Tidal Stripping of a White Dwarf by an Intermediate-Mass Black Hole

During the inspiralling of a white dwarf (WD) into an intermediate-mass black hole (~ 10^{2-5} M_sun), both gravitational waves (GWs) and electromagnetic (EM) radiation are emitted. Once the eccentric orbit's pericenter radius approaches the tidal radius, the WD would be tidally stripped upon each pericenter passage. The accretion of these stripped mass would produce EM radiation. It is suspected that the recently discovered new types of transients, namely the quasi-periodic eruptions and the fast ultraluminous X-ray bursts, might originate from such systems. Modeling these flares requires a prediction of the amount of stripped mass from the WD and the details of the mass supply to the accretion disk. We run hydrodynamical simulations to study the orbital parameter dependence of the stripped mass. We find that our results match the analytical estimate that the stripped mass is proportional to z^{5/2}, where z is the excess depth by which the WD overfills its instantaneous Roche lobe at the pericenter. The corresponding fallback rate of the stripped mass is calculated, which may be useful in interpreting the individual flaring light curve in candidate EM sources. We further calculate the long-term mass-loss evolution of a WD during its inspiral and the detectability of the GW and EM signals. The EM signal from the mass-loss stage can be easily detected: the limiting distance is ~ 320(M_h/10^4 M_sun) Mpc for Einstein Probe. The GW signal, for the space-borne detectors such as Laser Interferometer Space Antenna or TianQin, can be detected only within the Local Supercluster (~ 33 Mpc).Comment: 18 pages, 13 figures, Accepted for publication in Ap

Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high success rates in the white-box setting, they also exhibit weak transferability in the black-box setting. Recently, various methods have been proposed to improve adversarial transferability, in which the input transformation is one of the most effective methods. In this work, we notice that existing input transformation-based works mainly adopt the transformed data in the same domain for augmentation. Inspired by domain generalization, we aim to further improve the transferability using the data augmented from different domains. Specifically, a style transfer network can alter the distribution of low-level visual features in an image while preserving semantic content for humans. Hence, we propose a novel attack method named Style Transfer Method (STM) that utilizes a proposed arbitrary style transfer network to transform the images into different domains. To avoid inconsistent semantic information of stylized images for the classification network, we fine-tune the style transfer network and mix up the generated images added by random noise with the original images to maintain semantic consistency and boost input diversity. Extensive experimental results on the ImageNet-compatible dataset show that our proposed method can significantly improve the adversarial transferability on either normally trained models or adversarially trained models than state-of-the-art input transformation-based attacks. Code is available at: https://github.com/Zhijin-Ge/STM.Comment: 10 pages, 2 figures, accepted by the 31st ACM International Conference on Multimedia (MM '23

Structural deformation of shale pores in the fold-thrust belt: The Wufeng-Longmaxi shale in the Anchang Syncline of Central Yangtze Block

The gas-rich Wufeng-Longmaxi shale has been intensely deformed within the fold-thrust belt of the Yangtze Block. To better understand the impact of structural deformation on the shale pore system, this paper systematically investigated the matrix components, porosity and pore structures in core samples from theWufeng-Longmaxi shale, newly collected from various structural domains in the first commercial shale gas field of the Central Yangtze Block, the Anchang Syncline. The shale porosity generally showed a positive relationship with total organic carbon content. Nevertheless, even at a constant total organic carbon content, the shale porosity decreased from the syncline limb to the syncline hinge zone and with a decreasing interlimb angle in the syncline hinge zone, which aligned with the structural deformation strain during folding. The artificial axial compression of shale samples also confirmed that the decrease in shale porosity was stronger at an elevated axial compression stress and was relatively higher in samples with higher total organic carbon content. The organic pore size decreased with higher structural deformation strain, but the aspect ratio of the pore shape increased. Even quartz failed to resist the effective stress under the intensive structural deformation, changing the correlation between porosity and quartz from positive to negative. In contrast, pore spaces generated by the slipping between clay flakes under intensive deformation accounted for a positive relationship between clay content and bulk porosity. Considering the shale porosity reduction caused by the intensive structural deformation of shale pores, the Wufeng-Longmaxi shale, that is rich in fracture networks between roof and floor layers, may still be an excellent exploration target in the fold-thrust belt of the Yangtze Block.Cited as: Guo, X., Liu, R., Xu, S., Feng, B., Wen, T., Zhang, T. Structural deformation of shale pores in the fold-thrust belt: The Wufeng-Longmaxi shale in the Anchang Syncline of Central Yangtze Block. Advances in Geo-Energy Research, 2022, 6(6): 515-530. https://doi.org/10.46690/ager.2022.06.0

Enhancing Security Patch Identification by Capturing Structures in Commits

With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the deployed software that integrated them being unable to get a timely update. Hence, it is critical to design a security patch identification system to ensure the security of the utilized software. However, most of the existing works for security patch identification just consider the changed code and the commit message of a commit as a flat sequence of tokens with simple neural networks to learn its semantics, while the structure information is ignored. To address these limitations, in this paper, we propose our well-designed approach E-SPI, which extracts the structure information hidden in a commit for effective identification. Specifically, it consists of the code change encoder to extract the syntactic of the changed code with the BiLSTM to learn the code representation and the message encoder to construct the dependency graph for the commit message with the graph neural network (GNN) to learn the message representation. We further enhance the code change encoder by embedding contextual information related to the changed code. To demonstrate the effectiveness of our approach, we conduct the extensive experiments against six state-of-the-art approaches on the existing dataset and from the real deployment environment. The experimental results confirm that our approach can significantly outperform current state-of-the-art baselines