dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The individual-level genotype and phenotype data are available through formal application to the UK Biobank http://www. ukbiobank. ac. uk. The R package biglmmz, developed to perform lowrank mixed-model GWAS and calculate the effective size multiplier, is available at https://github. com/variani/biglmmz. The scripts to reproduce results of simulations and UK Biobank analyses can be found at https://github. com/variani/paper-neff.
2015; Finucane et al. 2018 ) explicitly includes the sample size in its model, and the empirical multiplier in Equation (  18 ) was proposed for correction  (Gazal et al. 2017). While the assumptions underlying this approach seems reasonable, especially for large powerful GWAS including numerous genome-wide significant SNPs, our real data analysis suggests it should be used with caution. For some other methods, such as meta-analysis, correction by the ESS multiplier is required when weighting the effect estimates by the sample size. However, the inverse-variance weighted approach implicitly solves the problem, as the variance of estimates from the LMM carries on the information from the ESS multiplier. Since most GWAS designs to-date are composed predominantly of unrelated individuals, we expect the adjustment to the ESS due to family relatedness in existing datasets to be modest.
Data analysis To test our hypothesis that there is an ancestral priority in the cognitive strategies mobilized in the human mind to find medicinal plants to treat diseases, we developed mixed generalized linear models (GLMs) with binomial distribution, using the R lme4 package  [45]. As the environment and relevance of the information are factors that influence memory in the recall test, the dependent variable was the recall of the words evaluated by the participants. Therefore, the independent variables were the types of simulated environments, the words' relevance, and the interaction between these variables. As each participant has a different memorization capacity, we included the participants as a random factor to discard the effect of the difference in memorization among the three models developed.
The effect size of the variables was measured using the R broom. mixed package  [46]. All analyses were performed in the R version 3. 6. 1 environment  [47]. We adjusted the model to obtain the lowest AIC value, as proposed by Agresti  [48]. Although the variable relevance when isolated, was not significant (assuming a significance level of p < 0. 001), we included it in the adjusted model (AIC = 9013. 2), as analysis of the more complex model showed significant interactions between the variables (relevance + environment) that influenced the recall. For Agresti  [48] , when the variables interact significantly, the variables that make up this interaction should not be removed from the model. The resulting model explains about 4. 5% of the variation observed in the recall (R2 marginal = 0. 044).
When a CH receives data from its subpeers, via the second-degree CH2, it selects randomly one SCH among the SCHs set as a key to look for. Hence, the given CH checks in its finger table, the CH with the largest closest identifier to the key and relays the data to it. In this way, the data are passed around the CHEARP ring through the successor pointers until achieving the random selected SCH (the requested key), which takes in charge the transmission of the data to the sink. An example of routing in CHEARP protocol is given in Figure  7 , where a data transmission is supposed from the node N 7. The latter sends the data packet to the appropriate CH2 in its cluster for eventual data aggregation. Then, the CH2 sends the packet to the cluster head N 9 that takes in charge the data transmission.
Simulation Environment and Parameters To position the efficiency of the CHEARP protocol, extensive simulation experiments are conducted under Matlab/Simulink environment, and the obtained results versus the recent DHT-based routing protocol: CLEVER  [8]  are likened. In this regard, a random deployed network of 100 -500 nodes is considered on a squared size field of 500 * 500 m 2 , which means that the abscissa (horizontal) and ordinate (vertical) coordinates of each sensor are randomly selected between 0 and the maximum value of the space dimension. For each node is assigned a transmission range equals to 150 m and an initial energy value of 2 j, while the energy values of E elec and E amp are respectively set to 50 nj and 0, 0013 pj. The summary of simulation parameters used in this model is given in Table  2.
| Software and visualization Basic image and video manipulation were performed with ImageJ 11 as well as 3D volumetric visualization figures and videos using the ImageJ 3D Viewer plugin. Our image analysis software was scripted with Matlab (MathWorks, Natick, MA, USA) including VP/CF/IP/DM/SG/ST toolboxes. Some video sequences were realized with Amira for Life & Biomedical Sciences (Thermo Fisher Scientific, Waltham, MA, USA). Videos were encoded in MP4 with the opensource video transcoder HandBreak (https://handb rake. fr/). Before entering the image analysis pipeline, the 3D reconstructions realized from the recorded scans were checked for preparation artefacts such as bubbles in the lumen or huge blood clots. Scans exhibiting such features were discarded. In addition, we did not consider stacks where the signal/noise ratio was poor, making reliable automated image processing impossible.
| Image analysis The computer used for this work was a dual processor Intel Xeon E5 2680 V2 (2 × 10 cores) with 128 GB RAM, high speed SSD disks, and a NVIDIA GeForce 2080Ti with 11Gb of RAM. Whenever possible, Matlab computing was parallelized with the dedicated toolbox on 20 cores. Three hours were necessary to compute the segmentation of 1500 × 1500 × 2500 stacks. Measurements of elastic structures took about 3 hours for 200 images. Briefly, 16 bit-depth TIFF stacks were first reduced to 8-bit manually using ImageJ by selecting a subset of the dynamic range that provides the best contrast for elastic structures. In rare cases, if extra-bright artefacts considerably reduced the 16-bit image dynamic, we created a Matlab script that identified the well exposed images, extracted their 16-bit histograms, and created a reference average histogram from all of them.
This second step of detection ensured the possibility to detect the boundary between media/adventitia and also the interlamellar regions, with a good accuracy. In a third and last step, the original image of the artery wall was slightly deblurred with a 5 × 5 Wiener filter and segmented using both masks. Measurements were then carried out by first using a smoothed version of the luminal mask. Orthogonal lines to this mask were used for profiling pixel intensities. Peak intensities and half-height widths were determined using Matlab-dedicated functions. The estimation of the thickness and the width of the filamentous structures were performed with bespoke Matlab script using the following procedure. First, an image thresholding level was manually chosen on the basis that (1) it allowed the view of the filamentous structure and (2) modifying its value did not change drastically the measurements, indicating that we used a robust thresholding value.
Despite their small size, the elastic lamellae present in the arterial wall appeared clearly contrasted from the interstitial and surrounding tissues. The images had a good signal/noise ratio in most cases and, consequently, they were compatible for the automated image processing method we developed in Matlab. Image stacks that passed the quality control (11 in total: 5 control, 2 db/db, and 4 ApoE -/-) were automatically segmented. This automated step was developed so as to avoid human bias during the segmentation process. Despite possible local noise or lack of contrast, our method allowed the segmentation of key arterial structures (elastic lamellae) throughout the vessel wall. Indeed, the tubular shape of the aorta and the continuity of elastic structures through each dimension allowed segmentation without any complex assumption.
With regards to 3D reconstruction and visualization, it is important to note that our imaging program segments images and does not modify the pixel intensity of the original tomogram. However, as 3D visualization is a critical step in detecting these filamentous structures, we have also demonstrated that three alternative programs (Matlab, ImageJ, and Amira) also visualize these structures. And as ripples and filamentous structures do not fully colocalize, we conclude that they do not originate from a 3D visualization artifact. Collectively our observations provide evidence for the existence of a previously unreported internal structure in arterial elastic lamellae, however future studies will be required to confirm these observations using alternative experimental approaches. The bright filaments of the lattice-like network indicate high local phase-contrast in the absence of labeling.
ACKNOWLEDGMENT The authors gratefully acknowledge the assistance provided by  Dr CM Disney ,  Dr R Bradley ,  Dr K Madi , and  Dr M Yusuf  during data collection. The chair MAgICS is acknowledged for financial and technical support. This work was funded by  DLS  under MT12776 proposal. ABZ and AG were funded by the  University of Reims Champagne-Ardenne. DRRT of  Champagne-Ardenne  funded this work via the " Projet Incitatif Amont" program. The financial support of  Centre National de la Recherche Scientifique (CNRS)  is also acknowledged.
Particle image velocimetry Two continuous lasers, positioned opposing one another in order to avoid shades, provided a light sheet with thickness 2 mm. A Phantom V2511 high-speed camera captured the fluid flow with 4 kHz and 1280 × 800 px 2 (see Table  2  for details). The fluidimage software package was deployed for data processing. A multistep postprocessing with a final interrogation window of 16 × 16 px 2 and 50% overlap led to 1. 5 × 1. 5 mm 2 spatial resolution in a plane section of 120 × 74 mm 2. Final vector fields were compared to those obtained by processing with the DaVis software package. A significant difference in the direct vicinity of the hydrofoils was found, which is considered to be a result of different mask treatment strategies. The averaged differences were found to be -0.
Acknowledgements The authors thank  Roberto Leidhold  (University Otto-von-Guericke of Magdeburg),  Christian-Toralf Weber  ( Magdeburg University of Applied Sciences ) and  Laure Vignal  ( LEGI, Grenoble ) for their advice. The work of the student assistants in this project is grateful acknowledged:  Iring Koesters ,  Yanneck Kiiski ,  Carsten Laddey  and  Stephanie Müller. The technical support of  Michael Haarmann  ( CATLAB, Berlin ),  Michel Riondet  and  Jean-Marc Barnoud  ( LEGI, Grenoble ), is also gratefully acknowledged.
State of the art Modern 3D clothing CAD systems include physical based fabric simulation, virtual fitting, 3D costume design technology, and 2D and 3D mutual conversion technology  [5]. With the application of 3D virtual clothing prototyping, the 2D patterns of the clothing can be placed and draped on the virtual human body. When the virtual prototyping is accurate, the garment fitted to the body model reflects and combines characteristics of the clothing style, the garment pattern design, the virtual body model, and the mechanical properties of textiles  [6]. The success of 3D virtual garment prototyping programs depends heavily on the definition of the material properties used in the program. Nowadays there are several 3D CAD software packages for clothing on the market: Modaris 3D Fit by Lectra is one of the 3D virtual prototyping solutions, which links 2D patterns, fabric information and 3D virtual models together; Assyst Vidya connects well with 2D pattern development Assyst CAD  [7].
Several recent products like CLO3D and MarvelousDesigner of CLO Virtual Fashion Inc. concentrate on simplified user interfaces. Users can check silhouettes of garments and understand the feelings about the final product without making physical samples, and they can directly simulate different 2D patterns. Changing colors, textures and fabric materials can shorten the time for sample preparation  [8]. Actually for the physical testing of samples for CLO3D, a separated testing kit is required. OptiTex is another fabric simulation software for 3D draping and visualization of garments and is based on 2D CAD patterns and real fabric features. In this software, the user can customize different mannequins according to their preferences. Moreover, the designer can see all the cutting details and check the fit on the virtual fitting model in static or dynamic positions  [9].
VStitcherTM from Browzwear software can convert 2D patterns into 3D virtual garments, and the virtual models can be customized according to customers' needs  [10]. This software is coupled with pattern drafting, grading and marking software. It integrates excellently with Grafis, which provides a parameter based pattern development tool with large number of clothing construction systems. The PAD system is visualization software that can be used to create virtual 3D garments from 2D pattern pieces. It includes a fabric simulation tool and texture mapping tools that allow users to simulate fabrics, apply colors, textures and prints. The software also allows the user to adjust fabric properties and see the effects on the drape of the garment  [3]. Users can create virtual 3D prototypes of garments based on 2D patterns and fabric properties on the virtual models to view a garment from any angle in a static or dynamic pose and create dynamic storyboards for presentation purposes using the eFit SimulatorTM software.
The software also enables digital prototype models that can be sent by e-mail around the world, allowing designers to perform a "virtual fitting" online and in real time  [11, 12]. Other products such as Gemini and Gerber became part of Lectra and allow users asking which of these products will remain in the near future on the market. One of the complex questions of all 3D clothing programs is the accuracy of the drape simulation, based on the material properties of the fabrics. Modaris provides a broad library of more than 120 materials along with their mechanical properties, which are tested and prepared and in this way ensure correct simulations. The software also allows the designer entering new fabric properties to view different drape  [13] , but then the user is responsible for the correct measurement, too.
The same options -large library and the possibility to insert new samples with correct material parameters which the user obtained during standard testing -are provided in Assyst Vidya, too. Despite of this, some research institutions try to motivate software companies to use unified format for the material data, as until now each CAD software requires different values, based on different testing procedures and stored in different formats. Principally, engineers with good mechanics knowledge are able to re-compute these values  [14] , but the solvers in the CAD programs remain closed and hidden and the reproducibility of the drape results remains not enough investigated. Generating and importing avatars is another important feature, which is required for evaluation of the clothing fit. Most of the software packages allow import of externally created avatars.
An example of creating pregnant avatars using blender and importing in Lectra Modaris is demonstrated in  [15]. The avatars and the CAD software allow as well investigation of body temperature and additional comfort related parameters  [16]. 3D clothing simulation software has many advantages, however, there are also some limitations, to name just a few: mechanical simulation, collision detection, geometric modeling, interactivity and others are difficult to simulate as real properties. For example, how a fabric responds to folds, creases, tears, and other conditions is the result of the many properties that make up a given fabric. Even more important is how a fabric responds to its environment, with certain fabrics responding very differently, such as how a fabric looks and stretches in the wind and in different lighting conditions.
The fit of the garment and the ease of body movement are very much related to the stretchability and resilience of fabrics. 3D technology can reduce or eliminate costly sample making and fitting. However, there is no more systematic study on 3D clothing simulation for the analysis of clothing style and fabric type, especially considering the fabric properties. So the aim of this paper is to study the influence of different fabric types for different skirt styles to represent and analyze the appearance of virtual garments, create the most suitable patterns and select appropriate fabric types based on the different skirt styles to achieve the intended design using "Modaris (Lectra) Software".
Materials and methods The skirt pattern was created using Lectra Modaris software. First, a basic skirt was created with two darts in the front waist and two darts in the back, as shown in Figure  1. This style was modified to create a flared skirt (Fig. 2a ) and a bell-shaped skirt (Fig. 2b ) as well as a straight or rectangular shape (the skirt hangs straight from the hip line to the hem) (Figure  2 c ). After creating these skirt variations, the cuts were sewn in the virtual plane to verify the 3D fit. A standard mannequin was used for the current evaluation fit of the garment with the different materials (Fig. 2 d ). The two-dimensional pattern for this study was created for a size 38 woman with body measurements of 70 cm at the waist, 101 cm at the full hip, and 15 cm from the waist to the hip.
The three types of skirts are simulated for six different fabrics, whose main properties are given in Table  1. These are produced of cotton elastane, polyester satin, felting, and linen with silk, cotton voile, and Botany wool serge fabrics and are all randomly selected from the fabrics which are available in the Swatch Book of Lectra  [22]. This book contains real samples, so that the user can touch them, and at the same time all their properties for correct draping simulation within the Lectra Modaris V8 software are available in the system. Elastane fabric has great stretch and recovery properties. Polyester satin fabric is a smooth, shiny fabric with a satin weave and 100% polyester fibers. Felting fabric is a non-woven fabric that is manufactured by matting and compressing fibers together to form the structure of the fabric.
For linen with silk, the strength of both threads makes this fabric durable and abrasion resistant. The silk fiber also gives the linen more softness. Cotton voile, voile made of 100% cotton or a blend of cotton and linen, allows air to pass through and provides comfort. Because of its fine texture, voile can also be used to hem clothing. Botany wool serge wool is marketed under the name Botany wool to indicate that the wool comes from Botany Bay. The term is reminiscent of knitwear made from very fine wool and has its own special labeling. All these different fiber types provide different appearances and properties of the products. The question in the current study is if and which influence on the appearance can be detected using the 3D simulation capabilities of the Lectra Modaris software.
A central question is, therefore, what happens when the data dimension exceeds the latent space dimension? To answer this question, we consider some simple, lowdimensional examples where n z = dim(Z) = 2. We assume that Z has iid normal components and consider linear mappings of these components to the output space. We consider the joint data PDF in three possible cases (i) n x < n z ; (ii) n x = n z , and (iii) n x > n z. In the following examples, for vector Gaussian random variables x with mean vector µ and covariance matrix P , we write x ∼ N{µ, P } and adopt N{x; µ, P } for the corresponding probability density function. For compactness, we also use Matlab TM notation; thus x = [x 1 ; x 2 ] is the same as x = [x 1 , x 2 ] T , with a similar convention for matrices. The construction of the examples reflects the fact that G(•) is a deterministic function whose domain (or input space) is in a space of lower dimension than its codomain (or output space).
This work presents the simulation of 3D crack propagation in samples in order to determine fatigue life. The modellings have been achieved by using MBEMv3. 0: a fast software based on the Symmetric Galerkin Boundary Element Method (SGBEM) accelerated with the Fast Multipole Method (FMM) in 3D elasticity. Fatigue crack propagation has been simulated with Paris law. We present the simulations of a tensile/compression fatigue test on cylindrical samples of a semi-coarse asphalt concrete considered as homogeneous, containing very small cracks. When the number of cycles increases, the cracks propagate, and we can observe a loss of rigidity of the sample. Parametric studies of the modelling parameters have been performed where the damage evolutions exhibit a typical shape that proves that, for asphalt concrete materials subjected to T/C fatigue test, the shape of the fatigue test curve is mainly governed by biasing effects at the beginning then by mechanical damage at the end.
Introduction Fatigue cracks are among the major causes of structural degradation. Experimental methods have been widely used since a long time to predict their propagation. However numerical methods are very interesting because some destructive experimental tests, which can entail important costs and long delays, can be avoided. This work presents the simulation of 3D crack propagation in order to determine fatigue life by using a fast-numerical software developed in our laboratory: MBEMv3. 0. This software is based on two sophisticated methods of integral equations: The Boundary Element Method (BEM) and the Fast Multipole Method (FMM). Although the Finite Element Method (FEM) is the best-known method in fracture mechanics, BEM has significant advantages, for example, the reduction by one of thr problem dimension.
2 Crack propagation simulation by MBEMv3. 0 Based on SGBEM and FMM, Pham  [8]  and then Trinh  [9]  developed a Fortran code for 3D elasticity problems, called MBEMv2. 0. This software inherits a number of innovative algorithms from the boundary element community, such as the singular integration schemes, the index of severity for adjusting the Gaussian quadrature density to the interelement distance, the nested Flexible GMRES (FGMRES) which uses the near-interaction matrix as a preconditioner and the extension to multizone configurations of the SGBEM. These features and attendant performance enhancements are reported in  [7, 9]. Matrix-vector operations are performed using subroutines from the BLAS library. The FGMRES algorithm used is available at www. cerfacs. fr and described. A new version of the code noted MBEMv3.
0, is developed by Dansou et al. in  [10, 11, 12]. In this version, multiple strategies have been proposed and implemented for performance enhancement, including a data reusing technique, a shared memory parallelism using OpenMP directives and the proposal of a new sparse matrix method. The enhanced code has been run on various large-scale tests (N = O(10 6 )) and has proved to be very robust and of excellent accuracy. The time speedup compared to MBEMv2. 0 has exceeded 50 in some crack propagation simulations. For example, ten propagation cycles of 64 circular cracks in a cube sample are simulated in two hours on a 20-core computer. The new version can deal with surface breaking cracks, complex multizone problems and proposed a direct coupling with FEM to simulate thin structures. In MBEM v3.