Point cloud completion is a fundamental task in 3D vision. A persistent challenge in this field is simultaneously preserving fine-grained details present in the input while ensuring the global structural integrity of the completed shape. While recent works leveraging local symmetry transformations via direct regression have significantly improved the preservation of geometric structure details, these methods suffer from two major limitations: (1) These regression-based methods are prone to overfitting which tend to memorize instant-specific transformations instead of learning a generalizable geometric prior. (2) Their reliance on point-wise transformation regression lead to high sensitivity to input noise, severely degrading their robustness and generalization. To address these challenges, we introduce Simba, a novel framework that reformulates point-wise transformation regression as a distribution learning problem. Our approach integrates symmetry priors with the powerful generative capabilities of diffusion models, avoiding instance-specific memorization while capturing robust geometric structures. Additionally, we introduce a hierarchical Mamba-based architecture to achieve high-fidelity upsampling. Extensive experiments across the PCN, ShapeNet, and KITTI benchmarks validate our method's state-of-the-art (SOTA) performance.

As LLMs rapidly advance and enter realworld use, their privacy implications are increasingly important.We study an authorship de-anonymization threat: using LLMs to link anonymous documents to their authors, potentially compromising settings such as double-blind peer review.We propose De-Anonymization at Scale (DAS), a largelanguage-model-based method for attributing authorship among tens of thousands of candidate texts.DAS uses a sequential progression strategy: it randomly partitions the candidate corpus into fixed-size groups, prompts an LLM to select the text most likely written by the same author as a query text, and iteratively requeries the surviving candidates to produce a ranked top-k list.To make this practical at scale, DAS adds a dense-retrieval prefilter to shrink the search space and a majority-voting-style aggregation over multiple independent runs to improve robustness and ranking precision.Experiments on anonymized review data show DAS can recover same-author texts from pools of tens of thousands with accuracy well above chance, demonstrating a realistic privacy risk for anonymous platforms.On standard authorship benchmarks (Enron emails and blog posts), DAS also improves both accuracy and scalability over prior approaches, highlighting a new LLM-enabled de-anonymization vulnerability.

ABSTRACT Recent advances in wireless sensor networks (WSNs) technologies have brought significant convenience for location‐based services. And the problem of enabling nodes of WSNs to determine their locations in a hostile environment has become more and more important in WSNs applications. In this article, we propose a novel Dempster–Shafer based secure localization algorithm to diminish the effect of the unreliable beacons that provide false localization information in the WSNs. First, the framework of trust evaluation is established on the basis of evidence theory. According to the characteristics of time and space of beacons, we set up the basic probability assignment (BPA) values and BPA evidences of beacons. Then, an improved Dempster–Shafer method is utilized to resolve the conflicts among the BPA evidences and to integrate the trust values of beacons. Themax_massfunction is used to decide which beacon is attacked, and then, we eliminate the attacked beacons before the localization stage. Finally, we employ the weighted Taylor‐series least squares method to estimate the normal nodes coordinates. Simulation results demonstrate that our proposed algorithm is robust and effective. Copyright © 2013 John Wiley & Sons, Ltd.