Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its ability to process size-varying inputs, and prove its permutation equivariance. Besides, prior work uses heuristics dependence on the input data (e.g., Furthest Point Sampling) to hierarchically select subsets of input points. Thereby, we for the first time propose an end-to-end learnable and taskagnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points. Equipped with Gumbel-Softmax, it produces a "soft" continuous subset in training phase, and a "hard" discrete subset in test phase. By selecting representative subsets in a hierarchical fashion, the networks learn a stronger representation of the input sets with lower computation cost. Experiments on classification and segmentation benchmarks show the effectiveness and efficiency of our methods. Furthermore, we propose a novel application, to process event camera stream as point clouds, and achieve a state-of-theart performance on DVS128 Gesture Dataset.

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X-ray repair cross-complementing group 1 (XRCC1) plays an important role in base excision and single-strand break repair, as a scaffold protein that brings together proteins of the DNA repair complex, and appears to be a candidate for cancer risk. However, studies on the association between polymorphisms in this protein and cancer have yielded conflicting results. We performed a meta-analysis to investigate the association between the breast cancer and the XRCC1 polymorphisms Arg194Trp (9411 cases and 9783 controls), Arg399Gln (22 481 cases and 23 905 controls) and Arg280His (6062 cases and 5864 controls) in different inheritance models. Our analysis suggested that Arg399Gln was associated with a trend of increased breast cancer risk when using both dominant [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.00-1.13] and recessive models (OR = 1.12, 95% CI: 1.02-1.23) to analyse the data. In ethnic subgroups and using recessive model analysis: Arg399Gln increased breast cancer risk in Asians (OR = 1.26, 95% CI: 0.96-1.64) and Africans (OR = 1.80, 95% CI: 0.97-3.32), and also while only slightly increasing the breast cancer risk in Caucasians (OR = 1.08, 95% CI: 0.95-1.22). However, Arg194Trp (recessive model, OR = 0.95, 95% CI: 0.75-1.20) and Arg280His (recessive model, OR = 1.28, 95% CI: 0.64-2.55) did not appear to be risk factors for breast cancer. Larger scale primary studies are required to further evaluate the interaction of XRCC1 polymorphisms and breast cancer risk in specific populations.

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