dataesr/scientific-paragraphs-categorization huggingface dataset:
The vertices on the topological graph represent the between the cables and the pieces of equipment. Finally, the Electrical Matrix, detailed in Figure  21 , is automatically generated by the Matlab developed program. Then, in accordance with the process described in Section 3. 2, the qualitative evaluation starts with the identification of the current loops present in the EVPT scenario. They have been singled out in Figure  22. For each identified victim loop, the corresponding potential aggressors have been determined (Fig. 23 ) by  Finally, to distinguish inductive and capacitive crosstalk, based on the physical solicitation described in the initial requirements, in our example we consider disruptive variable currents in the HV cables (C0 and C2). Then we identify each possible inductive crosstalk between these HV cables and its related victim loop (ABDC), in order to thereafter quantitatively evaluate the corresponding EMI disturbances.
This calculation is carried out in Matlab by summing up all the electromagnetic fields calculated in each point related to a loop discretized area. Then, from the calculated magnetic field, we can determine the electromotive force e induced in the surface loop S, using the Lenz-Faraday law, which corresponds to the additional voltage that will be generated in the loop due to the inductive crosstalk: e ¼ À df dt ¼ Àm 0 S dH ! dt ⋅ n ! ð5Þ with f ¼ B ! :S n ! ð6Þ Assuming that the lengths of the parallel cables constituting the loops, are electrically short (l < 0. 25 l of the disturbance signal), it can be assumed that the distributed wire-to-wire capacity and the distributed mutual inductance are assimilated to a localized capacity and mutual inductance. Then, each current loop can be defined by a characteristic impedance, a line inductance, and a line capacitance, which are expressed according to  the relevant cables' geometry and position, based on the following expressions  [30, 31, 40, 42, 48] : Characteristic impedance : Z c ¼ 120 ln d r ð7Þ Line inductance : L a ¼ z c v l ð8Þ Line capacitance : C a ¼ 1 z c v l ð9Þ Line resistance : R a ¼ r s l ð10Þ with v: propagation speed, l: length of the line, d: distance between the cables, r: cable radius.
The inductive crosstalk between these two circuits is represented by a mutual inductance M 12. The Kron's method can be described in four steps: -Determining all the edge impedances Z i , and the source voltage vector E b involved in the whole system related to the edges' space (Fig. 27) : Z e ¼ C0 C1 C2 C3 Z S 0 0 0 0 Z 1 0 0 0 0 Z 2 0 0 0 0 Z v 2 6 6 4 3 7 7 5 ; E b ¼ C0 C1 C2 C3 V S 0 0 0 2 6 6 4 3 7 7 5 with Z S = R S ; Z 1 ¼ R 1 þ L 1 p þ 1 c 1 p ; Z 2 ¼ R 2 þ L 2 p þ 1 c 2 p ; Z v = R v - Adding the coupling M i (here the mutual inductance) between the edges: M e ¼ C0 C1 C2 C3 C0 C1 C2 C3 0 0 0 0 0 0 M 0 0 M 0 0 0 0 0 0 2 6 6 4 3 7 7 5 with M = M 12 p Where M 12 = the mutual inductance between edge 1 and edge 2; p = the Laplace operator. -Defining the connectivity matrix C C!m to identify the edges involved in each loop/mesh and to correlate the edges' space with the mesh space; C e!m ¼ C0 C1 C2 C3 m 1 m 2 1 0 1 0 0 1 0 1 2 6 6 4 3 7 7 5 -Analyzing the Lagrangian problem in the mesh space, then eventually solving it by resolving these following equations  [50]  with a numerical tool (here Matlab): Z m ¼ E m ¼ C t e!m :E e C t e!m :ðZ e À M e Þ:C e!m E m ¼ Z m :I m V v ¼ R v :C e!m ð4; :Þ:I m 8 > > > < > > > : The resolution of the previous equations is shown by the curves in Figure  28  and Figure  29 , reflecting the resulting voltage Vv at the terminals of the victim component.
This objective function is simply expressed 1 https://github. com/TMacquart/OptiBLESS as the root mean square error (RMSE) between the continuous and discrete lamination parameters as shown in (  28 ) and (29). Fitness(θ , ) = 1 N lam N lam s=1 RMSE s (θ , ) (28) RMSE s (θ , ) = 1 8 8 i=1 w i LP i,s -LP i,s (θ , ) 2 (29) where N lam is the total number of laminate sections in a structural component (i. e. upper wing skin), LP i,s is the vector of input parameters for section s, w i is a weighting factor and LP i,s is the vector of lamination parameters obtained by the GA. Stacking sequences are converted into lamination parameters in order to evaluate the fitness using the following notation: LP = [v A 1 v A 2 v A 3 v A 4 , v D 1 v D 2 v D 3 v D 4 ] (30) According to the fitness function given in (  28 ), the bestretrieved stacking sequence would be a manufacturable stacking sequence best matching the optimised lamination parameters obtained by MSC.
Participants The present study included 447 healthy volunteers recruited during the same period whose native language tongue was French and who had no past history of neuropsychiatric disorders. The data were derived from the BIL&GIN database  (Mazoyer et al. , 2016) , and we excluded four participants with unavailable data and two participants with suspected dyslexia. The experimental protocol was approved by the local ethics committee (Comité de Protection des Personnes Nord-Ouest). All participants provided written informed consent and received compensation for their participation. The study sample comprised 228 women and 219 men with a mean age of 26. 6 years (range from 18 to 58 years) and was balanced for handedness. The mean level of education was 15. 3 years, which corresponds to 3 years of university education.
Cognitive Tests Data were extracted from the standardized battery of 10 cognitive tests in the BIL&GIN, a database acquired by the team and previously described in detail  (Mazoyer et al. , 2016). Briefly, the participants completed 10 cognitive tests distributed over two sessions separated by an approximately 3-h MRI acquisition period. We used six verbal tests that were ordered within each cognitive session as follows: -Session 1: the RAVLT, vocabulary scope, a rhyme judgment task and the listening span test. -Session 2: the pseudoword memory test and the reading span test.
After a 20-min delay (during which a nonverbal task was performed), the participants were asked to recall this list again. A similar procedure was applied using 15 pseudowords created with the WordGen software  (Duyck et al. , 2004)  and matched to the list of words by the number of letters, phonemes, syllables and bigram frequencies (guice, anire, ficot, meple, flaxion, jaron, asue, ecrot, diare, doussant, boidir, sato, fince, veigne, and gouage). As the measures were collected in a healthy adult population, we expected a relatively high level of DR  (Van Der Elst et al. , 2005). Additionally, since recognition requires fewer processing resources than recall  (Craik and McDowd, 1987) , we did not include a recognition task and focused instead on three scores  (Ivnik et al. , 1990)  corresponding to the following three stages of memory: 1.
According to the Daneman and Carpenter method, truncated spans were scored for each test by starting with the highest level (2-6) at which the participant recalled the majority of the blocks (2 out of 3) and adding half a point for recalling 1 out of 3 blocks at the subsequent level. The truncated spans for reading and listening were averaged to obtain the WMC score. Vocabulary scope, rhyming task and reading span test were conducted with E-Prime (Version 2, Pittsburgh, PA: Psychology Software Tools. ).
Collection and Categorization of Self-Reported Strategies Self-reported strategies were collected after the word and pseudoword DR tasks by a psychologist trained in interviewing. The self-reported strategies corresponded to all three memory stages (STR, LOT, and DR). The participants were asked to explain how they retained the lists, and their answers were recorded as field notes. An open question was used to avoid influencing the nature of the answers. At this stage, data related to words from three participants were missing, as were data related to pseudowords from five participants. Based on the psychologist's field notes, two experimenters compiled an exhaustive list of the strategies used for the words and pseudowords and standardized the wording of the strategies on the basis of consensus.
Data Analysis The statistical analyses were conducted in R Version 3. 5. 2. 1 The figures were created with the following packages: ggplot2  (Wickham, 2016)  and interactions  (Long, 2019). The alpha level of 0. 05 or the 95% confidence interval (CI) was used to determine the significance of the hypothesis tests. Group Comparisons of the Word and Pseudoword Memory Scores Each memory score (STR, LOT, and DR) was averaged across participants, and the mean scores for the words and pseudowords were compared with a paired sample t-test Bonferroni corrected for the multiplicity of tests (p < 0. 016). Each individual memory score was further standardized, thereby allowing a direct comparison in the subsequent analyses.
Specifically, we address the tasks of learning discriminative features and global labeling smoothing. The key contribution of our study is threefold. First, a hierarchical data augmentation strategy is applied to enhance the learning of deep features based on the PointNet++ network and simultaneously eliminate the artifacts caused by division and sampling while dealing with large-scale datasets. Subsequently, the learned hierarchical deep features are globally optimized and embedded into a low-dimensional space with a nonlinear manifoldbased joint learning method with the removal of redundant and disturbing information. Finally, a graphstructured optimization based on the Markov random fields algorithm is performed to achieve global optimization of the initial classification results that are obtained using the embedded deep features by constructing a weighted indirect graph and solving the optimization problem with graph-cuts.
We conducted thorough experiments on ALS point cloud datasets to assess the performance of our framework. Results indicate that compared to other commonly used advanced classification methods, our method can achieve high classification accuracy. The overall accuracy (OA) of our approach on the ISPRS benchmark dataset can scale up to 83. 2% for classifying nine semantic classes, thereby outperforming other compared point-based strategies. Additionally, we evaluated our framework on a selected portion of the AHN3 dataset, which provided OA up to 91. 2%.
, 2016; Zhang et al. , 2016; Wang et al. , 2018). However, prior to using ALS data in applications, one of the essential tasks is to interpret semantic information of the observed scenes presented by 3D point clouds. The primary objective of the semantic interpretation of ALS point clouds is to assign each 3D point with a unique semantic label indicating the class of specific objects in the scene, in accordance with geometric or radiometric information provided by the point itself and the points in its neighborhood. To achieve the semantic labeling of points, supervised classification is typically implemented  (Vosselman et al. , 2017; Li et al. , 2019a). It comprises two main steps: generation of distinctive features and classification of 3D points with corresponding features with a classifier.
Point cloud classification with deep learning techniques In the last decade, high-performance computation resources and data acquisition techniques have experienced exponential growth, thereby leading to the availability of large-scale datasets. Deep learning techniques, which highly rely on these two factors, have gained immense popularity over recent years. They have achieved advances in numerous fields such as scene interpretation, object detection, and target tracking. Recent investigations, especially, moving beyond the applications on 2D images, have addressed the applications of such techniques in the field of point cloud processing. Different from the traditional strategies for point cloud classification, deep learning techniques are utilized in an end-to-end manner by integrating feature extraction with classifiers.
Moreover, both the rendering and voxelization processes increase the complexity of procedures in classifying point clouds. Furthermore, the network would experience a considerable increase with the expansion of spatial resolution (i. e. , dimension) for these voxel-based neural networks. Most of the aforementioned deep-learning-based point cloud classification techniques are based on traditional networks designed for 2D images or their extensions. The emergence of PointNet has introduced a new idea. PointNet  (Qi et al. , 2017a)  and its variants  (Qi et al. , 2017b; Qi et al. , 2018)  have shown superior performance on several representative benchmark datasets with both indoor  (Armeni et al. , 2016; Dai et al. , 2017)  and outdoor  (Geiger et al. , 2012; Hackel et al. , 2017; Zhang et al.
, 2019)  scenarios. For the deep learning techniques derived from PointNet, the points with unstructured and unordered characteristics are considered and transformed so that 3D pointsets can be processed by the proposed networks directly. In this framework, feature extraction and classification can be conducted in an end-to-end manner, and it can also be achieved without initial preprocessing, which considerably streamlines the process of point cloud classification. Moreover, in the workflow of PointNet, both the point-based and global features were learned, which enhanced the capability of considering the local contextual information. In the base of PointNet,  Yousefhussien et al. (2018)  added the concept of multi-scale approach and successfully applied it to a large-scale ALS dataset. Furthermore, PointNet serves as the deep feature descriptor in numerous applications  (Rethage et al.
Our contributions Unlike classic point-wise classification methods that utilize the proliferation of various handcrafted features and require considerable efforts for the design of neighborhood and the description of features, in our work, we use an efficient deep learning technique that is capable of directly dealing with raw point data. To enhance the generalization capability of PointNet++, we developed a hierarchical data augmentation strategy, in which the sizeable urban point cloud datasets are divided into multi-scale pointsets. Deep features are extracted with the network by utilizing these multi-scale pointsets. Then, they are aggregated to generate a concatenated vector representing deep features in multi-scales. Multi-scale deep features (MDF) are highly correlated and spatially discontinuous, and classification with high-dimensional deep features is computationally expensive and time-consuming; therefore, global optimization and dimensionality reduction are conducted on the high-dimensional deep features.
Through this regularization process, spatial regularity is considered to produce locally continuous and globally optimal classification results. The major highlights of this work are: • A novel deep embedding framework for ALS point cloud classifica- tion is proposed with a joint feature embedding method bridging multi-scale deep contextual feature learning and global regularization of initial results. • Hierarchical data augmentation strategy is employed to enhance deep feature learning by using the given deep embedder (i. e. , PointNet++) to eliminate the artifacts caused by division and sampling while dealing with large-scale datasets. • A solution with large-scale manifold learning is proposed to opti- mize the obtained hierarchical deep features in a global manner. In this approach, spatial and deep feature contextual information is jointly considered while embedding the high-dimensional deep features into an optimal low-dimensional space.
Methodology The proposed methodology for point cloud classification comprises three essential steps: hierarchical deep feature learning (HDL), joint manifold-based embedding (JME), and global graph-based optimization (GGO). Fig. 1  illustrates the framework presenting the essential steps of the involved methods and sample results. The detailed explanation of each step in the framework is introduced in the following sub-sections. The HDL step is designed for learning the original features of points. Here, hierarchical splitting is applied to the input datasets. Specifically, the training and test point clouds are subdivided into non-overlapped sub-pointsets, and then, these pointsets are downsampled to a fixed number, which fulfills the input requirement of the network. The features extracted from the network comprise point-based description and texture information of the entire sub-pointsets; therefore, the deep features extracted from hierarchical pointsets can be used to represent texture information from different scales.
Large-scale LML For commonly used small-sized 2D datasets, the LLE-based methods always work quite well and provide satisfactory outputs  (Hong et al. , 2017; Bachmann et al. , 2005; Ma et al. , 2010). However, for an enormous number of points in a point cloud, the computation of lowdimensionality embedding is challenging owing to the massive amount of data. More than millions of points are collected for each point cloud; this renders algorithms with high demands for memory and computational cost impractical. To mitigate this problem, it is essential that both construction of the affinity matrix and spectral decomposition of the matrix are intractable for a large number of samples. Therefore, under the framework of local manifold embedding, we propose a new algorithm, K-JME, to improve efficiency by adopting k-means clustering before embedding.
Experiments For the performance assessment of our method, experiments using two ALS datasets were conducted, and their results were evaluated and analyzed. The first experiment uses the ISPRS benchmark dataset of Vaihingen for 3D labeling of ALS point clouds (ISPRS benchmark dataset)  (Cramer, 2010; Rottensteiner et al. , 2012) , whereas the second uses the ALS dataset of a selected area provided by Actueel Hoogtebestand Nederland  (Xu et al. , 2014; Vosselman et al. , 2017 ). 1.
Testing datasets The ISPRS benchmark dataset was acquired in August 2008 by an airborne Leica ALS50 system with average flying height of 500 m and a 45°field of view. The point density is approximately 4 points/m 2 , and there are approximately 753,000 points for training 412,000 for testing. In this dataset, all LiDAR points contain the x-, y-, z-coordinates in Euclidean space, intensity values, and the number of returns. These points have been manually assigned to the following nine classes of different objects: powerline, low vegetation, impervious surfaces, cars, fences/hedges, roofs, facades, shrubs, and trees. The test areas are located in the city center of Vaihingen, Germany, where buildings are present in a dense and complex pattern with an area of 389 m × 419 m. Moreover, the training area contains mainly residential houses and high-rise buildings covering an area of 399 m × 421 m.
Evaluation criteria For evaluation, we utilize the following evaluation metrics: precision (pr), recall (r), F 1 score (F 1 ), overall accuracy (OA), and average F 1 score (AvgF 1 ). Among them, precision, recall, and F 1 score are specifically used to evaluate the classification performance on each single class, whereas OA and AvgF 1 score are applied to assess the performance on the whole dataset.
Preprocessing and initial classification Prior to the training of the deep neural network, training samples must be formatted into subsets of a fixed number of points. Consequently, point clouds of the training and test scenes were subdivided into small 3D sub-pointsets without overlaps. In the ISPRS benchmark dataset, each point within the sub-pointset is represented by a 4D vector with x-, y-, z-coordinates and intensity. Then, to sample points from each block, 8192 points were chosen without replacements from each sub-pointset. To learn objects from different scales with our HDL method, we generated multi-scale pointsets and then trained our network with these pointsets of different scale separately, thereby acquiring deep features encapsulating different levels of contextual information.
Fixed values of 10,000, 20,000, and 30,000 were empirically selected as the size of the pointset because they perform satisfactorily in our experiments considering the scale of the objects of interest, e. g. , powerline and roof. For the AHN3 dataset, considering the different point densities and the types of urban objects, the scales of the pointsets are different from those we used for the ISPRS benchmark dataset. They were set to 10,000, 30,000, and 50,000. Furthermore, these pointsets were downsampled to the same number of points. Moreover, each point within the pointset is represented only by a 3D vector owing to the provided information that comprises the x-, y-, z-coordinates without the intensities. To assess and supervise the performance of our model during the training process, for the aforementioned two datasets, we chose 10% of the training samples from the training datasets directly for validation.
Results using ISPRS benchmark dataset To test the performance of our feature learning method, we conducted comparisons between the results with the following conditions: (i) using original single-scale deep features (SDF) with original PointNet ++, (ii) using the proposed MDF with RF classifier, and (iii) using joint embedded features (JEF) with RF classifier. Furthermore, to evaluate the performance of the feature dimensionality reduction in addition to our method, we implemented several classic dimensionality reduction methods to provide fair comparison; with such a comparison, we can investigate the effectiveness of the robust dimensionality reduction strategy proposed herein. Moreover, to validate the feasibility of the GGO designed for smoothing the classification results, we conducted comparisons between the results with and without GGO.
Comparison with other published methods using the ISPRS benchmark dataset For further evaluation, the proposed method was compared to other published high-ranking methods using the ISPRS benchmark dataset. Here, the compared baseline methods: NANJ2  (Zhao et al. , 2018) , WUY3  (Yang et al. , 2017) , RIT_1  (Yousefhussien et al. , 2018) , LUH  (Niemeyer et al. , 2016)  are from the ISPRS 3D semantic labeling dataset website. 2  Table  6  provides a comparison of the classification accuracy with the aforementioned evaluation metrics among different methods. The LUH method classified the ALS point clouds by learning the MDF with a 2D CNN, which integrated the height, intensity, roughness, and RGB attributes to predict the labels for the urban scenes. In WHY3 method, whose strategy is similar to that of the LUH method, the 3D point clouds were transferred to a 2D image by a point-based feature image-generation method, and then these feature images were classified using a 2D CNN.
Results using the AHN3 dataset In addition to the ISPRS benchmark dataset, we obtained the pointwise classification results using the AHN3 dataset to further investigate the versatility of our classification method on a large-scale ALS point cloud dataset. The results are listed Tables  7  and 8  and Figs. 14  and 15. For the part of deep feature extraction, hyperparameters of the training process are the same as the setting given in the description of the processing of the ISPRS benchmark dataset. For the feature embedding part, the 384-dimensional deep features were embedded into a 30-dimensional feature vector with the neighborhood size set as 20. The number of trees used in the RF classifier is 100. As for the graph-based optimization, the regularization strength is set as 1. 0. As seen in Table  7 , JME outperforms the other feature extraction methods with the AHN3 dataset.