dataesr/scientific-paragraphs-categorization huggingface dataset:
• Obstacle avoidance and terrain following: Obstacle avoidance, as the name suggests, implies the detection and following avoidance of obstacles present in the flight path. Many passive and active sensors can be used for this purpose, and a number of algorithms have proven effective in this domain  [60, 61]. Terrain following compares measurements from onboard sensors with a database terrain map so the minimum clearance is respected. It can also be used for drift-free localisation purposes in case of GPS-denied environments.
In this table, we show the link between the number of concurrent onboard tasks that are required, in either VLOS or BVLOS operations, in order to ensure application success. We also draw the relationship between applications, the ALFUS model and UAS AL. For instance, the reasoning behind Table 2 is as follows: • Infrastructure Inspection can be conducted in two modalities, local (VLOS) or remote (BVLOS). Local inspections are intended to identify issues on the spot, in addition to recording data for further analysis. These operations are mostly flown in manual mode (pilot in control at all times, HI low ∈  [1, 3] ) and some automation might exist in the sensors used to acquire data or flight control. The environment and mission carry a degree of complexity mainly due to the fact that the unmanned aircraft will be flying close to a structure that might be functioning (power pole, tower, bridge, factory chimney, etc.
Furthermore, the nature of parallelism, e. g. , whether a program can be parallelizable or not (Amdhal's law), will define the efficiency in a multi-processor model. As an alternative, we propose that a CPU-FPGAs architecture would overcome some of these limitations by being able to be dynamically reconfigured (FPGA dynamic reconfiguration). FPGA Dynamic Partial Reconfiguration (DPR) allows to reduce the FPGA size (large FPGAs are costly) and to maximize its use. DPR can be seen as a tile-based FPGA with multiple configuration ports that work like many embedded FPGAs in a single chip. A tile-based FPGA architecture also allows power gating when a resource is unused. It is also worth noting that available parallelism will be efficiently exploited is this approach if combined with a dedicated NoC that provide performance and flexibility comparable to NoC designed for multicore architectures  [119]  and not a light NoC [120] optimized but degraded to fit with FPGA resource.
In terms of applications, FPGA still remains complex to program. The application complexity (design programming language) issue can be solved with an upgradeable library of IP cores designed for a set of standard functions/routines that will be mapped to the tiles on demand. To conclude, autonomy requires embedded intelligence. This means that complexity will grow over time including more sensors, data and more processing. In the UAS case, it means that SWaP will likely lead to FPGAs with DPR capabilities. The objective is then to design an architecture and programming methodology that makes this evolution possible and efficient.
Acknowledgments: The research was supported by the CNRS PICS Swarm project and was a part of the HPEC project funded by French National Research Agency (ANR), project ID ANR-15-CE24-0022-01. This work was also supported in part by an Australian Endeavour Executive Fellowship and the Queensland University of Technology professional development program (PDL). We would also like to acknowledge the support from UBO University for the invited professor program.
Discussion The Martian magnetosphere contains elements of induced and intrinsic origin (see e. g. ,  Chai et al. , 2019; Di-Braccio et al. , 2018; Dubinin et al. , 2017; Ramstad et al. , 2020; Weber et al. , 2019; Xu et al. , 2017 Xu et al. , , 2018 Xu et al. , , 2020)). To display these components one must use specific coordinate systems. Because of a strong asymmetry of the magnetosphere controlled by the direction of the motional electric field, the MSE coordinate system adequately describes aspects of the induced magnetosphere but loses features caused by the presence of the local crustal magnetizations. To study effects of the induced origin we used here the MAVEN data obtained for the period from November 2014 to May 2018 and utilized the MSE coordinate system. One should keep in mind that the     2017 ) of the trapped electrons with two-sided loss cones near the terminator, in the area not connected to the strong crustal sources, also support the reconnection scenario in this region.
Acknowledgments The authors thank  M. Drissi  for reporting a bug in the previous version of the program. This publication is based on work supported by the  UK Science and Technology Facilities Council (STFC)  through grants  ST/P005314/1  and  ST/L005516/1 , the  Max Planck Society , the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Project-ID  279384907 -SFB 1245 , and the framework of the  Espace de Structure et de réactions Nucléaires Théorique (ESNT)  at  CEA.
21om the source filesOnce the ADG source files are downloaded from the CPC library or the GitHub repository21, one must enter the project folder and either run With this method, pip 22 also takes care of downloading and installing NumPy, NetworkX and SciPy. pip3 install. or python3 setup. py install 9. 2. Run the program 9. 2. 1. Batch mode 20https://pypi. org/project/adg/ 9. 1. 2.
Deformable registration of medical images generally relies on the iterative minimization of a cost function involving a large number of parameters. For complex deformations and large datasets, this process is computationally very demanding, leading to processing times that are incompatible with the clinical routine workflow. The recent breakthroughs in deep learning have led to revisit most of image processing problems, including deformable image registration. While building large annotated dataset can be a bottleneck for implementing such techniques, recent contributions demonstrated the potential of Convolutional Neural Networks (CNN) trained in an unsupervised way for achieving fast and efficient deformable registration of 2D and even 3D medical images. The present study proposes to evaluate one of the reference state-of-the-art methods VoxelMorph [5], in the context of inter-subject registration of abdominal CT images.
Standard similarity measure-based approaches to deformable image registration amount to iteratively minimizing cost (objective) functions embedding thousand or millions of variables  [2, 3, 4]. These registration algorithms are thus time-consuming, and typically require several minutes to hours for 3D images while running on CPU. Difficulties may also arise due to the highly non-convex nature of these optimization problems. In the last few years, deep learning-based frameworks have been introduced in deformable image registration to overcome these issues. After a computationally intensive offline learning on large training datasets, CNN-based registration methods are able to match a new pair of images within (fractions of) seconds. Among all the proposed approaches, the VoxelMorph learning-based framework  [5] , made a major breakthrough since it does not require any ground truth deformation field for the learning phase, contrary to previous related approaches  [15, 16, 17].
This is a crucial feature since it is extremely difficult to obtain accurate ground truth deformations from real data. An alternative strategy is to generate the learning datasets using synthetic warpings, but they may not necessarily be representative of the whole morphological variabilities that may be observed in real data. To overcome these limitations, VoxelMorph learns in an unsupervised way the complex mapping between every couple of 2D or 3D pairs of images and the corresponding deformation field by minimizing a standard intensity-based similarity metrics over the whole learning database. Any differentiable objective function can be considered. The training database simply consists of representative pairs of images. The mapping is parameterized using CNN. After the training step, evaluating the CNN output from an unseen pair of images enables us to obtain a deformation field within seconds.
This learning-based approach allows, in a certain sense, to transfer the time consuming iterative minimizing procedure done by standard registration methods for each new pair of images to an offline computationally intensive learning phase, which needs to be carried out only once and for all. In this paper, we present an evaluation of the reference CNN-based registration method VoxelMorph  [5]  in the context of inter-subject abdominal CT image registration by considering two datasets: LiTS  [6]  and 3D-IRCADb-01  [7]. Both image datasets are complemented with anatomical segmentations, which are used for evaluating the registration accuracy by calculating the overlap of each region of interest. VoxelMorph has so far only been evaluated on brain images. Abdominal images present a greater challenge in terms of registration than brain images, due to greater anatomical variability and significant organ deformations.
Our experimental results highlight that VoxelMorph performance is on a par with the classical non-learning-based state-of-the-art registration algorithm ANTs  [4] , but with a drastic reduction of the computation time to a fraction of seconds for 2D images and to a few seconds for 3D images which is more than two order of magnitude faster compared to ANTs. The paper is organized as follows. In Section 2, the two databases of abdominal CTs used in this evaluation are described and the image registration method VoxelMorph is introduced. Section 3 is dedicated to the experimental results. Finally, conclusions are drawn in Section 4.
Datasets LiTS  [6]  and 3D-IRCADb-01  [7]  are the two datasets used for the experiments conducted in this work. The LiTS dataset contains 130 contrast-enhanced 3D abdominal CT scans and corresponding liver segmentation masks, which were created in collaboration with seven hospitals and research institutions and manually reviewed independently by three radiologists. The 3D-IRCADb-01 dataset is composed of 3D CT scans of 20 patients (10 men and 10 women) with the hepatic tumor in 75% cases. Masks of various segmented regions of interest (liver, spleen, skin, bone, kidney, etc) are also provided.
The deformation  is often defined by a displacement vector field, but it can also be specified as a velocity vector field  [13]. Standard registration approaches minimize the cost function  # for each pair of images that have to be registered  [14]. VoxelMorph relies on a different paradigm which consists in the estimation of global mapping, that associates to every couple of images( " ,  ! ) the corresponding deformation field. This mapping modeled as a CNN is learned once and for all by minimizing the cost  # averaged on the whole training database.
CNN-based Registration: VoxelMorph The block diagram of the image registration method VoxelMorph is shown in Fig. 1. The basic idea is to learn a registration function  ( ( " ,  ! ), that associates for every pair of input images of the training dataset the corresponding deformation field (), where () are the convolution kernels that parameterize the network architecture. Stochastic gradient descent optimization is used to get the optimal parameters for the final registration function by minimizing the cost function. As an output, a registration field () is obtained such that  " () and [ ! ○ ]() hopefully correspond to the same anatomical locations ( is considered as pixel/voxel). During the testing phase, the fixed ( " ) and moving ( ! ) image are evaluated as a new input of the network, which outputs the corresponding deformation field.
CNN Architecture For the study and the experiments conducted in this paper, the VoxelMorph-2 network architecture is used  [5]. The CNN considered for the parameterization of the registration function ( is similar to the standard UNet network  [18] , which is divided into an encoder and decoder portions with skip connections between them (see Fig. 1 ). The fixed and moving images are concatenated into a 2-channel image and given as input to the network. In both the encoder and decoder sections, convolutions are applied at each layer with kernels of size 3 ) (n being the image dimension). These layers capture the image features and finally encode the deformation field (). A stride of 2 is used in the encoder part to decrease the image dimension into half of its previous layer's input to get the image features from its fine to coarse representation.
Experiments and Results The experiments have been performed on two databases of abdominal CT images. VoxelMorph is benchmarked with the competitive deformable registration method Symmetric Normalization (SyN)  [12]  available through the software package of ANTs (http://stnava. github. io/ANTs/). The registration methods are evaluated for both 2D and 3D image registration. In this section, we first describe the experimental setup including the preparation of the dataset and the metrics used for the evaluation. Then, results are presented focusing on three scenarios, namely 2D registration on a reference image (considered as an atlas), 2D pairwise registration (without a reference image) and 3D pairwise registration (without a reference image). The influence of the weighting regularization parameter is finally investigated.
Dataset Preparation Abdominal 3D CT-scan volumes from the LiTS  [6]  and 3D-IRCADb-01  [7]  dataset are used for the assessment of VoxelMorph. As the LiTS dataset has more 3D volumes than 3D-IRCADb-01 (130 against 20), only the volumes from LiTS are taken into account for training the CNN, while the volumes from both datasets are considered for the evaluation.
2D dataset This dataset was created by extracting 2D slices from the cropped 3D volumes. Firstly, the 3D volume intensities are clipped between 0 and 255. Artefacts in the volume image (structures other than the abdominal portion, e. g. patient bed) are removed using morphological operators (see Fig. Elsevier © 2020. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ 3D volume is considered for further processing. Then, the histogram matching technique implemented in ANTs is applied to normalize the 2D images. As a result, 81 affinely aligned 2D images and a reference image are obtained. To increase the number of images for training, data augmentation is performed using the Gryds  [19]  package (https://github. com/tueimage/gryds).
Gryds implements deformable transformations modeled as B-splines. It has been modified as per the requirement of this study. The control points on the ribs regions of the abdominal images have been constrained to remain stationary, while the control points inside the abdominal portion are allowed to move (shown in Fig. 2. (d), (e), (f)). This constraint is introduced to obtain anatomically plausible deformed images. Finally, a dataset has been produced, with 810 abdominal 2D images of size (512 × 512) with a 1 mm isotropic pixel dimension. The dataset has been split into 730 and 80 images for training and testing respectively. The same dataset is used for 2D atlas-based registration and 2D pairwise registration.
3D dataset For the preprocessing of the 3D volumes, again the first step is to clip the volume intensities between 0 and 255 and to discard structures other than the abdominal portion. The height and the width of the cropped 3D volumes are all (512 × 512), but the depth of each volume is different. All cropped 3D volumes are thus resampled to (512 × 512 × 256) with 1mm isotropic voxels. Next, one random volume from the cropped resampled 3D volumes of the LiTS dataset is considered as a reference volume, and intensity normalization is carried out for all other volumes using the histogram matching technique implemented in ANTs. Finally, an affine transformation is used to align the 3D volumes with the reference volume (using ANTs-Registration). As a result, 111 abdominal 3D volumes of size (512 × 512 × 256) with 1mm isotropic voxel dimension are obtained.
Hausdorff Distance Elsevier © 2020. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ The Hausdorff distance quantifies how far two subsets of points (here, two binary segmentation maps) are according to a given metric (here, the Euclidean norm is considered). More concretely, it is defined as the greatest of all the distances from a point in the segmentation map of  " to the closest point in the warped segmentation map associated to  ! ○  and conversely. This distance reflects the worst degree of mismatch between two segmentation maps. Defining the two segmented anatomical structures  5 $ $ = { * ,  0 ,. ,. } and  5 % ○> $ = { * ,  0 ,. ,  C } as two finite point sets, the Hausdorff distance is defined as: 𝐻𝐷 k 𝑅 5 $ $ , 𝑅 5 % ○> $ m = 𝑚𝑎𝑥 tℎ𝑑 k𝑅 5 $ $ , 𝑅 5 % ○> $ m , ℎ𝑑 k 𝑅 5 % ○> $ , 𝑅 5 $ $ mv … (9) where, ℎ𝑑 k 𝑅 5 $ $ , 𝑅 5 % ○> $ m = 𝑚𝑎𝑥 R∈M ' $ * 𝑚𝑖𝑛 S∈M ' % ○) * w|𝑎 -𝑏|w … ( 10 ) The Dice Score and the Hausdorff Distance are calculated in the experiments on 2D atlas-based registration, 2D pairwise registration, and 3D pairwise registration for the liver region only, since this is the only segmented structures provided in the LiTS dataset.
Comparison with a State-of-the-Art Method In this study, the accuracy of VoxelMorph is compared with the Symmetric Normalization (SyN) method  [12] , which is a competitive method that has been proven to be efficient on several image modality and organs, such as brain MRI  [3, 4]  or thoracic CT scan  [20]. SyN is an implementation of the large deformation diffeomorphic metric mapping (LDDMM) algorithm, available in the ANTs software package. We adopted a step size of 0. 25 and used cross-correlation (CC) as the similarity metric. The CC metric available in ANTs is a fast implementation of the standard normalized cross-correlation metric computed in a local neighborhood. The CC metric is known to result in registrations that are robust to differences in intensity contrast. We adopted a local neighborhood of size 3.
2D Pairwise Registration Similarly to the previous experiment, 730 2D affine registered abdominal images are used as the training dataset (in this case without the atlas image), and eight images are kept for testing the accuracy (after discarding the rest 72 images augmented from those eight images and only considering the original ones). Again the similarity metric used is MSE and the images are downsampled to (256 × 256). In this experiment, pairs of images are randomly selected at a time and concatenated together to be used as input in batches for the model. Here 3600 steps per epoch are utilized while the number of epochs is 750. The regularization parameter is set to 0. 25. It took 24. 33 hours to train the model with an NVIDIA GeForce GTX 1080 GPU. The average Dice scores and the Hausdorff distances for the liver mask obtained using Affine, VoxelMorph, and ANTs (SyN) are listed in Table  2.
This illustrates the ability of VoxelMorph to model very complex registration function  ( ( " ,  ! ). In terms of computational burden, VoxelMorph still produces output on average 120 times faster than ANTs (SyN). For this experiment, the training dataset consists of 100 3D affine registered abdominal images from the LiTS dataset. The original cropped images (512 × 512 × 256) are downsampled to (128 × 128 × 64) in order to contain the computational cost so that the training of the network can be performed faster while using only a single GPU, and test results can be computed using CPU only (no need to use GPU, hence memory allocation time is saved). Here also, two random images from the training dataset are picked up, concatenated and given as input in batches to train the model. The model is trained with two different similarity metrics, namely MSE and CC, using two appropriate values for the regularization parameter, Elsevier © 2020.
This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ 0. 25 and 1. 5 respectively. Using the NVIDIA GeForce GTX 1080 GPU, it takes around 11. 023 hours to train the model with MSE (number of epochs = 75, steps per epoch = 600), while using CC, the time is about 19. 83 hours (number of epochs = 90, steps per epoch = 600). 11 images from the LiTS dataset (Fig. 7  and 8 ) and 5 images from the 3D-IRCADb-01 dataset (Fig. 9 ) are used to evaluate the accuracy. Table  3  and 4    The quality of image registration is known to depend on the smoothness term in the cost function, the effect of which is controlled by the regularization parameter. The test dataset from LiTS data is evaluated on 2D atlas-based registration, with varying regularization parameter ranging from 0 to 1 and the average Dice Scores are calculated (Fig.
Discussion In this study, we have proposed an evaluation of VoxelMorph, a state-of-the-art unsupervised learningbased framework for deformable image registration on 2D and 3D abdominal CT-scan images. The performances of VoxelMorph were compared with a competitive non-learning-based deformable registration method "Symmetric Normalization" (SyN), implemented in ANTs, on two representative databases: LiTS and 3D-IRCADb-01. Three different experiments were carried out on 2D or 3D data, on atlas-based or pairwise registration, using two different similarity metrics, namely MSE and CC. Accuracy Elsevier © 2020. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ of the registration was measured by the Dice score and Hausdorff distance metrics, which respectively quantify the volume overlap and the farthest point to point distance for selected anatomical regions.
19 seconds taken by ANTs (SyN), which represents a drastic reduction of the computational burden. Similar conclusion can be drawn on a 3D pairwise registration experiment:VoxelMorph exhibits drastically reduced computation time, while achieving almost similar registration accuracy. It is also interesting to notice that VoxelMorph still performs well on data from another dataset (3D-IRCADb-01), thus illustrating the generalizability of the learned model. Here Dice scores and Hausdorff distance values are calculated not only for liver but for five other segmented anatomical regions. In most cases, the performance obtained with VoxelMorph is similar to ANTs (SyN), while computational cost for a single registration estimation is less than 7 seconds on a single CPU for VoxelMorph compared to about 5 mins for ANTs.
Concerning the influence of the similarity metric for the training of VoxelMorph, MSE and CC lead to similar performance, with a slight gain of performance with MSE. The drop in values of the Dice scores for CC could be the effect of inconsistent anatomical structures present in the 3D volumes. While MSE is computed globally for the entire volumes, CC is calculated locally in our experiment. As the anatomical structures vary abruptly for the abdominal volumes, the performance of local CC for the entire abdominal volumes might not be up to the mark (although it is one of the best metrics). Hence we observed a slight decline in the Dice scores. Fig. 9  and Fig. 10  present triplanar and 3D view of abdominal volume registration between two random test volumes taken from LiTS dataset and 3D-IRCADb-01 dataset respectively.