3D body scan has been adopted for body composition assessment due to its ability to accurately capture body shape measurements. However, the complexity of mesh representation and the lack of fine-shape descriptors limit its applications in body fat percentage analysis. Most studies rely on algorithms applied to anthropometric values derived from 3D scans, such as multiple girth measurements, which fail to account for the body's detailed shape. To address these issues, we explore the feasibility of using point cloud representation. However, few existing point-based methods are aimed at the human body or regression tasks. In this study, we introduce a new model, D3BT, which utilizes a transformer-based network on the body point cloud to efficiently learn shape information for regional and global fat percentage regression tasks. The model dynamically divides the points into voxels for enhanced transformer training, providing higher density and better alignment across different subjects, which is more suitable for body shape learning. We evaluate various models for predicting body fat percentage from 3D body scans, using ground truth data from dual-energy X-ray absorptiometry (DXA) reports. Compared to traditional methods that depend on anthropometric measurements and other point-based approaches, the proposed model shows superior results. In extensive experiments, the model reduces the Root Mean Square Error (RMSE) by an average of 10.30% and achieves an average R-squared score of 0.86.

Recently, there has been much interest in developing image captioning models. State-of-the-art models reached a good performance in producing human-like descriptions from image features that are extracted from neural network models such as CNN and R-CNN. However, none of the previous methods have encapsulated explicit features that reflect a human perception of the images such as gazing patterns without the use of the eye-tracking systems. In this paper, we hypothesize that the nouns (i.e. entities) and their orders in the image description reflect human gazing patterns and perception. To this end, we estimate the sequence of the gazed objects from the words in the captions and then train a pointer network to learn to produce such sequence automatically given a set of objects in new images. We incorporate the suggested sequence by pointer network in existing image caption models and investigate its performance. Our experiments show a significant increase in the performance of the image captioning models when the sequence of the gazed objects are utilized as additional features (up to 13 points improvement in CIDEr score when combined with Neural Image Caption model).

Neonatal Endotracheal Intubation (ETI) is a critical resuscitation skill that requires tremendous practice of trainees before clinical exposure. However, current manikin-based training regimen is ineffective in providing satisfactory real-time procedural guidance for accurate assessment due to the lack of see-through visualization within the manikin. The training efficiency is further reduced by the limited availability of expert instructors, which inevitably results in a long learning curve for trainees. To this end, we propose an intelligent Augmented Reality (AR) training framework that provides trainees with a complete visualization of the ETI procedure for real-time guidance and assessment. Specifically, the proposed framework is capable of capturing the motions of the laryngoscope and the manikin and offer 3D see-through visualization rendered to the head-mounted display (HMD). Furthermore, an attention-based Convolutional Neural Network (CNN) model is developed to automatically assess the ETI performance from the captured motions as well as identify regions of motions that significantly contribute to the performance evaluation. Lastly, augmented user-friendly feedback is delivered with interpretable results with the ETI scoring rubric through the color-coded motion trajectory that classifies highlighted regions that need more practice. The classification accuracy of our machine learning model is 84.6%.

Neonatal endotracheal intubation (ETI) is a complex procedure. Low intubation success rates for pediatric residents indicate the current training regimen is inadequate for achieving positive patient out-comes. Computer-based training systems in this field have been limited due to the complex nature of simulating in real-time, the anatomical structures, soft tissue deformations and frequent tool interactions with large forces which occur during actual patient intubation. This paper addresses the issues of neonatal ETI training in an attempt to bridge the gap left by traditional training methods. We propose a fully interactive physics-based virtual reality (VR) simulation framework for neonatal ETI that converts the training of this medical procedure to a completely immersive virtual environment where both visual and physical realism were achieved. Our system embeds independent dynamics models and interaction devices in separate modules while allowing them to interact with each other within the same environment, which offers a flexible solution for multi-modal medical simulation scenarios. The virtual model was extracted from CT scans of a neonatal patient, which provides realistic anatomical structures and was parameterized to allow variations in a range of features that affect the level of difficulty. Moreover, with this manikin-free VR system, we can capture and visualize an even larger set of performance parameters in relation to the internal geometric change of the virtual model for real-time guidance and post-trial assessment. Lastly, validation study results from a group of neonatologists are presented demonstrating that VR is a promising platform to train medical professionals effectively for this procedure.

In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate the regularization term for the deformation field in our non-rigid registration. The local coordinate regularizations vary for each pair of neighboring nodes based on the tracking status of the surface regions. We propose our tracking strategies for different surface regions to minimize misalignments and reduce error accumulation. This method can thus preserve local geometric features and prevent undesirable distortions. Moreover, we introduce a geodesic-based correspondence estimation algorithm to align surfaces with large displacements. Finally, we demonstrate the effectiveness of our proposed method with detailed experiments.

Body composition can be assessed in many different ways. High-end medical equipment, such as Dual-energy X-ray Absorptiometry (DXA), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) offers high-fidelity pixel/voxel-level assessment, but is prohibitive in cost. In the case of DXA and CT, the approach exposes users to ionizing radiation. Whole-body air displacement plethysmography (BOD POD) can accurately estimate body density, but the assessment is limited to the whole-body fat percentage. Optical three-dimensional (3D) scan and reconstruction techniques, such as using depth cameras, have brought new opportunities for improving body composition assessment by intelligently analyzing body shape features. In this paper, we present a novel supervised inference model to predict pixel-level body composition and percentage of body fat using 3D geometry features and body density. First, we use body density to model a fat distribution base prediction. Then, we use a Bayesian network to infer the probability of the base prediction bias with 3D geometry features. Finally, we correct the bias using non-parametric regression. We use DXA assessment as the ground truth in model training and validation. We compare our method, in terms of pixel-level body composition assessment, with the current state-of-the-art prediction models. Our method outperforms those prediction models by 52.69% on average. We also compare our method, in terms of whole-body fat percentage assessment, with the medical-level equipment-BOD POD. Our method outperforms the BOD POD by 23.28%.

We contribute a new integrated system designed for high-quality 3D reconstructions. The system consists of a sparse set of commodity RGB-D cameras, which allows for fast and accurate scan of objects with multi-view inputs. We propose a robust and efficient tile-based streaming pipeline for geometry reconstruction with TSDF fusion which minimizes memory overhead and calculation cost. Our multi-grid warping method for texture optimization can address misalignments of both global structures and small details due to the errors in multi-camera registration, optical distortions and imprecise geometries. In addition, we apply a global color correction method to reduce color inconsistency among RGB images caused by variations of camera settings. Finally, we demonstrate the effectiveness of our proposed system with detailed experiments of multi-view datasets.

Recently, image captioning has received much attention from the artificial-intelligent (AI) research community. Most of the current works follow the encoder-decoder machine translation model to automatically generate captions for images. However, most of these works used Convolutional Neural Network (CNN) as an image encoder and Recurrent Neural Network (RNN) as a decoder to generate the caption. In this paper, we propose a sequence-to-sequence model that uses RNN as an image encoder that follows the encoder-decoder machine translation model, such that the input to the model is a sequence of images that represents the objects in the image. These objects are ordered based on their order in the captions. We demonstrate the results of the model on Flickr30K dataset and compare the results with the state-ofthe-art methods that use the same dataset. The proposed model outperformed the state-of-the-art methods on all metrics.

The cover image is based on the Research Article Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors, by Yao Lu et al., https://doi.org/10.1002/cav.1807.

In the last decade, 3D modeling techniques enjoyed a booming development in both hardware and software. High-end hardware generates high fidelity results, but the cost is prohibitive, whereas consumer-level devices generate plausible results for entertainment purposes but are not appropriate for medical uses. We present a cost-effective and easy-to-use 3D body reconstruction system using consumer-grade depth sensors, which provides reconstructed body shapes with a high degree of accuracy and reliability appropriate for medical applications. Our surface registration framework integrates the articulated motion assumption, global loop closure constraint, and a general as-rigid-as-possible deformation model. To enhance the reconstruction quality, we propose a novel approach to accurately infer skeletal joints from anatomical data using multimodality registration. We further propose a supervised predictive model to infer the skeletal joints for arbitrary subjects independent from anatomical data reference. A rigorous validation test has been conducted on real subjects to evaluate the reconstruction accuracy and repeatability. Our system has the potential to make accurate body surface scanning systems readily available for medical professionals and the general public. The system can be used to obtain additional health data derived from 3D body shapes, such as the percentage of body fat.

Robotic-assisted surgeries are commonly used today as a more efficient alternative to traditional surgical options. Both surgeons and patients benefit from those systems, as they offer many advantages, including less trauma and blood loss, fewer complications, and better ergonomics. However, a remaining limitation of currently available surgical systems is the lack of force feedback due to the teleoperation setting, which prevents direct interaction with the patient. Once the force information is obtained by either a sensing device or indirectly through vision-based force estimation, a concern arises on how to transmit this information to the surgeon. An attractive alternative is sensory substitution, which allows transcoding information from one sensory modality to present it in a different sensory modality. In the current work, we used visual feedback to convey interaction forces to the surgeon. Our overarching goal was to address the following question: How should interaction forces be displayed to support efficient comprehension by the surgeon without interfering with the surgeon’s perception and workflow during surgery? Until now, the use the visual modality for force feedback has not been carefully evaluated. For this reason, we conducted an experimental study with two aims: (1) to demonstrate the potential benefits of using this modality and (2) to understand the surgeons’ perceptual preferences. The results derived from our study of 28 surgeons revealed a strong positive acceptance of the users (96%) using this modality. Moreover, we found that for surgeons to easily interpret the information, their mental model must be considered, meaning that the design of the visualizations should fit the perceptual and cognitive abilities of the end user. To our knowledge, this is the first time that these principles have been analyzed for exploring sensory substitution in medical robotics. Finally, we provide user-centered recommendations for the design of visual displays for robotic surgical systems.

The evolution and affordability of depth cameras like Microsoft Kinect make it a great source for object detection and surveillance monitoring. Information available from depth cameras includes depth in addition to color. Using depth cameras, the provided depth information can be incorporated for object detection in still and video images, but needs special care to pair it with color information. In this work, we propose a simple, yet novel real time unsupervised object detection method in spatio-temporal videos. The RGB color frame is mapped into Hunter Lab color space to reduce emphasis on image illuminations, while the depth frame is back-projected into the 3D real world coordinate in order to distinguish between objects in space. Once combined, the mapped color information and the back-projected depth information are fed into automatic, unsupervised clustering framework in order to detect scene objects. The framework runs in parallel to provide real time spatio-temporal object detection.

ABSTRACT Volumetric datasets are increasingly used in medical applications. In many of these applications, visualization and interaction is generally performed on cross‐sectional two‐dimensional (2D) views of three‐dimensional (3D) imaging modalities. Displaying 3D volumetric medical datasets on traditional 2D screens can present problems such as occlusion and information overload, especially when multiple data sources are present. Displaying desired information while showing the relationship to the rest of the dataset(s) can be challenging. In this paper, we present an interactive focus + context visualization approach that uses the volumetric Magic Lens interaction paradigm. We propose to use the Magic Lens as a volumetric brush to perform volume editing tasks, therefore combining data exploration with volumetric editing. Polygon‐assisted ray casting methods are used for real‐time rendering and editing frame rates, while providing compact storage of editing states for undo/redo operations. We discuss the application of our methods to radiation therapy, which is an important cancer treatment modality. We envision that this approach will improve the treatment planning process by improving the therapists' understanding of information from various sources and will help identify if the alignment of the patient in the treatment room coincides with the prepared treatment plan. Copyright © 2013 John Wiley & Sons, Ltd.

The field of computer graphics has impacted a number of application domains in medicine. Medical volume visualization, in particular, has a long and active history in the computer graphics community. However, there are many other techniques in computer graphics that have been applied successfully in medicine. Virtual Reality Simulators have been used to train physicians on difficult procedures. Image-guidance has been used to assist surgeons using different imaging modalities. Different types of visualization techniques have been applied to analyze medical images. This special issue is devoted to novel applications of computer graphics in medical domains. The emphasis has been on technical novelty as opposed to novel medical applications of well-known techniques. The hope is that these papers will be a catalyst for computer graphics community to become more aware of the enormous potential that they hold in making significant contributions to medical domain. The first two papers deals with visualization of medical datasets. Virtual colonoscopy has been used successfully as a faster and painless alternative to real colonoscopy. The paper “Coherence aware GPU based ray casting for virtual colonoscopy” proposes using Graphic Processing Units (GPUs) to ray-trace volumes for use in real-time virtual colonoscopy. An emerging imaging modality is the 3D ultrasound. In “GPU-based Interactive Visualization Framework for Ultrasound Datasets,” the volumetric data generated from a 3D ultrasound is rendered in real-time using the GPU. Simulation can be used in two different ways. They can be use used to train surgeons to perform difficult procedures as described in the next two papers. They can also be used to analyze the physiology as a part of the diagnostic process. In “Haptic Ventriculostomy Simulation in a Grid Environment,” a full-featured surgery simulator that encompass haptic interaction with deformable models in a shared virtual environment is described. Currently, there is little work on evaluating surgical simulators for their effectiveness in training. In “A New Assessment Methodology for Virtual Reality Surgical Simulators,” an assessment methodology for evaluating virtual reality surgical simulators is presented. Simulation offer physicians the ability to analyze biomechanical properties. In “From MRI to Anatomical Simulation of The Hip Joint,” a system for simulating a patient-specific hip joint is presented. The anatomy is generated from Magnetic Resonance Imaging and motion capture is used to drive the simulation. “Image Guided Medialization Laryngoplasty,” describes a system to guide surgeons intra-operatively using image-based modeling and registration.

Techniques that originate in computer graphics and computer vision have found prominent applications in the medical domain. In this paper, we have seamlessly developed techniques from computer graphics and computer vision together with domain knowledge from medicine to develop an image guided surgical system for medialization laryngoplasty. The technical focus of this paper is to register the preoperative radiological data to the intraoperative anatomical structure of the patient. With careful analysis of the real-world surgical environment, we have developed an ICP-based partial shape matching algorithm to register the partially visible anatomical structure to the preoperative CT data. We extracted distinguishable features from the human thyroid cartilage surface and applied image space template matching to find the initial guess for the shape matching. The experimental result shows that our feature-based partial shape matching method has better performance and robustness compared with original ICP-based shape matching method. Although this paper concentrates on the medialization laryngoplasty procedure, its generality makes our methods ideal for future applications in other image guided surgical areas.

Abstract In this paper, we describe a novel method to automatically generate synchronized dance motion that is perceptually matched to a given musical piece. The proposed method extracts 30 musical features from musical data as well as 37 motion features from motion data. A matching process is then performed between the two feature spaces considering the correspondence of the relative changes in both feature spaces and the correlations between musical and motion features. Similarity matrices are introduced to match the amount of relative changes in both feature spaces and correlation coefficients are used to establish the correlations between musical features and motion features by measuring the strength of correlation between each pair of the musical and motion features. By doing this, the progressions of musical and dance motion patterns and perceptual changes between two consecutive musical and motion segments are matched. To demonstrate the effectiveness of the proposed approach, we designed and carried out a user opinion study to assess the perceived quality of the proposed approach. The statistical analysis of the user study results showed that the proposed approach generated results that were significantly better than those produced using a random walk through the dance motion database. Copyright © 2009 John Wiley & Sons, Ltd.

Projective texture mapping is used to project a texture map onto scene geometry. It has been used in many applications, since it eliminates the assignment of fixed texture coordinates and provides a good method of representing synthetic images or photographs in image-based rendering. But conventional projective texture mapping has limitations in the field of view and the degree of navigation because only simple rectangular texture maps can be used. In this work, we propose the concept of panoramic projective texture mapping (PPTM). It projects cubic or cylindrical panorama onto the scene geometry. With this scheme, any polygonal geometry can receive the projection of a panoramic texture map, without using fixed texture coordinates or modeling many projective texture mapping. For fast real-time rendering, a hardware-based rendering method is also presented. Applications of PPTM include panorama viewer similar to QuicktimeVR and navigation in the panoramic scene, which can be created by image-based modeling techniques. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Viewing Algorithms; I.3.7 [Computer Graphics]: Color, Shading, Shadowing, and Texture

Abstract Many fine art pieces have been reproduced in digital form. The digital reproductions have been used to store and transmit the original work. In contrast, mobiles, or moving sculptures, such as those designed by Alexander Calder cannot be reproduced realistically by photographs and/or static images. The real characteristics of mobiles come from the motions generated by interactive external forces applied to their structures. Hence people could not fully enjoy them through static images or even static three‐dimensional models. We present a virtual mobile system where users can easily control the mobile and can feel the impressions that the artist originally intended to provide. Virtual winds are generated by blowing on a microphone which then exert external forces to the mobile. This microphone interface lets users control the mobile while they are watching it through a monitor. We introduce a linear time solution for the constraint dynamics and an improved impulse dynamics to speed up the simulation. Using these techniques, we achieve a real‐time simulation of the mobile on personal computers. The techniques presented can easily be extended to simulate other interactive dynamics systems. Copyright ©2001 John Wiley & Sons, Ltd.

This paper presents an interactive hierarchical motion control system dedicated to the animation of human figure locomotion in virtual environments. As observed in gait experiments, controlling the trajectories of the feet during gait is a precise end-point control task. Inverse kinematics with optimal approaches are used to control the complex relationships between the motion of the body and the coordination of its legs. For each step, the simulation of the support leg is executed first, followed by the swing leg, which incorporates the position of the pelvis from the support leg. That is, the foot placement of the support leg serves as the kinematics constraint while the position of the pelvis is defined through the evaluation of a control criteria optimization. Then, the swing leg movement is defined to satisfy two criteria in order: collision avoidance and control criteria optimization. Finally, animation attributes, such as controlling parameters and pre-processed motion modules, are applied to achieve a variety of personalities and walking styles.

This paper reports empirical results from a study into the use of 2D widgets in 3D immersive virtual environments. Several researchers have proposed the use of 2D interaction techniques in 3D environments, however little empirical work has been done to test the usability of such approaches. We present the results of two experiments conducted on low-level 2D manipulation tasks within an immersive virtual environment. We empirically show that the addition of passive-haptic feedback for use in precise UI manipulation tasks can significantly increase user performance. Furthermore, users prefer interfaces that provide a physical surface, and that allow them to work with interface widgets in the same visual field of view as the objects they are modifying.

The study of human-computer interaction within immersive virtual environments requires us to balance what we have learned from the design and use of desktop interfaces with novel approaches to allow us to work effectively in three dimensions. While some researchers have called for revolutionary interfaces for these new environments, devoid of two-dimensional (2D) desktop widgets, others have taken a more evolutionary approach. Windowing within immersive virtual environments is an attempt to apply 2D interface techniques to three-dimensional (3D) worlds. 2D techniques are attractive because of their proven acceptance and widespread use on the desktop. With current methods environments, however, it is difficult for users of 3D worlds to perform precise manipulations, such as dragging sliders, or precisely positioning or orienting objects. We have developed a testbed designed to take advantage of bimanual interaction, proprioception, and passive-haptic feedback. We present preliminary results from an empirical study of 2D interaction in 3D environments using this system. We use a window registered with a tracked, physical surface, to provide support for precise manipulation of interface widgets displayed in the virtual environment.

A new paradigm is presented for the control of dynamic simulations involving articulated figures. A clone of a figure is manipulated by an animator using traditional kinematic control techniques. The realized dynamic simulation is influenced by this animation through dampened spring forces that connect each link to its corresponding link in the clone. Varying tensions dictate the tightness of the correlation between the animated actions and the computed movements of the simulation. This paradigm strikes a compromise between the absolute control of kinematics and the realistic automation of dynamics. Such a compromise is often sought by animators who want realistic motion but do not want to lose the ability to impart feeling and emotion in a character.

This paper presents a new image morphing method using a two-dimensional deformation technique which provides an intuitive model for a warp. The deformation technique derives aC1-continuous and one-to-one warp from a set of point pairs overlaid on two images. The resulting in-between image precisely reflects the correspondence of features specified by an animator. We also control the transition behaviour in a metamorphosis sequence by taking another deformable surface model, which is simpler and thus more efficient than the deformation technique for a warp. The proposed method separates transition control from feature interpolation and is easier to use than the previous techniques. The multigrid relaxation method is employed to solve a linear system in deriving a warp or transition rates. This method makes our image morphing technique fast enough for an interactive environment.

Abstract We present an approach to articulated figure motion in which motion tasks are defined in terms of goals and ratings. The agents are dynamically‐controlled robots whose behaviour is determined by robotic controller programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI technique of genetic programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative to key framing which allows a highly automated, learning‐based approach to generation of motion. This method of motion control is very general (it can be applied to any type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically plausible and aesthetically appealing motion can be generated using these methods.

Abstract Until recently, sound has been given little attention in computer graphics and related domains of computer animation and virtual environments, although sounds which are properly synchronized to motion provide a great deal of information about events in the environment. Sounds are often not properly synchronized because the sounds and the phenomena that caused the sounds are not considered in an integrated way. In this paper, we present an integrated approach to motion and sound as it applies to computer animation and virtual environments. The key to this approach is synchronization by mapping the motion parameters to sound parameters so that the sound changes as a result of changes in the motion. This is done by representing sounds using a technique for functional composition analogous to the ‘shade trees’ which we calltimbre trees.These timbre trees are used as a part of a sound description language that is analogous to scene description languages such as RenderMan. Using this methodology, we have produced convincing sound effects for a wide variety of animated scenes including the automatic generation of background music.

This paper presents a new image morphing technique using deformable surfaces. Drawbacks of previous techniques are overcome by a physically-based approach which provides an intuitive model for a warp. A warp is derived by two deformable surfaces which specify horizontal and vertical displacements of points on an image. This paper also considers the control of transition behavior in a metamorphosis sequence. The presented technique separates the transition control from interpolating features making it much easier to use than the previous techniques. The multigrid relaxation method is used to compute a deformable surface for a warp or transition rates. This method makes the presented image morphing technique fast enough for an interactive environment.>

This paper presents an object-oriented system design supporting the composition of scientific data visualization techniques based on the definition of hierarchies of typed data objects and tools. Traditional visualization systems focus on creating graphical objects which often cannot be re-used for further processing. Our approach provides objects of different topological dimension to offer a natural way of describing the results of visualization mappings. Serial composition of data extraction tools is allowed, while each intermediate visualization object shares a common description and behavior. Visualization objects can be re-used, facilitating the data exploration process by expanding the available analysis and correlation functions provided. This design offers an open-ended architecture for the development of new visualization techniques. It promotes data and software re-use, eliminates the need for writing special purpose software and reduces processing requirements during interactive visualization sessions.>

We present a general methodology to produce synchronized soundtracks for animations.A sound world is modeled by associating a characteristic sound for each object in a scene.These sounds can be generated from a behavioral or physically-based simulation.Collision sounds can be computed from vibrational response of elastic bodies to the collision impulse.Alternatively, stereotypic recorded sound effects can be associated with each interaction of objects.Sounds may also be generated procedurally, The sound world is described with a sound event file, and is rendered in two passes.First the propagation paths from 3D objects to each microphone are analyzed and used to calculate sound transformations according to the acoustic environment.These effects are convolutions, encoded into two essential parameters, delay and attenuation of each sound.Timeciependency of these two parameters is represented with key frames, thus being completely independent of the original 3D animation script.In the second pass the sounds associated with objects are instantiated, modulated by interpolated key parameters, and summed up to the final soundtrack.The advantage of a modular architecture is that the same methods can be used for all types of animations, keyframed, physically-based and behavioral.We also discuss the differences of sound and light, and the remarkable similarities in their rendering processes.

The theoretical background and implementation for a computer animation system to model a general class of three dimensional dynamic processes for arbitrary rigid bodies is presented. The simulation of the dynamic interaction among rigid bodies takes into account various physical characteristics such as elasticity, friction, mass, and moment of inertia to produce rolling and sliding contacts. If a set of bodies is statically unstable, the system dynamically drives it toward a stable configuration while obeying the geometric constraints of the system including general non-holonomic constraints. The system also provides a physical environment with which objects animated using more traditional techniques can interact. The degree of interaction is easily controlled by the animator. A computationally efficient method to merge kinematics and dynamics for articulated rigid bodies to produce realistic motion is presented.