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The observed dispersion measures (DMs) of fast radio bursts (FRBs) are a good indicator of the amount of ionized material along the propagation paths. In this work, we present a forecast of He II reionization detection using the DM and redshift measurements of FRBs from the upcoming Square Kilometre Array (SKA). Assuming a model of the Universe in which \he reionization occurred at a specific redshift $z_{\rm re}$, we analyze what extent the signal-to-noise ratio ($\mathrm{S/N}$) for the detection of the amplitude of reionization can be achieved in the era of SKA. Using $10^{6}$ mock FRB data from a one-year observation of the second phase of SKA, we find that the $\mathrm{S/N}$ for detecting He II reionization can approach the $32-50\sigma$ level and the uncertainty on the reionization redshift can be constrained to be $\sigma(z_{\rm re})\approx 0.022-0.031$, depending on the assumed FRB redshift distribution. We also examine the influence of different fiducial $z_{\rm re}$ values, finding that this effect has a modest impact on the forecasts. Our results demonstrate the potentially remarkable capability of SKA-era FRBs in constraining the epoch of He II reionization., Comment: 9 pages, 6 figures, 1 table

Observations of large amount of massive galaxies with relatively old populations found at high redshifts are challenging galaxy formation scenarios within standard cosmology. Precise determinations of the average age of these galaxies would be useful for the discussion of this problem. Here we carry out a better constraint of the age of 200 V-shaped SED non-AGN galaxies at redshifts $22.5$, we do not see a significant evolution of their average age, with all average ages of galaxies mostly remaining between 1 and 2 Gyr. Our research find that most massive galaxies ($\sim 10^{10} M_\odot$ ) are older (typically $>\sim 1$ Gyr old) and formed earlier than less massive galaxies in our sample., Comment: 8 pages, 8 figures, 1 table, accepted for publication in ApJ

Visual Prompt Learning (VPL) differs from traditional fine-tuning methods in reducing significant resource consumption by avoiding updating pre-trained model parameters. Instead, it focuses on learning an input perturbation, a visual prompt, added to downstream task data for making predictions. Since learning generalizable prompts requires expert design and creation, which is technically demanding and time-consuming in the optimization process, developers of Visual Prompts as a Service (VPaaS) have emerged. These developers profit by providing well-crafted prompts to authorized customers. However, a significant drawback is that prompts can be easily copied and redistributed, threatening the intellectual property of VPaaS developers. Hence, there is an urgent need for technology to protect the rights of VPaaS developers. To this end, we present a method named \textbf{WVPrompt} that employs visual prompt watermarking in a black-box way. WVPrompt consists of two parts: prompt watermarking and prompt verification. Specifically, it utilizes a poison-only backdoor attack method to embed a watermark into the prompt and then employs a hypothesis-testing approach for remote verification of prompt ownership. Extensive experiments have been conducted on three well-known benchmark datasets using three popular pre-trained models: RN50, BIT-M, and Instagram. The experimental results demonstrate that WVPrompt is efficient, harmless, and robust to various adversarial operations., Comment: 11 pages, 7 figures

The recent discoveries of a remarkable glitch/antiglitch accompanied by fast radio burst (FRB)-like bursts from the Galactic magnetar SGR J1935+2154 have revealed the physical connection between the two. In this work, we study the statistical properties of radio bursts from the hyperactive repeating source FRB 20201124A and of glitches from the pulsar PSR B1737--30. For FRB 20201124A, we confirm that the probability density functions of fluctuations of energy, peak flux, duration, and waiting time well follow the Tsallis q-Gaussian distribution. The derived q values from q-Gaussian distribution keep approximately steady for different temporal interval scales, which indicate that there is a common scale-invariant structure in repeating FRBs. Similar scale-invariant property can be found in PSR B1737--30's glitches, implying an underlying association between the origins of repeating FRBs and pulsar glitches. These statistical features can be well understood within the same physical framework of self-organized criticality systems., Comment: 9 pages, 3 figures, 2 tables, Accepted by Apj

Deep neural networks (DNNs) are extensively employed in a wide range of application scenarios. Generally, training a commercially viable neural network requires significant amounts of data and computing resources, and it is easy for unauthorized users to use the networks illegally. Therefore, network ownership verification has become one of the most crucial steps in safeguarding digital assets. To verify the ownership of networks, the existing network fingerprinting approaches perform poorly in the aspects of efficiency, stealthiness, and discriminability. To address these issues, we propose a network fingerprinting approach, named as GanFinger, to construct the network fingerprints based on the network behavior, which is characterized by network outputs of pairs of original examples and conferrable adversarial examples. Specifically, GanFinger leverages Generative Adversarial Networks (GANs) to effectively generate conferrable adversarial examples with imperceptible perturbations. These examples can exhibit identical outputs on copyrighted and pirated networks while producing different results on irrelevant networks. Moreover, to enhance the accuracy of fingerprint ownership verification, the network similarity is computed based on the accuracy-robustness distance of fingerprint examples'outputs. To evaluate the performance of GanFinger, we construct a comprehensive benchmark consisting of 186 networks with five network structures and four popular network post-processing techniques. The benchmark experiments demonstrate that GanFinger significantly outperforms the state-of-the-arts in efficiency, stealthiness, and discriminability. It achieves a remarkable 6.57 times faster in fingerprint generation and boosts the ARUC value by 0.175, resulting in a relative improvement of about 26%., Comment: 9 pages, 6 figures