dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Esperanza Alonso  for excellent technical assistance and  Dr Javier Casas  ( Microscopy Unit, IBGM ) for technical support with confocal image analyses. Supported by the  Spanish Ministerio de Economía y Competitividad (Mineco , grant  PID 2020-118517RB-I00  to  M. Teresa Pérez-García  and  José R. López-López ), the  Junta de Castilla y León  (grants  VA172P20  to  M. Teresa Pérez-García ;  Unidad de Excelencia   CLU-2019-02  to IBGM, and postdoctoral [DPP] and predoctoral [SME] contracts) and funds from  Institut Curie, INSERM ,  Agence Nationale de la Recherche  RetroTact  ANR-20CE15-0009-01 ,  ANR-10-IDEX-0001-02 PSL* , and  ANR-11-LABX-0043 , and  Fondation pour la Recherche Médicale   FRM EQU202003010280  ( Claire Hivroz ). Julia Serna  and  Dino Gobelli  are funded by predoctoral contracts of  Universidad de Valladolid.
Statistical analysis Initial sample size calculation. We estimated that 15 participants per arm would be necessary to detect a within group difference in Hct of 5%, with a power of 80% and a type I error of 5%. This computation was based on Hct values of 74 § 6% previously observed in CMS inhabitants of La Rinconada  22, 25  and the 5%-effect of ACZ on Hct previously demonstrated, albeit at lower altitude, for treatment durations ranging from 3 to 24 weeks. 13À15  To account for a potential high rate of dropouts at follow-up, we planned to include a total of 60 CMS participants (i. e. , 20 participants per arm). Statistical analysis. After confirming the normality of the main data (using Shapiro-Wilk test and quantile-quantile plots), continuous data were expressed as mean § standard deviation (SD) or expressed in median [25th-75th percentiles], as appropriate.
Acknowledgements The authors would like to thank  Blandine Deschamps  ( RN ) for her expert technical assistance during the study, the medical students of the scientific association of medical students from the  Altiplano University (Puno, Peru ) who actively contributed to the research program implemented in La Rinconada,  Detalo Instruments  ( Prof. Carsten Lundby ) for support with the OpCO system and  Severine Lambing  and  Vincent Rocher  from  Radiometer France  for their excellent technical assistance.
The method uses the camera model presented in  Labussière et al. (2022)  which explicitly includes the defocus blur in the projection model. We derive its inverse which provides depth estimates up to a scale factor. Thus, we empirically take into account depth scaling to achieve precise and accurate metric depth estimates. For that purpose, calibration is required. Especially, we propose a method to calibrate the inverse model. To validate our framework, we present a new dataset of 3D real-world scenes with ground truths acquired with a 3D lidar scanner, and a methodology to calibrate the extrinsic parameters. We make our source code and datasets publicly available to the community on our github page's https://github. com/comsee-research/. Paper Organization. The remainder of this paper is organized as follows.
Experimental setup To validate our blur aware depth estimation framework, we proceeded as follows: first, we analyzed the scaling error modelings; second, we compared our method on relative depth estimation with state-of-the-art methods, including the Raytrix software, corresponding to the model of  Heinze et al. (2016) , and estimation with the model of  Noury et al. (2017)  using only the disparity; finally, we evaluated the depth estimation on realworld 3D complex scenes with ground truths acquired with a lidar. Our experimental setup is illustrated in Figure  6.
Software environment All images have been acquired using the MultiCamStudio free software (v6. 15. 1. 3573) of the Euresys company. We set the shutter speed to 5 ms. For Raytrix data, we use their proprietary software RxLive (v4. 0. 50. 2) to calibrate the camera, and compute the depth maps used in the evaluation. Our source code has been made publicly available at https://github. com/ comsee-research/libpleno, and https://github. com/ comsee-research/blade.
Datasets R12-A, B, C. For the relative depth evaluation, we used the datasets presented in  Labussière et al. (2020). We evaluated then our depth estimation framework for focus distances h ∈ {450, 1000, ∞} mm (i. e. , datasets R12-A,B,C respectively), representing then three different working depth ranges. R12-E, ES, ELP20. For the 3D scenes evaluation, we introduced a new dataset, namely R12-E, corresponding to a new focus distance h = 2133 mm. The camera has been calibrated using Compote  (Labussière et al. , 2022). From this configuration, we created two sub-datasets: 1) a simulated dataset built upon our own simulator based on raytracing 2 to generate images with known absolute position, named R12-ES; 2) a dataset composed of several real-world 3D scenes with metric ground truth acquired with the LP20, for object distances ranging from 400 mm to 1500 mm, exploiting then the whole distance range offered by the camera setup.
The latter dataset, named R12-ELP20, includes fives scenes: 1) a scene for extrinsics calibration, containing checker corner targets, named Calib; 2) two scenes containing textured planar objects, named Plane-1 and Plane-2; 3) and two scenes containing various figurines, named Figurines-1 and Figurines-2. Each scene is composed of: a colored point cloud (with spatial (x, y, z) information, color information (r, g, b), and intensity information) in format. ptx,. pts and. xyz; 3D positions of the targets in the lidar reference frame; two raw plenoptic images in rgb color and two raw plenoptic images in bayer; finally, photos and labels of the scene. Our datasets have been made publicly available, and can be downloaded from our github repository with their descriptions. We believe it is a first step toward helping future research to evaluate their method on metric space.
Lidar-camera calibration In order to transform the lidar point cloud data P l in the same reference frame as the camera, we calibrate the extrinsic parameters between those frames, i. e. , the transformation c T l ∈ SE (3) such that P c = c T l P l , where P c is the point cloud data expressed in the camera frame. The calibration is a four-steps process. First, using the Leica station, we acquired a point cloud of a scene containing calibration targets, and associated the 3D coordinates p l of the corners manually from the point cloud. This set of corners forms a points constellation, noted C l. Second, a raw plenoptic image of the same scene is acquired with the plenoptic camera, and BAP features { p k,l } are extracted. The features are clustered and associated to the points constellation.
For each cluster of observations, the barycenter is computed. Those barycenters are initial estimates of the projections of the points constellation through the main lens using a standard pinhole model. Third, the initial transformation c Tl is estimated using the Perspective-n-Point (PnP) algorithm  Kneip et al. (2011) , like in classic pinhole imaging system. Finally, the transformation c T l is refined by minimizing the reprojection error of the points constellation, such that arg min c T l p l ∈C l k,l p k,l -Π k,l c T l p l 2. ( 40 ) The optimization is conducted using the Levenberg-Marquardt algorithm. The point cloud data P l can now be expressed in the camera frame. It is thus used as ground truth for quantitative evaluations. 2 Available at https://github. com/comsee-research/prism.
Depth scaling error analysis Depth scaling correction. Coarse depth estimation for datasets R12-A,B,C,ES is performed on images corresponding to planar checkerboards orthogonal to the optical axis and uniformly distributed in the range of distances, whilst for R12-E, depth estimation is performed on hand-held checkerboards, leading to noisier evaluation of depth estimates. The reported depth associated to each frame is the median of the depth estimates. Calibrated depth scaling coefficients are reported in Table  1 , along with their median scale error after correction for the evaluation datasets. Depths before and after correction are illustrated in Figure  7. A positive error means the estimated object is smaller than the ground truth, and a negative error means that the estimated object is bigger than the ground truth.
The absolute error grows when getting farther from the focus distance. All corrected distances have a nearly null scale error. For all datasets, with real data from four configurations and also in simulation, our methodology successfully corrects the scale, with a final median scale error ε scale of less than 0. 05 %, indicating thus nearly no scale error. Analysis on simulated data. We investigated scale error on simulated images of the R12-ES dataset. Depths are estimated based on the coarse depth estimation framework, for ground truth distances from 500 mm to 1900 mm with a step of 100 mm. Without scale correction, depths are estimated from 610. 9 mm to 1917. 4 mm with a mean relative error ε z = 8. 06 %. After scale correction, depths are closer to the ground truth and are estimated from 495.
7 mm to 1841. 8 mm with a mean relative error reduced by a factor two, ε z = 3. 78 %. As shown by Figure  7 , depth scaling error appears even in simulated data, showing that this phenomenon must be added to the inverse projection model to reach precise and accurate depth measurements. Scaling model comparison. Secondly, we evaluated the choice of the empirical scaling model and presented the results for the dataset R12-C. As illustrated in Figure  7 , the quadratic model performs slightly better than the linear model, as the slope of the fitted line is closer to zero compared to the slope of the linear fitted model. This is confirmed by the median scale error after correction reported in Table  1  which is reduced by a factor two with the quadratic model. In the following, depths will be corrected with the quadratic model.
Relative depth evaluation We compared our method with (BLADE) and without (BLADEu) depth scaling on relative depth estimation with state-of-the-art methods, including the Raytrix software  (Perwass and Wietzke, 2012) , corresponding to the model of  Heinze et al. (2016)  (denoted RTRX), and depth estimation with the model of  Noury et al. (2017)  using only the disparity (denoted DISP) in our framework. The latter provides a baseline using a classic block matching technique but that does not leverage blur information. The relative depth errors along the z-axis with respect to the ground truth displacement from the closest frame are reported in Figure  8  for datasets R12-A (a), R12-B (b) and R12-C (c). The mean error with its confidence interval across all datasets is illustrated in (d) for each method, and is: for BLADE, ε z = 4.
09 ± 0. 85 %; for BLADEu, ε z = 11. 05 ± 6. 61 %; for DISP, ε z = 7. 88 ± 3. 74 %; and, for RTRX, ε z = 6. 76 ± 3. 96 %. First, we see that our scale correction effectively improves the depth estimation results , as expected from our depth scaling error analysis. With scale correction it outperforms the other methods, meaning that leveraging both defocus and correspondences provides accurate results. This behavior is also validated in simulation on R12-ES, where the mean error after correction is of the same order, i. e. , ε z = 3. 78 %. Secondly, the BLADE method presents the lowest standard deviation, a stable error across all distances, and errors of the same order for all configurations. The other methods vary significantly across the datasets, making our method the only one able to generalize to several configurations without losing precision.
Absolute depth evaluation on 3D scenes We used the dataset R12-ELP20 to evaluate our depth estimation framework on absolute metric depth estimates. Central sub-aperture depth map. To compare depth estimates, we generated for each scene the central sub-aperture depth map (CSAD) as follows: 1. From the raw depth map, we back-project each pixel having a virtual depth hypothesis into a 3D point in metric space; 2. We replace the plenoptic camera model by a pinhole model, where the sensor is now at a distance F from the main lens, and we increase the pixel size by a factor S (here, S = 4, the final resolution is thus 1020 × 767 pixels); 3. We project each point of the point cloud with the new pinhole model, using a z-buffer like technique, and attributing the minimum depth value to the pixel (or the median value for noisy data); 4.
The mean error with its confidence interval across all datasets for our method with (BLADE) and without scale correction (BLADEu), for the model of  Noury et al. (2017)  using only disparity (DISP), and for the proprietary software RxLive (RTRX) are reported in (d). Please refer to the color version for better visualization. for each of the five scenes of R12-ELP20, and compared them to the ground truths. It allows us to validate both the scaling model, and the improvement of accuracy due to the defocus information.
With better strategy to deal with occlusion, results are expected to be similar for both. The choice of using which variation depends on the application behind: if we need more details, refined is preferred; if a denser map is required, the coarse is preferred. Note that for the scene Figurines-2, the variation D/R/U has a smaller median error than the others. Recall that for R12-E, objects are reconstructed farther when unscaled (i. e. , the scale error is negative, see Figure  7 ). As most of the errors appear on the objects' boundaries, objects in foreground are closer to the reference ones in background, and the difference of depth Table  2 : Statistics (percentiles and median) of the absolute difference (AD) error of central sub-aperture depth map for each variation of our BLADE framework (using relative blur information (B) or only disparity (D); using the coarse (C) or the refined (R) estimation; and using the scale-corrected (S) or scale-uncorrected (U) model), on the scenes of dataset R12-ELP20.
Acknowledgments This work was supported by the  AURA Region  and the  European Union (FEDER)  through the  MMII  project of CPER 2015-2020 MMaSyF challenge. We would like to thank  Laurent Malaterre ,  Trong-Lanh Rémi Nguyen ,  Ruddy Theodose  and  Etienne Cadet  for their help during the acquisitions. We also thank the reviewers for their insightful comments.
Introduction The accelerating scale of recorded neurophysiological data has been accompanied by a shift from the view that individual neurons are the basic unit of computation, to one that explains computational processes in terms of the dynamics of neural populations  (Saxena & Cunningham, 2019). Electrophysiology studies routinely record data from ever larger neural populations using chronically implanted multi-electrode arrays. These datasets are challenging to analyze because of their dimensionality, but recent evidence suggests that neural population activity in low dimensional subspaces, or manifolds, may be more important in neural computation than the firing of particular single neurons  (Barack & Krakauer, 2021; Gallego et al. , 2017 Gallego et al. , , 2018;; Humphries, 2021; Saxena & Cunningham, 2019).
5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey. Throughout the task, the monkey sat in front of a table containing a handle and a semicircular groove. A metallic cube, the target, was placed at 11 cm from the handle, in a slot within a metallic grasping platform which could be placed within the groove (Figure  1B ). Contact with the handle, target object, and the bottom of the groove was recorded by a circuit which detected changes in resistance.
The task was programmed and controlled by EventIDE software (OkazoLab Ltd). Trials started when the monkey grasped the handle. After a variable delay period (500 -1000 ms), an auditory go signal (900 Hz tone) instructed the monkey to reach for the target object, grasp it, and place it into the groove beside the grasping platform. The monkey was then rewarded with a few drops of water. Data was recorded from 1 second before the handle grasp until the reward. The task was run in blocks of 10 trials per condition (with the grasping platform placed in the left, right, or center of the groove). The left and right target positions were each 25° from the center of the semicircular groove, relative to the handle. The orientation of the slot in the grasping platform depended on the condition. In the center condition the slot was oriented at 0° with respect to the animal, and in the left and right conditions the slot was oriented at 45° to left and right, respectively.
Model training Model training was performed using R (R Core Team, 2021) and the developer version of the package mHMMbayes  (Aarts, 2019). In mHMMbayes, models are fitted using a hybrid Metropolis within Gibbs MCMC algorithm, which expands on classic HMM implementations by using Bayesian estimation as outlined in  Scott (2002). Estimation results from a conventional expectation-maximization HMM trained on aggregated spike count data were used as starting values for the MCMC chain (depmixS4;  Visser & Speekenbrink, 2010). The Bayesian multilevel HMM was fit to binned (10 ms bin width) spike counts from each electrode used with trial-specific intercepts, and covariates encoding each trial's condition. The model was fit with 4000 iterations, with the first 2000 being discarded to dissipate the effect of starting conditions (burn-in).
1. 26; Bates et al. , 2018). Factor significance was determined using type II Wald Χ 2 tests using the car package (v3. 0. 10; Fox et al. , 2019) , and pairwise Tukey-corrected follow-up tests were performed using estimated marginal means using the emmeans package (v1. 5. 3; Lenth et al. , 2020)  with Kenward-Roger approximated degrees of freedom. Code for all analyses is freely available (https://github. com/danclab/motor_mea_mbhmm).
Acknowledgements We acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC-CoG 864550  to JB), and the  National Institutes of Health  ( NICHD P01HD064653  to PFF). This work made use of the Dutch national e-infrastructure Snellius with the support of the SURF Cooperative using grant no. EINF-2570 , which is (partly) financed by the  Dutch Research Council (NWO). SK was supported by the  Fondation pour la Recherche Médical (FRM). We thank  H Scherberger  and  PA Beuriat  for assistance with array implantation. We thank  M Montagna  and  S Pinède  for their help with the construction of the laboratory set up.
Genotyping and genome-wide association studies Whole genome sequencing was utilized for genotyping, and the clean reads were mapped to the reference genome of D. alata v2. 1  [62]. A total of. 1. 9 million high-quality SNPs were used for association studies using the advanced multiple loci model statistical model Bayesianinformation and linkage-disequilibrium iteratively nested keyway (BLINK) implemented in the R package GAPIT3  [63]. It has been proven more robust than the traditional Mixed Linear Model  [64]. In this study, the genome-wide association study (GWAS) was independently conducted with phenotypic data from each location. The threshold for significant associations was set at log10 P = 6. At the two locations, we identified significant associations for all the traits, except for R.
𝑇𝑈𝐸 = 𝐴 𝐸 Genome-wide association studies Dossa et al. [29]  fully detailed the DNA extraction, sequencing and data processing steps. GWAS was performed with all 1. 9 million SNPs using the advanced multiple loci model statistical model Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) implemented in the R package "GAPIT3"  [63]. BLINK provides an efficient platform with increased power compared to other methods, while reducing the calculation time. The Manhattan plots were also generated in R4. 0. 23 with the "CMplot" package  [99]. SNPs having a significant association with traits were determined by the adjusted p-value. The threshold of P < 10 -8 (0. 05/n, with n = number of SNPs) was set to report a significant association. The quantile-quantile (QQ) plots were generated by plotting the negative logarithms (-log10) P-values relative to their expected p-values to fit model relevance GWAS with the null hypothesis of no association and to determine to what extent the models considered the structure of the population.
Putative candidate gene identification To inventory potential genes near associated SNP markers for target traits, we used ANNOVAR version 2. 4 to identify genes downstream and upstream of the significant SNPs based on linkage disequilibrium (LD = 5 Kb)  [29]. The identified candidate genes were screened for their putative functional attributes. Subsequently, we extracted the related genes and conducted a Gene Ontology (GO) enrichment analysis using the KOBAS-i tool. The significantly enriched GO terms were then visualized using the "ggplot" package from the R4. 0. 23 software.
Statistical analysis The effect of alleles at significant SNPs was assessed by comparing phenotyping data for haplotype groups. A Student's t-test was used to compare the groups of haplotypes (P < 0. 05) in the R4. 0. 23 software with the "ggpubr" and "rstatix" packages. Using the LME4 package [100] available on R4. 0. 23, we considered the genotype as a random effect to obtain the variance components of all the traits. We calculated phenotypic variance share of genetic source as broad sense heritability (H²)： 𝐻 " = 𝜎 " 𝑔 𝜎 " 𝑔 + 𝜎 " 𝑒 𝑁 G Where 𝜎 2 𝑔 is genotype variance, 𝜎 2 𝑒 is residual variance, and N is the average value of the replicates for each genotype. The basic descriptive analysis of the stress response-related leaf traits was performed by All relevant institutional, national, and international guidelines and legislations were followed while conducting this experiment.
Use of Electron Backscatter Diffraction for Grain Size and Plastic Strain Measurement As the grain refinement achieved by MHP is crucial to quantify the effectiveness of the parameters used, electron backscatter diffraction (EBSD) maps were carried out and compared with the OM measurements. Due to the large number of conditions tested, EBSD was only carried out for a few selected samples, as a validation method. The difference in the average grain diameter measurements between the OM and EBSD was approximately 15 to 3%, which was considered acceptable. To obtain the β grain size from the EBSD maps, a method for reconstruction of the β phase was carried out. Due to the limited and extremely fine residual β phase found in Ti-6Al-4V WAAM material  [32] , directly indexing it from EBSD data presents a significant challenge.
Therefore, Davies et al. [33, 34]  developed a method (based on  [35, 36] ) that allows the calculation of the parent β orientation for each α variant through the Burgers Orientation Relationship (BOR)  [9]  and determines the most likely prior β orientation for neighboring α-lath variants. In the present work, neighboring α were considered the same variant for misorientations of up to 2 • and maximum angular deviation from the ideal BOR of up to 3 •. The EBSD maps for this purpose were carried out using a Tescan Mira3 FEG-SEM (Kohoutovice, Czech Republic) with a 10-15 µm step size and a voltage of 20 kV. The system was equipped with a Symmetry 1 EBSD detector from Oxford Instruments and AZtec acquisition software (version 6. 0). Grain size measurements were extracted from the EBSD maps using Channel 5and ATEX software (version 3.