arXiv subjects

Qiang Wu

Publications and source records attributed to Qiang Wu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Signatures of Quantum-Corrected Black Holes in Gravitational Waves from Periodic Orbits

We investigate gravitational wave emission from periodic timelike orbits of a test particle around a loop quantum gravity-inspired Schwarzschild black hole. The spacetime is characterised by a holonomy-correction parameter that modifies the radial metric component while preserving asymptotic flatness and the classical location of the horizon. The bound geodesics are systematically classified using the zoom--whirl representation labelled by three integers $(z,w,v)$. Gravitational waveforms are computed within a numerical framework that combines exact geodesic motion with the quadrupole approximation, which is suitable for extreme mass ratio inspirals. We demonstrate that the quantum corrections lead to distinct phase shifts, amplitude variations, and modifications to the harmonic structure of the waveforms, with increasingly complex features for orbits with larger zoom numbers. The corresponding frequency spectra and characteristic strain peak, which fall within the millihertz band, are within the sensitivity ranges of space-based detectors such as LISA, Taiji, and TianQin. For specific orbital configurations and values of the quantum-correction parameter, the characteristic strain exceeds the projected detector noise, indicating potential observability. Our results demonstrate that gravitational waves from periodic orbits provide a sensitive probe of quantum-corrected black hole spacetimes in the strong-field regime.

gr-qc↗

arXiv2025

CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning

Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effective strategy for enhancing safety at these urban junctures. Despite the growing popularity of Reinforcement Learning (RL) methods in optimizing TSC, these methods often prioritize driving efficiency over safety, thus failing to address the critical balance between these two aspects. Additionally, these methods usually need more interpretability. CounterFactual (CF) learning is a promising approach for various causal analysis fields. In this study, we introduce a novel framework to improve RL for safety aspects in TSC. This framework introduces a novel method based on CF learning to address the question: ``What if, when an unsafe event occurs, we backtrack to perform alternative actions, and will this unsafe event still occur in the subsequent period?'' To answer this question, we propose a new structure causal model to predict the result after executing different actions, and we propose a new CF module that integrates with additional ``X'' modules to promote safe RL practices. Our new algorithm, CFLight, which is derived from this framework, effectively tackles challenging safety events and significantly improves safety at intersections through a near-zero collision control strategy. Through extensive numerical experiments on both real-world and synthetic datasets, we demonstrate that CFLight reduces collisions and improves overall traffic performance compared to conventional RL methods and the recent safe RL model. Moreover, our method represents a generalized and safe framework for RL methods, opening possibilities for applications in other domains. The data and code are available in the github https://github.com/AdvancedAI-ComplexSystem/SmartCity/tree/main/CFLight.

cs.LG↗

arXiv2025

Spatial-spectral mapping for long-duration broadband terahertz pulse generation in on-chip waveguide arrays

Conventional approaches to terahertz (THz) pulse generation are restricted by the Fourier-transform limit, which hinders the creation of sources that combine long duration with broad bandwidth--a capability crucial for many spectroscopic and sensing applications. In this work, we overcome this challenge in the terahertz domain using an on-chip gradient waveguide array. The key is to spectrally disperse the pulse into spatially separated channels within a lithium niobate chip, effectively decoupling the design of temporal and spectral properties. We validate the source by distinguishing amino acid mixtures, demonstrating its tailored biosensing potential. This work establishes a novel mechanism for integrated THz generation, offering considerable promise for broadband spectroscopy and on-chip photonics.

physics.optics↗

arXiv2025

Spinning Primordial Black Holes and Scalar Induced Gravitational Waves from Single Field Inflation

We investigate the formation of primordial black holes (PBHs), their spin and abundance, in a single-field inflationary model based on a mutated hilltop potential inserted with a small step-like feature. This step induces a brief phase of ultra-slow-roll inflation, producing the large enhancement of the scalar power spectrum required for an appreciable amount of PBH abundance. Instead of the commonly used analytical power spectra, we compute the primordial power spectrum accurately by numerically solving the Mukhanov-Sasaki equation. Using the obtained power spectrum, we apply peak theory with $\nabla^2 ζ$ treated as a Gaussian random field and parametrize the curvature profile by its amplitude $μ$ and characteristic width $K$. Confining the study to Type-I PBH, the threshold value is calculated using two robust methods: the average of the compaction function and the q-function method. Using the result, the dimensionless spin parameter of the resulting PBHs is calculated at linear order and found to be $\sqrt{\langle a_\star^2 \rangle} \sim 10^{-3}$; however, it can be higher for smaller masses. We present detailed predictions for two representative parameter sets, calculate the present-day PBH mass function $f_{\rm PBH}(M)$ and the associated scalar-induced gravitational waves (SIGW). The first produces PBHs of mass $M \simeq 10^{-13}\,M_\odot$ that can account for $100\%$ of dark matter, while the second yields $M \simeq 10^{-2}\,M_\odot$ PBHs contributing approximately $2.4\%$ of the dark matter density. The predicted signals of SIGWs lie within the sensitivity bands of future experiments such as LISA, DECIGO, BBO, and SKA. In particular, the second parameter set produces a SIGWs compatible with the recent NANOGrav evidence for a low-frequency gravitational-wave signal.

astro-ph.CO↗

arXiv2025

Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction

Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhaustively enumerate feature combinations up to that order. This paradigm depends heavily on prior domain knowledge to delimit the interaction space and incurs substantial computational overhead. As a result, conventional CTR models face a persistent tension between enriching representations with complex high-order interactions and keeping computation tractable. To address this dual challenge, this study introduces the Kolmogorov-Arnold Represented Sparse Efficient Interaction Network (KarSein). Drawing inspiration from the learnable activation mechanism in the Kolmogorov-Arnold Network (KAN), KarSein leverages this mechanism to adaptively transform low-order basic features into high-order feature interactions, offering a novel approach to feature interaction modeling. KarSein extends the capabilities of KAN by introducing a more efficient architecture that significantly reduces computational costs while accommodating two-dimensional embedding vectors as feature inputs. Furthermore, it overcomes the limitation of KAN's its inability to spontaneously capture multiplicative relationships among features. Extensive experiments highlight the superiority of KarSein, demonstrating its ability to surpass not only the vanilla implementation of KAN in CTR prediction tasks but also other baseline methods. Remarkably, KarSein achieves exceptional predictive accuracy while maintaining a highly compact parameter size and minimal computational overhead. Moreover, KarSein exhibits strong interpretability and structural sparsity. As the first systematic adaptation of KAN to CTR prediction, KarSein offers a practical, parameter-efficient, and interpretable alternative for modeling complex feature interactions in CTR prediction.

cs.LG↗

arXiv2025

Probing Loop Quantum Gravity black holes through gravitational lensing

We investigate strong gravitational lensing by a charged loop quantum gravity (LQG) black hole obtained through the polymerisation scheme of Borges \textit{et al.} \cite{Borges:2023fog}. These effective geometries replace the Reissner--Nordström singularity with a symmetric transition surface and admit an extremal, cold remnant determined by the minimal area gap in LQG. In turn, we derive the null geodesic equations, investigate the photon effective potential, and obtain expressions for the photon-sphere radius and critical impact parameter. We compute the weak-field deflection angle and Einstein ring size, highlighting the deviations induced by the polymerisation parameter and the Barbero--Immirzi parameter. In the strong-field regime, we compute the strong deflection coefficients $(\bar{a},\bar{b})$ and evaluate the lensing observables $θ_\infty$, $s$, and $r_{\rm mag}$. Unlike the Reissner--Nordström case, the LQG corrections enhance the deflection angle and increase the angular separation of relativistic images, with deviations growing as the geometry approaches the LQG remnant limit. We further compute the corresponding observables for Sgr~A* and M87*, finding that the quantum-gravity modifications lie within the potential sensitivity of next-generation VLBI facilities. For M87*, the angular separation $s\in(0.05712,0.19123)\,μ\text{as}$, while it is $s\in(0.07595,0.25426)\,μ\text{as}$ for Sgr A*. The relative flux ratio is found to lie in the range, $r_{\rm mag}\in(4.49272,5.96397)$. Our analysis demonstrates that LQG-induced corrections leave characteristic strong and weak-lensing imprints, offering a promising observational pathway to probe quantum gravity using near-future high-resolution observations.

gr-qc↗

arXiv2025

OTARo: Once Tuning for All Precisions toward Robust On-Device LLMs

Large Language Models (LLMs) fine-tuning techniques not only improve the adaptability to diverse downstream tasks, but also mitigate adverse effects of model quantization. Despite this, conventional quantization suffers from its structural limitation that hinders flexibility during the fine-tuning and deployment stages. Practical on-device tasks demand different quantization precisions (i.e. different bit-widths), e.g., understanding tasks tend to exhibit higher tolerance to reduced precision compared to generation tasks. Conventional quantization, typically relying on scaling factors that are incompatible across bit-widths, fails to support the on-device switching of precisions when confronted with complex real-world scenarios. To overcome the dilemma, we propose OTARo, a novel method that enables on-device LLMs to flexibly switch quantization precisions while maintaining performance robustness through once fine-tuning. OTARo introduces Shared Exponent Floating Point (SEFP), a distinct quantization mechanism, to produce different bit-widths through simple mantissa truncations of a single model. Moreover, to achieve bit-width robustness in downstream applications, OTARo performs a learning process toward losses induced by different bit-widths. The method involves two critical strategies: (1) Exploitation-Exploration Bit-Width Path Search (BPS), which iteratively updates the search path via a designed scoring mechanism; (2) Low-Precision Asynchronous Accumulation (LAA), which performs asynchronous gradient accumulations and delayed updates under low bit-widths. Experiments on popular LLMs, e.g., LLaMA3.2-1B, LLaMA3-8B, demonstrate that OTARo achieves consistently strong and robust performance for all precisions.

cs.LG↗

arXiv2025

Periodic orbits and observational accretion disk around a Schwarzschild-like black hole surrounded by dark matter halo

In this work, we investigate the dynamics of periodic orbits and the properties of accretion disks around a Schwarzschild-like black hole (BH) immersed in a King-type dark matter (DM) halo. Our analysis focuses on how the presence of the King DM halo influences both the behavior of periodic orbits and the radiative characteristics of the accretion disk. We begin by examining time-like periodic geodesic orbits for various configurations characterized by different energy and angular momentum values, represented by the integers $(z, w, v)$. Furthermore, we explore the effects of the King DM halo on time-like periodic geodesics, marginally bound orbits, and innermost stable circular orbits, thereby providing a deeper understanding of how the DM halo environment modifies the behavior of these stable orbits and timelike particle geodesics. Finally, we analyze the null geodesics and the accretion disk properties by studying their direct and secondary images, redshift distributions, and radiation fluxes as observed at infinity for a range of inclination angles. This approach allows us to gain valuable insights into the spacetime geometry of a Schwarzschild-like BH within the King-type DM halo, its physical and radiative properties in the accretion disk, and the corresponding observational implications.

gr-qc↗

arXiv2025

VAEVQ: Enhancing Discrete Visual Tokenization through Variational Modeling

Vector quantization (VQ) transforms continuous image features into discrete representations, providing compressed, tokenized inputs for generative models. However, VQ-based frameworks suffer from several issues, such as non-smooth latent spaces, weak alignment between representations before and after quantization, and poor coherence between the continuous and discrete domains. These issues lead to unstable codeword learning and underutilized codebooks, ultimately degrading the performance of both reconstruction and downstream generation tasks. To this end, we propose VAEVQ, which comprises three key components: (1) Variational Latent Quantization (VLQ), replacing the AE with a VAE for quantization to leverage its structured and smooth latent space, thereby facilitating more effective codeword activation; (2) Representation Coherence Strategy (RCS), adaptively modulating the alignment strength between pre- and post-quantization features to enhance consistency and prevent overfitting to noise; and (3) Distribution Consistency Regularization (DCR), aligning the entire codebook distribution with the continuous latent distribution to improve utilization. Extensive experiments on two benchmark datasets demonstrate that VAEVQ outperforms state-of-the-art methods.

cs.CV↗

arXiv2025

AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM

SRAM Processing-in-Memory (PIM) has emerged as the most promising implementation for high-performance PIM, delivering superior computing density, energy efficiency, and computational precision. However, the pursuit of higher performance necessitates more complex circuit designs and increased operating frequencies, which exacerbate IR-drop issues. Severe IR-drop can significantly degrade chip performance and even threaten reliability. Conventional circuit-level IR-drop mitigation methods, such as back-end optimizations, are resource-intensive and often compromise power, performance, and area (PPA). To address these challenges, we propose AIM, comprehensive software and hardware co-design for architecture-level IR-drop mitigation in high-performance PIM. Initially, leveraging the bit-serial and in-situ dataflow processing properties of PIM, we introduce Rtog and HR, which establish a direct correlation between PIM workloads and IR-drop. Building on this foundation, we propose LHR and WDS, enabling extensive exploration of architecture-level IR-drop mitigation while maintaining computational accuracy through software optimization. Subsequently, we develop IR-Booster, a dynamic adjustment mechanism that integrates software-level HR information with hardware-based IR-drop monitoring to adapt the V-f pairs of the PIM macro, achieving enhanced energy efficiency and performance. Finally, we propose the HR-aware task mapping method, bridging software and hardware designs to achieve optimal improvement. Post-layout simulation results on a 7nm 256-TOPS PIM chip demonstrate that AIM achieves up to 69.2% IR-drop mitigation, resulting in 2.29x energy efficiency improvement and 1.152x speedup.

cs.AR↗

arXiv2025

Classifier Enhancement Using Extended Context and Domain Experts for Semantic Segmentation

Prevalent semantic segmentation methods generally adopt a vanilla classifier to categorize each pixel into specific classes. Although such a classifier learns global information from the training data, this information is represented by a set of fixed parameters (weights and biases). However, each image has a different class distribution, which prevents the classifier from addressing the unique characteristics of individual images. At the dataset level, class imbalance leads to segmentation results being biased towards majority classes, limiting the model's effectiveness in identifying and segmenting minority class regions. In this paper, we propose an Extended Context-Aware Classifier (ECAC) that dynamically adjusts the classifier using global (dataset-level) and local (image-level) contextual information. Specifically, we leverage a memory bank to learn dataset-level contextual information of each class, incorporating the class-specific contextual information from the current image to improve the classifier for precise pixel labeling. Additionally, a teacher-student network paradigm is adopted, where the domain expert (teacher network) dynamically adjusts contextual information with ground truth and transfers knowledge to the student network. Comprehensive experiments illustrate that the proposed ECAC can achieve state-of-the-art performance across several datasets, including ADE20K, COCO-Stuff10K, and Pascal-Context.

cs.CV↗

arXiv2025

Probing the Schwarzschild black hole immersed in a dark matter halo through astrophysical tests

We investigate a recently derived Schwarzschild-like black hole immersed in a Dehnen-type $(α,β,γ)=(1,4,5/2)$ dark matter (DM) halo. We obtain constraints on the two model parameters, i.e., the halo core radius $r_s$ and the DM density parameter $ρ_s$ in both the weak and the strong field regimes. In the weak field, we model test particle geodesics and match the predicted perihelion shift to Mercury (Solar System) and the orbit of the S2 star data. We obtain upper limits on $r_s$ and $ρ_s$ and highlight that the DM halo effects become observable only around supermassive BHs. In the strong field, we analyse twin high frequency quasiperiodic oscillations (QPOs) from four microquasars (e.g., GRO~J1655-40, GRS~1915+105, XTE~J1859+226, and XTE~J1550-564). Because QPO frequencies depend only on the local spacetime curvature, they can serve as a probe of halo-induced deviations from general relativity. Our MCMC analysis produces posterior distributions for model parameters, revealing close agreement between the theoretical QPO frequencies and the observations for GRS 1915+105 and GRO J1655-40. The same analysis also yielded best-fit values and upper bounds for each parameter. Our combined geodesic and QPO analysis demonstrates that timelike orbits and epicyclic oscillations can act as sensitive probes of DM halos around BHs, offering a pathway to distinguish Dehnen-type profiles from alternative DM distributions in future analysis and observations.

gr-qc↗

arXiv2025

QUICstep: Evaluating connection migration based QUIC censorship circumvention

Internet censors often rely on information in the first few packets of a connection to censor unwanted traffic. With the rise of the QUIC transport protocol, prior work has suggested the method of using QUIC connection migration to conceal the first few handshake packets using a different network path (e.g., an encrypted proxy channel). However, the use of connection migration for censorship circumvention has not been explored or validated in terms of feasibility or performance. We bridge this gap by providing a rigorous quantitative evaluation of this approach that we name QUICstep. We develop a lightweight, application-agnostic prototype of QUICstep and demonstrate that QUICstep is able to circumvent a real-world QUIC SNI censor. We find that not only does QUICstep outperform a fully encrypted channel in diverse settings, but also that it can significantly reduce traffic load for encrypted channel providers. We also propose using QUICstep as a tool for measuring QUIC connection migration support in the wild and show that support for connection migration is on the rise. While as of now QUIC and connection migration support is limited, we envision that QUICstep can be a useful tool for the future where QUIC is the de facto norm for the Internet.

cs.CR↗

arXiv2025

Understanding Robust Machine Learning for Nonparametric Regression with Heavy-Tailed Noise

We investigate robust nonparametric regression in the presence of heavy-tailed noise, where the hypothesis class may contain unbounded functions and robustness is ensured via a robust loss function $\ell_σ$. Using Huber regression as a close-up example within Tikhonov-regularized risk minimization in reproducing kernel Hilbert spaces (RKHS), we address two central challenges: (i) the breakdown of standard concentration tools under weak moment assumptions, and (ii) the analytical difficulties introduced by unbounded hypothesis spaces. Our first message is conceptual: conventional generalization-error bounds for robust losses do not faithfully capture out-of-sample performance. We argue that learnability should instead be quantified through prediction error, namely the $L_2$-distance to the truth $f^\star$, which is $σ$-independent and directly reflects the target of robust estimation. To make this workable under unboundedness, we introduce a \emph{probabilistic effective hypothesis space} that confines the estimator with high probability and enables a meaningful bias--variance decomposition under weak $(1+ε)$-moment conditions. Technically, we establish new comparison theorems linking the excess robust risk to the $L_2$ prediction error up to a residual of order $\mathcal{O}(σ^{-2ε})$, clarifying the robustness--bias trade-off induced by the scale parameter $σ$. Building on this, we derive explicit finite-sample error bounds and convergence rates for Huber regression in RKHS that hold without uniform boundedness and under heavy-tailed noise. Our study delivers principled tuning rules, extends beyond Huber to other robust losses, and highlights prediction error, not excess generalization risk, as the fundamental lens for analyzing robust learning.

cs.LG↗

arXiv2025

FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities

Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has been the research trend for TSC. However, existing RL algorithms face several real-world challenges that hinder their practical deployment in TSC: (1) Sensor accuracy deteriorates with increased sensor detection range, and data transmission is prone to noise, potentially resulting in unsafe TSC decisions. (2) During the training of online RL, interactions with the environment could be unstable, potentially leading to inappropriate traffic signal phase (TSP) selection and traffic congestion. (3) Most current TSC algorithms focus only on TSP decisions, overlooking the critical aspect of phase duration, affecting safety and efficiency. To overcome these challenges, we propose a robust two-stage fuzzy approach called FuzzyLight, which integrates compressed sensing and RL for TSC deployment. FuzzyLight offers several key contributions: (1) It employs fuzzy logic and compressed sensing to address sensor noise and enhances the efficiency of TSP decisions. (2) It maintains stable performance during training and combines fuzzy logic with RL to generate precise phases. (3) It works in real cities across 22 intersections and demonstrates superior performance in both real-world and simulated environments. Experimental results indicate that FuzzyLight enhances traffic efficiency by 48% compared to expert-designed timings in the real world. Furthermore, it achieves state-of-the-art (SOTA) performance in simulated environments using six real-world datasets with transmission noise. The code and deployment video are available at the URL1

eess.SY↗

arXiv2025

RSAVQ: Riemannian Sensitivity-Aware Vector Quantization for Large Language Models

Large language models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their exponentially increasing parameters pose significant challenges for deployment on resource-constrained devices. Vector Quantization (VQ) shows great promise for low-bit quantization (e.g., 2 to 4 bits), but existing work faces two key challenges: unconstrained direction error and suboptimal bit allocation. In this paper, we propose RSAVQ, a novel VQ framework to enhance extremely low-bit quantization for LLMs. RSAVQ introduces two geometry-driven innovations that effectively mitigate above limitations: (1) Error Direction Sensitivity Guidance (EDSG), which leverages the Fisher Information Matrix (FIM)-induced Riemannian metric to project quantization errors onto low-sensitivity directions in the parameter space. Specifically, this projection is performed along the negative natural gradient direction, which effectively suppresses error expansion. (2) Weight Channel Sensitivity Guidance (WCSG) , which constructs a channel-wise sensitivity metric via FIM curvature analysis to dynamically guide bit resource allocation. The approach facilitates a globally optimal quantization solution within prescribed bit constraints. Experiments demonstrate that RSAVQ outperforms existing methods for LLMs. For example, in 2-bit quantization of LLaMA-3 8B, RSAVQ leads baselines like VPTQ and QuIP# by 0.4 in perplexity (PPL) and 1.5 in zero-shot accuracy. This work offers a practical solution for constrained environments and a theoretical bridge between information geometry and the quantization of neural networks, advancing efficient deep learning.

cs.LG↗

arXiv2025

Joint parameter estimations for spin glasses

Spin glass models with quadratic-type Hamiltonians are disordered statistical physics systems with competing ferromagnetic and anti-ferromagnetic spin interactions. The corresponding Gibbs measures belong to the exponential family parametrized by (inverse) temperature $β>0$ and external field $h\in\mathbb{R}$. Given a sample from these Gibbs measures, a statistically fundamental question is to infer the temperature and external field parameters. In 2007, Chatterjee (Ann. Statist. 35 (2007), no.5, 1931-1946) first proved that in the absence of external field $h=0$, the maximum pseudolikelihood estimator for $β$ is $\sqrt{N}$-consistent under some mild assumptions on the disorder matrices. It was left open whether the same method can be used to estimate the temperature and external field simultaneously. In this paper, under some easily verifiable conditions, we prove that the bivariate maximum pseudolikelihood estimator is indeed jointly $\sqrt{N}$-consistent for the temperature and external field parameters. The examples cover the classical Sherrington-Kirkpatrick model and its diluted variants.

math.PR↗

arXiv2025

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models

Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their applicability in dynamic environments where user preferences evolve. Incremental learning offers a promising solution, yet conventional methods from computer vision or NLP face challenges in recommendation tasks due to data sparsity and distinct task paradigms. Existing approaches for neural recommenders remain limited and often lack generalizability. To address this, we propose MEGG, Replay Samples with Maximally Extreme GGscore, an experience replay based incremental learning framework. MEGG introduces GGscore, a novel metric that quantifies sample influence, enabling the selective replay of highly influential samples to mitigate catastrophic forgetting. Being model-agnostic, MEGG integrates seamlessly across architectures and frameworks. Experiments on three neural models and four benchmark datasets show superior performance over state-of-the-art baselines, with strong scalability, efficiency, and robustness. Implementation will be released publicly upon acceptance.

cs.IR↗