arXiv subjects

Zijian Zhang

Publications and source records attributed to Zijian Zhang.

At least 127 records · Page 7Linked to original sources

arXiv2025

Decoupling What to Count and Where to See for Referring Expression Counting

Referring Expression Counting (REC) extends class-level object counting to the fine-grained subclass-level, aiming to enumerate objects matching a textual expression that specifies both the class and distinguishing attribute. A fundamental challenge, however, has been overlooked: annotation points are typically placed on class-representative locations (e.g., heads), forcing models to focus on class-level features while neglecting attribute information from other visual regions (e.g., legs for "walking"). To address this, we propose W2-Net, a novel framework that explicitly decouples the problem into "what to count" and "where to see" via a dual-query mechanism. Specifically, alongside the standard what-to-count (w2c) queries that localize the object, we introduce dedicated where-to-see (w2s) queries. The w2s queries are guided to seek and extract features from attribute-specific visual regions, enabling precise subclass discrimination. Furthermore, we introduce Subclass Separable Matching (SSM), a novel matching strategy that incorporates a repulsive force to enhance inter-subclass separability during label assignment. W2-Net significantly outperforms the state-of-the-art on the REC-8K dataset, reducing counting error by 22.5% (validation) and 18.0% (test), and improving localization F1 by 7% and 8%, respectively. Code will be available.

cs.CV↗

arXiv2025

Field-Free Superconducting Diode Enabled by Geometric Asymmetry and Perpendicular Magnetization

The superconducting diode effect (SDE)- manifested as directional, dissipationless supercurrents - is pivotal for realizing energy-efficient superconducting logic and memory technologies. Achieving high-efficiency SDE without external magnetic fields, however, remains a fundamental challenge. Here, we report a strongly enhanced, field-free SDE in Pt/Co/Nb heterostructures, enabled by the interplay of engineered geometric asymmetry and stray fields from a perpendicularly magnetized Co layer. This configuration promotes directional vortex entry and spatially selective pinning, yielding diode efficiencies that exceed all previously reported field-free values. Temperature- and field-dependent transport measurements, supported by micromagnetic simulations, reveal that the enhanced nonreciprocity stems from three cooperative mechanisms: asymmetric vortex entry, localized magnetic pinning, and Lorentz-force imbalance. These findings establish a scalable, CMOS-compatible platform for high-performance superconducting rectifiers, offering new opportunities for cryogenic spintronics and quantum electronics.

cond-mat.supr-con↗

arXiv2025

Discovery of a Little Red Dot candidate at $z\gtrsim10$ in COSMOS-Web based on MIRI-NIRCam selection

JWST has revealed a new high-redshift population called little red dots (LRDs). Since LRDs may be in the early phase of black hole growth, identifying them in the early universe is crucial for understanding the formation of the first supermassive black holes. However, no robust LRD candidates have been identified at $z>10$, because commonly-used NIRCam photometry covers wavelengths up to $\sim5\,{\rm μm}$ and is insufficient to capture the characteristic V-shaped spectral energy distributions (SEDs) of LRDs. In this study, we present the first search for $z\gtrsim10$ LRD candidates using both NIRCam and MIRI imaging from COSMOS-Web, which provides the largest joint NIRCam-MIRI coverage to date ($0.20\,{\rm deg^2}$). Taking advantage of MIRI/F770W to remove contaminants, we identify one robust candidate, CW-LRD-z10 at $z_{\rm phot}=10.5^{+0.7}_{-0.6}$ with $M_{\rm UV}=-19.9^{+0.1}_{-0.2}\,{\rm mag}$. CW-LRD-z10 exhibits a compact morphology, a distinct V-shaped SED, and a non-detection in F115W, all consistent with being an LRD at $z\sim10$. Based on this discovery, we place the first constraint on the number density of LRDs at $z\sim10$ with $M_{\rm UV}\sim-20$ of $1.2^{+2.7}_{-1.0}\times10^{-6}\,{\rm Mpc^{-3}\,mag^{-1}}$, suggesting that the fraction of LRDs among the overall galaxy population increases with redshift, reaching $\sim3\%$ at $z\sim10$. Although deep spectroscopy is necessary to confirm the redshift and the nature of CW-LRD-z10, our results imply that LRDs may be a common population at $z>10$, playing a key role in the first supermassive black hole formation.

astro-ph.GA↗

arXiv2025

TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting

Temporal non-stationarity, the phenomenon that time series distributions change over time, poses fundamental challenges to reliable time series forecasting. Intuitively, the complex time series can be decomposed into two factors, \ie time-invariant and time-varying components, which indicate static and dynamic patterns, respectively. Nonetheless, existing methods often conflate the time-varying and time-invariant components, and jointly learn the combined long-term patterns and short-term fluctuations, leading to suboptimal performance facing distribution shifts. To address this issue, we initiatively propose a lightweight static-dynamic decomposition framework, TimeEmb, for time series forecasting. TimeEmb innovatively separates time series into two complementary components: (1) time-invariant component, captured by a novel global embedding module that learns persistent representations across time series, and (2) time-varying component, processed by an efficient frequency-domain filtering mechanism inspired by full-spectrum analysis in signal processing. Experiments on real-world datasets demonstrate that TimeEmb outperforms state-of-the-art baselines and requires fewer computational resources. We conduct comprehensive quantitative and qualitative analyses to verify the efficacy of static-dynamic disentanglement. This lightweight framework can also improve existing time-series forecasting methods with simple integration. To ease reproducibility, the code is available at https://github.com/showmeon/TimeEmb.

cs.LG↗

arXiv2025

Enhancing Channel Estimation in RIS-aided Systems via Observation Matrix Design

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for enhancing wireless communications through dense antenna arrays. Accurate channel estimation is critical to unlocking their full performance potential. To enhance RIS channel estimators, this paper proposes a novel observation matrix design scheme. Bayesian optimization framework is adopted to generate observation matrices that maximize the mutual information between received pilot signals and RIS channels. To solve the formulated problem efficiently, we develop an alternating Riemannian manifold optimization (ARMO) algorithm to alternately update the receiver combiners and RIS phase-shift matrices. An adaptive kernel training strategy is further introduced to iteratively refine the channel covariance matrix without requiring additional pilot resources. Simulation results demonstrate that the proposed ARMO-enhanced estimator achieves substantial gains in estimation accuracy over state-of-the-art methods.

cs.IT↗

arXiv2025

Can Representation Gaps Be the Key to Enhancing Robustness in Graph-Text Alignment?

Representation learning on text-attributed graphs (TAGs) integrates structural connectivity with rich textual semantics, enabling applications in diverse domains. Current methods largely rely on contrastive learning to maximize cross-modal similarity, assuming tighter coupling between graph and text representations improves transfer performance. However, our empirical analysis reveals that both natural gap expansion and forced gap reduction result in performance degradation by disrupting pre-trained knowledge structures and impairing generalization. This arises from the geometric incompatibility between encoders, where graph encoders capture topological patterns, while text encoders capture semantic structures. Over-alignment compresses these distinct spaces into shared subspaces, causing structure collapse that diminishes both topological reasoning and semantic understanding. We propose \textbf{LLM4GTA}, a gap-aware alignment framework that preserves representation gaps as geometric necessities for maintaining modality-specific knowledge and improving transfer performance. LLM4GTA includes an adaptive gap preservation module to prevent over-alignment by monitoring similarity evolution and an intra-modal compensation mechanism that boosts discriminative power using auxiliary classifiers in graph space. Extensive experiments show significant improvements over existing methods in zero-shot and few-shot scenarios.

cs.GR↗

arXiv2025

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling

In recent years, densifying multiple-input multiple-output (MIMO) has attracted much attention from the communication community. Thanks to the subwavelength antenna spacing, the strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI). This inspires the careful design of observation matrices (e.g., transmit precoders and receive combiners), that exploits the CSI prior knowledge, to boost channel estimation performance. Aligned with this vision, this work proposes to jointly design the combiners and precoders by maximizing the mutual information between the received pilots and densifying MIMO channels. A two-dimensional ice-filling (2DIF) algorithm is proposed to efficiently accomplish this objective. The algorithm is motivated by the fact that the eigenspace of MIMO channel covariance can be decoupled into two sub-eigenspaces, which are associated with the correlations of transmitter antennas and receiver antennas, respectively. By properly setting the precoder and the combiner as the eigenvectors from these two sub-eigenspaces, the 2DIF promises to generate near-optimal observation matrices. Moreover, we further extend the 2DIF method to the popular hybrid combining systems, where a two-stage 2DIF (TS-2DIF) algorithm is developed to handle the analog combining circuits realized by phase shifters. Simulation results demonstrate that, compared to the state-of-the-art schemes, the proposed 2DIF and TS-2DIF methods can achieve superior channel estimation accuracy.

cs.IT↗

arXiv2025

Distilling Lightweight Language Models for C/C++ Vulnerabilities

The increasing complexity of modern software systems exacerbates the prevalence of security vulnerabilities, posing risks of severe breaches and substantial economic loss. Consequently, robust code vulnerability detection is essential for software security. While Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, their potential for automated code vulnerability detection remains underexplored. This paper presents FineSec, a novel framework that harnesses LLMs through knowledge distillation to enable efficient and precise vulnerability identification in C/C++ codebases. FineSec utilizes knowledge distillation to transfer expertise from large teacher models to compact student models, achieving high accuracy with minimal computational cost. By integrating data preparation, training, evaluation, and continuous learning into a unified, single-task workflow, FineSec offers a streamlined approach. Extensive evaluations on C/C++ codebases demonstrate its superiority over both base models and larger LLMs in identifying complex vulnerabilities and logical flaws, establishing FineSec as a practical and scalable solution for real-world software security. To facilitate reproducibility, the datasets, source code, and experimental results are made publicly available at: https://github.com/yangxiaoxuan123/FineSec_detect.

cs.CR↗

arXiv2025

Formalization Driven LLM Prompt Jailbreaking via Reinforcement Learning

Large language models (LLMs) have demonstrated remarkable capabilities, yet they also introduce novel security challenges. For instance, prompt jailbreaking attacks involve adversaries crafting sophisticated prompts to elicit responses from LLMs that deviate from human values. To uncover vulnerabilities in LLM alignment methods, we propose the PASS framework (\underline{P}rompt J\underline{a}ilbreaking via \underline{S}emantic and \underline{S}tructural Formalization). Specifically, PASS employs reinforcement learning to transform initial jailbreak prompts into formalized descriptions, which enhances stealthiness and enables bypassing existing alignment defenses. The jailbreak outputs are then structured into a GraphRAG system that, by leveraging extracted relevant terms and formalized symbols as contextual input alongside the original query, strengthens subsequent attacks and facilitates more effective jailbreaks. We conducted extensive experiments on common open-source models, demonstrating the effectiveness of our attack.

cs.AI↗

arXiv2025

Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy

Current image generation systems produce high-quality images but struggle with ambiguous user prompts, making interpretation of actual user intentions difficult. Many users must modify their prompts several times to ensure the generated images meet their expectations. While some methods focus on enhancing prompts to make the generated images fit user needs, the model is still hard to understand users' real needs, especially for non-expert users. In this research, we aim to enhance the visual parameter-tuning process, making the model user-friendly for individuals without specialized knowledge and better understand user needs. We propose a human-machine co-adaption strategy using mutual information between the user's prompts and the pictures under modification as the optimizing target to make the system better adapt to user needs. We find that an improved model can reduce the necessity for multiple rounds of adjustments. We also collect multi-round dialogue datasets with prompts and images pairs and user intent. Various experiments demonstrate the effectiveness of the proposed method in our proposed dataset. Our dataset and annotation tools will be available.

cs.CV↗

arXiv2025

FinSentLLM: Multi-LLM and Structured Semantic Signals for Enhanced Financial Sentiment Forecasting

Financial sentiment analysis (FSA) has attracted significant attention, and recent studies increasingly explore large language models (LLMs) for this field. Yet most work evaluates only classification metrics, leaving unclear whether sentiment signals align with market behavior. We propose FinSentLLM, a lightweight multi-LLM framework that integrates an expert panel of sentiment forecasting LLMs, and structured semantic financial signals via a compact meta-classifier. This design captures expert complementarity, semantic reasoning signal, and agreement/divergence patterns without costly retraining, yielding consistent 3-6% gains over strong baselines in accuracy and F1-score on the Financial PhraseBank dataset. In addition, we also provide econometric evidence that financial sentiment and stock markets exhibit statistically significant long-run comovement, applying Dynamic Conditional Correlation GARCH (DCC-GARCH) and the Johansen cointegration test to daily sentiment scores computed from the FNSPID dataset and major stock indices. Together, these results demonstrate that FinSentLLM delivers superior forecasting accuracy for financial sentiment and further establish that sentiment signals are robustly linked to long-run equity market dynamics.

cs.CE↗

arXiv2025

On the Variability Features of Active Galactic Nuclei in Little Red Dots

The high-redshift ($z>4$) compact sources with ``V-shaped" spectral energy distributions (SEDs), known as Little Red Dots (LRDs), are discovered by the James Webb Space Telescope and provide valuable clues to the physics of active galactic nuclei (AGNs) in the early universe. The nature of LRDs is controversial. Recently, several studies have investigated LRDs through variability, a characteristic feature of AGNs. These studies explore LRD variability by extrapolating empirical relationships from local quasars. Here, we adopt the Corona-heated Accretion-disk Reprocessing (CHAR) model, which is motivated by accretion physics and applicable to reproduce AGN conventional variability, to study the variability of $22$ LRDs in \citet{Tee2025}. Our results indicate that the observed variability in LRDs is dominated by measurement uncertainties. Within the CHAR model, the lack of variability in LRDs can be explained by two scenarios: either AGNs contribute $\lesssim30\%$ of the observed luminosities, or they are intrinsically luminous AGNs. We use simulations to demonstrate the observational requirements to effectively investigate LRDs via variability: first, a sample of about $200$ LRDs; second, each LRD has two observations separated by at least two years in the observed frame; third, the photometric uncertainty is $\leq 0.07$ mag. If the LRDs still lack variability under these conditions, the time-domain study would provide independent evidence that the accretion mode of LRDs differs significantly from low-redshift quasars.

astro-ph.GA↗

arXiv2025

Numerical Study of Oblique Detonation Initiation Assisted by Local Energy Deposition

Reliable initiation of oblique detonation waves (ODWs) is crucial for the stable operation of oblique detonation engines (ODEs), especially under flight conditions of low Mach numbers and/or high altitudes. In this case, conventional initiation approaches relying solely on a fixed-angle wedge may engender risks of initiation failure, which necessitates extra initiation assistance measures. In this study, ODW initiation over a finite wedge with local energy deposition is numerically investigated to assess the thermal effects of plasma-based initiation assistance techniques. Particular emphasis is put on the effects of forms and magnitudes of energy deposition on initiation modes and flow field structures of ODWs. The results demonstrate that on-wedge initiation of ODWs fails at a low Mach number without any energy depositions. In contrast, both continuous and pulsatile local energy depositions can effectively initiate ODWs, leading to sustainable detonation on the finite wedge. As continuous energy deposition power or pulsatile single pulse energy increases, several key detonation initiation modes emerge sequentially. Analysis of the spatiotemporal evolution of the primary wave structures under single-pulse energy deposition reveals the minimum pulse repetition frequency required for sustainable on-wedge detonation, which is subsequently verified through multi-pulse energy deposition simulations. Nevertheless, it is found that sustainable on-wedge detonation can be achieved by pulsatile energy deposition with an average power consumption of less than 10% of that required for continuous energy deposition while maintaining a same initiation length, suggesting that the pulsatile one is an efficient way of energy deposition for initiation assistance of ODWs on finite wedges under extreme flight conditions.

physics.flu-dyn↗

arXiv2025

El Agente: An Autonomous Agent for Quantum Chemistry

Computational chemistry tools are widely used to study the behaviour of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.

cs.AI↗

arXiv2025

SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration

Current dark image restoration methods suffer from severe efficiency bottlenecks, primarily stemming from: (1) computational burden and error correction costs associated with reliance on external priors (manual or cross-modal); (2) redundant operations in complex multi-stage enhancement pipelines; and (3) indiscriminate processing across frequency components in frequency-domain methods, leading to excessive global computational demands. To address these challenges, we propose an Efficient Self-Mining Prior-Guided Joint Frequency Enhancement Network (SPJFNet). Specifically, we first introduce a Self-Mining Guidance Module (SMGM) that generates lightweight endogenous guidance directly from the network, eliminating dependence on external priors and thereby bypassing error correction overhead while improving inference speed. Second, through meticulous analysis of different frequency domain characteristics, we reconstruct and compress multi-level operation chains into a single efficient operation via lossless wavelet decomposition and joint Fourier-based advantageous frequency enhancement, significantly reducing parameters. Building upon this foundation, we propose a Dual-Frequency Guidance Framework (DFGF) that strategically deploys specialized high/low frequency branches (wavelet-domain high-frequency enhancement and Fourier-domain low-frequency restoration), decoupling frequency processing to substantially reduce computational complexity. Rigorous evaluation across multiple benchmarks demonstrates that SPJFNet not only surpasses state-of-the-art performance but also achieves significant efficiency improvements, substantially reducing model complexity and computational overhead. Code is available at https://github.com/bywlzts/SPJFNet.

cs.CV↗

arXiv2025

BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement

Current low-light image enhancement (LLIE) methods face significant limitations in simultaneously improving brightness while preserving semantic consistency, fine details, and computational efficiency. With the emergence of state-space models, particularly Mamba, image restoration has achieved remarkable performance, yet existing visual Mamba approaches flatten 2D images into 1D token sequences using fixed scanning rules, critically limiting interactions between distant tokens with causal relationships and constraining their ability to capture meaningful long-range dependencies. To address these fundamental limitations, we propose BSMamba, a novel visual Mamba architecture comprising two specially designed components: Brightness Mamba and Semantic Mamba. The Brightness Mamba revolutionizes token interaction patterns by prioritizing connections between distant tokens with similar brightness levels, effectively addressing the challenge of brightness restoration in LLIE tasks through brightness-guided selective attention. Complementing this, the Semantic Mamba establishes priority interactions between tokens sharing similar semantic meanings, allowing the model to maintain contextual consistency by connecting semantically related regions across the image, thus preserving the hierarchical nature of image semantics during enhancement. By intelligently modeling tokens based on brightness and semantic similarity rather than arbitrary scanning patterns, BSMamba transcends the constraints of conventional token sequencing while adhering to the principles of causal modeling. Extensive experiments demonstrate that BSMamba achieves state-of-the-art performance in LLIE while preserving semantic consistency. Code is available at https://github.com/bywlzts/BSMamba.

cs.CV↗

arXiv2025

Beyond Illumination: Fine-Grained Detail Preservation in Extreme Dark Image Restoration

Recovering fine-grained details in extremely dark images remains challenging due to severe structural information loss and noise corruption. Existing enhancement methods often fail to preserve intricate details and sharp edges, limiting their effectiveness in downstream applications like text and edge detection. To address these deficiencies, we propose an efficient dual-stage approach centered on detail recovery for dark images. In the first stage, we introduce a Residual Fourier-Guided Module (RFGM) that effectively restores global illumination in the frequency domain. RFGM captures inter-stage and inter-channel dependencies through residual connections, providing robust priors for high-fidelity frequency processing while mitigating error accumulation risks from unreliable priors. The second stage employs complementary Mamba modules specifically designed for textural structure refinement: (1) Patch Mamba operates on channel-concatenated non-downsampled patches, meticulously modeling pixel-level correlations to enhance fine-grained details without resolution loss. (2) Grad Mamba explicitly focuses on high-gradient regions, alleviating state decay in state space models and prioritizing reconstruction of sharp edges and boundaries. Extensive experiments on multiple benchmark datasets and downstream applications demonstrate that our method significantly improves detail recovery performance while maintaining efficiency. Crucially, the proposed modules are lightweight and can be seamlessly integrated into existing Fourier-based frameworks with minimal computational overhead. Code is available at https://github.com/bywlzts/RFGM.

cs.CV↗

arXiv2025

Estimating Bolometric Luminosities of Type 1 Quasars with Self-Organizing Maps

We present a new method to calculate bolometric luminosities for unobscured, type 1 quasars with multi-band photometric data. Bolometric luminosity is a fundamental property to understand quasars and it is commonly estimated from monochromatic luminosities using bolometric corrections that often neglect quasar SED diversity. We take advantage of the fact that most quasars now have multi-band observations from UV to mid-IR, and construct SEDs for a well-defined sample of SDSS quasars at $0.5\leq z\leq 2$. Based on this fiducial sample, we explore quasar SEDs, their diversity, and their relations with bolometric luminosities. We then use unsupervised neural network self-organizing maps (SOM) to describe the SED diversity and compute the bolometric luminosities with a fully-trained SOM model. This method reduces systematical uncertainties compared to the traditional method. In addition, we update the multi-linear regression relations between bolometric luminosity and monochromatic luminosities at restframe 1450Å, 3000Å, and 5100Å. Our method is applicable to large quasar samples with a wide range of luminosity and redshift. We have applied it to the SDSS DR16 quasars. We have also made our code publicly available.

astro-ph.CO↗