Towards Designing Spatial Robots that are Architecturally Motivated

While robots are increasingly integrated into the built environment, little is known how their qualities can meaningfully influence our spaces to facilitate enjoyable and agreeable interaction, rather than robotic settings that are driven by functional goals. Motivated by the premise that future robots should be aware of architectural sensitivities, we developed a set of exploratory studies that combine methods from both architectural and interaction design. While we empirically discovered that dynamically moving spatial elements, which we coin as spatial robots, can indeed create unique life-sized affordances that encourage or resist human activities, we also encountered many unforeseen design challenges originated from how ordinary users and experts perceived spatial robots. This discussion thus could inform similar design studies in the areas of human-building architecture (HBI) or responsive and interactive architecture

GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers

Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer vision, where state-of-the-art methods are mainly based on full supervision. As annotating ground truth dense instance masks is tedious and expensive, solving 3DIS with weak supervision has become more practical. In this paper, we propose GaPro, a new instance segmentation for 3D point clouds using axis-aligned 3D bounding box supervision. Our two-step approach involves generating pseudo labels from box annotations and training a 3DIS network with the resulting labels. Additionally, we employ the self-training strategy to improve the performance of our method further. We devise an effective Gaussian Process to generate pseudo instance masks from the bounding boxes and resolve ambiguities when they overlap, resulting in pseudo instance masks with their uncertainty values. Our experiments show that GaPro outperforms previous weakly supervised 3D instance segmentation methods and has competitive performance compared to state-of-the-art fully supervised ones. Furthermore, we demonstrate the robustness of our approach, where we can adapt various state-of-the-art fully supervised methods to the weak supervision task by using our pseudo labels for training. The source code and trained models are available at https://github.com/VinAIResearch/GaPro.Comment: Accepted to ICCV 202

PERSISTENT ORGANOCHLORINE RESIDUES AND THEIR BIOACCUMULATION PROFILES IN RESIDENT AND MIGRATORY BIRDS FROM NORTH VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Structure and physico-chemical properties of silica gels doped with optically activated Er3+ ions by sol-gel process

This paper is devoted  to the study of the relationship between structure and optical properties of silica glasses doped with optically active Er3+ ions and reported the influence of the suitably selected heat treatment on the optical properties of silica glasses. The physico-chemical properties of Er3+doped silica are presented, which has been related with the effects of hydroxyl groups on the luminescence property  of Er3+ in the host matrix of silica