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We introduce the notion of point affiliation into feature upsampling. By abstracting a feature map into non-overlapped semantic clusters formed by points of identical semantic meaning, feature upsampling can be viewed as point affiliation-designating a semantic cluster for each upsampled point. In the framework of kernel-based dynamic upsampling, we show that an upsampled point can resort to its low-res decoder neighbors and high-res encoder point to reason the affiliation, conditioned on the mutual similarity between them. We therefore present a generic formulation for generating similarity-aware upsampling kernels and prove that such kernels encourage not only semantic smoothness but also boundary sharpness. This formulation constitutes a novel, lightweight, and universal upsampling solution, Similarity-Aware Point Affiliation (SAPA). We show its working mechanism via our preliminary designs with window-shape kernel. After probing the limitations of the designs on object detection, we reveal additional insights for upsampling, leading to SAPA with the dynamic kernel shape. Extensive experiments demonstrate that SAPA outperforms prior upsamplers and invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, image matting, and depth estimation. Code is made available at: https://github.com/tiny-smart/sapa.

Human thermal comfort researches mainly focus on the relation between the environmental factors (e.g. ambient temperature, air humidity, and air velocity, etc.) and the thermal comfort sensation based on a large amount of subjective field investigations. Although some physiological factors, such as skin temperature and metabolism were used in many thermal comfort models,they are not enough to establish a perfect thermal comfort model. In this paper,another two physiological factors, i.e. heart rate variation (HRV) and electroencephalograph (EEG), are explored for the thermal comfort study. Experiments were performed to investigate how these physiological factors respond to the environmental temperatures, and what is the relationship between HRV and EEG and thermal comfort. The experimental results indicate that HRV and EEG may be related to thermal comfort, and they may be useful to understand the mechanism of thermal comfort.

Intravascular optical coherence tomography (iOCT) is being used to assess viability of new coronary artery stent designs. We developed a highly automated method for detecting stent struts and measuring tissue coverage. We trained a bagged decision trees classifier to classify candidate struts using features extracted from the images. With 12 best features identified by forward selection, recall (precision) were 90%–94% (85%–90%). Including struts deemed insufficiently bright for manual analysis, precision improved to 94%. Strut detection statistics approached variability of manual analysis. Differences between manual and automatic area measurements were 0.12 ± 0.20 mm2 and 0.11 ± 0.20 mm2 for stent and tissue areas, respectively. With proposed algorithms, analyst time per stent should significantly reduce from the 6–16 hours now required.

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