arXiv subjects

Ying Liu

Publications and source records attributed to Ying Liu.

At least 73 records · Page 4Linked to original sources

arXiv2026

MeDocVL: A Visual Language Model for Medical Document Understanding and Parsing

Medical document OCR is challenging due to complex layouts, domain-specific terminology, and noisy annotations, while requiring strict field-level exact matching. Existing OCR systems and general-purpose vision-language models often fail to reliably parse such documents. We propose MeDocVL, a post-trained vision-language model for query-driven medical document parsing. Our framework combines Training-driven Label Refinement to construct high-quality supervision from noisy annotations, with a Noise-aware Hybrid Post-training strategy that integrates reinforcement learning and supervised fine-tuning to achieve robust and precise extraction. Experiments on medical invoice benchmarks show that MeDocVL consistently outperforms conventional OCR systems and strong VLM baselines, achieving state-of-the-art performance under noisy supervision.

cs.CV↗

arXiv2026

ImmCOGNITO: Identity Obfuscation in Millimeter-Wave Radar-Based Gesture Recognition for IoT Environments

Millimeter-Wave (mmWave) radar enables camera-free gesture recognition for Internet of Things (IoT) interfaces, with robustness to lighting variations and partial occlusions. However, recent studies reveal that its data can inadvertently encode biometric signatures, raising critical privacy challenges for IoT applications. In particular, we demonstrate that mmWave radar point cloud data can leak identity-related information in the absence of explicit identity labels. To address this risk, we propose {ImmCOGNITO}, a graph-based autoencoder that transforms radar gesture point clouds to preserve gesture-relevant structure while suppressing identity cues. The encoder first constructs a directed graph for each sequence using Temporal Graph KNN. Edges are defined to capture inter-frame temporal dynamics. A message-passing neural network with multi-head self-attention then aggregates local and global spatio-temporal context, and the global max-pooled feature is concatenated with the original features. The decoder then reconstructs a minimally perturbed point cloud that retains gesture discriminative attributes while achieving de-identification. Training jointly optimizes reconstruction, gesture-preservation, and de-identification objectives. Evaluations on two public datasets, PantoRad and MHomeGes, show that ImmCOGNITO substantially reduces identification accuracy while maintaining high gesture recognition performance.

cs.HC↗

arXiv2026

Nonlinear quadrupole topological insulators

Higher-order topological insulators (HOTIs) represent a family of topological phases that go beyond the conventional bulkboundary correspondence. d-dimensional n-th order HOTIs maintain (d - n)-dimensional gapless boundary states (in particular, zero-dimensional corner states in the case of d = n = 2). HOTIs of the Wannier type cam be extended into the nonlinear regime. Another prominent class of HOTIs, in the form of multipole insulators, was investigated only in the linear regime, due to the challenge of simultaneously achieving both negative hopping and strong nonlinearity. Here we propose the concept of nonlinear quadrupole topological insulators (NLQTIs) and report their experimental realization in an electric circuit lattice. Quench-initiated dynamics gives rise to nonlinear topological corner states and topologically trivial corner solitons, in weakly and strongly nonlinear regimes, respectively. Furthermore, we reveal the formation of two distinct types of bulk solitons, one existing in the middle finite gap under the action of weak nonlinearity, and another one found in the semi-infinite gap under strong nonlinearity. This work realizes another member of the nonlinear HOTI family, suggesting directions for exploring novel solitons across a broad range of topological insulators.

physics.optics↗

arXiv2026

Modality-Tailored Age of Information for Multimodal Data in Edge Computing Systems

As Internet of Things (IoT) systems scale and device heterogeneity grows, multimodal data have become ubiquitous. Meanwhile, evaluating the freshness of multimodal data is essential, as stale updates would delay task execution, degrade decision accuracy, and undermine safety in latency-sensitive services. However, existing freshness metrics such as Age of Information (AoI) are not suitable for multimodal data, as they do not capture modality-specific characteristics. In this paper, we propose a metric, namely, Modality-Tailored Age of Information (MAoI), to provide a unified and decision-relevant evaluation of freshness for resource management and policy optimization for multimodal data. This metric integrates modality-specific semantic and temporal characteristics, reflecting both age evolution and content importance for multimodal data in multi-access edge computing (MEC) systems. Then, the closed-form expression of the average MAoI is derived, and an MAoI minimization problem is formulated, where sampling intervals and offloading decisions are optimized with practical energy constraints. To effectively solve this problem, a Joint Sampling Offloading Optimization (JSO) algorithm is proposed to jointly optimize the sampling intervals and offloading decisions. It is a block coordinate descent-based algorithm where an optimal sampling-interval subalgorithm is used to update the sampling intervals, and an interference-aware best-response offloading subalgorithm is proposed to update the offloading decisions alternately. Finally, a comprehensive simulation is performed, confirming that the MAoI metric effectively quantifies multimodal freshness compared to traditional AoI, and the JSO algorithm significantly minimizes the average MAoI compared to state-of-the-art algorithms.

cs.NI↗

arXiv2026

Destination Drone: A Comprehensive Analysis of Japanese Consumer Choice Behavior and Intentions for Drone Delivery Services

The potential for drone delivery services to transform logistics systems and consumer behavior has gained increasing attention. However, comprehensive empirical evidence on consumer delivery choice behavior within the context of transportation and urban air logistics remains limited, particularly in Japan. This study addresses this gap by examining Japanese consumers' preferences and behavioral intentions toward drone delivery services. Using a stated preference (SP) survey and discrete choice modeling approaches, including multinomial logit (MNL) and mixed logit (MMNL) models, the analysis evaluates how delivery cost, delivery time, drop-off location, product type, and social influence affect delivery mode choices across different demographic groups. The results indicate that although consumers express interest in drone delivery, perceived cost and concerns related to reliability continue to constrain adoption. Younger and male consumers exhibit higher preferences for drone delivery, while product type, particularly daily consumer goods and medical or healthcare items, plays a significant role in shaping preferences. Post-estimation willingness-to-pay and elasticity analyses further highlight consumers' sensitivity to delivery pricing and speed attributes. Overall, the findings provide actionable insights for logistics service providers and policymakers regarding pricing strategies, service targeting, and deployment approaches for integrating drone delivery into Japan's evolving logistics system.

econ.GN↗

arXiv2026

PointSLAM++: Robust Dense Neural Gaussian Point Cloud-based SLAM

Real-time 3D reconstruction is crucial for robotics and augmented reality, yet current simultaneous localization and mapping(SLAM) approaches often struggle to maintain structural consistency and robust pose estimation in the presence of depth noise. This work introduces PointSLAM++, a novel RGB-D SLAM system that leverages a hierarchically constrained neural Gaussian representation to preserve structural relationships while generating Gaussian primitives for scene mapping. It also employs progressive pose optimization to mitigate depth sensor noise, significantly enhancing localization accuracy. Furthermore, it utilizes a dynamic neural representation graph that adjusts the distribution of Gaussian nodes based on local geometric complexity, enabling the map to adapt to intricate scene details in real time. This combination yields high-precision 3D mapping and photorealistic scene rendering. Experimental results show PointSLAM++ outperforms existing 3DGS-based SLAM methods in reconstruction accuracy and rendering quality, demonstrating its advantages for large-scale AR and robotics.

cs.CV↗

arXiv2026

Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness

In this work, we study how to efficiently apply reinforcement learning (RL) for solving large-scale stochastic optimization problems by leveraging intervention models. The key of the proposed methodology is to better explore the solution space by simulating and composing the stochastic processes using pre-trained deep learning (DL) models. We demonstrate our approach on a challenging real-world application, the multi-sourcing multi-period inventory management problem in supply chain optimization. In particular, we employ deep RL models for learning and forecasting the stochastic supply chain processes under a range of assumptions. Moreover, we also introduce a constraint coordination mechanism, designed to forecast dual costs given the cross-products constraints in the inventory network. We highlight that instead of directly modeling the complex physical constraints into the RL optimization problem and solving the stochastic problem as a whole, our approach breaks down those supply chain processes into scalable and composable DL modules, leading to improved performance on large real-world datasets. We also outline open problems for future research to further investigate the efficacy of such models.

cs.LG↗

arXiv2026

Ramp Josephson junctions of Al/Ti/Sr2RuO4: Observation of single-domain quantum oscillations and the detection of chiral edge current

The determination of how the phase of the superconducting order parameter in a superconductor varies with the spatial direction, which can be done only through the Josephson-effect-based phase-sensitive measurements, is crucial for the establishment of the precise pairing symmetry of the superconductor. So far, such measurements have been done on high-Tc cuprate superconductors but only at a couple of directions for Sr2RuO4 because of the difficulty in preparing Josephson junctions between Sr2RuO4 and an s-wave superconductor with a chosen mutual orientation. Another long-standing issue in Sr2RuO4, which was shown previously to feature a spontaneously broken time-reversal symmetry by muon spin rotation and other measurements, is that the expected presence of chiral surface currents, domains, and domain walls is yet to be explicitly shown experimentally. To address these issues, we have long sought the preparation of high-quality Josephson junctions between Sr2RuO4 and a conventional s-wave with a controllable orientation relative to symmetry axes in Sr2RuO4. We report in this article the successful fabrication of ramp Josephson junctions of Al/Ti/Sr2RuO4 on thin single crystals of Sr2RuO4 obtained by mechanical exfoliation. These junctions were found to show high-quality quantum oscillations consistent with a single-domain Josephson coupling. The normal junction resistance was found to depend extremely sensitively on the supercurrent flowing in the Sr2RuO4 crystal on which the Josephson junction was made. This finding was used in the present work to provide an estimate of the size of the chiral surface current, which is shown to agree with its upper bound established previously.

cond-mat.supr-con↗

arXiv2025

SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration

High-fidelity and controllable 3D simulation is essential for addressing the long-tail data scarcity in Autonomous Driving (AD), yet existing methods struggle to simultaneously achieve photorealistic rendering and interactive traffic editing. Current approaches often falter in large-angle novel view synthesis and suffer from geometric or lighting artifacts during asset manipulation. To address these challenges, we propose SymDrive, a unified diffusion-based framework capable of joint high-quality rendering and scene editing. We introduce a Symmetric Auto-regressive Online Restoration paradigm, which constructs paired symmetric views to recover fine-grained details via a ground-truth-guided dual-view formulation and utilizes an auto-regressive strategy for consistent lateral view generation. Furthermore, we leverage this restoration capability to enable a training-free harmonization mechanism, treating vehicle insertion as context-aware inpainting to ensure seamless lighting and shadow consistency. Extensive experiments demonstrate that SymDrive achieves state-of-the-art performance in both novel-view enhancement and realistic 3D vehicle insertion.

cs.CV↗

arXiv2025

Fun-ASR Technical Report

In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep integration with large language models (LLMs). However, LLMs are prone to hallucination, which can significantly degrade user experience in real-world ASR applications. In this paper, we present Fun-ASR, a large-scale, LLM-based ASR system that synergistically combines massive data, large model capacity, LLM integration, and reinforcement learning to achieve state-of-the-art performance across diverse and complex speech recognition scenarios. Moreover, Fun-ASR is specifically optimized for practical deployment, with enhancements in streaming capability, noise robustness, code-switching, hotword customization, and satisfying other real-world application requirements. Experimental results show that while most LLM-based ASR systems achieve strong performance on open-source benchmarks, they often underperform on real industry evaluation sets. Thanks to production-oriented optimizations, Fun-ASR achieves state-of-the-art performance on real application datasets, demonstrating its effectiveness and robustness in practical settings. The code and models are accessible at https://github.com/FunAudioLLM/Fun-ASR .

cs.CL↗

arXiv2025

Super-Resolution Posterior Ocular Microvascular Imaging Using 3-D Ultrasound Localization Microscopy With a 32X32 Matrix Array

The purpose of this study is to enable in-vivo three-dimensional (3-D) ultrasound localization microscopy (ULM) of posterior ocular microvasculature using a 256-channel system and a 1024-element matrix array, and to overcome limitations of restricted transmit angles, sound speed mismatch caused by the crystalline lens and surrounding tissues, and the low signal-to-noise ratio (SNR) of microbubble signals. To address phase distortions from the crystalline lens, which has a higher speed of sound (SOS) than surrounding tissues, a region-dependent SOS beamforming approach was implemented to improve microbubble resolution. A 4-D non-local means filter was subsequently applied to suppress background noise and enhance microbubble contrast. The proposed method improved localization accuracy and image quality, achieving a spatial resolution of 63 um, while Fourier shell correlation (1/2-bit threshold) confirmed a global resolution of approximately 59 um. Higher mean normalized cross-correlation coefficients between the microbubbles and the system point-spread function, obtained with the proposed method (approximately 0.67), compared with those without the proposed method (approximately 0.60), indicate enhanced microbubble signal quality. Furthermore, the 3-D bi-directional vessel density and flow-velocity maps were reconstructed, capturing detailed choroidal vascular and hemodynamic patterns. These results demonstrate that region-dependent SOS beamforming combined with spatiotemporal denoising enables high-resolution posterior ocular ULM and provides a practical pathway toward quantitative 3-D assessment of retinal and choroidal microvasculature for potential clinical use.

physics.med-ph↗

arXiv2025

Anomalous shift in scattering from topological nodal-ring semimetals

An electron beam may experience an anomalous spatial shift during an interface scattering process. Here, we investigate this phenomenon for reflection from mirror-symmetry-protected nodal-ring semimetals, which are characterized by an integer topological charge $χ_h$. We show that the shift is generally enhanced by the presence of nodal rings, and the ring's geometry can be inferred from the profile of shift vectors in the interface momentum plane. Importantly, the anomalous shift encodes the topological information of the ring, where the circulation of the shift vector field $κ_s$ over a semicircle is governed by the topological charge, with a simple relationship: $κ_s=-2πχ_h$. Furthermore, we demonstrate that the shift and its circulation reflect distinct features of topological phase transitions of the charged rings. This study uncovers a novel physical signature of topological nodal rings and positions anomalous scattering shifts as a powerful tool for probing topological band structures.

cond-mat.mtrl-sci↗

arXiv2025

Indirect Tunneling Enabled Spontaneous Time-Reversal Symmetry Breaking and Josephson Diode Effect in TiN/Al$_2$O$_3$/Hf$_{0.8}$Zr$_{0.2}$O$_2$/Nb tunnel junctions

Josephson diode (JD) effect found in Josephson tunnel junctions (JTJs) has attracted a great deal of attention due to its importance for developing superconducting circuitry based quantum technologies. So far, the highly desirable electrical control of the JD effect has not been demonstrated in any JTJ prepared by techniques used in semiconductor industry. We report the fabrication of JTJs featuring a composite tunnel barrier of Al$_2$O$_3$ and Hf$_{\mathrm{0.8}}$Zr$_\mathrm{0.2}$O$_2$ prepared by complementary-metal-oxide-semiconductor (CMOS) compatible atomic layer deposition (ALD). These JTJs were found to show the JD effect in nominally zero magnetic fields with the nonreciprocity controllable using an electric training current, yielding a surprisingly large diode efficiency not achieved previously. The quasiparticle tunneling, through which the Josephson coupling in a JTJ is established, was found to show no nonreciprocity. We attribute these observations to the simultaneous presence of positive and negative Josephson couplings, with the latter originating from indirect tunneling. The resulted spontaneous time-reversal symmetry breaking and the double-minima washboard potential for the ensemble averaged phase difference in the resistively and capacitively shunted junction (RCSJ) model are shown to fully account for the experimentally observed JD effect.

cond-mat.supr-con↗

arXiv2025

Near-Infrared Light Emitting Metal Halides: Materials, Mechanisms, and Applications

Near-Infrared (NIR) light emitting metal halides are emerging as a new generation of optical materials owing to their appealing features, which include low-cost synthesis, solution processability and adjustable optical properties. NIR emitting perovskite-based light-emitting diodes (LEDs) have reached an external quantum efficiency (EQE) over 20% and a device stability of over 10,000 h. Such results have sparked an interest in exploring new NIR metal halide emitters. In this review, we summarize several different types of NIR-emitting metal halides, including lead/tin bromide/iodide perovskites, lanthanide ions doped/based metal halides, double perovskites, low dimensional hybrid and Bi3+/Sb3+/Cr3+ doped metal halides, and assess their recent advancements. The characteristics and mechanisms of narrow-band or broadband NIR luminescence in all these materials are discussed in detail. We also highlight the various applications of NIR-emitting metal halides and provide an outlook for the field.

cond-mat.mtrl-sci↗

arXiv2025

Light-Emitting Diodes based on Metal Halide Perovskite and Perovskite Related Nanocrystals

Light-emitting diodes (LEDs) based on halide perovskite nanocrystals have attracted extensive attention due to their considerable luminescence efficiency, wide color gamut, high color purity, and facile material synthesis. Since the first demonstration of LEDs based on MAPbBr3 nanocrystals were reported in 2014, the community has witnessed a rapid development in their performances. In this review, we provide a historical perspective of the development of LEDs based on halide perovskite nanocrystals and then present a comprehensive survey of current strategies to high-efficiency lead-based perovskite nanocrystals LEDs, including synthesis optimization, ion doping/alloying and shell coating. We then review the basic characteristics and emission mechanisms of lead-free perovskite and perovskite-related nanocrystals emitters in environmentally friendly LEDs, from the standpoint of different emission colors. Finally, we cover the progress in LED applications and provide an outlook of the opportunities and challenges for future developments in this field.

cond-mat.mtrl-sci↗

arXiv2025

Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series

Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the primary challenge lies in determining the appropriate values to fill in. While previous deep learning methods have proven effective for time series imputation, they often produce overconfident imputations, which poses a potentially overlooked risk to the reliability of the intelligent system. Diffusion methods are proficient in estimating probability distributions but face challenges under a high missing rate and are, moreover, computationally expensive due to the nature of the generative model framework. In this paper, we propose Quantile Sub-Ensembles, a novel method that estimates uncertainty with ensembles of quantile-regression-based task networks and incorporate Quantile Sub-Ensembles into a non-generative time series imputation method. Our method not only produces accurate and reliable imputations, but also remains computationally efficient due to its non-generative framework. We conduct extensive experiments on five real-world datasets, and the results demonstrates superior performance in both deterministic and probabilistic imputation compared to baselines across most experimental settings. The code is available at https://github.com/yingliu-coder/QSE.

cs.LG↗

arXiv2025

FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions

We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evaluation benchmark for vision language models intended to test reasoning from visual clues. We attach links to the benchmark, evaluation data, and other updates on this website: https://flageval-baai.github.io/LRM-Eval/

cs.CL↗

arXiv2025

Self-Supervised Learning for Glass Composition Screening

Glass composition screening is essential for advancing new glass materials, yet the inherent complexity of multicomponent systems presents significant challenges. Current supervised learning methods for this task rely heavily on large amounts of high-quality data and are prone to overfitting on noisy samples, which limits their generalization ability. In this work, we propose a novel self-supervised learning framework designed specifically for screening glass compositions within pre-defined glass transition temperature (Tg) ranges. We reformulate the screening task as a classification problem, aiming to predict whether the glass transition temperature of a given composition falls within a target interval. To improve the model's robustness to noise, we introduce an innovative data augmentation strategy grounded in asymptotic theory. Additionally, we present DeepGlassNet, a dedicated network architecture developed to capture and analyze the complex interactions among constituent elements in glass compositions. Experimental results demonstrate that DeepGlassNet achieves superior screening accuracy compared to traditional methods and exhibits strong adaptability to other composition-related screening tasks. This study not only provides an efficient methodology for designing multicomponent glasses but also establishes a foundation for applying self-supervised learning in material discovery. Code and data are available at: https://github.com/liubin06/DeepGlassNet

cs.CE↗