dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional studies Next, we present intermediate experiments that were performed during the design of KShapeNet. In particular, we discuss the different configurations that were tested in terms of data preprocessing, and the variants of the transformation layer and the logarithmic map block. Comparison of preprocessing techniques: We used the code of Maosen et al. [38]  to generate input data for our algorithm, which is composed of three main steps: 1) extraction of skeleton landmark configurations across frames, 2) extraction of joint coordinates for each configuration, and 3) splitting of sequences into training and test sets for the different protocols. As an additional data processing step, we interpolated the sequences, using cubic splines, to estimate equally-spaced skeleton landmark configuration trajectories, with constant time change between frames.
For comparison, we tested the network by zero padding the missing frames. Since this operation results in frames that contain "wrong" data, the network is misled during the learning stage and recognition performance deteriorates significantly. Table  4  reports recognition results, on the NTU dataset, obtained with zero padding and with cubic spline interpolation. Effects of nuisance variation: As detailed previously, the representation in Kendall's shape space consists of filtering out the three sources of nuisance variation: translation, scale and rotation. In this section, we report recognition performance when only a subset of the nuisance variations is accounted for. Table  5  contains recognition accuracies when two out of the three nuisance variations are filtered out from the representation space, for both the NTU and NTU120 datasets.
The first row in Table  5  reports the accuracy obtained when translation variation is retained while filtering out scale and rotation. The second row reports the accuracy when keeping the initial rotations of the landmark configurations while filtering out scale and translation. Finally, the third row reports the accuracy when keeping scale while removing translation and rotation. From these results, it is clear that accounting for nuisance variation due to rotation and translation is an important aspect of the proposed approach. Comparison of transformation layer variants: Table  6  presents a comparison of recognition results computed using the four different variants of the transformation layer, for the NTU and NTU120 datasets. Each row in the table refers to one of the four variants: 1) optimization over rigid rotations using the matrix-based variant (Rigid Matrix), 2) optimization over rigid rotations using the angle-based variant (Rigid Angle), 3) optimization over non-rigid rotations using the matrix-based variant (Non-rigid Matrix), and 4) optimization over non-rigid rotations using the angle-based variant (Non-rigid Angle).
At a global level, we notice that each version of the transformation layer preserves state-of-the-art results on the NTU and NTU120 datasets. Further, performance is generally better on the CV protocol than the CS protocol for NTU, and on the CS protocol than the Cset protocol for NTU120. At a granular level, we highlight two different behaviors of the optimization over rigid transformations and the optimization over non-rigid transformations, with regards to the two different variants: rotation matrixbased and angle-based. On the one hand, the rotation matrix-based variant, which gives the network the liberty to optimize matrix coefficients without any constraints (updated matrices may not be in SO(  3 )), yields better results for the optimization over rigid transformations than for the optimization over non-rigid ones.
We then concatenate the output features before using them as input into the fully connected layer. The combination of features is applied on the linear functions of the two subnetworks. Table  7  summarizes the recognition results of this study, for the NTU and NTU120 datasets, and compares them to the KShapenet accuracy. Comparing recognition accuracies reported in Table  7  to those in Table  6 , we observe that, for the matrix-based variant, the combination approach generates a slight improvement over rigid transformations alone. For the angle-based variant, the combination approach decreases accuracy compared to rigid and non-rigid transformations applied separately. In all cases, the proposed KShapeNet architecture, based on the non-rigid anglebased transformation layer, yields the best recognition accuracy.
NTU-RGB+D Dataset Deep learning methods Cross Subject Cross View Directed Graph Neural Networks  [70]  89. 9% 96. 1% Two stream adaptive GCN  [71]  88. 5% 95. 1% LSTM based RNN  [34]  89. 2% 95. 0% AGC-LSTM(Joints&Part)  [72]  89. 2% 95. 0%.
The issue is exacerbated when projecting landmark configuration shapes that are far away from the reference configuration. To push the capabilities of our model, we next tried to incorporate parallel transport (PT) (refer to Section 3) as an alternative approach to map the landmark configuration sequences from the pre-shape space to the tangent space. In this approach, we first computed the shooting vectors between each consecutive frame within each sequence (using the logarithmic map). We then used PT to map these shooting vectors to the tangent space at the reference landmark configuration. Table  8  presents the results of applying the one-shot logarithmic map and the PT approach, on the NTU dataset. Theoretically, PT should perform better than the direct projection to a tangent space at a reference landmark configuration since it remedies the distortion issues mentioned earlier.
e. , the KShapeNet implementation. Figure  4  shows the (a) training and (b) test loss, as a function of epochs, for scenario 1) (green) and scenario 2) (red) for the NTU dataset under the CS protocol. Under both scenarios, we observe convergence to a final error value. However, convergence under scenario 2) (KShapeNet) is faster and to a lower error value, resulting in higher recognition accuracy. To summarize, at the end of the various experiments, we decided to adopt the following configurations for KShapeNet: projection on the tangent space using the logarithmic map with respect to a reference frame and optimization over non-rigid transformations using the angle-based variant (corresponding results are cited in Table  2  and Table 3 ). For precision, since the frame in all of the landmark configuration sequences in the two datasets is neutral, i.
Dataset and implementation settings For gait recognition, we evaluate the KShapenet framework on the CMU Mocap 3D gait dataset. CMU Mocap is a database from the CMU Graphics Lab that contains multiple human motion sequences such as playing, running and walking. Motions of 144 subjects were recorded with an optical marker-based Vicon system. Subjects wore a black jumpsuit with 41 markers taped to it. The tracking space of 30m 2 was surrounded by 12 cameras with a sampling rate of 120Hz at heights ranging from 2m to 4m. In our work, we used the CMU Mocap 3D gait dataset extracted and released by Balazia et al. [78] ,  [79] , which includes 3843 gait cycles for 54 subjects. Following the "Homogeneous" experimental setup described in  [79] , we used 10-fold cross validation to assess the recognition performance by dividing the evaluation set into one unlabeled fold as a test set and nine other labeled folds as a gallery set.
Ablation Study To confirm the efficacy of the proposed KShapeNet architecture for 3D gait recognition, we conducted an ablation study on the CMU Mocap dataset in the same fashion as described in Section 5. 1. 2. Table  9  reports the recognition results. The adopted evaluation metric is average recognition accuracy across the different folds. These results, being in line with the results obtained for action recognition, consolidate our conclusion about the efficiency of KShapeNet due to the convenient data representation of 3D landmark configuration shapes in the linear tangent space, and the optimization over local rotation transformations.
Comparison to state-of-the-art approaches Next, we compare the performance of KShapenNet to state-of-theart approaches on the CMU Mocap dataset. Table  10  reports these results. Again, KShapeNet consistently outperforms all of the competing deep learning-based architectures for gait recognition. Indeed, it outperforms the top competitor  [80]  by a small margin (96. 02% vs. 95. 97%).
Additional studies As in the case of action recognition, we performed similar intermediate experiments using the CMU Mocap dataset for gait recognition. Here, we only focus on the comparison of the transformation layer variants and the comparison of the different methods of projection to the tangent space. Table  11  summarizes the results of these studies. The two columns in Table  11  correspond to the two approaches that can be used to map sequences to a tangent space, as discussed in Section 5. 1. 4: using the logarithmic map to a tangent space at a reference configuration and using PT. We can see that projection via the logarithmic map to a common reference frame outperforms projection via PT. Each of the four rows in Table  11  correspond to the four different transformation layer variants.
Datasets and implementation settings For the facial expression recognition task, we evaluate the KShapeNet framework on two datasets: CK+ and Oulu-CASIA. As the first frame of all sequences in the two datasets is neutral, the first frame is considered as an approximation of the common reference frame. Cohn-Kanade Extended (CK+)  [82]  is a dataset consisting of 327 video sequences of facial expressions performed by 118 subjects with seven emotion labels: anger, contempt, disgust, fear, happiness, sadness and surprise. Each sequence contains the two first temporal phases of the expression, i. e. , neutral and onset (with apex frames). Each video shows a facial shift from the neutral expression to a targeted peak expression. The 118 subjects were divided into ten groups by ID in ascending order.
Nine subsets were used for training the network, and the remaining subset was used for validation. This process is the same as the 10-fold cross validation protocol in  [83]. Oulu-CASIA  [84]  is a dataset that includes 480 image sequences performed by 80 subjects. They are labeled with one of the six basic emotions (the same as in CK+, except contempt). Each sequence begins with a neutral expression and ends with the expression apex. The imaging hardware worked at the rate of 25 frames per second and the image resolution was 320×240 pixels.
Additional studies As for the action recognition and gait recognition applications, we report results of intermediate experiments for the facial expression Method CK+ Oulu-CASIA (G) DTGN  [57]  92. 35% 74. 17% (A+G) DTAGN  [57]  97. 25% 81. 46% (R) Gram matrix trajectories  [59]  96. 87% 83. 13% (R) Extrinsic SCDL (SVM)  [56]  95. 62% 77. 06% (R) Extrinsic SCDL (Bi-LSTM)  [56]   recognition task based on the CK+ and Oulu-CASIA datasets. Here, we focus only on comparing the four different transformation layer variants and use the logarithmic map for projection to a tangent space at the reference configuration. Results for the CK+ and Oulu-CASIA datasets are summarized in Table  14. We observe that the optimization over rigid transformations and the optimization over non-rigid transformations behave the same way as for gait recognition, but slightly differently compared to action recognition, with regards to the two different variants: rotation matrix-based and angle-based.
CONCLUSION In this paper, we proposed a geometric deep architecture, KShapeNet, for human motion analysis based on modeling landmark sequences on Kendall's shape space. As part of the framework, we introduced a novel transformation layer to increase global or local dissimilarities between different types of motion. In the transformation layer, we optimize over rigid or non-rigid transformations. In addition, we explored the use of two optimization variants: 1) rotation matrix-based, and 2) angle-based. We showed that the matrix-based variant yields better performance when optimizing over rigid transformations, while the second yields better performance when optimizing over non-rigid transformations. Extensive experiments on challenging datasets, two for action recognition, one for gait recognition and two for facial expression recognition, demonstrate that the proposed framework performs very well compared to state-of-the-art approaches.
Method The survey implemented for this study consists of 6 complementary questionnaires, comprising a total of 166 items divided into 3 main blocks: socio-demographic factors (12 items), the acceptance questionnaire based on the 11 constructs of UTAUT4-AV (32 items, with 4 replications of 24 of them to explore the acceptance of 4 different types of automated vehicles), and a set of other individual profiling factors (50 items). Data were collected online, via a personal computer or a smartphone. During the pre-tests carried out to validate the questionnaire among a sample of 18 participants, the mean time taken to complete the survey was of 30 min, ranged from 21 (achieved by a young man of 22 years old who was highly familiar with completing online surveys using his smartphone) to 43 min (achieved by an elderly woman of 82 years old, who was unfamiliar with online surveys, and who completed the questionnaire from her personal computer).
Participants involved and sample representativeness On-line data collection was carried out from October to December 2020. A total of 2,612 persons participated to this survey (1,252 males and 1,360 females). For legal issues, all the participants were aged of 18 years old and more. The sample of participants was formed on the basis of 5 demographic criteria: age, gender, socio-professional category, size of the Fig. 2. Visual Analogue Scales (raging from 0 to 100) used for the survey. 1 As highlighted by  Chimi et Russell (2009) : « the fundamental problem with Likert-type item is the response they generate qualitative data consisting of a count of responses in each category. The resulting analysis is inherently limited, primarily to a frequency table, typically with relative and cumulative relative frequencies computed.
Linear regression analysis to explain the intention to use automated shuttles To identify the predictors of the IU of AS (i. e. dependent variable), we performed a linear regression analysis by implementing a one-way procedure, using SPSS IBM Statistics version 26. The 10 independent variables composing the UTAUT4-AV model were integrated simultaneously. The results are presented in the regression Table  4. The first result to be highlighted here is that the regression model applied to the UTAUT4-AV constructs explains 89. 1% of the IU of Automated Shuttles. This result means that the 10 constructs contribute significantly, to a greater or lesser extent, to the explanation of the variance of the IU of AS. In this respect, it is nevertheless necessary to distinguish two groups of constructs, i.
Profiles of users most "favourable" versus "reluctant" to use shuttles To further investigate the acceptance of AS according to the socio-demographic characteristics of the participants and the mean of transport they currently use, a set of parametric tests were implemented (using the IBM SPSS Statistics 26. 0 statistical processing software). For the "Gender" variable (Table  6 ), it appears that men are significantly more likely to use AS than women (Student's t-test). This result confirms hypothesis H12. For the other socio-demographic variables, results of the analyses of variance (ANOVA, associated with Bonferroni post-hoc tests) presented in Table  7  show several significant differences according to the participants' characteristics: 1) Age: people of 18 to 44 years old have the highest intention to use AS, and people aged of 65 years and more are the most reluctant to this idea.
Data collected from the online survey implemented among a representative sample of 2,612 French citizens were used to explore the validity and the explanatory power of this extended version of the UTAUT model. In this aim, we successively examined (1) the reliability of the UTAUT4-AV constructs, (2) the model's ability to explain the intention to use automated shuttles, (3) the impact of individual factors, and (4) the UTAUT4-AV's robustness to study acceptance of other types of automated vehicles.
Conclusion: Main findings, limitations and perspectives The objective of this study was to propose a model able to explain individuals' intentions to use Automated Shuttles by tailoring the UTAUT model of  Venkatesh et al (2003 Venkatesh et al ( , 2008 Venkatesh et al ( , 2012) )  to this new type of vehicles. We gathered data from an online survey implemented among a representative sample of 2,612 French citizens, which allowed us to evaluate an extended version of the UTAUT model to study AV acceptance. Linear regression analyses showed that UTAUT4-AV model is capable to explain 89. 1% of the variance in the intention to use automated shuttles. This is 20-30% higher than other existing models such as UTAUT2 used by  Madigan et al. (2017)  and  Nordhoff et al. (2017). This better explanatory power may be explained by the inclusion of 3 constructs related to the Mean of Transport currently used in terms of satisfaction towards it, mobility performance increasing thanks to automated shuttles, and price value.
Acknowledgement This study was preformed in the frame of the French national research project  EVRA-ENA. This project was funded by the  French Government  as part of the " Programme d'investissements d'avenir" , now part of " France 2030 ", and was operated by  ADEME. It was also labelled by the  CARA European Cluster  for Mobility Solutions. Fig. 7. Consolidated version of UTAUT4-AV to study acceptance of different types of AVs (β values come from Table 8).
Spread rate and carrying capacity The estimate of the local spread rate C (6 km/year) was based on the maximum flight capacity of T. erytreae determined by  Van den Berg and Deacon (1988) , i. e. 1. 5 km, multiplied by 4, the estimated number of yearly generations that T. erytreae can have in Porto, where T. erytreae was first detected. The carrying capacity K was estimated for each cell in the model, as the product of the average maximum capacity of T. erytreae per host tree, k tree and the estimated number of citrus trees in the cell. k tree was determined using data from  Catling (1972) , corresponding to the mean number of individuals per citrus tree (adults, nymphs and eggs), observed in a 3-year study, under favourable conditions, using the following formula: k tree = Adults + (0. 289 × Nymphs + (0.
289 × 0. 95 × Eggs) eq. 3 where 0. 289 is the nymphal survival rate under natural conditions (Caitling 1970) and 0. 95 is the egg viability in optimum environmental conditions  (Catling 1972; Van den Berg et al. 1991 ) to obtain k tree = 2719 adults of T. erytreae per tree. To estimate the spatial density of citrus trees in orchards throughout the country, we used the data from the 2019 agricultural census  (INE 2021). This dataset does not include citrus plants in urban and peri-urban areas, as well as isolated citrus trees in rural landscapes. The dataset from INE (2021) provides the area of citrus orchards per county. The density of citrus trees in orchards was assumed, for simplicity, to be the same all over the territory, i. e. 400 citrus trees/ha, considering 5 m × 5 m per tree. Although citrus-tree spacing may vary between 5 m × 4 m or lower and 7 m × 5 m  (Cavaco and Calouro 2005; Vacante and Gerson 2012) , we considered for simplicity a median value of 5 m × 5 m.
The citrus tree density for each 25 km 2 grid cell of the model was estimated, based on spatial data from the Land Use and Occupancy Mapping -COS2018, (available at https://www. dgterritorio. gov. pt/) and the data from the 2019 agriculture census  (INE 2021) , about the area of citrus orchards. As data on the density of citrus trees in urban and peri-urban areas were not available from the 2019 agriculture census  (INE 2021)  and considering its possible influence on the dispersal of T. erytreae, we developed an innovative approach to estimate it. We used the spatial data of the COS2018 dataset, to classify the urban and peri-urban areas. Then, we divided these areas into three different classes: Vertical urban areas; Horizontal urban areas; and Discontinuous urban areas (see S1). For each class, the mean density of citrus trees was estimated using Google Street view imagery to survey the number of visible trees in randomly selected polygon areas extracted from the COS2018 spatial dataset throughout the country  (Rousselet et al.
Growth rate To calculate the growth rate, r, for T. erytreae in Portugal, we used climatic modelled data collected for 30 years  (Palma 2017 ). These data were collected for each centroid of a grid of 25 km × 25 km covering Portugal. The climatic variables considered were the daily mean temperature, daily maximum temperature and the daily minimum relative air humidity. The average daily climatic data were grouped into three periods per month. Each period had 10 days, except the last third of each month, which varied from 8 to 11 days, depending on the month. These periods are henceforth called as "10-day periods", needed to calculate the T. erytreae survival rate, using the method developed by  Catling (1969). For each 10-day periods, the number of "viable days" (i. e. the number of days above the lower temperature threshold for development) was calculated.
This complete dataset was provided to us by the Portuguese National Plant Protection Authority (DGAV). We compiled it yearly and used it to validate our model, as well as determining the dispersal capacity, from 2015 until the end of 2021. All the parameters used for the model were estimated independently from this presence/absence observed data, allowing for independent validation. To validate the model and identify the dispersal scenarios that best fit the observed data, the species' spread was simulated between 2014 and 2021 for different combinations of model parameters (Table  1 ). For the simulations considering long-distance dispersal (stochastic sub-model), we ran 300 replicate simulations using randomly generated long-distance events. We consider that a cell is infested when N ≥ 1 individual.
erytreae in Portugal. However, this hypothesis cannot be excluded, based on our results, since our model was not primarily designed to test the hypothesis. Additionally, our work showed that citrus trees from urban and peri-urban environments had a very important role in the spread of T. erytreae in Portugal. This is highlighted by the major impact that they had on model performance, considering their very low relative number in comparison with the estimated orchard trees. Our results contribute to highlighting the importance that isolated host trees can have for species invasive dynamics. These trees are generally disregarded due to lack of statistical data. Finally, we showed that Google Street view imagery can be an efficient tool to estimate the density of urban and peri-urban trees. This study was granted by HOMED project, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no.
Trioza eytreae Reports -Available at https://www. dgav. pt/plantas/conteudo/sanidade-vegetal/inspecao-fitossanitaria/informacao-fitossanitaria/trioza-erytreae/. Cos2018, Land Use and Occupancy Map 2018 -Available at https://www. dgterritorio. gov. pt/. Agricultural census of 2019 (INE 2021) -Available at https://www. ine. pt/xurl/pub/437178558. Human population density in Portugal from 2017 -Available at https://www. ine. pt/. The R script and the data needed to run the model -Available at https://zenodo. org/record/7096566.