arXiv subjects

Zhen Zhong

Publications and source records attributed to Zhen Zhong.

At least 19 recordsLinked to original sources

arXiv2026

Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

This study proposes a privacy-enhanced federated learning framework to address secure collaborative training in distributed data environments. The framework integrates Dynamic Differential Privacy (DDP), lightweight Homomorphic Encryption (HE), and Local Differential Privacy (LDP) mechanisms to ensure data privacy protection during model training. Additionally, the framework employs an asynchronous aggregation strategy with version control to support distributed training in asynchronous environments. Experimental validation on the CIFAR-10 and Purchase-100 benchmark datasets demonstrates that the method maintains high classification accuracy (up to 82.6%) even under stringent privacy constraints (ε = 0.1), while reducing communication overhead by 21.3% compared to FedAvg. Experimental results demonstrate that this framework effectively balances privacy protection and model performance in distributed machine learning scenarios, providing a scalable technical foundation for large-scale distributed collaborative computing.

cs.LG↗

arXiv2026

Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability.

cs.AI↗

arXiv2026

AI Assisted Workflow Optimization and Automation

Against the backdrop of digital transformation and stricter regulation, enterprise compliance work demands higher efficiency and accuracy. The auxiliary compliance process has become an important entry point for optimizing the compliance system due to its strong transactional nature and high degree of repetition. This study focuses on the process characteristics of auxiliary compliance work, sorts out its structural composition and organizational mechanism, proposes an optimization path with process reengineering, system modeling, and technology integration as the core, and focuses on exploring the collaborative application of key technologies such as RPA, rule engine, and semantic recognition in process automation. Research suggests that the systematic optimization and intelligent upgrading of auxiliary processes will help build a modern compliance operation system that is responsive, efficient, structurally clear, and risk controllable.

cs.SE↗

arXiv2026

Dynamics and Frequency Conversion of Accreting Axion Clouds

Axion fields can form exponentially growing gravitational clouds around compact objects through self-interaction-driven relaxation of ambient axion waves. As the field amplitude approaches the axion decay constant, nonlinear effects become important. We identify two distinct regimes of late-time evolution, determined by the gravitational fine-structure constant and the cloud growth rate: a Bosenova regime, characterized by collapse accompanied by explosive axion bursts, and a saturation regime, in which self-interaction-induced axion emission balances accretion. In the latter regime, the emitted axion radiation exhibits stable discrete spectral lines at odd multiples of the bound-state energy, directly probing the global structure of the axion potential beyond its quadratic minimum. We show that single-cosine potentials and QCD axion-like potentials predict distinct emission spectra, enabling probes of the underlying axion self-interaction structure and its ultraviolet completion through terrestrial detection of relativistic axion fluxes from compact objects.

hep-ph↗

arXiv2026

Ultralight Boson Ionization from Comparable-Mass Binaries

Detection of gravitational waves enables probes of environmental effects around compact binaries. Ultralight bosons, well motivated in particle physics and capable of forming core-like dark matter structures, induce environmental dynamics that differ qualitatively from those produced by stars or particle dark matter. For comparable-mass binaries, such bosons can form gravitationally bound states analogous to molecules once the binary separation falls below the characteristic wavelength of the bound states, with an inner region co-moving with the binary. We combine numerical simulations and a semi-analytic framework to characterize the structure and ionization of these gravitational molecules. We determine the extent of the co-moving region and compute the ionization flux driven by orbital motion over a range of eccentricities. Using these results, we estimate the backreaction on the binary orbital evolution and identify a new environmental effect: eccentricity-induced ionization of the co-moving component leads to efficient circularization. We further show that this molecular phase can be astrophysically viable and significantly modify the stochastic gravitational wave background from supermassive black hole binaries.

gr-qc↗

arXiv2026

FusionRelight: Relighting Portraits in Real Time via Hybrid Domain Knowledge Fusion

Portrait relighting is a low-level vision problem in which physically plausible illumination transfer, identity preservation, and compact real-time inference must be considered together. Iterative diffusion-style methods can synthesize fine detail, but stochastic inference and cost complicate deterministic live video creation; physically grounded relighting preserves identity, but controlled synthetic or light-stage supervision transfers poorly to unconstrained cameras. We present Hybrid Domain Knowledge Fusion (HDKF), a relighting-specific training framework that learns complementary physics, reflectance, and realism priors from synthetic, One-Light-at-A-Time (OLAT), and in-the-wild data, then distills their source-routed supervision into a compact student with clean teacher labels and degraded student inputs. The framework is trained with pixel-aligned RGB, albedo, and normal supervision, providing a simulation substrate for physically grounded low-level relighting. On a held-out OLAT benchmark, HDKF obtains the best MSE, PSNR, and SSIM among evaluated methods while remaining competitive in LPIPS. The distilled model runs in real time at 512x512, reaching 11.89 ms on an RTX 2060 and 1.82 ms on an RTX 4090.

cs.CV↗

arXiv2026

Black Hole Ringdown Seen in Photon Polarization Swings

Light propagating through a perturbed spacetime could imprint the underlying gravitational waveform directly onto electromagnetic observables. In this Letter, we develop a covariant perturbative framework for polarized photon propagation in generic curved spacetimes, and derive a compact expression for the observable polarization-angle (PA) swing during Kerr ringdown, explicitly demonstrating its time-domain locking to the quasi-normal modes. We confirm this behavior using dynamical ray-tracing calculations for a broad class of photon trajectories. Photons grazing the strong-field region exhibit an achromatic, damped PA oscillation that tracks the ringdown, with a phase set by the mode's angular structure. The swing amplitude can reach $\sim 10^{\circ}$ and leaves distinctive signatures in spatially resolved autocorrelations. These results open a new polarimetric window onto black hole mergers and ringdown.

astro-ph.HE↗

arXiv2026

PAKAN: Pixel Adaptive Kolmogorov-Arnold Network Modules for Pansharpening

Pansharpening aims to fuse high-resolution spatial details from panchromatic images with the rich spectral information of multispectral images. Existing deep neural networks for this task typically rely on static activation functions, which limit their ability to dynamically model the complex, non-linear mappings required for optimal spatial-spectral fusion. While the recently introduced Kolmogorov-Arnold Network (KAN) utilizes learnable activation functions, traditional KANs lack dynamic adaptability during inference. To address this limitation, we propose a Pixel Adaptive Kolmogorov-Arnold Network framework. Starting from KAN, we design two adaptive variants: a 2D Adaptive KAN that generates spline summation weights across spatial dimensions and a 1D Adaptive KAN that generates them across spectral channels. These two components are then assembled into PAKAN 2to1 for feature fusion and PAKAN 1to1 for feature refinement. Extensive experiments demonstrate that our proposed modules significantly enhance network performance, proving the effectiveness and superiority of pixel-adaptive activation in pansharpening tasks.

cs.CV↗

arXiv2025

Whispers from the Early Universe: The Ringdown of Primordial Black Holes

We investigate the stochastic gravitational wave background (SGWB) generated by the ringdown phase of primordial black holes (PBHs) formed in the early universe. As the ringdown signal is independent of the PBH formation mechanism, the resulting SGWB offers a model-independent probe of PBHs. We numerically compute the ringdown waveform and derive the corresponding SGWB. We show that such a signal could be detected by future pulsar timing arrays (PTAs) for PBHs heavier than the solar mass. Additionally, we evaluate the SGWB from binary PBH mergers and demonstrate that it lies within the sensitivity bands of next-generation ground-based interferometers such as Cosmic Explorer and Einstein Telescope, suggesting a multi-band observational strategy for probing the PBH dark matter scenario.

astro-ph.CO↗

arXiv2025

Fisher's Randomization Test for Causality with General Types of Treatments

We extend Fisher's randomization test (FRT) to test conditional independence between observed outcomes and treatments given covariates in both randomized experiments and observational studies, with no restriction on the variable type of treatments. Under a generalized unconfoundedness assumption, we provide causal identification for this hypothesis. Our approach requires neither the no-interference nor the positive overlap assumption, making it a widely applicable tool for detecting causal effects. A unique advantage of FRT lies in the separated roles of assignment and outcome models. The former, whether known from randomized experiments or estimated in observational studies, guarantees valid Type I error control at least asymptotically. The latter, even if misspecified, is used to construct optimal test statistics derived from Bayes factors. The synthesis of two classes of models through FRT yields a calibrated Bayesian procedure with desired frequentist properties. Recognizing that the generalized unconfoundedness assumption is untestable in observational studies, we develop a novel sensitivity analysis to assess the robustness of causal conclusions to unobserved confounding. Through a re-analysis of a panel dataset, we show how our methods can be integrated into a pipeline for observational causal inference.

stat.ME↗

arXiv2025

Dynamical Lensing Tomography of Black Hole Ringdown

Strong gravitational lensing occurs when photons pass through the vicinity of a black hole. We investigate this phenomenon in the context of a gravitational-wave event, specifically when a black hole is settling into its final state. The deflection angle of photons mimics the ringdown pattern of the gravitational wave at intermediate times. At late times it has an inverse cubic dependence on observation time. The deviation angle increases exponentially as photons approach the photon ring orbit, reflecting its unstable nature. Our findings are directly applicable to imaging scenarios involving stars against the background of compact binaries, or circumbinary accretion disks, particularly during the merger of two black holes.

gr-qc↗

arXiv2025

Conditionally Affinely Invariant Rerandomization and its Admissible Complete Class

Rerandomization utilizes modern computing ability to improve covariate balance while adhering to the randomization principle originally advocated by RA Fisher. Affinely invariant rerandomization has the ``Equal Percent Variance Reducing'' (EPVR) property. When dealing with covariates of varying importance and/or mixed types, the conditionally EPVR property is often more desired. We discuss a general class of conditionally affinely invariant rerandomization methods and obtain their conditionally EPVR property. In addition, we set up a decision-theoretical framework to evaluate balance criteria for rerandomization. Popular rerandomization methods, such as the covariate balance table check, are found to be inadmissible. We suggest an admissible complete class of conditionally affinely invariant balance criteria, which can be applied to experimental designs involving tiers of covariates, stratification, and multiple treatment arms.

stat.ME↗

arXiv2025

Forward Ray Tracing and Hot Spots in Kerr Spacetime

Hotspots, often characterized as pointlike emissions, frequently appear near black holes with significantly enhanced luminosity compared to the surrounding accretion flow. Notably, such hotspots are regularly observed near the black hole at the center of the Milky Way. Light rays emitted from these sources follow complex trajectories around the black hole before reaching distinct locations on the observer's image plane. Precisely resolving both direct emissions and their higher-order images--despite the latter's intensity suppression--is essential for extracting detailed spacetime information, including the black hole's mass, spin, and inclination angle. To improve the accuracy and efficiency of hotspot modeling, we develop a forward ray tracing method based on the analytic integral solution of Kerr geodesics, leveraging conserved quantities. Our approach traces geodesics from a given emission point near the black hole to a distant observer, effectively capturing multiple images with a tailored parametrization scheme for root-finding. By perturbing these geodesics, we map finite-size emissions to distinct regions on the image plane, enabling the quantification of image shapes and amplification rates. This method not only enhances the identification of strongly lensed photons from black holes but also enables efficient spacetime tomography and hotspot localization, leveraging observations from the Event Horizon Telescope and its upcoming next-generation upgrades.

gr-qc↗

arXiv2024

Separate, Dynamic and Differentiable (SMART) Pruner for Block/Output Channel Pruning on Computer Vision Tasks

Block pruning, which eliminates contiguous blocks of weights, is a structural pruning method that can significantly enhance the performance of neural processing units (NPUs). In industrial applications, an ideal block pruning algorithm should meet three key requirements: (1) maintain high accuracy across diverse models and tasks, as machine learning deployments on edge devices are typically accuracy-critical; (2) offer precise control over resource constraints to facilitate user adoption; and (3) provide convergence guarantees to prevent performance instability. However, to the best of our knowledge, no existing block pruning algorithm satisfies all three requirements simultaneously. In this paper, we introduce SMART (Separate, Dynamic, and Differentiable) pruning, a novel algorithm designed to address this gap. SMART leverages both weight and activation information to enhance accuracy, employs a differentiable top-k operator for precise control of resource constraints, and offers convergence guarantees under mild conditions. Extensive experiments involving seven models, four datasets, three different block types, and three computer vision tasks demonstrate that SMART pruning achieves state-of-the-art performance in block pruning.

cs.CV↗

arXiv2024

A Two-stage Inference Procedure for Sample Local Average Treatment Effects in Randomized Experiments

In a given randomized experiment, individuals are often volunteers and can differ in important ways from a population of interest. It is thus of interest to focus on the sample at hand. This paper focuses on inference about the sample local average treatment effect (LATE) in randomized experiments with non-compliance. We present a two-stage procedure that provides asymptotically correct coverage rate of the sample LATE in randomized experiments. The procedure uses a first-stage test to decide whether the instrument is strong or weak, and uses different confidence sets depending on the first-stage result. Proofs of the procedure is developed for the situation with and without regression adjustment and for two experimental designs (complete randomization and Mahalaonobis distance based rerandomization). Finite sample performance of the methods are studied using extensive Monte Carlo simulations and the methods are applied to data from a voter encouragement experiment.

stat.ME↗

arXiv2024

Hushing black holes: tails in dynamical spacetimes

Stationary, asymptotically flat, black hole solutions of the vacuum field equations of General Relativity belong to the Kerr family. But how does one approach this state, dynamically? Linearized fluctuations decay at late times, at fixed spatial position, as a Price power law for generic initial conditions. However, little attention was paid to forced and nonlinear spacetimes, where matter and nonlinearities play a role. We uncover a new, source-driven tail governing waves generated by pointlike matter and nonlinearities, which can dominate over Price's decay.

gr-qc↗

arXiv2023

Inference of Sample Complier Average Causal Effects in Completely Randomized Experiments

In randomized experiments with non-compliance scholars have argued that the complier average causal effect (CACE) ought to be the main causal estimand. The literature on inference of the complier average treatment effect (CACE) has focused on inference about the population CACE. However, in general individuals in the experiments are volunteers. This means that there is a risk that individuals partaking in a given experiment differ in important ways from a population of interest. It is thus of interest to focus on the sample at hand and have easy to use and correct procedures for inference about the sample CACE. We consider a more general setting than in the previous literature and construct a confidence interval based on the Wald estimator in the form of a finite closed interval that is familiar to practitioners. Furthermore, with the access of pre-treatment covariates, we propose a new regression adjustment estimator and associated methods for constructing confidence intervals. Finite sample performance of the methods is examined through a Monte Carlo simulation and the methods are used in an application to a job training experiment.

stat.ME↗

arXiv2023

Piercing of a solitonic boson star by a black hole

Recently, the piercing of a mini boson star by a black hole was studied, with tidal capture and the discovery of a "gravitational atom" being reported ( arXiv:2206.00021 [gr-qc] ). Building on this research, we extend the study by including a hexic solitonic potential and explore the piercing of a solitonic boson star by a black hole. Notably, the solitonic boson star can reach higher compactness, which one might expect could alter the dynamics in this context. Our findings suggest that even when the black hole's size approaches the test particle limit, the solitonic boson star is easily captured by the black hole due to an extreme tidal capture process. Regardless of the black hole initial mass and velocity, our results indicate that over 85% of the boson star material is accreted. Thus, the self-interaction does not alter the qualitative behavior of the system.

gr-qc↗