Asynchronous design ideation is increasingly vital in virtual reality (VR) environments. Annotations, referring to notes, marks, or comments overlaid on virtual designs, are essential for providing context, feedback, and clarity, facilitating communication among designers using Creativity Support Tools (CSTs) in the design sector. However, the effectiveness of various annotation methods in enhancing creativity during VR design ideation remains underexplored. This study aims to identify effective annotation methods that promote creativity in VR-based design ideation, addressing the shift from traditional paper-and-pen practices to immersive environments. We adopted a three-phase approach: (1) semi-structured interviews with design experts, (2) an empirical mixed-design study with design professionals, leading to the development of AsyncCreativity, our VR CST system, and (3) a deployment study evaluating annotations via AsyncCreativity in VR design ideation. Our findings reveal that multimodal annotations, especially audio annotations, significantly enhance user engagement and creativity compared to unimodal annotations. Participants reported improved ideation experiences when utilizing diverse annotation types during ideation when searching, sketching, and presenting. Our study provides valuable insights for developing effective CSTs in VR and design field, advancing the understanding of how optimized annotation strategies can foster creativity in immersive design environments.

Human-AI romantic relationships have gained wide popularity among social media users in China. The technological impact on romantic relationships and its potential applications have long drawn research attention to topics such as relationship preservation and negativity mitigation. Media and communication studies also explore the practices in romantic para-social relationships. Nonetheless, this emerging human-AI romantic relationship, whether the relations fall into the category of para-social relationship together with its navigation pattern, remains unexplored, particularly in the context of relational stages and emotional attachment. This research thus seeks to fill this gap by presenting a mixed-method approach on 1,766 posts and 60,925 comments from Xiaohongshu, as well as the semi-structured interviews with 23 participants, of whom one of them developed her relationship with self-created AI for three years. The findings revealed that the users' willingness to self-disclose to AI companions led to increased positivity without social stigma. The results also unveiled the reciprocal nature of these interactions, the dominance of 'self,' and raised concerns about language misuse, bias, and data security in AI communication.

Sexual harassment has been recognized as a significant social issue. In recent years, the emergence of harassment in social virtual reality (VR) has become an important and urgent research topic. We employed a mixed-methods approach by conducting online surveys with VR users ( N = 166) and semi-structured interviews with social VR users ( N = 18) to investigate how users perceive sexual harassment in social VR, focusing on the influence of avatar appearance. Moreover, we derived users' response strategies to sexual harassment and gained insights on platform regulation. This study contributes to the research on sexual harassment in social VR by examining the moderating effect of avatar appearance on user perception of sexual harassment and uncovering the underlying reasons behind response strategies. Moreover, it presents novel prospects and challenges in platform design and regulation domains.

Alzheimer's disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer's Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.

The problem of power control in the uplink network of cellular users communicating with Device-to-Device (D2D) is mainly studied. Since cellular users and D2D users share spectrum resources, several serious interference will be caused by them. Reasonable measures are taken to control the interference caused by the sharing spectrum resources, otherwise that influences the quality of service (QoS) of cellular users. The reduction of entire system interference can be achieved by power control, so a method of power control based deep reinforcement learning (DRL), namely Asynchronous Advantage Actor Critic (A3C) Algorithm, is proposed. At the same time, the spectrum resources utilization of the system is improved and QoS of cellular users is guaranteed. The simulation results prove rationality of the proposed algorithm, and have better convergence performance than the traditional DRL algorithm.

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its long-term progression prediction is definitely important. The structural Magnetic Resonance Imaging (sMRI) can be used to characterize the cortical atrophy that is closely coupled with clinical symptoms in AD and its prodromal stages. Many existing methods have focused on predicting the cognitive scores at future time-points using a set of morphological features derived from sMRI. The 3D sMRI can provide more massive information than the cognitive scores. However, very few works consider to predict an individual brain MRI image at future time-points. In this article, we propose a disease progression prediction framework that comprises a 3D multi-information generative adversarial network (mi-GAN) to predict what one's whole brain will look like with an interval, and a 3D DenseNet based multi-class classification network optimized with a focal loss to determine the clinical stage of the estimated brain. The mi-GAN can generate high-quality individual 3D brain MRI image conditioning on the individual 3D brain sMRI and multi-information at the baseline time-point. Experiments are implemented on the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our mi-GAN shows the state-of-the-art performance with the structural similarity index (SSIM) of 0.943 between the real MRI images at the fourth year and the generated ones. With mi-GAN and focal loss, the pMCI vs. sMCI accuracy achieves 6.04% improvement in comparison with conditional GAN and cross entropy loss.

Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the development of magnetic resonance imaging (MRI), the neuroimaging-based longitudinal analysis is gradually becoming an important research direction to understand and trace the process of the AD. In addition, regression analysis has been commonly adopted in the AD pattern analysis and progression prediction. However, most existing methods assume that all input features are equally related to the output variables, which ignore the difference in terms of the correlation. In this paper, we proposed a novel multi-task learning formulation, which considers a correlation-aware sparse and low-rank constrained regularization, for accurately predicting the cognitive scores of the patients at different time points and identifying the most predictive biomarkers. In addition, an efficient iterative algorithm is developed to optimize the proposed non-smooth convex objective formulation. We also have performed experiments using data from the AD neuroimaging initiative dataset to evaluate the proposed optimization formulation. Especially, we will predict cognitive scores of multiple time points through the baseline MRI features. The results not only indicate the rationality and correctness of the proposed method for predicting disease progression but also identify some stable and important MRI features that are consistent with the previous research.