LidPose: Real-Time 3D Human Pose Estimation in Sparse Lidar Point Clouds with Non-Repetitive Circular Scanning Pattern.

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena.

Identifying Types of Eco-Anxiety, Eco-Guilt, Eco-Grief, and Eco-Coping in a Climate-Sensitive Population: A Qualitative Study.

Background: Climate change is one of the greatest challenges of the 21st century and it can affect mental health either directly through the experience of environmental traumas or indirectly through the experience of emotional distress and anxiety about the future. However, it is not clear what possible subtypes of the emerging "psychoterratic" syndromes such as eco-anxiety, eco-guilt, and eco-grief exist, how much distress they may cause, and to what extent they facilitate ecofriendly behavior.

Methods: We analyzed semi-structured interviews (N = 17) focusing on the thoughts, emotions, and behaviors related to climate change by using a combination of inductive and deductive qualitative methods.

An Embedded Marked Point Process Framework for Three-Level Object Population Analysis.

In this paper, we introduce a probabilistic approach for extracting the complex hierarchical object structures from the digital images used by various vision applications. The proposed framework extends conventional marked point process (MPP) models by: 1) admitting object-subobject ensembles in parent-child relationships; and 2) allowing corresponding objects to form coherent object groups, by a Bayesian segmentation of the population. Different from earlier, highly domain specific attempts on MPP generalization, the proposed model is defined at an abstract level, providing clear interfaces for applications in various domains.

Exact density oscillations in the Tonks-Girardeau gas and their optical detection.

We construct the exact time dependent density profile for a superposition of the ground and singly excited states of a harmonically trapped one dimensional Bose gas in the limit of strongly interacting particles, the Tonks-Girardeau gas. We obtain analytic results that allows one to determine the number of particles in the gas, as well as the quantum amplitudes in the superposition, from measurement results in an off-resonant light scattering experiment.

Detection of object motion regions in aerial image pairs with a multilayer markovian model.

We propose a new Bayesian method for detecting the regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map.