arXiv subjects

Qi Wen

Publications and source records attributed to Qi Wen.

At least 19 recordsLinked to original sources

arXiv2026

Continuum modeling of fluidic and elastic flow during growth-driven wound closure in partial-EMT cell monolayers

Large-scale circular gap closure occurs over a time scale on which cell growth and proliferation become important. Growth is the main driver of the closing process, while cell dynamics such as elongation and intercalation reflect elastic and fluidic contributions to tissue deformation. We develop a novel fluidized growth-elasticity framework as a nonlinear analogue of a Maxwell fluid with growth. The framework decomposes the experimentally observable strain rate into the additive sum of the growth, elastic, and fluidic strain rates, thus enabling the separate quantification of these contributions from tissue kinematics and allowing the roles of tissue elasticity and fluidity (the inverse of viscosity) to be characterized. We apply the model to large circular gaps ($\sim$1.7 mm in diameter) in confluent monolayers of mouse embryonic epicardial cells (MEC1) under two conditions, without and with TGF-$\beta$ treatment. We show that both tissue fluidity and the elastic properties associated with fiber reinforcement are critical for reproducing the closure kinematics. Specifically, we predict that the treated condition has lower fluidity, associated with a lower fluidic deformation rate and a higher elastic deformation rate than the untreated condition, in agreement with the experimental observations.

physics.flu-dyn↗

arXiv2026

Comb-enabled spectral-domain image transport through perturbation-prone multimode fibers

Multimode fibers (MMFs) offer a compact platform for imaging, sensing, and information transport, but their practical deployment is hindered by sensitivity to fiber perturbations, which alter modal coupling and invalidate conventional speckle-based calibrations. Here, we demonstrate perturbation-resilient image transport through MMFs by combining image-to-spectrum encoding with dual-comb spectroscopy. Two-dimensional images are converted into comb-line-resolved spectral signatures before fiber transmission, allowing spatial information to be carried in the spectral domain rather than in the output speckle field. After propagation, dual-comb heterodyne detection maps the encoded spectrum into the radio-frequency domain, enabling massively parallel spectral readout with a single photodetector. Neural-network-assisted compressive reconstruction further enables high-fidelity imaging from sparse, noisy, and spectrally aliased measurements. Our approach achieves Pearson correlation coefficients exceeding 0.9 under strong fiber perturbations and supports frame rates up to 2.5 MHz, allowing the observation of transient switching dynamics in a digital micromirror device. These results establish a powerful tool for robust, real-time image transport through flexible MMFs, with potential applications in remote sensing and fiber-based optical instrumentation.

physics.optics↗

arXiv2026

Calibration Is Not Control: Why LLM-Agent Oversight Needs Intervention

Runtime oversight for LLM agents is commonly framed as scalar risk prediction: estimate failure likelihood, confidence, or uncertainty, then intervene once the score crosses a threshold. We argue that this framing targets the wrong object for control. The relevant question is not how likely the agent is to fail if it continues, but whether an available intervention would improve the outcome. Two trajectory prefixes can have the same risk estimate while requiring different actions, because one remains recoverable and the other does not. We formalize this mismatch as target error and identify intervention advantage, the expected utility gain from intervening rather than continuing, as the decision object for oversight. To measure this mismatch, we introduce prefix branching, a same-prefix counterfactual protocol that executes candidate actions from identical trajectory states. Across four benchmarks, action-conditioned control yields regime-dependent gains over scalar routing. In a calibration decomposition, recalibrating the same scalar score improves prediction metrics but leaves control regret unchanged, showing that calibration alone does not repair target error. A simple prefix-only action-conditioned controller substantially reduces regret in the strongest interactive regime, from 0.506 to 0.110 on ALFWorld. Gains shrink when interventions are weak or when scalar routing already preserves intervention-relevant information. These results suggest that LLM-agent oversight should move from calibrated risk scoring toward action-conditioned value estimation.

cs.AI↗

arXiv2026

Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs

The segmentation of thin linear structures is inherently topology allowbreak-critical, where minor local errors can sever long-range connectivity. While recent State-Space Models (SSMs) offer efficient long-range modeling, their isotropic serialization (e.g., raster scanning) creates a geometry mismatch for anisotropic targets, causing state propagation across rather than along the structure trajectories. To address this, we propose FGOS-Net, a framework based on frequency allowbreak-geometric disentanglement. We first decompose features into a stable topology carrier and directional high-frequency bands, leveraging the latter to explicitly correct spatial misalignments induced by downsampling. Building on this calibrated topology, we introduce frequency-aligned scanning that elevates serialization to a geometry-conditioned decision, preserving direction-consistent traces. Coupled with an active probing strategy to selectively inject high-frequency details and suppress texture ambiguity, FGOS-Net consistently outperforms strong baselines across four challenging benchmarks. Notably, it achieves 91.3% mIoU and 97.1% clDice on DeepCrack while running at 80 FPS with only 7.87 GFLOPs.

cs.CV↗

arXiv2026

S$^3$GND: An Effective Learning-Based Approach for Subgraph Similarity Search Under Generalized Neighbor Difference Semantics (Technical Report)

Subgraph similarity search over large-scale graphs is a fundamental task that retrieves subgraphs similar to a given query graph from a data graph, and it plays a crucial role in real applications such as protein discovery, social network analysis, and recommendation systems. While prior works on subgraph similarity search studied various graph similarity metrics, in this paper, we propose a novel graph similarity semantics, \textit{generalized neighbor difference} (GND), that accounts for both the keyword-set relationships between vertices and edge-weight differences. We formulate the problem of \textit{subgraph similarity search under the generalized neighbor difference semantics} (S$^3$GND), which retrieves those subgraphs similar to a query graph $q$ under GND semantics. To efficiently tackle the S$^3$GND problem, we propose an effective learning-based approach, which constructs a keyword hypergraph from the data graph, and trains a \textit{hypergraph neural network} (HGNN) model to obtain high-quality keyword embedding representations. We design effective pruning strategies, \textit{keyword embedding MBR}, \textit{vertex-Level ND lower bound}, and \textit{graph-level GND lower bound pruning}, to rule out false alarms of candidate vertices/subgraphs, and devise a tree-based indexing mechanism to facilitate efficient S$^3$GND query answering. We develop an efficient S$^3$GND query-processing algorithm that traverses the index, applies pruning strategies, and returns actual S$^3$GND answers. Finally, we conduct extensive experiments to verify the effectiveness and efficiency of our proposed S$^3$GND approach over both real and synthetic graphs.

cs.DB↗

arXiv2026

Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensing

The precise segmentation of geological linear features, spanning from planetary lineaments to terrestrial fractures, demands capturing long-range dependencies across complex anisotropic topologies. Although State Space Models (SSMs) offer near-linear computational complexity, their dependence on rigid, axis-aligned scanning trajectories induces a fundamental topological mismatch with curvilinear targets, resulting in fragmented context and feature erosion. To bridge this gap, we propose Fluxamba, a lightweight architecture that introduces a topology-aware feature rectification framework. Central to our design is the Structural Flux Block (SFB), which orchestrates an anisotropic information flux by integrating an Anisotropic Structural Gate (ASG) with a Prior-Modulated Flow (PMF). This mechanism decouples feature orientation from spatial location, dynamically gating context aggregation along the target's intrinsic geometry rather than rigid paths. Furthermore, to mitigate serialization-induced noise in low-contrast environments, we incorporate a Hierarchical Spatial Regulator (HSR) for multi-scale semantic alignment and a High-Fidelity Focus Unit (HFFU) to explicitly maximize the signal-to-noise ratio of faint features. Extensive experiments on diverse geological benchmarks (LROC-Lineament, LineaMapper, and GeoCrack) demonstrate that Fluxamba establishes a new state-of-the-art. Notably, on the challenging LROC-Lineament dataset, it achieves an F1-score of 89.22% and mIoU of 89.87%. Achieving a real-time inference speed of over 24 FPS with only 3.4M parameters and 6.3G FLOPs, Fluxamba reduces computational costs by up to two orders of magnitude compared to heavy-weight baselines, thereby establishing a new Pareto frontier between segmentation fidelity and onboard deployment feasibility.

cs.CV↗

arXiv2025

Rapid and precise distance measurement using balanced cross-correlation of a single frequency-modulated electro-optic comb

Ultra-rapid, high-precision distance metrology is critical for both advanced scientific research and practical applications. However, current light detection and ranging technologies struggle to simultaneously achieve high measurement speed, accuracy, and a large non-ambiguity range. Here, we present a time-of-flight optical ranging technique based on a repetition-frequency-modulated femtosecond electro-optic comb and balanced nonlinear cross-correlation detection. In this approach, a target distance is determined as an integer multiple of the comb repetition period. By rapidly sweeping the comb repetition frequency, we achieve absolute distance measurements within 500 ns and real-time displacement tracking at single-pulse resolution (corresponding to a refresh rate of 172 MHz). Furthermore, our system attains an ultimate ranging precision of 5 nm (with 0.3 s integration time). Our method uniquely integrates nanometer-scale precision, megahertz-level refresh rates, and a theoretically unlimited ambiguity range within a single platform, while also supporting multi-target detection. These advances pave the way for high-speed, high-precision ranging systems in emerging applications such as structural health monitoring, industrial manufacturing, and satellite formation flying.

physics.optics↗

arXiv2025

Laser ablated sub-wavelength structure anti-reflection coating on an alumina lens

We used laser ablation to fabricate sub-wavelength structure anti-reflection coating (SWS-ARC) on a 5 cm diameter alumina lens. With an aspect ratio of 2.5, the SWS-ARC are designed to give a broad-band low reflectance response between 110 and 290 GHz. SWS shape measurements conducted on both sides of the lens give 303 $\mu$m pitch and total height between 750 and 790 $\mu$m, matching or exceeding the aspect ratio design values. Millimeter-wave transmittance measurements in a band between 140 and 260 GHz show the increase in transmittance expected with the ARC when compared to finite element analysis electromagnetic simulations. To our knowledge, this is the first demonstration of SWS-ARC on an alumina lens, opening the path for implementing the technique for larger diameter lenses.

astro-ph.IM↗

arXiv2025

S3AND: Efficient Subgraph Similarity Search Under Aggregated Neighbor Difference Semantics (Technical Report)

For the past decades, the \textit{subgraph similarity search} over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely \textit{subgraph similarity search under aggregated neighbor difference semantics} (S$^3$AND), which identifies subgraphs $g$ in a data graph $G$ that are similar to a given query graph $q$ by considering both keywords and graph structures (under new keyword/structural matching semantics). To efficiently tackle the S$^3$AND problem, we design two effective pruning methods, \textit{keyword set} and \textit{aggregated neighbor difference lower bound pruning}, which rule out false alarms of candidate vertices/subgraphs to reduce the S$^3$AND search space. Furthermore, we construct an effective indexing mechanism to facilitate our proposed efficient S$^3$AND query answering algorithm. Through extensive experiments, we demonstrate the effectiveness and efficiency of our S$^3$AND approach over both real and synthetic graphs under various parameter settings.

cs.DB↗

arXiv2024

Unsupervised Cross-Domain Regression for Fine-grained 3D Game Character Reconstruction

With the rise of the ``metaverse'' and the rapid development of games, it has become more and more critical to reconstruct characters in the virtual world faithfully. The immersive experience is one of the most central themes of the ``metaverse'', while the reducibility of the avatar is the crucial point. Meanwhile, the game is the carrier of the metaverse, in which players can freely edit the facial appearance of the game character. In this paper, we propose a simple but powerful cross-domain framework that can reconstruct fine-grained 3D game characters from single-view images in an end-to-end manner. Different from the previous methods, which do not resolve the cross-domain gap, we propose an effective regressor that can greatly reduce the discrepancy between the real-world domain and the game domain. To figure out the drawbacks of no ground truth, our unsupervised framework has accomplished the knowledge transfer of the target domain. Additionally, an innovative contrastive loss is proposed to solve the instance-wise disparity, which keeps the person-specific details of the reconstructed character. In contrast, an auxiliary 3D identity-aware extractor is activated to make the results of our model more impeccable. Then a large set of physically meaningful facial parameters is generated robustly and exquisitely. Experiments demonstrate that our method yields state-of-the-art performance in 3D game character reconstruction.

cs.CV↗

arXiv2024

Efficient Community Detection Over Streaming Bipartite Networks (Technical Report)

The streaming bipartite graph is widely used to model the dynamic relationship between two types of entities in various real-world applications, including movie recommendations, location-based services, and online shopping. Since it contains abundant information, discovering the dense subgraph with high structural cohesiveness (i.e., community detection) in the bipartite streaming graph is becoming a valuable problem. Inspired by this, in this paper, we study the structure of the community on the butterfly motif in the bipartite graph. We propose a novel problem, named Community Detection over Streaming Bipartite Network (CD-SBN), which aims to retrieve qualified communities with user-specific query keywords and high structural cohesiveness at snapshot and continuous scenarios. In particular, we formulate the user relationship score in the weighted bipartite network via the butterfly pattern and define a novel $(k,r,\sigma)$-bitruss as the community structure. To efficiently tackle the CD-SBN problem, we design effective pruning strategies to rule out false alarms of $(k,r,\sigma)$-bitruss and propose a hierarchical synopsis to facilitate the CD-SBN processing. We develop efficient algorithms to answer snapshot and continuous CD-SBN queries by traversing the synopsis and applying pruning strategies. With extensive experiments, we demonstrate the performance of our CD-SBN approach on real/synthetic streaming bipartite networks.

cs.SI↗

arXiv2024

GCLS$^2$: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics

Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structural semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we will consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS$^2$) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS$^2$ online training. It is worth noting that we prove a lower bound on the training of GCLS$^2$ from the perspective of the information theory, explaining why GCLS$^2$ can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS$^2$ outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.

cs.SI↗

arXiv2024

Depth Awakens: A Depth-perceptual Attention Fusion Network for RGB-D Camouflaged Object Detection

Camouflaged object detection (COD) presents a persistent challenge in accurately identifying objects that seamlessly blend into their surroundings. However, most existing COD models overlook the fact that visual systems operate within a genuine 3D environment. The scene depth inherent in a single 2D image provides rich spatial clues that can assist in the detection of camouflaged objects. Therefore, we propose a novel depth-perception attention fusion network that leverages the depth map as an auxiliary input to enhance the network's ability to perceive 3D information, which is typically challenging for the human eye to discern from 2D images. The network uses a trident-branch encoder to extract chromatic and depth information and their communications. Recognizing that certain regions of a depth map may not effectively highlight the camouflaged object, we introduce a depth-weighted cross-attention fusion module to dynamically adjust the fusion weights on depth and RGB feature maps. To keep the model simple without compromising effectiveness, we design a straightforward feature aggregation decoder that adaptively fuses the enhanced aggregated features. Experiments demonstrate the significant superiority of our proposed method over other states of the arts, which further validates the contribution of depth information in camouflaged object detection. The code will be available at https://github.com/xinran-liu00/DAF-Net.

cs.CV↗

arXiv2024

Reverse Influential Community Search Over Social Networks (Technical Report)

As an important fundamental task of numerous real-world applications such as social network analysis and online advertising/marketing, several prior works studied influential community search, which retrieves a community with high structural cohesiveness and maximum influences on other users in social networks. However, previous works usually considered the influences of the community on arbitrary users in social networks, rather than specific groups (e.g., customer groups, or senior communities). Inspired by this, we propose a novel Top-M Reverse Influential Community Search (TopM-RICS) problem, which obtains a seed community with the maximum influence on a user-specified target community, satisfying both structural and keyword constraints. To efficiently tackle the TopM-RICS problem, we design effective pruning strategies to filter out false alarms of candidate seed communities, and propose an effective index mechanism to facilitate the community retrieval. We also formulate and tackle a TopM-RICS variant, named Top-M Relaxed Reverse Influential Community Search} (TopM-R2ICS), which returns top-M subgraphs with relaxed structural constraints and having the maximum influence on a user-specified target community. Comprehensive experiments have been conducted to verify the efficiency and effectiveness of our TopM-RICS and TopM-R2ICS approaches on both real-world and synthetic social networks under various parameter settings.

cs.SI↗

arXiv2023

Speech-Driven 3D Face Animation with Composite and Regional Facial Movements

Speech-driven 3D face animation poses significant challenges due to the intricacy and variability inherent in human facial movements. This paper emphasizes the importance of considering both the composite and regional natures of facial movements in speech-driven 3D face animation. The composite nature pertains to how speech-independent factors globally modulate speech-driven facial movements along the temporal dimension. Meanwhile, the regional nature alludes to the notion that facial movements are not globally correlated but are actuated by local musculature along the spatial dimension. It is thus indispensable to incorporate both natures for engendering vivid animation. To address the composite nature, we introduce an adaptive modulation module that employs arbitrary facial movements to dynamically adjust speech-driven facial movements across frames on a global scale. To accommodate the regional nature, our approach ensures that each constituent of the facial features for every frame focuses on the local spatial movements of 3D faces. Moreover, we present a non-autoregressive backbone for translating audio to 3D facial movements, which maintains high-frequency nuances of facial movements and facilitates efficient inference. Comprehensive experiments and user studies demonstrate that our method surpasses contemporary state-of-the-art approaches both qualitatively and quantitatively.

cs.CV↗

arXiv2021

A Large Diameter Millimeter-Wave Low-Pass Filter Made of Alumina with Laser Ablated Anti-Reflection Coating

We fabricated a 302 mm diameter low-pass filter made of alumina that has an anti-reflection coating (ARC) made with laser-ablated sub-wavelength structures (SWS). The filter has been integrated into and is operating with the MUSTANG2 instrument, which is coupled to the Green Bank Telescope. The average transmittance of the filter in the MUSTANG2 operating band between 75 and 105 GHz is 98%. Reflective loss due to the ARC is 1%. The difference in transmission between the s- and p-polarization states is less than 1%. To within 1% accuracy we observe no variance in these results when transmission is measured in six independent filter spatial locations. The alumina filter replaced a prior MUSTANG2 Teflon filter. Data taken with the filter heat sunk to its nominal 40 K stage show performance consistent with expectations: a reduction of about 50% in filters-induced optical power load on the 300 mK stage, and in in-band optical loading on the detectors. It has taken less than 4 days to laser-ablate the SWS on both sides of the alumina disk. This is the first report of an alumina filter with SWS ARC deployed with an operating instrument, and the first demonstration of a large area fabrication of SWS with laser ablation.

astro-ph.IM↗

arXiv2021

ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach

Chinese character style transfer is a very challenging problem because of the complexity of the glyph shapes or underlying structures and large numbers of existed characters, when comparing with English letters. Moreover, the handwriting of calligraphy masters has a more irregular stroke and is difficult to obtain in real-world scenarios. Recently, several GAN-based methods have been proposed for font synthesis, but some of them require numerous reference data and the other part of them have cumbersome preprocessing steps to divide the character into different parts to be learned and transferred separately. In this paper, we propose a simple but powerful end-to-end Chinese calligraphy font generation framework ZiGAN, which does not require any manual operation or redundant preprocessing to generate fine-grained target-style characters with few-shot references. To be specific, a few paired samples from different character styles are leveraged to attain a fine-grained correlation between structures underlying different glyphs. To capture valuable style knowledge in target and strengthen the coarse-grained understanding of character content, we utilize multiple unpaired samples to align the feature distributions belonging to different character styles. By doing so, only a few target Chinese calligraphy characters are needed to generated expected style transferred characters. Experiments demonstrate that our method has a state-of-the-art generalization ability in few-shot Chinese character style transfer.

cs.CV↗

arXiv2021

Picosecond Laser Ablation of Millimeter-Wave Subwavelength Structures on Alumina and Sapphire

We use a 1030 nm laser with 7 ps pulse duration and average power up to 100 W to ablate pyramid-shape subwavelength structures (SWS) on alumina and sapphire. The SWS give an effective and cryogenically robust anti-reflection coating in the millimeter-wave band. We demonstrate average ablation rate of up to 34 mm$^3$/min and 20 mm$^3$/min for structure heights of 900 $\mu$m and 750 $\mu$m on alumina and sapphire, respectively. These rates are a factor of 34 and 9 higher than reported previously on similar structures. We propose a model that relates structure height to cumulative laser fluence. The model depends on the absorption length $\delta$, which is assumed to depend on peak fluence, and on the threshold fluence $\phi_{th}$. Using a best-fit procedure we find an average $\delta = 630$ nm and 650 nm, and $\phi_{th} = 2.0^{+0.5}_{-0.5}$ J/cm$^2$ and $2.3^{+0.1}_{-0.1}$ J/cm$^2$ for alumina and sapphire, respectively, for peak fluence values between 30 and 70 J/cm$^{2}$. With the best fit values, the model and data values for cumulative fluence agree to within 10%. Given inputs for $\delta$ and $\phi_{th}$ the model is used to predict average ablation rates as a function of SWS height and average laser power.

physics.app-ph↗