arXiv subjects

Yingying Wang

Publications and source records attributed to Yingying Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Dynamic self-assembly of active particle systems controlled by light fields

Active particle systems are a class of non-equilibrium systems composed of self-propelled Brownian particles; through interactions between particles within the system, a variety of intriguing collective behaviors can emerge. Based on Brownian dynamics simulations, this study investigates the formation and transition mechanisms of ordered structures in active particle systems regulated by light fields. The study reveals that, under light field regulation, active particles undergo large-scale phase separation behavior, forming specific ordered structures and enabling the dynamic transition between multiple ordered structures. This study systematically explores the effects of light fields on this dynamic phase transition and the corresponding regulatory mechanisms. The research findings provide important references for the precise regulation of collective structures in active systems and the fabrication of micro-nano intelligent devices.

cond-mat.soft↗

arXiv2025

PCMamba: Physics-Informed Cross-Modal State Space Model for Dual-Camera Compressive Hyperspectral Imaging

Panchromatic (PAN) -assisted Dual-Camera Compressive Hyperspectral Imaging (DCCHI) is a key technology in snapshot hyperspectral imaging. Existing research primarily focuses on exploring spectral information from 2D compressive measurements and spatial information from PAN images in an explicit manner, leading to a bottleneck in HSI reconstruction. Various physical factors, such as temperature, emissivity, and multiple reflections between objects, play a critical role in the process of a sensor acquiring hyperspectral thermal signals. Inspired by this, we attempt to investigate the interrelationships between physical properties to provide deeper theoretical insights for HSI reconstruction. In this paper, we propose a Physics-Informed Cross-Modal State Space Model Network (PCMamba) for DCCHI, which incorporates the forward physical imaging process of HSI into the linear complexity of Mamba to facilitate lightweight and high-quality HSI reconstruction. Specifically, we analyze the imaging process of hyperspectral thermal signals to enable the network to disentangle the three key physical properties-temperature, emissivity, and texture. By fully exploiting the potential information embedded in 2D measurements and PAN images, the HSIs are reconstructed through a physics-driven synthesis process. Furthermore, we design a Cross-Modal Scanning Mamba Block (CSMB) that introduces inter-modal pixel-wise interaction with positional inductive bias by cross-scanning the backbone features and PAN features. Extensive experiments conducted on both real and simulated datasets demonstrate that our method significantly outperforms SOTA methods in both quantitative and qualitative metrics.

eess.IV↗

arXiv2025

FRN: Fractal-Based Recursive Spectral Reconstruction Network

Generating hyperspectral images (HSIs) from RGB images through spectral reconstruction can significantly reduce the cost of HSI acquisition. In this paper, we propose a Fractal-Based Recursive Spectral Reconstruction Network (FRN), which differs from existing paradigms that attempt to directly integrate the full-spectrum information from the R, G, and B channels in a one-shot manner. Instead, it treats spectral reconstruction as a progressive process, predicting from broad to narrow bands or employing a coarse-to-fine approach for predicting the next wavelength. Inspired by fractals in mathematics, FRN establishes a novel spectral reconstruction paradigm by recursively invoking an atomic reconstruction module. In each invocation, only the spectral information from neighboring bands is used to provide clues for the generation of the image at the next wavelength, which follows the low-rank property of spectral data. Moreover, we design a band-aware state space model that employs a pixel-differentiated scanning strategy at different stages of the generation process, further suppressing interference from low-correlation regions caused by reflectance differences. Through extensive experimentation across different datasets, FRN achieves superior reconstruction performance compared to state-of-the-art methods in both quantitative and qualitative evaluations.

cs.CV↗

arXiv2025

TeleEval-OS: Performance evaluations of large language models for operations scheduling

The rapid advancement of large language models (LLMs) has significantly propelled progress in artificial intelligence, demonstrating substantial application potential across multiple specialized domains. Telecommunications operation scheduling (OS) is a critical aspect of the telecommunications industry, involving the coordinated management of networks, services, risks, and human resources to optimize production scheduling and ensure unified service control. However, the inherent complexity and domain-specific nature of OS tasks, coupled with the absence of comprehensive evaluation benchmarks, have hindered thorough exploration of LLMs' application potential in this critical field. To address this research gap, we propose the first Telecommunications Operation Scheduling Evaluation Benchmark (TeleEval-OS). Specifically, this benchmark comprises 15 datasets across 13 subtasks, comprehensively simulating four key operational stages: intelligent ticket creation, intelligent ticket handling, intelligent ticket closure, and intelligent evaluation. To systematically assess the performance of LLMs on tasks of varying complexity, we categorize their capabilities in telecommunications operation scheduling into four hierarchical levels, arranged in ascending order of difficulty: basic NLP, knowledge Q&A, report generation, and report analysis. On TeleEval-OS, we leverage zero-shot and few-shot evaluation methods to comprehensively assess 10 open-source LLMs (e.g., DeepSeek-V3) and 4 closed-source LLMs (e.g., GPT-4o) across diverse scenarios. Experimental results demonstrate that open-source LLMs can outperform closed-source LLMs in specific scenarios, highlighting their significant potential and value in the field of telecommunications operation scheduling.

cs.CL↗

arXiv2025

EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records

Medical consultation dialogues contain critical clinical information, yet their unstructured nature hinders effective utilization in diagnosis and treatment. Traditional methods, relying on rule-based or shallow machine learning techniques, struggle to capture deep and implicit semantics. Recently, large pre-trained language models and Low-Rank Adaptation (LoRA), a lightweight fine-tuning method, have shown promise for structured information extraction. We propose EMRModel, a novel approach that integrates LoRA-based fine-tuning with code-style prompt design, aiming to efficiently convert medical consultation dialogues into structured electronic medical records (EMRs). Additionally, we construct a high-quality, realistically grounded dataset of medical consultation dialogues with detailed annotations. Furthermore, we introduce a fine-grained evaluation benchmark for medical consultation information extraction and provide a systematic evaluation methodology, advancing the optimization of medical natural language processing (NLP) models. Experimental results show EMRModel achieves an F1 score of 88.1%, improving by49.5% over standard pre-trained models. Compared to traditional LoRA fine-tuning methods, our model shows superior performance, highlighting its effectiveness in structured medical record extraction tasks.

cs.CL↗

arXiv2025

Guiding polaritonic energy and momentum through two-dimensional Bravais lattices

The strong exciton absorption in monolayer transition metal dichalcogenides provides a promising platform for studying polaritons with tunable dispersions, which are crucial for controlling polaritonic energy and momentum, but remain underexplored. In this work, monolayer MoS$_2$ is coupled with a Fabry-Pérot microcavity to form polaritons. Five types of Bravais lattices with sub-wavelength periods, based on polymethyl methacrylate (PMMA) nanopillars, are intentionally designed. The energy overlap between the periodic PMMA scattering wave and the polariton establishes a coupling channel that controls the directional flow of polaritonic energy, as demonstrated through angle-resolved reflectance measurements. Back-space image measurements further demonstrate that the dispersion in reciprocal space can be directly and manually tuned, allowing for control over their number and their positions. The coupling between the polariton and PMMA scattering wave is further demonstrated by analyzing the reflectance using the two-port two-mode model. The symmetries of 2D Bravais lattices allow the angle between energy and momentum flow to vary widely, from 90°, 60°, 45°, and 30° to arbitrary values. By adjusting the lattice vector lengths, the position of the dispersion branch in a specific direction can be fine-tuned, enabling full-range control over polariton dispersion. This work presents the first theoretical and experimental demonstrations of guiding the direction of polaritonic energy and momentum through Bravais lattice design.

physics.optics↗

arXiv2025

Fabry-Perot resonance modes in a MoS$_2$-based vertical stacking cavity for strong light-matter coupling and topological phase singularity

Rich dielectric properties in atomic transition metal dichalcogenides (TMDs) enhance light-matter interactions and contribute to a variety of optical phenomena. The direct transfer of TMDs onto photonic crystals facilitates optical field confinement and modifies photon dispersion through the generation of polaritons. However, light-matter interaction is severely limited by this stacking method. This limitation can be significantly improved by constructing a vertical stacking cavity with alternating layers of dielectric material and monolayer MoS$_2$. This multilayer structure is proven to be a compact, versatile, and customizable platform for controlling Fabry-Perot cavity resonance mode. Angle-resolved reflectance further aids in studying resonance mode dispersion. Moreover, the strong light-matter interaction results in multiple perfect absorptions, with the monolayer MoS$_2$ significantly contributing to the absorption in this system, as schematically revealed by the electric field distribution. The multiple perfect absorptions produce an unusual amounts of phase singularities with topological pairs, whose generation, evolution, and annihilation can be controlled by adjusting cavity parameters. Our findings provide a flexible and consistent framework for optimizing light-matter interactions and supporting further studies on wavefront shaping, optical vortices, and topological variants.

physics.optics↗

arXiv2024

Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas

Background: Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions remain elusive. FMRI is a cutting-edge medical imaging technology renowned for its high spatial resolution and dynamic temporal information, making it particularly suitable for the neural dynamics of depression research. Methods: To address this gap, our study firstly leveraged fMRI to delineate activated regions associated with positive and negative emotions in healthy individuals, resulting in the creation of positive emotion atlas (PEA) and negative emotion atlas (NEA). Subsequently, we examined neuroimaging changes in depression patients using these atlases and evaluated their diagnostic performance based on machine learning. Results: Our findings demonstrate that the classification accuracy of depressed patients based on PEA and NEA exceeded 0.70, a notable improvement compared to the whole-brain atlases. Furthermore, ALFF analysis unveiled significant differences between depressed patients and healthy controls in eight functional clusters during the NEA, focusing on the left cuneus, cingulate gyrus, and superior parietal lobule. In contrast, the PEA revealed more pronounced differences across fifteen clusters, involving the right fusiform gyrus, parahippocampal gyrus, and inferior parietal lobule. Limitations: Due to the limited sample size and subtypes of depressed patients, the efficacy may need further validation in future. Conclusions: These findings emphasize the complex interplay between emotion modulation and depression, showcasing significant alterations in both PEA and NEA among depression patients. This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.

cs.CV↗

arXiv2024

DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning

Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction have typically been limited to using labels of specific events as supervision, which renders them insufficient to address two significant challenges: (1) the bias caused by \textbf{highly imbalanced event distribution} where certain interaction types are vastly under-represented. (2) the \textbf{scarcity of labeled data for rare events}, a pervasive issue where rare yet potentially critical interactions are often overlooked or under-explored due to limited available data. In response, we offer ``DDIPrompt'', an innovative solution inspired by the recent advancements in graph prompt learning. Our framework aims to address these issues by leveraging the intrinsic knowledge from pre-trained models, which can be efficiently deployed with minimal downstream data. Specifically, to solve the first challenge, DDIPrompt features a hierarchical pre-training strategy to foster a generalized and comprehensive understanding of drug properties. It captures intra-molecular structures through augmented links based on structural proximity between drugs, further learns inter-molecular interactions emphasizing edge connections rather than concrete catagories. For the second challenge, we implement a prototype-enhanced prompting mechanism during inference. This mechanism, refined by few-shot examples from each category, effectively harnesses the rich pre-training knowledge to enhance prediction accuracy, particularly for these rare but crucial interactions. Extensive experiments on two benchmark datasets demonstrate DDIPrompt's SOTA performance, especially for those rare DDI events.

q-bio.BM↗

arXiv2024

Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors

Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. As a result, their practicality is limited. In this work, we devise a novel unsupervised LIE framework based on diffusion priors and lookup tables (DPLUT) to achieve efficient low-light image recovery. The proposed approach comprises two critical components: a light adjustment lookup table (LLUT) and a noise suppression lookup table (NLUT). LLUT is optimized with a set of unsupervised losses. It aims at predicting pixel-wise curve parameters for the dynamic range adjustment of a specific image. NLUT is designed to remove the amplified noise after the light brightens. As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in terms of visual quality and efficiency.

cs.CV↗

arXiv2024

Four-fold truncated double-nested anti-resonant hollow-core fibers with ultralow loss and ultrahigh mode purity

Hollow-core fibers are inherently multimode, making it crucial to filter out higher-order modes within the shortest possible fiber length for applications such as high speed coherent communications and fiber optic gyroscopes. However, current HCF designs face the challenges of simultaneously achieving ultralow fundamental mode loss and ultrahigh HOM suppression. In this study, we present a novel four fold truncated double nested anti resonant hollow core fiber structure that addresses this challenge. Our 4T-DNANF enables greater control over phase-matching between core modes and air modes in the cladding, allowing for minimized FM loss and substantially increased HOM loss. Experimentally, we fabricated several HCFs: one with an FM loss of 0.1 dB/km and an HOM loss of 430 dB/km, and another with an FM loss of 0.13 dB/km with a HOM loss of 6500 dB/km, resulting in a higher-order mode extinction ratio of 50,000.

physics.optics↗

arXiv2024

Navigation-grade interferometric air-core antiresonant fibre optic gyroscope with enhanced thermal stability

We present a groundbreaking navigation-grade interferometric air-core fibre optic gyroscope (IFOG) using a quadrupolar-wound coil of four-tube truncated double nested antiresonant nodeless fibre (tDNANF). This state-of-the-art tDNANF simultaneously achieves low loss, low bend loss, single-spatial-mode operation, and exceptional linear polarization purity over a broad wavelength range. Our 469 m tDNANF coil demonstrated a polarization extinction ratio (PER) of ~20 dB when illuminated by an amplified spontaneous emission (ASE) source spanning 1525-1565 nm. Under these conditions, the gyro archives an angular random walk (ARW) of 0.0038 deg h-1/2 and a bias-stability (BS) drift over 8500 s of 0.0014 deg h-1, marking the first instance of navigation-grade performance in air-core FOGs. Additionally, we validated the low thermal sensitivity of air-core FOGs, with reductions of 9.24/10.68/6.82 compared to that of conventional polarization-maintaining solid-core FOGs of the same size across various temperature ranges. These results represent a significant step towards long-standing promise of high-precision inertial navigation applications with superior environmental adaptability.

physics.optics↗

arXiv2024

AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement

Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limitations arise from two inherent challenges in real-world LIE: 1) the collection of distorted/clean image pairs is often impractical and sometimes even unavailable, and 2) accurately modeling complex degradations presents a non-trivial problem. To overcome them, we propose the Attribute Guidance Diffusion framework (AGLLDiff), a training-free method for effective real-world LIE. Instead of specifically defining the degradation process, AGLLDiff shifts the paradigm and models the desired attributes, such as image exposure, structure and color of normal-light images. These attributes are readily available and impose no assumptions about the degradation process, which guides the diffusion sampling process to a reliable high-quality solution space. Extensive experiments demonstrate that our approach outperforms the current leading unsupervised LIE methods across benchmarks in terms of distortion-based and perceptual-based metrics, and it performs well even in sophisticated wild degradation.

cs.CV↗

arXiv2024

Training-Free Large Model Priors for Multiple-in-One Image Restoration

Image restoration aims to reconstruct the latent clear images from their degraded versions. Despite the notable achievement, existing methods predominantly focus on handling specific degradation types and thus require specialized models, impeding real-world applications in dynamic degradation scenarios. To address this issue, we propose Large Model Driven Image Restoration framework (LMDIR), a novel multiple-in-one image restoration paradigm that leverages the generic priors from large multi-modal language models (MMLMs) and the pretrained diffusion models. In detail, LMDIR integrates three key prior knowledges: 1) global degradation knowledge from MMLMs, 2) scene-aware contextual descriptions generated by MMLMs, and 3) fine-grained high-quality reference images synthesized by diffusion models guided by MMLM descriptions. Standing on above priors, our architecture comprises a query-based prompt encoder, degradation-aware transformer block injecting global degradation knowledge, content-aware transformer block incorporating scene description, and reference-based transformer block incorporating fine-grained image priors. This design facilitates single-stage training paradigm to address various degradations while supporting both automatic and user-guided restoration. Extensive experiments demonstrate that our designed method outperforms state-of-the-art competitors on multiple evaluation benchmarks.

cs.CV↗

arXiv2024

AMSA-UNet: An Asymmetric Multiple Scales U-net Based on Self-attention for Deblurring

The traditional ingle-scale U-Net often leads to the loss of spatial information during deblurring, which affects the deblurring accracy. Additionally, due to the convolutional method's limitation in capturing long-range dependencies, the quality of the recovered image is degraded. To address the above problems, an asymmetric multiple scales U-net based on self-attention (AMSA-UNet) is proposed to improve the accuracy and computational complexity. By introducing a multiple-scales U shape architecture, the network can focus on blurry regions at the global level and better recover image details at the local level. In order to overcome the limitations of traditional convolutional methods in capturing the long-range dependencies of information, a self-attention mechanism is introduced into the decoder part of the backbone network, which significantly increases the model's receptive field, enabling it to pay more attention to semantic information of the image, thereby producing more accurate and visually pleasing deblurred images. What's more, a frequency domain-based computation method was introduced to reduces the computation amount. The experimental results demonstrate that the proposed method exhibits significant improvements in both accuracy and speed compared to eight excellent methods

cs.CV↗

arXiv2024

Extremely long transverse optical needle focus for reflective metalens enabled by monolayer MoS$_2$

Line-scan mode facilitates fast-speed and high-throughput imaging with developing a suitable optical transverse needle focus. Metasurface with periodic structures such as diffractive rings, ellipses, and gratings could enable discrete focus evolving into line focus under momentum conservation, but still face the challenge of extremely low light power utilization brought by inevitably multiple high-order diffractions. In addition, the designed focus requires the selection of particular optical functional materials. High dielectric constants in atomic transition metal dichalcogenides make significant phase modulation by bringing phase singularity at zero-reflection possible. However, no light power is available for use at zero-reflection and a balance between phase and amplitude modulation is needed. In this work, above issues are simultaneously solved by designing a monolayer MoS2 based Fresnel strip structure. An optical needle primary focus with a transverse length of 40 μm (~80 λ) is obtained, which is the longest value recorded so far, together with a sub-diffraction-limited lateral spot and a broad working wavelength range. This specially developed structure not only concentrates light power in primary diffraction by breaking restriction of momentum conservation, but also guarantees a consistent phase across different strips. The novel optical manipulation way provided here together with the longer focus length for flat optics will show promising applications in biology, oncology, nanofabrication, energy harvesting, and optical information processing.

physics.optics↗

arXiv2024

A mid-infrared Brillouin laser using ultra-high-Q on-chip resonators

Ultra-high-Q optical resonators have facilitated recent advancements in on-chip photonics by effectively harnessing nonlinear phenomena providing useful functionalities. While these breakthroughs, primarily focused on the near-infrared region, have extended interest to longer wavelengths holding importance for monitoring and manipulating molecules, the absence of ultra-high-Q resonators in this region remains a significant challenge. Here, we have developed on-chip microresonators with a remarkable Q-factor of 38 million, surpassing previous mid-infrared records by over 30 times. Employing innovative fabrication techniques, including the spontaneous formation of light-guiding geometries during material deposition, resonators with internal multilayer structures have been seamlessly created and passivated with chalcogenide glasses within a single chamber. Major loss factors, especially airborne-chemical absorption, were thoroughly investigated and mitigated by extensive optimization of resonator geometries and fabrication procedures. This allowed us to access the fundamental loss performance offered by doubly purified chalcogenide glass sources, as demonstrated in their fiber form. Exploiting this ultra-high-Q resonator, we successfully demonstrated Brillouin lasing on a chip for the first time in the mid-infrared, with a threshold power of 91.9 μW and a theoretical Schawlow-Townes linewidth of 83.45 Hz, far surpassing carrier phase noise. Our results showcase the effective integration of cavity-enhanced optical nonlinearities into on-chip mid-infrared photonics.

physics.optics↗

arXiv2024

Cohomology and geometry of Deligne--Lusztig varieties for ${\rm GL}_n$

We give a description of the cohomology groups of the structure sheaf on smooth compactifications $\overline{X}(w)$ of Deligne--Lusztig varieties $X(w)$ for ${\rm GL}_n$, for all elements $w$ in the Weyl group. As a consequence, we obtain the ${\rm mod}\ p^m$ and integral $p$-adic étale cohomology of $\overline{X}(w)$. Moreover, using our result for $\overline{X}(w)$ and a spectral sequence associated to a stratification of $\overline{X}(w)$, we deduce the ${\rm mod}\ p^m$ and integral $p$-adic étale cohomology with compact support of $X(w)$. In our proof of the main theorem, in addition to considering the Demazure--Hansen smooth compactifications of $X(w)$, we show that a similar class of constructions provide smooth compactifications of $X(w)$ in the case of ${\rm GL}_n$. Furthermore, we show in the appendix that the Zariski closure of $X(w)$, for any connected reductive group $G$ and any $w$, has pseudo-rational singularities.

math.AG↗