Image-guided percutaneous needle puncture is a critical yet challenging clinical procedure, constrained by the high cognitive demand of mental model construction and manipulation of 3D anatomy via scrolling through 2D cross-sectional images. While virtual reality (VR) simulators provide a risk-free training platform, many focus on simulation fidelity but lack structured, self-directed learning frameworks. In this paper, we present PunctVR, a VR system that incorporates the instructional principles of scaffolding. PunctVR features a training mode employing a phased subgoal workflow and instructional guidance scaffolding, and an assessment-only test mode where both the workflow and enhanced 3D visualization are removed. We conducted a between-subject experiment with 16 physicians, comparing training using a baseline multiplanar reconstruction (MPR) view with a combined MPR + 3D visualization across two difficulty levels. Our test mode results indicate that all trainees significantly improved their performance after training. Furthermore, those who trained with the integrated 3D visualization achieved a greater reduction in puncture time in both easy and hard cases. These findings suggest that PunctVR effectively enhances procedural efficiency in simulated needle puncture training and provides important insights into how learning scaffolding can accelerate skill acquisition and retention for image-guided interventions.

As 3D Gaussian Splatting (3DGS) emerges as a leading approach for novel view synthesis and scene reconstruction, its potential in digital asset creation has gained significant attention. An increasing number of asset libraries based on GS are being established. However, generating physics-based dynamic assets remains a time-consuming and expertise-intensive task, especially for non-experts. In this paper, we propose LIVE-GS, a highly realistic Virtual Reality (VR) system powered by Large Language Models (LLMs), which enables rapid creation of dynamic Gaussian assets and real-time VR interactions. To inform our system design, we conducted interviews to examine challenges faced by current GS-based VR systems and the specific demands of users. Based on these insights, we employed GPT-4o to analyze key physical properties of objects that significantly impact user interactions, ensuring physics-based interactions in VR align with real-world phenomena. A key innovation of LIVE-GS is its ability to predict reasonable parameters in just 10 seconds from static Gaussian assets while maintaining high-quality VR interactions. To validate our approach, we invited participants experienced in physical simulation to manually adjust physical parameters, providing a baseline for comparison in both asset quality and authoring efficiency. We also conducted a comprehensive user study to evaluate system usability and user satisfaction. Experimental results demonstrate that LIVE-GS, leveraging LLMs' scene understanding capabilities, can achieve efficient physical scene creation and natural interactions without requiring manual design or annotation.

The poor transparency of occlusion-capable optical see-through head-mounted displays (OC-OSTHMDs) deteriorates the visibility of the real scene, hindering the practical application of the devices. Previous works mitigate the issue by upgrading the transmittance of the spatial light modulator (SLM). However, the strategy soon reaches a limit because further optimization requires improving the transmittance of all optical elements, e.g., lenses and beam splitters. Moreover, pixelated occlusion usually relies on polarizing the real scene light, inevitably cutting the input optical power by half. To overcome this limitation, we propose a mask balancing method that improves real-scene brightness through polarization blending. Specifically, the s-polarized component, which passes through the optical system to provide occlusion-capable vision, is blended with the p-polarized component, which bypasses the system to preserve the raw view of the real scene. The blending is realized by simply modulating the cross-angle between a polarizing beam splitter and a linear polarizer, benefiting the robustness and versatility of the proposed method. We introduce a perception-driven blending approach, where the cross-angle is optimized in real-time to balance the visibility of the real scene and the texture and lighting of the virtual object. A benchtop prototype is built. A user study with 12 participants is conducted to quantify the visibility threshold of the texture and lighting of virtual objects. Then, a user study with 12 participants proves that the proposed method improves the visibility of the real scene while keeping a good appearance of the virtual object. We believe the proposed method is an important step toward developing practical solutions for OC-OSTHMDs.

This paper explores finger pressure as a continuous implicit input modality to enhance interaction precision in virtual reality (VR). While motion controllers are widely adopted, their limitations in delicate operations remain a critical challenge. We investigate whether finger pressure signals from conventional VR controllers could offer advantages over traditional kinematic metrics for precision interaction.Through empirical studies, we demonstrate a robust relationship between pressure dynamics and task precision requirements, leading to a lightweight sigmoid-based model that leverages detected pressure to infer desired control granularity. In a comparative evaluation of video-scrubbing tasks, our adaptive method outperforms static sensitivity baselines in both task performance and subjective preference, without elevating cognitive load. Further validation via a VR sketching application demonstrates that our technique maintains task performance while reducing mental demand compared to manual control. Our findings reveal the untapped potential of pressure-based input to bridge coarse and fine-grained VR interactions, offering a path toward more versatile and intuitive input systems.

Occlusion-capable optical see-through head-mounted displays (OC-OSTHMDs) overcome the deficiency of semi-transparent virtual images by selectively cutting off light emitted from the physical background, considerably improving the graphics performance of augmented reality (AR). Existing OC-OSTHMDs achieve compact form factors by compressing the optical system based on the modulation of light polarization. However, the wavelength sensitivity of polarizing optical elements (POEs) causes color aberration in the see-through view. In this paper, we propose the spectrum- tuning method that mitigates color aberration of the see-through view caused by the wavelength sensitivity of OC-OSTHMDs. The methods operate on a spectrum-based color perception model that formulates the variation of the visible spectrum through OC-OSTHMDs. The optimization is performed globally, requiring minimal computation at runtime. A bench-top prototype of the OC-OSTHMD was built to validate these methods. Experimental results demonstrate that the spectrum-tuning method reduces the color difference by 18.1%. Additionally, the advantages of OC-OSTHMDs in presenting fluid animations in AR scenarios are demonstrated based on the prototype and a multi-buffer mask synthesis method.

Clouds in satellite imagery pose a significant challenge for downstream applications.A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset.To address this problem, we introduce the largest public dataset -- *AllClear* for cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with diverse land-use patterns, comprising 4 million images in total. Each ROI includes complete temporal captures from the year 2022, with (1) multi-spectral optical imagery from Sentinel-2 and Landsat 8/9, (2) synthetic aperture radar (SAR) imagery from Sentinel-1, and (3) auxiliary remote sensing products such as cloud masks and land cover maps.We validate the effectiveness of our dataset by benchmarking performance, demonstrating the scaling law - the PSNR rises from $28.47$ to $33.87$ with $30\times$ more data, and conducting ablation studies on the temporal length and the importance of individual modalities. This dataset aims to provide comprehensive coverage of the Earth's surface and promote better cloud removal results.

Foveated rendering (FR) improves the rendering performance of virtual reality (VR) by allocating fewer computational loads in the peripheral field of view (FOV). Existing FR techniques are built based on the radially symmetric regression model of human visual acuity. However, horizontal-vertical asymmetry (HVA) and vertical meridian asymmetry (VMA) in the cortical magnification factor (CMF) of the human visual system have been evidenced by retinotopy research of neuroscience, suggesting the radially asymmetric regression of visual acuity. In this paper, we begin with functional magnetic resonance imaging (fMRI) data, construct an anisotropic CMF model of the human visual system, and then introduce the first radially asymmetric regression model of the rendering precision for FR applications. We conducted a pilot experiment to adapt the proposed model to VR head-mounted displays (HMDs). A user study demonstrates that retinotopic foveated rendering (RFR) provides participants with perceptually equal image quality compared to typical FR methods while reducing fragments shading by 27.2% averagely, leading to the acceleration of 1/6 for graphics rendering. We anticipate that our study will enhance the rendering performance of VR by bridging the gap between retinotopy research in neuroscience and computer graphics in VR.

Kernel point convolution (KPConv) defines convolutional weights based on Euclidean distances between kernel points and input points and has shown good segmentation results on several datasets. However, it does not consider the intrinsic connection between input points and features, which is crucial for the semantic segmentation of airborne laser scanning (ALS) point clouds with sparse density and complex backgrounds. To address this problem, we design a dual attention KPConv network (DAKAG-Net) combined with attention gates for semantic segmentation of ALS point clouds. Specifically, we design the channel and spatial attention KPConv (CSAKPConv) block in the encoding process, which first performs adaptive feature refinement of the input mapping along two separate dimensions, channel and spatial, and then performs kernel point convolution. In addition, to enhance the use of high-level semantic information and detect objects of varying sizes, DAKAG-Net incorporates multiple attention gates (MAGs) that merge the lowest-level features, skip-connected features, and corresponding upsampled features during the decoding process. The decoded features are ultimately convolved with convolution kernels of various sizes and then merged to acquire multiscale perceptual field features. The proposed DAKAG-Net improves the OA, mF1, and mIoU by 3.5%, 3.1%, and 3.5%, respectively, compared with the baseline results on the ISPRS 3-D dataset, and yields the segmentation accuracy rates of 85.2% (OA), 73.7% (mF1), and 61.2% (mIoU). Moreover, the DAKAG-Net also obtains new state-of-the-art segmentation results on the DFC2019 dataset and the LASDU dataset.

BACKGROUND: Identifying polymorphism clades on phylogenetic trees could help detect punctual mutations that are associated with viral functions. With visualization tools coloring the tree, it is easy to visually find clades where most sequences have the same polymorphism state. However, with the fast accumulation of viral sequences, a computational tool to automate this process is urgently needed. RESULTS: Here, by implementing a branch-and-bound-like search method, we developed an R package named sitePath to identify polymorphism clades automatically. Based on the identified polymorphism clades, fixed and parallel mutations could be inferred. Furthermore, sitePath also integrated visualization tools to generate figures of the calculated results. In an example with the influenza A virus H3N2 dataset, the detected fixed mutations coincide with antigenic shift mutations. The highly specificity and sensitivity of sitePath in finding fixed mutations were achieved for a range of parameters and different phylogenetic tree inference software. CONCLUSIONS: The result suggests that sitePath can identify polymorphism clades per site. The clustering of sequences on a phylogenetic tree can be used to infer fixed and parallel mutations. High-quality figures of the calculated results could also be generated by sitePath.

Accessory proteins play important roles in the interaction between coronaviruses and their hosts. Accordingly, a comprehensive study of the compositional diversity and evolutionary patterns of accessory proteins is critical to understanding the host adaptation and epidemic variation of coronaviruses. Here, we developed a standardized genome annotation tool for coronavirus (CoroAnnoter) by combining open reading frame prediction, transcription regulatory sequence recognition and homologous alignment. Using CoroAnnoter, we annotated 39 representative coronavirus strains to form a compositional profile for all of the accessary proteins. Large variations were observed in the number of accessory proteins of 1-10 for different coronaviruses, with SARS-CoV-2 and SARS-CoV having the most (9 and 10, respectively). The variation between SARS-CoV and SARS-CoV-2 accessory proteins could be traced back to related coronaviruses in other hosts. The genomic distribution of accessory proteins had significant intra-genus conservation and inter-genus diversity and could be grouped into 1, 4, 2 and 1 types for alpha-, beta-, gamma-, and delta-coronaviruses, respectively. Evolutionary analysis suggested that accessory proteins are more conservative locating before the N-terminal of proteins E and M (E-M), while they are more diverse after these proteins. Furthermore, comparison of virus-host interaction networks of SARS-CoV-2 and SARS-CoV accessory proteins showed that they share multiple antiviral signaling pathways, those involved in the apoptotic process, viral life cycle and response to oxidative stress. In summary, our study provides a tool for coronavirus genome annotation and builds a comprehensive profile for coronavirus accessory proteins covering their composition, classification, evolutionary pattern and host interaction.

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) shows great potential for forest height retrieval. However, there are abundant noise photons in the ICESat-2 data, which make the accurate extraction of global forest heights challenging. In this letter, a novel algorithm based on the clustering method of ordering points to identify the clustering structure (OPTICS) was proposed to remove noise photons. First, we modified the circular shape of the search area in the OPTICS algorithm to an elliptical shape. Second, a distance ordering of all photons was generated using the modified OPTICS algorithm. Finally, signal photons were effectively detected using distance thresholds set by the Otsu method. To evaluate the algorithm performance, both the simulated and real ICESat-2 data were applied to our proposed algorithm. In addition, we compared our algorithm with another noise removal algorithm based on the modified density-based spatial clustering of applications with noise (DBSCAN). The results show that our algorithm works well in distinguishing the signal and noise photons as indicated by high$F$values. Compared with the modified DBSCAN, our algorithm performs better in filtering out noise photons regardless of the simulated or real ICESat-2 data sets. In addition, the results also indicate that our algorithm is robust because it is insensitive to the clustering parameters. Overall, the new proposed algorithm is effective for removing noise photons in the ICESat-2 data.