dataesr/scientific-paragraphs-categorization huggingface dataset:
Otherwise, the one-way coupling scheme returns to providing input data (without back exchange) to single physics solvers, which is quite outside the definition of multiphysics coupling. At IRESNE, the remaining one-way coupling schemes are progressively upgraded into full coupling schemes, especially through the developed multiphysics platforms. Another key point in numerical multiphysics coupling concerns physics field transfer between non-matching meshes. The question of data projection and conservation then becomes crucial. In practice, current multiphysics tools developed and/or used at IRESNE very often rely on compatible meshes: the physics fields are defined on the same meshes or on interface-matching meshes. However, this can become a practical limitation for some advanced multiphysics simulation.
However, a step-wise validation process is required, and consequently, experimental databases need to be extended, even though the current CEA database as well as the results of partner facilities are already widely used. New experiments need to be designed and performed with consideration of the development of an innovative instrumentation, capable of characterizing coupled phenomena at lower scales in parallel. With this objective, the IRESNE institute still operates experimental facilities of interest for each research field (see Table  3 ). The experimental data obtained contribute to the understanding of physical phenomenology, the associated development of new models, as well as the experimental validation of multiphysics tools. These data are not readily available as they are not measured in industrial reactors.
Regarding fuel behavior under irradiation The challenge consists in the development of full 3D modeling and simulation of a fuel rod/pin, with the representation of its exact geometry including the stacking of pellets in the cladding, without any symmetry and cracking assumption. Currently, the PLEIADES-based fuel performance software programs relie on an axial multislice (or layered) representation of the rod/pin geometry, where each 3D slice is formed of one 32nd of a pellet surrounded by its cladding piece. This representation is based on strong assumptions regarding the pellet geometry (symmetry) and its cracking but also the contacts between fuel fragments. The simulations on this simplified geometry are now reaching their limits to further analyze irradiation configurations in an heterogeneous environment (e.
Regarding behavior of reactors in normal, off-normal and accident situations The challenge consists in the definition, design and realization of a new experiment for a PWR application, with the aim of contributing to the validation of the high-fidelity multiphysics tools. The established approach to validation of LWR multiphysics modeling and simulation is mainly based on measurement data coming from the operation of the current reactor fleet or legacy experiments, insufficiently instrumented for the validation of the detailed multiphysics coupling (neutronics/ thermal-hydraullics/fuel performance) currently under development at the assembly, rod and even sub-rod levels. Discussions have been initiated to define new highly instrumented integral experiments focusing on a first application, the validation of reactivity feedback effects, internally at IRESNE, but also within the framework of international collaborations.
Most of these tools are developed and/or coupled within multiphysics platforms. Platforms used at IRESNE are generic and able to treat different types of reactor concepts (PWRs, SMRs, AMRs, SFRs, etc. ). As the modeling/simulation/qualification process is an always ongoing work, a snapshot of the current situation has been provided. The physical models as well as their coupling schemes and data transfer processes have been reviewed, which opens the way to the mutualization (or at least expert scientist exchange) of some models and numerical methods. IRESNE is also equipped with different experimental devices in research reactors or in hot labs which in particular contribute to the validation of multiphysics simulation tools. This makes IRESNE a key player in multiphysics simulations supporting reactor design, operation and safety.
In this work, we study the ability to use hand gestures for human-machine interaction from wrist-worn sensors. Towards this goal, we design a wrist-worn prototype to capture RGB video stream of hand gestures. Then we built a new wrist-worn gesture dataset (named WiGes) with various subjects in interaction with home appliances in different environments. To the best of our knowledge, this is the first benchmark released for studying hand gestures from a wrist-worn camera. We then evaluate various CNN models for vision-based recognition. Furthermore, we deeply analyze the models that produce the best trade-off between accuracy, memory requirement, and computational cost. We point out that among studied architectures, MoviNet produces the highest accuracy. Then, we introduce a new MoviNet-based two-stream architecture that takes both RGB and optical flow into account.
Such a mounting position is more suitable and comfortable for users to wear than head or chest mounting. Some similar wrist-worn designs have been introduced in  [11] ,  [12] , and  [13]. However, they usually convey a number of limitations, i. e. , types and mounting positions of sensors being inconsistent, activities being collected in different contexts (daily life activities or finger taps only), or not publically available datasets. With the designed prototype, we collect a set of dynamic gestures. Concerning hand gesture recognition methodology, we investigate and produce baseline results with some state-of-theart CNN models. We finally propose a novel two-stream MoviNet architecture that processes RGB and optical flow simultaneously to improve the overall accuracy. In summary, the main contributions of this paper are as follows: i We design a system that consists of a wrist-worn device connected to an embedded computer for capturing visual data of human hand gestures.
This system can be utilized in various practical applications, such as human-machine interaction or healthcare. ii We collect and publish a new dataset of twelve dynamic hand gestures using the wrist-worn device with 50 subjects in the context of home-appliance control. To the best of our knowledge, this is the first dataset of hand gestures captured by a wrist-worn camera publicly available to the research community. iii We investigate various CNN architectures and their performance in terms of accuracy, memory footprint, and computational time. They serve as baseline approaches for further improvements as well as for finding suitable architectures for the deployment of practical applications. iv We propose a two-stream MoviNet architecture that takes both RGB and optical flow into account.
It improves the Top-1 accuracy by 1. 36% (from 94. 87% to 96. 23%) and 3. 67% (from 94. 81% to 98. 48%) compared to single RGB stream MoviNet according to two evaluation protocols on our collected dataset. Figure  1  illustrates the main components of our proposed framework. It contains six parts: designing the wearable device for collecting data, designing the hand gesture set for human-machine interaction, conducting data collection and annotation, implementing and evaluating the baseline methods as well as proposing a new method, analyzing and selecting the best model to be deployed on edge device for application of human-machine interaction. The remaining sections of this paper are organized as follows: Section II briefly reviews related works on existing wearable devices, gesture datasets, and hand gesture recognition methods.
A. EXISTING WEARABLE DEVICES, DATASETS, AND APPLICATIONS The number of wrist-worn prototypes, as well as the number of egocentric datasets of hand gestures compared to those captured by ambient cameras, are still very limited. The type, the characteristics, and the mounting position of each integrated sensor vary from prototype to prototype. The activities are dependent on applications. Table  1  summarizes the attributes of existing datasets of hand gestures collected by wrist-worn devices and their availability. In  [18] , Maekawa et al. designed a wrist-worn device integrating a camera, a microphone, an accelerometer, and a compass. Their objective was to collect data using heterogeneous sensors for object-based action recognition. The dataset contains 15 daily life activities with a duration from 0.
67 to 3. 65 minutes. Unfortunately, the dataset is not available for evaluation. Ohnishi et al. proposed a network to capture and recognize activities of daily living using a wrist-mounted and a head-mounted camera  [13]. The wrist-mounted camera also gazes at the hand, allowing to facilitate the observation of the hand when interacting with objects. Twenty-three ADL classes were collected and annotated using both cameras. Yeo et al. [19]  introduced a prototype of a wearable camera with a view of the opisthenar (back of the hand) area. The device used a single infrared camera (Leap Motion device) with an active infrared light source for easy removal of the background. They collected ten static hand postures and five individual finger tapping actions for dynamic gestures. In  [20] ,  [21] , and  [11] , the authors deployed an RGB camera worn on the backside of user's right to capture hand gestures for human-machine interaction application.
III. PROTOTYPE DESIGN AND DATA COLLECTION A. PROTOTYPE DESIGN We use an ordinary low-cost wide-angle RGB camera. The camera model is IMX219-160, which gives the highest resolution of 3280 × 2464 at 15 fps, and a side field of view of 160 o. At 1280 × 720 resolution, the acquisition rate may reach 90 fps. A device that integrates the camera is worn on the backside of user's wrist. As a result, the camera will capture images from the back of the hand. The camera is connected to an embedded computer (Jetson Nano) to transfer data through a CSI port. To accommodate greater flexibility for the subject, we developed a compact pack to put the embedded computer inside and power the computer for up to four hours using a 5v 10000mA battery. The solution for wireless connection between the sensors and the embedded computer is under consideration.
B. DATASET COLLECTION AND ANNOTATION 1) DESIGN OF HAND GESTURE SET As mentioned in the related works section, there are some existing works on human hand gesture recognition using wearable sensors. However, each work has designed and built a proper dataset for a specific purpose, which makes it impossible to generalize or re-use them in different applications. Moreover, most of them are not publicly available. Our work aims to develop a system for controlling home appliances (e. g. , fans, air conditioners, television) through hand gestures captured by wrist-worn devices. As a consequence, a new set of hand gestures is designed. The main required characteristic for any new gesture set in human-machine interaction is that they have to be intuitive, distinguishable, easy to memorize, and performed by users.
After a careful design process, a set of twelve dynamic hand gestures named from G 1 to G 12 has been designed. The trajectory of each hand gesture is shown in Figure  3. Each hand gesture can be mapped to one command to control in-home appliances, such as turning on/off the light switch or increasing/decreasing the temperature of air-conditioners. Intuitively, according to the subjects participated in data collection, these gestures are easy to perform and memorize. The shapes of gesture trajectories are distinctive in appearance. We will validate their discriminance by experiments in later sections. We recruited 12 individuals aged between 23 to 50 years to conduct a survey and evaluate our gesture set. Based on the easy-to-implement criteria, 50% of the participants found the gestures to be very easy to implement, 25% found them easy, and 25% found them normal.
2) DATA COLLECTION AND ANNOTATION To ease data collection by a large number of subjects in different places, we have produced four identical kits, each of which includes a wrist-worn camera, a Jetson Nano, and a power supply. In all data acquisition sessions, image data was captured at 30fps with a resolution of 1280 × 720 pixels. We invite 50 volunteers (33 men and 17 women, aged from 10 to 65 years old) to perform twelve designed gestures while standing or sitting in different environments, such as at offices, lab rooms, or at home. In each collection session, volunteers are informed consent to provide data for research purposes and explained how to wear the device and implement the gestures correctly. Each subject performs in his natural manner 12 gestures; each gesture is repeated from 2 to 12 times.
All visual frames from the camera are stored in the memory of the embedded device. Furthermore, each gesture's starting and ending times are marked during data acquisition via a keypad or a remote control device to facilitate the labeling process. Therefore, all gesture instances can be automatically segmented. In total, we conducted 50 sessions of collection for 50 subjects to obtain a dataset of 5408 samples. We obtain a multimodal dataset of 5408 gesture samples. Figure  4  shows an example of gesture G 3. The top part of the figure shows some frames extracted from a sequence   captured by a third-person view camera that observes the subject in action. The bottom part of the figure shows the corresponding frames extracted from the wrist-worn camera. We denote extraction time above each frame.
As a result, when a subject implements a dynamic hand gesture, his/her hand's position and orientation are static relative to the camera while the background will change. The last challenge of our dataset is the poor quality of images due to the use of the low-cost camera. We compare our dataset with some relevant existing ones in Table  1. Our dataset is significant in terms of the number of subjects, number of gesture instances, diversity of background environments, number of modalities, and accessibility for the research community. The dataset and our pre-trained models are available at https://www. mica. edu. vn/perso/Tran-Thi-Thanh-Hai/MuWiGes. html.
3) EVALUATION PROTOCOL The collected dataset is divided distinctly into training and testing sets based on two evaluation protocols we defined for the comparison of recognition algorithms, i. e. , cross-subject evaluation and cross-scene-subject evaluation: • Cross-subject evaluation: This evaluation protocol aims to evaluate the robustness of investigated models   • Cross-scene-subject evaluation: This evaluation protocol is double strict in the sense that the investigated models are tested on videos of subjects and scenes both unseen during the training process. We also select data from 35 subjects performing the gestures in various scenes for training and 15 remaining subjects in other scenes for testing. Accordingly, the training set has 3633 video clips (i. e. , gesture instances), while the test set contains 1775 instances of 12 gestures.
5) EfficientNet3D While C3D and R3D, R(2+1)D are heavy models, Effi-cientNet3D has been introduced in  [30]  as an additional resource-efficient architecture to be deployed on mobile devices. It has been shown that proportional feature extraction for video analysis gives relatively good results. The network takes input images with dimensions of N × C × T × H × W, where N and C are the batch size and channels, respectively, H and W are the height and width of the frames, T is the duration of video of the dataset. Each video is sampled to 32 frames, both of which are resized to 224 × 224.
V. EXPERIMENTS A. COMPLEXITY COMPARISON OF EXPERIMENTED MODELS In this section, we elaborate on our findings conducted for different network architectures on our testing dataset. Various experiments have been carried out with various variations of hyperparameters, such as the number of sampling frames, temporal sampling strategies, learning rate, input resolution, and input transformation, to optimize our proposed two-stream frameworks. The reported results in this work are of models with the best hyperparameters for conducting experiments regarding system efficiency (i. e. , suitable for low-performance devices and time, etc. ). • C3D and R3D are common architectures for action recognition problems but are not suitable for real-time and mobile applications due to their high complexity. They require the most memory as their number of parameters is very high (63.
85%) by 13. 46%. It is even slightly better than R3D-50 by 1. 92%. It could be explained by the fact that the decomposition of 3D convolution into two separate convolutions (2D spatial convolution and 1D temporal convolution) is advantageous, as indicated in  [28]. All experimented MoviNet models output very high accuracy ranging from 92. 44% (MoviNet-a0) to 94. 81% (MoviNet-a2 ⋆ ), exceeding all CNN models mentioned above. The best model is MoviNet-a2 ⋆ with the input resolution of 224 × 224, which achieves Top-1 accuracy of 94. 81% -1. 53% higher than its variant with the resolution of 172 × 172. This can be explained by the fact that the bigger input size would better capture the hand and background movement than the small size. The more complex MoviNet-a5 model fails to achieve better results than MoviNet-a2 and MoviNet-a2 ⋆ on this dataset as it uses only input with the resolution of 172 × 172 in our experiments due to its memory requirement as well as our system's capacity.
4) EVALUATION OF THE PROPOSED TWO-STREAM MoviNet ON EgoGesture DATASET To confirm the efficiency of our proposed two-stream MoviNet, we conduct experiments on another benchmark Egogesture  [9]. The Egogesture dataset was collected by an egocentric camera Intel RealSense SR300 that provides both RGB and depth modalities with a resolution of 640 × 480 with a frame rate of 30 fps in four indoor and two outdoor scenes. 50 distinct subjects were invited to perform 83 classes of gestures. Totally, 24,161 video gesture samples and 2,953,224 frames are collected in RGB and depth modality, respectively. We follow the same data-splitting strategy for training and testing our framework as in the original paper. Specifically, we trained MoviNet family models with both training and validation sets (i. e.
, a total of 1450 videos, of which 1239 videos belong to the training set and 411 belong to the validation set) and test the model with the testing set (i. e. , 431 videos). To speed up the training process, the labeled actions in each RGB video are split into smaller clips but keep their original video's parameters such as resolution, frame rate, and so on. These clips are then converted to the corresponding OF clips for OF branch of our proposed framework. We conduct two types of experiments where the frames per clip are 16 and 32 respectively. Table  4  shows the experimental results on the Egogesture dataset. We compare our performance with existing results using C3D and ResNeXt-101 reported in  [35]. It is noticed that on 16-frame clip configuration, our models achieved the highest performance (Top-1 accuracy of 91.
5) DISCUSSIONS Although most of the studied methods achieved promising results, our current method has some following limitations: • The current method is only executed with segmented videos. In a practical application where the frames come continuously, it requires a temporal segmentation of gestures, which in turn is a more challenging task. In our previous work  [36] , we proposed a method to deal with continuous recognition on other datasets using other methods of recognition. We may apply the same idea to this problem but with an adaptation for dealing with continuous gesture recognition. • The combined methods of OF with RGB are more time-consuming than using a single stream which may cause some issues when deploying it on an embedded device. In the future, we will optimize the architecture, (e.
VI. CONCLUSION In this paper, we designed a sensory smart-watch-like device and introduced a new benchmark for hand gesture recognition from a wrist-worn camera. The dataset is challenging because it covers many real-world issues (different in-home and office environments, large intra-class variability of gestures, large extra-class similarity, and a high number of participants). It could be considered the first dataset for training and evaluating deep learning models. We also evaluated different vision-based CNN models for hand gesture recognition, including the most conventional 3D models such as C3D and R3D, as well as lightweight models (R3D-18, R(2+1)D-18), resource-efficient models such as MobileNet3D, Effi-cientNet3D, and MoviNets. Our experiment shows that the MoviNet variant gets high accuracy.
, 2013). Time-calibrated phylogenetic trees built from the genetic data of extant contain topological and temporal information that, through models, indirectly informs how speciation operates. Fossil information, on the other hand, remains the only direct evidence of extinction and past species distributions, yet its incompleteness and fragmentary nature hinders a comprehensive characterization of species relationships and their branching process for most clades  (Behrensmeyer et al. , 2000; Silvestro et al. , 2016). Approaches using simulation tools have been useful  (Rangel et al. , 2018; Hagen et al. , 2021) , but these frameworks do not provide inference methods to estimate evolutionary parameters from empirical datasets. We first developed a new set of phylogenetic models that account for the effect of paleoenvironmental variations and observed as well as unobserved ('hidden') species states on spatial diversification, which we call 'ESSE' (Materials and Methods & Supplementary Information).
Elevated extratropical extinction maintains diversity gradient We explored geographic patterns of diversification, first ignoring potential temporal and hidden heterogeneity (model 'G'; Materials and Methods). In light of inferential concerns about diversification models  (Louca and Pennell, 2020, 2021) , we stress that our models are identifiable, and we show that biogeographic information strengthens the confidence we can have in extinction rate estimates (see Supplementary Information "Macroevolutionary Rates"). Contrary to previous studies integrating deep timescales on previous biological datasets that suggest a negative latitudinal trend with origination rates  (Jablonski et al. , 2006; Mittelbach et al. , 2007; Rolland et al. , 2014)  and to studies focusing on recent speciation rates that suggest a positive latitudinal trend  (Weir and Schluter, 2007; Rabosky et al.
e. , p); iii) number of states (i. e. , |K|); and iv) number of hidden states (i. e. , |H|). Furthermore, any number of logical constraints between parameters, or fixing of parameters to a determined real value can be specified. Code is available online at https://github. org/ignacioq/Tapestree. jl (Note to reviewers: code will be merged into the master version of Tapestree and PANDA once the article is published. ).
Biogeographic fossil analyses of Mammals To test for congruent historical evolutionary dynamics from both neontological and paleontological evidence, a clade with both well-known fossil record and complete phylogenetic tree is required. We thus focused on Mammals, which, during the Cenozoic, remain the best sampled major tetrapod radiation in the fossil record  (Mannion et al. , 2014; Davies et al. , 2017). We downloaded Mammal fossil occurrence data from the Paleobiology Database (PBDB) on July 2022  (Database, 2022)  for all mammals (i. e. ,'Class' = 'Mammalia') excluding Cetaceans, since we work with terrestrial regions. We first vetted the resulting database by removing occurrences with a reported interval larger than 5 My for occurrences younger than 23. 04 Mya, larger than 10 My for occurrences younger than 66.