dataesr/scientific-paragraphs-categorization huggingface dataset:
LAM + LOLA Merged DEM We used the adjusted LAM points and the LOLA points to produce a LAM + LOLA merged DEM with a 1/256 • resolution in the Mons Rümker region by Kriging interpolation (Figure  9 ). The LAM + LOLA merged DEM possesses many distortions and exhibits a banded distribution. We think this is mainly due to the horizontal errors of the CE-1 orbit and the LAM pointing errors and partly to the remaining residuals after the crossover adjustment. The crossover adjustment did not handle the horizontal error of the LAM data. If we use the LAM data at greater distances from the LOLA data, the problem of distortion is reduced, but the amount of LAM data is small, which makes little contribution to the merged DEM. The LAM data need to be relocated by reconstructing the CE-1 orbit.
LALT + LOLA Merged DEM We used the adjusted LALT points and the LOLA points to create a LALT + LOLA merged DEM with a 1/256° resolution in Mons Rümker region by Kriging interpolation (Figure  10a ). There is no distortion in the merged DEM because of the more accurate horizontal geolocation of the LALT data. The crossover adjustment did not handle the horizontal error of the LAM data. If we use the LAM data at greater distances from the LOLA data, the problem of distortion is reduced, but the amount of LAM data is small, which makes little contribution to the merged DEM. The LAM data need to be relocated by reconstructing the CE-1 orbit.
A further comparison is shown in Figure  11a ; we calculated the difference between each pixel in the LOLA-only DEM and the LALT + LOLA merged DEM. The large differences follow a banded distribution, reflecting the position of the added LALT points. The added LALT points contribute to the gaps of the LOLA-only DEM, in which the maximum change in the pixel value was about 200 m. Figure  11b, c   The crossover adjustment did not handle the horizontal error of the LAM data. If we use the LAM data at greater distances from the LOLA data, the problem of distortion is reduced, but the amount of LAM data is small, which makes little contribution to the merged DEM. The LAM data need to be relocated by reconstructing the CE-1 orbit.
SLDEM2015 Barker et al. [17]  used a five-parameter coordinate transformation and a bounded downhill simplex minimization algorithm to register the stereo-derived DEMs (each 1° × 1°) from the SELENE TC to the LOLA reference geodetic framework. Subsequently, the authors used the co-registered TC data to estimate and correct the orbital and pointing geolocation errors from the LOLA altimeter profiles. Typically, empty pixels in the LOLA-only map were interpolated with a continuous curvature surface but the empty pixels were filled with the corresponding pixels of the geodetically-controlled TC tiles to create the SLDEM2015. We compared the differences between SLDEM2015 and LALT + LOLA merged DEM. Figure  12  shows overall differences in the entire study area and Table  1  gives the statistics of difference.
As a whole, the two DEMs are more consistent, with a mean difference of only 0. 02 m and a standard deviation of 7. 72 m. However, there is still large difference in local areas, and the maximum difference is 323. 12 m, the minimum is -500. 59 m, mainly concentrated in the impact crater, mountains, ridges and other topographic undulating areas. There are two main reasons for the large difference. One is that there are still many data blank areas in LALT + LOLA merged DEM, which is limited by the current amount and distribution of altimetry data. The other is that SELENE TC DEM has large residual error after adjustment in a local area and the errors are taken into the production process of SLDEM 2015.
SLDEM2015 Barker et al. [17]  used a five-parameter coordinate transformation and a bounded downhill simplex minimization algorithm to register the stereo-derived DEMs (each 1 • × 1 • ) from the SELENE TC to the LOLA reference geodetic framework. Subsequently, the authors used the co-registered TC data to estimate and correct the orbital and pointing geolocation errors from the LOLA altimeter profiles. Typically, empty pixels in the LOLA-only map were interpolated with a continuous curvature surface but the empty pixels were filled with the corresponding pixels of the geodetically-controlled TC tiles to create the SLDEM2015. We compared the differences between SLDEM2015 and LALT + LOLA merged DEM. Figure  12  shows overall differences in the entire study area and Table  1  gives the statistics of difference.
As a whole, the two DEMs are more consistent, with a mean difference of only 0. 02 m and a standard deviation of 7. 72 m. However, there is still large difference in local areas, and the maximum difference is 323. 12 m, the minimum is -500. 59 m, mainly concentrated in the impact crater, mountains, ridges and other topographic undulating areas. There are two main reasons for the large difference. One is that there are still many data blank areas in LALT + LOLA merged DEM, which is limited by the current amount and distribution of altimetry data. The other is that SELENE TC DEM has large residual error after adjustment in a local area and the errors are taken into the production process of SLDEM 2015. The SLDEM2015 possesses good quality and combines the finer details of the stereo-derived DEM and the LOLA reference geodetic framework but we find that this map shows inconsistency in some areas.
Conclusions A method combining filtering and cluster analysis was used to eliminate the noise points in multisource altimeter data and a special crossover adjustment was used to model the radial errors in the LAM and LALT data. The LAM radial residual RMS was reduced from 154. 83 ± 43. 60 m to 14. 29 ± 27. 84 m and the LALT radial residual RMS was reduced from 3. 50 ± 5. 0 m to 2. 75 ± 4. 4 m. To fill the gaps in the LOLA data, the adjusted LAM and LALT data were used to produce the merged DEMs. The LAM + LOLA merged DEM shows that there are large horizontal geolocation errors in the adjusted LAM data but the LALT + LOLA merged DEM is more credible than the LOLA-only DEM. The combined multisource altimetry data improves the spatial coverage and helps to create more precise DEMs. The target region in this study is only 15 • × 15 • of the lunar surface and the adjustment model has only one parameter.
The adjustment model must be replaced by a higher order polynomial when handling an expanded research area. Future work will cover latitudes from 0 • N to 60 • N, as determined by the LOLA data coverage. The horizontal geolocation of the LALT and LAM data is another problem that must be tackled. The 3-D crossover adjustment is a good way to reduce horizontal and radial errors. In our study, there are few crossover points between a LAM/LALT track and LOLA cloud points. And it is hardly possible to do a 3D match with limited crossover points. If the number of the new data by the future lunar mission becomes as much as LOLA data, horizontal position error can be adjusted as the radial one.
Acknowledgments: We thank the  LRO Mission Operations Center  and the  LOLA  science teams for the LOLA RDR data used in this study. We also thank the  SELENE LALT team  and the  SELENE Data Archive  for providing the LALT data. We also thank the CE-1 LAM team and  Data Publishing  and  Information Service System of the Chinese Lunar Exploration Project. We are grateful to the anonymous reviewers and editors for constructive feedback, which improved the quality of this paper. Jean-Pierre Barriot was funded by a  DAR  grant in planetology from the  French Space Agency (CNES).
The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images.
These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3. 4% on MSR Action 3D dataset, 0. 67% on KARD dataset, and 2. 5% on NTU-RGB+D dataset.
In the literature of HAR, many studies have indicated that CNNs have the ability to learn complex motion features better than hand-crafted approaches (see Figure  1 ). However, most authors have just focused on the use of relatively small and simple CNNs such as AlexNet  (Krizhevsky et al. , proaches reported on the Cross-View evaluation criteria of NTU-RGB+D dataset  (Shahroudy et al. , 2016). The traditional approaches are marked with circles  (Oreifej and Liu, 2013; Ohn-Bar and Trivedi, 2013; Yang and Tian, 2014; Evangelidis et al. , 2014; Hu et al. , 2015). The deep learning based approaches are marked with squares  (Du et al. , 2015; Shahroudy et al. , 2016; Liu et al. , 2016; Song et al. , 2017; Li et al. , 2017).
We evaluate the proposed method on three benchmark skeleton datasets (MSR Action 3D  (Li et al. , 2010) ; Kinect Activity Recognition Dataset -KARD  (Gaglio et al. , 2015) ; NTU-RGB+D  (Shahroudy et al. , 2016))  and obtain state-of-the-art recognition accuracies on all these datasets. Furthermore, we also point out the effectiveness of our learning framework in terms of computational complexity, the ability to prevent overfitting and to reduce the effect of degradation phenomenon in training very deep networks. The contributions of our work lie in the following aspects: • Firstly, we propose an end-to-end learning framework based on ResNets to effectively learn the spatial-temporal evolutions carried in RGB images which encoded from skeleton sequences for 3D human action recognition. To the best of our knowledge, this is the first time ResNetbased models are applied successfully on skeletal data to recognize human actions.
• Secondly, we present a novel ResNet building unit to construct very deep ResNets. Our experiments on action recognition tasks prove that the proposed architecture is able to learn features better than the original ResNet model  (He et al. , 2016). This architecture is general and could be applied for various image recognition problems, not only the human action recognition. • Finally, we show the effectiveness of our learning framework on action recognition tasks by achieving the state-ofthe-art performance on three benchmark datasets including the most challenging skeleton benchmark currently available, whilst requiring less computation. The rest of the paper is organized as follows: Section 2 discusses related works. In section 3, we present the details of our proposed method. Datasets and experiments are described in Section 4.
When the deep networks start converging, a degradation phenomenon occurs (see an example in Figure  2 ). If we add more layers to a deep network, this can lead to higher training and/or testing error  (He and Sun, 2015). This phenomenon is not as simple as an overfitting problem. To reduce the effect of vanishing gradients problems and degradation phenomenon,  (He et al. , 2016)  introduced Residual Networks (ResNets) with the presence of shortcut connections parallel to their traditional convolutional layers. This idea helps ResNets to improve the information flow across layers. Experimental results on two well-known datasets including  CIFAR-10 (Krizhevsky, 2009)  and ImageNet  (Russakovsky et al. , 2015)  confirmed that ResNets can improve the recognition performance and reduce degradation phenomenon.
Several authors have exploited the feature learning ability of CNNs on skeletal data  (Hou et al. , 2017; Wang et al. , 2016; Song et al. , 2017; Li et al. , 2017). However, such studies mainly focus on finding good skeletal representations and learning features with simple CNN architectures. In contrast, in this paper we concentrate on exploiting the power of D-CNNs for action recognition using a simple skeleton-based representation. We investigate and design a novel deep learning framework based on ResNet  (He et al. , 2016)  to learn action features from skeleton sequences and then classify them into classes. Our exper-imental results show state-of-the-art performance on the MSR Action 3D  (Li et al. , 2010) , KARD  (Gaglio et al. , 2015)  and NTU-RGB+D  (Shahroudy et al. , 2016)  datasets.
As shown in Fig- ure 4, each skeleton sequence is encoded into an RGB image. By this transformation, the raw data of skeleton sequences are converted to 3D tensors, which will then be fed into the learning model as the input features. The order of joints in each frame is non-homogeneous for many skeleton datasets. Thus, it is necessary to rearrange joints and find a better representations in which different actions can be easily distinguished by the learning model. In other words, the image-based representation needs to contain discriminative x l+1 = ReLU (F (x l , W l ) + id(x l )) (5.
Datasets and evaluation criteria In this work, we evaluate the proposed deep learning framework on MSR Action 3D  (Li et al. , 2010) , KARD  (Gaglio et al. , 2015)  and NTU-RGB+D  (Shahroudy et al. , 2016). For each dataset, we follow the same evaluation criteria as provided in the original papers. For the interested reader, some public RGB-D datasets for HAR can be found in recent surveys  (Zhang et al. , 2016; Liu et al. , 2017b).
MSR Action 3D dataset The MSR Action 3D dataset 1  (Li et al. , 2010)  consists of 20 different action classes. Each action is performed by 10 subjects for three times. There are 567 skeleton sequences in total. However, 10 sequences are not valid since the skeletons were either missing. Therefore, our experiment was conducted on 557 sequences. For each skeleton frame, the 3D coordinates of 20 joints are provided. The authors of this dataset suggested dividing the whole dataset into three subsets, named AS1, AS2, and AS3. The list of actions for each subset is shown in Table  1. For each subset, we follow the cross-subject evaluation method used by many other authors working with this dataset. More specifically, a half of the dataset (subjects with IDs: 1, 3, 5, 7,  9 ) is selected for training and the rest (subjects with IDs: 2, 4, 6, 8, 10) for test.
Kinect Activity Recognition Dataset (KARD) This dataset 2   (Gaglio et al. , 2015)  contains 18 actions, performed by 10 subjects and each subject repeated each action three times. KARD provides 540 skeleton sequences in total. Each frame comprises 15 joints. The authors suggested the different evaluation methods on this dataset in which the whole dataset is divided into three subsets as shown in  For Cross-Subject evaluation, the sequences performed by 20 3 The NTU-RGB+D dataset can be obtained at: http://rose1. ntu. edu. sg/Datasets/actionRecognition. asp with authorization. subjects with IDs: 1, 2, 4, 5, 8,  9, 13, 14, 15, 16, 17, 18, 19, 25, 27, 28, 31, 34, 35, and.
Data augmentation Very deep neural networks require a lot of data to train. Unfortunately, we have only 557 skeleton sequences on MSR Action 3D dataset  (Li et al. , 2010)  and 540 sequences on KARD  (Gaglio et al. , 2015). Thus, to prevent overfitting, some data augmentation techniques have been applied. The random cropping, flip horizontally and vertically techniques were used to generate more training samples. Specifically, 8 × cropping has been applied on 40 × 40 images to create 32 × 32 images. Then, their horizontally and vertically flipped images are created. For the NTU-RGB+D dataset  (Shahroudy et al. , 2016) , due to the very large-scale of this dataset, data augmentation techniques were not applied.
Implementation details Different configurations of ResNet with 20-layers, 32-layers, 44-layers, 56-layers, and 110-layers were designed, based on the original Resnet  (He et al. , 2016)  building unit (Figure  8a ) and the proposed ResNet building unit (Figure  8b ). Totally, we have ten different ResNets. The baseline of the proposed architectures can be found in Appendix B. All networks are designed for the acceptable images with the size of 32 × 32 pixels as input features and classifying them into n categories corresponding to n action classes in each dataset. In the experiments, we use a mini-batch of size 128 for 20-layer, 32-layer, 44-layer, and 56-layer networks and a mini-batch of size 64 for 110layer networks. We initialize the weights randomly and train all networks in an end-to-end manner using Stochastic Gradient Descent (SGD) algorithm  (Bottou, 2010)  for 200 epochs from scratch.
The learning rate starts from 0. 01 for the first 75 epochs, 0. 001 for the next 75 epochs and 0. 0001 for the remaining 50 epochs. The weight decay is set at 0. 0001 and the momentum at 0. 9. In this project, MatConvNet 4  (Vedaldi and Lenc, 2015)  is used to implement the solution. Our code, models, and pre-trained models will be shared with the community at: https://github. com/huyhieupham.
Results on MSR Action 3D dataset The experimental results on MSR Action 3D dataset  (Li et al. , 2010)  are shown in Table  3. We achieved the best classification accuracy with the 44-layer ResNet model which is constructed from the proposed ResNet building unit. Specifically, classification accuracies are 99. 9% on AS1, 99. 8% on AS2, and 100% on AS3. We obtained a total average accuracy of 99. 9%. Table  3. Recognition accuracy obtained by the proposed method on AS1, AS2, and AS3 subsets of the MSR Action 3D dataset  (Li et al. , 2010). The best accuracy rates and configuration are highlighted in bold. result with the state-of-the-art methods reported on this benchmark. This comparison indicates that the proposed model outperforms many prior works, in which we improved the accuracy rate by 3.
Results on KARD dataset The experimental results on KARD dataset  (Gaglio et al. , 2015)  are reported in Table  4. It can be observed the same learning behavior as experiments on MSR Action 3D dataset, in which the best results are achieved by the proposed 44layer ResNet model. Table  6  provides the accuracy comparison between this model and other approaches on the whole KARD dataset. Based on these comparisons, it can be concluded that our approach outperformed the prior state-of-theart on this benchmark. Figure  12c  and Figure  12d  show the learning curves of all networks on Activity Set 1 subset for Experiment C.
Results on NTU RGB-D dataset Table  7  shows the experimental results on NTU-RGB+D dataset  (Shahroudy et al. , 2016). The best network achieved an accuracy of 78. 2% on the Cross-Subject evaluation and 85. 6%  on the Cross-View. The performance comparison between the proposed method and the state-of-the-art methods on these two evaluations are provided in Table  8  and Table 9. These results showed that our proposed method can deal with very large-scale datasets and outperforms various state-of-the-art approaches for both evaluations. Comparing with the best published result reported by  (Li et al. , 2017)  for the Cross-Subject evaluation, our method significantly surpassed this result by a margin of +2. 0%. For the Cross-View evaluation, we outperformed the state-of-the-art accuracy in  (Kim and Reiter, 2017 ) by a margin of +2.
Computational efficiency We take the Cross-View evaluation criterion of the NTU-RGB+D dataset  (Shahroudy et al. , 2016)  and the Proposed- With the implementation in Matlab using MatConvNet toolbox  (Vedaldi and Lenc, 2015)  on a single NVIDIA GeForce GTX 1080 Ti GPU system 6 , without parallel processing, we take 7. 83 × 10 -3 s per skeleton sequence during training. After about 80 epochs, our network starts converging with an accuracy around 85%. While the prediction time, including the time for encoding skeletons into RGB images and classification by pre-trained ResNet, takes 0. 128s per skeleton sequence. This speed is fast enough to respond to many real-time applications. Our method is therefore more practical for large-scale problems and real-time applications.
Conclusion and future work We have presented a novel deep learning framework based on ResNets for human action recognition with skeletal data. The idea is to combine two important factors, i. e. , a good spatio-temporal representation of 3D motion and a powerful deep learning model. By encoding skeleton sequences into 6 For more information about the specifications of this GPU, please refer to: https://www. nvidia. com/en-us/geforce/products/10series/ geforce-gtx-1080-ti/. RGB images and proposing a novel ResNet architecture for learning human action from these images, higher levels of performance have been achieved. We show that the approach is effective for recognizing actions as we achieved state-of-the-art performance on three well-established datasets, while requiring less computation resource.
The resizing step may lead to the change in the accuracy rate. To identify the effects of different resizing methods on the recognition performance of the proposed model, we conducted an additional experiment on the MSR Action 3D/AS1 dataset (Li et al. , 2010) with Proposed-ResNet-20 network. In this experiment, two different resizing methods, including Nearest-Neighbor interpolation and Bicubic interpolation were used for resizing imagebased representations before feeding to deep networks. Experimental results indicate that the difference between the accuracy.
Acknowledgments This research was carried out at the  Cerema Research Center (CEREMA)  and  Toulouse Institute of Computer Science Research (IRIT) ,  France. Sergio A Velastin  is grateful for funding received from the  Universidad Carlos III de Madrid , the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement n  600371 , el  Ministerio de Economía, Industria y Competitividad  ( COFUND2013-51509 ) el  Ministerio de Educación, Cultura y Deporte  ( CEI-15-17 ) and  Banco Santander.
The North Atlantic Ocean is a major sink region for atmospheric CO 2 and contributes to the storage of anthropogenic carbon (Cant). While there is general agreement that the intensity of the meridional overturning circulation (MOC) modulates uptake, transport and storage of Cant in the North Atlantic Subpolar Ocean, processes controlling their recent variability and evolution over the 21st century remain uncertain. This study investigates the relationship between transport, air-sea flux and storage rate of Cant in the North Atlantic Subpolar Ocean over the past 53 years. Its relies on the combined analysis of a multiannual in situ data set and outputs from a global biogeochemical ocean general circulation model (NEMO-PISCES) at 1/2 • spatial resolution forced by an atmospheric reanalysis. Despite an underestimation of Cant transport and an overestimation of anthropogenic air-sea CO 2 flux in the model, the interannual variability of the regional Cant storage rate and its driving processes were well simulated by the model.