dataesr/scientific-paragraphs-categorization huggingface dataset:
Ground truth datasets Beyond the experiments, we collected multiple annotated ground truth datasets for training and validation. The first one (GT1) was recorded by a researcher sitting in a classroom and logging the state of interaction/no-interaction between a pair of children and their relative orientation using the scan sampling method (7 pairs of children were observed for Σ = 3 hours 35 minutes, in ranges of 17-54 minutes). Advantage of this setting was to gather annotated data in-situ with all noise and interference present (the class containing 20-25 individuals wearing a badge). To obtain annotated data in more controlled settings, a second dataset (GT2) was recorded by statically positioning a pair of children, at a given distance and orientation from each other for ∼10 minutes periods, in a separate room (4 pairs of children positioned in two different settings each, observation time in a given position range was 2-10 minutes, summing up to ∼56 minutes of observation).
These data provided stationary observations of packet exchange, however, it failed to capture the dynamicity of social interactions. Finally, a third dataset (GT3) was recorded by a researcher sitting in a classroom and logging the relative distance and orientation of the individuals present, both children and adults, during regular class activities (28 individuals wearing a badge observed for 30 min). Advantage of this dataset was to make possible a direct comparison of the RSSI values collected by the badges with the actual distance between social partners. Both the datasets GT1 and GT3 were collected using the scan sampling method  [41]  (data recorded at intervals of 10 seconds for GT1, and at intervals of 2 minutes for GT3) with the Animal Observer application for iPad  [42]. For a more detailed description on how ground truth data were collected see Appendix A.
These dependencies are demonstrated by the measurements depicted in Fig. 2b  and c. There, in panel Fig. 2b , the density plot of RSSI values (y-axis) of captured packets shows a non-linear negative correlation with the distance between participants (x-axis). This measure suggests, for a realistic distance of maximum 1. 5-2 meters for verbal interactions, a corresponding RSSI range between -70 • • • -75 dBm, while high intensity regions for lower RSSI values are due to noise and situations of close, non-interacting proximities. This is verified by other measures based on GT2 dataset (see panel Fig. 2c ), where RSSI values remain within this range for several orientations and radically change only when participants are back to back to each other. However, only visually inspecting these results, it is very difficult to determine a precise RSSI threshold separating real and false social interactions.
This is shown in Fig. 3a , where we plot handshake pairs using their RSSI values as coordinates. Colours code a handshake being an interaction (green) or non-interaction (red). Since different handshake pairs could appear with the same RSSI values but different interaction states, in Fig. 3a  we represent with a small pie chart their fraction at a given location. The strong diagonal component indicates that the RSSI values of mutual observations are very similar to each other as expected, while the interactions seem to separate from noninteractions around ∼-70 dBm, which corresponds well to the earlier estimated threshold  range. To solve this classification problem in a more systematic way, we trained a logistic regression model on the annotated GT1 dataset. As input we gave vectors of handshake pairs and we used their annotated labels for the training task.
Hidden Markov model The second reconstruction method we chose is the Hidden Markov Model. To set the parameters for HMM with supervised learning method we used the GT1 dataset with the annotated states of handshake sequences as states sequence, and the sequence of binary states (explained in Sect. 3. 1) as the observations. After training on the annotated data (GT1), we determined the values of the conditional transition probability of hidden states as transition matrix, conditional emission probability from hidden states to observation states as emission matrix and the initial states probability as start matrix. In turn, we used these as parameters for the Viterbi algorithm to solve the most likely sequence problem and use the output as the reconstructed sequence. To enrich the information coded in the input sequence, instead of providing a sequence of binary values for each time step, we define a backward window, which contains some short term information before the actual state being reconstructed.
Event reconstruction To train all these models we used GT1 since it was recorded in the most realistic setting. These data were built from 7 observation clips of 1290, 3060, 3200, 1230, 1740, 1350 and 1030 sec, covering 3 hours 35 minutes combined. For training and validation purposes, we divided evenly observations longer than 3000 seconds (2 clips) into 3 shorter periods, and retained 10 observation clips all with length between 1000 and 1740 seconds (for a total of 3 hours and 18 minutes). To determine the best hyper-parameter set for each method, we applied a nested cross validation strategy. In the outer loop, we selected one clip (each of them once) for testing purposes thus we kept it out from the training of the model at this round. From the remaining 9 clips we perform the traditional 9-fold cross validation.
This corresponds to a gap length of 35 seconds, which is somewhat smaller than the gap window size of 75 seconds reported by Elmer et al. [24]  on another RFID dataset. In the case of the HMM model, the best performance corresponds to the same window size win = 6. For the BiLSTM models, the accuracy increases with the window size but reaches a plateau at window size win = 27 for the BiLSTM-bin, win = 25 for the BiLSTM-logi, and win = 27 for the BiLSTM-RSSI, after which the reconstruction accuracy decreases.
Discussion The goal of this work was manyfold. First, we developed a filtering and temporal network reconstruction pipeline to obtain the best approximation of temporal social interaction sequences from proximity data recorded via wearable wireless devices. We used ground truth data recorded in various settings and explored different reconstruction strategies involving supervised methods of classification and sequence reconstruction. We found that, while all tested methods provide reasonable performance, naïve methods commonly used in the literature show the worst performance. At the same time, bi-directional LSTM methods, which take into account information from the past and future of the actually predicted state, solve the reconstruction task the best, with accuracy up to ∼90%. Furthermore, we wanted to highlight the importance of precise reconstruction of temporal interactions from raw data.
Over the last few years, experiments using wearable wireless devices provided an ideal way to study collective social phenomena through the precise recording of temporal social interactions of people/animals in various settings. At the same time, these datasets became inductive resources to study ongoing dynamical processes such as epidemics  [13, 47] , opinion dynamics  [48] , etc. evolving on the temporal social fabric. However, without the careful reconstruction of social interactions, any study addressing the dynamics or structure of the evolving networks or any ongoing collective dynamics would risk to draw wrong or inaccurate conclusions. We demonstrated the sensitivity of this issue by simulating susceptible-infected processes on the reconstructed networks, which in turn follow significantly different scenarios depending on the actual method used for event reconstruction, even for those with comparable accuracy.
Second, we aim at measuring, quantifying and modelling the processes that have longterm influence on social and linguistic development. Our three-year longitudinal followup design makes it possible to analyse the processes underlying the dynamics between changes in the social network and language skills. In particular, we will examine the effect of integration within a new community: does a community always have a homogenising role, by absorbing linguistic change, or, by contrast, can it accommodate the linguistic usages brought by new members and augment these by disseminating them through the community? As any data-driven study intending to predict or infer human behaviour, our study has also limitations. First of all, the collected data contain certain noise, which cannot be reconstructed with any actual method.
e. continuous recording of interaction events)  [41] , however social interactions (and kids position/distance too) were sometimes too short and fluctuating for continuous observation, thus it was impossible to record the beginning and end of each interacting event without using video-recording. In addition, for scan sampling, we made trials to find the appropriate time intervals between two observations (scans) that allowed us to record data without loss (i. e. the shortest step possible for recording the data of interest without taking the risk to miss one observation point). In the end, we chose 10 second steps for pair observations (GT1) and 2 minute steps for group observations (GT3). • For GT1 observations, we worked during free play time to be able to observe spontaneous play interactions.
To minimise such fluctuations in recording the distances in GT3, we used a customised behavioural observation app originally developed to record the positions and distances of animals in a fixed environment (Animal Observer application for iPad  [42] ). This app projects a scaled map of the classroom with indicated reference objects (furnitures, doors, windows, etc. ) and allows to record by an observer the positions of individuals on this map as the function of time. Using this temporal location dataset inter-individual distances of children have been computed afterward. Relative to the objects on the classroom map the location of children could be estimated precisely with a very small error margin (around 10 cm), something that would have been impossible to accurately assess through "naked eye" estimations and "hand" recording.
Acknowledgements We are especially grateful to all the children, their family, and the members of the school teaching staff who took part in our experiment. This work has been supported by the DyLNet  ANR  project ( ANR-16-CE28-0013 ). MK, JPC and EF are grateful for partial support from the  SoSweet   ANR  project ( ANR-15-CE38-0011 ). MK acknowledges support from the  DataRedux   ANR  project ( ANR-19-CE46-0008 ) and the  SoBigData++ H2020  project. SD benefits from sponsorship by  China Scholarship Council (CSC)  for his PhD program.
Landscapes have been photographed dozens of times at scales ca. 1/25,000 and better since World War II. Scans are distributed freely online (e. g. remonterletemps. ign. fr). In parallel, Structure-from-Motion (SFM) software made photogrammetric processing easy to non-specialists. Yet puzzling questions crop up to use both: (i) Can raw scans be used as is? (ii) Can Ground Control Points (GCP) and checkpoints be safely collected from a web portal? (iii) How many parameters are sufficient for camera interior orientation? (iv) Are single flight camera networks sufficient to constrain camera models compared to multiple flights? (v) Are photogrammetric Digital Surface Models (DSM) fit for quantifying landslide activity? Processing of scanned black-and-white 1/27,000 photographs from IGN flown in May 1978 over Cirque de Salazie in La Réunion Island answer these questions.
INTRODUCTION Structure-from-Motion (SFM) is a method widely applied in geoscience to produce topographic data of a landscape from digital photos  (Eltner et al. , 2016). Such data is useful to extract information from the landform and monitor its evolution over time (e. g. Cook, 2017; Dewez et al. , 2016; James and Robson, 2012; Johnson et al. , 2014; Smith and Vericat, 2015). Systematic aerial photogrammetric surveys image the earth surface since the middle of the 20th century, every few years. Freely distributed online in many countries, geomorphologists are longing to exploit this archive to build historical digital surface models and accurate ortho-photographs to retrieve quantitative landscape evolution metrics. Recently, it has been demonstrated that Structure-from-Motion software could leverage this information to quantify landscape changes  (Bakker and Lane, 2016; Gomez et al.
Using these flight properties, we investigate selfcalibrating bundle adjustment behaves in SFM software Agisoft Metashape v1. 5. 2, which is used by a large community. In this contribution, we ask and answer a series of practical questions: (i) Can raw photo scans be readily used without preprocessing? (ii) Can GCP be collected effectively on a webmapping portal? (iii) How many parameters are sufficient to parametrize the camera interior orientation? (iv) Is minimal stereoscopic coverage (single flight) sufficient to constrain the camera model compared to more redundant multiple flight configuration? (v) Are photogrammetric Digital Surface Models fit for quantifying landsliding?.
PREPARATION OF THE AERIAL PHOTOGRAPHS Previous work using archive photographs with SFM software  (Gomez et al. , 2015; Riquelme et al. , 2019)  were somewhat elusive regarding their image preparation. Scanned file comparison shows that film placement on the scanner's window was not strictly reproduced. Cropped larger than film extent, the images cannot be readily mapped to a unique camera reference system. Although Agisoft Metashape can handle fiducial mark objects, these could not be used in this project because the software expects calibrated mark coordinate values. Without camera calibration certificate, fiducial positions are unknown. This is why we wrote a Python code to perform the inner orientation task. Our code reads JPEG2000 images, automatically detects fiducial marks with one pixel accuracy, with pattern matching correlation using Lucas-Kanade method, rotates the image coordinates so that XY camera axes align to image lines/columns and crops the images to the same origin (upper left corner fiducial).
Cropped images, removing the black film edge, are output as lossless TIF files. In this way, the camera principal point should always occur in a unique image location corresponding to the true photo center. Figure  2  shows that individual photo image coordinates are indeed very variable from one scan to the next: individual photo coordinate origin on the scanned image vary by up to 150 pixels (3 mm), and rotation spans 1° of rotation. Assigning all images to being captured with the same camera, Metashape expects that all images reflect a unique, static, image coordinate system. If Metashape had managed to calibrate such poor camera situation at all, calibration results would still be grossly erroneous. Our Python code automates this inner orientation pre-processing. Its validity was checked manually with 40 fiducials.
SFM-BASED CAMERA CALIBRATION Aerial photogrammetric surveys were historically designed to shoot minimally overlapping photos with parallel and nadirlooking axes images (60% along-track overlap, 30% across-track side lap, e. g. Kraus, 1993). 3D reconstruction by SFM, by contrast, is optimal if the photo sets contain oblique photographs and large coverage redundancy (e. g. James and Robson, 2014b; Wenzel et al. , 2013). Historical photograph networks are considered weak photogrammetric network  (James et al. , 2017). Nevertheless, air photo archives contain such a wealth of landscape evolution information and SFM softwares integrate much improved dense matching algorithm nowadays, that investigating their combined use is worth being framed again. We therefore explore to what limits SFM-derived Digital Surface Models (DSM) are appropriate tools for geomorphologic application and change detection.
CAMERA PARAMETER SENSITIVITY TO REDUNDANT COVERAGE Because Metashape offers the possibility to interact programmatically through its Python API, further tests were run to assess the robustness of camera calibration parameters and the impact of redundant camera network on calibration parameters. For this, a bootstrapping method was employed. A reference "chunk" containing all 60 aligned photos with high accuracy contained a set of 63 079 tie points with a multiplicity of 2. 64 in average, 11 GCP and 9 checkpoints. 85% of the reference points are observed on more than four photos. A bootstrapping method reproduced one hundred times the following: a random subset of 70% of tie points was sampled and an optimize operation run for f, cx, cy and K1. The camera network (positions and angles) was saved, along with reference point residuals, number of unique tie points effectively sampled and camera calibration parameters.
DSM COMPARISON AND LANDSCAPE EVOLUTION The SFM pipeline was further unfolded to extract dense point clouds with UltraHigh/aggressive, filtering settings recommended by Metashape for aerial photos, all 60 photos of 1978 and a camera model with 4 parameters (f, cx, cy, K1). The "DEM" (in the sense of the Metashape function) was processed at the default resolution of 0. 66 m/pixel. But to limit noise, comparison were conducted with the DSM Geotif grid resampled at 5 m, using bilinear interpolation in ArcGIS. The 1978 DSM was compared to a reference lidar elevation dataset acquired for La Réunion Regional Council in 2015 (Figure  6 ). According to the comparison with 25 geodetic markers distributed in the Cirque, this MNT is accurate at 18 cm +/-12 cm in elevation. This helicopter-borne lidar data set was acquired with an average point density of 67.
From DGNSS permanent and monitoring campaigns surveys benchmarks, it is known that Grand Ilet experiences 0. 05 m/yr -0. 52 m/yr horizontal motion on up to 15° average slopes, vertical displacement of 0. 01 m/yr to 0. 13 m/yr  (Belle et al. , 2014). It is not surprising that Cirque de Salazie exhibits such massive changes in 37 years. In more quantitative terms, Figure  7  shows the elevation change observed in a narrow (20 m x 1000 m) N-S box located in a stable area (location between reference points p5 and p17, Figure  1 ) illustrates that there is a fair agreement between elevations estimated by photogrammetry in 1978 and lidar in 2015. The photogrammetric data is biased by -1. 39 m with a median absolute deviation (descriptor advocated by  Höhle and Höhle, 2009)  equals to 3. 34 m (Figure  7 ).
Characteristic elevation difference quantiles (q17% and q83%, therefore bracketing 2/3 of the elevation samples) are respectively -6. 92 m and 1. 12 m. Admittedly, elevation discrepancies are sizeable. Beyond identifying ground movements (landslides and erosion), our approach is valid to assess with a 5 m resolution volumes of landslides and eroded areas. This precision may seem coarse compared to today's lidar accuracy. But photogrammetry provides a landscape-wide topographic image for a period at which none existed so far. It is pretty amazing. Elevation errors arise for several reasons. First, a photogrammetric surface model is compared to a lidar terrain model. Elevation on tropical vegetation is susceptible to explain a significant part of the elevation errors. Second, the ground control points used to georeference the photogrammetric dataset show an agreement to within 7 m.
DISCUSSION AND CONCLUDING REMARKS In this contribution, we spent time exploring the effects of SFM processing on digital landscape reconstruction. Early papers evoking this topic (e. g. Gomez et al. 2015 , Riquelme et al. 2019 ), marvelled at the possibility of doing so. They spent less time on deeper photogrammetric or SFM processing pipeline considerations. Here, we show that image preparation is a significant source of improvement in the image geometry (image origin and image to camera axes alignments). An automatic Python code is used to produce images in a reproducible, unique and stable reference frame. Results show that raw scanned images may happen to produce 3D results with SFM by chance, but individual image origin shifts and rotations are bound to dramatically alter principal point estimates and the entire photogrammetric chain.
In numerical terms, however, the benefits of increased stereoscopic coverage was not visible from estimated camera parameters or reference point adjustment quality figures. Finally, comparison with recent Digital Terrain Model revealed a never-seen-before 37 years of intense mass wasting history in Cirque de Salazie. This image freezes the combined geomorphic signature of 13 cyclones intervened in the interval. Such data is a critical piece of information for the improvement of risk mitigation maps. It brings the hindsight of decades history. Our work, although mostly technical will have significant consequences for the risk management community. Council, European Union (FEDER), French Government and BRGM. In memory of J. -M Dewez who passed away when finalizing this work.
Gene Expression Data Pre-processing We preprocessed and normalized gene expression data from the GTEx project (version 6. 0) in a tissue-aware manner using YARN, a software pipeline designed to perform quality control, gene filtering, and normalization, allowing gene expression levels to be comparable between tissues while preserving information regarding tissue-specific expression  (Paulson et al. , 2017). The data included expression information for 30,243 genes, including 1,074 encoded on the sex chromosomes. For the analyses shown here, we only considered tissues with samples collected from both men and women with more than 30 samples in either of the sexes. This included 8,279 samples representing 29 tissue types from 548 research subjects (360 males and 188 females). Table  S1  shows the demographic information, including a comparison of covariates by sex (age, race, body mass index [BMI], RNA integrity number  [RIN] , and RNA isolation kit).
However, there are many challenges in studying sex as a biological variable. This includes ensuring adequate sample sizes for well-powered analyses of each sex and issues with sequencing data mapping, including poor mapping quality and bias from both highly similar regions between the sex chromosomes and the lack on Y chromosome in females. Several studies have found sex differences in the expression levels of autosomal genes, although the expression differences are generally small. The differentially expressed genes between males and females are enriched for biological processes with known sex differences, such as reproduction, fat biogenesis, muscle contraction, and cardiomyopathy  (Gershoni and Pietrokovski, 2017; InanlooRahatloo et al. , 2017). In an analysis of a small GTEx pilot dataset (1,641 tissue samples from 175 individuals), Meleet al.
Tissues are ordered based on the ESR1 expression-targeting correlation value. For each tissue, the names of the top 3 TFs with the highest expression-targeting correlation values are annotated, and the locations of ESR1, ESR2, AR, and MAZ are marked by arrows. See also Table  S8. for spurious correlation in females, we set the edge weights to Y chromosome genes in females to the minimum value found, which can cause significant inflation of male-biased Y chromosome edges. As network methods evolve, we need to develop better approaches that can balance these types of technical and biological artifacts to improve and extend comparisons of male and female networks and to understand how these regulatory differences contribute to sex differences in health and disease. Our analyses were done on the GTEx dataset, and need to be repeated in additional datasets to confirm that our findings are generalizable.