dataesr/scientific-paragraphs-categorization huggingface dataset:
The framework is adapted to improve the perceptual capabilities of Automated Ground Vehicles (AGVs) in the factory field. To this end, first, the acquired point cloud is passed to a preprocessing module that prunes the point cloud and generates the features. Then the point cloud is segmented into local patches, where features are aggregated and constitute the final object proposals. An object classifier iteratively trained through hard negative mining is used to determine object classes. Our contribution in this paper is three-fold: Firstly, we propose a new framework (Section 3) that is capable of object detection by feature aggregation from large point clouds using only point cloud information in cluttered indoor scenes. Secondly, we provide a challenging, partially-labeled synthetic object classification and detection dataset (Section 4) suitable for testing object detection frameworks.
Post-processing The detection process can result in multiple overlapping object bounding boxes because of the dense nature of the dataset that includes lots of objects placed in a close range from each other. To avoid this problem, we employ a 3D non-maximum suppression approach which has been widely used in many detection tasks  (Neubeck, Van Gool, 2006 , Felzenszwalb et al. , 2009). First, the detection bounding boxes are listed with descending order of their detection scores. By comparing those on top of the list with the currently accepted object list, we can make a decision to keep them or not. If the overlap of the bounding box with the previously accepted one is no more than a given threshold then, it is kept. The bounding boxes overlap is calculated by intersection over union (IoU) metric.
We introduce novel simulated dataset representing a scenario in a modeled factory. In the simulation, a ground vehicle starts from a specific point in the 3D model of a factory and navigates throughout the factory and comes back to its primary position. Figure  3  shows a point cloud retrieved from a scan captured from the simulated 3D factory model. As it can be partially seen from the point cloud, there are lots of racks in the model where at each row of the rack various object types are located. The objects are interconnected with racks and also the other nearby object. This introduces difficulties for segmentation and makes object detection in this dataset a very challenging task. As shown in Figure  4  twelve sensors (S1 to S12) are mounted on the vehicle. The 3D sensors have a 120°horizontal and 45°v ertical field-of-view.
Each sensor has its own yaw, pitch and raw angles and records with a maximum scan range of about 100 m. We evaluate our framework on this dataset. The dataset contains 3 minutes and 20 seconds of simulation consisted of 6389 scans. The scans are recorded in 30 scans per second rate and each scan on average includes 80 000 points. For evaluations, we selected 2/3 of the scans (4260 frames) for training and the rest for testing. For object recognition/detection, there are around 344 objects annotated in the global map of the factory. Considering the whole scans, the total number of the annotated object instances in the dataset are 69 738. The initial instances    1  shows the details of the dataset. Unlike other recent datasets recorded for autonomous driving scenarios which main focus is on cars, pedestrians and bikes, our dataset includes seven different object classes emphasizing on industrial environment that are not addressed by most of the current solutions.
SUN RGB-D SUN RGB-D is a scene understanding 3D benchmark dataset consisting of 37 object classes. The dataset includes around 10K RGB-D indoor images with accurately annotated object categories and orientations. Prior to feeding the dataset into our framework, the RGB-D images are converted to point cloud data. For evaluation, we followed the standard protocol provided in the original study and for comparison, we use 10 commonly used object categories. This dataset is used to evaluate performance of the proposed framework in real-world scenarios, enabling us to compare with state-of-the-art.
EXPERIMENTS To evaluate performance of the framework, we use mean average precision (mAP) and F-score metrics (calculated as Fscore = 2 × P recision×Recall P recision+Recall ). The intersection over union threshold for the reported best results is set to 0. 6 for the synthetic dataset while it is set to 0. 25 for SUN RGB-D dataset. Table  2  shows the results of the object recognition using the trained final classifier. For training the network, the batch size is set to 64, decay rate to 0. 7 and learning rate to 0. 001. For the representation of the feature vectors, the number of Gaussian functions and their variances are empirically chosen to be 5 and 0. 04, respectively. The minimum number of points for an object is 2048. For optimization, Adam optimizer is used. For training, only 50 epochs were used on an Nvidia Geforce RTX 2060 and each training iteration took about two hours on synthetic (and 10 hours on SUN RGB-D datasets).
The obtained average class accuracy is 99. 84% (mean loss is 0. 010). As it can be noticed, the trained classifier achieved a very high recognition accuracy. It is even possible to further increase accuracy by increasing the number of epochs at each iteration. Table  3  and Figure  8  show object detection results on our dataset. The table presents the results which are obtained after the final training iteration (the hard negative mining iteration is set to 50). We achieve satisfactory performance in most of the classes. However, for some classes, such as "Barrel" and "Truck", the detection results are not desirable. This is related to several problems. In some scenes (especially, the ones including "Barrels") the object points are so interconnected that the segment-Figure  8. Qualitative results of the proposed object detection framework applied on a sample scene from our synthetic dataset ation algorithm fails to separate the object and therefore, generates improper proposals.
Moreover, the provided dataset is partially annotated. Thus, for some classes, such as "Box" and "Bobbin", the classifier performs a correct detection of the objects (TP) which are not initially annotated in the ground-truth ("Bobbin" instances on the back row of the racks on the right side of Figure  8 ). Those instances can be added to the background class in the training set and reduce the accuracy of the classifier and detection. The dataset is also not balanced and for some classes, such as "Cone" and "Truck", the training data is not sufficient. The Precision-Recall curve in Figure  6  underlines that despite all these challenges, the overall accuracy of the baseline framework is promising. Resolution of the grid cell is an important factor in producing accurate detections. Figure  7  depicts influence of this parameter on performance.
As the resolution of grids increases, the performance increases until it tops when the grid resolution is 0. 2 m 3. Increasing grid resolution further makes the feature blocks bigger hence less detailed. Accordingly, the feature aggregation is affected in a negative way which decreases the performance. We compare performance of our framework on synthetic dataset with  (Pepik et al. , 2012)  which is an extension of 2D deformable part models to 3D. Table  3  and Figure  6  show that our framework significantly outperforms this baseline (by 0. 39 in F-score). Higher performance of 3D-DPM method (which is not a CNN based method) on the "Barrel" category explains the low performance of our method on classes with low number of instances and thereby We also made a comparison to prior state-of-the-art methods using SUN RGB-D dataset.
To be able to compare with these methods, we use mean Average Precision (mAP) metric. Notice that we use the same set of hyperparameters to train object detection network in both datasets. Deep Sliding Shapes (DSS)  (Song, Xiao, 2016 ) is a 3D CNN based method that uses RGB images with 3D information together. Cloud of Oriented Gradients (COG)  (Ren, Sudderth, 2016 ) is a sliding window based detection method that produces HOG like descriptors for 3D detectors. 2D-driven  (Lahoud, Ghanem, 2017)  and F-Point-Net  (Qi et al. , 2018)  use 2D detectors to reduce search space for 3D detection. VoteNet  (Qi et al. , 2019)  is current state-ofthe-art method that uses Hough voting for 3D object detection using only point cloud information. As shown in Table  4 , we achieve competitive results compared to the current best methods in the literature.
Out of ten categories, we achieve the best performance in one category ("bed") and second best performance on three other categories. It achieves third best overall performance, however, notice that our method and VoteNet are the two methods that use only geometric information. Other methods use both RGB and geometry information to perform object detection. Figure  9  represents qualitative examples of our framework on SUN RGB-D dataset. Despite various challenges introduced in these scenes (e. g. cluttered, attached objects etc. ), our framework produces robust detection bounding boxes. For example, in bottom right and bottom left of the Figure  9 , the chairs and the sofa are attached to the tables, however, the framework is able to distinguish between them and produce correct detection boxes.
It is interesting to see that interconnected objects make more trouble for the network in synthetic dataset as the density of the point clouds is much higher than a real scene. It therefore fails to produce correct proposals and misses on those objects. Similar to synthetic dataset that the framework was able to detect several similar attached objects in a scene (such as "Bobbin"s and "Boxes"), it also detects vast majority of similar objects such as chairs in real scenes (top right of Figure  9 ). Like other methods, the proposed framework has some limitations. Difficulties in segmentation of very dense and attached objects and false hallucinations of occluded objects are among them. The future work will target these issues to further increase the performance in object detection task.
CONCLUSION In this paper, a novel object detection framework is proposed with a focus on the indoor factory environment. Unlike most of the recent state-of-the-art methods that use image and point cloud together, it only uses point cloud information. The features of the grid representation are computed and stored as a sparse representation. These features are restored and aggregated after local segmentation of the objects. A deep classifier trained with generalized Fisher representation is employed to learn object models. The developed framework is evaluated on a dataset which simulates a factory of the future setting and also SUN RGB-D object detection benchmark dataset. Based on the evaluations, the proposed framework achieves a competitive performance in object recognition as well as object detection task.
(Rusinkiewicz, Levoy, 2001)he point cloud in the current time from vehicle coordinates to global coordinates, we can apply the following transformation to the point cloud P : Ri × Pi + Ti. Moreover, we can combine several scans to create a dense point cloud, since 12 sensors are available in the recorded dataset. When the transformation parameters among the sensors are available, we can merge points from different sensors by aligning the source cloud to the target point cloud coordinate to construct the dense point cloud. This process can be done efficiently by the Iterative Closest Point (ICP) algorithm(Rusinkiewicz, Levoy, 2001). The output is a point set of tightly aligned point clouds. Finally, a combined dense point cloud is obtained knowing the correct transformation from ICP (Nc == [Rc|Tc]) to our target reference frame (ego-vehicle) and the parameters of each scan Ni:.
ACKNOWLEDGEMENTS This work was sponsored by a public grant overseen by the  French National Research Agency as part of the "Investissements d'Avenir"  through the  IMobS3 Laboratory of Excellence  ( ANR-10-LABX-0016 ) and the  IDEX-ISITE initiative CAP 20-25  ( ANR-16-IDEX-0001 ). Financial support was also received from the  European Union  through the  European Regional Development Fund program  ( ERDF -AURA region ) and by the  Auvergne-Rhône-Alpes region.
The purpose of GeneGeneInteR is to propose a collection of tools for all the steps involved in gene-based gene-gene interaction testing in case-control association studies. Illustrated by an example of a dataset related to rheumatoid arthritis, this paper details the implementation of the functions available in GeneGeneInteR to perform an analysis of a collection of SNP sets. Such an analysis aims at addressing the complete statistical pipeline going from data importation to the visualization of the results through data manipulation and statistical analysis.
In this article we focus on two complementary sources of missing heritability: (1) SNP-set association and (2) SNPxSNP interaction testing for which efforts have been made in the literature. In SNP-set association testing, all the SNPs within the region of a gene (or more generally a locus) are jointly modeled as a set. Since the gene is considered as the functional unit of the genome, SNP-set testing has gain in popularity the last few years and has been implemented in most of the above cited softwares (PLINK, SNPStats, GenABEL, SNPAssoc, postgwas). Additional softwares have been dedicated to the implementation of SNP-sets methods such as PUMA  (Hoffman, Logsdon, and Mezey 2013)  and the R packages hapassoc  (Burkett, Graham, and McNeney 2006) , cpvSNP  (McHugh, Larson, and Hackney 2020) , SKAT  (Lee, Zhao, Miropolsky, and Wu 2020) , MixMAP  (Matthews and Foulkes 2015)  and aSPU  (Kwak et al.
2016). SNPxSNP interaction consists in testing the association between the case-control status and the interaction between two SNPs. Such testing allow the detection of an epistatic effect between two loci which is very often considered as a main source of missing heritability. Therefore, popular softwares have proposed an implementation of SNPxSNP interaction testing in randomly-sampled design (PLINK, GenABEL, SNPStats SNPAssoc and CGEN). Many other tools have also been developed to tackle the issue of detecting SNPxSNP interaction, as, for example, the independent programs BOOST  (Wan et al. 2010)  and TEAM  (Zhang, Huang, Zou, and Wang 2010)  as well as the R packages MDR  (Winham 2012) , logicFS  (Schwender 2020)  and etma  (Lin 2016). However, none of these softwares and, to our knowledge, no R package has implemented functionality to simultaneously tackle the two objectives of SNP-set association and SNPxSNP interaction into a SNP-set × SNP-set interaction test.
2014) , partial least square path modeling (PLSPM,  Zhang et al. 2013 ) and gene-based information gain method (GBIGM,  Li et al. 2015). A second class of methods aims at aggregating interaction tests performed at the SNP level by the minimum p value (minP, Emily 2016), a gene-based association test using extended Simes (GATES,  Li, Gui, Kwan, and Sham 2011) , and two truncated tests, the truncated tail strength (tTS,  Jiang, Zhang, Zuo, and Kang 2011)  and the truncated product p values (tProd,  Zaykin, Zhivotovsky, Westfall, and Weir 2002). However, all these methods have been implemented in home made softwares that are for most of them available only on request to the authors and at best have a web interface. Thus, searching for gene-gene interaction at the gene level is not straightforward.
Furthermore, a comprehensive comparison of such methods, in terms of power and computational performances, remains hardly feasible. In this paper, we present the R package GeneGeneInteR  (Emily, Sounac, Kroell, and Houee-Bigot 2020 ) that provides an assembly of tools for all the steps involved in gene-based gene-gene interaction testing in case-control association studies when dealing with randomlysampled individuals. Our package, available from Bioconductor (https://bioconductor. org/packages/GeneGeneInteR/) and from GitHub (https://github. com/MathieuEmily/ GeneGeneInteR), proposes a set of functions that allow to (1) download data in various standardized formats (PED, PLINK, VCF), (2) impute missing genotypes, (3) perform a gene-based gene-gene interaction analysis and (4) visualize the results.
Section 2 provides an overview of the implementation of the package and aims at summarizing the dependencies to other R packages. In Section 3, technical details and implementation are given regarding the 10 above mentioned statistical procedures (PCA, CLD, CCA, KCCA, PLSPM, GBIGM, minP, GATES, tTS, tProd) proposed in GeneGeneInteR. Section 4 is devoted to the description of the functions implemented in GeneGeneInteR in order to analyze a collection of SNP sets. Through the example of a dataset related to rheumatoid arthritis and composed of 17 genes, we introduce tools for the importation and the manipulation of the data as well as the visualization of the results.
Overview of the implementation of GeneGeneInteR The implementation of GeneGeneInteR can be divided into three main functionality modules. The first module is a set of functions dedicated to the importation and the manipulation of raw data. This module exploits functionalities attached to the class 'SnpMatrix'. 'SnpMatrix' is a S4 class of objects developed under the snpStats package that is dedicated to large SNP association studies. A detailed example of the functionalities of this module is proposed in Sections 4. 1 and 4. 2. Our second module consists in functions devoted to the visualization of the results. One functionality of this module allows a network representation, for which functionalities from the igraph package  (Csardi and Nepusz 2006)  are imported with Gene-GeneInteR. The functionalities of this module are detailed in Section 4.
x 1 (nc+n d )m 1             , X 2 =          X c 2 X d 2          =             x 2 11. x 2 1m 2. x 2 nc1. x 2 ncm 2 x 2 (nc+1)1. x 2 (nc+1)m 2. x 2 (nc+n d )1. x 2 (nc+n d )m 2            . In our package we propose 10 methods for testing interaction at the gene level. These 10 methods are all based on three main parameters: Y, a numeric or factor vector with exactly two distinct values, G1 and G2, two 'SnpMatrix' objects as proposed by the R Bioconductor package snpStats. 'SnpMatrix' objects have been chosen because of their capacity to handle large datasets. Furthermore, 'SnpMatrix' objects benefit from numerous methods provided by the snpStats package, allowing flexible data manipulation and efficient computation of summary statistics.
In the remainder of this section, we provide details on the methods implemented in our package and highlight the specific parameters used for each method. Our implementation is illustrated by the dataset gene. pair provided with the GeneGeneInteR package. The next code line displays the header of the status coding variable Y: R> head(gene. pair$Y) [1] HealthControl HealthControl HealthControl HealthControl HealthControl Levels: HealthControl RheumatoidArthritis The two following code lines provide the main characteristics of the two 'SnpMatrix' objects, G1 and G2:.
Such a percentage is defined by the user and corresponds to the threshold parameter. In our package, two distinct principal component decompositions are provided by the functions PCA. test via the argument method. With method = "Std", the dataset is standardized using variables' standard deviation while with method = "GenFreq", the dataset is standardized using standard deviation under Hardy-Weinberg equilibrium, as proposed in the snpStats package. When the percentage of inertia asked by the user is high, the number of PCs can be important and fitting logistic models M Inter and M No is likely to fail. In that case, the number of PCs retained in model (  2 ) is reduced until convergence of the glm function for fitting models M Inter and M No. The following code line illustrates the PCA-based method when the dataset is standardized using standard deviation under Hardy-Weinberg equilibrium: R> PCA.
test(Y = gene. pair$Y, G1 = gene. pair$G1, G2 = gene. pair$G2, + threshold = 0. 7, method = "GenFreq") Gene-based interaction based on Principal Component Analysis -GenFreq data: gene. pair$Y and (gene. pair$G1 , gene. pair$G2) Deviance = 8. 2157, df = 6. 0, threshold = 0. 7, p-value = 0. 2227 alternative hypothesis: true deviance is greater than 0 sample estimates: Deviance without interaction Deviance with interaction 615. 2977 607. 0821 In the next code line, the dataset is standardized using variables' standard deviation: R> PCA. test(Y = gene. pair$Y, G1 = gene. pair$G1, G2 = gene. pair$G2, + threshold = 0. 7, method = "Std) Gene-based interaction based on Principal Component Analysis -Std data: gene. pair$Y and (gene. pair$G1 , gene. pair$G2) Deviance = 8. 5074, df = 6. 0, threshold = 0.
Since the performance of kernel methods strongly relates to the choice of kernel functions, the default is the radial basis kernel function (RBF) owing to its flexibility in parameter specification. However, other kernel functions, such as linear, polynomial or spline kernels, can be used. Thus, in addition to the three arguments Y, G1 and G2, our implementation of the KCCA test proposes two optional arguments: n. boot that determines the number of bootstrap samples and kernel that provides the kernel function to be used. This kernel parameter is a character string matching one of the kernel name provided by the kernlab package (Karatzoglou, Smola, Hornik, and Zeileis 2004) such as "rbfdot", "polydot", "tanhdot", "vanilladot", "laplacedot", "besseldot", "anovadot", "splinedot". The arguments, sigma, degree, scale, offset and order, can also be passed to the kcca.
tTS and tProd The tTS and tProd procedures are two truncated tail strength methods that aim at combining signals from all single-SNP p values less than a predefined cutoff value  (Jiang et al. 2011). Denoting by τ the cutoff value, the two truncated p values are defined as follows  (Zaykin et al. 2002) : tTS = 1 m 1 m 2 m 1 m 2 i=1 I(p (i) < τ ) 1 -p (i) m 1 m 2 + 1 i , ( 18 ) tProd = m 1 m 2 i=1 p I(p i <τ ) i , ( 19 ) where I is the indicator function. When p values are correlated, the null distributions of tTS and tProd are unknown. Following the approach proposed by  Zaykin et al. (2002) , a p value is obtained in the GeneGeneInteR package by computing an empirical null distribution using Monte Carlo (MC) simulations. For each MC iteration, an empirical value for tTS (or tProd) is obtained by simulating a vector of W jk with respect to a multivariate normal distribution with a vector of 0 means and Σ as covariance matrix.
The empirical p value is calculated as the proportion of simulated statistics larger than the observed statistic on the "true" set of W jk. The tTS and tProd methods have been implemented in the functions tTS. test and tProd. test of the GeneGeneInteR package. Additional to the mandatory Y, G1 and G2 arguments, these two functions have two optional arguments: tau and n. sim that control the cutoff value and the number of simulations used to estimate the empirical value respectively. The following code lines give an example of the use of the tTS. test and tProd. test functions:.
Analysis of a set of genes In practice, it is very common to investigate the interaction between a collection of k > 2 genes. In this context, our package GeneGeneInteR proposes several methods to perform a complete pipeline of analysis, going from data importation to results visualization via data A list object has been returned with elements: snpX & genes. info The following code lines show that the dataset now contains only 209 SNPs, meaning that 103 SNPs have been filtered out.