Aphasia following stroke affects millions globally, yet rehabilitation remains severely limited by speech therapist shortages. Existing digital systems rely on static video demonstrations, single-modality assessment, and rule-based feedback, failing to address authentic clinical needs. Through formative investigation with five therapists, three patients, and their caregivers, we identified concrete clinical challenges: therapists spending 30-40% of time on repetitive demonstrations, existing tools providing only speech scores without articulatory evaluation, and patients struggling with complex interfaces and monotonous content. To address these challenges, we developed an integrated rehabilitation system combining an embodied digital therapist for Action Observation Therapy, tri-dimensional assessment coordinating speech quality, lip movement accuracy, and semantic understanding, and large language model-driven personalization for content generation and adaptive training. We conducted a proof-of-concept evaluation across two in-situ hospital training sessions with six patients, six caregivers, and three therapists. Results demonstrated substantial efficiency gains, with therapists spending 69-78% less time per patient. Patient acceptance improved 42.6% across sessions, and low digital literacy patients showed steepest gains (+69.0%). However, human intervention remained necessary for 24-30% of session time to provide emotional support. These findings empirically characterize human-AI collaboration boundaries in clinical rehabilitation, revealing both automation’s potential to enhance efficiency and bridge digital divides, and the persistent necessity of human therapeutic presence—providing evidence for responsible deployment of AI-assisted healthcare systems.

Cross-domain medical image segmentation has been challenging due to the extraordinary cost of collecting sufficient data in various imaging conditions. Mainstream methodologies enhance generalizability by directly adapting large-scale vision foundation models for medical image segmentation tasks. However, these approaches frequently incur high memory costs and necessitate additional prompts for deployment. In this study, we utilize dark knowledge in pretrained segmentation models as external regularization to improve the model’s generalizability. Furthermore, an activation-restricted regularization item is proposed to eliminate the noise/errors within the generated pseudo labels. Experiments on medical cross-domain datasets demonstrate the SOTA performance (above 2.66% improvement) of the proposed method with no additional cost during inference.

Microswarms face challenges in precise delivery within dynamic biological fluids due to fluid disturbances and limited operational intuitiveness. Current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities, particularly in complex tasks that require a balance between flexibility and precision. In this study, we propose a haptic-assisted magnetic actuation control strategy, establishing a human-in-the-loop control framework. The haptic perception system provides the operator with haptic feedback reflecting the interactions between the microswarm and the environment. A real-time tracking system monitors the position and pattern of the controlled microswarm in remote environments, and transmits this information to the control system for decision-making. After characterizing the magnetic field parameters and magnetic nanoparticles, we have achieved real-time navigation and morphology modulation of the microswarm in dynamic flow conditions and three-dimensional (3D) space. Comparative experiments under various flow rate conditions demonstrate that the haptic-assisted strategy enhances microswarm control stability and precision across different flow regimes. Moreover, the human-machine collaboration mechanism improves delivery success rates (97%) under sudden disturbances compared to preprogrammed automated control and purely manual control, validating its potential for applications in complex biomedical scenarios. Our work provides a haptic-assisted microswarm control method in dynamic conditions, expanding an adaptive microswarm control strategy in complex biomedical environments.

Traditional force-based graph layout models are rooted in virtual physics, while criteria-driven techniques position nodes by directly optimizing graph readability criteria. In this article, we systematically explore the integration of these two approaches, introducing criteria-driven force-based graph layout techniques. We propose a general framework that, based on user-specified readability criteria, such as minimizing edge crossings, automatically constructs a force-based model tailored to generate layouts for a given graph. Models derived from highly similar graphs can be reused to create initial layouts, users can further refine layouts by imposing different criteria on subgraphs. We perform quantitative comparisons between our layout methods and existing techniques across various graphs and present a case study on graph exploration. Our results indicate that our framework generates superior layouts compared to existing techniques and exhibits better generalization capabilities than deep learning-based methods.

Artificial Intelligence for IT Operations (AIOps) platform needs to monitor the status of massive microservices to alert potential failures. In China Mobile Cloud, we develop a time-series anomaly detection service consisting of architecture and algorithm, that captures the anomaly metrics in time-series effectively. The architecture comprises four modules that work together: data collection, data storage, anomaly monitoring, and experimental platform. For enhancing the efficiency and accuracy of anomaly detection, a novel Saliency Time-Series Anomaly Detection Algorithm with minLSTM (STAD-minLSTM) is proposed to solve the challenges of high real-time, lack of labels, inefficiency, and poor accuracy in AIOps. A massive of experimental results on two real-world datasets demonstrate that our approach has substantially improved, which outperforms several state-of-the-art baselines.

Modular robotic structures simplify robot design and manufacturing by using standardized modules, enhancing flexibility and adaptability. However, the need for manual input in design and assembly limit their potential. Current methods to automate this process still require significant human effort and technical expertise. This paper introduces a novel approach that employs Large Language Models (LLMs) as intelligent agents to automate the creation of modular robotic structures. We decompose the modular robot creation task and develop two agents based on LLM to plan and assemble the modular robots from text prompts. By inputting a textual description, users can generate robot designs that are validated in both simulated and real-world environments. This method reduces the need for manual intervention and lowers the technical barrier to creating complex robotic systems.

Chinese acupuncture practitioners primarily depend on muscle memory and tactile feedback to insert needles and accurately target acupuncture points, as the current workflow lacks imaging modalities and visual aids. Consequently, new practitioners often learn through trial and error, requiring years of experience to become proficient and earn the trust of patients. Medical students face similar challenges in mastering this skill. To address these challenges, we developed an innovative system, MRUCT, that integrates ultrasonic computed tomography (UCT) with mixed reality (MR) technology to visualize acupuncture points in real-time. This system offers offline image registration and real-time guidance during needle insertion, enabling them to accurately position needles based on anatomical structures such as bones, muscles, and auto-generated reference points, with the potential for clinical implementation. In this paper, we outline the non-rigid registration methods used to reconstruct anatomical structures from UCT data, as well as the key design considerations of the MR system. We evaluated two different 3D user interface (3DUI) designs and compared the performance of our system to traditional workflows for both new practitioners and medical students. The results highlight the potential of MR to enhance therapeutic medical practices and demonstrate the effectiveness of the system we developed.

ABSTRACT We propose a novel method, entitled “Folding by Skinning”, which creatively integrates skinning techniques with folding simulations. This method allows users to specify a two‐dimensional crease pattern along with the desired folding angles for each crease. Based on this input, the system computes the final three‐dimensional shape of the fold. Rather than employing costly physics‐based simulations, we explore the skinning method, noted for its effectiveness in handling the geometry of the folded shape. We recommend extracting the skinning weights directly from the user‐defined crease patterns. By combining the obtained skinning weights with the user‐input folding angles, the initial shape undergoes dual quaternion skinning to produce the folding result. Users can further optimize the shape using post‐processing and targeted filtering of weights to generate more realistic results. Our experimental results demonstrate that “Folding by Skinning” yields high‐quality outcomes and offers relatively fast computation, making it an effective tool for computer‐aided design, animation, and fabrication applications.

In this study, for the musculoskeletal robot system, we aim at optimizing the angle tracking control based on the system model. First, we analyze the driving principle of muscles and establish a bionic muscle dynamics model. Further, based on the geometric relationship between muscles and skeletons, we establish the kinematics model and dynamics model of the musculoskeletal robot system. The adaptive dynamic programming (ADP) algorithm is used for solving the Hamilton-Jacobi-Bellman (HJB) equation. Based on the proposed updating law, the neural network is trained to approximate the solution. Using the Lyapunov’s direct method, we can prove that the system is stable with the proposed controller. Furthermore, simulation indicates that this presented control law is feasible.

Existing works on controlling a concentric tube robot (CTR) mostly focus on the trajectory of its tip position or pose. In order to safely send CTRs in a confined lumen space, we propose to continuously steer the CTRs so that its entire shape will always attempt to approximate target curves over time. We focus on stiffness-dominant CTRs. Considering the differential geometry of such CTR shapes, we propose to work on the curvature domain to reduce the computational cost in searching the configuration of the CTRs. With our formulation, we model the curvature control of the CTR to find the optimal translation of each tube and then search for the rotation of the tubes to fit the target shapes. We demonstrate our method using sets of different target paths. The computational time per frame, ranging between 0.1 to 0.3 seconds across all experiments, highlights the efficiency of our approach in aligning the complete shape of the CTR with specified paths. Notably, for time-varying trajectories that could be reproduced by the CTR with its maximum deployment length reaching 150 mm, the root mean square error and median error were 0.98mm and 0.46mm, respectively.

Robot-assisted vascular interventional surgery is a hot topic in the interdisciplinary field of medical science and engineering, it has important research significance. Compared with traditional manual operation, it shows obvious advantages, such as avoiding the radiation exposure to the interventionist, reducing the workload of the interventionist, improving the precision of the operation, and guaranteeing the safety of the operation. However, the human–robot interaction ability has always been a major challenge that cannot be ignored. In this article, we designed and developed a leader device that enables the interventionists to experience the state of surgical instruments naturally and realistically when operating it, as if they were operating actual surgical instruments. The innovations can be summarized as follows: the structure of the leader device is highly consistent with the interventionist’s operating habit, which greatly reduces the interventionist’s operating difficulty. Haptic feedback in both translational direction and circumferential direction is achieved to provide haptic guidance to the interventionists and improve their surgical presence. The leader device is optimized in both structural design and functional realization compared to existing leader devices. This study has great potential in improving human-robot interaction ability, and could provide a reference for the design and evaluation of the leader device.

Clinical coding translates medical information from Electronic Health Records (EHRs) into structured codes such as ICD-10, which are essential for healthcare applications. Advances in deep learning and natural language processing have enabled automatic ICD coding models to achieve notable accuracy metrics on in-domain datasets when adequately trained. However, the scarcity of clinical medical texts and the variability across different datasets pose significant challenges, making it difficult for current state-of-the-art models to ensure robust generalization performance across diverse data distributions. Recent advances in Large Language Models (LLMs), such as GPT-4o, have shown great generalization capabilities across general domains and potential in medical information processing tasks. However, their performance in generating clinical codes remains suboptimal. In this study, we propose a novel ICD coding paradigm based on code verification to leverage the capabilities of LLMs. Instead of directly generating accurate codes from a vast code space, we simplify the task by verifying the code assignment from a given candidate set. Through extensive experiments, we demonstrate that LLMs function more effectively as code verifiers rather than code generators, with GPT-4o achieving the best performance on the CodiEsp dataset under zero-shot settings. Furthermore, our results indicate that LLM-based systems can perform on par with state-of-the-art clinical coding systems while offering superior generalizability across institutions, languages, and ICD versions.

Elasticity is one of therepresentative parameters that reflect the mechanical properties of soft materials. Detecting the underneath elasticity distribution called elastography is a key step for understanding and interacting with objects. Existing solutions for capturing the interior elasticity distribution typically rely on expensive apparatus. In this work, the dense tactile signal captured by the high-resolution vision-based tactile sensor is introduced as a new modality for reconstructing 3D elasticity distribution. We propose a model-based method, which exploits the tactile maps from active pressing trials for the elastography task. The interior elasticity distribution for non-rigid objects is reconstructed from an inverse physics model. We analyze the credibility of the estimated elasticity distribution obtained from our method. Varying design factors are also discussed. We experiment our method on a set of synthesized 3D models and physical models in robot-assisted scenes. Various experimental results have been gathered, demonstrating the efficacy of our approach in perceiving elasticity distribution.

This article proposes a distributed passivity-based bilateral teleoperation control for optimizing the velocity/force manipulability of the coordinated remote redundant manipulators during the task execution. Following the leader–follower paradigm, the control connects a local haptic device with a leader remote manipulator and coordinates all the leader and follower remote manipulators. The approach is novel in reconciling the potential conflicts between the pose synchronization task and the manipulability optimization task for the remote manipulators by two-layer auxiliary systems. The first layer decouples the pose synchronization constraints into separable position and orientation constraints, and the second layer optimizes the manipulability under the position and orientation constraints. The approach is robust by designing smooth controls for the manipulators without knowing their dynamic parameters. Finally, the control renders the bilateral teleoperator output strictly passive for stable physical interactions with the human user and the environment. Comparative experiments verify the effectiveness of the proposed control in the presence of time-varying communication delays.

This article presents a passivity-based shared control for a multirobot teleoperation system to perform extravehicular assembly tasks. At the remote site, an eye-in-hand camera is integrated with three manipulators that cooperatively drive a customized tool. A haptic device at the local site allows a human operator to intervene when necessary via bilateral teleoperation. To enable intuitive user control, a homography-based method seamlessly integrates visual servoing and operator input to guide the remote camera. To accommodate different peg geometries and enable adaptive tool actuation, a partial pose synchronization method decouples roll from the other pose dimensions. It allows each manipulator to independently actuate a gripper by rotating its end-effector frame around the roll axis, without interfering with cooperative motion in position, pitch, and yaw. In addition, to minimize undesired internal forces, we formulate and solve a passivity-constrained interaction wrench optimization problem that enhances stability. The proposed control ensures smooth transitions between autonomous and teleoperated modes, supporting flexible human intervention. Theoretical analysis and experimental validation confirm the system’s stability and effectiveness in achieving precise, robust, and responsive multirobot teleoperation for complex space assembly tasks.

Handheld haptic devices often face challenges in delivering stiffness feedback with both high force output and good backdrivability, especially under practical constraints on power consumption, size, and weight. These difficulties stem from the inherent performance limitations of conventional actuation mechanisms. To address this issue, we propose a lightweight, low-power handheld device that provides wide-range stiffness feedback through a novel dual actuation design composed of two key components. A programmable spring (PS), implemented via an adjustable lever arm, enables tunable physical stiffness. Two electrostatic clutches (ECs) are integrated to compensate for the inherent limitations of PS-based interactions in stiffness display range, rendered object size, and free motion capability. The feedback force arises passively from the reaction of the PS and ECs to user input, effectively lowering both power consumption and actuator torque demands. A fully integrated prototype was developed, incorporating wireless communication, control, and power modules. The results of the evaluation experiments and user studies demonstrate that the device effectively renders stiffness across the full range, from free motion to full rigidity, and delivers more realistic elastic feedback compared to conventional electric motor-based systems.

As virtual objects contain increasingly rich attribute information, small wearable fingertip devices need to have higher degrees of freedom (DoFs) to convey the haptic sensation of virtual objects. In order to effectively display the shape features of virtual objects to users through curvature, we designed a 6-DoF wearable fingertip device (WFD). This WFD combines a 6-DoF Stewart parallel mechanism, consisting of a static platform and a mobile platform connected by six revolute-spherical-spherical kinematic chains. The translation and rotation of the mobile platform are driven by six miniature servo motors, which can simulate haptic sensations such as making and breaking contact, sliding, and skin stretch when the fingertip interacts with a virtual surface. The WFD is fixed at the user's dominant index finger using hook-and-loop fasteners, with a size of 68 × 59 × 56 mm$^{3}$3 and a mass of 45.5 g. We analyzed and validated the kinematic model of the WFD and tested its force output capability. Finally, we invited 15 adults to conduct three subjective perception experiments to evaluate the performance of the WFD in curvature perception and shape display. The experimental results show that: (1) The just noticeable difference (JND) for curvature identification using the WFD is 3.02$\pm$±0.23 m$^{-1}$-1; (2) The 6-DoF haptic feedback provided by the WFD improves the accuracy of curved surface recognition from 53.4$\pm$±7.1% in 3-DoF to 72.0$\pm$±5.9%; (3) Even without visual feedback, the shape recognition accuracy of the WFD when combined with the Touch device reaches 82.3$\pm$±8.2% . Experimental results show that the WFD has good performance and potential in curvature perception and shape display.

Elastic rods are commonly seen in our daily life. Although humans are sensitive to the shape change of rods, it is not intuitive to estimate the external forces applied to generate the deformation. We propose a method to interactively track the elastic linear objects by using the Cosserat rod model to regulate the captured noisy points. We develop a framework based on particle filters to work with the physics-based model, turning the inverse physics problem into a forward simulation and search problem. We show that with the proposed method, we can simultaneously digitalize the shape as well as the external forces on real-world elastic rods. With these capabilities, we demonstrate virtual and augmented reality applications to facilitate the interaction with elastic linear objects. The tracking performance is also validated with experiments.

For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best quality of the rehabilitation process, customized game levels for each user are desired, while it is labor-intensive to design and adjust game levels for different individuals. We work on generating training content for a desktop end-effector rehabilitation robot and propose a method to automatically generate individualized training plans. By modeling the search of the training motions as finding optimal hand paths and trajectories, we introduce solving the design problem with a multi-objective optimization (MO) solver. We further improve the MO solver to enhance the diversity of the solutions. With the proposed approach, our system is capable of automatically generating various training plans considering the training intensity and dexterity of each joint in the upper limb. In addition, the enhanced diversity avoids repeated training plans, which helps motivate the user in the rehabilitation. We test our method with different requirements on the training plans and validate the solutions.

Teleoperated techniques enable remote human-robot interaction and have been widely accepted in robot-assisted surgeries. However, it is still hard to guarantee the safety of teleoperated surgery due to the imperfect input commands limited by remote perception, preventing teleoperated surgery from being widely used. We propose a new framework to avoid the collision of surgery robots and human tissue caused by inaccurate inputs. We directly take the medical volume data and propose to use the implicit neural field to guide teleoperated robot-assisted surgery. With guidance, the trajectory of the robot manipulator is optimized to safely work inside a narrow workspace. We evaluated our method in several aspects and conducted a real-world experiment on a head phantom. Experimental results show that our proposed method can effectively avoid the collision between the surgical tool and the human tissue during teleoperation.

The neural radiance field (NeRF) is attracting increasing attentions from researchers in various fields. While NeRF has produced visually plausible results and found its potential applications in virtual reality, users are only allowed to rotate the camera to observe the scene represented as NeRF. We study the haptic interaction with NeRF models in this paper to enable the experience of touching objects reconstructed by NeRF. Existing haptic rendering algorithms do not work well for NeRF-represented models because NeRF is often noisy. We propose a stochastic haptic rendering method to deal with the collision response between the haptic proxy and NeRF. We validate our method with complex NeRF models and experimental results show the efficacy of our proposed algorithm.

We propose a novel approach for enabling comic-style conversation in mixed reality to assist face-to-face conversation on-site or remotely. Our approach brings word balloons of comic-style conversation to the real world. The word balloons can adapt to mixed reality scenes, such as the 3-D head motion of the speaker, the comic styles, and the speech. During the conversation, our approach updates the word balloons continuously in the object space and discretely in the image space, guided by a field learned from comics. Quantitative experiments and perceptual studies were conducted to evaluate and compare our approach with alternatives. The results from the user study and ablation study demonstrated that our approach turns out to be practical for assisting face-to-face conversation.

With the development of virtual reality, the practical requirements of the wearable haptic interface have been greatly emphasized. While passive haptic devices are commonly used in virtual reality, they lack generality and are difficult to precisely generate continuous force feedback to users. In this work, we present SmartSpring, a new solution for passive haptics, which is inexpensive, lightweight and capable of providing controllable force feedback in virtual reality. We propose a hybrid spring-linkage structure as the proxy and flexibly control the mechanism for adjustable system stiffness. By analyzing the structure and force model, we enable a smart transform of the structure for producing continuous force signals. We quantitatively examine the real-world performance of SmartSpring to verify our model. By asymmetrically moving or actively pressing the end-effector, we show that our design can further support rendering torque and stiffness. Finally, we demonstrate the SmartSpring in a series of scenarios with user studies and a just noticeable difference analysis. Experimental results show the potential of the developed haptic display in virtual reality.

Haptic feedback is widely used to enhance realism in virtual reality (VR). Shape and softness are two common factors perceived by the users in the haptic rendering of soft objects. To integrate these factors, we propose a new handheld shape-changing device, TapeTouch, to provide various shapes and softness in real time. TapeTouch is based on a controllable shape-changing tape, which is mainly composed of four motors and a section of brass tape. We design a structure of the components to fit a portable controller and allow to flexibly adjust the shape of the brass tape. After decoding desired shapes into the signals to control the motor, we automatically reproduce varying shapes and levels of softness to the finger or palm touching the shape-changing tape. We conducted two user studies to understand the capability of TapeTouch to render shape and softness, and the results showed that TapeTouch could provide a variety of distinguishable shapes as well as multiple levels of softness. Based on the results, we performed two VR experience studies to verify that the haptic feedback from TapeTouch enhances VR realism.

This paper proposes a new path planning strategy - the Rapid Visible Tree (RVT) algorithm to guide a robot to its goal in a complex environment without dangerous collisions. By fusing the visibility information with the classic tree-based searching method, RVT only takes the noisy points locally acquired from the environment as input and computes the visible region at each location to decide the growing direction of the path tree. Compared with traditional methods, RVT is more efficient, lightweight, and robust. We demonstrate that the RVT algorithm can not only complete the path planning task in real-time but also explore the unknown environment in simulated or real scenes.

Conventional coverage path planning algorithms are mainly based on the zigzag and spiral patterns or their combinations. The traversal order is limited by the linear or inside-outside manner. We propose a new set of coverage patterns induced from geometric folding operations, called the geometric folding pattern, to make coverage paths with more flexible traversal order. We study the modeling and parameterization of the geometric folding patterns. Then, a sampling operator is introduced. Based on the computational tools, we demonstrate the application of the proposed patterns in designing coverage paths. We show that the simple geometric folding patterns are flexible and controllable, which enables more choices for the coverage path planning problem.

Prediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from sensing technology and the dynamic environment. It leads to failure of motion planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-inspired model to estimate collision risk in road scenes. Multi-object states are detected and tracked, and then a stable fluid model is adopted to construct the risk field. Objects’ state spaces are used as the boundary conditions in the simulation of advection and diffusion processes. We have evaluated our approach on the public KITTI dataset; our model can provide predictions in the cases of misdetection and tracking error caused by occlusion. It proves a promising approach for collision risk assessment in road scenes.

Thermoplastic and Fused Deposition Modeling (FDM) based 4D printing are rapidly expanding to allow for space- and material-saving 2D printed sheets morphing into 3D shapes when heated. However, to our knowledge, all the known examples are either origami-based models with obvious folding hinges, or beam-based models with holes on the morphing surfaces. Morphing continuous double-curvature surfaces remains a challenge, both in terms of a tailored toolpath-planning strategy and a computational model that simulates it. Additionally, neither approach takes surface texture as a design parameter in its computational pipeline. To extend the design space of FDM-based 4D printing, in Geodesy, we focus on the morphing of continuous double-curvature surfaces or surface textures. We suggest a unique tool path - printing thermoplastics along 2D closed geodesic paths to form a surface with one raised continuous double-curvature tiles when exposed to heat. The design space is further extended to more complex geometries composed of a network of rising tiles (i.e., surface textures). Both design components and the computational pipeline are explained in the paper, followed by several printed geometric examples.

Dynamic effects are commonly added offline to pre-recorded videos. In this work, we propose to synthesize online dynamic effects with controlled vector fields by using mobile augmented reality. By modelling typical primitives of a flow field to oriented markers, we use image detection and tracking techniques to build and control a virtual vector field in the real world. Virtual objects are then added and animated in real time. We prototype our system and the results show the possibility that dynamic effects can be created in mobile augmented reality to enhance the visual communication with the real world.

Sensing and presenting on-road information of moving vehicles is essential for fully and semi-automated driving. It is challenging to track vehicles from affordable on-board cameras in crowded scenes. The mismatch or missing data are unavoidable and it is ineffective to directly present uncertain cues to support the decision-making. In this paper, we propose a physical model based on incompressible fluid dynamics to represent the vehicle’s motion, which provides hints of possible collision as a continuous scalar riskmap. We estimate the position and velocity of other vehicles from a monocular on-board camera located in front of the ego-vehicle. The noisy trajectories are then modeled as the boundary conditions in the simulation of advection and diffusion process. We then interactively display the animating distribution of substances, and show that the continuous scalar riskmap well matches the perception of vehicles even in presence of the tracking failures. We test our method on real-world scenes and discuss about its application for driving assistance and autonomous vehicle in the future.

We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood-aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state-of-the-art methods and show that we can improve user performance, user satisfaction, as well as the layout itself.

Line graphs are usually considered to be the best choice for visualizing time series data, whereas sometimes also scatter plots are used for showing main trends. So far there are no guidelines that indicate which of these visualization methods better display trends in time series for a given canvas. Assuming that the main information in a time series is its overall trend, we propose an algorithm that automatically picks the visualization method that reveals this trend best. This is achieved by measuring the visual consistency between the trend curve represented by a LOESS fit and the trend described by a scatter plot or a line graph. To measure the consistency between our algorithm and user choices, we performed an empirical study with a series of controlled experiments that show a large correspondence. In a factor analysis we furthermore demonstrate that various visual and data factors have effects on the preference for a certain type of visualization.

We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes - an approach which previous methods cannot support.

The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and improved aspect ratio selection method, namely the -LOR (local orientation resolution), with a certain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45 principle, RV (resultant vector) and AS (average absolute slope), as well as -LOR and AO (average absolute orientation). We verify these connections by a comparative evaluation involving various data sets, and show that the selections by RV and -LOR are complementary to each other for most data. Accordingly, we propose the dual-scale banking technique that combines the strengths of RV and -LOR, and demonstrate its practicability using multiple real-world data sets.

With the recent advances in sequencing technology, researchers now have opportunities to study microbiomes associated with various environments. Recent studies have shown that the composition of microbiomes in our bodies and our environments play a significant role in our health. For example, 90% of human DNA is composed of bacterial microbiomes. In this study, we propose a systems biology approach using split graphs to analyze the composition of microbiomes and the impact of such composition on the health and growth of organisms living in associated environments. We focus on a case study related to the composition of microbiomes in fish guts and its impact on various growth parameters for three types of fish. The proposed model explores features in the aquatic ecosystem including correlations among its microorganisms and their abundance levels. The results of the study show that single or groups of bacteria are significantly associated with multiple growth phenotypes in different gut portions of the fish. We also identify bacterial clusters that provide new insight to functional relevance of these bacteria and their contribution to the fish gut microbial ecosystem.

Abstract We present a shape manipulation technique capable of producing deformations of 2D and 3D meshes, guaranteeing that no elements will be inverted. We achieve this by augmenting the quadratic ex‐rotated elastic energy with additional convex terms that penalize the presence of inverted elements. Using a schedule of increasing penalty coefficients, we efficiently and robustly converge to an inversion free state by solving a sequence of unconstrained convex minimization problems. This process can be interpreted as a special purpose Semi‐Definite Programming (SDP) solver. We demonstrate that our method outperforms solvers used in previous work, including commercial‐grade SDP software (MOSEK). As an additional benefit, our method also converges to the solution via a more intuitive path, which can be used for quick preview. We demonstrate the efficacy of our scheme in a number of 2D and 3D shapes undergoing moderate to drastic deformation.

Abstract We present a system to reconstruct subject‐specific anatomy models while relying only on exterior measurements represented by point clouds. Our model combines geometry, kinematics, and skin deformations (skinning). This joint model can be adapted to different individuals without breaking its functionality, i.e., the bones and the skin remain well‐articulated after the adaptation. We propose an optimization algorithm which learns the subject‐specific (anthropometric) parameters from input point clouds captured using commodity depth cameras. The resulting personalized models can be used to reconstruct motion of human subjects. We validate our approach for upper and lower limbs, using both synthetic data and recordings of three different human subjects. Our reconstructed bone motion is comparable to results obtained by optical motion capture (Vicon) combined with anatomically‐based inverse kinematics (OpenSIM). We demonstrate that our adapted models better preserve the joint structure than previous methods such as OpenSIM or Anatomy Transfer.

We introduce a new method to synthesize mechanical toys solely from the motion of their features. The designer specifies the geometry and a time-varying rotation and translation of each rigid feature component. Our algorithm automatically generates a mechanism assembly located in a box below the feature base that produces the specified motion. Parts in the assembly are selected from a parameterized set including belt-pulleys, gears, crank-sliders, quick-returns, and various cams (snail, ellipse, and double-ellipse). Positions and parameters for these parts are optimized to generate the specified motion, minimize a simple measure of complexity, and yield a well-distributed layout of parts over the driving axes. Our solution uses a special initialization procedure followed by simulated annealing to efficiently search the complex configuration space for an optimal assembly.

In conceptual design of models, designers usually express their ideas in curve networks, without generating final 3D surfaces. In this paper, we propose a method to utilize the structures and properties of curve networks to create mesh surfaces with controllable sharp features. We demonstrated our method on various curve networks, the created surfaces are both visually plausible and faithfully follow the input curve networks.

We present a novel method to approximate a freeform shape with a planar quadrilateral (PQ) mesh for modeling architectural glass structures. Our method is based on the study of conjugate direction fields (CDF) which allow the presence of ±κ/4(κ ε Z) singularities. Starting with a triangle discretization of a freeform shape, we first compute an as smooth as possible conjugate direction field satisfying the user's directional and angular constraints, then apply mixed-integer quadrangulation and planarization techniques to generate a PQ mesh which approximates the input shape faithfully. We demonstrate that our method is effective and robust on various 3D models.

Various applications, such as mesh composition and model repair, ask for a natural stitching for polygonal surfaces. Unlike the existing algorithms, we make full use of the information from the two feature lines to be stitched up, and present an accurate stitching method for polygonal surfaces, which minimizes the error between the feature lines. Given two directional polylines as the feature lines on polygonal surfaces, we modify the general placement method for points matching and arrive at a closed-form solution for optimal rotation and translation between the polylines. Following calculating out the stitching line, a local surface optimization method is designed and employed for postprocess in order to gain a natural blending of the stitching region.