dataesr/scientific-paragraphs-categorization huggingface dataset:
The proposed TenGeoP-SARwv dataset could be used as a training dataset for the identification and classification of different key geophysical phenomena occurring over open ocean. It can be used to directly fine-tune existing CNN models for straightforward geophysical applications, or explore new CNN architectures to improve feature representations. In fact, one relevant work has been conducted and the preliminary result was presented at the International Geoscience and Remote Sensing Symposium (IGARSS) in 2018. We believe that the large number of images in each of these ten classes satisfy the requirement to train a deep CNN model. Moreover, unsupervised learning algorithms by combining deep learning and reinforcement learning will become far more important (LeCun.
ACKNOWLEDGEMENTS The authors would like to thank the anonymous reviewers for their helpful comments and suggestions regarding this manuscript. This image dataset is created thanks to Sentinel-1A data access via  ESA  and through  Sentinel-1A Mission Performance Center  (Contract No. 4000107360/12/I-LG ). Copernicus data (2016) are used. A. Mouche ,  B. Chapron ,  N. LongÉpÉ  and  C. Wang  were supported by  S1-4SCI   Ocean Study  (Contract No. 4000115170/15/I-SBo ) and  CNES (TOSCA program ,  COWS  project). R. Foster  and  D. Vandemark  were supported by  NASA Physical Oceanography  grant  NNX17AH17G. C. Wang  also would like to acknowledge the financial support of  China Scholarship Council (CSC)  for his PhD study.
SAR image numbers of the labelled dataset for each class in every month of 2016 WANG et Al. | 9 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total POW 406 407 408 409 406 408 410 409 409 409 408 411 4,900 WS 406 409 403 407 404 391 396 398 397 398 384 404 4,797 MCC 396 384 370 384 385 365 386 388 380 391 384 385 4,598 RC 398 399 398 395 398 391 395 393 393 396 394 390 4,740 BS 398 394 395 398 397 339 397 398 397 400 398 398 4,709 SI 387 150 282 396 396 392 393 393 396 396 396 393 4,370 IB 399 417 308 146 58 29 10 14 12 29 159 399 1,980 LWA 137 137 138 220 201 95 214 144 207 207 241 219 2,160 AF 360 282 301 348 363 234 361 377 378 367 364 365 4,100 OF 61 85 64 102 131 60 116 135 96 108 132 109 1,199 Total 3,348 3,064 3,067 3,205 3,139 2,704 3,078 3,049 3,065 3,101 3,260 3,473 37,553 T A B L E 1.
, 2017; Santee et al. , 2017)  and ERA-Interim reanalysis data (https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era-interim, last access: 20 November 2018,  Dee et al. , 2011)  are available. The O 3 , AoA, w * , RCTT and age spectrum data set can be requested from the corresponding author Felix Ploeger (f. ploeger@fz-juelich. de). Author contributions. All co-authors made substantial contributions to the analysis, interpretation of the data as well as contributing to provide the analysed data and drafting the article. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "The SPARC Reanalysis Intercomparison Project (S-RIP) (ACP/ESSD inter-journal SI)". It is not associated with a conference.
Acknowledgements. We particularly thank the  NASA Jet Propulsion Laboratory , the  European Centre for Medium-Range Weather Forecasts  and the  Japan Meteorological Agency  for providing Aura Microwave Limb Sounder product (https://mls. jpl. nasa. gov/,last access: 9 January 2019), the  ERA-Interim  and JRA-55 reanalyses data. Work at the  Jet Propulsion Laboratory, California Institute of Technology , was done under contract with the  National Aeronautics and Space Administration. This work was funded by the  Helmholtz Association  under grant number  VH-NG-1128  ( Helmholtz-Hochschul-Nachwuchsforschergruppe ), enabling a research stay at the  Institute of Energy and Climate Research, Stratosphere  ( IEK-7 ), Forschungszentrum in Jülich during which this work was carried out. The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association.
6, Hines and Carnevale 2003) and a CA1 pyramidal neuron implemented with the simplified 15-compartments morphology taken from  Bianchi et al. (2014) (ModelDB acc. n. 151126). All models and simulation files will be uploaded to the public ModelDB database (https://senselab. med. yale. edu/ModelDB, acc. n. 256388). The model neuron included Hodgkin-Huxley type currents (axon and somadendritic, Na + and Kdr), two A-type potassium current (proximal and distal dendrites), one M-type potassium current, a h-current, three types of voltage dependent calcium currents (CaL, CaT, CaR), two types of calcium dependent potassium current (a slow AHP and a medium AHP current) and a calcium pump. The channel peak conductances and the dendrites diameters and lengths were modified from their original values (tuned to be consistent with recordings in mice), to take into account the specific set of experimental recordings in mice used in this work.
Object configurations for Sample and Test trials were counter-balanced. Experiments and analysis were done blind as to condition (SynGFP-or synAICD transduced rats). Object exploration was scored only when the animal was facing an object, with its nose within 1 cm of the object. Moments when the rat was touching the object with another part of the body, or when leaning or rearing on the object were not scored as object exploration. For the Test trial, a discrimination index was calculated: [time spent exploring object in the novel location]/[ total objects exploring time]. The index value is $0. 5 (chance value) if the animal does not show any object preference, spending similar time exploring each object. The trajectory of the animals was video recorded and tracked with ANYmaze software (Stoelting, Wood Dale, USA).
QUANTIFICATION AND STATISTICAL ANALYSES Results are shown as mean ± SEM 'n' refers to the number of cells examined, unless otherwise stated. Statistical significance of differences between means was determined through Prism 6. 0 software (GraphPad) and is presented in details in the Supplement (Tables  S1, S2 , S3, S4, S5, and S6). Significance was taken at p < 0. 05. the Student's t test or the Wilcoxon test were performed whenever two conditions were compared, according to the samples distribution normality. One factor analysis of variance (ANOVA) was performed to analyze significance among the different conditions. In experiments where mean firing frequency was analyzed as a function of injected current, a Two-way ANOVA with repeated-measures was applied. In studies where two variables were tested (e.
ACKNOWLEDGMENTS This work was supported by the  ATIP/AVENIR program  ( Centre National de la Recherche Scientifique [CNRS] ) to P. A. P. and H. M. ;  Fondation pour la Recherche Medicale  (FRM; postdoctoral fellowship) to P. A. P. ; the  French Fondation Plan Alzheimer  to E. F. R. , X. M. , and H. M. ; the  EU Joint Program-Neurodegenerative Disease Research (JPND)  Project  CIRCPROT  (jointly funded by  BMBF ,  MIUR , and  EU Horizon   2020 Grant Agreement 643417 ) to H. M. and P. A. P. , D. B. , and M. M. ;  EU   Horizon 2020 Framework Programme for Research and Innovation (Specific Grant Agreement 785907 ;  Human Brain  Project  SGA2 ); the  UniversiteCôte d'Azur  to H. M. and P. A. P. ;  Santa Casa da Miserico  ´rdia (MB-7-2018); and  Fundacão para a Ciência e Tecnologia  ( PD/BD/ 135516/2018 ,  SFRH/BD/52228/2013 , and  PTDC/BIMMEC/4778/2014 ) to L.
For k = 4, n = 2, the hyper-Pfaffian is reduced into the ordinary Pfaffian as Pf [4] ( f ) = ∑ σ∈S 4 sgn(σ)Pf ( f σ(1)σ(2),σ(3)σ(4) ) (11) where σ(1) < σ(2) and σ(3) < σ(4). For k = 4, n = 3, the hyper-Pfaffian is also reduced into the ordinary Pfaffian as Pf [4] ( f ) = ∑ σ∈S 6 sgn(σ)[Pf ( f 3 σ(1),σ(2),σ(3),σ(4),σ(5),σ (6) ) + z σ(1),σ(2),9,10 Pf ( f 2 σ(3),σ(4),σ(5),σ (6) ) ] where σ(1) < σ(2), σ(3) < σ(4) and σ(5) < σ  (6). These relations are derived in  [14] , while for n > 3, there is no simple relation. To find a more general formula beyond this limitation and the gradient formula is currently under our investigation. This work was supported by the research grant of the Senshu Research Abroad Program (2018) for one of the authors (T. M. ).
Comparisons. A detailed comparison between Progonomys manolo, all known species of Progonomys and other murines is available as online Supplementary Content (Supplementary Text S1). Morphometrics. The teeth of the Lebanese species are amongst the shortest of the dataset (Fig. 4a ). For the majority, they are within the range of variation of early Progonomys and Mus auctor. The length/width ratio provided a measure of the elongation of the tooth (Fig. 4b ). The teeth from Zahleh appear heterogeneous in this respect. Most teeth share a high length/width ratio, indicating elongate teeth. Such elongation has also been found in early Progonomys, as well as in Mus and Antemus. In contrast, one specimen (Zahleh 02, Supplementary Fig. S2 ) displays a very low length/width ratio, corresponding to a broad tooth, such as those of Castillomys, Occitanomys and Progonomys clauzoni.
The occlusal measurements (greatest length and greatest width; Supplementary Tables  S1  and S2 ) of the teeth of the taxon from Lebanon have been obtained with a Nikon digital counter CM-6S measuring device. Morphometrics. Nine first upper molars (M1) from Zahleh were considered for morphometric analyses. They were compared to a set of rodent molars of Miocene age documenting the following genera: Antemus, Castillomys, Huerzelerimys, Karnimata, Mus, Occitanomys, Parapodemus, Parapelomys, Progonomys (including Sinapodemus) (Supplementary Table  S3 ). The data set includes a total of 541 M1. Data resulting from the analysis of the cheek teeth of Huerzelerimys, Occitanomys, European Parapodemus and Progonomys correspond to previously published morphometric analyses  55, 59  (see Supplementary Table  S3 ).
Castillomys crusafonti has been documented on the basis of M1 from Sète (Pierre Mein collection), France, housed in the paleontological collections of the University Claude Bernard, France  74. Data obtained for the first upper molars of the Siwalik murines (Antemus chinjiensis, Karnimata darwini, Karnimata huxleyi, Mus auctor, Parapelomys robertsi and Progonomys debruijni) come from photos kindly provided by Yuri Kimura and those from Progonomys hussaini, Progonomys morganae, Parapodemus badgleyae and Karnimata fejfari are from Kimura et al. 4. Data from the first upper molars of Progonomys sinensis, Progonomys shalaensis, Progonomys ibrahimi and Progonomys minus result from measurements taken from the plates of Qiu et al. 75  , Qiu and Li 76 and Sen  15  , respectively. First upper molars have been photographed so that the occlusal surface matches best the horizontal plane.
Based on these photographs, the shape of the M1 was described using 64 points sampled at equal curvilinear distance along their 2D outline. The starting point was tentatively positioned towards the anteriormost part of the tooth. Using the Momocs package  77  , the outlines were then aligned along their long axis, and the starting point slid towards the uttermost point along this long axis, consistently adjusting its position at the most anterior part of the tooth. From the 64 points, 64 radii (i. e. distance of each point to the centre of gravity of the outline) were computed. Expressed as a function of the curvilinear distance along the outline, this set of radii constituted a function that was analysed using a Fourier analysis. Accordingly, the empirical function is decomposed into a sum of trigonometric functions of decreasing wavelength (the harmonics).
by 14 variables (FCs), representing more than 99% of the total information. The 64 points along the outline are enclosed into a bounding box (i. e. the smallest rectangle that enclosed the object delineated by the points). The length and width of this bounding box provided an estimate of these dimensions for each tooth (function coo_lw in Momocs). The shape of each molar was described by a multivariate dataset (14 FCs). A Principal Component Analyses (PCA) on the variance-covariance matrix of the FCs was performed to represent the total variance on few synthetic shape axes. Relationships between groups (species per deposits) were further assessed by calculating the Euclidean distances between the group means of the 14 FCs. An unrooted neighbor-joining tree allowed a representation of these relationships (Fig.
Various modern populations from the wood mouse Apodemus sylvaticus allowed the estimation of the variation in a context in which interspecific mixing can be definitely discarded based on genetic analyses. The sample from Mimizan (AS-Mim, n = 63) included specimens from four neighbouring populations and the sample from Lantabat (AS-Lant, n = 41) included seasonal variation within a population. A third sample (AS-geo, n = 15 + 15 + 15) included specimens from Belgium, France and Spain to cover geographic variation  82. For all these samples, the size and shape variation was estimated on the original dataset and on 10 bootstrapped datasets. In samples with n > 10, the sample size was rarefied to n = 10, in order to be comparable to the limited sample size in Zahleh (n = 8). Multivariate analyses, statistical analyses and representations were performed using Momocs, ade4  83  and ape 84 under R  85.
Acknowledgements We acknowledge much the students and staff of the  Lebanese University  for their assistance both on campus and in the field in the Bekaa. We are indebted to  I. Antoñanzas-Asso  for sorting the sediment. We sincerely thank  L. Flynn  ( Harvard University, Cambridge ) and  Y. Kimura  ( National Museum of Nature and Science, Tokyo ) for kindly providing unpublished photos of murine specimens and improving this work through careful reading and very useful comments. W. Wessels  ( Utrecht University, Utrecht ),  J. van Dam  ( Utrecht University, Utrecht ),  J. Aguilar  ( Université de Montpellier, Montpellier ) and  S. Sen  ( Muséum National d'Histoire Naturelle, Paris ) for having provided measurements of various specimens of Progonomys. Thanks are due also to  R. Portela Minguez  ( Natural History Museum, London ) for making available the extant murine material under his care and  J.
Encouraged by this structure-property tuning of H 2 S and CO 2 adsorption affinity using this MOF, we expected KAUST-8 to be more selective toward SO 2 than CO 2. Similar to gain better insight of the KAUST-8 system, SCXRD data were collected on the SO 2 -loaded KAUST-8 crystal. The structure analysis revealed the resultant compound crystallized in the tetragonal space group P4/mmm with the unit cell parameters a = b = 6. 9996(2) Å and c = 7. 7033(2) Å (Fig. 2b-d , Supplementary Table  2 ). Contrary to KAUST-7, a rotational disorder of the (AlF 5 ) 2-is caused by trigonal bipyramidal shape of the anions located at the axis 4 among four 1D channels in the structure. When one fluorine atom of a pillar is directed perfectly to the channel center (φ = 0°), in the two other adjacent channels Al-F ).
Stainless steel delivery lines or perfluoroalkoxy alkane, Perfluoroalkoxy (PFA) tubing (in regions requiring flexibility and resistivity to volatile organic compounds (VOCs)) were used on the setup with Vernier metering valves (from Swagelok) as a flow regulator. To detect the change in humidity level inside the chamber, a commercial humidity sensor (Honeywell HIH-4000-003) was used as a reference, which has an error < 0. 5% RH. The QCM sensor was exposed to the analyte stream until a stable response was obtained; a two-port network (Keysight E5071C ENA) circuit was used to monitor the change in resonance frequency. A LabVIEW interface was used for synchronization and data acquisition by controlling the LCR meter (L = Inductance, C = Capacitance, R = Resistance) and the multimeter. Hence, the possibility of data loss was minimized.
Data availability The X-ray crystallographic data for KAUST-7 (SO 2 ) and KAUST-8 (SO 2 ) have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition numbers 1871683 and 1871684, respectively. These data can be obtained free of charge from the CCDC via www. ccdc. cam. ac. uk. All other relevant data supporting the findings of this study are available from the corresponding authors on request.
mRNA was quantified for the following six genes: complement (C3-1), β-defensin (βdef), interleukin 1β (IL-1β), interferon (IFN), tumor necrosis factor α (TNF1&2), and toll-like receptor 3 (TLR3). In addition, the elongation factor gene (Ef) was included in the analysis as a housekeeping gene. Primers were obtained from the literature and are available in GenBank (http://www. ncbi. nlm. nih. gov/genbank/) (Table  1 ). Very low variation in expression levels for the elongation factor (i. e. similar cycle threshold values, Ct) was observed between the control and contaminated fish. Quantitative real-time PCR (qPCR) amplifications were carried out in triplicate in 25 μL volumes, containing 2 μL of cDNA (1/20 dilution), 12. 5 μL of Quantifast SYBR Green Mix (Qiagen), 2. 5 μL of each primer, and 5 μL of water.
Statistical analysis All statistical analyses were performed using XLSTAT statistical software  (2007) , with a significance level of 5%. Chemical and immune parameters of control and contaminated fish were evaluated using a two-way analysis of variance (ANOVA), taking into account the treatment and sampling date for each sample. Interaction effects between factors were always tested. Newman-Keuls significant difference tests were performed a posteriori to detect differences between means. All data were expressed as the mean ± standard deviation (SD).
We will refer to the latter catalog as P18. Details on the use of these additional catalogs for the forest selection are given in Sec. 3. 1, and for the estimate of the associated systematic uncertainties in Sec. 4. We also use a list of wavelength regions contaminated by sky emission lines. This list is available at https://github. com/igmhub/picca/blob/master/etc/list veto line Pk1D. txt.
0 for all redshifts in PYB13. Minimum SNR  Sky lines affect the data quality by increasing the pixel noise. We mask major sky lines (e. g. , lines at 5577 Å, 5890 Å, 6300 Å, 6364 Å, 6864 Å) in each forest. We locate the position of DLAs in our forest using catalog N12 introduced in Sec. 2. 2. We mask the DLAs following the same procedure as in  [28] : all pixels where the DLA absorption is higher than 20% are masked, and the absorption in the wings is corrected using a Voigt profile. 10 -3 10 -2 k >0. 015(km/s) -1 z Lyα = 2. 2 z Lyα = 2. 4 z Lyα = 2. 6 z Lyα = 2. 8 z Lyα = 3. 0 z Lyα = 3. 2 z Lyα = 3. 4 z Lyα = 3. 6 z Lyα = 3. 8 z Lyα = 4. 0 z Lyα = 4. 2 z Lyα = 4. 4 z Lyα = 4. 6 We discard forests shorter than 75 pixels, where the reduced length can be due to the CCD UV cut or to the presence of a strong DLA at the forest boundary.
Estimator of noise power spectrum A quasar spectrum is typically measured with five to eight individual exposures. We use these N exp individual exposures to compute the noise power spectrum P noise in a two-step process. We first construct a spectrum that contains the same noise as the data coadded spectrum, but devoid of any power from an astrophysics or cosmology signal. To this end, we compute the semidifference ∆φ between two customized coadded spectra obtained from weighted averages of the evennumber exposures, for the first spectrum, and of the odd-number exposures, for the second one, with weights taken as the pixel inverse variances. In this first step, we assign zero weight to pixels flagged by the COMBINEREJ bit of the SDSS pipeline, i. e. , pixels rejected when computing the coadded spectrum from the individual exposures.
Pixel masking effect We mask pixels affected by strong absorption caused by DLA or by emission from sky lines, by setting their flux to zero. Otherwise, sky lines would impact the data quality by increasing significantly the pixel noise, and DLAs, responsible for saturated absorptions on the scale of several pixels, would generate additional correlations. We evaluate the impact of pixel masking by applying the same procedure on mock spectra that include neither sky lines nor DLAs, and we measure the ratio: b(k, z) = P m (k, z) P u (k, z) , (4. 2) with P m the masked power spectrum and P u the unmasked power spectrum. In order to only evaluate the masking impact, both power spectra are computed using the generated values for the quasar continuum C q (λ) and for the mean transmitted flux F(z Lyα ).
7 < z Lyα < 3. 3, since such forests contain no strong sky lines. Like done in the masking of the sky lines, we assign a 30% systematic uncertainty associated to the correction for the masking of DLA absorbers. Because the DLA masking yields at most a 1% correction (cf. Fig. 9 ), the related systematic is sub-dominant compared to all others. The DLA correction shows almost no dependence on k, and the redshift dependence is explained by the increasing percentage of contaminated forest. The residual effect of unmasked DLAs was not taken into account in the systematics budget of PYB13. Our analysis uses the automated DLA catalog of N12  [56] , as was done in  [28, 30]. We compute a systematic uncertainty associated to this sample from the data themselves, using an alternative DLA catalog from P18  [57].
The systematic uncertainty related to the correction for the noise power is slightly reduced compared to PYB13. Other error contributions, such as systematics related to side bands, sky line and DLA masking, are similar to what was measured before. The agreement between the two analyses can be examined with a pull distribution, i. e. the distribution of the difference in P(k, z) divided by the statistical error on that difference. Because a fraction of the quasars selected in the DR9 analysis of PYB13 are included in the present work, the combined statistical error overestimates the error on the difference. However, given the increased size of the data set and more optimized selection of the present analysis (leading to a factor of two reduction of σ stat ), the overestimate is 10% at most.
Finally, the last category groups all nuisance parameters that allow us to account for uncertainties or corrections related to noise in the data, spectrograph resolution, bias from the splicing technique, UV fluctuations in the IGM, residual contamination from unmasked DLA, supernova and AGN feedbacks. Details on the fit parameters and on the dependance with scale and redshift of the nuisance parameters can be found in PY15. As the data statistical uncertainties improve, systematic uncertainties also have to be looked into with great care. In the present work, we did a thorough revision of all data-related systematics (cf. Sec. 5). Simulation uncertainties were studied in detail in  [11]  and in PYB15. Among these, there are two dominant sources of systematics, both modeled as nuisance parameters and left free in the fit.
33, demonstrates. We check the robustness of the results by performing several data splits shown in Table  6. The splits divide the data according to the spectral resolution, the SNR per pixel, the QSO catalogs (DR9, corresponding to MJD < 55753, and post DR9), the spectrograph number (1 or 2), and the Galactic hemisphere (NGC, SGC). None of these data splits show any unexpected shift in the cosmological nor astrophysical parameters. Table  6 : Best-fit value and 68% confidence levels of the cosmological parameters of the model fitted to the flux power spectrum. The dataset is split in several subsamples based on the spectral resolution, the SNR per pixel, the QSO catalog (DR9, post DR9), the spectrograph used and the Galactic hemisphere (NGC, SGC). To study a potential effect due to residual DLAs not detected and thus not masked in the spectra, we introduce a multiplicative factor 1-[1/(15000.