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In parallel simulation, convergence and parallelism are often seen as inherently conflicting objectives. Improved parallelism typically entails lighter local computation and weaker coupling, which unavoidably slow the global convergence. This paper presents a novel GPU algorithm that achieves convergence rates comparable to fullspace Newton's method while maintaining good parallelizability just like the Jacobi method. Our approach is built on a key insight into the phenomenon ofovershoot.Overshoot occurs when a local solver aggressively minimizes its local energy without accounting for the global context, resulting in a local update that undermines global convergence. To address this, we derive a theoretically second-order optimal solution to mitigate overshoot. Furthermore, we adapt this solution into a pre-computable form. Leveraging Cubature sampling, our runtime cost is only marginally higher than the Jacobi method, yet our algorithm converges nearly quadratically as Newton's method. We also introduce a novel full-coordinate formulation for more efficient pre-computation. Our method integrates seamlessly with the incremental potential contact method and achieves second-order convergence for both stiff and soft materials. Experimental results demonstrate that our approach delivers high-quality simulations and outperforms state-of-the-art GPU methods with 50× to 100× better convergence. 

Context. To explain the well-known tension between cosmological parameter constraints obtained from the primary cosmic microwave background (CMB) and those drawn from X-ray-selected galaxy cluster samples identified with early data, we propose a possible explanation for the incompleteness of detected clusters being higher than estimated. Specifically, we suggest that certain types of galaxy groups or clusters may have been overlooked in previous works. Aims. We aim to search for galaxy groups and clusters with especially extended surface brightness distributions by creating a new X-ray-selected catalog of extended galaxy clusters from the XMM-SpitzerExtragalactic Representative Volume Survey (XMM-SERVS) data, based on a dedicated source detection and characterization algorithm optimized for extended sources. Methods. Our state-of-the-art algorithm is composed of wavelet filtering, source detection, and characterization. We carried out a visual inspection of the optical image, and spatial distribution of galaxies within the same redshift layer to confirm the existence of clusters and estimated the cluster redshift with the spectroscopic and photometric redshifts of galaxies. The growth curve analysis was used to characterize the detections. Results. We present a catalog of extended X-ray galaxy clusters detected from the XMM-SERVS data. The XMM-SERVS X-ray eXtended Galaxy Cluster (XVXGC) catalog features 141 cluster candidates. Specifically, there are 53 clusters previously identified as clusters with intracluster medium (ICM) emission (class 3); 40 that were previously known as optical or infrared (IR) clusters, but detected as X-ray clusters for the first time (class 2); and 48 identified as clusters for the first time (class 1). Compared with the class 3 sample, the “class 1 + class 2” sample is systematically fainter and exhibits a flatter surface brightness profile. Specifically, the median flux in [0.5–2.0] keV band for “class 1 + class 2” and class 3 sample is 1.288 × 10⁻¹⁴erg/s/cm²and 1.887 × 10⁻¹⁴erg/s/cm², respectively. The median values ofβ(i.e., the slope of the cluster surface brightness profile) are 0.506 and 0.573 for the “class 1 + class 2” and class 3 samples, respectively. The entire sample is available at the CDS. 

High-quality large-scale scene rendering requires a scalable representation and accurate camera poses. This research combines tile-based hybrid neural fields with parallel distributive optimization to improve bundle-adjusting neural radiance fields. The proposed method scales with a divide-and-conquer strategy. We partition scenes into tiles, each with a multi-resolution hash feature grid and shallow chained diffuse and specular multilayer perceptrons (MLPs). Tiles unify foreground and background via a spatial contraction function that allows both distant objects in outdoor scenes and planar reflections as virtual images outside the tile. Decomposing appearance with the specular MLP allows a specular-aware warping loss to provide a second optimization path for camera poses. We apply the alternating direction method of multipliers (ADMM) to achieve consensus among camera poses while maintaining parallel tile optimization. Experimental results show that our method outperforms state-of-the-art neural scene rendering method quality by 5%--10% in PSNR, maintaining sharp distant objects and view-dependent reflections across six indoor and outdoor scenes. 

Abstract We consider semantic image segmentation. Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output. In contrast to uncertainty, our method directly learns to predict the erroneous pixels of a segmentation network, which is modeled as a binary classification problem. It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning. It also allows us to train a branch to correct the labels of erroneous pixels. Our method consists of three stages: (i) predict pixel-wise error probability of the initial result, (ii) redetermine new labels for pixels with high error probability, and (iii) fuse the initial result and the redetermined result with respect to the error probability. We formulate the error-pixel prediction problem as a classification task and employ an error-prediction branch in the network to predict pixel-wise error probabilities. We also introduce a detail branch to focus the training process on the erroneous pixels. We have experimentally validated our method on the Cityscapes and ADE20K datasets. Our model can be easily added to various advanced segmentation networks to improve their performance. Taking DeepLabv3+ as an example, our network can achieve 82.88% of mIoU on Cityscapes testing dataset and 45.73% on ADE20K validation dataset, improving corresponding DeepLabv3+ results by 0.74% and 0.13% respectively.