dataesr/scientific-paragraphs-categorization huggingface dataset:
In a second series of experiments, we compared the relative performances of lfmm2 with genome scans based on redundancy analysis (RDA)  (Mardia, Kent, & Bibby, 1979; Forester, Lasky, Wagner, & Urban, 2018). To this objective, we re-analyzed one hundred simulated data sets from  Capblancq, Luu, Blum, & Bazin (2018). Each data set was obtained from forward simulations of biologically realistic scenarios with weak population structure, and contained n = 640 diploid individuals genotyped at L = 1, 000 SNPs. In these scenarios, three quantitative traits were controlled by 10 unlinked loci (QTLs). Ten environmental variables were also created. The first three environmental variables determined selective pressure on the traits, whereas the other ones had no effect on the phenotypes. Power and FDR were computed for lfmm2, RDA and partial RDA.
Data availability. The R package LEA 3. 0 and higher versions are available from Bioconductor: www. bioconductor. org/packages/release/bioc/html/LEA. html. Development versions are available from www. bioconductor. org/packages/devel/ bioc/html/LEA. html, and github. com/bcm-uga/LEA. The computer scripts and the data used in the examples or in the simulation studies of this manuscript are available from a GitHub repository github. com/bcm-uga/LEA3_simulation_script under GNU General Public License v3. 0.
ACKNOWLEDGMENTS We are indebted to  Drs. Georgina Rodríguez de Lores Arnáiz ,  Alicia Brusco ,  Juana Pasquini ,  Claudio Cuello , and  Fabian Loidl  for their fundamental collaboration to reconstruct some biographical details on  Amanda Pellegrino de Iraldi , as well as images included as Figures  4A ,C,D,6A,B. We thank Archives at the newspaper  ABC (Madrid, Spain)  for  Figures   6C , D. Francisco Tomás  y  Valiente Fellowship (MIAS-UAM ) funded CN.
These results were obtained by standardized re-evaluation of the collected spectra, incorporating information of wind velocity from a re-analysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). We present daily statistics of emission and corresponding information of plume parameters. A database for access to the results is described in detail, providing a substantial basis for further investigations of volcanic degassing patterns over time. We compare the emission inventory of NOVAC with past compilations of degassing intensity on these volcanoes. These topics determine the structure of the paper.
In order to adopt a standardized methodology for the evaluation of data collected by each station, a program called NOVAC Post-Processing-Program (NovacPPP) was developed by  Johansson (2009b). This program retrieves all scan measurements collected at a specified volcano within a given period and proceeds to evaluate them selecting the best information available for each variable. For instance, measurements of plume speed, direction and height are prioritized over modelled-derived or default information (e. g. plume height equal to difference in altitude between volcano summit and scanner). In this work we used wind speed from the ERA-interim re-analysis database of ECMWF, which is based on the IFS Cycle 31r2 4D-Var assimilation system, using a TESSEL land-surface model. This database, with a coverage period since 1979 until 2019, has an assimilation period of 12 h, while the spatial resolution is 79 km (TL255) in the horizontal and 60 vertical levels from sea level pressure up to 10 Pa, with a typical difference equivalent to 200 m  (Dee et al.
NOVAC emission data repository The results presented here are made public through a data repository hosted on the website https://novac. chalmers. se/. The site shows a map with the location of the volcanoes for which valid data has been produced. The dataset produced according to the methodology described here is labelled Version 1, and updates (temporal increments) and upgrades (different versions of data produced with improved methodology) are planned in the future. The dataset shows a summary of available raw data (i. e. , scans) collected by the instruments, along with a summary of valid fluxes derived from those measurements. After selection of a volcano, a dedicated window presents a map with the setup of monitoring instruments, including coordinates and measurement parameters, a link to generic information about the volcano hosted on the Smithsonian Notice that each dataset is assigned a registered and permanent Digital Object Identifier (DOI).
The data files were prepared following the guidelines of the Generic Earth Observation Metadata Standard (GEOMS)  (Retscher et al. , 2011) , which are generic metadata guidelines on atmospheric and oceanographic datasets adopted for global initiatives, such as the Network for Detection of Atmospheric Composition Change (NDACC). The GEOMS standard requires a file format such as netCDF. This data format can be explored using openly available tools such as Panoply (https://www. giss. nasa. gov/tools/panoply/). For users not familiarized with the netCDF format, a text format file, easily accessible through standard workbook or text editor applications and containing the same information that the netCDF file, is also available. An example of such a file is presented in the Supplementary Information SI3 and a list of all files is listed in the Supplementary Information SI1.
The GEOMS standard requires data and metadata to be included in the same file. In the case of NOVAC, the metadata, required for the description and interpretation of the data, includes the following: -General information about the dataset https://doi. org/10. 5194/essd-2020-295 Open values that account for a considerable fraction of the total emission); and (iii) the large difference between the characteristic emission of different volcanoes (fig. 4 ). Access Earth System Science Data Discussions Preprint. With respect to the intra-variability (for a particular volcano), we consider this to be one of the most important findings of long-term monitoring. As mentioned in the introduction, the production of high-sampling rate, long-term measurements is relatively recent. Most compilations of measurements in the past include campaign-based estimates of gas emissions, typically during periods of enhanced activity, when a plume was visible and during short periods of time.
Ground-based vs. space-based observations The recent compilation of global volcanic degassing from satellite-based measurements of OMI  (Carn et al. , 2017)  offers an excellent opportunity for comparison with the measurements obtained from the ground with NOVAC instruments. First, both methods have operated for about the same period (since 2005); second, both sets of measurements are analysed independently in a consistent manner; and, third, the two datasets are focused on passive degassing. This was achieved for OMI by stacking of wind-rotated images over the course of a year, discarding both pixels contaminated by clouds and pixels with elevated column densities resulting from explosive eruptions. The co-added annual image is then fitted to a Gaussian distribution, following  Fioletov et al.
This is not surprising, as all volcanoes not observed by OMI are weak sources of emission, confined to the lower atmosphere, and in some cases located in areas of persistent cloud cover. Consequently, our dataset provides new data for several volcanoes, such as Sangay, Cotopaxi, Planchón Peteroa and Sinabung, and the largest dataset for all other volcanoes ever published. Figure  5 , as well as the supplementary dataset SI1, also show that the difference between ground-and space-based observations is reduced by the method of filling-in low-emission values in the patchy time-series in NOVAC. Notwithstanding this better convergence, the differences are, in general, biased towards higher emission observed from satellites. There are many possible reasons for this, for example, that the selection of data for OMI may tend to pick images from higher plumes that reach altitudes above low-level clouds, which may be the result of more explosive activity.
Another reason could be that the data selection for NOVAC favours plumes with clear boundaries, and in some instances, high gas-content plumes may be too wide and thus completely overcast the instruments, and, as a consequence, are filtered out by the strict quality control filters applied to the dataset during our analysis. However, the larger differences are caused by obvious reasons: for example, in the case of two nearby volcanoes, such as Nyiragongo-Nyiamulagira (with a footprint of 1324 km 2 ), OMI cannot separate completely the contributions of each source, so they are reported as a complex. In this respect, NOVAC can aid to discriminate between these sources, since the stations are deployed with a focus on Nyiragongo and the finer time resolution allows disentangling contributions, especially during periods of heightened activity in any of them.
The NOVAC inventory and past compilations of emission It is interesting to compare the emission statistics obtained from the NOVAC data with past compilations of emissions presented in other studies. We refer in particular to  Andres and Kasgnoc (1998) , who report the volcanic input during 1970-1997 to the Global Emission Inventory Activity (GEIA) database. The results of a one to one comparison between the emissions reported for quiescent degassing volcanoes in GEIA and NOVAC is presented in fig. 6. There are a few volcanoes reported in GEIA which are not part of NOVAC yet; conversely, some volcanoes monitored in NOVAC that were not active and thus not considered during the period reported in GEIA. A comparison can only be done for the 16 volcanoes present in both datasets. Undoubtedly, the reported values are not expected to coincide, considering that the measurements were not obtained during the same periods and, as revealed by the NOVAC results, volcanic gas emission is by no means a stationary process over time.
Conclusions and outlook In this study, we report the results of post-processing of SO2 mass emission rate measurements at 32 volcanoes of the NOVAC network during 2005-2017. This is, to our knowledge, the densest (10-50 measurements per volcano per day during up to 12 years, 32 volcanoes) database of volcanic degassing obtained by a standardized method. Since the ScanDOAS method is subject to multiple and potentially large sources of uncertainty, considerable attention has been given to the selection of highquality measurements on which to base the reported statistics. Independent studies (e. g. ,  Stoiber and Jepsen, 1983; Krueger et al. , 1995; Halmer et al. , 2002; Andres and Kasgnoc, 1998; Carn et al. , 2017)  have demonstrated over the years that passive degassing dominates, in time and magnitude, the time-averaged global volcanic emission.
At the same time, this component of volcanic emission can produce a persistent impact on local, regional or global scales, and potentially affect the climate system. Observational limitations have hindered quantifying the magnitude and variability of global volcanic degassing in the past at the level of detail obtained by a global ground-based network like NOVAC. This database will therefore represent an important contribution to global emission inventories, which are typically based on sporadic and short-term investigations of emission during periods of heightened activity. Moreover, the results from measurements in NOVAC complement the observations from satellite platforms, which on an operational basis during the past decade were more suited for quantification of explosive degassing. The measurements performed in NOVAC provide more information than the gas emission rate of SO2.
Technology and a Career Grant from the Swedish National Space Agency (149/18), and the ECMWF CAMS_81 Global and Regional Emissions project. The authors are thankful for the valuable support from the USGS-USAID Volcano Disaster Assistance Program (https://volcanoes. usgs. gov/vdap/) provided to NOVAC since 2015. Thanks for data management assistance to Urban Andersson, Andreas Backström and Maria Kinger (Chalmers U. ), and for data-base development to Grégoire Détrez, Fredrik Gustafsson, Bengt Rydberg, Andreas Skyman, and Martin Zackrisson 560 (MolFlow). Thanks to Sarah Ogburn (USGS) for revision of an earlier version of this manuscript.
Data availability More information about NOVAC can be found in the website https://novac-community. org/. Raw data from NOVAC is accessible by request to the local observatory responsible for the measurements. The dataset obtained for this study can be accessed, free of charge, through a dedicated website https://novac. chalmers. se/. The datasets of individual volcanoes can be accessed through the DOI-links provided in Table  1. Updates of the time series, addition of new volcanoes and release of data versions resulting from improved analysis is planned. The NOVAC Post Processing Program and other software used in NOVAC are open source projects available in GitHub (https://github. com/NOVACProject/).
Spectral pre-treatments and software OMNIC 8. 1 (Thermo Nicolet) was used to record FTIR-ATR spectra. Spectral zone between 4000 and 1800 cm -1 was withdrawn to keep region with the most significant information and Multiplicative Scatter Correction (MSC) was used to compensate for additive and/or multiplicative effects in spectral data  [25]. These pre-treatments were realized by the help of Unscrambler version 10. 3 from CAMO (Computer Aided Modeling, Trondheim, Norway). AComDim computations were performed using Matlab 7. 14 (R2012a) and a procedure adapted from the ComDim function of the free toolbox SAISIR  [26]. The code (Matlab) of the ComDim (CCSWA) method and examples of applications, are available on the web of the French group of chemometrics: http://www. chimiometrie. fr/comdim. html.
The Monomodal Forest harvested zones (Batoké, Douala, Dibombari and Pendja) are rather subject to a north coast equatorial climate with two seasons and abundant precipitations while Bimodal Forest harvested zone (Bertoua, Doumé, Ebolowa and Mbalmayo) have four well-marked seasons with abundant precipitations. On the basis of weather station data reported by Sighomnou  [41] , rainfall days could not differentiate these Cameroonian zones because rainfall varies with proximity to the sea, altitude and latitude. Rainfall decreases from the sea inland and from south to north. The Sudano-Sahelian zone has the longest dry period. But the discrimination of the FTIR-ATR MOL signatures according to agroclimatic zones along CC6 axis is not due to their rainfall profile. The CC6 distribution of these agroclimatic zones was not due to temperature effect because the maximum average temperatures are recorded around Garoua in Sudano-Sahelian zone.
Methods: We name our CNN as ART-Net (Augmented Reality Tool Network). It has a Single Input Multiple Output (SIMO) architecture with one encoder and multiple decoders to achieve detection, segmentation, and geometric primitive extraction. These primitives are the tool edge-lines, mid-line, and tip. They allow the tool's 3D pose to be estimated by a fast algebraic procedure. The framework only proceeds if a tool is detected. The accuracy of segmentation and geometric primitive extraction is boosted by a new Full resolution feature map Generator (FrG). We extensively evaluate the proposed framework with the EndoVis and new proposed datasets. We compare the segmentation results against several variants of the Fully Convolutional Network (FCN) and U-Net. Several ablation studies are provided for detection, segmentation, and geometric primitive extraction.
The proposed datasets are surgery videos of different patients. Results: In detection, ART-Net achieves 100. 0 % in both average precision and accuracy. In segmentation, it achieves 81. 0 % in mean Intersection over Union (mIoU) on the robotic EndoVis dataset (articulated tool), where it outperforms both FCN and U-Net, by 4. 5 pp and 2. 9 pp, respectively. It achieves 88. 2 % in mIoU on the remaining datasets (non-articulated tool). In geometric primitive extraction, ART-Net achieves 2. 45 • and 2. 23 • in mean Arc Length (mAL) error for the edge-lines and mid-line, respectively, and 9. 3 pixels in mean Euclidean distance error for the tool-tip. Finally, in terms of 3D pose evaluated on animal data, our framework achieves 1. 87 mm, 0. 70 mm, and 4. 80 mm mean absolute errors on the X, Y, and Z coordinates, respectively, and 5.
Conclusions: The proposed framework outperforms existing ones in detection and segmentation. Compared to separate networks, integrating the tasks in a single network preserves accuracy in detection and segmentation but substantially improves accuracy in geometric primitive extraction. Overall, our framework has similar or better accuracy in 3D pose estimation while largely improving robustness against the very challenging imaging conditions of laparoscopy. The source code of our framework and our annotated dataset will be made publicly available at https://github. com/kamruleee51/ART-Net.
Surgical tool segmentation More recently, the use of deep learning has substantially boosted segmentation performance, especially with the introduction of the U-Net  ( Ronneberger et al. , 2015 ) , as reported by  Garcia-Peraza-Herrera et al. (2016 ) , Pakhomov et al. (2019 ) , Garcia-Peraza-Herrera et al. (2017)  ,  Laina et al. (2017). An automatic realtime method based on an FCN with an improved learning process was proposed by  Garcia-Peraza-Herrera et al. (2016). The Cyclical Learning Rate (CLR)  ( Smith, 2017 )  and the pre-trained model on the PASCAL-context dataset  ( Mottaghi et al. , 2014 )  were used to improve segmentation accuracy. Attia et al. (2017)  applied a hybrid-CNN method utilizing both recurrent and convolutional networks simultaneously. To prevent the coarse segmentation  ( Pakhomov et al.
We use the pinhole camera model, which is known to work well in laparoscopy  ( Melo et al. , 2011 ). This model is a mere perspective projection with matrix K [ I 3 0 3 ] in homogeneous coordinates, where K ∈ R 3 ×3 represents the camera intrinsics. We calibrate the intrinsic camera parameters, namely the effective focal lengths f u , f v and principal point u 0 , v 0 , and optical distortion using the software Agisoft Lens by filming a checkerboard at the start of surgery. The laparoscopic images are then undistorted to remove the effect of radial and tangential distortion before geometric primitive extraction, and the geometric coordinates are normalized using K -1 to 'undo' the effect of K , leaving [ I 3 0 3 ] as projection matrix.
Datasets We use two datasets. The first one is from the Endoscopic Vision Challenge (EndoVis Instrument Segmentation and Tracking sub-challenge), which contains two sub-datasets, namely robotic (articulated) and non-robotic (non-articulated). The robotic subdataset is a collection of 9050 images from 6 one minute laparoscopic videos of a large articulated needle driver tool used in an ex-vivo setup. The first 45 s of 4 videos consist of 4500 images used for training. The last 15 s belong to the testing dataset, which also includes the 2 other videos, leading to a total of 4550 testing images. The non-robotic sub-dataset is a collection of 300 images with 160 training images extracted from 4 laparoscopic colorectal surgeries ( 4 × 40 ), and 140 testing images from 6 laparoscopic surgeries ( 4 × 10 + 2 × 50 ).
These two sub-datasets already include the binary segmentation masks. The geometric primitive annotations were added for images containing non-articulated tools, namely the non-robotic sub-dataset. The second dataset is our proposed annotated data, which is non-robotic. We annotated the tool presence, the segmentation masks, and the geometric primitives for 635 laparoscopy images (see Fig. 7  ). We annotated the tool presence for another set of 30 0 0 images, namely 1500 positive and 1500 negative images, respectively, for which some positive images contain multiple tools, therefore not usable for the geometric primitive extraction. These images are from 29 laparoscopic hysterectomy videos from the gynecology department of CHU Estaing (Clermont-Ferrand, France), which were randomly split into train and test sets patient-wise.
Training strategy In the proposed strategy, the encoder's kernels are initialized using the pre-trained weights from ImageNet  ( Deng et al. , 2009 ) , and the kernels of the decoders are initialized using the 'glorot uniform' distribution. Two stages of training and testing were used (see Section 4. 2 for details). In the first stage, referred to as stage-1 , we trained and tested only the segmentation sub-network of ART-Net on the EndoVis (robotic) dataset. In the second stage, referred to as stage-2 , we trained and tested the whole ART-Net on the combined EndoVis (non-robotic) and our annotated data.
Tool segmentation Several state-of-the-art methods for tool segmentation  ( Garcia-Peraza-Herrera et al. , 2016; 2017; Pakhomov et al. , 2019 )  evaluate their methods on the EndoVis robotic sub-dataset, which comprises a very large number of images compared to the non-robotic one. Milletari et al. (2018)  ,  Laina et al. (2017)  do not share their implementations, which prevents us from comparing ART-Net against their methods on the EndoVis non-robotic and the proposed subdatasets. In order to increase the possible comparisons, we, there-Table  3  Quantitative metrics for the segmented tool masks from ART-Net, U-Net, FCN8s, and state-of-the-art segmentation networks on  EndoVis-2015 (robotic). The metrics were computed using the true labels and semantic labels obtained from the networks. Best results are in bold, second-best underlined, third-best underlined twice.
fore, propose a preliminary training and testing stage of ART-Net, referred to as stage-1, over the EndoVis robotic dataset. Specifically, in stage-1, ART-Net is reduced to its segmentation sub-network and the loss function to the L seg term only in Eq. (  1 ). Importantly, stage-1 also allows us to pre-train ART-Net on a large laparoscopic image dataset. A second training and testing stage, referred to as stage-2, was then performed on the non-robotic laparoscopic images, namely images associated with a tool model presenting a cylindrical shaft, as considered in our generic method. Stage-2 uses the full loss function L in Eq. (  2 ) , which includes the detection, segmentation, and geometric primitive extraction terms. An evaluation of ART-Net for segmentation in stage-2 was possible but against fewer state-of-the-art methods than in stage-1, namely against several instances of U-Net, FCN8, and ART-Net with several ablations.
Metrics Results from stage-1 We report the segmentation results using several scores and compare ART-Net against state-of-the-art methods. U-Net and FCN8s were trained and tested on the same dataset, namely EndoVis (robotic), and optimized using the same loss as for ART-Net, namely the cross-entropy term L seg in Eq. (  1 ). The obtained quantitative and qualitative results, without any postprocessing, are shown in Table  3  and Fig. 11  , respectively. ART-Net is very close to the winning method for most of the criteria, namely 0. 2 pp for mDSC and 0. 4 pp for mean specificity 2 , and wins for the mean IoU together with CFCM, which comprises about twice as many parameters as ART-Net. ART-Net's results represent a very good compromise between specificity and sensitivity, reaching a specificity very close to the winning one while maintaining an average sensitivity.