arXiv subjects

Meng Wang

Publications and source records attributed to Meng Wang.

At least 307 records · Page 17Linked to original sources

arXiv2025

Revealing nanoscale structural phase separation in La$_{3}$Ni$_{2}$O$_{7-δ}$ single crystal via scanning near-field optical microscopy

The discovery of superconductivity in La3Ni2O7-$δ$ under high pressure,with an onset critical temperature around 80 K, has sparked significant interest in the superconducting phases of Ruddlesden-Popper nickelates, Lan+1NinO3n+1. While La4Ni3O10 exhibits nearly 100% superconductivity with Tc~30 K under high pressure, magnetic susceptibility studies on La3Ni2O7-$δ$, however, reveal a more complex picture, indicating either filamentary superconductivity or that approximately 50% of crystal phase becomes superconducting in polycrystalline samples. In this study, we employed scattering-type scanning near-field optical microscopy to visualize nanoscale structural phase separation in La3Ni2O7-$δ$, identifying enhanced optical conductivity with stripes approximately 183 nm wide. These stripes run diagonally with respect to the Ni-O-Ni bond directions in the a-b plane, ruling out the possibility that they arise from impurity phases, like the '1313', '214' or '4310' structures. The dark regions and bright stripes exhibit optical conductivities ~ 22% and 29% of gold's, respectively. Additionally, we find that the bright stripes constitute about 38% of the total field of view, while the remainder consists of dark regions and the transitional region between dark regions and bright stripes. Our results suggest that optical conductivity stripes originate from nanoscale structural phase separation. In contrast, La4Ni3O10 exhibits uniform and higher optical conductivity with no observable evidence of phase separation. Thus, our study represents a pioneering effort to directly image nanoscale phase separation in Lan+1NinO3n+1 nickelates. This observation could provide crucial insights into the factors that limit the superconducting volume fraction of La3Ni2O7-$δ$, highlighting SNOM as a powerful probe for exploring nanoscale low-energy physics in correlated quantum materials.

cond-mat.supr-con↗

arXiv2025

Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs

Knowledge editing aims to update outdated information in Large Language Models (LLMs). A representative line of study is locate-then-edit methods, which typically employ causal tracing to identify the modules responsible for recalling factual knowledge about entities. However, we find these methods are often sensitive only to changes in the subject entity, leaving them less effective at adapting to changes in relations. This limitation results in poor editing locality, which can lead to the persistence of irrelevant or inaccurate facts, ultimately compromising the reliability of LLMs. We believe this issue arises from the insufficient precision of knowledge localization. To address this, we propose a Fine-grained Neuron-level Knowledge Editing (FiNE) method that enhances editing locality without affecting overall success rates. By precisely identifying and modifying specific neurons within feed-forward networks, FiNE significantly improves knowledge localization and editing. Quantitative experiments demonstrate that FiNE efficiently achieves better overall performance compared to existing techniques, providing new insights into the localization and modification of knowledge within LLMs.

cs.CL↗

arXiv2025

Ground state and magnetic transitions of the orthorhombic antiferromagnet CaCo$_2$TeO$_6$

We report the systematic synthesis, crystal structure, magnetization, and powder neutron diffraction of single crystalline and polycrystalline CaCo$_2$TeO$_6$ samples. CaCo$_2$TeO$_6$ crystallizes in an orthorhombic structure with $Pnma$ space group, featuring chains of edge-shared CoO$_6$ octahedra arranged in a honeycomb pattern. Two antiferromagnetic transitions are observed at $T$$_{N1}$ = 14.4 K and $T$$_{N2}$ = 16.2 K, corresponding to two long-range magnetic orders with propagation vectors of $\bf{k}$$_1$ = (0, 0, 0) and $\bf{k}$$_2$ = (0.125, 0, 0.25), respectively. The ground state is determined as a canted up-up-down-down zigzag spin configuration along the $c$ axis, wherein the magnetic moments of Co1 and Co2 ions are 3.4(1) and 2.1(1)$μ$$_B$, respectively. Successive spin-flop transitions appear with the increasing magnetic field applied along the easy axis ($c$ axis), accompanied by depression of the antiferromagnetic orders and enhancement of residual magnetic entropy. The field-induced spin-disordered state suggests that CaCo$_2$TeO$_6$ may be an ideal candidate for studying frustrated magnetism.

cond-mat.str-el↗

arXiv2025

Doping dependence of the magnetic ground state in the frustrated magnets Ba$_2$$M$Te$_{1-x}$W$_{x}$O$_6$ ($M$ = Mn, Co)

Theoretically, the relative change of the Heisenberg-type nearest-neighbor coupling $J_1$ and next-nearest-neighbor coupling $J_2$ in the face-centered-cubic lattice can give rise to three main antiferromagnetic orderings of type-I, type-II, and type-III. However, it is difficult to tune the $J_2/J_1$ ratio in real materials. Here, we report studies on the influence of Te$^{6+}$ and W$^{6+}$ ions replacement to the magnetic interactions and the magnetic ground states in the double-perovskite compounds Ba$_2$$M$Te$_{1-x}$W$_{x}$O$_6$ ($M$ = Mn, Co). For Ba$_2$MnTe$_{1-x}$W$_{x}$O$_6$, the W$^{6+}$ doping on Te$^{6+}$ site is successful in $0.02 \leq x \leq 0.9$ with short-range orders of the type-I ($0.02 \leq x \leq 0.08$) and type-II ($0.1 \leq x \leq 0.9$). In Ba$_2$CoTe$_{1-x}$W${_x}$O$_6$, x-ray diffraction measurements reveal two crystal structures, including the trigonal phase ($0 \leq x \leq 0.1$) and the cubic phase ($0.5 \leq x \leq 1$), between which is a miscibility gap. Two magnetic transitions are identified in the trigonal phase due to two magnetic subsystems, and the type-II magnetic order is observed in the cubic phase. Magnetic phase diagrams of Ba$_2M$Te$_{1-x}$W$_{x}$O$_6$ ($M$ = Mn, Co) are established. Our work shows that the magnetic interactions and ground states of Ba$_2$$M$Te$_{1-x}$W$_x$O$_6$ can be tuned effectively by the replacement of Te$^{6+}$ by W$^{6+}$ ions.

cond-mat.str-el↗

arXiv2025

An Information-Theoretic Regularizer for Lossy Neural Image Compression

Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be challenging due to its nature of learning quantized latent representations. In this paper, our key finding is that minimizing the latent entropy is, to some extent, equivalent to maximizing the conditional source entropy, an insight that is deeply rooted in information-theoretic equalities. Building on this insight, we propose a novel structural regularization method for the neural image compression task by incorporating the negative conditional source entropy into the training objective, such that both the optimization efficacy and the model's generalization ability can be promoted. The proposed information-theoretic regularizer is interpretable, plug-and-play, and imposes no inference overheads. Extensive experiments demonstrate its superiority in regularizing the models and further squeezing bits from the latent representation across various compression structures and unseen domains.

cs.CV↗

arXiv2025

CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning

The integration of Internet of Things (IoT) technology in pulmonary nodule detection significantly enhances the intelligence and real-time capabilities of the detection system. Currently, lung nodule detection primarily focuses on the identification of solid nodules, but different types of lung nodules correspond to various forms of lung cancer. Multi-type detection contributes to improving the overall lung cancer detection rate and enhancing the cure rate. To achieve high sensitivity in nodule detection, targeted improvements were made to the YOLOv8 model. Firstly, the C2f\_RepViTCAMF module was introduced to augment the C2f module in the backbone, thereby enhancing detection accuracy for small lung nodules and achieving a lightweight model design. Secondly, the MSCAF module was incorporated to reconstruct the feature fusion section of the model, improving detection accuracy for lung nodules of varying scales. Furthermore, the KAN network was integrated into the model. By leveraging the KAN network's powerful nonlinear feature learning capability, detection accuracy for small lung nodules was further improved, and the model's generalization ability was enhanced. Tests conducted on the LUNA16 dataset demonstrate that the improved model outperforms the original model as well as other mainstream models such as YOLOv9 and RT-DETR across various evaluation metrics.

eess.IV↗

arXiv2025

Towards Open-Vocabulary Audio-Visual Event Localization

The Audio-Visual Event Localization (AVEL) task aims to temporally locate and classify video events that are both audible and visible. Most research in this field assumes a closed-set setting, which restricts these models' ability to handle test data containing event categories absent (unseen) during training. Recently, a few studies have explored AVEL in an open-set setting, enabling the recognition of unseen events as ``unknown'', but without providing category-specific semantics. In this paper, we advance the field by introducing the Open-Vocabulary Audio-Visual Event Localization (OV-AVEL) problem, which requires localizing audio-visual events and predicting explicit categories for both seen and unseen data at inference. To address this new task, we propose the OV-AVEBench dataset, comprising 24,800 videos across 67 real-life audio-visual scenes (seen:unseen = 46:21), each with manual segment-level annotation. We also establish three evaluation metrics for this task. Moreover, we investigate two baseline approaches, one training-free and one using a further fine-tuning paradigm. Specifically, we utilize the unified multimodal space from the pretrained ImageBind model to extract audio, visual, and textual (event classes) features. The training-free baseline then determines predictions by comparing the consistency of audio-text and visual-text feature similarities. The fine-tuning baseline incorporates lightweight temporal layers to encode temporal relations within the audio and visual modalities, using OV-AVEBench training data for model fine-tuning. We evaluate these baselines on the proposed OV-AVEBench dataset and discuss potential directions for future work in this new field.

cs.CV↗

arXiv2025

VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion

Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving. However, images provide limited information making the model susceptible to geometric ambiguity caused by occlusion and perspective distortion. Existing methods often lack explicit semantic modeling between objects, limiting their perception of 3D semantic context. To address these challenges, we propose a novel method VLScene: Vision-Language Guidance Distillation for Camera-based 3D Semantic Scene Completion. The key insight is to use the vision-language model to introduce high-level semantic priors to provide the object spatial context required for 3D scene understanding. Specifically, we design a vision-language guidance distillation process to enhance image features, which can effectively capture semantic knowledge from the surrounding environment and improve spatial context reasoning. In addition, we introduce a geometric-semantic sparse awareness mechanism to propagate geometric structures in the neighborhood and enhance semantic information through contextual sparse interactions. Experimental results demonstrate that VLScene achieves rank-1st performance on challenging benchmarks--SemanticKITTI and SSCBench-KITTI-360, yielding remarkably mIoU scores of 17.52 and 19.10, respectively.

cs.CV↗

arXiv2025

Feedback Favors the Generalization of Neural ODEs

The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural ordinary differential equations (neural ODEs), leading to a prominent generalization improvement. The feedback neural network is a novel two-DOF neural network, which possesses robust performance in unseen scenarios with no loss of accuracy performance on previous tasks.} A linear feedback form is presented to correct the learned latent dynamics firstly, with a convergence guarantee. Then, domain randomization is utilized to learn a nonlinear neural feedback form. Finally, extensive tests including trajectory prediction of a real irregular object and model predictive control of a quadrotor with various uncertainties, are implemented, indicating significant improvements over state-of-the-art model-based and learning-based methods.

cs.LG↗

arXiv2025

Search for Double Beta Decay of $^{136}$Xe to the $0^+_1$ Excited State of $^{136}$Ba with PandaX-4T

We perform a search of double beta decay of $^{136}$Xe to the excited state, $0^+_1$, of $^{136}$Ba (2$νββ$-0$_1^+$), using the dual-phase xenon detector of PandaX-4T with the first 94.9-day commissioning data. The multi-site events are reconstructed up to the MeV energy scale, which helps to improve the background model significantly. The background contribution from the stainless steel platform outside PandaX-4T cryostat is evaluated for the first time. No significant evidence for 2$νββ$-$0_1^+$ is observed, resulting in a lower limit on half-life of $7.5 \times 10^{22}$ yr at the 90% confidence level. This is the first experimental limit on such a rare decay in a natural xenon-based detector.

nucl-ex↗

arXiv2025

SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network

Existing image steganography methods face fundamental limitations in hiding capacity (typically $1\sim7$ images) due to severe information interference and uncoordinated capacity-distortion trade-off. We propose SMILENet, a novel synergistic framework that achieves 25 image hiding through three key innovations: (i) A synergistic network architecture coordinates reversible and non-reversible operations to efficiently exploit information redundancy in both secret and cover images. The reversible Invertible Cover-Driven Mosaic (ICDM) module and Invertible Mosaic Secret Embedding (IMSE) module establish cover-guided mosaic transformations and representation embedding with mathematically guaranteed invertibility for distortion-free embedding. The non-reversible Secret Information Selection (SIS) module and Secret Detail Enhancement (SDE) module implement learnable feature modulation for critical information selection and enhancement. (ii) A unified training strategy that coordinates complementary modules to achieve 3.0x higher capacity than existing methods with superior visual quality. (iii) Last but not least, we introduce a new metric to model Capacity-Distortion Trade-off for evaluating the image steganography algorithms that jointly considers hiding capacity and distortion, and provides a unified evaluation approach for accessing results with different number of secret image. Extensive experiments on DIV2K, Paris StreetView and ImageNet1K show that SMILENet outperforms state-of-the-art methods in terms of hiding capacity, recovery quality as well as security against steganalysis methods.

cs.CV↗

arXiv2025

Search for MeV-Scale Axionlike Particles and Dark Photons with PandaX-4T

Axionlike particles (ALPs) and dark photons (DPs) are viable dark matter particle candidates. We have searched for possible ALP/DP signals in the PandaX-4T liquid xenon detector using 440 kg$\cdot$yr of data. A binned likelihood fit is constructed to search for possible mono-energetic peaks induced by the absorption processes between ALPs/DPs and atomic electrons of xenon. A detailed temporal model of decays associated with xenon isotopes is introduced to constrain the number of background events. No signal excess over background expectations is observed, and we have established the most stringent exclusion limits for most ALP/DP masses across the range of 150 keV/$c^2$ to 1 MeV/$c^2$. The improvement is particularly significant within the mass range of \text{150-400}~keV/$c^2$, with the average factor of 3.5 compared to previous results.

hep-ex↗

arXiv2025

Vision-Language Model IP Protection via Prompt-based Learning

Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard the intellectual property (IP) of well-trained models. Effective IP protection extends beyond ensuring authorized usage; it also necessitates restricting model deployment to authorized data domains, particularly when the model is fine-tuned for specific target domains. However, current IP protection methods often rely solely on the visual backbone, which may lack sufficient semantic richness. To bridge this gap, we introduce IP-CLIP, a lightweight IP protection strategy tailored to CLIP, employing a prompt-based learning approach. By leveraging the frozen visual backbone of CLIP, we extract both image style and content information, incorporating them into the learning of IP prompt. This strategy acts as a robust barrier, effectively preventing the unauthorized transfer of features from authorized domains to unauthorized ones. Additionally, we propose a style-enhancement branch that constructs feature banks for both authorized and unauthorized domains. This branch integrates self-enhanced and cross-domain features, further strengthening IP-CLIP's capability to block features from unauthorized domains. Finally, we present new three metrics designed to better balance the performance degradation of authorized and unauthorized domains. Comprehensive experiments in various scenarios demonstrate its promising potential for application in IP protection tasks for VLMs.

cs.CV↗

arXiv2025

A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal

Single Image Reflection Removal (SIRR) is a canonical blind source separation problem and refers to the issue of separating a reflection-contaminated image into a transmission and a reflection image. The core challenge lies in minimizing the commonalities among different sources. Existing deep learning approaches either neglect the significance of feature interactions or rely on heuristically designed architectures. In this paper, we propose a novel Deep Exclusion unfolding Network (DExNet), a lightweight, interpretable, and effective network architecture for SIRR. DExNet is principally constructed by unfolding and parameterizing a simple iterative Sparse and Auxiliary Feature Update (i-SAFU) algorithm, which is specifically designed to solve a new model-based SIRR optimization formulation incorporating a general exclusion prior. This general exclusion prior enables the unfolded SAFU module to inherently identify and penalize commonalities between the transmission and reflection features, ensuring more accurate separation. The principled design of DExNet not only enhances its interpretability but also significantly improves its performance. Comprehensive experiments on four benchmark datasets demonstrate that DExNet achieves state-of-the-art visual and quantitative results while utilizing only approximately 8\% of the parameters required by leading methods.

eess.IV↗

arXiv2025

Learning Dual Transformers for All-In-One Image Restoration from a Frequency Perspective

This work aims to tackle the all-in-one image restoration task, which seeks to handle multiple types of degradation with a single model. The primary challenge is to extract degradation representations from the input degraded images and use them to guide the model's adaptation to specific degradation types. Building on the insight that various degradations affect image content differently across frequency bands, we propose a new dual-transformer approach comprising two components: a frequency-aware Degradation estimation transformer (Dformer) and a degradation-adaptive Restoration transformer (Rformer). The Dformer captures the essential characteristics of various degradations by decomposing the input into different frequency components. By understanding how degradations affect these frequency components, the Dformer learns robust priors that effectively guide the restoration process. The Rformer then employs a degradation-adaptive self-attention module to selectively focus on the most affected frequency components, guided by the learned degradation representations. Extensive experimental results demonstrate that our approach outperforms existing methods in five representative restoration tasks, including denoising, deraining, dehazing, deblurring, and low-light enhancement. Additionally, our method offers benefits for handling, real-world degradations, spatially variant degradations, and unseen degradation levels.

cs.CV↗

arXiv2025

Origin of the density wave instability in trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$ revealed by optical and ultrafast spectroscopy

In the intricate phase diagram of unconventional superconductors characterized by intertwined electronic orders and superconductivity, a key step in understanding the superconducting mechanism is to investigate the parent compounds from which superconductivity emerges through doping or pressure. In this study, we employed optical spectroscopy and ultrafast reflectivity measurements to examine the density wave instability in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$, which displays pressure-induced superconductivity up to 30 K. Our optical spectroscopy measurements reveal that La$_{4}$Ni$_{3}$O$_{10}$ behaves as a metal with a high plasma frequency. Upon cooling, we observed a distinct formation of a density wave energy gap in both optical conductivity and pump-probe measurements. The gap feature is more pronounced compared to the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. Through a comparison of the experimentally determined plasma frequency with first-principles calculations, we classify La$_{4}$Ni$_{3}$O$_{10}$ as a moderately electron-correlated material, resembling the parent compound of iron-based superconductors but exhibiting weaker correlation than the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. The enhanced gap feature and weaker electronic correlation in La$_{4}$Ni$_{3}$O$_{10}$ may explain its lower superconductivity transition temperature under high pressure. These findings significantly advance our comprehension of the density wave and superconductivity mechanisms in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$.

cond-mat.str-el↗

arXiv2025

Pressure-enhanced spin-density-wave transition in double-layer nickelate $La_{3}Ni_{2}O_{7-δ}$

Recently, a signature of high-temperature superconductivity above the liquid nitrogen temperature (77 K) was reported for $La_{3}Ni_{2}O_{7-δ}$ under pressure. This finding immediately stimulated intense interest in the possible mechanism of high-$T_{c}$ superconductivity in double-layer nickelates. Notably, the pressure-dependent phase diagram inferred from transport measurements indicates that the superconductivity under high pressure emerges from the suppression of density-wave-like order at ambient pressure, which is similar to high-temperature superconductors. Here, nuclear magnetic resonance (NMR) spectroscopy of $^{139}La$ nuclei was performed to study the density-wave-like transition in a single crystal of $La_{3}Ni_{2}O_{7-δ}$. At high temperatures, two sets of sharp $^{139}La$ NMR peaks are clearly distinguishable from a broad background signals, which are ascribed to La(1) sites from two bilayer Ruddlesden-Popper phases with different oxygen vacancy $δ$. As the temperature decreases, the temperature-dependent $^{139}La$ NMR spectra and nuclear spin-lattice relaxation rate $(1/T_{1}$) for both La(1) sites provide evidence of spin-density-wave (SDW) ordering below the transition temperature ($T_{SDW}$), which is ~ 150 K. The anisotropic splitting in the NMR spectra suggests the formation of a possible double spin stripe with magnetic moments aligned along the c-axis. Furthermore, we studied the pressure-dependent SDW transition up to ~ 2.7 GPa. Surprisingly, the $T_{SDW}$ inferred from NMR measurements of both La(1) sites increases with increasing pressure, which is opposite to the results from previous transport measurements under pressure and suggests an intriguing phase diagram between superconductivity and SDW. All these results will be helpful for building a connection between superconductivity and magnetic interactions in double-layer nickelates.

cond-mat.supr-con↗

arXiv2025

Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning

Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability remains a significant challenge, particularly when the task ID is unknown. To address this, our study reveals that the gap in feature distribution between novel and existing tasks is primarily driven by differences in mean and covariance moments. Building on this insight, we propose a novel semantic drift calibration method that incorporates mean shift compensation and covariance calibration. Specifically, we calculate each class's mean by averaging its sample embeddings and estimate task shifts using weighted embedding changes based on their proximity to the previous mean, effectively capturing mean shifts for all learned classes with each new task. We also apply Mahalanobis distance constraint for covariance calibration, aligning class-specific embedding covariances between old and current networks to mitigate the covariance shift. Additionally, we integrate a feature-level self-distillation approach to enhance generalization. Comprehensive experiments on commonly used datasets demonstrate the effectiveness of our approach. The source code is available at \href{https://github.com/fwu11/MACIL.git}{https://github.com/fwu11/MACIL.git}.

cs.CV↗