Isolating the chiral magnetic effect from backgrounds by pair invariant mass

Topological gluon configurations in quantum chromodynamics induce quark chirality imbalance in local domains, which can result in the chiral magnetic effect (CME)--an electric charge separation along a strong magnetic field. Experimental searches for the CME in relativistic heavy ion collisions via the charge-dependent azimuthal correlator ($\Delta\gamma$) suffer from large backgrounds arising from particle correlations (e.g. due to resonance decays) coupled with the elliptic anisotropy. We propose differential measurements of the $\Delta\gamma$ as a function of the pair invariant mass ($m_{\rm inv}$), by restricting to high $m_{\rm inv}$ thus relatively background free, and by studying the $m_{\rm inv}$ dependence to separate the possible CME signal from backgrounds. We demonstrate by model studies the feasibility and effectiveness of such measurements for the CME search.Comment: 16 preprint pages 5 figures. v2: added a test with a broad "instanton/sphaleron" peak, and added clarifying texts; v3: added event-shape engineering (and two new figures) and expanded discussions on the low invariant mass region; v4: repeated cautionary discussions in introduction and conclusion sections, published versio

RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network

In recent years, convolutional neural network has shown good performance in many image processing and computer vision tasks. However, a standard CNN model is not invariant to image rotations. In fact, even slight rotation of an input image will seriously degrade its performance. This shortcoming precludes the use of CNN in some practical scenarios. Thus, in this paper, we focus on designing convolutional layer with good rotation invariance. Specifically, based on a simple rotation-invariant coordinate system, we propose a new convolutional operation, called Rotation-Invariant Coordinate Convolution (RIC-C). Without additional trainable parameters and data augmentation, RIC-C is naturally invariant to arbitrary rotations around the input center. Furthermore, we find the connection between RIC-C and deformable convolution, and propose a simple but efficient approach to implement RIC-C using Pytorch. By replacing all standard convolutional layers in a CNN with the corresponding RIC-C, a RIC-CNN can be derived. Using MNIST dataset, we first evaluate the rotation invariance of RIC-CNN and compare its performance with most of existing rotation-invariant CNN models. It can be observed that RIC-CNN achieves the state-of-the-art classification on the rotated test dataset of MNIST. Then, we deploy RIC-C to VGG, ResNet and DenseNet, and conduct the classification experiments on two real image datasets. Also, a shallow CNN and the corresponding RIC-CNN are trained to extract image patch descriptors, and we compare their performance in patch verification. These experimental results again show that RIC-C can be easily used as drop in replacement for standard convolutions, and greatly enhances the rotation invariance of CNN models designed for different applications

Improving the Transferability of Adversarial Examples via Direction Tuning

In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models. Although considerable efforts have been developed on improving the transferability of adversarial examples generated by transfer-based adversarial attacks, our investigation found that, the big deviation between the actual and steepest update directions of the current transfer-based adversarial attacks is caused by the large update step length, resulting in the generated adversarial examples can not converge well. However, directly reducing the update step length will lead to serious update oscillation so that the generated adversarial examples also can not achieve great transferability to the victim models. To address these issues, a novel transfer-based attack, namely direction tuning attack, is proposed to not only decrease the update deviation in the large step length, but also mitigate the update oscillation in the small sampling step length, thereby making the generated adversarial examples converge well to achieve great transferability on victim models. In addition, a network pruning method is proposed to smooth the decision boundary, thereby further decreasing the update oscillation and enhancing the transferability of the generated adversarial examples. The experiment results on ImageNet demonstrate that the average attack success rate (ASR) of the adversarial examples generated by our method can be improved from 87.9\% to 94.5\% on five victim models without defenses, and from 69.1\% to 76.2\% on eight advanced defense methods, in comparison with that of latest gradient-based attacks

Why cuckoos remove host eggs: Biting eggs facilitates faster parasitic egg‐laying

Brood parasitism by cuckoos relies on manipulating hosts to raise their offspring and has evolved stunning adaptations to aid in their deception. The fact that cuckoos usually but not always, remove one or two host eggs while laying their eggs has been a longstanding focus of intensive research. However, the benefit of this behavior remains elusive. Moreover, the recently proposed help delivery hypothesis, predicting that egg removal by cuckoos may decrease the egg‐laying duration in the parasitism process caused by biting action, lacks experimental verification. Therefore, in this study, we examined the effects of egg removal/biting on the egg‐laying speed in the common cuckoo (Cuculus canorus) to experimentally test this hypothesis. We compared the duration of cuckoo egg‐laying in empty nests, nests with host eggs, and nests with artificial blue stick models to test whether cuckoos biting an egg/stick can significantly hasten the egg‐laying speed than no biting action. Our results showed that biting an egg or an object is associated with cuckoos laying approximately 37% faster than when they do not bite an egg or an object. This study provides the first experimental evidence for the help delivery hypothesis and demonstrates that when cuckoos bite eggs or other objects in the nest, they lay eggs more quickly and thereby avoid suffering the hosts' injurious attack