arXiv subjects

Heng Xu

Publications and source records attributed to Heng Xu.

At least 55 records · Page 3Linked to original sources

arXiv2024

X-ray hardening preceding the onset of SGR 1935+2154's radio pulsar phase

Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts have been detected (\citealt{Camilo2006,camilo2007a}), albeit its physical origin is unclear. It has long been speculated that the origin of magnetars' radio signals is different from those from canonical pulsars, although convincing evidence is still lacking. Five months after magnetar SGR 1935+2154's X-ray outburst and its associated Fast Radio Burst (FRB) 20200428, a radio pulsar phase was discovered. Here we report the discovery of X-ray spectral hardening associated with the emergence of periodic radio pulsations from SGR 1935+2154 and a detailed analysis of the properties of the radio pulses. The observations suggest that radio emission originates from the outer magnetosphere of the magnetar, and the surface heating due to the bombardment of inward-going particles from the radio emission region is responsible for the observed X-ray spectral hardening.

astro-ph.HE↗

arXiv2024

Scintillation velocity and arc observations of FRB 20201124A

We present the scintillation velocity measurements of FRB~20201124A from the FAST observations, which reveal an annual variation. This annual variation is further supported by changes detected in the scintillation arc as observed from the secondary spectrum. We attribute the annual velocity variation to the presence of a moderately anisotropic scattering screen located at a distance of 0.4$\pm$0.1~kpc from Earth. Our results prove that the scintillation of this FRB is mainly caused by material close to Earth on a Galactic scale. However, scintillation observations of other FRBs may expose their surrounding environment or uncover possible orbital motion if scintillation is caused by materials in their host galaxy.

astro-ph.HE↗

arXiv2024

Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation

Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine unlearning, which is required when the effect of some specific training samples needs to be removed from a learning model due to privacy, security, usability, and/or legislative factors. However, problems arise when current centralized unlearning methods are applied to existing federated learning, in which the server aims to remove all information about a class from the global model. Centralized unlearning usually focuses on simple models or is premised on the ability to access all training data at a central node. However, training data cannot be accessed on the server under the federated learning paradigm, conflicting with the requirements of the centralized unlearning process. Additionally, there are high computation and communication costs associated with accessing clients' data, especially in scenarios involving numerous clients or complex global models. To address these concerns, we propose a more effective and efficient federated unlearning scheme based on the concept of model explanation. Model explanation involves understanding deep networks and individual channel importance, so that this understanding can be used to determine which model channels are critical for classes that need to be unlearned. We select the most influential channels within an already-trained model for the data that need to be unlearned and fine-tune only influential channels to remove the contribution made by those data. In this way, we can simultaneously avoid huge consumption costs and ensure that the unlearned model maintains good performance. Experiments with different training models on various datasets demonstrate the effectiveness of the proposed approach.

cs.CR↗

arXiv2024

Don't Forget Too Much: Towards Machine Unlearning on Feature Level

Machine unlearning enables pre-trained models to remove the effect of certain portions of training data. Previous machine unlearning schemes have mainly focused on unlearning a cluster of instances or all instances belonging to a specific class. These types of unlearning might have a significant impact on the model utility; and they may be inadequate for situations where we only need to unlearn features within instances, rather than the whole instances. Due to the different granularity, current unlearning methods can hardly achieve feature-level unlearning. To address the challenges of utility and granularity, we propose a refined granularity unlearning scheme referred to as ``feature unlearning". We first explore two distinct scenarios based on whether the annotation information about the features is given: feature unlearning with known annotations and feature unlearning without annotations. Regarding unlearning with known annotations, we propose an adversarial learning approach to automatically remove effects about features. For unlearning without annotations, we initially enable the output of one model's layer to identify different pattern features using model interpretability techniques. We proceed to filter features from instances based on these outputs with identifying ability. So that we can remove the feature impact based on filtered instances and the fine-tuning process. The effectiveness of our proposed approach is demonstrated through experiments involving diverse models on various datasets in different scenarios.

cs.CR↗

arXiv2024

Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs

Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategies. However, the verification of machine unlearning, or in other words, how to guarantee that a sample has been successfully unlearned, has been overlooked for a long time. Existing verification schemes typically rely on machine learning attack techniques, such as backdoor or membership inference attacks. As these techniques are not formally designed for verification, they are easily bypassed when an untrustworthy MLaaS undergoes rapid fine-tuning to merely meet the verification conditions, rather than executing real unlearning. In this paper, we propose a formal verification scheme, IndirectVerify, to determine whether unlearning requests have been successfully executed. We design influential sample pairs: one referred to as trigger samples and the other as reaction samples. Users send unlearning requests regarding trigger samples and use reaction samples to verify if the unlearning operation has been successfully carried out. We propose a perturbation-based scheme to generate those influential sample pairs. The objective is to perturb only a small fraction of trigger samples, leading to the reclassification of reaction samples. This indirect influence will be used for our verification purposes. In contrast to existing schemes that employ the same samples for all processes, our scheme, IndirectVerify, provides enhanced robustness, making it less susceptible to bypassing processes.

cs.CR↗

arXiv2024

Towards Efficient Target-Level Machine Unlearning Based on Essential Graph

Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, have resulted in this need to allow a trained model to forget some of its training data. Existing studies of machine unlearning mainly focus on unlearning requests that forget a cluster of instances or all instances from one class. While these approaches are effective in removing instances, they do not scale to scenarios where partial targets within an instance need to be forgotten. For example, one would like to only unlearn a person from all instances that simultaneously contain the person and other targets. Directly migrating instance-level unlearning to target-level unlearning will reduce the performance of the model after the unlearning process, or fail to erase information completely. To address these concerns, we have proposed a more effective and efficient unlearning scheme that focuses on removing partial targets from the model, which we name "target unlearning". Specifically, we first construct an essential graph data structure to describe the relationships between all important parameters that are selected based on the model explanation method. After that, we simultaneously filter parameters that are also important for the remaining targets and use the pruning-based unlearning method, which is a simple but effective solution to remove information about the target that needs to be forgotten. Experiments with different training models on various datasets demonstrate the effectiveness of the proposed approach.

cs.LG↗

arXiv2024

New Timing Results of MSPs from NICER Observations

Millisecond pulsars (MSPs) are known for their long-term stability. Using six years of observations from the Neutron Star Interior Composition Explorer (NICER), we have conducted an in-depth analysis of the X-ray timing results for six MSPs: PSRs B1937+21, B1821$-$24, J0437$-$4715, J0030+0451, J0218+4232, and J2124$-$3358. The timing stability parameter $σ_z$ has been calculated, revealing remarkable timing precision on the order of $10^{-14}$ for PSRs B1937+21 and J0437$-$4715, and $10^{-13}$ for PSRs B1821$-$24, J0218+4232, and J0030+0451 over a timescale of 1000 days. These findings underscore the feasibility of autonomous in-orbit timekeeping using X-ray observations of MSPs. In addition, the consistency of long-term spin-down noise in the X-ray and radio bands has been investigated by comparison with IPTA radio data.

astro-ph.HE↗

arXiv2023

Scintillation Arc from FRB 20220912A

We present the interstellar scintillation analysis of fast radio burst (FRB) 20220912A during its extremely active episode in 2022 using data from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). We detect a scintillation arc in the FRB's secondary spectrum, which describes the power in terms of the scattered FRB signals' time delay and Doppler shift. The arc indicates that the scintillation is caused by a highly localized region of the ionized interstellar medium (IISM). Our analysis favors a Milky Way origin for the localized scattering medium but cannot rule out a host galaxy origin. We present our method for detecting the scintillation arc, which can be applied generally to sources with irregularly spaced bursts or pulses. These methods could help shed light on the complex interstellar environment surrounding the FRBs and in our Galaxy.

astro-ph.HE↗

arXiv2023

A radio pulsar phase from SGR J1935+2154 provides clues to the magnetar FRB mechanism

The megajansky radio burst, FRB 20200428, and other bright radio bursts detected from the Galactic source SGR J1935+2154 suggest that magnetars can make fast radio bursts (FRBs), but the emission site and mechanism of FRB-like bursts are still unidentified. Here we report the emergence of a radio pulsar phase of the magnetar five months after FRB 20200428. 795 pulses were detected in 16.5 hours over 13 days by the Five-hundred-meter Aperture Spherical Radio telescope, with luminosities about eight decades fainter than FRB 20200428. The pulses were emitted in a narrow phase window anti-aligned with the X-ray pulsation profile observed by the X-ray telescopes. The bursts, conversely, appear in random phases. This dichotomy suggests that radio pulses originate from a fixed region within the magnetosphere, but bursts occur in random locations and are possibly associated with explosive events in a dynamically evolving magnetosphere. This picture reconciles the lack of periodicity in cosmological repeating FRBs within the magnetar engine model.

astro-ph.HE↗

arXiv2023

Scientific Objectives of the Hot Universe Baryon Surveyor (HUBS) Mission

The Hot Universe Baryon Surveyor (HUBS) is a proposed space-based X-ray telescope for detecting X-ray emissions from the hot gas content in our universe. With its unprecedented spatially-resolved high-resolution spectroscopy and large field of view, the HUBS mission will be uniquely qualified to measure the physical and chemical properties of the hot gas in the interstellar medium, the circumgalactic medium, the intergalactic medium, and the intracluster medium. These measurements will be valuable for two key scientific goals of HUBS, namely to unravel the AGN and stellar feedback physics that governs the formation and evolution of galaxies, and to probe the baryon budget and multi-phase states from galactic to cosmological scales. In addition to these two goals, the HUBS mission will also help us solve some problems in the fields of galaxy clusters, AGNs, diffuse X-ray backgrounds, supernova remnants, and compact objects. This paper discusses the perspective of advancing these fields using the HUBS telescope.

astro-ph.GA↗

arXiv2023

Searching for the nano-Hertz stochastic gravitational wave background with the Chinese Pulsar Timing Array Data Release I

Observing and timing a group of millisecond pulsars (MSPs) with high rotational stability enables the direct detection of gravitational waves (GWs). The GW signals can be identified from the spatial correlations encoded in the times-of-arrival of widely spaced pulsar-pairs. The Chinese Pulsar Timing Array (CPTA) is a collaboration aiming at the direct GW detection with observations carried out using Chinese radio telescopes. This short article serves as a `table of contents' for a forthcoming series of papers related to the CPTA Data Release 1 (CPTA DR1) which uses observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here, after summarizing the time span and accuracy of CPTA DR1, we report the key results of our statistical inference finding a correlated signal with amplitude $\log A_{\rm c}= -14.4 \,^{+1.0}_{-2.8}$ for spectral index in the range of $α\in [-1.8, 1.5]$ assuming a GW background (GWB) induced quadrupolar correlation. The search for the Hellings-Downs (HD) correlation curve is also presented, where some evidence for the HD correlation has been found that a 4.6-$σ$ statistical significance is achieved using the discrete frequency method around the frequency of 14 nHz. We expect that the future International Pulsar Timing Array data analysis and the next CPTA data release will be more sensitive to the nHz GWB, which could verify the current results.

astro-ph.HE↗

arXiv2023

Results of 23 yr of Pulsar Timing of PSR J1453-6413

In this paper, we presented the 23.3 years of pulsar timing results of PSR J1456-6413 based on the observation of Parkes 64m radio telescope. We detected two new glitches at MJD 57093(3) and 59060(12) and confirmed its first glitch at MJD 54554(10). Using the "Cholesky" timing analysis method, we have determined its position, proper motion, and two-dimensional transverse velocities from the data segments before and after the second glitch, respectively. Furthermore, we detected exponential recovery behavior after the first glitch, with a recovery time scale of approximately 200 days and a corresponding exponential recovery factor Q of approximately 0.15(2), while no exponential recovery was detected for the other two glitches. More interestingly, we found that the leading component of the integral pulse profile after the second glitch became stronger, while the main component became weaker. Our results will expand the sample of pulsars with magnetosphere fluctuation triggered by the glitch event.

astro-ph.HE↗

arXiv2023

Machine Unlearning: A Survey

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.

cs.CR↗

arXiv2023

FAST Observations of FRB 20220912A: Burst Properties and Polarization Characteristics

We report the observations of FRB 20220912A using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We conducted 17 observations totaling 8.67 hours and detected a total of 1076 bursts with an event rate up to 390 hr$^{-1}$. The cumulative energy distribution can be well described using a broken power-law function with the lower and higher-energy slopes of $-0.38\pm0.02$ and $-2.07\pm0.07$, respectively. We also report the L band ($1-1.5$ GHz) spectral index of the synthetic spectrum of FRB~20220912A bursts, which is $-2.6\pm0.21$. The average rotation measure (RM) value of the bursts from FRB~20220912A is $-0.08\pm5.39\ \rm rad\,m^{-2}$, close to 0 $\rm rad\,m^{-2}$ and maintain relatively stable over two months. Most bursts have nearly 100\% linear polarization. About 45\% of the bursts have circular polarization with SNR $>$ 3, and the highest circular polarization degree can reach 70\%. Our observations suggest that FRB~20220912A is located in a relatively clean local environment with complex circular polarization characteristics. These various behaviors imply that the mechanism of circular polarization of FRBs likely originates from an intrinsic radiation mechanism, such as coherent curvature radiation or inverse Compton scattering inside the magnetosphere of the FRB engine source (e.g. a magnetar).

astro-ph.HE↗

arXiv2023

Lipschitz continuity of the solutions to the Dirichlet problems for the invariant laplacians

This short note is motivated by an attempt to understand the distinction between the Laplace operator and the hyperbolic Laplacian on the unit ball of $\mathbb{R}^n$, regarding the Lipschitz continuity of the solutions to the corresponding Dirichlet problems. We investigate the Dirichlet problem \begin{equation*} \left\{\begin{array}{ll} Δ_{\vartheta} u = 0, & \text{ in }\, \mathbb{B}^n,\\ u=ϕ, & \text{ on }\, \mathbb{S}^{n-1}, \end{array}\right. \end{equation*} where \[ Δ_{\vartheta} := (1-|x|^2) \bigg\{ \frac {1-|x|^2} {4} Δ+ \vartheta \sum_{j=1}^n x_{j} \frac {\partial } {\partial x_j} + \vartheta \left( \frac {n}{2}-1- \vartheta \right) I\bigg\}. \] We show that the Lipschitz continuity of boundary data always implies the Lipschitz continuity of the solutions if $\vartheta > 0$, but does not when $\vartheta \leq 0$.

math.AP↗

arXiv2023

Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation

Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradation problem of deep neural networks. We take inspiration from the biological plausibility learning where the neuron responses are tuned based on a local synapse-change procedure and activated by competitive lateral inhibition rules. Based on these feed-forward learning rules, we design a soft Hebbian learning process which provides an unsupervised and effective mechanism for online adaptation. We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer. It is able to fine-tune the neuron responses based on the external feedback generated by the error back-propagation from the top inference layers. This leads to our proposed neuro-modulated Hebbian learning (NHL) method for fully test-time adaptation. With the unsupervised feed-forward soft Hebbian learning being combined with a learned neuro-modulator to capture feedback from external responses, the source model can be effectively adapted during the testing process. Experimental results on benchmark datasets demonstrate that our proposed method can significantly improve the adaptation performance of network models and outperforms existing state-of-the-art methods.

cs.CV↗

arXiv2023

An X-Ray-dim "Isolated'' Neutron Star in a Binary?

We report the discovery of a dark companion to 2MASS J15274848+3536572 with an orbital period of 6.14 hr. Combining the radial velocity from LAMOST observations and modelling of the multiband light curve, one obtains a mass function of $\simeq 0.131~\rm M_{\odot}$, an inclination of $45.20^\circ{}^{+0.13^{\circ}}_{-0.20^{\circ}}$, and a mass ratio of $0.631^{+0.014}_{-0.003}$, which demonstrate the binary nature of the dark companion with mass of $0.98 \pm 0.03\rm M_{\odot}$ and a main-sequence K9-M0 star of $0.62 \pm 0.01~\rm M_{\odot}$. LAMOST optical spectra at a range of orbital phases reveal extra-peaked Halpha emission that suggests the presence of an accretion disk. The dark companion does not seem to be a white dwarf because of the lack of any observed dwarf nova outbursts in the long-term data archive, although a magnetic white dwarf cannot be excluded. Alternatively, we propose a scenario wherein the dark companion is a neutron star, but we have not detected radio pulsations or a single pulse from the system with the FAST (Five-hundred-meter Aperture Spherical radio Telescope), which hints at a radio-quiet compact object. If the dark companion is identified as a neutron star, it will be the nearest ( ~ 118 pc) and lightest neutron star. Furthermore, a kinematic analysis of the system's orbit in the galaxy may suggest its supernova event is associated with the radionuclide $^{60} \rm Fe$ signal observed from the deep-sea crusts. This radio-quiet and X-ray-dim nearby neutron star may resemble an XDINS (X-ray-dim isolated neutron star), but in a binary.

astro-ph.HE↗

arXiv2023

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Fast radio bursts (FRBs) are highly dispersed millisecond-duration radio bursts, of which the physical origin is still not fully understood. FRB 20201124A is one of the most actively repeating FRBs. In this paper, we present the collection of 1863 burst dynamic spectra of FRB 20201124A measured with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The current collection, taken from the observation during the FRB active phase from April to June 2021, is the largest burst sample detected in any FRB so far. The standard PSRFITs format is adopted, including dynamic spectra of the burst, and the time information of the dynamic spectra, in addition, mask files help readers to identify the pulse positions are also provided.

astro-ph.HE↗