Warner  ( Albert Einstein, NY ) for the kind gift of uL3 antibodies. We are grateful to  Dr Luftalla  ( Université Montpellier II ) for very useful discussions and to  Fabrice Giusti  ( IBPC, Paris, France ) for the synthesis of methylated glutamine used to produce methylated peptide used to immunize rabbits. We thank  Leslie Muller  for initial mass spectrometry experiments and  Sarah Cianferani  for invaluable discussions.
FUNDING Research in the Lab of D. L. J. L. is supported by the  Belgian Fonds de la Recherche Scientifique (F. R. S. /FNRS) ['RiboEurope' European Joint Programme on Rare Diseases  ( EJP RD/JTC2019/PINT-MULTI) grant R. 8015. 19  and  PDR grant T. 0144. 20 ]; Université Libre de Bruxelles (ULB) , the  Région Wallonne (SPW EER) ['RIBOcancer' FSO  grant  1810070 ];  Fonds Jean Brachet , the  Internationale Brachet Stiftung , and the  Epitran COST  action [CA16120]; K. S. was the recipient of a  doctoral fellowship  from  'Fonds de la Recherche dans l'Industrie et l'Agriculture' (FRIA) ; the work of V. H. H. was supported by the  Agence Nationale pour la Recherche  [ ANR14-CE09-0016-01 ];  Initiative d'Excellence program  from the  French State Grant   DYNAMO [ANR-11-LABX-0011 ]; CNRS; the work of M.
G. was supported by the  CNRS ATIP-AVENIR program ;  Agence Nationale pour la Recherche  [ ANR-14-CE09-0016-02 ]; CNRS and Ecole Polytechnique; Proteomics experiments were supported by the  French Proteomic Infrastructure [ProFI ;  ANR-10-INBS-08-03 ]. Funding for open access charge: CNRS.
Acknowledgments I. V. A and G. P. G. are grateful for the support of the  National Science Foundation  (Grant  CHE-1800329 ). G. P. G gratefully acknowledges  Canada's Natural Sciences and Engineering Research Council (NSERC)  for the  Banting Postdoctoral Fellowship  (starting April 2020). Part of computational resources was provided by  NSF   XSEDE (TG-CHE160006 ), and  FSU Research Computing Center. G. P. G. thanks  Professor Alán Aspuru-Guzik  ( University of Toronto, Vector Institute for Artificial Intelligence ) for the support and  Compute Canada  for computational resources. NEDA computations were performed on the niagara supercomputer at the  SciNet HPC Consortium. SciNet is funded by: the  Canada Foundation for Innovation ; the  Government of Ontario; Ontario Research Fund -Research Excellence ; and the  University of Toronto.
X. L. and G. Z. thank the  CSC  for a PhD grant. P. B. A, S. R. , M. S. thank the  LabEx MiChem part of French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02 , and  Cyclolab (Hungary)  for a generous supply of acyclodextrin. We thank  Dr. Robert Pollice  ( University of Toronto ) and  Dr. Kjell Jorner  ( AstraZeneca ) for valuable discussions. The first version of this manuscript has been posted as a preprint on ChemRxiv. 43.
Manual Optimization Within the CGMD formalism the AM is treated as a collection of spherical particles with a certain density and size distribution, interacting between them and with CBDs through the FFs. [9, 11, 12, 24] The CBDs are modeled as effective spheres, which represent a certain mass of carbon, binder and (for the slurry phase) solvent in a volume with an effective density. The FF used to mimic the slurry physicochemical properties is a combination of the Lennard-Jones FF (LJFF) and the Granular Hertz FF (GHFF), which are both available in LAMMPS computational software  [25]  and that are reported in Equations (  1 )-(  5 ): E LJ r ð Þ ¼ 4e s r � � 12 À s r � � 6 h i (1) F LJ r ð Þ ¼ À r E LJ r ð Þ (2) if r � r c F LJ=SF r ð Þ ¼ F LJ r ð Þ À F LJ r c ð Þ (3) if r > r c F LJ=SF r ð Þ ¼ 0 ( 4 ) F GH ¼ ffi ffi ffi d p ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi R i R j R i þ R j s k n dn ij À m eff g n v n À � À k t Ds t þ m eff g t v t ð Þ � � (5) The LJFF is used to describe the adhesive forces between particles and their bonds, due to the presence of binder in the suspension [Eqs.
The latter can give to the algorithm a hint of which zone of the parameters space is the most interesting one to analyze (lower cost function respect to the ones obtained by using random FFs parameters). On the other hand, the ML algorithm determines the parameters of one particle for the whole optimization procedure. The latter is described schematically in Figure  6. The comparison between the results obtained by H-D PSO and ML-D PSO allows to verify if the latter was able to accelerate the optimization procedure Batteries & Supercaps at least as much as an expert operator, but with the advantage of being completely automatized. The ML algorithm was built using a Python-based code that relies on the Keras library. Finally, the ML algorithm dataset was constructed with the results previously obtained during the PSO and the manual optimization procedures (by excluding the optimal ones that lead to the results showed in Figures  2  and 3 ).
It should also be underlined that this dataset is updated iteration by iteration during the ML-D PSO by adding to the dataset the results obtained during the previous iteration, as showed in Figure  6 B. Figure  7  reports the average cost function [Eq. (  6 )] for the eight particles (per iteration step) as function of the number of iterations for PSO, H-D PSO and ML-D PSO. The curves show clearly that our PSO-based algorithms are effective in terms of FFs parameters optimization, i. e. the average cost function decreases rapidly during the optimization procedure, especially during the first iterations. However, even if the classical PSO displays acceptable performance during the first iterations, it reaches a local minimum from which it was not able to get out from. This drawback could be solved by introducing in the PSO code a procedure "to escape" more easily from local minima, by changing randomly the FFs parameters of the eight particles, if the global best does not change after a certain number of iterations.
Concerning the H-D and ML-D PSO, both of them were able to reach the global minimum and the cost function decreases even faster than the classical PSO. Moreover, the performances of the H-D and ML-D PSO are comparable, which shows that in this context a ML algorithm can be used to accelerate the optimization procedure at least as much as an expert operator, but through a fully automated approach. Furthermore, the performances of the ML-D PSO are expected to improve by increasing the dataset size and by improving the ML model. We compare in the following the computational cost of the PSO, H-D PSO and ML-D PSO. The calculations were performed using eight nodes (384 GB of RAM each), each composed of 2 processors (Intel® Xeon® Gold 6148 CPU @ 2. 40 GHz, 20 cores). To estimate the time needed to obtain the curves showed in Figure  7 , it should be taken into account that each iteration step needs around 36 h.
The latter corresponds to the iteration 0 of the LPSO. Afterwards, the FFs parameters associated to these results are modified randomly to launch eight new CGMD simulations, whose results will be associated to the iteration 1 of the LPSO. From this point onwards, the optimization procedure follows the workflow of the classical PSO (Figure  5 ). Figure  8  A depicts the average cost function in terms of the number of iterations for the LPSO procedure, in which all the previous results (PSO, H-D PSO and ML-D PSO) were used as initial dataset. For both 70 % and 73 % SCs, the average cost function is approximately equal to zero from the iteration 0. At the first iteration the function increases due to the random modification of the parameters value, while with further iteration steps it decreases and oscillates around zero.
Particularly, the cost function approximately equal to zero at iteration 0 indicates that the initial dataset (obtained by using the PSO, ML-D PSO and H-D PSO) already contained set of FFs parameters values suited for all the SCs analyzed for the slurry composition 96 : 2 : 2. Consequently, the results obtained at the iteration 0 would have been enough to determine FFs parameters values suited for both SC 70 and 73 %. The further iterations were performed only to verify that the LPSO code would correctly converge to approximately the same FFs parameters values obtained at the iteration 0. All the above represents a further advantage of the PSO approaches in terms of saving time and computational resources. Indeed, it indicates that during the FFs parametrization for a specific slurry it is possible to find parameters sets suited for slurries similar to the one studied (as in the case of slurry with constant composition but different SCs).
Acknowledgements The authors acknowledge the European Union's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges the  Institut Universitaire de France  for the support. Results have been achieved thanks to the  MatriCS HPC  platform from  Université de Picardie-Jules Verne , particularly using the ARTISTIC projectdedicated computational nodes.
Acknowledgments This research was supported by the  Deutsche Forschungsgemeinschaft (DFG)  in the framework of the priority program SPP 2017 " Mountain Building in Four Dimensions  ( MB-4D )" by grant  JO 349/11-1. Funding for TBA was provided from  Norges forskningsråd (NFR)  project  250327. We would like to thank  Whitney Behr  and one anonymous reviewer for their thorough and thoughtful reviews and  Maureen Long  for editorial handling.
To complement the new European Strong-Motion dataset and the ongoing efforts to update the seismic hazard and risk assessment of Europe and Mediterranean regions, we propose a new regionally adaptable ground-motion model (GMM). We present here the GMM capable of predicting the 5% damped RotD50 of PGA, PGV, and SA(T = 0. 01 -8 s) from shallow crustal earthquakes of 3 ≤ M W ≤ 7. 4 occurring 0 < R JB ≤ 545 km away from sites with 90 ≤ V s30 ≤ 3000 m s -1 or 0. 001 ≤ slope ≤ 1 m m -1. The extended applicabil- ity derived from thousands of new recordings, however, comes with an apparent increase in the aleatory variability (σ). Firstly, anticipating contaminations and peculiarities in the dataset, we employed robust mixed-effect regressions to down weigh only, and not eliminate entirely, the influence of outliers on the GMM median and σ.
Secondly, we regionalised the attenuating path and localised the earthquake sources using the most recent models, to quantify region-specific anelastic attenuation and locality-specific earthquake characteristics as random-effects, respectively. Thirdly, using the mixed-effect variancecovariance structure, the GMM can be adapted to new regions, localities, and sites with specific datasets. Consequently, the σ is curtailed to a 7% increase at T < 0. 3 s, and a substantial 15% decrease at T ≥ 0. 3 s, compared to the RESORCE based partially non-ergodic GMM. We provide the 46 attenuating region-, 56 earthquake localities-, and 1829 site-specific adjustments, demonstrate their usage, and present their robustness through a 10-fold cross-validation exercise.
Introduction Ground-Motion Models (GMMs) characterise the random distribution of ground-motions at site for a combination of earthquake source, wave travel-path, and the affected site's geological properties. Typically, GMMs are regressed over a compendium of strong groundmotion recordings collected from several earthquakes recorded at multiple sites scattered across a variety of geographical regions. The necessity of compiling such large datasets is to expand the range of magnitude and distance, and diversity of site-types, in order to derive a GMM capable of predicting realistic ground-motions for prospective seismic risk scenarios, e. g. large magnitudes at short distances from a reference rock site. NGA-West2  (Ancheta et al. 2014 ) is one such dataset compiled of ground-motion observations recorded around the globe-primarily from Western US, and in smaller fractions from Alaska, China, Italy, Japan, Taiwan, Turkey, etc.
Several GMMs have been derived from this dataset for application in probabilistic seismic hazard (PSHA) and risk assessments. Given the clear tectonic and geological diversity of the data, possible regional differences in observed ground-motions needed to be quantified  (Douglas 2004). Four of the NGA-West2 GMMs  (Abrahamson et al. 2014; Boore et al. 2014; Campbell and Bozorgnia 2014; Chiou and Youngs 2014 ) accounted for regional differences in ground-motions through region-specific regression coefficients. Through region-specific adjustments, these GMMs were able to capture and predict, for example, the faster attenuation of short-period ground-motions with distance in Japan compared to Western US, and the relatively slower attenuation in China. Earlier GMMs were not capable of such predictions simply owing to the lack of sufficient data from individual regions to quantify the differences.
In case of Europe and the Middle-East, RESORCE  (Akkar et al. 2014b ) is one such dataset compiled of data from Italy, Turkey, Greece, and other active seismic regions in pan-Europe. Using mixed-effects regression algorithms  (Abrahamson and Youngs 1992) , a few GMMs  (Akkar et al. 2014a; Bindi et al. 2014)  were derived and used in regional  (Grünthal et al. 2018 ) and continental PSHA  (Giardini et al. 2018; Woessner et al. 2015). However, these GMMs were not regionalised despite known geological differences between Italy and Turkey. In fact, even the regionalised GMMs  (Bora et al. 2017; Kale et al. 2015; Kotha et al. 2016; Kuehn and Scherbaum 2016)  limited the distinction to geopolitical boundaries, yet the geological diversity within these regions is far more complex. In essence, quantification of regional differences requires, first, a regionalisation scheme, and then, sufficient data from each region.
With the arrival of the new European Strong-Motion (ESM) dataset  (Bindi et al. 2018b; Lanzano et al. 2019a ) and regionalisation models, and ongoing efforts to update the pan-European PSHA, a revision of the regionalised pan-European GMMs is proposed. In this study, we present an upgrade of the RESORCE dataset based region-and sitespecific GMM  (Kotha et al. 2016 (Kotha et al. , 2017) )  for shallow crustal earthquakes using the new ESM dataset. We often see GMMs evolve with progressively larger datasets, and supersede their older versions in terms of applicability  (Bommer et al. 2010 ). However, with increasing data and complex parametrization, a reduction in the apparent aleatory variability (σ) of GMMs could not be achieved  (Douglas and Edwards 2016; Strasser et al. 2009). Of course, increasing amount of data comes with increasing the spatiotemporal diversity of ground-motion observations, and thus an increasing σ.
One approach would be to introduce new predictor variables into the GMM, but then, uncertainty or unavailability of predictor values then becomes an issue during application  (Bindi et al. 2017; Kuehn and Abrahamson 2017). With this in mind, this revision of the  Kotha et al. (2016)  GMM will attempt to regionalise and refine the aleatory variability, while maintaining its original parametrization. In addition, assuming a possible contamination of data and deviation from the assumption of log-normality, instead of the usual ordinary least-square estimates of GMM median and variances we compute their robust counterparts; while flagging outlier events, stations, and records in the dataset. Table  1  summarizes the additional features we introduced in this new GMM (abbreviated K20) with respect to the recent pan-European GMMs: AK14  (Akkar et al.
Ground-motion data and selection criteria Figure  1  compares the data distribution between the RESORCE and ESM datasets. The increase in amount of data between 2014 and 2018 for GMM development is dramatic. While the K16 GMM was regressed over 1251 records, the proposed revision (K20) derives from 18,222 records. One striking feature of the ESM dataset is the number of stations with ≥ 3 ground-motion recordings. RESORCE dataset had about 150 stations with ≥ 3 records, while ESM has 1077. This increase is highly sought in empirical site-specific GMM, PSHA, and seismic risk applications  (Faccioli et al. 2015; Kohrangi et al. 2020; Kotha et al. 2017; Rodriguez-Marek et al. 2013). Another feature is the remarkable increase in the number of small earthquakes with M W ≤ 4. 5. This could imply an increase in σ of the revised GMM over K16, similar to the reported increase for NGA-West2 GMMs with respect to their superseded NGA-West counterparts.
Nevertheless, ground-motions from frequent small-moderate sized earthquakes drive the hazard in low-moderate seismicity regions of Europe, such as France  (Drouet et al. 2020)  and Germany  (Grünthal et al. 2018). It is necessary that the GMM is well behaved in these small-moderate magnitude ranges. Moreover, if the site-specific terms S2S s were to be estimated with low uncertainty, data from several small-moderate sized earthquakes is indispensable. The distance (Joyner-Boore metric, R JB ) range and density of data is also superior to RESORCE. The region-specific anelastic attenuation terms of K16, NGA-West2 and other GMMs  (Sedaghati and Pezeshk 2017)  were estimated from records at R JB ≥ 80 km. This revised GMM aims to refine the regionalisation of K16, and therefore, such increase in farsource data is quite useful.