dataesr/scientific-paragraphs-categorization huggingface dataset:
Our pipeline is highly dynamic and can be modified to include intermediate steps, such as the addition of dynamic objects (e. g. , moving vehicles and pedestrians). This is demonstrated in the experiments in Section 6. 3. 4, where we introduce the SimKITTI32 dataset, a simulated Velodyne HDL-32 LiDAR in a splatted environment from SemanticKITTI acquired by a Velodyne HDL-64  [18]. Our contributions can be summarized as follows: • AdaSplats: a novel adaptive splatting approach for accurate 3D geometry modeling of large outdoor noisy point clouds; • Splat-based point cloud resampling, dealing with highly varying densities and scalable to large data; • Faster-than-real-time GPU ray casting in the splat model for LiDAR sensor simulation and rendering; • SimKITTI32: a dataset simulating a Velodyne HDL-32 inside a sequence of SemanticKITTI dataset  [18].
Moreover, we change the error bound parameter Ē , the criterion that stops the growth of the splats. The parameters used for the four groups are as follows: The choice of parameters follows the analysis on points density to identify one that is sufficient to locally resemble a plane on the different class groups. The analysis takes into consideration the maximum size of a generated splat on a given structure that does not result in a geometry deformation. For example, a big splat on a linear structure, or a tree leaf, and do not leave holes in large planar areas like the ground. Through visual inspection, we observe the impact of varying the splats parameters for the different class groups. Finally, the parameters were chosen as the best set that could be used on the three datasets tested in the experiments section.
LiDAR Simulation Previous approaches  [2, 3]  have not tackled the real-time aspect of LiDAR simulation. We focus our work on accelerating the sensor simulation and achieving real-time LiDAR simulation. To do this, we use the splat ray tracing method we introduced. We implement the firing sequence of multiple LiDAR sensor models, launch the rays in the splatted environment, and return the ray-splat intersection points, leveraging the high parallelization capability of the GPU (see Figure  5 ). The first generates accurate 3D modeling of the static environment using our adaptive splatting method. The second module takes the sensor model as input and simulates the sensor, including but not limited to camera and LiDAR, and generates the corresponding rays. In the third module, the rays are cast in parallel using the GPU architecture, then a bounding volume hierarchy (BVH) structure containing the generated splats is traversed, and ray-splat intersection point or color information is reported to generate the LiDAR or camera output, respectively.
Experiments and Results We designed experiments to test the accuracy of our AdaSplats approach and the ability of our ray tracing method to achieve real-time performance on rendering and LiDAR simulation. Validating the correct modeling of the environment to simulate a LiDAR is a complex task. The only experiments done by LiDARsim  [3]  and AugmentedLiDAR  [2]  were comparisons of learning results of deep networks between their simulated 3D data and data simulated under a simplified synthetic environment from CARLA  [1]. In this section, we detail the task protocol and how we compare the performance of the LiDAR simulation using our splatting technique (AdaSplats) and other surface representations. We split the experiments and results according to the different datasets that we use. Moreover, we show that using deep learning for semantic segmentation achieves similar results to AdaSplats using ground truth semantic information.
Experiments To be able to precisely compare different modeling techniques, we choose three different datasets: two acquired by different mobile LiDAR sensors and mounted in different configurations and one built from a terrestrial laser scanner (TLS) (see Figure  6 ). Moreover, the datasets used were acquired in different cities around the world and in different environments (e. g. , urban and suburban). We generate the scenes from the different datasets, using the different surface representations, then we render the scenes and simulate the Velodyne HDL-64 LiDAR in AV configuration using the generated splats. Finally, we provide quantitative results to evaluate the rendering quality provided by the different surface representations and perform quantitative evaluation of the different methods using the point cloud resulting from the accumulation of the simulated LiDAR scans.
Surface Representation To demonstrate the accuracy of our approach, we chose to compare it to three other methods, namely Basic Splats, IMLS  [8] , and Screened Poisson  [32]. Basic splats can be considered as a baseline representing the geometric precision of concurrent methods based on splatting, namely LiDARsim  [3]  and AugmentedLiDAR  [2]. IMLS  [8] , and Screened Poisson  [32]  are two well-known and efficient meshing techniques. We use the latest available code (version 13. 72) for Screened Poisson surface reconstruction to mesh the point cloud, obtaining the finest mesh with: octree depth of 13, Neumann boundary constraints, samples per node as 2, and other parameters as default values. We also used SurfaceTrimmer to remove parts of the reconstructed surface that are generated in low-sampling-density regions with trim value as 10.
Paris-Carla-3D We use the PC3D-Paris part from the PC3D dataset  [19] , which is a dataset acquired in mapping configuration (LiDAR pitched at a 45 • angle). The high diversity of complex objects (barriers, street lamps, traffic lights, vegetation, facades with balconies) present in this dataset allows us to compare the modeling capacities of the different techniques in real situations. We remove dynamic objects using the semantic information; that is, we use ground truth semantic labels to remove the dynamic objects for all the methods excluding AdaSplats-KPConv. To remove dynamic objects for AdaSplats-KPConv, we use the corresponding predicted point-wise semantic classes. In a last step, we apply the different surface representations on the static background. In the Results section, we first provide qualitative evaluation of the different K-nn values to validate the choice of the parameters used for the different semantic groups.
SemanticKITTI Validating the accuracy of our modeling method requires testing it on other types of data. More specifically, we use a SemanticKITTI  [18]  dataset, which was acquired using a different sensor mounted in a different configuration with respect to PC3D (mapping), namely the Velodyne HDL-64 in AV configuration. The different sensor and mount result in a different scan pattern, which helps in validating if our method generalizes to other types of sensors or data. Moreover, the geometry in SemanticKITTI is different to that in PC3D-Paris, since it had been acquired in a different country and not in the heart of a large city. The same as in PC3D-Paris, we first remove dynamic objects using the semantic information and apply the different surface representations on the static background.
M-City We take the testing even further and model a point cloud that was acquired using a TLS, namely M-City. This is an in-house dataset that was acquired in an AV testing site in Michigan using a TLS (Leica RTC360), at fixed points in a controlled environment without dynamic objects. The acquired point cloud was then used by 3D artists to perform manual surface reconstruction, which took 4 months. For this work, we use 25% of the original point cloud, which contains 17. 5 million points. The interest in using M-City lies in the fact that it has a completely different scanning pattern, since the point cloud had been acquired at fixed points from a TLS. For M-City, we do not have the sensor positions to re-orient the normals. Not having a correct normal orientation makes it difficult to properly reconstruct the surface using automatic meshing techniques (Poisson and IMLS).
New Trajectory Simulation To simulate the LiDAR sensor, we generate new trajectories. We shift the original trajectories provided by the sensor positions in PC3D and SemanticKITTI. As for M-City, since we do not have the sensor positions, and because the dataset had been acquired using a TLS, we perform the simulation on interpolated 3D points on a linear line segment. For PC3D-Paris and SemanticKITTI, we take the original sensor positions provided with the datasets and offset them on the three axes (see Figure  6 ). More precisely, the offset for PC3D-Paris is [1. 0, 1. 0, -1. 0] T along the x, y, and z axes and [1. 0, 1. 0, -0. 5] T for SemanticKITTI. For M-City, we generate a linear trajectory across the dataset.
AdaSplats-KPConv includes KPConv for automatic SS (trained on the training set of PC3D dataset). On Paris-CARLA-3D, KPconv has an average mIoU of 52% over all classes and 68% IoU for the class "vehicles" (computed on test set Soufflot-0 and Souffot-3). AdaSplats-GT uses the ground truth semantic (manual annotation), and AdaSplats-Descr computes the local descriptors to arrange points into the three semantic groups. We obtain the lowest number of primitives with our AdaSplats method. We observe that Basic Splats has the lowest generation time, and this is because there is no resampling, which results in generating the splatted environment only once. Moreover, the generation time of AdaSplats-KPConv includes the inference time of KPConv, which is 600 seconds for 10 million points. Adasplats-Descr achieves similar C2C distance to Basic Splats; however, it performs better on thin structures, as we see in the comparisons below.
M-City Figures  12  and 13  show renderings and the simulated point clouds, respectively, using the different surface representation methods on M-City. As a reminder, for M-City, we did not perform Poisson and IMLS surface reconstruction, since we do not have the position of the scanners to orient the normals. However, we use the manually reconstructed mesh to compare a manual reconstruction of the scene to Basic Splats and our AdaSplats method. Moreover, we cannot train KPConv on this small dataset; therefore, we do not include AdaSplats-KPConv in the comparisons. However, AdaSplats-Descr can be computed, and we can see that it is able to achieve lower C2C distance and higher simulation frequency compared to Basic Splats. Looking at the results, we can observe that our method (AdaSplats-GT) obtains a better surface representation, which can be clearly seen on the grass and vegetation that are hard to manually reconstruct due to the complexity of the geometry.
Model Gen In the context of AVs, 3D SS methods  [12] [13] [14] [15]  provide important information about the surroundings of the vehicles, increasing the level of scene understanding. Many manually labeled datasets are available  [18, 67] ; however, not all datasets were acquired using the same sensor model or configuration. When SS networks are trained on a given dataset, they perform poorly when tested on datasets acquired using different LiDAR sensors, such as training on data acquired using a Velodyne HDL-64 and testing on datasets acquired using a Velodyne HDL-32. This is mainly due to the domain gap arising from the different sensor model, which affects the points density and scan pattern. We introduce SimKITTI32, which is an automatically annotated dataset simulated using a Velodyne HDL-32 LiDAR sensor model in SemanticKITTI  [18]  sequence 08 (used in the validation procedure of 3D SS methods) that was acquired originally using a Velodyne HDL-64.
SimKITTI32 is created with the aim to test the ability of SS methods to generalize to different sensor models. We use our AdaSplats method to model the full sequence 08 using the point-wise semantic labels provided with the original dataset. For the simulation of the Velodyne HDL-32 sensor, we use a slightly different LiDAR placement. More specifically, we use the original trajectory of the LiDAR sensor and offset its position by -0. 5 m on the zaxis. This offset provides more scan lines on high elevations when simulating the Velodyne HDL-32, since it has a larger vertical field of view with respect to the Velodyne HDL-64. First, we obtain the static scene by removing the dynamic objects from the dataset. Here, the dynamic objects refer to points belonging to the semantic classes of moving objects only while static objects, such as parked vehicles, are considered part of the static background.
Finally, when we concatenate all of the separate dynamic objects frames with the static background, we obtain trails of splats representing the displaced dynamic objects through time. If we simulate the LiDAR sensor directly on the concatenated splatted environment, we also obtain a trail of points. We make use of the semantic labels that we associate with the splat during generation time and check if the intersected splat belongs to a moving object. In that case, we invoke the any-hit program of OptiX to accumulate all the intersections along the ray. At generation time, we also assign the splats that belong to a moving object the corresponding frame number as an attribute, which we use at intersection time inside OptiX. If the number of the simulated frames matches the frame number of the splat intersected inside any-hit, we report only this intersection back.
This ensures that we only intersect the splats belonging to the frame currently being simulated. Figure  14  shows a frame from the SemanticKITTI dataset sequence 08, the same frame simulated with an HDL-64 LiDAR model at the same position without any offset, and the simulated frame using an HDL-32 LiDAR model shifted by -0. 5 m on the z-axis. We can see that, with our implementation, we are able to accurately simulate the data with a different sensor model.
Finally, we introduce SimKITTI32, a simulated dataset with a Velodyne HDL-32 LiDAR sensor inside a scene acquired using a Velodyne HDL-64 LiDAR. SimKITTI32 can be used to test the ability of semantic segmentation methods to generalize to different sensor models. Our pipeline creates a "simulable" representation of the world, making it possible to generate in real-time virtual point clouds from simulated LiDAR sensors. The same pipeline could be used to generate virtual images from simulated cameras by adding textures to splats (and taking advantage of neural renderers such as NeRFs), but we leave that for further work.
These surface reconstruction methods result in artifacts on open shapes; borders are dilated because these functions attempt to close the surface, as they are performing inside/outside classification. To limit this effect, we truncate the IMLS function at three voxels and perform surface trimming with Poisson. However, we can still see artifacts in Figures8 and 9(e. g. , red, orange, and green areas). Our method is also verified quantitatively on PC3D-Paris. We report the generation time of the different surface representations, rendering, and LiDAR simulation details of PC3D-Paris in Table1. AdaSplats-KPConv includes KPConv for automatic SS (trained on the training set of PC3D dataset). On Paris-CARLA-3D, KPconv has an average mIoU of 52% over all classes and 68% IoU for the class "vehicles" (computed on test set Soufflot-0 and Souffot-3).
For our analysis, the Dis-pEn 3 D entropy values were thus analyed for 1 ≤ τ ≤ 6. Besides, a new variable θ was defined based on the sum of these MDispEn 3 D values: θ = 6 τ =1 MDispEn 3 D (τ ) , ( 7 ) where MDispEn 3 D (τ ) corresponds to the multiscale entropy value obtained for the scale factor τ using DispEn 3 D as the entropy measure. The algorithms were excuted using MATLAB®.
Clinical data The magnetic resonance imaging (MRI) scans were obtained from 42 patients who went through UFE (at 3 time instances for the follow-up), in the Centre Hospitalier Regional Universitaire (CHRU) La Cavale Blanche and the Polyclinique Keraudren, both in Brest, between 2018 and 2020. Consent was obtained from the patients and the work has been approved by the ethical committee. The study of MRI scans was performed for sagittal T2 scans without gadolinium as recommended by the medical doctors. The average age of the patients was 45 +/-9 years, that is goes back to the most common age of the onset of fibroids  [1, 2]. This study included the UFS-QOL and FSFI surveys. The patients had to complete these surveys at 3 time instances: the day of the UFE procedure (D0), after 10 days (D10), and after six months (M6).
The protocol was carried out by an experienced radiologist (Pr. M. N. ). The patients underwent the procedure under local anesthesia. The embolization was realized with Embosphere®. The definition of the region of interest (ROI) was performed by Dr. S. S. , a radiology intern; the  volume of the fibroid and the apparent diffusion coefficient (ADC) were computed. ADC is related to the degree of water diffusion in tissues  [23]. Several variables were studied, including clinical and imagebased features. The clinical data included the following: age, body mass index (BMI), gestiture, parity, number of spontaneous miscarriages, presence of a previous fibroid, type of contraception used, treatment taken by the patient before embolization (D0), and the scores obtained from the surveys (FSFI, SSS, and HRQL) at D0 and M6.
Clinical data As mentioned above, the MRI scans of uterine fibroids were assessed using MDispEn 3 D. For this work, 6 scale factors were studied and used in the computation of the θ score (see Eq. (  7 ) ). Fig. 8  shows the mean and standard deviation (SD) of the entropy values obtained for our dataset for each scale factor at D0, D10, and M6. We observe increasing entropy values with scale factors. This is true for entropy values computed at D0, D10, and M6. This can be explained by the fact that, when increasing the scale factor, the degree of down-sampling increases, and therefore, the resulting samples become less similar between each other. Given the lower similarity between samples, a more irregular downsampled volume is obtained. When volumes are more irregular, the entropy is expected to be higher.
We first define and annotate the most prominent categories of fallacious arguments in political discourse on an existing dataset of U. S. presidential debates  [Haddadan et al. , 2019] , i. e. , ad hominem, appeal to authority, appeal to emotion, false cause, slogan, slippery slope. We then train a neural classifier based on a transfomer-based model with an attention mechanism called Longformer  [Beltagy et al. , 2020]  to address the task in an automated way. Our core contribution is twofold: • We built a novel large linguistic resource of political debates where we annotated 1628 fallacious arguments. This is the largest existing dataset of political debates annotated with heterogeneous fallacious arguments (6 main categories, 14 sub-categories). • We propose a new transformer-based model architecture, fine-tuned on argumentation features, and we address an extensive evaluation obtaining very promising results.
Related Work In the last years, there have been a few works aiming at automatically detecting fallacious content in argumentation, relying on Natural Language Processing methods. Habernal et al. [2017]  developed the open-source software "Argotario", a gaming platform that serves both for educational purposes and as a crowd-sourcing data-acquisition platform to annotate fallacy types in everyday argumentation (ad hominem, appeal to emotion, red herring, hasty generalization, irrelevant authority). As a result of their study, they release an annotated dataset of fallacious arguments in both English and German. The German dataset contains 430 gold-labeled arguments. They experiment with fallacious argument type classification using Support Vector Machine and BiLSTM with German word vectors, achieving 50.
9% accuracy and 42. 1% macro-F1 on this dataset  [Habernal et al. , 2018a]. In this work, each argument is considered to be entirely in one of the categories of "fallacy" or "non-fallacious". Focusing only on the "Ad hominem" fallacy,  [Habernal et al. , 2018b]  created another annotated dataset from the Change My View subreddit on the Reddit platform, achieving a high inter-annotator agreement of 0. 79 Cohen's κ. They investigate the importance of the context in distinguishing fallacious argumentation. They also prove insights into the triggers of the "Ad hominem" fallacy using a Self Attentive Embedding Neural Network Architecture. They obtain an accuracy of 0. 810 using a Convolutional Neural Network architecture for the prediction of ad hominem arguments using a balanced dataset of negative/positive fallacies in 7,242 arguments.
[  Sahai et al. , 2021]  create a dataset of fallacious arguments, retrieving data using fallacy keywords extracted from Wikipedia on Reddit platform and then filter out false positives. They come up with 8 fallacy types recognized mostly in comments which correspond to some extent to the propaganda techniques annotated by  [Da San Martino et al. , 2019]. They reach a moderate inter annotator agreement. Their dataset consists of 1708 fallacious arguments balanced over different fallacy types. They later examine different models to identify the occurrence of a fallacious argument and to classify the fallacy type both at comment and at token level. Related to fallacies,  [Da San Martino et al. , 2019]  define an annotation scheme of 18 propaganda techniques and annotate 451 news articles with such labels, ending up with a dataset of 7,485 spans containing propaganda techniques.
Such dataset is released in the context of the shared task NLP4IF'19 1 on fine-grained propaganda detection. The same authors propose a multi-granularity network architecture on top of the BERT contextualized embeddings to identify propagandist samples on different levels of granularity, e. g. , documentlevel, paragraph-level, sentence-level. As a follow up, in 2020 another shared task on the same topic was proposed at SemEval (T11)  [Da San Martino et al. , 2020]  reducing the number of propaganda categories, and proposing a more restrictive evaluation scheme. As already introduced, some of the categories used to annotate propaganda partially overlap with the ones used to define fallacious arguments. 1 https://propaganda. qcri. org/nlp4if-shared-task/.
The ElecDeb60To16-fallacy Dataset To investigate fallacious arguments in political debates, we extend the annotations of the ElecDeb60To16 dataset  [Haddadan et al. , 2019] , that collects televised debates of the presidential election campaigns in the U. S. from 1960 to 2016. To the best of our knowledge, it is the biggest available dataset of political debates annotated with both argument components (evidence, claim) and relations  (support, attack). Given that we aim at investigating possible correlations in the occurrence of fallacious arguments within certain argument components or relations, this dataset is an optimal starting point.
List of Annotated Fallacies Before starting the annotation process, an expert annotator carried out an exploratory study on the arguments put forward by the candidates in the ElecDeb60To16 dataset, to verify which of the fallacy types -among those mentioned in the annotation scheme of  [Da San Martino et al. , 2019]  and in the categorization of  [Walton, 1987]  -were mainly present in political discourse. As a result, in this study, we decided to focus on the 6 types of fallacies which occur more frequently in political debates, meaning Ad Hominem, Appeal to Emotion, Appeal to Authority, Slippery Slope, False Cause, and Slogans. The first three types of fallacies are further divided into sub-categories. In the rest of this section, we provide a definition of each of these categories, and Table  1  shows some examples from the ElecDeb60To16 dataset.