arXiv subjects

Hua Li

Publications and source records attributed to Hua Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Broadband terahertz comb with sub-Hz comb linewidth

Terahertz (THz) frequency combs are increasingly essential for spectroscopy, metrology, and quantum science. However, generating a dense array of evenly spaced ultra-narrow THz comb lines is challenging. Here, we demonstrate broadband THz comb generation using a photoconductive antenna that transfers a noise-suppressed near-infrared electro-optical (EO) comb into the THz domain. Our noise-suppression strategy, leveraging soliton self-frequency shift and spectral filtering, effectively suppresses EO comb phase noise without requiring active stabilization. The resulting THz comb exhibits broad spectral coverage (0.05-4 THz), narrow comb linewidths (0.3 Hz at the Fourier-transform limit), and excellent frequency stability (8.6*10^-14 at 1-second integration). We further demonstrate asynchronous THz time-domain spectroscopy, resolving ~36,000 comb lines with 50 MHz spacing. Crucially, the inherent frequency agility of the EO comb enables rapid and wide-range tuning of the THz comb line spacing. These attributes position our THz comb as a versatile tool for high-resolution molecular spectroscopy and precision THz metrology.

physics.optics↗

arXiv2026

Beyond Global Scanning: Adaptive Visual State Space Modeling for Salient Object Detection in Optical Remote Sensing Images

Salient object detection (SOD) in optical remote sensing images (ORSIs) faces numerous challenges, including significant variations in target scales and low contrast between targets and the background. Existing methods based on vision transformers (ViTs) and convolutional neural networks (CNNs) architectures aim to leverage both global and local features, but the difficulty in effectively integrating these heterogeneous features limits their overall performance. To overcome these limitations, we propose an adaptive state space context network (ASCNet), which builds upon the state space model mechanism to simultaneously capture long-range dependencies and enhance regional feature representation. Specifically, we employ the visual state space encoder to extract multi-scale features. To further achieve deep guidance and enhancement of these features, we design a Multi-Level Context Module (MLCM), which module strengthens cross-layer interaction capabilities between features of different scales while enhancing the model's structural perception, allowing it to distinguish between foreground and background more effectively. Then, we design the Adaptive Patchwise Visual State Space (APVSS) block as the decoder of ASCNet, which integrates our proposed Dynamic Adaptive Granularity Scan (DAGS) and Granularity-aware Propagation Module (GPM). It performs adaptive patch scanning on feature maps enhanced by local perception, thereby capturing rich local region information and enhancing state space model's local modeling capability. Extensive experimental results demonstrate that the proposed model achieves state-of-the-art performance, validating its effectiveness and superiority.

cs.CV↗

arXiv2026

WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation

Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable information they contain. We propose WaterFlow, a rectified flow-based framework for underwater salient object detection that innovatively incorporates underwater physical imaging information as explicit priors directly into the network training process and introduces temporal dimension modeling, significantly enhancing the model's capability for salient object identification. On the USOD10K dataset, WaterFlow achieves a 0.072 gain in S_m, demonstrating the effectiveness and superiority of our method. https://github.com/Theo-polis/WaterFlow.

cs.CV↗

arXiv2025

Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization

Diffusion models recently emerged as a powerful paradigm for recommender systems, offering state-of-the-art performance by modeling the generative process of user-item interactions. However, training such models from scratch is both computationally expensive and yields diminishing returns once convergence is reached. To remedy these challenges, we propose ReFiT, a new framework that integrates Reinforcement learning (RL)-based Fine-Tuning into diffusion-based recommender systems. In contrast to prior RL approaches for diffusion models depending on external reward models, ReFiT adopts a task-aligned design: it formulates the denoising trajectory as a Markov decision process (MDP) and incorporates a collaborative signal-aware reward function that directly reflects recommendation quality. By tightly coupling the MDP structure with this reward signal, ReFiT empowers the RL agent to exploit high-order connectivity for fine-grained optimization, while avoiding the noisy or uninformative feedback common in naive reward designs. Leveraging policy gradient optimization, ReFiT maximizes exact log-likelihood of observed interactions, thereby enabling effective post hoc fine-tuning of diffusion recommenders. Comprehensive experiments on wide-ranging real-world datasets demonstrate that the proposed ReFiT framework (a) exhibits substantial performance gains over strong competitors (up to 36.3% on sequential recommendation), (b) demonstrates strong efficiency with linear complexity in the number of users or items, and (c) generalizes well across multiple diffusion-based recommendation scenarios. The source code and datasets are publicly available at https://anonymous.4open.science/r/ReFiT-4C60.

cs.IR↗

arXiv2025

Pattern Division Random Access (PDRA) for M2M Communications with Massive MIMO Systems

In this work, we introduce the pattern-domain pilot design paradigm based on a "superposition of orthogonal-building-blocks" with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems.Specifically, the pattern-domain pilot is constructed based on the superposition of $L$ cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to $\binom{N}{L}$, where $\binom{N}{L}$ denotes the number of all L-combinations of a set N, and low correlation valueslead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems.We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems.Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one,while preserving the existing physical layer virtually unchanged. The extension of the "superposition of orthogonal-building-blocks" scheme to "superposition of quasi-orthogonal-building-blocks" is straightforward.

cs.IT↗

arXiv2025

Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset

With the development of underwater exploration and marine protection, underwater vision tasks are widespread. Due to the degraded underwater environment, characterized by color distortion, low contrast, and blurring, camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings. Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes. To address these issues, we introduce the first underwater camouflaged instance segmentation (UCIS) dataset, abbreviated as UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations. In addition, we propose an Underwater Camouflaged Instance Segmentation network based on Segment Anything Model (UCIS-SAM). Our UCIS-SAM includes three key modules. First, the Channel Balance Optimization Module (CBOM) enhances channel characteristics to improve underwater feature learning, effectively addressing the model's limited understanding of underwater environments. Second, the Frequency Domain True Integration Module (FDTIM) is proposed to emphasize intrinsic object features and reduce interference from camouflage patterns, enhancing the segmentation performance of camouflaged objects blending with their surroundings. Finally, the Multi-scale Feature Frequency Aggregation Module (MFFAM) is designed to strengthen the boundaries of low-contrast camouflaged instances across multiple frequency bands, improving the model's ability to achieve more precise segmentation of camouflaged objects. Extensive experiments on the proposed UCIS4K and public benchmarks show that our UCIS-SAM outperforms state-of-the-art approaches.

cs.CV↗

arXiv2025

Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation

With recent breakthroughs in large-scale modeling, the Segment Anything Model (SAM) has demonstrated significant potential in a variety of visual applications. However, due to the lack of underwater domain expertise, SAM and its variants face performance limitations in end-to-end underwater instance segmentation tasks, while their higher computational requirements further hinder their application in underwater scenarios. To address this challenge, we propose a large-scale underwater instance segmentation dataset, UIIS10K, which includes 10,048 images with pixel-level annotations for 10 categories. Then, we introduce UWSAM, an efficient model designed for automatic and accurate segmentation of underwater instances. UWSAM efficiently distills knowledge from the SAM ViT-Huge image encoder into the smaller ViT-Small image encoder via the Mask GAT-based Underwater Knowledge Distillation (MG-UKD) method for effective visual representation learning. Furthermore, we design an End-to-end Underwater Prompt Generator (EUPG) for UWSAM, which automatically generates underwater prompts instead of explicitly providing foreground points or boxes as prompts, thus enabling the network to locate underwater instances accurately for efficient segmentation. Comprehensive experimental results show that our model is effective, achieving significant performance improvements over state-of-the-art methods on multiple underwater instance datasets. Datasets and codes are available at https://github.com/LiamLian0727/UIIS10K.

cs.CV↗

arXiv2025

Impact of RHIs and ipSIC on Active RIS-NOMA Systems with Low-Precision ADCs

This study evaluates the performance of an active reconfigurable intelligent surface (ARIS)-assisted non-orthogonal multiple access (NOMA) system employing low-precision analog-to-digital converters (ADCs). Analytical approximations for the outage probability (OP) are derived, considering residual hardware impairments (RHIs) and imperfect successive interference cancellation (ipSIC). Additionally, we analyze the asymptotic OP, system throughput, and diversity order at high signal-to-noise ratios (SNRs). Simulation results demonstrate that the proposed quantized ARIS-NOMA system outperforms its passive counterpart (PRIS-NOMA), achieving lower OP and higher throughput with reduced transmit power requirements and fewer reflecting elements. Moreover, the outage performance of both quantized ARIS-NOMA and PRIS-NOMA systems demonstrates significant improvement as the number of reflecting elements increases. The negative impacts of low-precision ADCs can be effectively mitigated by optimizing transmit power and scaling the number of reflecting elements.

eess.SP↗

arXiv2025

On the Design of Capacity-Achieving Distributions for Discrete-Time Poisson Channel with Low-Precision ADCs

This paper investigates the design of the capacity-achieving input distribution for the discrete-time Poisson channel (DTPC) under dark current effects with low-precision analog-to-digital converters (ADCs). This study introduces an efficient optimization algorithm that integrates the Newton-Raphson and Blahut-Arimoto (BA) methods to determine the capacity-achieving input distribution and the corresponding amplitudes of input mass points for the DTPC, subject to both peak and average power constraints. Additionally, the Karush-Kuhn-Tucker (KKT) conditions are established to provide necessary and sufficient conditions for the optimality of the obtained capacity-achieving distribution. Simulation results illustrate that the proposed algorithm attains $72\%$ and $83\%$ of the theoretical capacity at 5 dB for 1-bit and 2-bit quantized DTPC, respectively. Furthermore, for a finite-precision quantized DTPC (i.e., ${\log _2}K$ bits), the capacity can be achieved by a non-uniform discrete input distribution with support for $K$ mass points, under the given power constraints.

eess.SP↗

arXiv2025

Photo-Thermally Tunable Photon-Pair Generation in Dielectric Metasurfaces

Photon-pair sources based on spontaneous four-wave mixing (SFWM) in integrated photonics are often spectrally static. We demonstrate and model a fundamental thermo-optical mechanism that modulates photon-pair generation in amorphous silicon (a-Si) thin films and metasurfaces via SFWM. Femtosecond-pulsed excitation yields g2(0) higher than 400 in unpatterned a-Si, confirming high-purity nonclassical emission. Resonant a-Si metasurfaces produce photon pairs at rates exceeding 3.8 kHz under 0.6 mW pump power through Mie-type modes. Pump absorption induces localized heating that redshifts resonances, altering modal overlap and SFWM efficiency, leading to deviations from the quadratic power scaling expected in the undepleted regime. Coupled electromagnetic and heat-transfer simulations quantitatively reproduce these trends. Polarization-resolved measurements show nearly isotropic nonlinear responses, with 3 times higher third-order susceptibility of a-Si compared to poly-Si. This work positions a-Si as a bright, CMOS-compatible quantum photonics platform and identifies thermo-optical detuning as a key mechanism that should be considered-and potentially harnessed-in integrated photon-pair sources.

physics.optics↗

arXiv2025

Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes

While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversely misinterpret salient objects as camouflaged. We hypothesize that this can be attributed to two factors: (i) the specific annotation paradigm of current SOD and COD datasets, and (ii) the lack of explicit attribute relationship modeling in current models. Prevalent SOD/COD datasets enforce a mutual exclusivity constraint, assuming scenes contain either salient or camouflaged objects, which poorly aligns with the real world. Furthermore, current SOD/COD methods are primarily designed for these highly constrained datasets and lack explicit modeling of the relationship between salient and camouflaged objects. In this paper, to promote the development of unconstrained salient and camouflaged object detection, we construct a large-scale dataset, USC12K, which features comprehensive labels and four different scenes that cover all possible logical existence scenarios of both salient and camouflaged objects. To explicitly model the relationship between salient and camouflaged objects, we propose a model called USCNet, which introduces two distinct prompt query mechanisms for modeling inter-sample and intra-sample attribute relationships. Additionally, to assess the model's ability to distinguish between salient and camouflaged objects, we design an evaluation metric called CSCS. The proposed method achieves state-of-the-art performance across all scenes in various metrics. The code and dataset will be available at https://github.com/ssecv/USCNet.

cs.CV↗

arXiv2025

Do ATCOs Need Explanations, and Why? Towards ATCO-Centered Explainable AI for Conflict Resolution Advisories

Interest in explainable artificial intelligence (XAI) is surging. Prior research has primarily focused on systems' ability to generate explanations, often guided by researchers' intuitions rather than end-users' needs. Unfortunately, such approaches have not yielded favorable outcomes when compared to a black-box baseline (i.e., no explanation). To address this gap, this paper advocates a human-centered approach that shifts focus to air traffic controllers (ATCOs) by asking a fundamental yet overlooked question: Do ATCOs need explanations, and if so, why? Insights from air traffic management (ATM), human-computer interaction, and the social sciences were synthesized to provide a holistic understanding of XAI challenges and opportunities in ATM. Evaluating 11 ATM operational goals revealed a clear need for explanations when ATCOs aim to document decisions and rationales for future reference or report generation. Conversely, ATCOs are less likely to seek them when their conflict resolution approach align with the artificial intelligence (AI) advisory. While this is a preliminary study, the findings are expected to inspire broader and deeper inquiries into the design of ATCO-centric XAI systems, paving the way for more effective human-AI interaction in ATM.

cs.HC↗

arXiv2025

Task Facet Learning: A Structured Approach to Prompt Optimization

Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large language model. Humans solve this problem by also considering the different facets that define a task (e.g., counter-examples, explanations, analogies) and including them in the prompt. However, it is unclear whether existing algorithmic approaches, based on iteratively editing a given prompt or automatically selecting a few in-context examples, can cover the multiple facets required to solve a complex task. In this work, we view prompt optimization as that of learning multiple facets of a task from a set of training examples. We exploit structure in the prompt optimization problem and break down a prompt into loosely coupled semantic sections. The proposed algorithm, UniPrompt, (1) clusters the input space and uses clustered batches so that each batch likely corresponds to a different facet of the task, and (2) utilizes a feedback mechanism to propose adding, editing or deleting a section, which in turn is aggregated over a batch to capture generalizable facets. Empirical evaluation on multiple datasets and a real-world task shows that prompts generated using \shortname{} obtain higher accuracy than human-tuned prompts and those from state-of-the-art methods. In particular, our algorithm can generate long, complex prompts that existing methods are unable to generate. Code for UniPrompt is available at https://aka.ms/uniprompt.

cs.AI↗

arXiv2025

Mamba Based Feature Extraction And Adaptive Multilevel Feature Fusion For 3D Tumor Segmentation From Multi-modal Medical Image

Multi-modal 3D medical image segmentation aims to accurately identify tumor regions across different modalities, facing challenges from variations in image intensity and tumor morphology. Traditional convolutional neural network (CNN)-based methods struggle with capturing global features, while Transformers-based methods, despite effectively capturing global context, encounter high computational costs in 3D medical image segmentation. The Mamba model combines linear scalability with long-distance modeling, making it a promising approach for visual representation learning. However, Mamba-based 3D multi-modal segmentation still struggles to leverage modality-specific features and fuse complementary information effectively. In this paper, we propose a Mamba based feature extraction and adaptive multilevel feature fusion for 3D tumor segmentation using multi-modal medical image. We first develop the specific modality Mamba encoder to efficiently extract long-range relevant features that represent anatomical and pathological structures present in each modality. Moreover, we design an bi-level synergistic integration block that dynamically merges multi-modal and multi-level complementary features by the modality attention and channel attention learning. Lastly, the decoder combines deep semantic information with fine-grained details to generate the tumor segmentation map. Experimental results on medical image datasets (PET/CT and MRI multi-sequence) show that our approach achieve competitive performance compared to the state-of-the-art CNN, Transformer, and Mamba-based approaches.

cs.CV↗

arXiv2025

Significant tuning of dispersive mode coupling in doubly clamped MEMS beam resonators using thermally induced buckling effect

Dispersive mode coupling is a promising mechanism for the development of advanced micro/nanoelectromechanical devices. However, strong coupling strength remains a key challenge limiting the practical applications of dispersive mode coupling effect. Here, we experimentally demonstrate the significant tuning of the mode coupling coefficient of two flexural vibrational modes in a doubly-clamped MEMS beam resonator using thermally-induced buckling effect, which enables variable coupling strengths to be implemented for practical applications. Furthermore, a theoretical model is developed to describe the mode coupling coefficient, showing that the tunability is owing to the breakdown of the symmetric shape of the MEMS beam caused by buckling. Moreover, the theoretical model defines a simple relation between the coupling coefficient and the nonlinearity of the two coupled modes. These results provide valuable insight into physical mechanisms underlying dispersive mode coupling effect, as well as pave the way for the development of advanced MEMS devices with application-specified mode coupling strength.

physics.app-ph↗

arXiv2025

Spectrally-Corrected and Regularized QDA Classifier for Spiked Covariance Model

Quadratic discriminant analysis (QDA) is a widely used method for classification problems, particularly preferable over Linear Discriminant Analysis (LDA) for heterogeneous data. However, QDA loses its effectiveness in high-dimensional settings, where the data dimension and sample size tend to infinity. To address this issue, we propose a novel QDA method utilizing spectral correction and regularization techniques, termed SR-QDA. The regularization parameters in our method are selected by maximizing the Fisher-discriminant ratio. We compare SR-QDA with QDA, regularized quadratic discriminant analysis (R-QDA), and several other competitors. The results indicate that SR-QDA performs exceptionally well, especially in moderate and high-dimensional situations. Empirical experiments across diverse datasets further support this conclusion.

cs.LG↗

arXiv2025

Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers

Medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend. As such, estimation of IO performance can provide valuable guidance when designing under-sampled data-acquisition techniques by enabling the identification of designs that will not permit the reconstruction of diagnostically inappropriate images for a specified task - no matter how advanced the reconstruction method is or how plausible the reconstructed images appear. The need for such analysis is urgent because of the substantial increase of medical device submissions on deep learning-based image reconstruction methods and the fact that they may produce clean images disguising the potential loss of diagnostic information when data is aggressively under-sampled. Recently, convolutional neural network (CNN) approximated IOs (CNN-IOs) was investigated for estimating the performance of data space IOs to establish task-based performance bounds for image reconstruction, under an X-ray computed tomographic (CT) context. In this work, the application of such data space CNN-IO analysis to multi-coil magnetic resonance imaging (MRI) systems has been explored. This study utilized stylized multi-coil sensitivity encoding (SENSE) MRI systems and deep-generated stochastic brain models to demonstrate the approach. Signal-known-statistically and background-known-statistically (SKS/BKS) binary signal detection tasks were selected to study the impact of different acceleration factors on the data space IO performance.

physics.med-ph↗

arXiv2024

Field equations and Noether potentials for higher-order theories of gravity with Lagrangians involving $\Box^i R$, $\Box^i R_{μν}$ and $\Box^i R_{μνρσ}$

In this paper, we aim to perform a systematical investigation on the field equations and Noether potentials for the higher-order gravity theories endowed with Lagrangians depending on the metric and the Riemann curvature tensor, together with $i$th ($i=1,2,\cdot\cdot\cdot$) powers of the Beltrami-d'Alembertian operator $\Box$ acting on the latter. We start with a detailed derivation of the field equations and the Noether potential corresponding to the Lagrangian $\sqrt{-g}L_R(R,\Box R,\cdot\cdot\cdot,\Box^m R)$ through the direct variation of the Lagrangian and a method based upon the conserved current. Next the parallel analysis is extended to a more generic Lagrangian $\sqrt{-g}L_{\text{Ric}}(g^{μν}, R_{μν},\Box R_{μν}, \cdot\cdot\cdot,\Box^m R_{μν})$, as well as to the generalization of the Lagrangian $\sqrt{-g}L_{\text{Ric}}$, which depends on the metric $g^{μν}$, the Riemann tensor $R_{μνρσ}$ and $\Box^i R_{μνρσ}$s. Finally, all the results associated to the three types of Lagrangians are extended to the Lagrangian relying on an arbitrary tensor and the variables via $\Box^i$ acting on such a tensor. In particular, we take into consideration of equations of motion and Noether potentials for nonlocal gravity models. For Lagrangians involving the variables $\Box^i R$, $\Box^i R_{μν}$ and $\Box^i R_{μνρσ}$, our investigation provides their concrete Noether potentials and the field equations without the derivative of the Lagrangian density with respect to the metric. Besides, the Iyer-Wald potentials associated to these Lagrangians are also presented.

gr-qc↗