Discovery of Dependency Tree Patterns for Relation Extraction

PACLIC 23 / City University of Hong Kong / 3-5 December 200

A Hybrid Model for Sense Guessing of Chinese Unknown Words

PACLIC 23 / City University of Hong Kong / 3-5 December 200

SESS: A Self-Supervised and Syntax-Based Method for Sentiment Classification

PACLIC 23 / City University of Hong Kong / 3-5 December 200

GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer, or GeoTransformer for short, to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it invariant to rigid transformation and robust in low-overlap cases. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to $100$ times acceleration. Extensive experiments on rich benchmarks encompassing indoor, outdoor, synthetic, multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our method improves the inlier ratio by $18{\sim}31$ percentage points and the registration recall by over $7$ points on the challenging 3DLoMatch benchmark. Our code and models are available at \url{https://github.com/qinzheng93/GeoTransformer}.Comment: Accepted by TPAMI. Extended version of our CVPR 2022 paper [arXiv:2202.06688

Health risks and respiratory intake of submicron particles in the working environment: A case study

Background: Powder-coating processes have been extensively used in various industries. The submicron particles generated during the powder-coating process in the workplace have complex compositions and can cause serious diseases. The purpose of this study was to better understand the health risks and respiratory intake of submicron particles during the powder coating process.Methods: The concentrations of and variations in submicron particles were measured using real-time instruments. The health risks of submicron particles were analyzed using the Stoffenmanager Nano model. A new computational fluid dynamics model was used to assess the respiratory intake of ultrafine particles (UFPs), which was indicated by the deposited dosage of UFPs in the olfactory area, nasal cavity, and lungs. The deposited doses of UFPs were used to calculate the average daily doses (ADDs) of workers, according to the method described by the Environmental Protection Agency.Results: The number concentration (NC), mass concentration, surface area concentration, personal NC, and lung-deposited surface area concentration of submicron particles were >105 pt/cm3, 0.2–0.4 mg/m3, 600–1,200 μm2/cm3, 0.7–1.4 pt/cm3, and 100–700 μm2/cm3, respectively. The size distribution showed that the submicron particles mainly gathered between 30 and 200 nm. The health risk of submicron particles was high. Upon respiratory intake, most UFPs (111.5 mg) were inhaled into the lungs, a few UFPs (0.272 mg) were trapped in the nasal cavity, and a small minority of UFPs (0.292 mg) were deposited in the olfactory area. The ADD of male workers with 10 years of exposure in the olfactory area, nasal cavity, and lung were 1.192 × 10–3 mg/kg·d−1, 1.11 × 10–3 mg/kg·d−1, and 0.455 mg/kg·d−1, respectively.Conclusion: Owing to the high concentrations of submicron particles, the workers involved in the powder-coating process are at a high health risk. Moreover, the respiratory intake of UFPs by workers is high, which is suggested by the highly deposited dosage of UFPs in the lungs and the corresponding high ADD in workers. Control measures, including engineering control, management control, and personal protective equipment, must be improved for the protection of workers

Gray Matter Alterations in Post-Traumatic Stress Disorder, Obsessive-Compulsive Disorder, and Social Anxiety Disorder

Post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD) and social anxiety disorder (SAD) all bear the core symptom of anxiety and are separately classified in the new DSM-5 system. The aim of the present study is to obtain evidence for neuroanatomical difference for these disorders. We applied voxel-based morphometry (VBM) with Diffeomorphic Anatomic Registration Through Exponentiated Lie (DARTEL) to compare grey matter volume (GMV) in Magnetic Resonance (MR) images obtained for thirty patients with PTSD, twenty nine patients with OCD, twenty patients with SAD and thirty healthy controls. GMV across all four groups differed in left hypothalamus and left inferior parietal lobule and post hoc analyses revealed that this difference is primarily due to reduced GMV in the PTSD group relative to the other groups. Further analysis revealed that the PTSD group also showed reduced GMV in frontal lobe, temporal lobe and cerebellum compared to the OCD group, and reduced GMV in frontal lobes bilaterally compared to SAD group. A significant negative correlation with anxiety symptoms is observed for GMV in left hypothalamus in three disorder groups. We have thus found evidence for brain structure differences that in future could provide biomarkers to potentially support classification of these disorders using MRI