dataesr/scientific-paragraphs-categorization huggingface dataset:
Intervention program The NeuroStratE program  (Cherrier et al. , 2020)  is structured around 4 themes: learning to learn, learning to concentrate, learning to memorize, learning to put strategies in place. Each intervention comprises: neurophysiological and neurocognitive content; activities allowing students to experience and test the theme; and finally, the implementation of strategies which were evaluated from 1 week to the next. In the interests of space, we will not detail here the entire program provided during the training  (Cherrier et al. , 2020). Rather, we will direct our attention to the effect of "the learning to put strategies in place, " which directly concerns procrastination, the focus of our study. This study aspect allowed students to address two main elements: knowing how to manage time and knowing how to set goals, enabling them to plan their own work.
Statistics We ran separate mixed effect 2 (Group: Test vs. Control) X 2 (Time: T initial vs. T final) analysis of variances (ANOVAs) on each of the procrastination scales and on the scores in the Tower of Hanoi task. The scores in the Tower of Hanoi test (i. e. , the number of moves) were first power transformed because of a noncompliance for the ANOVA hypothesis. We performed the lmer and anova functions of the lmerTest R package which is able to compute type III ANOVA tables for fixed-effect terms with Satterthwaite's method  (Giesbrecht and Burns, 1985; Hrong-Tai Fai and Cornelius, 1996; Schaalje et al. , 2002; Kuznetsova et al. , 2017). Effect sizes are reported in terms of partial eta-squared (η p 2 ) computed with the eta_squared function from the effectsize R package  (Ben-Shachar et al.
, 2020). The contrasts and post-hoc tests were computed with the lsmeans function from the emmeans R package, p-values were adjusted with Holm's correction and Cohen's d for effect sizes were computed with the eff_size function from the emmeans R package  (Holm, 1979; Lenth et al. , 2021). These analyzes were conducted using R 4. 1. 0 (R Core Team, 2021) with an accepted type 1 error probability set at α = 0. 05.
Discussion In this study we investigated whether a neuroscience program (NeuroStratE) through metacognition, could have an impact on procrastination and executive planning function in university students. Impact was measured through changes in behavior, procrastination and the executive function of planning. We hypothesized an improvement of these aspects following an intervention program (NeuroStratE;  Cherrier et al. , 2020) , with a more significant improvement to be seen after a longer duration of the intervention. Utilizing a methodological approach  (Johnson and Onwuegbuzie, 2004) , in particular explanatory design  (Creswell and Plano Clark, 2018) , an examination of results highlighted a number of elements: the program leads to an improvement in procrastination behavior in parallel with planning ability, better time management and planning, and better self-knowledge.
Improvement of planning ability Planning ability assessed in the Tower of Hanoi task improved to a greater extent from the start to the end of each semester in students who followed the program compared to those who did not. For each planning ability test (4 and 5 disks), our results showed significant two-way interaction among time and group. Thus, the post-hoc tests notably revealed significant differences in the test group between T initial and T final. Although the effect sizes of interactions were small (η p 2 0 03 =. ), the highlighted significant differences suggest a small effect of the NeuroStratE program on planning ability. Succeeding on 4 disks is an easier task than on 5 disks because it requires less planning  (Welsh, 1991). If we compare the scores obtained in the test groups with 4 disks and 5 disks, we note that the improvement is more significant on the scores of the 5 disks (Table  3 ).
The interview of the students who followed the program in 2019 (Table  6 ) shows that 90% of students continued to use the methods and tools, which is a very positive reflection on the long-term impact of the NeuroStratE program. Nevertheless, students could have been re-tested on the degree of procrastination and the planning function in order to compare their results with the initial ones, taking into account that students will have acquired more maturity after 2 or 3 additional years spent at university. It is also noted that even if students state that the theoretical aspects on the knowledge of brain function are necessary for a better use of the tools, in order to be sure of our results it would have been necessary to provide the training with and without the neurobiological theoretical content and to carry out the same measurements for confirmation, something which remains difficult to put in parallel with the initial program.
Purpose Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities. Methods This work proposes a new simplified augmentation method, called TRandAugment, specifically designed for long surgical videos, that treats each video as an assemble of temporal segments and applies consistent but random transformations to each segment.
Results The effectiveness of the proposed method is demonstrated on two surgical video datasets, namely Bypass40 and CATARACTS, and two tasks, surgical phase and step recognition. TRandAugment adds a performance boost of 1-6% over previous state-of-the-art methods, that uses manually designed augmentations. Conclusion This work presents a simplified and automated augmentation method for long surgical videos. The proposed method has been validated on different datasets and tasks indicating the importance of devising temporal augmentation methods for long surgical videos.
This temporality present in both surgical videos and activities needs to be considered and exploited, while designing augmentation policies for training spatiotemporal models. To this end, the paper introduces a new simplified and automated data augmentation method, called TRandAugment, that aims to incorporate the essential temporal dimension. Inspired by work  [24] , the TRandAugment method proposes a compact and simple parameterization consisting of only 3 parameters, where one parameter is dedicated to the temporal dimension. TRandAugment is extensively evaluated on the task of surgical activity recognition at two levels of granularity, i. e. , phase and step  [4] , using two large surgical video datasets: Bypass40  [10]  and CATARACTS  [28].
Bypass40 (BY40) The Bypass40 dataset  [10] , courteously shared by the authors of  [10] , comprises 40 Laparoscopic Roux-en-Y gastric bypass (LRYGB) procedures with average video duration of different levels of granularity. A full list of all the phases and steps is presented in  [10]. All the videos have a resolution of 854 × 480 or 1920 × 1080 pixels and are recorded at 25 fps. Following the same data split as  [10] , the dataset has been segregated into 24, 6, and 10 videos for training, validation, and test sets, respectively. The frames have been extracted at 1 fps and resized to ResNet-50's input size of 224 × 224.
CATARACTS (CA50) The CATARACTS dataset 1  [28, 35]  consists of 50 videos of cataract surgical procedures. The dataset is annotated per frame with only steps as part of the CATARACTS2020 challenge. A complete list of all 19 steps is tabulated on the challenge website. 2 The 50 videos are split into 25, 5, and 20 subsets for training, validation, and test sets, respectively. Frames are extracted at 1 fps and resized from 1920 × 1080 to 224 × 224.
Results and discussion In this section, we analyze the different components that influence the design of TRandAugment. Initially, we study the importance of temporally consistent augmentations in Sect. "Do temporally consistent augmentations matter?", then we analyze the impact of parameter M in Sect. "Effect of magnitude (M)", the number of transformations in Sect. "Do all augmentations help?" and impact of the parameter T in Sect. "Impact of parameter T on TRA". Finally, we present the performance of the proposed method considering the optimal parameters on both datasets (Sect. "TRandAugment").
Do temporally consistent augmentations matter? One of the key differences between videos and images is the additional temporal dimension. An obvious question is to study the importance of temporally consistent augmentations when training models on videos. To study the effect of temporal consistency, Table  1  compares the image-based augmentation method, RA, against the temporally consistent URA method on the CATARACTS dataset. The comparison is carried out at different settings (M = {15, 30}, N = 1, τ ⊂ τ : |τ | = {3, 5, 9}). URA consistently performs better.
Effect of magnitude (M) To study the effect of augmentation magnitude, Table  2  compares model performance over various settings of M = {5, 10, 15, 20, 30} for URA and TRA while keeping all other parameters fixed (|τ | = 5, N = 1, T = 5). Both URA and TRA show higher performance at higher magnitudes with the best results obtained at M = 30 on both tasks and datasets. Irrespective of the augmentation method used, higher magnitudes seem to have a direct effect on the performance of the model for different tasks and datasets. However, we notice that TRA performance is below URA at M = 30. This is not a valid comparison as the other parameters |τ |, N , and T are fixed and sub-optimal. Hence, we perform these experiments to solely study the effect of magnitude on URA and TRA independently. The full comparison of TRA against other methods is discussed in Sect.
Do all augmentations help? To study the importance of using all the augmentations, Table  3  lists different experiments in terms of F1-score on the validation set, with N = 1 and T = 5, where subsets of transforms (τ ⊂ τ : |τ | = {3, 5, 9}) are randomly sampled from τ. For the task of step recognition on both datasets, the best model performances are obtained when all transforms are utilized. On the other hand, the model performs best at an.
Impact of parameter T on TRA The key component of the proposed TRA method is the parameter T that captures the variance in the appearance of the frames across a video. TRA is inspected with different settings of parameter T = {1, 3, 5, 8} at two different magnitudes M = {15, 30} while fixing N = 1 and |τ | = 10. The results in Table  4  show that at T = 5, M = 30 the model achieves the best performance on all the different tasks and across the two datasets. This indicates that augmenting at the clip level benefits the training of activity recognition models and the proposed TRA parameterization (M, N , T ) allows us to easily find optimal parameters.
TRandAugment Table  5  compares different augmentations methods on the test set with optimal parameters. As noticed earlier, tem- We can further notice a significant improvement of 5-11% across all the metrics for recognizing steps on Bypass40. This improvement could be attributed to the larger set of transforms |τ | = 10. TRA, on the other hand, outperforms URA on both the phase and step recognition tasks and both datasets. TRA achieves a 1-3% improvement in phase and step recognition on Bypass40 and CATARACTS, respectively. Moreover, for step recognition on Bypass40, TRA achieves a +2% and +1. 5% improvement in precision and F1-score over URA. The performance improvement of the proposed TRA method over URA could be attributed to the temporally consistent augmentations applied at the clip level.
Limitations The (M, N , T ) parameterization of TRandAugment simplifies the process of selecting a good augmentation policy, for training, that induces both spatial and temporal variations in the input videos. Yet, it does not completely eliminate the search for optimal parameters, which adds computational expense. Further studies are required to better understand if or when datasets or tasks may require a separate search to achieve optimal performance. Another drawback of TRan-dAugment is that it works only in the input space. Few works in the literature have proposed adding variations in the model's feature space to improve generalizability  [36, 37]. Unlike input space augmentations, designing feature space augmentations is extremely challenging because the domain or the noise characteristics of the feature space is not wellstudied.
Conclusion This paper introduced a new augmentation method called TRandAugment that simplifies data augmentation pipelines. Given a video, creates pseudo-videos with different clips augmented differently. The method is parameterized with magnitude (M), the number of augments (N), and the number of temporal augments (T). This parameterization provides a simple framework to search for optimal configuration and operates at a level with significantly reduced search space, in line with current research in data augmentation. The proposed method has been validated on two large surgical video datasets, considering both the phase and step recognition tasks, obtaining a boost in the performances thus demonstrating the impact of TRandAugment. New open questions arise on how this method may improve model robustness  [19] , federated learning or semi-/self-supervised learning  [20] [21] [22] [23] 34].
Acknowledgements The authors would like to thank  Deepak Alapatt ,  Vinkle Srivastav , and  Adit Murali  for their helpful comments. This work was supported by the  ATLAS  project. The  ATLAS  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 813782. This work was also supported by  French  state funds managed within the  Investissements d'Avenir program  by  BPI France  (project  CONDOR ) and by the  ANR  ( ANR-16-CE33-0009 ,  ANR-10-IAHU-02 ). This work was performed using HPC resources from  GENCI-IDRIS  (Grant  2021-AD011012832 ).
40 Custom* [10] - -, -, - 75. 46 ± 9. 34 55. 58 ± 9. 88 52. 78 ± 9. 22 50. 35 ± 9. 75 BY40 URA (ours) 10 30, 2, -80. 55 ± 6. 61 61. 32 ± 8. 11 62. 13 ± 7. 74 58. 52 ± 8. 46 Step TRA (ours) 10 30, 2, 5 80. 80 ± 7. 90 63. 66 ± 9. 08 63. 94 ± 8. 31 60. 06 ± 9. 22 Bold values indicate the best performance per dataset/task per metric porally consistent augmentations are beneficial, and hence, both URA and TRA, which enforce this consistency, out- perform image-level augmentation method RA by 1-2% in F1 and ∼3% in accuracy for the task of step recognition on CATARACTS. Additionally, URA and TRA both show improvement over the state-of-the-art MTMS-TCN model, which utilized a 'Custom' set of augmentations by 1-5% across all the metrics for phase recognition on Bypass40.
The work is performed on RC beam equivalent systems. The experimental data used were obtained from previously carried out tests keeping the steel reinforcement in the elastic range. Therefore, the developed work is restricted to RC material non-linearities in the case of low amplitude dynamic excitations. After presenting the RC beam used and the experimental data in Section 2, the SDOF equivalent model of the RC beam is given in Section 3. Next, the proposed identification algorithms are derived and validated in Section 4. The methods are then applied on a damaging test in Section 5, leading to the proposition and validation of damping models in Section 6. Finally, conclusions are discussed in Section 7.
Experimental data Different tests were carried out: (i) quasi-static tests, (ii) dynamic tests (Figure  1 ) and (iii) hammer shock tests to follow the decrease in beam dynamic properties between each forced vibration test. The quasi-static tests were performed on a strong floor with two actuators. When the actuators were acting in phase, the four-point bending test excited the first (A) (B) F I G U R E 2 Experimental dynamic acceleration loadings. mode of the beam. It consisted of imposed displacement cycles with increasing amplitudes. Three cycles were performed for each amplitude to stabilise the energy dissipation. The obtained experimental data from an HA16-C1A-1 beam are used to calibrate the non-linear beam model in Section 3. 3. Then, two dynamic tests (Figure  2 ) successively performed on the HA16-C1A-2 beam are considered to identify damping models.
Evaluation of the studied beam modal basis The first three modes of the experimental beam can be determined with the hammer shock test data on the undamaged beam. For example, Figure  3  presents the imaginary part of the acceleration FFT responses for the beam HA16-C1A-1, in the initial stage and with shocks applied at the mid-span of the beam. The plot corresponds to the mean response of four shocks. A similar figure can be plotted with the shocks at the beam quarter to characterise the second mode. The frequencies obtained for the first three modes are 7. 11, 23. 1 and 58. 2 Hz. If no experimental data are available concerning the undamaged structures, a numerical or analytical modal basis should be evaluated. The studied beam is a complex system composed of a simply supported beam with elastic supports and additional masses placed at one-quarter and three-quarters of the beam.
Energy descriptions: The dissipated energy is defined in Equation (  25 ) by the difference between the hysteretic energy  ℎ (Equation  26 ) and the work hardening associated energy  ℎ (Equation  27 ). The last two energies are computed by the products between the associated forces and the rates of the internal variables. (A) (B) F I G U R E 5 Quasi-static responses of the calibrated IDEFIX numerical model. 𝐸 𝑑𝑖𝑠𝑠 = 𝐸 ℎ -𝑊 ℎ (25)  ℎ =  ℎ, +  ℎ, =  ±. ḋ± +. u  ℎ =  ℎ, +  ℎ, =  ±. ż± +. α Model calibration: The IDEFIX model has been implemented in the MATLAB R computer program and is calibrated to reproduce the beam quasi-static response. It is composed of eight parameters. The calibrations were performed using an error minimisation method compared to the experimental data. The parameter calibrations were decomposed between the first parameters influencing the envelope response and then those influencing the hysteresis cycles.
ALGORITHM AND VALIDATION OF THE DAMPING IDENTIFICATION METHOD The damping identification method proposed here is inspired by Demarie and Sabia,  32  which aimed at identifying non-linear damping and frequencies of damaged RC elements with a linear material model. The idea was taken up in Heitz et al. 27  and adapted to identify equivalent viscous damping ratios in low amplitude white-noise signals (small non-linearities) based on experimental displacement measurements, and to compare with values from the half-power bandwidth method. Furthermore, dynamic data will be analysed using a physical constitutive material model. Therefore, this paper aims to apply the method to more intense non-linear dynamic tests with two non-linear models: (i) a piecewise linear model presented in Section 3. 3. 1 (used in Heitz et al.
Minimisation method to solve the problem and numerical scheme: Using the error defined in Equation (  32 ), parameters are identified on each window considering an error minimisation function. The function fmincon implemented in MATLAB R is used to minimise the error function. This function is based on the 'interior point' method,  34  allowing minimisations to be carried out under constraints. It should be noted that, because negative values can be obtained with Russel's amplitude error, absolute values are considered in the analyses. Finally, to solve Equations (  19 ) and (  21 ), Newmark's algorithm is used with   = 1∕2 and   = 1∕4, which is unconditionally stable and induces no numerical dissipation. In addition, for the non-linear parts, a quasi-Newton algorithm is considered based on the approximated tangent modulus   = Δ  ∕Δ.
Method validation on an elastic test A large parametric study was performed to calibrate the parameters of the method. The results of the identification method can be studied with the two non-linear models and the properties described in Table  3. First, the identified accelerations are plotted in Figure  7. A very good correlation is obtained between the experimental data and the identified responses. The global identification error (Equation  32 ) is 13% for the piecewise model and 12% with the IDEFIX model. The errors are on the same order of magnitude with the two models, yet strong differences are obtained between the dynamic response components, as presented in Table  4. With the piecewise linear model, the displacement is best identified, while with the IDEFIX model, it is the acceleration that is best identified.
Performance of the proposed models Different damping models have been proposed in the previous section leading to   or   and. Their calibrations were performed on the experimental data from RC beam HA16-C1A-A. Other beams were tested during the experimental However, for the energy representation (grey data), the constant damping model always gives the poorest results. Therefore, the strategy used in engineering to consider constant damping seems to be performative, provided that the objective is to obtain dynamic quantities as the acceleration. However, it is shown here that it does not allow for accurately characterising the energy dissipation. Furthermore, the evolution of energy is particularly interesting with the damping model based on the friction variable because (i) the final energies are larger than with the two other models and (ii) more dissipation occurs at the material level (green curve corresponding to the absorbed energy).
Matthew et al. [24]  suggested the feasibility of using a single-depth camera to assess STS movements, opening new assessment perspectives. In addition, the feasibility of remote 30s-STS tests appears good as a recent study suggested that a video-guided STS test is suitable for participants of varying ages body sizes, and activity levels  [25]. In consequence, it would be conceivable to estimate the distance walked or the number of repetitions performed during the tests using the smartphone's GPS or smartphone camera of the user. In this perspective, the Kiplin company developed in collaboration with the present authors and the CEA tech Nantes a medical device to empower patients in conducting the 6MWT and the 30s-STS test. MediEval is a stand-alone software module integrated within a mobile app and certified class 1 medical device under the European Medical Device Regulation.
System design/ app development MediEval is a class 1 medical device (Unique Device Identifier 3770024180008) allowing individuals to perform the 6MWT and the 30s-STS test, in autonomy. This app module is incorporated within the Kiplin app (available on iOS and Android smartphones, with iOS version 13 and Android version 7 as minimum configurations). Subjects need to have a physical activity prescription to access the content of MediEval. Technical functioning of the 6MWT. The distance traveled by the user during the 6MWT is computed on the basis of the phone's location by GPS position, recorded every 5 seconds  [26]. The triangulation of the GPS points is then used to calculate the distance traveled and the associated speed in a straight line over each 5-second interval. The total distance walked is computed by summing the distances obtained for each interval.