dataesr/scientific-paragraphs-categorization huggingface dataset:
Model of relative phase Compared with the phase, the relative phase has a clear statistical characteristic. An  [25]  found that the Wrapped Cauchy Distribution (WCD) fits the relative phase distribution well. From a preliminary study of the relative phase distribution of the images in the LIVE 2 database, we found that the Generalized Gaussian Distribution (GGD) also fits the distribution well. Thus, we compared the two models by an experimental way in this section. The WCD is obtained by wrapping the Cauchy distribution on the real line with density f (x) around the circle, where f (x) = σ w π[σ 2 w + (x -µ w ) 2 ] , -∞ < x < ∞. (8) It has the probability density function  [27, 28] p(θ) = 1 2π 1 -ρ 2 1 + ρ 2 -2ρ cos(θ -µ w ) , -π ≤ θ ≤ π, ( 9 ) where ρ = e -σw is the sharp parameter, µ w is the mean.
Its main function can be represented as  [45]  Ŷ (x) = n i=1 Y i exp D 2 i 2σ 2 n i=1 exp - D 2 i 2σ 2 , ( 14 ) where D 2 i = (X -X i ) T (X -X i ), D 2 i is the Euclidean distance between the measured value X and the sample observation X i , Y i is the score associated with X i , n is the number of sample observations of training database, and σ is the standard deviation of radial kernal (a larger σ may bring a smoother regression surface but less accuracy while a smaller one may provide higher approximation accuracy but a worse smooth surface  [46] ). We implement the generalized regression neural network by a Matlab function newgrnn(). The only parameter involved is the spread parameter p ∈ [0, 2], linked with the standard deviation σ. To set this parameter, we optimize the value of p with the 10-fold method.
Comparing the correlation coefficients between the prediction data and actual data, and taking the value of p that maximizes the correlation coefficient as the optimal value. The experiment results show the optimal value is around 0. 8. Thus, we set the spread parameter to 0. 8 in the next step. Another important issue is to design the training and testing subsets. To have a robust evaluation of the metric, two strategies can be employed to design subsets. Strategy 1: two-thirds of data are randomly picked out for training, while the rest data are for testing. The final result is the median value of 1000 replicates of the process. Strategy 2: 10-fold cross-validation style. The database is randomly partitioned into 10 equally sized subgroups. A single subgroup is selected as the testing data, and the remaining 9 subgroups are used as training data, then switching the testing and training data.
Each of the 10 subgroups is selected just once as the validation (testing) data. Since the proposed metric maps the RR features into the objective scores with a generalized regression neural network, the objective score will vary with the subjective score provided by the database. If the subjective score is Mean Opinion Score (MOS), the objective score will also be MOS. In addition, if the subjective score is the Difference of MOS (DMOS), the objective score will be DMOS. The MOS measures the similarity of the reference image and the distorted one (larger MOS means less distortion) while the DMOS measures the difference (larger DMOS means larger distortion). Suppose we take the LIVE 2 database (DMOS as the subjective score) as the train-ing set, the objective score will be DMOS. Some examples will be shown in Subsection 4.
Image quality database To evaluate the performance of the proposed metric, four publicly available image databases are selected as test objects, including LIVE 2  [47] , CSIQ  [48] , TID2013  [49]  and Toyama  [50]. The characteristics of these four databases are shown in Table  1. Note that CSIQ and TID2013 include some distortions, such as contrast change, change of colour saturation and intensity shift, which are utilized to test the FR-IQA metrics and usually do not occur in the communication system. Thus, we remove these types of distortions from CSIQ and TID2013, remaining 5 types of distortions of CSIQ (6 in total) and 18 types of distortions of TID2013 (24 in total), and name them CSIQ* 2  and TID2013* 3  , respectively. According to the suggestion given by the Vide Quality Experts Group in  [51] , we employ three evaluation criteria to quantify the performance of our metric.
Decomposition scales and the features As introduced in Section 3 the influences of the image degradation mainly lie on the first three decomposition scales of the DT-CWT. Table  2  lists the prediction accuracy of different decomposition scales for all images of the LIVE 2 database. Obviously, a 3-scale decomposition is better than a 2-scale one and almost with the same accuracy as a 4-scale one. Thus, the 3-scale decomposition is selected, which provides the best trade-off between accuracy and the size of transmitted information. To further analyze how the magnitude and the relative phase features vary with distortion types, we take magnitude and relative phase as independent metrics respectively and compare them with the feature in the Discrete Wavelet Transform (DWT) domain. The experimental results are illustrated in Table  3 , where we utilize the LIVE 2 database as a test database, including JPEG compression, JPEG2000 compression, Gaussian blur, white noise and fast fading distortions.
Comparison with other metrics To futher assess the performance, we compared the FMRP metric with six other metrics, including two FR metrics: SSIM  [1] , MS-SSIM  [52]  and four RR metrics: Wang et al. 's RR method  [7]  (we call it FDWT), ADI  [8] , QWT  [9]  and Lin et al. 's RR method  [19]  (we call it RP). The implementation code of the SSIM, MS-SSIM and FDWT methods are available at the Waterloo IVC Code Repository  [53]. According to the algorithms in  [8, 9, 19] , we respectively implemented the other metrics. All the performances of these metrics are summarized in Table  4. We utilize Strategy 1 (referring to section 3. 6) to design the training and testing subsets. To increase the readability of the results, we mark the first-best and the second-best metrics in bold. It is shown that the proposed metric FMRP performs quite well for the four databases.
It provides better prediction accuracy (with higher PLCC), better prediction monotonicity (with higher SROCC) and better prediction consistency (with lower OR) than most other metrics. Compared with other RR metrics, the FMRP metric achieves the best results on all the databases. Compared with the other representative FR metric MS-SSIM, it also performs quite well, with a higher accuracy in the LIVE 2 database and a very close accuracy in the other databases. By recalling that the FMRP metric is an RR metric, it uses less information in image evaluation than the FR metrics. The experimental results verify that the FMRP metric is a useful and reasonable RR metric for communication systems. Further, we compared the FMRP with three other RR metrics to test their performances for different types of distortions.
Robustness and cross-validation The FMRP metric is based on machine learning regression techniques, so its performance depends upon the training sets to some extent. To evaluate the robustness of the proposed FMRP metric, cross-validation and cross-database validation are performed. The experimental results of crossvalidation in databases are listed in Table  6 , where Strategies 1 and 2 are two subsets design strategies introduced in Subsection 3. 6. The numerical values of the three evaluation criteria are very close. The experimental results show that the FMRP metric is not sensitive to the selection of the training subset. Moreover, to perform a cross-database validation, we integrate all the images of four databases and unify the subjective scores. With the integration, we have around 4200 images and then carry out the training and testing process with Strategy 1.
Two other methods based on machine learning, ADI  [8]  and RP  [19] , are compared with the proposed FMRP metric. The results are shown in Table  7 , where stdCC and stdSR respectively are the standard deviations of 1000 PLCC and SROCC (1000 iterations for the training and testing process). Obviously, the FMRP metric performs better than the ADI and the RP metrics in cross-database validation.
00 GB RAM, and the soft platform is Matlab R2016a. The computation time of the FMRP is 1. 3618 seconds, which is slightly more than the FDWT metric. Thus, it is an acceptable time consumption for a communication system. Some inaccurate samples of the FMRP metric are illustrated in Fig. 8 , where the objective scores of the FMRP are not accordant with the MOS of assessors. Taking Fig. 8  (a) and (b) as an example, their MOSs are sorted in descending order, but their FMRP scores are in ascending order. This phenomenon is caused by the JPEG 2000 transmission error in the TID2013 database. For this distortion, image degradation mainly occurs in some local blocks (we call it local distortion), as shown with the black rectangle in Fig. 8. The local distortion is a challenge to most metrics, especially for the metrics with global statistical features.
Finally, to build the relation between features and subjective scores, a general regression neural network is introduced. Comparing with the representative FR-IQA and RR-IQA metrics, the proposed reduced-reference image quality metric performs well in prediction accuracy, prediction monotonicity, prediction consistency and robustness, and demonstrates that the features are quality-aware and the metric is highly correlated with the human visual system. Moreover, the experimental results indicate our strategy is independent on the used database for the training process. Recently, for the image transmission in a wireless channel, some adaptive strategies have been exploited to improve the quality of service by adjusting the system parameters. However, these strategies do not consider the quality of experience.
Performance of cross-database validation for FMRP and two other RR metrics. The extracted parameters are quantized into 8-bit precision. As introduced in Section 3, both the magnitude and the relative phase have two parameters and one modeling error. Thus, the total memory cost is 36 × (8 + 8 + 8) = 864 bits, i. e. , 0. 27% of the reference image, as shown in Table.
Intestinal microbiota composition is modified by hypergravity. A more in-depth taxonomic analysis of bacterial types led us to highlight taxa influenced by hypergravity (Table  1 , details in Suppl. Database S1). Only few variations occurred in microbiota of mice submitted to 2G-hypergravity in comparison to 1G mice, whatever the phylogenetic level observed. These variations were clearly greater in microbiota of 3G mice and occurred at all phylogenetic levels. The caecal microbiota consisted of bacteria from 7 divisions. Most of the identified sequences belonged to the Firmicutes (ranging from 71. 8 to 93. 3%) and Bacteroidetes (ranging from 2. 2 to 26. 5%) phyla. In 3G mice, we observed higher levels of Bacteroidetes by comparison to 1G and 2G mice, associated to a significant decrease of the Firmicutes/Bacteroidetes ratio (Fig.
Bacterial species significantly enriched or impoverished are presented in Fig. 5  (Suppl. Database S1, Suppl. Fig. S3 ). Compared to 1G microbiota, only 1% of the species presented a relative abundance that was significantly impacted at 2G whereas 12. 1% of the species presented a relative abundance significantly altered at 3G hypergravity. Of the 50 species significantly impacted at 3G-hypergravity level, 78. 0% were enriched compared to 1G microbiota with relative abundance that could be strongly enhanced (for example, Alistipes onderdonkii, 63-fold; Lactobacillus murinus, 51-fold; Prevotella sp. oral taxon 472, 30-fold; Enterococcus columbae, 22-fold; Marvinbryantia formatexigens, 14-fold). Two taxa were enriched at both level of hypergravity: Alistipes massiliensis (28-fold at 2G; 11-fold at 3G) and Anaerocolumna xylanovorans (5-fold at 2G; 4-fold at 3G).
It is only recently that these critical issues have begun to receive the attention they deserve. Moreover, among bacterial taxa impacted by hypergravity, some of them should be closely investigated to explore their deleterious or beneficial effects within the gut. Data obtained from such studies are important as they could lead to new strategies, such as pre-or probiotics supplementation or dietary approaches, to countermeasure spaceflight-associated dysbiosis and its consequences on health. Mice used in this study were C57BL/6j males (8-week-old, mean body mass of 20 g) purchased from Charles River (Les Oncins, France). For acclimation to room conditions, they were housed during a week in standard cages (four mice per cage, 36 × 20 × 14 cm) in a quiet room under constant conditions (22 °C, 50% relative humidity, 12-h light/dark cycles) with free access to standard food and water.
0 Fluorometer (Invitrogen) using the dsDNA HS Assay Kit. To ensure equal representation of each sample in the sequencing run, each barcoded sample was standardized by calculating equimolar amounts (100 ng/sample) using the SequalPrep Normalization Plate Kit (Invitrogen) prior to pooling. Pooled samples of the 16S rDNA multiplexed amplicons were sequenced on a Roche 454 Genome Sequencer FLX Titanium instrument using the GS FLX Titanium XLR70 sequencing reagents and protocols (Beckman Coulter Genomics, Danvers, USA). Amplicon sequencing data analysis. Analysis of amplicon sequencing data was carried out using the MEGAN pipeline  60. After demultiplexing, combined raw sequencing data plus metadata were filtered to exclude low-quality reads. Next, data were denoised and clustered using the MIRA 4 software (http://mira-assembler.
sourceforge. net). Sequences with ≥98% similarity were binned and assigned to the same OTU to approximate species-level phylotypes. Representative sequences of each OTU, derived from clusters or singletons, were assigned at different taxonomic level by using the Ribosomal Database Project II Classifier 61. To avoid a potential bias linked to variation of sequence coverage between samples, the data were normalized to 100000 sequences per samples. Rarefaction curves were constructed to evaluate sequencing depth. Relative abundances of each OTU were compared according to the different experimental conditions. Bacterial richness and diversity across samples were estimated by calculating the following indexes: Shannon index (H′ = -∑ p i ln(p i ), where p i equals the frequency of species i in the popu-.
arb-silva. de) and the Ribosomal Database Project Probe Match pipeline (RDP II, http://rdp. cme. msu. edu). The standard curves used for quantification were optimized and based on serial dilutions of numerous bacterial DNAs extracted by using the QIAamp DNA Mini Kit (Qiagen). DNAs were extracted from strain Barnesiella intestinihominis DSM 21032 T for pan-bacterial qPCR, and from strains P. distasonis DSM 20701 T , P. clara DSM 19731 T , P. excrementihominis DSM 21040 T and F. plautii DSM 4000 T for specific qPCR. All assays were performed in triplicate. The following thermocycling conditions were applied with the MyiQ ™ 2 real-time PCR system (Bio-Rad Laboratories): initial denaturation at 95 °C for 5 min followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Melting curves were obtained immediately after the amplification under the following conditions: 70 cycles of 10 s with an increment of 0.
Each qPCR was performed in duplicate and repeated at least two times. Data analysis and relative expressions by comparison to 4 housekeeping transcripts (Eef2, Eif3f, Ppia, and HRPT1) were performed as previously described  62. Amplification and quantification of immunological markers or stress markers encoding mRNA (IgM, Ror-γ, NR3C1) were done as previously described  21. All primers used were targeted to different exons to ensure that they could not hybridize to potential traces of genomic DNA. Their specificity was checked using a BLAST search through the U. S. National Center for Biotechnology Information (Bethesda, MD, USA).
Acknowledgements We thank  Amandine Siméon  and  Sébastien Clerc  for technical assistance,  Christine Legrand-Frossi  for constructive discussions and her help in animal experimentation design, as well as  Arnaud Vanden-Bossche  and  Priscilla Orlando  from the  INSERM   U1059  unit and the PLEXAN platform ( University of Saint-Etienne, France ) for efficient management of hypergravity exposure. This work was supported by the  French Space Agency (DAR 4800000724 ,  4800000786 & 4800000841 ), the  French Ministry of Higher Education and Research , the  Université de Lorraine  and the  Région Lorraine.
Mass spectrometry analysis The TMT labelled sample was analysed by RPLC-MS/ MS/MS (145 min linear gradient) on a Fusion Tribrid Orbitrap operating in Data Dependent Acquisition mode (MultiNotch Simultaneous Precursor Selection method; MS1: profile mode, Orbitrap resolution 120 k, 400-1600 m/z, AGC target 400,000, 100 milliseconds (ms) maximum injection time, RF lens 60%; MS2: centroid mode, IonTrap, 10 dependent scans, 1. 2 Th isolation window, charge states 2-6, 60 s dynamic exclusion, CID fragmentation (35%, activation Q 0. 25), AGC target 10,000, 70 ms maximum injection time; MS3: profile mode, 5 precursors, 2 Th isolation window, Orbitrap resolution 30 k, 100-500 m/z, AGC target 50,000, 105 ms maximum injection time, HCD fragmentation (55%). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD013744.
Data analysis The acquired raw file was searched with MaxQuant (1. 6. 0. 13) against a human proteome Fasta database downloaded from UniProtKB. Because there was a single file to search, this allowed more variable modifications than normal without the search becoming impractically long. Variable modifications included in the search were "M-oxidation", "N-terminal acetylation", "deamidation (NQ)", "Gln → pyroGlu", "Phospho (STY)", as well as two putative glyphosate-induced modifications: "A1" = glyoxylate-modified cysteine (+H 2 O 3 C 2 => expected monoisotopic mass shift +74. 0003939305 Da) and "A2" = Glycine replaced by glyphosate (+H 3 O 3 CP => expected monoisotopic mass shift +93. 9819804726 Da); finally, "N-ethylmaleimide" was unusually set as a variable modification (no fixed modifications) since it would compete with modification "A1" for the same sites.
All false discovery rates (FDRs) were set to 1%. Dependent peptide search was ticked. Following MaxQuant analysis, data was reprocessed starting from the level of individual evidences for modified peptides (peptidoforms) using DC Biosciences' TMT-labelled data processing scripts. Briefly, the Levenberg-Marquardt procedure was applied by column to normalise samples. Peptidoform reporter intensities were calculated as the sum of those of individual evidences and re-normalised as above. Peptide ratios were calculated (glyphosate vs average control), re-normalised as above, and summarized at protein groups level using an in house, mean based algorithm. Protein groups with a single identified peptidoform were excluded from the analysis. Moderated Welch's t-tests were calculated and, in order to address the multiple hypothesis testing problem, p-value significance thresholds for 10, 20 and 30% False Discovery Rate were identified using the Benjamini-Hochberg procedure.
In Situ Data We use the forest database produced in the framework of the GLORI project  [53, 54] , from an in situ measurement campaign that was carried out in 2016 before the beginning of the tree growth. 83 stands of maritime pine were selected in an effort to cover all the range of forest structure parameters specific to maritime pine in the Landes forest. As in  [41] , one sample plot is selected in a rather homogeneous area within each stand. The plot size varies from 0. 05 to 0. 2 hectares. The center of each sample plot is geolocated using global positioning system (GPS). All trees are counted and the tree density is calculated. DBH and height are measured for the trees closest to the center (10 and 5 trees respectively). BA is estimated from the individual trunk cross-section derived from DBH and the tree density.
Optical Data Sentinel-2 images: Sentinel-2 data consist in optical time series acquired in the visible (blue, green, red), near infrared (nir) and short waves infrared (swir) with a spatial resolution from 10 to 20 m. The raw images are provided by the European Space Agency (ESA). The Theia Land Data Center (website: theia. cnes. fr) produces the level 2A images (top of canopy reflectance: TOC) with ortho-rectification, atmospheric corrections and cloud mask (MAJA processing,  [56, 57] ). Eighteen dates are available in 2016, and seven dates have all the reference plots cloud free in May, August, September, November and December. Spot-6 images: Spot-6 data consists of annual mosaics with a spatial resolution from 1. 5 m (panchromatic image) to 6 m (multi-spectral image in the visible and near infrared).
ALOS-PALSAR-2: L-band backscatter data (~23 cm wavelength) are considered to be more sensitive to the woody components of the canopy, and thus to forest height and AGB. We use HV, HH and the HV/HH ratio from 2016. No texture metrics are extracted from ALOS-PALSAR because the spatial resolution is too coarse (~25 m): the window size would be too large compared to the size of some stands in these forests. Sentinel-1 and 2 provide a resolution more suitable for texture extraction.
Choice of Regression Algorithms and Parametrization Statistical regressions have been used to establish the connections between the remote sensing features and AGB, BA, DBH, age, density and height variables. The choice of the regression algorithm is very important for the quality of the results. We investigated non parametric and multiple regression methods that could perform with several input predictors and a small number of samples. Deep learning approaches like neural network for regression are not relevant for a small dataset. Multi-linear regressions (MLRs) are very simple and frequently used in papers related to forest variables estimation using remote sensing. MLRs assume that predictive variables have linear relationships with predicted variables, although it has been shown it is not always an acceptable approximation when applied to remote sensing features and forest structure parameters.
The best estimates are obtained using SVR, RF and MLR in this order. The results confirm the benefits of using non-linear and non-parametric algorithms, and SVR is better at handling small datasets. Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 25 the relative LOO RMSE (smaller values are better) obtained for MLR, RF and SVR using as input the 28 features from open access images with a forward feature selection. These three algorithms produce equivalent results for AGB and BA estimations. Differences are more pronounced for the estimation of DBH, age, density and height. The best estimates are obtained using SVR, RF and MLR in this order. The results confirm the benefits of using non-linear and non-parametric algorithms, and SVR is better at handling small datasets.
Feature selection approaches We study the effectiveness of dimensionality reduction for improving the estimation of forest parameters from the perspective of four approaches: PCA, forward selection, backward selection, and no selection. The evaluation is performed with SVR on 28 features from Sentinel-1, Sentinel-2 and ALOS-PALSAR. Results are presented in Figure  4b. Backward selection was expected to perform best for it maintains groups of features and draws on their synergies. Expectations toward forward selection were lower since the approach adds one feature at a time and is known to dismiss prematurely features that have a beneficial contribution. Yet forward selection performed better and is our recommendation when dealing with small reference dataset.