dataesr/scientific-paragraphs-categorization huggingface dataset:
As was the case for MSR Action3D dataset  [59] , data augmentation techniques (i. e. , random cropping, vertically flipping, and rotation with α = 90 • ) were also applied. SBU Kinect Interaction dataset  [61] : This dataset was collected using the Kinect v1 sensor. It contains 282 skeleton sequences and 6822 frames performed by 7 participants. Each frame of the SBU Kinect dataset  [61]  contains skeleton joints of two subjects corresponding to an interaction, each skeleton has 15 key joints. There are 8 interactions in total, including approaching, departing, pushing, kicking, punching, exchanging objects, hugging, and shaking hands. This dataset is challenging due to the fact that the joint coordinates exhibit low accuracy. Moreover, they contain non-periodic actions as well as very similar body movements.
For instance, there are some pairs of actions that are difficult to distinguish such as exchanging objects-shaking hands or pushing-punching. We randomly split the whole dataset into 5 folds, in which 4 folds are used for training and the remaining 1 fold is used for test. It should be noted that each skeleton frame provided by the SBU dataset  [61]  contains two separate subjects. Therefore, we consider them as two data samples and feature computation is conducted separately for the two skeletons. Additionally, data augmentation (i. e. , random cropping, vertically flipping, rotation with α = 90 • ) has been also applied on the SBU dataset  [61].
MSR Action3D dataset NTU-RGB+D dataset  [39] : This Kinect 2 captured dataset is a very large-scale RGB-D dataset. To the best of our knowledge, the NTU-RGB+D dataset  [39]  is currently the largest and state-of-the-art benchmark dataset with skeletal data for human action analysis. It provides more than 56 thousand video samples, 4 million frames, collected from 40 distinct subjects for 60 different action classes. The following actions are provided by the NTU-RGB+D dataset  [39]  (please see Figure  6  for some examples): drinking, eating, brushing teeth, brushing hair, dropping, picking up, throwing, sitting down, standing up, clapping, reading, writing, tearing up paper, wearing jacket, taking off jacket, wearing a shoe, taking off a shoe, wearing on glasses, taking off glasses, putting on a hat/cap, taking off a hat/cap, cheering up, hand waving, kicking something, reaching into self pocket, hopping, jumping up, making/answering a phone call, playing with phone, typing, pointing to something, taking selfie, checking time, rubbing two hands together, bowing, shaking head, wiping face, saluting, putting palms together, crossing hands in front.
sneezing/coughing, staggering, falling down, touching head, touching chest, touching back, touching neck, vomiting, fanning self. punching/slapping other person, kicking other person, pushing other person, patting others back, pointing to the other person, hugging, giving something to other person, touching other persons pocket, handshaking, walking towards each other, and walking apart from each other. In the NTU-RGB+D dataset  [39] , each skeleton contains the 3D coordinates of 25 body joints. The authors  [39]  of this dataset suggested two different evaluation criteria, including Cross-Subject evaluation and Cross-View evaluation. For the Cross-Subject setting, the sequences performed by 20 subjects (with IDs 1, 2, 4, 5, 8, 9, 13, 14,  15, 16, 17, 18, 19, 25, 27, 28, 31, 34, 35, and 38)  are used for training and the rest sequences are used for test.
Implementation Details For all the datasets, the proposed Enhanced-SPMF representations are computed directly from the raw skeleton sequences without using a fixed number of frames. For computational efficiency, all the image representations are resized to 32 × 32 pixels. The three network configurations: DenseNet (L = 100, k = 12); DenseNet (L = 250, k = 24); and DenseNet (L = 190, k = 40) were implemented and evaluated in Python with the support of the Keras framework using TensorFlow as back-end. During the training stage, we use mini-batches of 32 images for all networks. The weights are initialized as per the He initialization technique  [82]. Adam optimizer  [81]  is used with default parameters (i. e. , β 1 = 0. 9 and β 2 = 0. 999). Additionally, we use a dynamic learning rate during training.
Results and Comparisons with the State-of-the-Art Results on MSR Action3D dataset: Experimental results and comparisons of the proposed method with the current state-of-the-art approaches on the MSR Action3D dataset  [59]  are summarized in Table  4. We compare the proposed method with Bag of 3D Points  [59] , Depth Motion Maps  [69] , Bi-LSTM  [72] , Lie Group Representation  [23] , FTP-SVM  [72] , Hierarchical LSTM  [37] , ST-LSTM Trust Gates  [40] , Graph-Based Motion  [36] , ST-NBNN  [70] , ST-NBMIM  [83] , S-T Pyramid  [84] , Ensemble TS-LSTM v2  [71]  and our previous model SPMF Inception-ResNet-222  [48]  using the same evaluation protocol. The proposed DenseNets (L = 100, k = 12) and DenseNet (L = 190, k = 40) achieve average accuracies of 98. 76% and 98. 94%, respectively. Meanwhile, the best recognition accuracies are obtained by the proposed DenseNet (L = 250, k = 24) with a total average accuracy of 99.
10%. This result outperforms many previous approaches  [23, 36, 37, 40, 59, 69, 70, 72, 83, 84] , demonstrating the superiority of the proposed method. Figure  7  (first row) shows learning curves of the proposed DenseNets on the AS1 subset/MSR Action3D dataset  [59]. The recognition accuracy for each action class in the AS1 subset by the DenseNet (L = 250, k = 24) is provided in Figure  8  via its confusion matrix. Results on KARD dataset: We performed a total of 9 experiments over three experiments A, B, and C on the KARD dataset  [60]. Table  5  summarizes the obtained results on this dataset. We compute the average recognition accuracy over the three experiments and compare it with existing techniques including Hand-crafted Features  [60] , Posture Feature+Multi-class SVM  [85] , and Key Postures+Multi-class SVM  [86].
As can be seen in Table  5 , the proposed DenseNet (L = 250, k = 24) is able to improve state-of-the-art accuracy by 9. 15% over Hand-crafted Features  [60] , 2. 78% over Posture Feature+Multi-class SVM  [85]  and 0. 68% over Key Postures+Multi-class SVM  [86]. This result confirms that the proposed deep learning framework trained on the Enhanced-SPMFs is able to achieve better performance in the recognition of actions compared to hand-crafted based approaches. [59] , KARD  [60] , SBU Kinect Interaction  [61] , and NTU-RGB+D  [39]  datasets. Almost all designed networks are able to reach the optimal weights after the first 100 epochs. The symbols k and L and denote the "growth rate" and the depth of the network, respectively. Table  4. Experimental results and comparison of the proposed method with state-the-art approaches on the MSR Action3D dataset  [59].
The list is ordered by recognition performance, in which results that outperform previous works are in bold, while the best accuracies are in blue. Our previous work on SPMF  [48]     Results on SBU Kinect Interaction dataset: As reported in Table  6 , the proposed DenseNet (L = 250, k = 40) achieved an accuracy of 97. 86% and outperforms many existing state-of-the-art approaches including Raw Skeleton  [61] , Joint Features  [61] , HBRNN  [37] , CHARM  [87] , Deep LSTM  [41] , Joint Features  [88] , ST-LSTM  [40] , Co-occurrence+Deep LSTM  [41] , STA-LSTM  [89] , ST-LSTM+Trust Gates  [40] , ST-NBMIM  [83] , Clips+CNN+MTLN  [90] , Two-stream RNN  [91] , and GCA-LSTM network  [92]. Using only skeleton modality, the proposed method outperforms hand-crafted feature based approaches such as Raw Skeleton  [61] , Joint Features  [61]  and recent state-of-the-art RNN-based approaches  [37, 40, 41, 89, 91, 92].
In particular, the proposed method achieves a significant accuracy gain of 2. 96% compared to the nearest competitor GCA-LSTM network  [92]. This result demonstrates that the proposed deep learning framework is able to learn discriminative spatio-temporal features of skeleton joints containing in the proposed motion representation for classification task. Results on NTU-RGB+D dataset: For the NTU-RGB+D dataset  [39] , the best configuration DenseNet (L = 250, k = 40) achieves an accuracy of 80. 11% on the Cross-Subject evaluation and 86. 82% on the Cross-View evaluation, as summarized in Table  7. These results demonstrate the effectiveness of the proposed representation and deep learning framework since they surpass previous state-of-the-art techniques such as Lie Group Representation  [23] , Hierarchical RNN  [37] , Dynamic Skeletons  [93] , Two-Layer P-LSTM  [39] , ST-LSTM Trust Gates  [40] , Geometric Features  [74] , Two-Stream RNN  [91] , Enhanced Skeleton  [94] , Lie Group Skeleton+CNN  [95] , and GCA-LSTM  [92].
An Ablation Study on the Proposed Enhanced-SPMF Representation We believe that the use of the AHE algorithm  [49]  and the Savitzky-Golay smoothing filter  [37, 76]  helps the proposed representation to be more discriminative, which improves recognition accuracy. To verify this hypothesis, we carried out an ablation study on the Enhanced-SPMF representation provided by the SBU Kinect Interaction dataset  [61]. Specifically, we trained the proposed DenseNet (L = 250, k = 24) on both the SPMFs and Enhanced-SPMFs. During training, the same hyper-parameters and training methodology were applied. The experimental results indicate that the proposed deep network achieves better recognition accuracy when trained on the Enhanced-SPMFs. As reported in Figure  9 , applying the AHE algorithm  [49]  and and the Savitzky-Golay smoothing filter  [37, 76]  helps improving the accuracy by 4.
Visualization of Deep Feature Maps Different action classes have different discriminative characteristics. To better understand the internal operation of the proposed deep networks and to study what they learned from the skeleton-based representation, we input different Enhanced-SPMFs corresponding to different action classes of the MSR Action3D dataset  [59]  to the DenseNet (L = 100, k = 12) and visualize the individual feature maps learned by the network at the end of a dense block (intermediate layer). We observe that the designed network is able to extract discriminative features from the Enhanced-SPMF representations. This is expressed through the color of each learned feature map, as can be seen in Figure  10. These discriminative features play a key role in classifying actions.
Computational Efficiency Evaluation In this section, we take the AS1 subset of MSR Action3D dataset  [59]  and the DenseNet (L = 100, k = 12) to evaluate the computational efficiency of the proposed method. Figure  11  illustrates three main stages of the deep learning framework for learning and recognizing actions from skeleton sequences, including an encoding process from input skeleton sequences to color images (Stage 1), a supervised training stage (Stage 2), and an inference stage (Stage 3). The implementation is in Python/Keras and when training on a single GeForce GTX 1080 Ti GPU, the proposed deep network only has 6. 0M parameters and it takes less than six hours to reach convergence. Latency in predicting an action for a new skeleton sequence (including encoding it to color images, executed on a CPU) is about 74.
Conclusions In this paper, we have presented an efficient and effective deep learning framework for 3D human action recognition from skeleton sequences. A novel motion representation, termed Enhanced-SPMF, which captures the spatio-temporal information of skeleton movements and transforms them into color images has been proposed. We exploited the Adaptive Histogram Equalization (AHE) technique to enhance the local textures of color images and generate more discriminative features for learning and classification tasks. Different Deep Convolutional Neural Networks (D-CNNs) based on the DenseNet architecture have been designed and optimized to learn and recognize actions from the proposed representation, in an end-to-end manner. Extensive empirical evaluations on four challenging public datasets demonstrate the effectiveness of the proposed approach on both individual actions, interactions, multiview and large-scale datasets.
Acknowledgments: This research was carried out at the  Cerema Research Center and Informatics Research Institute of Toulouse, Paul Sabatier University, France. The authors would like to express our thanks to all the people who have made helpful comments and suggestions on a previous draft. S. A. Velastin is grateful to 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 no. 600371 , el  Ministerio de Economía y Competitividad  ( COFUND2013-51509 ) and  Banco Santander.
data collection Independent clinical research assistants will be available at each participating hospital to help in running the study and with data collection. Study documents will be deidentified and stored for 15 years, as per the protocol for non-clinical trial notification interventional studies. Data will be entered into the web-based eCRF (CSOnline, Clinsight) and electronically stored on double password-protected computers. Hard copies of data (clinical research files) will be stored in a secure locked office. All personnel involved in data analysis will be masked to study groups. Only the principal investigators and the statisticians will have access to the final data set. The following data will be recorded. Open access mechanical cardiac support (extra-corporeal life support (ECLS) or intra-aortic balloon pump); adverse events (especially local and systemic side effects possibly linked to antiseptic use).
Postoperative data until hospital discharge Type and number of blood products given during the 48 hours following surgery; type and length of vasopressor and/or inotropic drugs administered during the 48 hours following surgery; use of mechanical cardiac support (ECLS, intra-aortic balloon pump); atrial fibrillation episode; number and results of blood cultures; number, type and results of bacteriological sampling at surgical site; wound status at surgical site (until dressing withdrawal): local signs of infection (local incisional pain/tenderness, localised redness, heat or swelling, purulent drainage from the superficial incision, superficial/deep incision spontaneously or deliberately opened by the surgeon), status of dressing, date of dressing changes; physical examination (temperature, chest pain, sternal instability); antibiotics used (molecule, duration and indication); results of blood samples (standard lab values); duration of mechanical ventilation; length of stay in ICU, surgical ward and high dependency unit; date of hospital discharge; reoperation at sternal site or saphenous vein/radial artery site occurring after surgery (date and reason); SSI occurrence: type (superficial, deep, organ-space), site and date and hour of SSI diagnosis; adverse events (especially local and systemic side effects possibly linked to antiseptic use) and survival status (if the patient is deceased, date of death).
Postoperative data monthly after surgery (until 90 days following surgery) Phone contact: date; SSI occurrence, date of diagnosis, site and type; planned or unplanned surgical consultation; need for hospital readmission: date, total duration of hospital stay; need for reoperation at sternal site (within 90 days following surgery) or at saphenous vein/ radial artery site (within 30 days following surgery): date, reason; date of rehabilitation unit discharge and survival status (if the patient is deceased, date of death). safety According to the French Public Health Code, all suspected unexpected serious adverse events will be reported to the Agence Nationale de Sécurité du Médicament (ANSM). Adverse events will be evaluated at each visit during clinical interview and physical examination. In agreement with ANSM, all serious adverse events related to heart disease (except infections) and not related to antiseptic use will not be declared immediately but will be reported in the eCRF.
statistical analysis The data will be analysed blindly on an intention-to-treat basis. No interim analysis is planned. Demographic data will be described as number and percentage or median and IQR and compared with the χ² test or Mann-Whitney test, as appropriate. For primary analysis, incidence of reoperation between groups will be compared with χ² test. We will assess antiseptic efficacy with a marginal Cox model and adjusted for covariates that will be significantly imbalanced between groups. We will calculate HR and 95% CIs, as well as incidence density and Kaplan-Meier estimates. Proportions of each secondary endpoint assessed at day 30 and day 90 will be compared using similar principles. We will use χ² tests. A multiple logistic regression will be computed with covariates clinically relevant according to our outcomes (Centre; patients' characteristics: age, gender, body mass index, EuroSCORE II, active smoking, insulin-dependent diabetes, use of preoperative S.
aureus decontamination; Intraoperative data: type of surgery of the heart (valve, coronary, combined surgery, other) or of the aorta, type of scheduling (elective, semi-elective or emergency), skin scrubbing before skin antisepsis; number of antiseptic application, use of iodophor-impregnated incise drapes, number of internal thoracic arteries sampled, length of surgery, duration of cardiopulmonary bypass, minimal body temperature during surgery, volume infused during surgery, use of mechanic cardiac support) and with covariates statistically relevant (covariates with difference between groups <0. 20 in the univariate analysis). All tests will be two-tailed, stratified by centre and unadjusted for multiple comparisons. Analyses will be done with SAS V. 9. 4 and R software.
All authors approved the final manuscript prior to submission. Funding This work is being funded by unrestricted research grants from the  French Ministry of Social Affairs and Health  (# 16-0619 ) and  CareFusion/ Becton Dickinson. Competing interests OM has received grant support from  3M  and  Carefusion-BD  and honoraria for giving lectures from  3M  and  Carefusion-BD. Funders will have no role in the trial initiation, study design, choice of antiseptic products, data collection, data analysis, data interpretation or writing of the report. Patient consent for publication Not required. Ethics approval Ethical aspects of this research project have been approved by the  ethics committee of Ambroise Paré Hospital (CPP Ile de France VIII, Boulogne-Billancourt, France ).
I. INTRODUCTION New kinds of HPC applications have emerged, that rely on a communication scheme different from regular MPI applications. For examples multi-threaded stencil applications push MPI implementations beyond their limits  [1]. Applications using a task-based runtime system  [2] ,  [3]  are another class of applications that stresses MPI communications in an unusual way: the MPI communication schemes when driven by a task-based runtime and when written explicitly in the application are radically different. Most notably, for heavy communication phases, regular MPI applications typically use collective operations, that let the MPI library decide on the communication graph and scheduling. By contrast, a taskbased runtime translates data dependencies as a set of point-topoint communications.
The default MPI implementation on this cluster is OpenMPI 1. 8. Since this version is ancient, for the sake of fairness we have compiled OpenMPI 3. 0, MPICH 3. 2 with libfabric, and MVAPICH2 2. 3b for a large panel of MPI libraries. The second one is plafrim. Nodes are dual-Xeon E5-2680 with 24 cores and 128 GB RAM, equipped with Intel Omni-Path 100 Gb/s (HFI Silicon 100 Series). The default MPI implementation on this cluster is OpenMPI 1. 10. Since this version is ancient, for the sake of fairness we have compiled OpenMPI 3. 0. As an alternative, Intel MPI 2017 is available as a module. Additionally, we compiled MVAPICH2 2. 3b with PSM2.
C. Cholesky Factorization To evaluate the impact of this work on a computation code, we have used the task-based tile Cholesky factorization from the Chameleon software  [3]. This dense linear algebra code is based on tasks scheduled through the StarPU  [22]  runtime system, which itself uses MPI for inter-node communications. Since communications are triggered by tasks data-flow without synchronization, they do not use MPI collectives at all and rely exclusively on MPI point-to-point communications. Code instrumentation has shown that 50+ communications are typically active at the same time. We have run time_spotrf_tile, the benchmark integrated in the Chameleon package, on both clusters and using various MPI libraries. The results are shown as GFlop/s on the Y axis and matrix size on the X axis.
ACKNOWLEDGMENT Experiments presented in this paper were carried out using the PLAFRIM experimental testbed, being developed under the  Inria PlaFRIM development action  with support from  LABRI  and  IMB  and other entities:  Conseil Régional d'Aquitaine, FeDER ,  Université de Bordeaux  and  CNRS  (see https://plafrim. bordeaux. inria. fr/).
25 pF provided by the manufacturer are added to the design schematic. To achieve a broad bandwidth, two DRs are linked together. They are designated as DR-A and DR-B, as shown in Figure  6. DR-A is designed to operate across the band from 1-6 GHz. DR-A is manufactured using lumped elements L1, L2, L3, and C1, purchased from Murata Manufacturing Co. Ltd. (Nagaokakyo, Japan), and connected to two cascaded L-sections to provide broadband impedance matching. The value parameters (presented in Table  2 ) are obtained following a thorough optimization using the nonlinear harmonic balance simulation in the Agilent, ADS software. The input impedance of each rectifier is set as the ASDA input impedance Z in. This impedance is obtained from the electromagnetic HFSS simulation of the ASDA without considering the BALUN connection.
The measurement set-up is presented in Figure  8b. The measurements were done inside an anechoic chamber represented by the triangles on the side walls. The power transmitter is emitting power into space with RF-power value of P in. At the receiver side, we employed an antenna to measure the received power P r f and at the same time the rectenna is mounted on the same reception plane. The output characteristics of the rectenna is measured using a voltmeter. The rectenna output performance is calculated using  (5) , and the efficiency for different constraints is plotted in Figure  9. The response of the rectenna conversion efficiency with frequency is shown in Figure  9a. From the data shown, it is obvious that the results obtained from the measurements and EM model are in good agreement over the desired band with a maximum simulated efficiency at frequency 2 GHz.
Acknowledgments: We would like to thank  Yoshitomi ,  E-JUST center , who helped us in measuring the radiation pattern of the antenna. In addition, we are thankful to our colleagues who provided their expertise and contributions for conducting this research. This work was partly supported by  VLSI Design and Education Center (VDEC) , the  University of Tokyo  in collaboration with Keysight Corporation. This work was partly supported by  CREST  ( JPMJCR1431 ),  KAKENHI  ( JP18K04146 ),  Collaborative Research Based on Industrial Demand Program, JST  and the  Telecommunications Advancement Foundation.