dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental results  [143]  on BMC 2012 dataset and CDNet 2014 dataset  [203]  show that MF-Net outperforms SOBS, LOBSTER and SuBSENSE in the case of dynamic backgrounds. Lee and Kim  [108]  proposed a method to learn the pattern of the motions using the Factored 3-Way Restricted Boltzmann Machines (RBM)  [157]  and obtain the global motion from the sequential images. Once this global motion is identified between frames, background subtraction is achieved by selecting the regions that do not respect the global motion. These regions are thus considered as the foreground region 6 Adequacy for the background subtraction task All the previous works demonstrated the performance of DNN for background subtraction but not discuss the reason why DNN works well. A first way to analyze these performance is to compare these different methods.
Only few authors proposed a full designed architecture for background subtraction. Table  4  groups the solutions of the different methods for the limitations of ConvNets  [22]. To learn the process at different level, the most common solutions are multi-scale and cascaded strategies alleviating the drawback to work with patches. For the training, over-fitting is often the case producing scene-specific methods. For the dataset used for the training, most of the authors employed the CDnet 2014 dataset with a part devoted to the training phase and another part for the testing phase. End-to-end solutions are well proposed as well as spatial and temporal strategies. Most of the time, the architecture is a generative one even if a combination of generative and discriminative would be better suitable for background subtraction.
), otherwise it is required. In this last case, several authors employed an end-toend solution in which a DNN method for background generation is used to determine the background image over time. Then, the output of this DNN based background generation is the input of the DNN based background subtraction with the current image in order to determine the foreground mask. For issue (4), DNNs are time consuming without a specific GPU card and optimizer. Thus, the key point to have a suitable DNN methods for background subtraction is to have a large training dataset, additional spatial/temporal strategies, and to apply it with a specific card if possible. For issue  (5)  which concerns the challenges met in video sequences like illumination challenges and dynamic backgrounds, the DNN can be sufficient by itself if the architecture allow to learn these changes as in several works or additional networks can be added.
For the adequacy of the architecture, it is needed to check the features of DNNs that are (1) type of architecture, and (2) parameters such as number of neurons, number of layers, etc. In literature, we can only found two works which compared different architecture for background/foreground separation: Cinelli  [22]  tested both LeNet5  [43]  and ResNet  [73]  architectures while Chen et al. [32]  compared the VGG-16  [175] , the GoogLeNet  [185] , and the ResNet50  [73]. In these two works, ResNet  [73]  provided the best results. But, these architectures were first designed for different classification tasks with the ImageNet dataset  [104] , CIFAR-10 dataset or ILSVRC 2015 dataset but not for the background/foreground separation task with the corresponding dataset such as CDnet 2014 dataset.
CDnet 2014 dataset and Challenges CDnet 2014 dataset  [203]  was developed as part of Change Detection Workshop challenge (CDW 2014). This dataset includes all the videos from the CDnet 2012 dataset  [62]  plus 22 additional camera-captured videos providing 5 different categories that incorporate challenges that were not addressed in the 2012 dataset. Practically, the categories are as follows: baseline, dynamic backgrounds, camera jitter, shadows, intermittent object motion, thermal, challenging Weather, low frame-rate, night videos, PTZ and turbulence. In addition, whereas ground truths for all frames were made publicly available for the CDnet 2012 dataset for testing and evaluation, in the CDnet 2014, ground truths of only the first half of every video in the 5 new categories is made publicly available for testing.
The evaluation will, however, be across all frames for all the videos (both new and old) as in CDnet 2012. All the challenges of these different categories have different spatial and temporal properties. It is important to determine what are the solved and unsolved challenges. Both CDnet 2012 and CDnet 2014 datasets allow to highlight in which situations it is difficult to provide robust foreground detection for existing background subtraction methods. The following remarks can be made as developed in  [99] : -Conventional background subtraction methods can efficiently deal with challenges met in baseline and bad weather sequences. -Dynamic backgrounds, thermal video and camera jitter is a reachable challenge for top performing background subtraction. -Night videos, low frame-rate, and PTZ videos represent huge challenges.
Qualitative Evaluation We compared the visual results obtained on the CDnet 2014 dataset by the different deep learning algorithms with visual results of other representative background subtraction algorithms that are: Two statistical models (MOG  [180] , RMOG  [192] ), one multi-cues model (SubSENSE  [178] ), and two conventional neural networks (SC-SOBS  [126] , AAPSA  [155] ). The deep learning models are the following ones: five CNNs based methods (Cascaded CNN  [204] , DeepBS  [4] , FgSegNet  [114] , FgSegNet-SFPM  [115] , FgSegNet-V2  [116] ) and two GANs based methods (BSPV-GAN  [230] , DCP  [181] ). All the visual results come from the CDnet 2014 website except for DCP for which the authors kindly provided the results. We also let in the four figures the number ID as well as the name as it is provided in the CDnet 2014 website.
Quantitative Evaluation We compared the F-measures obtained by the different algorithms with the F-measures of other representative background subtraction algorithms over the complete evaluation dataset: (A) two conventional statistical models (MOG  [180] , RMOG  [192] , (B) three advanced non-parametric models (SubSENSE  [178] , PAWCS  [179] , and Spectral-360  [165] ), and (C) two conventional neural networks models (SOBS-CF  [125] , SC-SOBS  [126] ). Deep learning models for background separation are classified following their architecture: -Convolutional Neural Networks: We grouped scores of 20 algorithms based on CNN that are two basic CNN algorithms (two variants of ConvNet  [22] ), six multi-scale or/and cascaded CNN algorithms (cascaded CNN  [204] , FgSegNet-M  [114] , FgSegNet-S  [115] , FgSegNet-V2  [116] , MCSS  [112] , and Guided Multiscale CNN  [111] ), 1 fully CNN algorithms (MFCN  [221] ), seven deep CNN algorithms (DeepBS  [4] , TS-CNN  [226] , Joint TS-CNN  [226] , five variants of Attention ConvLSTM  [32] ), one structured CNN algorithm (Struct-CNN  [113] ), and two 3D CNN algorithms (3D CNN  [161] , 3D Atrous CNN  [82] ).
However, SOBS and its variants were the leader methods on the CDnet 2012 dataset  [62]  for a long time (around two years) showing the interest of neural networks for background subtraction. But, F-measure did not exceed 0. 6 in average, that were relatively low in absolute. The F-measure exceeded only 0, 9 for the baseline category making these methods only usable and reliable in applications where the environments were not too complex. Second, we can remark that advanced non parametric models as SuBSENSE  [178]  and PAWCS  [179]  developed in 2014/2015 achieved chronologically better performance than SOBS because of multifeatures and multi-cues strategies. The gain in performance was around 25% for the F-Measure. The average F-measure was around 0. 75 becoming to be more acceptable for a reliable use in real conditions especially that the F-measure was around 0.
Conclusion In this paper, we have firstly presented a full review of recent advances on deep neural networks applied to background generation, background subtraction and deep learned features for detection of moving objects in video taken by a static camera. Experimental results on the large-scale CDnet 2014 dataset show the gap of performance obtained by the supervised deep neural networks methods in this field. Even if deep neural networks has received significant attention much more recently for background subtraction in the last two years since the seminal paper of Braham and Van Droogenbroeck  [22] , there are many unsolved important issues: -The main question is what is the best suitable type of deep neural networks and its corresponding architecture for background initialization, background subtraction and deep learned features in presence of complex backgrounds? -Looking at the experiments, several authors avoid experiments on the "PTZ" category and when the F-Measure is provided the score is not always very high.
Thus, it seems that the current deep neural networks tested meet problems in the case of moving cameras. -For the inputs, all the authors employed either gray or color images in RGB, except  [224]  which used a distribution learning feature improving the performance of the basic CNNs. But, it would be surely interesting to employ RGB-D images because depth information is very helpful in several challenges like camouflage as developed in Maddalena and Petrosino  [130]. In addition, the conventional neural networks SOBS  [131]  is the top algorithm on the SBM-RGBD dataset  [25]. Thus, we can expect that CNNs with RGB-D features as inputs will also achieve great performance as ForeGAN-RGBD  [182] model. However, multi-spectral data would be also interesting to test. Furthermore, a study on the influence of the input feature's type would be interesting.
Lake watershed and geomorphology 120 A portable sonar system, equipped with an internal GPS antenna (Humminbird model 859XD) and mounted on a small zodiac, was used to map the lake bottom in July 2014  (Bouchard et al. , 2015b). Lake-depth signals were continuously recorded along regularly spaced (20-25 m apart) navigation lines, mainly of SW-NE and SE-NW orientations. Depth data were interpolated between navigation lines using the compatible software (AutoChart) to produce a geo-referenced 3D bathymetric map. The acquired data were also used to calibrate and extrapolate lake bottom depths inferred from GPR mapping conducted the 125 following year. Ground penetrating radar (GPR) surveying on lake ice cover allows accurate description of lake bottom topography  (Moorman, 2001; Paquette et al.
, 2015; You et al. , 2017). Three GPR survey lines crossing the lake were conducted in May 2015 (Fig. 2 ) using a sleigh-dragged Sensors and Software PulseEkko GPR and 50 MHz antennas. GPR line processing was performed using Ekko project software and included Dewow, Lowpass temporal filter to diminish background noise and a background 130 average subtraction to remove the overwhelming ice/water boundary signal. The base of the ice cover and lake bottom depth were manually identified and corresponded well to the dielectric properties of ice and water. Signal travel velocities of 0. 06 m ns -1 and 0. 13 m ns -1 were used respectively for unfrozen and frozen ground.
Sediment core sampling and logging Sediment cores were collected during two consecutive years: a short core (54 cm) from a boat during the ice-free season of 135 2014 (July), and a longer core (109 cm) from the ice cover in spring 2015 (June). The coring site location (> 4 m deep) was located in 2014 and 2015 using the bathymetric data from the sonar and GPR surveys  (Bouchard et al. , 2015b). Each core was retrieved using a handheld percussion corer equipped with a 7-cm diameter clear polycarbonate tube (Aquatic Research Instruments). The 2014 core was subsampled in the field immediately after retrieval at 1-cm intervals, and the subsamples were transferred into polyethylene bags and brought back to the laboratory, where they were kept in the dark at 4 °C. For the 140 2015 core, water from above the sediment surface was removed immediately after retrieval in order to prevent the mixing of the water-sediment interface  (Bouchard et al.
Acknowledgements We are grateful to the team of  Gilles Gauthier  (Dept. of biology, U. Laval), the  Center for Northern Studies (CEN)  and the 480 staff of the  Sirmilik National Park (Parks Canada)  for logistical support and access to Bylot Island. We sincerely thank  Jean-Philippe Tremblay  and  Yoan Lechasseur  for fieldwork preparation assistance,  Vilmantas Prėskienis ,  Audrey Veillette  and  Maxime Tremblay  for their help in the field,  Arianne Lafontaine  and  Andréanne Lemay  for their help in the laboratory, and  Stéphanie Coulombe  for inspiring discussions while preparing the first draft of the manuscript. We are also very thankful to the community and the people of Pond Inlet (Mittimatalik). This research was funded by  ArcticNet , the  Natural Sciences and 485 Engineering Research Council of Canada (NSERC) , the  Polar Continental Shelf Program (PCSP) of Natural Resources Canada , the  NSERC Discovery Frontiers  grant " Arctic Development and Adaptation to Permafrost in Transition" (ADAPT) ,  the EnviroNorth Training Program , and the  W.
Gene expression was normalized to Gapdh. The Taqman probes and primers used to detect transcript expression are listed in Supplementary Tables  2  and 3. Data were analyzed using Prism GraphPad Software by unpaired Student's t-test or by ANOVA p < 0. 05. Values are presented as mean ± SEM. Runx1 expression in other species. For the rainbow trout, Runx1 expression during gonadal development was assessed by quantitative PCR  63. Species-specific primers used are listed in Supplementary Table  3. For the red-eared slider turtle, Runx1 expression during gonadal development was assessed at Female-Promoting Temperature of 31 °C and at Male-Promoting Temperature of 26 °C by RNAsequencing (RNA-seq)  64. For the goat, Runx1 expression during gonadal development was assessed by quantitative PCR and two to three biological replicates were used for each stage of development  65.
Arrays were scanned in an Affymetrix Scanner 3000 and data were obtained using the GeneChip® Command Console Software (AGCC; Version 3. 2) and Expression Console (Version 1. 2). Microarray data have been deposited in GEO under accession code GSE129038. Gene expression analyses were conducted with Partek software (St. Louis, Missouri) using a one-way ANOVA comparing the Robust Multichip Average (RMA)-normalized log2 intensities. A full dataset Excel file containing the normalized log2 intensity of all genes for each genotype and a graphic view of their expression is provided in Supplementary Data 2. In order to identify differentially expressed genes, ANOVA was used to determine whether there was a statistical difference between the means of groups and the gene lists were filtered with p < 0.
ChIP-seq libraries were sequenced as single-end 75-mers by Illumina NextSeq 500, then filtered to retain only reads with average base quality score > 20. Reads were mapped against the mouse mm10 reference genome using Bowtie v1. 2 with parameter "-m 1" to collect only uniquely mapped hits. Duplicate mapped reads were removed using Picard tools MarkDuplicates. jar (v1. 110). The number of uniquely mapped non-duplicate reads for each biological replicate was 8,932,674 and 15,036,698. After merging the replicate datasets, binding regions were identified by peak calling using HOMER v. 4. 9  67  with a false discovery rate < 1e -5. Called peaks were subsequently re-defined as 300mers centered on the called peak midpoints and filtered for a fourfold enrichment over input and over local signal. Genomic distribution of RUNX1-bound regions was determined based on Refseq gene models as downloaded from the UCSC Genome Browser as of 9 August 2017.
Enriched motifs were identified using HOMER findMotifsGenome. pl de novo motif analysis with parameter "-size given". For RUNX1 and FOXL2 ChIP-seq comparisons, binding peaks that had at least 1 bp in common were considered overlapping. Peaks were assigned to the nearest gene based on RefSeq. Gene lists were analyzed for enrichment using the online tool EnrichR  68. The ChIP-seq data are available in the ReproGenomics Viewer (https://rgv. genouest. org)  69, 70 and Gene Expression Omnibus (GSE128767; http://www. ncbi. nlm. nih. gov/geo/). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. upon reasonable request. Microarray and ChIP-seq data generated in this study have been deposited in the GEO database under accession codes GSE129038 and GSE128767, respectively.
The ChIP-seq data are available in the ReproGenomics Viewer (https://rgv. genouest. org). Raw data underlying all reported mean values in graphs are provided in the Source Data File. All other relevant data supporting the key findings of this study are available in the Supplementary Information files. The source data underlying Figs. 1, 4d-e, 4h, 7d-g, 7i-j, and 9b-d are provided as a Source Data file.
Acknowledgements We thank late  Keith Parker  ( UT Southwestern Medical Center, USA ) for the Sf1-Cre mice,  David Schlessinger  ( National Institute on Aging, USA ) for the Foxl2 +/-mice,  Yoram Groner  and  Ditsa Levanon  (The  Weizmann Institute of Science, Israel ) for the RUNX1 antibody for ChIP,  Susan Brenner-Morton  and late  Thomas Jessell  ( Columbia University, USA ) for the RUNX1 antibody for immunofluorescences,  David Zarkower  ( University of Minnesota, USA ) for the DMRT1 antibody,  Ken Morohashi  ( Kyushu University, Japan ) for the SF1 and SOX9 antibodies, and  Dagmar Wilhelm  ( University of Melbourne, Australia ) for the SRY antibody. We are grateful to the  NIEHS Molecular Genomics Core, Integrative Bioinformatics Support Group, Comparative Medicine Branch, and Cellular and Molecular Pathology Branch  for their services.
This work was supported in part by the  Intramural Research Program  ( ES102965 ) of the  NIH, National Institute of Environmental Health Sciences  in the U. S. The goat study was supported by  Agence Nationale de la Recherche in France  ( ANR-16-CE14-0020 ). The human fetal gonads study was supported by l 'Institut national de la santé et de la recherche médicale (Inserm) , l' Université de Rennes 1 and l'Ecole des hautes études en santé publique (EHESP) in France. The study of red-eared slider turtle was supported by a grant ( IOS-1256675 ) from  National Science Foundation  in the US.
Numbers indicate locations of commonly used biodiversity metrics within the biodiversity space. Panel (b) shows explained variance of the fits across biodiversity space. Detailed relationships for focal biodiversity metrics are illustrated at in panels (c-e) with source data provided in the Source Data file. Central lines in boxplots illustrate medians, boxes illustrate interquartile ranges, and whiskers show 95% confidence intervals. Overlaid are univariate GAM-fits with colors representing type of the curve: blue is increasing; yellow is concave-; purple is concave+; and gray is not significant. Rao* represents 1/(1-Rao), with Rao being Rao's quadratic entropy  68. R 2 5 2 q 1 1 1 1 1 1 1 0 ∞ 10 5 2 q 1 1 1 1 3 2 1 1 1 0 ∞ 10 5 2 q 1 0 ∞ 10 5 2 q 1 0 ∞ 10 5 2 q 1 0 1 0. 8 0. 6 0. 05 0. 35 0.
Mean diversity [#] Species richness Overlaid curves are corresponding GAM-fits, colored according to curve type classification: yellow is unimodal; blue is increasing; and gray is not significant. Standard errors are hardly visible in this representation and thus not depicted. Histograms illustrate the frequency distribution of the vegetation types along the productivity gradient. Mini panels on the right show Rao* indices for traits (a = 1) for maximum height (HGT) diversity, specific leaf area (SLA) diversity and seed mass (SM) diversity. Rao* represents 1/(1-Rao), with Rao being Rao's quadratic entropy  68. Source data for main panels are provided in the Source Data file.
Finally, we compared the diversity of highproductivity environments to the diversity of random assemblages. Overlaid curves are corresponding GAM-fits, colored according to curve type classification: yellow is unimodal; blue is increasing; gray is not significant; and orange is decreasing. Rao* represents 1/(1-Rao), with Rao being Rao's quadratic entropy  68. Source data are provided in the Source Data file. Community data. Plant community observations were provided by the French National Alpine Botanical Conservatory (CBNA) and covered the French Alps (c. 41,500 km 2 , Fig. 2a ). The initial dataset was published in ref. 43  , consisted of about 43,000 observations and 3400 species of vascular plants with abundance information resolved in six coverage classes  44. We translated these coverage classes into percentages using the conversion suggested by Münkemüller et al.
Normalized Difference Vegetation Index data. We used the remotely-sensed Normalized Difference Vegetation Index (NDVI) to derive proxies for productivity  46  and land use intensity. Geo-corrected NDVI data were obtained from the Landsat project and had a horizontal resolution of 30 m. We downloaded NDVI estimates for all scenes (images) of the Landsat 7 satellite covering the study area from July 1999 to October 2017 and extracted NDVI values at the locations of the community plots. The extracted values were filtered for pixel quality 'clear' (no water, snow, clouds, or cloud shadow) and NDVI scores in the valid range, resulting in an average of 136 NDVI estimates per community plot (ranging from 38 to 483). Climate data. We used data on bioclimatic zones and growing season to investigate the impact of climate on productivity-biodiversity relationships, and to constrain estimates of land use intensity.
Trait data originated from Thuiller et al. 43. Functional dendrograms were derived from the combined information of SLA, HGT, and SM, and for each trait individually. First, all traits were log-transformed and scaled to unit variance. For species with complete trait information we then calculated distance matrices using the Euclidean distance measure and finally derived hierarchical clusters with the unweighted pair group method with arithmetic mean (UPGMA) algorithm to produce ultrametric, functional dendrograms  55. We checked to which degree the functional variation was hierarchical by applying Mantel tests, comparing phenetic distances (obtained from the dendrograms) with functional distances from the distance matrices  56. Based on 9999 randomizations, we found significant (p ≤ 0. 001) correlations of 0.
74, 0. 80, 0. 80, and 0. 73 for functional trees based on all traits, SLA, HGT, and SM, respectively. A substantial fraction of the functional variation was therefore preserved in the trees. Phylogenetic tree. We used an ultrametric genus-level phylogeny of plants occurring in the European Alps and extended it with random binary trees to obtain a species-level resolution. The original tree was developed by Thuiller et al. 43  based on Genbank sequences and included 947 genera, covering 99% of the species for which trait information was available (Supplementary Table  3 ). In order to bring the tree to species-level resolution, we randomly generated as many binary splits as were necessary to link the genus-level tree tips to all observed species within each genus, using the Yule model as implemented in the R package apTreeshape  57.
We repeated this procedure to create 100 and 2000 possible species-level trees and used them to calculate phylogenetic and functional-phylogenetic diversity of resampled sets of observed and null communities, respectively (see subsections Calculating biodiversity and Constructing null communities). Limited phylogenetic resolution had little effect on our analyses. Indeed, across the filtered community dataset, on average only 1% of genera were represented by three or more species in local communities and thus not fully resolved. Phylogenetic distances were not significantly correlated with functional distances. The phenetic distances obtained from the functional dendrogram and ten replicates of species-level phylogenetic dendrograms showed non-significant Mantel correlations (all p > 0. 05) of 0.