dataesr/scientific-paragraphs-categorization huggingface dataset:
Multilocus genotypic dataset quality control Assuming Hardy-Weinberg equilibrium, we tested for heterozygote deficit or excess for each locus in each population using Genetix  (Belkhir et al. , 2004)  to identify outlier loci presenting extreme inbreeding coefficient that could indicate presence of null allele (positive Fis) or paralogs (negative Fis). As clonal reproduction has been reported for oaks in harsh environmental conditions  (Alberto et al. , 2010) , we identified clones using Colony  (Wang, 2016)  and kept only one genotype from each detected genet. The studied loci were partly derived from expressed transcripted sequences  (Durand et al. , 2010)  or selected to differentiate Q. robur and Q. petraea  (Scotti-Saintagne et al. , 2004; Lepais et al. , 2006) , therefore all may not behave neutrally.
Population genetic diversity and structure Gene diversity (Nei's unbiased heterozygosity), allelic richness after rarefaction to 16 diploid individuals, Fis and Fst were computed for each sampled location with FSTAT  (Goudet, 1995) , private alleles were computed using HP-RARE  (Kalinowski, 2005) , genetic differentiation accounting for allele distance in number of repeat at microsatellite alleles (Rst) was computed using SPAGeDi  (Hardy and Vekemans, 2002)  and genetic differentiation between species tested using 10,000 permutations of individuals between species. To estimate genetic differentiation between populations, four different genotypic datasets were considered, accounting either for the whole haplotype, the flanking sequence haplotype, the number of microsatellite repeat or the whole allele size, to highlight potential difference in genetic structure between polymorphisms.
Statistical significance for difference of genetic diversity and differentiation within species estimated from different datasets was tested by comparing locus-level statistics using a Mann-Whitney test. Structure v 2. 3. 4  (Pritchard et al. , 2000; Falush et al. , 2003)  was used to explore genetic structure between and within species. We applied the Admixture model and the Correlated Allele Frequency model with 20 replicated runs for an assumed number of genetic clusters (k) ranging from 1 to 12. To remove runs that did not converged and produced outlier results, the 15 most likely runs for each k value were kept for further inspection. Structure Harverster  (Earl and vonHoldt, 2012)  was used to inspect likelihood trends of the runs and Clumpak  (Kopelman et al. , 2015)  to identify and filter-out sub-optimal runs.
Using the most informative whole haplotype genotypic dataset, contemporary effective population size was estimated for each sampled location using the linkage disequilibrium method implemented in NeEstimator v2. 1  (Do et al. , 2013)  assuming random mating, removing alleles with frequency below 0. 02  (Waples and Do, 2010)  and using the parametric chi-squared method to estimate 95% confidence interval. In addition, the geographical context of the population genetic structure was illustrated using a network graph built with EDENetworks  (Kivelä et al. , 2015)  with node location representing the geographical coordinates of sampled site locations and edges linking sites showing pairwise genetic differentiation values (Fst) lower than 0. 04 (determined by automatic thresholding), with edge width inversely proportional to Fst estimates.
Three additional models include long-term continuous interspecific gene flow between core populations of the two species (model Ab, Bb and Cb, respectively, Figure  2 ). Gene flow between contemporary populations and unsampled core range of the other species was modelled as directional from the core to the isolated heterospecific population because we were interested in the effect of interspecific gene flow on the studied populations specifically. In addition, we allowed interspecific gene flow only for populations showing putative admixture in Structure (Figure  1c  and 1g ). Effective population size was allowed to increase or decrease between the time at divergence and present. Prior distribution of demographic parameters was setup wide to reflect the lack of knowledge about the history of these populations (Table  4 , Supporting Material File 1) and checked for their capacity to produce realistic genotypic data by comparing observed and simulated genotyping datasets  (Cornuet, Ravigné & Estoup, 2010; Leroy et al.
, 2017, Supporting Material File 3). Using fastsimcoal2, we simulated realistic molecular diversity that closely matches the characteristics of each of the studied loci. We focused on two kind of variability that can be simulated: variation in repeat number at the 36 microsatellites and substitutions in the flanking sequence for the 20 loci for which flanking sequence could be reliably analysed (called HapSTR loci thereafter) and assuming complete linkage between the microsatellites and their flanking sequences. This procedure accounts for 90% of the source of variations in the dataset  (Lepais et al. , 2020) , leaving out additional polymorphisms (SNP in the repeated motif or indel in the flanking sequence) that are far more complex to model due to uncertainty of the mutational mechanisms and rates.
Each microsatellite was allowed to mutate at rates ranging from 10 -5 to 10 -2 mutation/generation/haploid genome  (Lye et al. , 2011)  following a Generalized Mutation Model with parameter p between 0. 1 and 0. 5. Similarly, substitutions in the flanking sequence could mutate at rates ranging between 10 -9 and 10 -6 substitution/position/generation/haploid genome following neutral model of evolution. Each locus system had its own prior distributions for these parameters to model a wide range of mutational dynamics. To compare the six alternative demographic models, 12,000 genotypic datasets were simulated for each model with demographic and mutational parameter values sampled from prior distributions. To summarize the distribution of genetic diversity among populations and locus, a total of 530 summary statistics were derived (Supporting Material File 2).
We used Arlsumstat  (Excoffier and Lischer, 2010)  to compute 294 summary statistics that take into account the different sources of molecular variation. A first set of 98 statistics were computed with microsatellites and, when available, flanking sequences combined as microhaplotypes "considered as mutationally equidistant from each other" (standard data in Arlequin nomenclature) using the 36 loci. A second set of 135 statistics were computed based on the number of repeats from all 36 loci (microsatellite data type in Arlequin). Finally, for the 20 HapSTR loci, an additional set of 61 statistics were computed based on substitution in the flanking sequences. We then computed 224 additional summary statistics using a custom R script (Supporting Material File 4). First, 136 of these statistics were locus-level variability such as variance in allele size for the microsatellite (V), number of microsatellite alleles, number of flanking haplotypes, nucleotide diversity (theta).
Second, as the covariation in diversity at linked polymorphisms might include useful information about mutational and demographic processes  (Payseur and Cutter, 2006) , we computed 88 statistics interrogating the association between the repeated motif number and the flanking haplotype, such as the correlation between theta and V  (Payseur and Cutter, 2006)  or the mean number of microsatellite alleles per flanking sequence haplotype. We performed model choice by comparing simulation-based summary statistics from the six models to the observed summary statistics from the real dataset within the approximate Bayesian computation framework  (Beaumont et al. , 2002)  and using the Random Forest approach implemented in the R package abcrf  (Pudlo et al. , 2016; Raynal et al. , 2019). We first built a classification random forest model using 1,000 trees and a training dataset consisting of simulation-based summary statistics obtained from all models.
We estimated the classification prior error rate for each model using an "out-of-bag" procedure to estimate the power of the genetic data to differentiate between the demographic models given the model and prior specifications  (Pudlo et al. , 2016). Then, we used the summary statistics computed based on the observed genotypic data to predict the demographic model that best fit the data using a regression forest with 1,000 trees. In addition, we also repeated the model choice analysis using only the 231 summary statistics derived from the microsatellite motifs from the 36 loci. We then used the overall most likely scenario to simulate 100,000 additional genetic datasets using parameters and prior distributions described above to estimate demographic model parameters. We built a regression random forest model implemented in the R package abcrf based on the summary statistics using 1,000 trees.
The out-of-bag procedure  (Raynal et al. , 2019)  was used to assess prediction error and inference power by comparing true (simulated) and estimated parameter values. We then estimated the posterior median, 0. 05 and 0. 95 quantiles of demographic and mutational parameters using a random forest regression model based on all summary statistics computed from the observed haplotypic dataset. Finally, we assessed the value of different or combined polymorphisms for demographic inference by quantifying summary statistics importance for both model choice and parameter estimation  (Estoup et al. , 2018). To better interpret the significance of variable importance, we introduced 12 additional random summary statistics ("noise variable") in the datasets  (Chapuis et al. , 2020). These consisted in four random floating point variables sampled between 0 and 1, four random floating variables sampled between 0 and 10 and four random natural number variable sampled between 1 and 30.
Genotyping After filtering for missing data, genotyping error, Hardy-Weinberg equilibrium, neutrality and clonality (see Supporting Material File 2), the final genotypic dataset consisted in 36 high quality sequenced loci genotyped in 318 individuals. This includes 20 linked flanking sequence and microsatellite loci, called HapSTR thereafter, and 16 loci that could not be reliably analysed across the whole haplotype and for which only the microsatellite motif was analysed, called STR thereafter (Supporting Material File 2).
7% and 44. 4% for HapSTR and respectively). Therefore, we selected scenario Ab to estimate its demographic parameters using the HapSTR dataset. representing 89% and 88% of the total variable importance for microsatellite mutation rates at HapSTR loci and SSR loci respectively (Figure  4f  and 4i ) and an average of 16 informative parameters for substitution rate that represents 47% of the total variable importance (Figure  4h ). On the contrary, the parameter p controlling the number of mutational steps at microsatellite was difficult to estimate (posterior MNSE between 2. 6 and 7. 6; Supporting Material File 2) and had only an average of 3 informative summary statistics per parameters that represented 7. 6% of the total variable importance for all loci (Figure  4g  and 4j ). Estimated microsatellite mutation rates varied from 2.
The value of compound markers for population history reconstruction Sequence information around microsatellite loci can uncover molecular variations caused by different mutation dynamics and mechanisms generate complementary patterns across populations and species  (Hey et al. , 2004; Barthe et al. , 2012). If the dataset combining all polymorphisms under microhaplotypes showed the most clear-cut results to depict the geographical distribution of the genetic clusters, we also observed different patterns of diversity distribution across populations according the polymorphisms considered. For instance, microsatellite variation drives the build-up of genetic differentiation among Q. faginea populations, but not among Q. canariensis populations. Given that microsatellite loci evolve faster than DNA in their flanking sequences, increased differentiation at fast-evolving loci might suggest recent demographic events affecting Q.
, 2018) , locus-specific inference contrasting flanking sequence and microsatellite mutation rates could help identifying functionally relevant microsatellite variation showing unusually low or high rate of evolution. Integrating different sources of polymorphism will thus further our understanding of the role of molecular variations in shaping species adaptation. The datasets of Q. faginea and Q. canariensis genotypes used in this study will be made publicly available in Dryad upon manuscript acceptance.
Random forest ABC Model choice (a) by using jointly microsatellite and flanking sequence information (HapSTR) or information derived only from the microsatellite variations (SSR only). Power to differentiate alternative demographic models for HapSTR dataset (b) given the model and prior range specifications. The table reports how many of the 12,000 simulated datasets generated under a specific scenario (rows) are classified into each demographic scenario (columns). The overall scenario classification error is computed based on the number of incorrectly classified dataset; the last column shows the percentage of simulated datasets classified as Ab, the most likely scenario for the observed genotypic Bold numbers indicate >10% incorrect classification, underlined number indicates correct classifications.
Acknowledgments We thank  Rémy Petit  and  Cecile Bacles  for advice and comments on a previous version of the manuscript. Sequence-based microsatellite genotyping was performed at the PGTB (doi: 10. 15454/1. 5572396583599417E12 )with the help of  Erwan Guichoux  and  Emilie Chancerel. Computer  time for the demographic simulations and ABC inferences was provided by the computing facilities  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Université de Bordeaux  and of the  Université de Pau et des Pays de l'Adour.
We evaluated the proposed AR training method, particularly the conveyance of the AR step-by-step instructions by conducting two field experiments. The first  [1] , a preliminary one that involved 12 participants, aimed at assessing the effectiveness and usability of our proposed low-cost AR training method. The second  [24] , an extension of the first, involved 20 additional participants and aimed at assessing the worthiness of using CAD data for conveying manual assembly expertise via AR. This paper presents a comprehensive overview of the two field experiments and discusses unpublished preliminary data in respect to the proposed AR authoring method, collected during a field study. The overall reported evaluation results collected from the considered experiments suggest that capturing and conveying expert knowledge via AR by using uniquely low-cost spatially registered visual assets is potentially the most efficient and viable option until the creation, manipulation and storage of CAD data and animations become more convenient, especially in industrial sectors.
Field Study (FS) The study presented in this section was conducted by one of the authors and aimed at evaluating the proposed AR authoring system, ATOFIS (see Section 3. 1), particularly from a time perspective. We conducted this work to provide an estimate of the time required for authoring the AR instructions of an assembly workstation and, concurrently, to compare our proposal with one of the most representative industrial AR authoring systems. We analyzed the creation of step-by-step low-cost-based AR instructions by measuring their authoring time and their media types composition. We note that the current study does not deal with CAD data nor does it evaluate the authoring difficulty. Unlike FE1 and FE2, this section discusses unpublished data; therefore, a complete description of the considered study is presented, as further described.
Yet, no non-chemical solution exists concerning crop pests. This is why the Greenshield (see https://anr-greenshield. insa-lyon. eu/, accessed on 9 September 2022) project's objective was to demonstrate the feasibility of using a laser-based detection-neutralisation module (DNM) embedded in a mobile robot dedicated to this purpose on aphids. This robot managed to do demonstrate its feasibility in lab conditions in September 2021. It also demonstrated the feasibility of using the DNM with a commercially available robot (Dino, Naio Tech) to combat weeds. Detecting aphids in crop fields is a complex task due to their small size and confounding color. As with numerous other applications, a deep learning approach has been envisaged. Indeed, this artificial intelligence approach is nowadays widespread for face recognition  [32] , image segmentation  [33] , signal analysis and automatic interpretation, and the control of autonomous cars.
Insects and Plants This study focused on the neutralization of aphids, as they are among the most harmful pests for crops and complex to control  [34]. A solution able to control their spread should most likely work on other invertebrates such as cockroaches, mosquitoes and fruit flies. To do so, we chose three aphid lines from parthenogenetic females in the fields, different in color and size, providing a diverse database to develop our DNM. Thus, we used two pea aphid lines, Acyrthosiphon pisum LL01 (green) and YR2 (pink), and one bird cherry-oat aphid line Rhopalosiphum padi LR01 (black, 2 to 3 times smaller than A. pisum). Aphids were kept under obligate parthenogenesis in laboratory conditions to rear unsynchronised parthenogenetic aphid populations and synchronized nymphs N1 (first instar) as described by Simonet et al.
To do that, we first built an image database featuring the two pea aphid lines, A. pisum LL01 (green) and YR2 (pink) on broad bean seedlings. We chose A. pisum on broad bean for the training set because of the color diversity of the aphid lines occurring on the same crop, which has an optimal leaf surface for plant and aphid detection. Furthermore, we used young plants for the detection of plants and aphids because of their vulnerability to pest attacks and their compactness compatible with the regular passage of a robot above the rows. Thus, this technology could be useful for the detection and neutralization of pests that can cause serious damage and economic losses to vulnerable young plants, such as soybean aphids  [39]  and black cutworms on corn  [40] , or crops with a compact habit, such as black bean aphids on sugar beets  [41].
The training set of images was annotated using LabelMe (see https://github. com/ wkentaro/labelme, accessed on 9 September 2022) software. Around 700 images were manually labeled, with each one featuring 20 to 30 aphids. It was necessary to manually draw a bounding box around each aphid as well as to put a dot on the center of each aphid. Since we did not have a sufficient amount of images for training the neural networks from scratch, we fine-tuned our network from an already trained network (trained from MS COCO data set) and we used data augmentation on our images during training to avoid the phenomenon of over-fitting and to improve the generalization ability of the trained model to new images. The following image transformations were applied randomly to generate modified images during training: We started to develop this localization function with YOLOv4  [38]  (whose code is available at https://github.
It is indeed a preparation step for the following detection. The YOLO detector predicts the class of the object, its bounding box, and the probability of its presence in its bounding box at three different scales. Because of its small characteristic maps, this kind of multi-scale detector struggles to detect small objects in the image. Thus, in practice, it could not detect aphids located at a distance greater than 30 cm from the camera. We tested several input image sizes (640 × 640 and 512 × 512) knowing that the size of the images acquired by the camera is 2208 × 1242 pixels to determine the best choice. We also optimized the network process using batch inference, which was not yet implemented in the original YOLOv4 code. To speed-up the inference time, we chose tkDNN (the source code for tkDNN is available on Github: https://github.
com/ceccocats/tkDNN, accessed on 9 September 2022), which is a neural network library specifically designed to work on NVIDIA Jetson boards. It is built with the primitives of cuDNN, a library from NVIDIA regarding neural networks, and TensorRT, an inference platform high performance that enables high throughput and low latency for inference applications related to deep learning. TensorRT engine optimizes models by quantifying all network layer parameters and converting them into formats that occupy less memory space. The tkDNN library allows inference with network parameters in three different numeric formats: FP32 (a 32-bit floating point format representation), FP16 (a 16-bits floating point format), and INT8 (integers represented with 8 bits). In this project, we chose the FP16 format as it is displayed the most accurate and efficient format (see https://github.
com/ceccocats/tkDNN#fpsresults, accessed on 9 September 2022). The data degradation caused by format conversion remained meaner than 0. 02%. We also studied an approach to detect smaller objects without a bounding box based on the weighted Hausdorff distance  [37]  (code available on Github: https://github. com/ javiribera/locating-objects-without-bboxes, accessed on 9 September 2022). The chosen neural network architecture was a U-Net optimized with a loss function based on this distance. Rather than taking bounding boxes as references (ground truth), the network seeks to localize objects according to reference points. Once the bounding box center or an aphid point was well located on the image in the form of pixel coordinates, it was then possible to convert these points into a 3D reference using the Software Development Kit of the ZED mini camera.
All the treatments were repeated five times. Statistical tests were performed using R Studio software. The mortality rate of the synchronized N1 aphids is presented here with a GLM Gaussian test with a 95% confidence interval. Moreover, the laser beam must be innocuous for host plants. Indeed, in case of false-positive detection, the entire laser energy would strike them. Hence, the impact of the energy delivered in our experiments has also been investigated on host plants in our previous work  [51]. V. faba and T. aestivum host plants were chosen for the A. pisum and R. padi aphid lines, respectively. The plants were cultivated as described in Section 2. 2 and shot at the seedling stage of 2-3 leaves. We shot the host plants 4 times with different values found in each experiment with the 10.
Sub-sampling the input image (2208 × 1242 pixels) to a 512 × 512 pixels image blurred the objects and caused a reduction in precision. To get better accuracy, we also tiled the area of 800 × 800 pixels into 4 or 16 subparts. The inference speed (frames per second) was computed when the network ended up processing all these areas. We observed an increase in sensitivity when using 400 × 400 pixel tiles (0. 73 versus 0. 51 for 800 × 800 pixel tiles). In the case of very small images (200 × 200 pixels), the sensitivity falls to 0. 53. We also noticed that this method greatly reduces the inference speed of the model because of the large number of images to be processed for each capture. We also observed that the code developed in C++ (gcc version 7. 5. 0-ubuntu 18. 04) was more efficient than that of Python API (Python 3.
, S. P. , T. G. , T. N. , V. D. , V. L. and V. N. ; writing-review and editing, A. L. , B. M. , K. R. , M. T. P. , P. D. S. , R. G. , R. M. , S. P. , T. G. and V. L. ; visualization, J. D. S. , K. R. , P. D. S. , R. G. , S. P. , T. G. , V. D. and V. L. ; supervision, A. L. , B. M. , F. G. F. , K. R. , M. T. P. , P. D. S. , R. M. and T. G. ; project administration, A. H. , A. L. , B. M. , F. G. F. and K. R. ; funding acquisition, A. H. , A. L. , B. M. , F. G. F. and K. R. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by ANR grant number ANR-17-CE34-0012.