InSocialNet: Interactive visual analytics for role-event videos

Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role–event videos. It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role–event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors

The effect of inhaled air temperature on thermal comfort, perceived air quality, acute health symptoms and physiological responses at two ambient temperatures

We explored the importance of inhaled air temperature on thermal comfort, perceived air quality, acute non-clinical health symptoms, and physiological responses. Sixteen subjects stayed in a stainless-steel chamber for 90 min. They experienced four conditions with two inhaled air temperatures of 22 and 30°C and two ambient temperatures of 22 and 30°C in a 2 × 2 design. They wore breathing masks covering their mouth and nose to control the inhaled air temperature; the air was provided from an adjacent twin stainless-steel chamber. The subjects evaluated thermal conditions and health symptoms on visual-analogue scales. Skin temperature and electrocardiography were recorded. Whole-body thermal sensation and skin temperature did not change when the temperature of inhaled air was changed. Perceived air quality was significantly improved when subjects sat in the chamber at 30°C and inhaled air with a temperature of 22°C; under these conditions lip and throat dryness were significantly reduced. The lower inhaled air temperature increased time-domain heart rate variability indicators and decreased heart rate and the LF/HF ratio, suggesting that the parasympathetic nervous system was activated and the sympathetic nervous system was suppressed

UniNeXt: Exploring A Unified Architecture for Vision Recognition

Vision Transformers have shown great potential in computer vision tasks. Most recent works have focused on elaborating the spatial token mixer for performance gains. However, we observe that a well-designed general architecture can significantly improve the performance of the entire backbone, regardless of which spatial token mixer is equipped. In this paper, we propose UniNeXt, an improved general architecture for the vision backbone. To verify its effectiveness, we instantiate the spatial token mixer with various typical and modern designs, including both convolution and attention modules. Compared with the architecture in which they are first proposed, our UniNeXt architecture can steadily boost the performance of all the spatial token mixers, and narrows the performance gap among them. Surprisingly, our UniNeXt equipped with naive local window attention even outperforms the previous state-of-the-art. Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone. All models and codes will be publicly available