Women re-entering the information and technology (IT) fields after a career break often encounter significant challenges such as diminished self-confidence and difficulty adjusting to a rapidly changing technological landscape. This study investigates whether structured online learning journals can help these individuals rebuild their skills and confidence, while also influencing their longer-term career activities. Data were collected from 48 women enrolled in a four-week online IT job training program, including 442 journal entries, quiz scores, instructor evaluations, and three-month follow-up records on career activities. Results indicate that (1) Frequent submissions with everaging 98 words, correlated with higher short-term performance; (2) cluster analysis identified three distinct journaling patterns: Reflective (25%), Engaged (54%), and Disengaged (21%); and (3) at the three-month follow-up, the participants in the journaling group demonstrated significantly higher instructor contract signings and total teaching sessions than the control group (n = 44) which did not engage in journaling. The article concludes with design recommendations for implementing journaling in online reentry programs.

We present BrainODE, a neural ordinary differential equation (ODE)-based framework for modeling continuous longitudinal deformations of brain shapes. BrainODE learns a deformation space over anatomically meaningful brain regions to facilitate early prediction of neurodegenerative disease progression. Addressing inherent challenges of longitudinal neuroimaging data-such as limited sample sizes, irregular temporal sampling, and substantial inter-subject variability-we propose a conditional neural ODE architecture that models shape dynamics with subject-specific age and cognitive status. To enable autoregressive forecasting of brain morphology from a single observation, we propose a pseudo-cognitive status embedding that allows progressive shape prediction across intermediate time points with predicted cognitive decline. Experiments show that BrainODE outperforms time-aware baselines in predicting future brain shapes, demonstrating strong generalization across longitudinal datasets with both regular and irregular time intervals.

Spatial control methods using additional modules on pretrained diffusion models have gained attention for enabling conditional generation in natural images. These methods guide the generation process with new conditions while leveraging the capabilities of large models. They could be beneficial as training strategies in the context of 3D medical imaging, where training a diffusion model from scratch is challenging due to high computational costs and data scarcity. However, the potential application of spatial control methods with additional modules to 3D medical images has not yet been explored. In this paper, we present a tailored spatial control method for 3D medical images with a novel lightweight module, Volumetric Conditioning Module (VCM). Our VCM employs an asymmetric U-Net architecture to effectively encode complex information from various levels of 3D conditions, providing detailed guidance in image synthesis. To examine the applicability of spatial control methods and the effectiveness of VCM for 3D medical data, we conduct experiments under single-and multimodal conditions scenarios across a wide range of dataset sizes, from extremely small datasets with 10 samples to large datasets with 500 samples. The experimental results show that the VCM is effective for conditional generation and efficient in terms of requiring less training data and computational resources. We further investigate the potential applications for our spatial control method through axial super-resolution for medical images. Our code is available at https://github.com/SSTDV-Project/VCM.git

Ground-level ozone is formed by chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC) under sunlight, primarily from industrial and vehicular emissions.

Estimating the poses of both a hand and an object has become an important area of research due to the growing need for advanced vision computing. The primary challenge involves understanding and reconstructing how hands and objects interact, such as contact and physical plausibility. Existing approaches often adopt a graph neural network to incorporate spatial information of hand and object meshes. However, these approaches have not fully exploited the potential of graphs without modification of edges within and between hand- and object-graphs. We propose a graph-based refinement method that incorporates an interaction-aware graph-attention mechanism to account for hand-object interactions. Using edges, we establish connections among closely correlated nodes, both within individual graphs and across different graphs. Experiments demonstrate the effectiveness of our proposed method with notable improvements in the realm of physical plausibility.

We present a mid-air haptic rendering method to explore volumetric data in augmented reality (AR) environments. Users directly interact with a volume-rendered hologram using their bare hands. Since the volume rendering method accumulates color along with its transparency, the depth perception of the user’s hand within the hologram is vague when used with only visual feedback. Therefore, to enhance the localization of internal structures within the volume data, we propose an AR system that provides a tactile presence for boundaries and associated information (e.g., texture). Leveraging Low-High (LH) histograms, our system provides boundary information to the user both visually and haptically. Specifically, when the user interacts with the volume data using their hands, the system computes a set of focal points or a set of tactile patterns using a GPGPU-based estimation. Additionally, we developed mid-air haptic rendering methods using amplitude modulation (AM) and spatiotemporal modulation (STM) techniques. Both methods were implemented on HoloLens 2 and could run in real-time. The effectiveness of each proposed method was evaluated with respect to various volume data sets, including synthetic and computed tomography (CT) scan data. Our results show that while both haptic rendering methods produce tangible experiences from the hologram interaction, our spatiotemporal modulation method generates better shape discrimination performance with respect to the amplitude modulation method in the case of multiple region of interests.

We present an interactive exhibition concept built on existing physical wall panels in a museum. By combining the unique exhibition contents with various rendering and interface techniques, we create immersive contents that have two-way communication and multi-modal feedback as key features to provide engaging experiences. We apply our interactive panel concept to a mummy exhibit in a natural history museum in Korea and demonstrate its effectiveness. The final goal of this project is for it to serve as a platform for virtual museums to provide an opportunity to expand offline museums to an online experience.

Accurately recovering the hippocampal shapes against rough and noisy segmentations is as challenging as achieving good anatomical correspondence between the individual shapes. To address these issues, we propose a mesh-to-volume registration approach, characterized by a progressive model deformation. Our model implements flexible weighting scheme for model rigidity under a multi-level neighborhood for vertex connectivity. This method induces a large-to-small scale deformation of a template surface to build the pairwise correspondence by minimizing geometric distortion while robustly restoring the individuals' shape characteristics. We evaluated the proposed method's (1) accuracy and robustness in smooth surface reconstruction, (2) sensitivity in detecting significant shape differences between healthy control and disease groups (mild cognitive impairment and Alzheimer's disease), (3) robustness in constructing the anatomical correspondence between individual shape models, and (4) applicability in identifying subtle shape changes in relation to cognitive abilities in a healthy population. We compared the performance of the proposed method with other well-known methods--SPHARM-PDM, ShapeWorks and LDDMM volume registration with template injection--using various metrics of shape similarity, surface roughness, volume, and shape deformity. The experimental results showed that the proposed method generated smooth surfaces with less volume differences and better shape similarity to input volumes than others. The statistical analyses with clinical variables also showed that it was sensitive in detecting subtle shape changes of hippocampus.

In this paper, we present a haptic plug-in system that operates on already-developed existing interactive 3D graphics applications. There are various high-quality graphics applications developed without taking into consideration the haptic features. Our system augments haptic interaction in a simple manner to enhance the experience. The proposed system consists of two separate modules. While one module captures the depth map from a graphics pipeline of the target application, the other module calculates the haptic feedback force based on the captured depth map. We address efficient and effective ways of capturing and transferring depth map data for haptic rendering and present a working solution.

While the human hand is the most natural tool for interacting with objects, hand interaction with virtual objects is still a challenging research field. We recognize that common hand interaction involves two separate stages with different aspects: One is the robustness of grasping, and the other is the dexterity of manipulation after grasping a virtual object. In this paper, we address virtual hand interaction by decoupling these two aspects and propose strategic simulation algorithms for two different stages. As for the initial grasping, a quality measure-based grasping algorithm is applied for robustness, and the manipulation is simulated by physically based methods to meet the requirements of dexterity. We conducted experiments to evaluate the effectiveness of our proposed method under different display environments -- monoscopic and stereoscopic. From the 2-way ANOVA test, we were able to show that the proposed scheme that separates a pre-contact grasping phase and a post-contact manipulation phase gives us the robustness of grasping quality and dexterity of tool operation. Furthermore, we demonstrate various assembly manipulations of the relatively complex models using our interaction scheme.

Mechanical removal of bone material is the most critical procedure during dental implant surgery because it can jeopardize patient safety in several ways such as damage in the mandibular canal and piercing of the maxillary sinus. With recognition of the effectiveness in virtual training, many simulators with haptic feedback have been proposed. Although there are many varieties in drill bits, most of previously developed simulators consider only a spherically shaped tool due to its simplicity in tool-bone interaction. In this paper, we propose a new simulation method that can handle any arbitrarily shaped tools with multiple contacts between the tool and the bone. The tool is represented by a signed-distance field, and the bone is represented as voxels surrounded by a point shell. Upon chipping away bone elements, the point shell is updated reflecting the deformation of bone in real-time, while the collision detection and the reflected force is efficiently and accurately computed from the distance field encoded in the tool. We also present the experimental results with 12 dental implantologists to evaluate realism of the proposed simulator.

BACKGROUND: Much effort is currently made to develop the Gene Ontology (GO). Due to the dynamic nature of information it addresses, GO undergoes constant updates whose results are released at regular intervals as separate versions. Although there are a large number of computational tools to aid the development of GO, they are operating on a particular version of GO, making it difficult for GO curators to anticipate the full impact of particular changes along the time axis on a larger scale. We present a method for tapping into such an evolutionary aspect of GO, by making it possible to keep track of important temporal changes to any of the terms and relations of GO and by consequently making it possible to recognize associated trends. RESULTS: We have developed visualization methods for viewing the changes between two different versions of GO by constructing a colour-coded layered graph. The graph shows both versions of GO with highlights to those GO terms that are added, removed and modified between the two versions. Focusing on a specific GO term or terms of interest over a period, we demonstrate the utility of our system that can be used to make useful hypotheses about the cause of the evolution and to provide new insights into more complex changes. CONCLUSIONS: GO undergoes fast evolutionary changes. A snapshot of GO, as presented by each version of GO alone, overlooks such evolutionary aspects, and consequently limits the utilities of GO. The method that highlights the differences of consecutive versions or two different versions of an evolving ontology with colour-coding enhances the utility of GO for users as well as for developers. To the best of our knowledge, this is the first proposal to visualize the evolutionary aspect of GO.

In interacting with a soft object, both visual and haptic senses play major role in providing the characteristics of the object. While visual feedback comes from the appearance of the overall shape, haptic feedback comes largely from the localized internal composition. However, it has been a common practice to represent the object with one model for both processing shape de formation and computing the feedback force, making it much challenging work to achieve real-time simulation of a soft and especially non-homogeneous object. In this paper, we claim that it may be sufficient to represent the graphical visual model as a surface model whereas it is necessary to represent the haptic model as a volumetric model. We propose, therefore, a hybrid model where volumetric haptic model and surface graphical model coexist. For constructing hybrid model, we employ S-chain model to compute the haptic behavior and mass-spring model to calculate graphic behavior. We conduct experiments with volumetric human liver model at a haptic update rate of 1000 Hz and graphic update rate of 30 Hz. The experimental results show that our proposed model satisfies the real-time performance and provides the realistic sense to be useful for a virtual reality application.

We present a real-time simulation of spray painting incorporated into a VR environment as an alternative training system for ship-building industries. The system allows the user to try out a painting work on life-size structures with a spray gun. Our goal is to provide a trainee realistic painting experience in real-time as well as to represent the thickness of the deposited paint on the surface for evaluation of his performance. The Gaussian model is used for a painting deposition model, and texture mapping technique is utilized to provide efficient visual feedback. We also present effective collision detection methods for a volume of spray paint particles

We present a new approach for the analysis of the left ventricular shape and motion based on the development of a new class of volumetric deformable models. We estimate the deformation and complex motion of the left ventricle (LV) in terms of a few parameters that are functions and whose values vary locally across the LV. These parameters capture the radial and longitudinal contraction, the axial twisting, and the long-axis deformation. Using Lagrangian dynamics and finite-element theory, we convert these volumetric primitives into dynamic models that deform due to forces exerted by the datapoints. We present experiments where we used magnetic tagging (MRI-SPAMM) to acquire datapoints from the LV during systole. By applying our method to MRI-SPAMM datapoints, we were able to characterize the 3-D shape and motion of the LV both locally and globally, in a clinically useful way. In addition, based on the model parameters we were able to extract quantitative differences between normal and abnormal hearts and visualize them in a way that is useful to physicians.

The authors present a new method for analyzing the motion of the heart's left ventricle (LV) from tagged magnetic resonance imaging (MRI) data. Their technique is based on the development of a new class of physics-based deformable models whose parameters are functions. They allow the definition of new parameterized primitives and parameterized deformations which can capture the local shape variation of a complex object. Furthermore, these parameters are intuitive and require no complex post-processing in order to be used by a physician. Using a physics-based approach, the authors convert the geometric models into dynamic models that deform due to forces exerted from the datapoints and conform to the given dataset. The authors present experiments involving the extraction of the shape and motion of the LV's mid-wall during systole from tagged MRI data based on a few parameter functions. Furthermore, by plotting the variations over time of the extracted LV model parameters from normal and abnormal heart data along the long axis, the authors are able to quantitatively characterize their differences.

We present a new method for analyzing the 3D motion of the heart's left ventricle (LV) from tagged magnetic resonance imaging (MRI) data. Our technique is based on the development of a new class of volumetric physics-based deformable models whose parameters are functions and can capture the local shape variation of an object. These parameters require no complex post-processing in order to be used by a physician. These volumetric models allow the accurate estimation of the shape and motion of the inner and outer walls of the LV as well as within the walls. We also present a new technique for calculating forces exerted by tagged MRI data to material points of the deformable model. Furthermore, by plotting the variations over time of the extracted LV model parameters from normal heart data we are able to quantitatively analyze and compare the epicardial and endocardial motion.>

We develop a new method for analyzing the motion of the left ventricle (LV) of a heart from tagged MRI data. Our technique is based on the development of a new class of physics-based deformable models whose parameters are functions allowing the definition of new parameterized primitives and parameterized deformations. These parameter functions improve the accuracy of shape description through the use of a few intuitive parameters such as functional twisting. Furthermore, these parameters require no complex post-processing in order to be used by a physician. Using a physics-based approach, we convert these geometric models into deformable models that deform due to forces exerted from the datapoints and conform to the given dataset. We present experiments involving the extraction of shape and motion of the LV from MRI-SPAMM data based on a few parameter functions. Furthermore, by plotting the variations over time of the extracted model parameters from normal and abnormal heart data we are able to characterize quantitatively their differences.>

This paper develops a new class of physics-based deformable models which can deform both globally and locally. Their global parameters are functions allowing the definition of new parameterized primitives and parameterized global deformations. These new global parameter functions improve the accuracy of shape description through the use of a few intuitive parameters such as functional bending and twisting. Using a physics-based approach we convert these geometric models into deformable models that deform due to forces exerted from the data-points so as to conform to the given dataset. We present an experiment involving the extraction of shape and motion of the Left Ventricle (LV) of a heart from MRI-SPAMM data based on a few global parameter functions. >