Electric Fields and Chiral Magnetic Effect in Cu + Au Collisions

The non-central Cu + Au collisions can create strong out-of-plane magnetic fields and in-plane electric fields. By using the HIJING model, we study the general properties of the electromagnetic fields in Cu + Au collisions at 200 GeV and their impacts on the charge-dependent two-particle correlator $\gamma_{q_1q_2}=$ (see main text for definition) which was used for the detection of the chiral magnetic effect (CME). Compared with Au + Au collisions, we find that the in-plane electric fields in Cu + Au collisions can strongly suppress the two-particle correlator or even reverse its sign if the lifetime of the electric fields is long. Combining with the expectation that if $\gamma_{q_1q_2}$ is induced by elliptic-flow driven effects we would not see such strong suppression or reversion, our results suggest to use Cu + Au collisions to test CME and understand the mechanisms that underlie $\gamma_{q_1q_2}$.Comment: V1: 7 pages, 8 figures. V2: Add 2 new figures. Published versio

Brezinski Inverse and Geometric Product-Based Steffensen's Methods for Image Reverse Filtering

This work develops extensions of Steffensen's method to provide new tools for solving the semi-blind image reverse filtering problem. Two extensions are presented: a parametric Steffensen's method for accelerating the Mann iteration, and a family of 12 Steffensen's methods for vector variables. The development is based on Brezinski inverse and geometric product vector inverse. Variants of these methods are presented with adaptive parameter setting and first-order method acceleration. Implementation details, complexity, and convergence are discussed, and the proposed methods are shown to generalize existing algorithms. A comprehensive study of 108 variants of the vector Steffensen's methods is presented in the Supplementary Material. Representative results and comparison with current state-of-the-art methods demonstrate that the vector Steffensen's methods are efficient and effective tools in reversing the effects of commonly used filters in image processing

A deep learning approach for marine snow synthesis and removal

Marine snow, the floating particles in underwater images, severely degrades the visibility and performance of human and machine vision systems. This paper proposes a novel method to reduce the marine snow interference using deep learning techniques. We first synthesize realistic marine snow samples by training a Generative Adversarial Network (GAN) model and combine them with natural underwater images to create a paired dataset. We then train a U-Net model to perform marine snow removal as an image to image translation task. Our experiments show that the U-Net model can effectively remove both synthetic and natural marine snow with high accuracy, outperforming state-of-the-art methods such as the Median filter and its adaptive variant. We also demonstrate the robustness of our method by testing it on the MSRB dataset, which contains synthetic artifacts that our model has not seen during training. Our method is a practical and efficient solution for enhancing underwater images affected by marine snow