arXiv subjects

Meilin Liu

Publications and source records attributed to Meilin Liu.

7 recordsLinked to original sources

arXiv2026

Orbital Dynamics and Gravitational-Wave Signatures of EMRIs in Self-Dual Loop Quantum Gravity Black Holes

Loop quantum gravity (LQG) predicts quantum modifications to classical black-hole spacetimes, which may leave imprints on the dynamics and gravitational-wave signals of compact objects in the strong-field regime. In this work, we investigate the orbital dynamics and gravitational-wave signatures of extreme mass-ratio inspirals (EMRIs) in a self-dual loop quantum gravity black hole spacetime. We analyze test-particle motion in the static, spherically symmetric self-dual LQG geometry characterized by two quantum parameters: the polymeric parameter $P$ and the minimal area parameter $a_0$. The effective potential and orbital structure are systematically studied, and we quantify the influence of quantum corrections on circular-orbit stability and strong-field orbital behavior. Compared with the classical Schwarzschild spacetime, LQG corrections modify the near-horizon orbital dynamics. Based on the orbital evolution, we construct gravitational-wave waveforms and investigate the impact of quantum corrections on waveform morphology. We find that LQG effects accumulate during the long inspiral phase, leading to noticeable signal deviations from the classical case. To incorporate rotation, we construct a rotating extension of the self-dual spacetime using the Newman--Janis algorithm. The resulting LQG-corrected Kerr geometry is used to analyze orbital motion, revealing the interplay between spin and quantum corrections in strong-field trajectories. Finally, we perform a Fisher matrix analysis to estimate potential constraints on quantum parameters from future space-based gravitational-wave detectors. Our results indicate that EMRI observations provide a promising avenue to probe quantum gravitational effects in black-hole spacetimes.

gr-qc↗

arXiv2026

Quasinormal Modes of Self-Dual Loop Quantum Gravity Corrected Kerr Black Holes

We investigate scalar quasinormal modes and ringdown signals of a rotating loop quantum gravity black hole constructed via the revised Newman--Janis algorithm. The spacetime incorporates two quantum parameters, the polymeric parameter $P$ and the minimal area parameter $a_0$, and reduces to the Kerr spacetime in the classical limit. Using the sixth-order WKB approximation and time-domain integration, we analyze the effective potential, quasinormal mode spectrum, and ringdown dynamics. The polymeric parameter $P$ systematically reduces the oscillation frequency $\mathrm{Re}(\omega)$, while the spin parameter $a$ enhances it. The time-domain signals exhibit clear dependence on the multipole number $\ell$ and overtone index $n$. Our results provide a theoretical framework for understanding the effects of loop quantum gravity corrections on black hole perturbations and gravitational-wave ringdown signals.

gr-qc↗

arXiv2026

OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging

Federated learning (FL) has become a promising paradigm for collaborative medical image analysis, yet existing frameworks remain tightly coupled to task-specific backbones and are fragile under heterogeneous imaging modalities. Such constraints hinder real-world deployment, where institutions vary widely in modality distributions and must support diverse downstream tasks. To address this limitation, we propose OmniFM, a modality- and task-agnostic FL framework that unifies training across classification, segmentation, super-resolution, visual question answering, and multimodal fusion without re-engineering the optimization pipeline. OmniFM builds on a key frequency-domain insight: low-frequency spectral components exhibit strong cross-modality consistency and encode modality-invariant anatomical structures. Accordingly, OmniFM integrates (i) Global Spectral Knowledge Retrieval to inject global frequency priors, (ii) Embedding-wise Cross-Attention Fusion to align representations, and (iii) Prefix-Suffix Spectral Prompting to jointly condition global and personalized cues, together regularized by a Spectral-Proximal Alignment objective that stabilizes aggregation. Experiments on real-world datasets show that OmniFM consistently surpasses state-of-the-art FL baselines across intra- and cross-modality heterogeneity, achieving superior results under both fine-tuning and training-from-scratch setups.

cs.CV↗

arXiv2025

Attack for Defense: Adversarial Agents for Point Prompt Optimization Empowering Segment Anything Model

Prompt quality plays a critical role in the performance of the Segment Anything Model (SAM), yet existing approaches often rely on heuristic or manually crafted prompts, limiting scalability and generalization. In this paper, we propose Point Prompt Defender, an adversarial reinforcement learning framework that adopts an attack-for-defense paradigm to automatically optimize point prompts. We construct a task-agnostic point prompt environment by representing image patches as nodes in a dual-space graph, where edges encode both physical and semantic distances. Within this environment, an attacker agent learns to activate a subset of prompts that maximally degrade SAM's segmentation performance, while a defender agent learns to suppress these disruptive prompts and restore accuracy. Both agents are trained using Deep Q-Networks with a reward signal based on segmentation quality variation. During inference, only the defender is deployed to refine arbitrary coarse prompt sets, enabling enhanced SAM segmentation performance across diverse tasks without retraining. Extensive experiments show that Point Prompt Defender effectively improves SAM's robustness and generalization, establishing a flexible, interpretable, and plug-and-play framework for prompt-based segmentation.

cs.CV↗

arXiv2021

A New Continuous Optimization Method for Mixed Integer Space Travelling Salesman Problem

The travelling salesman problem (TSP) of space trajectory design is complicated by its complex structure design space. The graph based tree search and stochastic seeding combinatorial approaches are commonly employed to tackle the time-dependent TSP due to their combinatorial nature. In this paper, a new continuous optimization strategy for the mixed integer combinatorial problem is proposed. The space trajectory combinatorial problem is tackled using continuous gradient based method. A continuous mapping technique is developed to map the integer type ID of targets on the sequence to a set of continuous design variables. Expected flyby targets are introduced as references and used as priori to select the candidate target to fly by. Bayesian based analysis is employed to model accumulated posterior of the sequence and a new objective function with quadratic form constraints is constructed. The new introduced auxiliary design variables of expected targets together with the original design variables are set to be optimized. A gradient based optimizer is used to search optimal sequence parameter. Performances of the proposed algorithm are demonstrated through a multiple debris rendezvous problem and a static TSP benchmark.

math.OC↗

arXiv2014

A Non-Cooperative Game Model for Reliability-Based Task Scheduling in Cloud Computing

Cloud computing is a newly emerging distributed system which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non-cooperative game model for reliability-based task scheduling approach. This model takes the steady-state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. And also, we design a task scheduling algorithm based on this model. The experiments can be seen that our task scheduling algorithm is better than the so-called balanced scheduling algorithm.

cs.DC↗

arXiv2014

A Task Allocation Schema Based on Response Time Optimization in Cloud Computing

Cloud computing is a newly emerging distributed computing which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum or the overall system performance is optimal. Unlike the previous task slices' sequential execution of an independent task in the model of which the target is processing time, we build a model that targets at the response time, in which the task slices are executed in parallel. Then we give its solution with a method based on an improved adjusting entropy function. At last, we design a new task scheduling algorithm. Experimental results show that the response time of our proposed algorithm is much lower than the game-theoretic algorithm and balanced scheduling algorithm and compared with the balanced scheduling algorithm, game-theoretic algorithm is not necessarily superior in parallel although its objective function value is better.

cs.DC↗