Zooming and panning are fundamental input actions for exploring complex 2D and 3D scenes and data such as images, maps, and designs. Multi-touch zoom/pan interactions have been proven effective on mobile devices, and have been directly ported to HMDs, where they are typically accomplished by analogous but relatively large-scale movements of both hands. We argue that such motions are inefficient and induce fatigue and explore how the eye-tracking features of HMDs can be leveraged to achieve improvements. We evaluated three interaction techniques that combine gaze with two-handed, one-handed, and head-based input in a study (N = 24) that contrasts them against a baseline two-handed technique. The results indicate that gaze-assisted two- and one-handed techniques outperform the baseline (17%-36% faster), while our head-based technique achieves similar performance to the Baseline but leaves the hands free for other tasks. We further developed a VR application demonstrating these techniques and validating their practical applicability.

Multi-view clustering (MVC) has attracted considerable attention in the signal processing field. However, two issues still remain: 1) They adopt the concatenation or weighted combination as the fusion strategies, which makes it difficult to ensure semantic robustness of fusion representations. 2) They suffer from dominant view dependency that models over-rely on views with stronger clustering signals and neglect weaker views. Therefore, an information-driven complementarity and consistency mining method (ICCM) is devised for multi-view clustering. Specifically, ICCM designs view-specific representation learning and cluster partitioning module to extract inherent information in each view. Then, ICCM introduces an entropy-oriented complementary aggregation module to learn semantics-robust fusion representations through inter-view nonlinear transformations. Meanwhile, it proposes an invariance-driven consistent partition module to capture consistent cluster assignments across views where an adaptive weighting strategy is introduced to balance contributions of each view via assigning greater weights to views with fuzzy structures. Finally, experiments on six datasets demonstrate that ICCM gains cutting-edge results in MVC. The code is available athttps://github.com/Yuzhe-Li123/ICCM.

In virtual reality (VR), our virtual body can have different characteristics from our real body, such as appearance, size, and even extra body parts. Previous research shows that haptic feedback enhances the user-perceived embodiment of those dissimilar avatars. In particular, weight-shifting devices showed the potential to enhance arm deformation. However, there has been no exploration of using such techniques to enhance embodiment with extra body parts, like wings. We introduce HapticWings, a back-wearable 2D weight-shifting device that provides haptic feedback for wing motions, enhancing the user embodiment of avatars with extra wings. In three user studies, we explored (1) users’ abilities to recognize different weight-shifting motions provided by HapticWings, (2) users’ perceived embodiment of avatars with extra wings when providing haptic feedback for wing motions, and (3) four possible applications and used two of them to evaluate users’ sense of realism and enjoyment in VR.

Thanks to the development of virtual reality (VR) technology, there is growing research on VR avatar body deformation effects. However, previous research mainly focused on passive body deformation expression, leaving users with limited methods to actively control their virtual bodies. To address this gap, we explored user-controlled forearm deformation by investigating how hand and forearm gestures can be mapped to various degrees of avatar forearm deformation. We conducted a gesture design workshop with six designers to generate gesture sets for different forearm deformations and deformation degrees, resulting in 15 gesture sets. Then, we selected the three highest-rated gesture sets and conducted a comparative study to evaluate the sense of embodiment and user performance across the three gesture sets. Our findings provide design suggestions for gesture-controlled forearm deformation in VR.

With the development of virtual reality (VR) technology, research is being actively conducted on how incorporating multisensory feedback can create the illusion that virtual avatars are perceived as an extension of the body in VR. In line with this research direction, we introduce ArmDeformation, a wearable device employing skin-stretching to enhance virtual forearm ownership during arm deformation illusion. We conducted five user studies with 98 participants. Using a developed tabletop device, we confirmed the optimal number of actuators and the ideal skin-stretching design effectively increases the user’s body ownership. Additionally, we explored the maximum visual threshold for forearm bending and the minimum detectable bending direction angle when using skin-stretching in VR. Finally, our study demonstrates that using ArmDeformation in VR applications enhances user realism and enjoyment compared to relying on visual feedback alone.

When we enter Virtual Reality (VR) in the first person, the avatar replaces our real body. Within a certain range of mismatches, we tend to identify with our avatar and perceive it as our own body. Previous research has shown that we can even believe that a deformed forearm is our own through haptic and visual feedback. However, the haptic devices used in earlier research were only explored using skin-stretching and weight-shifting. Vibrotactile feedback is the most common technique used to create a haptic VR experience in academia and industry because of its light weight and ease of application. Taking these advantages, we introduce VibroArm, a lightweight wearable haptic system with the vibrotactile funneling illusion that enhances the virtual forearm’s ownership in the forearm deformation illusion. In a perceptual study, we determined that users can make correct direction judgments about vibration on the forearm in different directions. The result shows that all vibration patterns can be sufficiently recognized, with an average recognition rate of $87.17 \%$. Based on our results, we designed 18 vibration patterns and compared their impact on the forearm deformation illusion. Finally, our study shows that using VibroArm in VR applications can significantly improve user realism and enjoyment compared to relying on visual feedback alone.

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data.

The advancements of single-cell RNA sequencing (scRNA-seq) technologies have provided us unprecedented opportunities to characterize cellular states and investigate the mechanisms of complex diseases. Due to technical issues such as dropout events, scRNA-seq data contains excess of false zero counts, which has a substantial impact on the downstream analyses. Although several computational approaches have been proposed to impute dropout events in scRNA-seq data, there is no strong consensus on which is the best approach. In this study, we propose a novel weighted ensemble learning method, named EnTSSR, to impute dropout events in scRNA-seq data. By using a multi-view two-side sparse self-representation framework, our model can exploit the consensus similarities between genes and between cells based on the imputed results of various imputation methods. Moreover, we introduce a weighted ensemble strategy to leverage the information captured by various imputation methods effectively. Down-sampling experiments, clustering analysis, differential expression analysis and cell trajectory inference are carried out to evaluate the performance of our proposed model. Experiment results demonstrate that our EnTSSR can effectively recover the true expression pattern of scRNA-seq data.