dataesr/scientific-paragraphs-categorization huggingface dataset:
Of course, BWGS does not deal with all challenges. It is now admitted that most methods give reasonably high accuracy, although recent studies claim that prediction accuracy could be improved with new alliances to share data across breeding programmes  [62]. Rather, the challenges for future wheat breeding are 1) efficient implementation in real breeding schemes and/or adapting selection schemes with step(s) of GS (e. g. [63] [64] ), 2) prediction of GxE (e. g. [65] for review) and 3) incorporating multitrait selection (e. g. [66] ). Future developments of BWGS are ongoing to address these challenges, particularly multitrait selection. The source code of BWGS R functions as well as example files and notice are freely available on https://forgemia. inra. fr/umr-gdec/bwgs.
Creating simplified visualizations of large 3D trail sets with limited occlusion and preservation of the main structures in the data is challenging. We address this challenge for the specific context of 3D fiber trails created by DTI tractography. For this, we propose to jointly simplify trails in both the geometric space (by extending and adapting an existing bundling method to handle 3D trails) and in the image space (by proposing several shading and rendering techniques). Our method can handle 3D datasets of hundreds of thousands of trails at interactive rate, has parameters for the most of which good preset values are given, and produces visualizations that have been found, in a small-scale user study involving five medical professionals, to be better in occlusion reduction, conveying the connectivity structure of the brain, and overall clarity than existing methods for the same data.
Introduction Trails or trajectories are generated by many applications, such as graph drawing and graph visualization  [1] , monitoring of vehicle motion along large geographical spaces  [2, 3]  and scanning techniques  [4] [5] [6]. Besides their spatial position, trails can be annotated by additional data attributes, such as weights, directions, and types of the entire trail, or speed and travel duration of the item traveling along the trail. Visualizing datasets consisting of hundreds of thousands of trails is challenging, due to the high levels of occlusion, and the difficulty of simultaneously displaying multiple attributes. In information visualization, bundling has emerged as an important technique to handle such datasets  [7]. Edge bundling creates simplified visualizations of large graphs by deforming the (typically straight-line) drawing of spatially close and data-similar edges so these get visually grouped, thereby creating whitespace that reduces clutter and allows one to discern the main structure of the graph drawing.
Trail bundling generalizes the process to arbitrary sets of curves, or trails, embedded in 2D space. Numerous bundling algorithms have been proposed in the last two decades, offering a wealth of drawing styles, heuristics for grouping the edges or trails, and acceleration strategies to handle datasets of hundreds of thousands of elements in real time  [8]. Although bundling has proven its added value for 2D graph-and-trail simplification, only a handful of works have explored its generalization to 3D datasets. Bundling graphs laid out on 3D surfaces has been met with success  [9]. However, for the graph visualization use-case, truly volumetric 3D layouts are far less common  [1]. The other data type-trails-is also approached by only a few methods. This leaves the question whether 3D trail bundling is an efficient and effective tool for the visual exploration of such data.
A salient use-case for 3D bundling is the visual exploration of Diffusion Tensor Imaging (DTI) datasets via tractography  [10] [11] [12]. This application generates massive sets of 3D curves, representing the paths followed by major fiber tracts in the brain. Due to the volumetric structure of the brain and its complex connection patterns, obtaining simplified views of such patterns has raised much interest in the visualization community. This has been approaches by two main method types. First, so-called illustrative rendering methods aim to draw the actual, raw, 3D fiber tract data in ways that make important spatial patterns show up more saliently  [13] [14] [15]. These methods work well for medium-density trail sets; too-high-density ones create, inevitably, occlusions that do not allow visual exploration of a full volume.
Secondly, 3D bundling methods take the more aggressive step of actually deforming the trails to create more structure at the expense of preserving spatial information  [16]. To our knowledge, these methods cannot handle large 3D datasets consisting of hundreds of thousands of trails. Moreover, the combination of illustrative techniques and 3D bundling techniques has been explored only to very limited extents. This is in stark contrast to the aforementioned results for bundling-and-rendering techniques for 2D trail sets  [8]. In this paper, we aim to answer the question of how to combine bundling and rendering techniques efficiently and effectively for the visual simplification of large DTI fiber tracts. For this, we adapt a state-of-the-art method for 2D trail bundling  [8]  to efficiently handle 3D DTI trail sets, taking into account the typical structures that exist and need to be emphasized in such data.
Next, we propose a range of rendering techniques to present the 3D bundling results so that the structures of interest in the data get further emphasized. Our joint 3D-bundling-and-rendering pipeline can handle large 3D fiber tract datasets of hundreds of thousands of trails in real time on consumer-grade hardware; offers several exploration modes that highlight different aspects of the DTI tract data; and allows various ways to control the process leading to simplified views of the data. We demonstrate our method on several real-world DTI datasets. We also present a qualitative user study of our method by five medical professionals that outlines several advantages in terms of occlusion reduction, conveying the connectivity structure of the brain, and overall clarity of the produced visualizations.
Diffusion Tensor Imaging and Tractography Conventional structural imaging techniques such as T1-, T2-and proton density-weighted imaging generally create high contrast between major tissue groups in the brain, which are: Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). Such structural imaging techniques are generally well suited for the study of tissue macrostructure yet provide little insight into the orientation of white matter fibers. DWI  [17, 18]  is a variant of conventional Magnetic Resonance Imaging (MRI) based on the tissue water diffusion rate, which is better suited for the study of white matter pathways. Although DWI refers to the contrast of the acquired images, DTI is a specific type of modeling (or abstraction) of the DWI datasets, in which diffusivity of water molecules is represented by tensors  [10].
As an in-vivo non-destructive technique that requires no chemical tracers, DTI is presently one of the most promising methods for the study of white matter architecture in living humans. DTI provides quantitative estimates of white matter integrity and orientation by measuring molecular diffusion of water molecules (or Brownian motion). It is based on the phenomenon of diffusion anisotropy in the nerve tissue: water molecules diffuse faster along the neural fiber direction and slower in the fiber-transverse direction. DTI tensor datasets record, essentially, the spectrum of diffusion strengths per diffusion direction at every point in a 3D scan. Such datasets can be visualized using glyph techniques, which render the local major, medium, and minor eigenvalues of the diffusion tensor, scaled by their corresponding eigenvalues, over a dense sampling of the 3D scanned volume  [29].
Visualizing these next is challenging. Conventional line rendering is a simple and efficient baseline rendering technique to show such datasets that has been employed since fiber tracking was first introduced  [12, 21]. Yet, such naive rendering of the densely seeded and criss-crossing 3D trails creates too much clutter and occlusion for many tasks to be accomplished effectively. Numerous visualization tools have been proposed to leverage interactivity to explore large DTI datasets, e. g. ,  [22]. However, navigating the large space of parameter settings can be daunting for users. More extensive illumination  [23] , ambient occlusion  [24]  and alpha-blending  [25]  have been applied to line rendering of fiber tracts to further improve their visual exploration. Rendering 3D tubes with Phong shading  [26]  is based on more detailed geometric modeling than line rendering, and can produce even higher quality visualizations.
Surface reconstruction or extraction from DTI datasets have also been applied as a means for indirect volume visualization  [29]. Merhof et al. [28]  have shown that encompassing fiber tract bundles with isosurfaces yields a preferable representation for use in neurosurgery. Ridge and valley surfaces  [29]  are demonstrated to capture the cores of sheet-like tracts. Visualizing such surfaces provides important added value compared to all fiber-based alternatives. Indeed, even if there is a dense surface-like distribution of fiber tracts, creating the appearance of a surface from this (without gaps) can be challenging when applying line-rendering-based techniques. Besides explicit surface extraction, such surfaces can be also created implicitly in the image space. The Depth-Dependent Halos (DDH) method  [13, 14, 37]  performs this merging of structures quite well, as colinear fibers are visually combined into thicker bundles with illustrative halos.
Trail and Edge-Bundling Techniques In contrast to the above-mentioned rendering techniques, which modify how a 3D trail set is depicted, trail and edge-bundling techniques modify the actual trail set to emphasize structures of interest. Bundling essentially aims to (a) identify trails that are spatially close and similar from the perspective of one or several data attributes; and (b) deform these trails so they get spatially closer, so as to create more visual structure in the ensuing rendering thereof. Applications of edge bundling include graph drawing simplification  [8, 38] , trajectory exploration  [7] , eye-tracking analysis  [5]  and streamline bundling  [39]. Bundling of 2D datasets has been extensively explored. Image-based edge-bundling techniques, including SBEB  [40] , ADEB  [5] , KDEEB  [38] , CUBu  [8] , and FFTEB  [41]  show that the problem of bundling of 2D trails, as well as the rendering of resulting bundles using a variety of styles, can be efficiently and effectively addressed using image processing approaches that scale well on graphics hardware.
Bundling is however far less explored for 3D volumetric trail datasets such as DTI tracts. Bottger et al. [42, 43]  have proposed mean-shift bundling of 3D connectivity graphs obtained through functional Magnetic Resonance Imaging (fMRI). Simplification of anatomical connectivity using edge bundling is a relatively new topic, one example thereof being KDEEB  [38]  applied to fiber tracts  [25, 44]. Other examples of fiber tract simplification methods include multi-scale local fiber tract contraction  [16]  and two-dimensional neural maps  [45]. To our knowledge, such 3D methods have two main limitations: They cannot, in the same time (1) recover well plausible anatomical structures, such as the mix of surfaces and tube-like structures present in a DTI dataset; and (2) allow interactive visual exploration of DTI datasets of hundreds of thousands of fibers in real time using different rendering styles.
For example, the method in  [16] , which is technically closest to ours, requires hours to complete on a dataset of 78 K fiber tracts. At a higher level, bundling-and-rendering methods for DTI data have evolved largely separated, with the notable exception of  [16]. This, we believe, is an artificial separation of two method classes which ultimately aim to the same goal-presenting a large, complex, dense, volumetric 3D trail dataset in a simplified manner to the user. Summarizing the above, our contribution to the creation of simplified visualization of large DTI trail sets is three-fold: 1. We present a method for bundling 3D DTI tracts that allows the user to preserve relevant underlying anatomical elements (fiber tubular bundles, sheets, and manifolds) in a controlled manner; 2. We propose several rendering styles of the 3D bundled structures that emphasize several aspects of interest in the data; 3.
We propose a joint implementation of the above two points that can handle 3D trail datasets of hundreds of thousands of trails in real time on consumer graphics hardware. As our method builds atop of the backbone of the CUBu technique  [8]. We inherit from CUBu the kernel density-based advection of trails that ultimately creates the bundles, and the full GPU-based bundling computation for computational scalability. As described next, we modify CUBu in several respects: We implement the entire pipeline in 3D (using a volumetric density map, Section 3. 2); allow trail endpoints to bundle under specific constraints (Section 3. 2. 1); use the DTI volume anisotropy rather than the CUBu isotropic bundling (Section 3. 2. 2); reseed tracts in sparsely populated volume areas rather than bundling a fixed, predefined, trail set (Section 3.
Method To describe our joint rendering-and-bundling method for 3D DTI tracts, we first introduce some notations. Let V : R 3 → R 6 be a 3D volume scan that encodes, per voxel, the symmetric 3 by 3 DTI diffusion tensor. For every point x ∈ V, we can compute the three eigenvectors e i and eigenvalues λ i , 1 ≤ i ≤ 3, of V's diffusion matrix, which give the directions, respectively strengths, of the largest, medium, and smallest diffusions recorded by the MRI data  [46]. Our visualization method then consists of three steps: seeding and tracing DTI trails (Section 3. 1); bundling trails to follow anatomical structures (Section 3. 2); and structure-emphasizing rendering (Section 3. 3).
Seeding and Tracing DTI Trails To start with, we need to construct the actual 3D trails representing streamlines in the major DTI eigenvector e 1. The standard method to seed such streamlines is to look at regions M ⊂ V of so-called high anisotropy, i. e. , points x ∈ V for which the diffusion is far stronger in one direction than in all others. M can be computed by finding high-anisotropy voxels in V, i. e. , points where the eigenvalues λ 1 ,. , λ 3 strongly differ  [46] ; alternatively, M is readily provided by so-called masks, suitably segmented from V by medical professionals based on their knowledge of fiber-rich versions  [47]. The masks M we use in all our work are of the latter type, and they are provided with the public DTI datasets we use in our experiments  [48] [49] [50]. Figure  1  shows such a mask for one of the datasets used in our work.
Constrained Endpoint Advection The original CUBu bundling  [8] , applied to 2D trails, keeps trail endpoints fixed (not affected by Equation (  2 )) and updates all other trail sample points x j. This is well motivated by the fact that CUBu (and, actually, for all other bundling methods we are aware of) address 2D trails where endpoints represent meaningful information, such as nodes in a graph layout or fixation points in a 2D eye-tracking dataset  [7]. In contrast, our 3D trail endpoints are far less meaningful, as they are simply locations of low confidence for the streamline tracing process (Section 3. 1). Hence, their location does not encode any significant information. Keeping them fixed creates fan-like structures that generate significant occlusion, preventing us to look into the 3D bundle structure.
Anisotropy-Constrained Bundling Figure  4d  shows that 3D bundling considerably decreases occlusion, allowing one to see structures created by fibers deep inside the trail set. However, a problem immediately appears: All manifold-like structures, such as known to exist in DTI fiber tracts, are now broken into a set of parallel bundles, separated by gaps. This is a serious problem as the resulting rendering does not faithfully convey the brain anatomy, as also reported in other works that use DTI fiber contraction  [16]. Figure  5  shows this. Image (a) shows an unbundled trail set. Given that this is a dense dataset (100 K trails), the manifold structure of the corpus callosum is well captured, albeit occluded by the outer fiber tracks. Image (b) shows the isotropic bundling computed by the constrained advection explained earlier (Section 3.
Splat-Based Rendering Since the trail set T is densely sampled, we can create solid-looking geometry by rendering circular splats, also called billboarded point sprites  [54] , oriented parallel to the view plane and centered at the sample points x i ∈ T. Our technique is related to the work of Jalba et al. [55]  that reconstruct 3D shapes from point-cloud representations of their surface skeletons and to other methods for surface splatting from point clouds  [56]. However, our aim is to render a complex set of fibers-either individual ones or grouped to form fiber sheets; in contrast, the other splatting works mentioned above aim to reconstruct a single, watertight, surface from a point cloud. In our method, we use different splat profiles than the above works; effectively use blending to merge splats into smooth-looking surfaces; show data atop of the reconstructed images by color coding; and create outlines to better convey the separation between the complex DTI fiber structures.
As visible in Figure  8b , this creates stronger shading contrasts allowing one to separate easier overlapping bundles. Figure  7f -i show the application of the above four shading profiles to a fill 100 K-trails bundled dataset. Here, we use color (hue) to encode the trails' directions, similar to earlier images in this paper (e. g. , Figure  2 ). Combining color with shading is immediately done by multiplying the luminance values produced by the profile textures by the color values computed by mapping the trail directions at every sample point x j. Directly splatting trail sets however creates artifacts where the borders of the circular splats overlap-see Figure  9a  and its insets. A better solution is to use alpha-blending to render the splats. This would require depth-sorting all splats every time the viewpoint changes.
For a dataset of hundreds of thousands of trails, each rendered with hundreds of splats, this is not (easily) done at interactive frame rates. Solutions such as Order-Independent Transparency (OIT)  [57, 58]  do not require depth-sorting the splats and are as such faster. However, OIT works well typically for a few overlapping surfaces with high transparency. Our datasets, in contrast, contain hundreds of overlapping fibers at a single location. More importantly, we do not need high-transparency rendering, but rather low-transparency, sufficient to allow the few overlapping splats in the 'topmost' layer, i. e. , closest to the viewpoint, to blend. We achieve this by a simple two-pass rendering technique: First, we render the entire trail set T using no shading but with Z buffering on. This delivers a depth buffer z recording the z value of the closest splat to the viewplane at every pixel.
In the second pass, we visit (again) all splats, but only render those whose depths, i. e. , the z components of the sample points x j where they are centered, are not larger than the z value at that pixel plus a small offset δ = 0. 005. This effectively renders only those splats that are within a 'peel', or thin layer, of δ units from the closest one to the viewer. Since there are only a few splats per pixel within this layer-typically under 10-we can use standard blending with a transparency value of α = 0. 1. Figure  9b  shows the same dataset as in Figure  9a  rendered with peel blending. As visible, peel blending removes the shading artifacts we had seen earlier.
Scalable Implementation We implemented our end-to-end pipeline fully on the GPU using NVidia CUDA and programmable OpenGL (pixel shaders and tessellation) and C++ for the main architecture. The various scalar volumes (DTI, fractional anisotropy, density ρ) are implemented as 32-bit floating-point 3D textures. Density estimation (Equation (  1 )) is implemented by separable convolution requiring three one-dimensional passes. For the density map ρ, we use volumes of higher (up to 8 times) resolution than the original DTI volumes (Table  1 ), as the latter are too coarse for the accuracy needed to compute the gradient ∇ρ (Equation (  2 )). Gradient estimation is done by central differences. The above implementation can efficiently compute and render the fiber bundling of large trail sets. Table  2  shows, for three different DTI datasets, the number of trails |T|, sampling points x j , average time per bundling iteration (over 15 total iterations), and total bundling-and-rendering time.
Experiments were done on a PC running Ubuntu Linux using a NVidia GTX 1080 graphics card (2560 CUDA cores). Time includes the CPU-to-GPU transfer of trail sets needed to load the input dataset T on the GPU prior to doing the bundling proper. The rendering time is a small fraction of the total time-under 4%-and as such not reported separately. For each of the three datasets, we constructed two trail sets, one with 50 K trails, and the other one with 500 K trails (all of them randomly seeded). Details over these available datasets are shown in Table  1. The table lists the (public) data sources, identification of the dataset in terms of patient (subject) ID and accession number, and data characteristics-DTI volume size, voxel size, and number of diffusion directions used to acquire the DTI tensor field.
Summarizing Table  1 , we argue that our data is compatible with real-world, large, and complex DTI volumes used in medical practice. Table  2  shows us several insights. First and foremost, we see that the end-to-end processing time of our pipeline is of a few seconds at most, even for the large (500 K) trail sets. Given that we bundle a 3D trail set, and that we perform additional steps during bundling-constrained advection (Section 3. 2. 1), anisotropy bundling (Section 3. 2. 2), and tract reseeding (Section 3. 2. 3), this is just slightly slower than the 2D bundling reported by CUBu  [8] , which, according to the survey in  [7]  is by one up to two orders of magnitude the fastest method for bundling general 2D trail sets. Additionally, note that the bundling step (Section 3. 2) is typically done only sparsely during a dataset investigation.
Once the desired geometry is obtained, that geometry is next interactively explored using different rendering settings (Section 3. 3) and viewpoints. Changing these is, as we mentioned above, real time. The memory requirements of our method are dominated by storing the density volume needed to sample the KDE density map ρ (Equation (  1 )). Additional costs involve storing the sample points for T. For a dataset of 1M trails, with 100 sample points per trail on average, this involves an extra of roughly 1. 2 GB. These costs are not high, given that DTI volumes are typically not acquired at high resolutions, meaning that this acquisition resolution (see Table  1 ) is an upper bound to that of our density maps. For example, even with 8-fold supersampling of ρ vs the actual DTI data (to ensure maximal accuracy for the KDE gradient estimation used in Equation (  2 ), Dataset 3, which has 86 × 116 × 88 voxels after cropping the actual brain tissue present in the 140 × 140 × 96 input voxel volume (Table  1 ), requires a density map of 688 × 928 × 704 voxels, which, if stored with 32-bit-per-voxel density precision, asks for a total of 1.