dataesr/scientific-paragraphs-categorization huggingface dataset:
For chromocenters, the Dice coefficients only slightly vary between methods and users (Figure  8 ). DL-based chromocenter segmentation shows a comparable or slightly higher Dice coefficients when compared to inter-user or inter-method comparisons. Importantly, applying the nucleus/chromocenter 0. 5/0. 25 threshold used in the One_User model largely improves the Dice coefficient as compared to the 0. 5/0. 5 threshold, indicating the importance of fine-tuning these parameters for accurate segmentation (Figure  8 ). Although DL efficiently reduces inter-user differences, thus providing ground for more powerful analysis of subtle changes, assisted segmentation by iCRAQ or any software is biased in the set of measurable features. While DL methods also suffer from the same drawbacks, the space of distinctive features they based their decision on is much larger and allows them to theoretically outperform any assisting software.
Nucl. Eye. D Analysis of the Ddm1 Dataset In order to confirm the ability of Nucl. Eye. D to measure altered or non-canonical heterochromatin features, we used mutant Arabidopsis plants for DDM1 These exhibit more pronounced alterations of heterochromatin patterns than the dark-grown plants  [35, 43, 49]. We used the three different Nucl. Eye. D models to automatically segment a dataset of more than 150 wild-type (WT) and 150 ddm1 nuclei, prepared following the same procedure as the one used to produce the training images. As shown in Figure  9A , the One_User, Ten_Users and Ten_Users_iCRAQ pipelines produced a coherent segmentation of low contrasted nuclei and small atypical ddm1 chromocenters (Figure  S4 ). The three Nucl. Eye. D pipelines allow for detecting chromocenter morphology and accurately reporting the well-described defects of the ddm1 mutant  [43, 49]  (Figure  9B ).
iCRAQ Analysis iCRAQ is a tool written in ImageJ macro language that relies on the FeatureJ (http://imagescience. org/meijering/software/featurej/, accessed on 1 January 2022) and Interactive H_Watershed (https://imagej. net/plugins/interactive-watershed, accessed on 1 January 2022) plugins; here, we used a version adapted from  [40]  to annotate images. Nuclei were detected via global thresholding of the median filtered z-projection (either standard deviation or maximum intensity) of the stack and the corresponding regions were saved as ImageJ regions of interest (ROIs). Incorrectly detected nucleus ROIs were suppressed manually. Likewise, missed nuclei were added manually. The input stack was cropped around each nucleus ROI. For chromocenter segmentation, the largest 3D structure tensor eigenvalue was calculated using the FeatureJ plugin, and its z-projection served as an input for the interactive H-watershed plugin.
Nucl. Eye. D The Nucl. Eye. D script was written in python using Keras and TensorFlow libraries for Neuronal network designing. U-net networks were built according to the original paper from Olaf Ronneberger  [48]. Model training was performed using Google Collab allocated a Cuda v 11. 2; Tesla P100-16 Go HBM2 GPU and Intel(R) Xeon(R) CPU @ 2. 20 GHz CPU  [40]. Full script, images and trained models are available in  [41]. Importantly, training set was performed using images captured from tissue fixed with either formaldehyde or ethanol:acetic acid.
Conclusions Our work describes user-specific issues in manual nucleus and chromocenter detection and proposes improved segmentation tools: the semi-automatic ImageJ plug-in iCRAQ and the DL-based tool Nucl. Eye. D. The central motivation of this work was to test and provide plug-in assisted methods, which can be used to facilitate the production of training datasets to build a fully automated DL tool. Both tools reduce the time of analysis and inter-user variability. iCRAQ can be easily implemented on a local computer (downloadable from  [40]  with a demonstration guide), and Nucl. Eye. D can be used directly online on a dedicated Google Collab environment to produce the binary masks  [41]. The masks are then treated on ImageJ with a dedicated combination of macro  [41]  to compute the different nuclear and chromocenter morphometric parameters.
In recent years, unsupervised learning techniques such as contrastive learning improved, especially for segmentation of medical images  [52, 53]. These have the benefit of needing much less (semi-supervised training) or no annotated images (self-supervised/unsupervised training)  [53]. Consequently, these methods also reduce the risk of inducing a bias through the segmentation method used to build training set. However, to which extent these more recently developed methods can outperform the established CNN models for the segmentation of nuclei and subnuclear structures remains to be evaluated. Although Nucl. Eye. D was trained here with a dataset composed of about 300 images, it provides accurate segmentation of nuclei and chromocenters, even on images produced from different protocols witnessing its adaptability.
Nucl. Eye. D as DL-based approach for segmentation of nuclear and subnuclear structures should provide an interesting leap in the field of plant cytogenetics, and complete the existing range of DL-based tools already existing for phenotypic analyses in organs  [28] , leaves  [27]  or individual cells  [29]. In contrast to tools such as DeepImage J  [25]  and cellPose  [54] , Nucl. Eye. D easily enables the training with user-specific datasets and purposes, and provides a ready to use workflow for object segmentation tasks inside ROI, without the need of self-building a workflow as previously proposed by ZerocostDL4Mic  [55] , providing a larger choice of combinable models. Our methodology assists nuclei and subnuclear structure segmentation, possibly encouraging biologists to include DL-based methods to minimize human-derived biases in quantitative approaches of nucleus imaging.
Collectively, our study highlights that algorithm-and DL-based tools are not free of human biases introduced during the training process when it results from image choice and object segmentation. The "programmer"-based bias starts to be investigated as a potential explanation for dataset-specific performance  [45, 56]. Additionally, according to our thoughts, the growing trend of automatizing image segmentation and analysis should be accompanied by substantial efforts in assessing inter-user variability of the segmentation method  [57]. We strongly recommend using DL based tools to enhance reproducibility (in the case that the dataset is large enough, we further recommend to retrain the DL tool on your own data, which then will be facilitated by iCRAQ). In perspective, Nucl. Eye. D should contribute to expend the use of a DL-based approach in chromatin biology, offering the possibility to segment any subnuclear structures revealed by FISH-or immuno-staining (e.
Sets of parallel cultures were imaged at fixed timepoints with immunohistochemistry (an histology technique using bio-engineered antibodies coupled with fluorescent probes to stain specific proteins and cellular structures within tissues), in order to identify the organelles generating the D-FFOCT signal. Depending on the origin of the cells (ppRPE or hiRPE) and the width of the scratch, cell behaviours differed. Two main features are usually used to quantify wound healing: the speed of closure, i. e. , the evolution of the width of the scratch, and the wound closure, measuring the evolution of the area of the scratch  16. Semi-automatic segmentation based software, named scratch assay velocity evolution (SAVE) profiler (see the "Methods" section), was developed to segment the wound and calculate the width and area of the scratch in order to quantify wound closure.
Average wound closing speed for this ppRPE sample was thus calculated to be 15. 7 μm/h. Figure  1g  presents an optical flow calculation over the same area. The velocity of movements throughout the acquisition is shown, with two fronts of motion in opposite directions revealed by the arrows. By averaging the velocities (i. e. , the absolute values without taking the direction into account) plotted in Fig. 1g , the medium speed of one front can be calculated as 8. 25 μm/h. As there are two opposite moving fronts, the total velocity of closing is the multiplication of the average speed of one front by 2, giving a total speed of 16. 5 μm/h. This result is consistent with the results obtained with our custom-written SAVE profiler software. The same analysis was performed on hiRPE cell cultures (Fig.
Their movement could be followed through live image sequences using a custom GPU-computing software (Holovibes http://holovibes. com/ 21 , see Supplementary Movie 6), for the first time to our knowledge. After a closed scratch assay, an accumulation of actin is visible in immunohistochemistry (Supplementary Fig. 4a ). It is composed of the border cells of the wound which seem to disintegrate after the closing, as observable in the corresponding D-FFOCT image. This is coherent with the literature  22. It was also observed that apical processes along the wound border are lost a few days after a scratch assay (Supplementary Fig. 5 ). Pigment particles (ovular, 2-3 μm × 0. 5-1 μm) absorb light, making them visible in conventional microscopy and phase contrast microscopy (here we used Normarski interference contrast and superimposed the fluorescent phalloidin image corresponding in green as reference image).
Our results are confirmed by those shown in literature with electron microscopy  10  , where the RPE cytoplasm is mainly filled with mitochondria (usually arranged in a network for unstressed cells) and pigment granules (of similar size to those measured here with D-FFOCT). Electron microscopy of microvilli, shown in the same paper  10  , is also consistent with what we observed with D-FFOCT, but is not capable of showing the movements that were visible with D-FFOCT. Discussion D-FFOCT is able to non invasively show mitochondrial dynamics, microvilli and fillipods, and pigment distribution in RPE cells via live imaging in real time. Comparison of wound healing parameters quantified using optical flow 17 , semi-automatic segmentation methods (SAVE Profiler) and Cell Profiler software  18  , showed that our custom-developed SAVE Profiler successfully combines results from both optical flow and Cell Profiler in a shorter calculation time.
In order to get closer to human in vivo RPE cells, we tried to study hiRPE at a mature development stage (pigmented, 90 days of growing). Unfortunately, we were not able to determine if the cell movements were due to the scratch assay or breaks in the cell layer caused by lack of membrane adhesion. This problem was also observed by Abu Khamidakh et al. 16. Future developments of this study would be to automatise the realisation of scratch assays with a new setup combining a laser to cut the cell layer with better repeatability. We could also test the addition of molecules to change the speed of closure  30. We showed that D-FFOCT combined with customdeveloped calculation software allow imaging and study of degeneration in RPE cell cultures, which may contribute to the comprehension and the following of in vivo degenerative retinal diseases (such as AMD) evolution, and could be an appreciated method for live patient diagnostics and direct treatment choice in the future.
Finally, the value, which codes for the intensity in the image, is calculated as the standard deviation over a moving window of 50 images, which are then averaged, to give the final intensity highlighting the intra-cellular motion. Finally, the three channels are combined to create a coloured image, representing the dynamic profile of the imaged sample (see Fig. 1c ). The acquisitions in depth were performed from the top of the cells to the membrane by acquiring D-FFOCT images in stacks with a step of 1 μm. For the timelapse acquisitions, D-FFOCT images were acquired every minute for up to several hours in the same plane, using a plane lock-in stabilisation software previously presented in Scholler et al. 9. This stabilisation software is based on the cross-correlation between the first image and the current image.
Images were observed and processed with FIJI  41. The SAVE profiler: custom-developed software to segment and analyse wound closure. In order to evaluate the movement of the cells following the scratch, we wished to study the speed and the percentage of closure of the wound by directly measuring the size of the wound over a time-stack of images. For this purpose, custom software was developed in Matlab, which we name the scratch assay velocity evolution (SAVE) profiler  42. The first step is to segment the scratch to create a binary image of the scratch and the cells. In order to obtain the best binary image (i. e. , which considers the cell interstices, which are not associated with the scratch, as part of the cells), we applied a multiple threshold to the image and retained only the first level, which separated the scratch from the rest of the image.
The mean width was plotted over time and fitted with a bi-exponential function, determining the characteristic time and the speed of closing. The limit of closing was fixed at around 1-2 μm, which is the average interstice between cells far from the wound. We validated our custom developed SAVE profiler software against an existing software. Cell Profiler is a cell image analysis software developed in 2005  18. We used the Wound Healing example available on the website https://cellprofiler. org. By analysing images of a healing wound over time, this software calculates the area occupied by the cells on each image. Thus, we were able to calculate the wound closure (formula explained above) in order to compare with the results obtained with our SAVE profiler. We observed that Cell profiler was efficient on non closing wounds (i.
Here, we first take the intensity channel of the images of an acquisition and apply a median filter to remove noise (e. g. , line noise from the camera). We average 8 by 8 frames to improve the intensity and reduce the time of calculation. Then, we apply the Optical Flow from Matlab 8 , using the Horn-Schunck method  17. The Horn-Schunck method is based on the derivatives of the brightness of the frames, assuming there is a certain smoothness in the flow between the images. The optical flow gives magnitude, orientation, and the velocity components on x and y axes. For the representation of the velocity and the angle of the optical flow, we choose a limit of 2 μm/h as a minimum velocity. The angle is plotted by summing all the different angles calculated through the optical flow process. Velocity is plotted from the magnitude using the quiver function from Matlab, which gives a representation of the velocity with arrows oriented in the direction of the movement and with a length proportional to the velocity (e.
Acknowledgements The authors would like to thank  Valérie Forster  for providing primary porcine samples. The authors thank  Jules Scholler ,  Pedro Mecê  and  Olivier Thouvenin  for fruitful discussions on the results. The authors thank  Marie Darche  and  Leyna Boucherit  for their help in immunohistochemistry experiments and analysis of results. The authors would like to thank the direction and management teams of the institutions involved. The authors thank the following sources of funding:  OREO  [ ANR-19-CE19-0023 ],  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ],  HELMHOLTZ (European Research Council (ERC)  (# 610110 )),  OPTORETINA (European Research Council (ERC)  (# 101001841 )),  LabEx LIFESENSES  [ ANR-10-LABX-0065 ],  Institut Carnot Fondation Voir et Entendre ,  RETINIT-iPS  [ ANR-19-CE18-0005 ],  LabEx WIFI , [ ANR-10-LABX-0024 ].
We used the sources cited in the detected reports to trace the path of each signal. Signals were categorized as official and unofficial according to the source. We have built a directed network where the nodes represented the sources (characterized by type, location and geographical focus) and the edges represented the signal flow. To describe the network, we used network centrality measurements (degree, betweenness and eigenvector) to determine which nodes were important in the data dissemination. We also included the reactivity, calculated as the difference (in days) between the detection of an outbreak by PADI-web and its official notification by Empres-i (gold-standard) with a distinction between wild and domestic birds. Results: PADI-web detected 202 official signals and 26 unofficial signals.
Topographic data The Digital Elevation Model (DEM) used in this study is derived from the very high-resolution optical imagery from Pleiades satellite (CNES/Airbus) -see Figure  1. Using Pleiades panchromatic image (0. 5 m ground pixel resolution), a tri-stereogrammetry method is performed using the NASA software ASP (Ames Stereo Pipeline;  Shean et al. , 2016)  to extract the topography of the Langue de Barbarie  (Almeida et al. , 2019; Salameh et al. , 2019; Taveneau et al. , 2021). The use of tri-stereo method (three images) is preferred over stereo one (two images) as it significantly improves the derived-topography  (Almeida et al. , 2018; Collin et al. , 2018; Taveneau et al. , 2021; James et al. , 2022). The images selected for this work are 3 acquired in March 2019, and further processed using the method further described by  Taveneau et al.
, 2021). ERA-Interim reanalysis data (global climate and weather data available from 1979 onward) at a 0. 5°x 0. 5°r esolution developed by the European Center for Medium-Range Weather Forecasting (ECMWF) model are used for waves data at a 6-hour resolution over the 1994-2015 period. Wave runup R is computed following the dissipative beach form of  Stockdon et al. , 2006 (see Almar et al. , 2021) : R = 0:043 ffiffiffiffiffiffiffiffiffi ffi H s L o p Where H s and L o are offshore significant wave height and wavelength. All the above-mentioned parameters are resampled on an hourly basis over the 2013-2015 period. All the hydrodynamic data used in this study are global dataset available worldwide are all extracted at the nearest point from Saint Louis city (latitude 16. 23°, longitude -16. 21°) in the corresponding grids, which ensures here a maximum distance of 50 km from the coarsest grid.
To compute overtopping and flooding potential, the topographic data in this work are set to a geoidal coordinate system (vertical datum). Here, the ECWL are converted to geodetic data using the vertical datum value (0. 981m) of  Wöppelmann et al. (2008) , so that they can be superimposed with the topographic data. It should be recalled that the vertical datum used is that of the tide gauge in Dakar, because there is no one in Saint-Louis. To study the trend of evolution of the extremes in Saint-Louis on the basis of annualized data, the regress function of the Matlab software (linear regression) is used. In addition, the statistical test p-value is calculated to determine the level of significance of the trend.
Flood zone and mapping and risk for infrastructures The methodological approach to flood mapping adopted in this work is based on the calculation of the percentage of potentially floodable surface through the satellite-derived DEM and the raster calculator tool of the Arc Gis software. Then, from the OSM (Open Street Map) databases, the properties (buildings, parcels, etc. ) are vectorized in Arc Gis. To identify the infrastructures potentially exposed to flooding, spatial queries similar to the selection by location are made in order to extract the properties that are under the spatial extension of a certain ECWL threshold.
Categories are important elements of databases of Product Listings, for e-commerce platforms, or of Points of Interest (POIs), for location-based services. However, category annotations are often incomplete, which calls for automatic completion. Hierarchical classification has been proposed as a solution to impute missing annotations. We address this task in one of Naver's production databases (POIs), in order to enhance its quality. In real-life applications, like ours, however, it is unrealistic to count on the existence of a perfectly annotated training set, and noisy training labels prevent us from casting the task as a straightforward classification problem. In order to overcome this difficulty, we propose an approach that takes into account the type of noise in the training set. We identified that the main deficiency is that the training labels tend to be under-specified i.
e. they point to categories found at higher levels of the hierarchy than the correct ones. This results in a lot of under-represented and a few over-represented categories. We call categories that are over-represented, due to under-specified labels, joker classes. To allow robust learning in the presence of joker classes we propose a simple and effective approach: First, we detect problematic categories, i. e. joker classes, based on the misclassifications of an initial hierarchical classifier. Then we re-train from scratch, introducing a weight to the standard cross-entropy loss function that targets incorrect predictions related to joker classes. Our model has enabled the correction of thousands of POIs in our production database.
Introduction Categories are important elements of databases of Product Listings, for e-commerce platforms, or of Points of Interest (POIs), for location-based services, such as Google Maps and Naver Maps. In this paper we are particularly interested in POIs, i. e. places that someone may find interesting. POI categories, i. e. tags denoting that a POI is a Falafel Restaurant, Bowling Alley etc. , can be used not only to guide humans, but also as input data to several applications such as recommender systems and trip planners. However, in real-life applications, tags are incomplete or imprecise, especially for unpopular or newly-established POIs  [1]. ML-based supervised category prediction has been proposed as a solution to impute missing tags  [2, 3, 4, 5, 6, 7, 8]. However, as Zhou et al.
[6]  argue, it is unrealistic to count on the existence of a perfectly annotated training set. This is due to the fact that tags are input either using automatic techniques (e. g. mining user comments), which necessarily comprises errors, and/or by humans who often fail to annotate database items comprehensively, especially when there are thousands of categories to select from. Our data illustrates this as well. In Figure  1 , we show how the distribution of the top one hundred most popular categories changes after our data analysis team verified and corrected our original (manually annotated by users) dataset (c. f. Section 3). In this paper, we propose a method that takes into account the fact that training annotations may be noisy. In particular, we identify that a major source of errors relates to under-specified hierarchical labels i.
To our knowledge this is the first study that identifies this problem in a real-world setting, and illustrates its impact on the quality of corresponding categorisation models. • We perform experiments on data from our POI database, one of the biggest search and services platforms worldwide. We also report the impact of the deployment of this model on our production database, where the categories of thousands of POIs were corrected. The rest of the paper is organised as follows. We review related work in Section 2 and the background in Section 3. We define the problem in Section 4 and describe our approach in 5. Experiments are presented in Section 6 and a deployment case study in 7.