Searcharxiv⌕ Search

arXiv subjects

Ying Zhou

Publications and source records attributed to Ying Zhou.

At least 37 records · Page 2Linked to original sources
arXiv2026

Coupling model of metallic target ablation-plasma evolution-radiation under nanosecond laser irradiation

The interaction of nanosecond laser pulses with metallic materials involves multiple complex physical processes. It is challenging to construct a self-consistent model capable of uniformly describing all stages. This work establishes a multi-physics coupling model for pure iron, encompassing laser energy deposition, solid-liquid phase transition, gas-liquid interfacial kinetic transport, plasma expansion and ionization, and spectral radiation. The numerical solution adopts a partition method, utilizing an implicit compact difference scheme for the target and a Mac-Cormack explicit scheme for the plasma. The simulations elucidate the emergence of plasma shielding and its inhibitory effect on the evaporation process, thereby confirming that 81.6% of the early-stage ablation products are transported through a supersonic expansion mode. The model successfully captures the complete evolution of the plasma plume from a high-temperature, highly ionized state to a low-temperature, neutral atomic state. Based on this, spectral calculations demonstrate the dynamic evolution of radiative characteristics from an early stage featuring a strong continuum background dominated by ion lines to a later stage where the continuum attenuates, atomic lines become prominent, and self-absorption appears. The emergence of self-absorption proves the ability of the model to effectively capture the optical thickness effects arising from spatial inhomogeneity within the plasma. Through systematic comparison between experimentally measured spectra and calculated results from the PrismSPECT and NIST LIBS spectral programs, the model presented here achieves the highest comprehensive scores in quantitative evaluations of multiple channels. This validates the necessity and superiority of the full-chain self-consistent modeling approach, especially in describing plasma inhomogeneity and radiation transport.

physics.plasm-ph↗
arXiv2026

Nonthermal melting and density wave instability coupled to the lattice in La$_4$Ni$_3$O$_{10}$

The recent discovery of high-temperature superconductivity in pressurized nickelates has renewed interest in the broken-symmetry states of their ambient-pressure parent phases, where a density-wave (DW) order emerges and competes with superconductivity, but its microscopic origin remains unresolved. Using ultrafast optical spectroscopy, we track quasiparticle relaxation dynamics across the DW transition at $T_{\rm DW} \approx$ 136 K in trilayer nickelate {\LNO} single crystals, revealing the opening of an energy gap of $\sim$52 meV. Multiple coherent phonons, including $A_g$ modes near 3.88, 5.28, and 2.09 THz, display pronounced mode-selective anomalies across the transition, indicating that the DW is strongly coupled to lattice degrees of freedom and suggesting an important role of electron-phonon coupling. At higher excitation densities, the DW is nonthermally suppressed, producing a temperature-fluence phase diagram that parallels pressure-tuned behavior. These results establish the DW in {\LNO} as a lattice-entangled instability involving multiple phonon modes, and highlight ultrafast optical excitation as a nonequilibrium tuning parameter for suppressing density-wave order in nickelates.

cond-mat.str-el↗
arXiv2026

Ultrafast Fluence-Reversal Fingerprint of Fragile Kondo Hybridization in CePt$_2$In$_7$

The emergence of heavy quasiparticles in a Kondo lattice is usually viewed as the formation of a low-energy hybridization gap. Whether this gap represents a rigid electronic structure or a fragile many-body state that can be dynamically reconfigured remains a central question for heavy-fermion systems near magnetic order, quantum criticality, and unconventional superconductivity. Here we use femtosecond pump-probe reflectivity to interrogate this problem in the weakly hybridized Kondo-lattice compound CePt$_2$In$_7$. At low fluence, a slow quasiparticle relaxation channel emerges below $T^* \sim$ 40 K and follows a Rothwarf-Taylor bottleneck response with a low-energy recombination scale 2$Δ\approx$ 7.4 meV. Coherent optical phonons, independently identified by Raman spectroscopy, act as an internal lattice thermometer and rule out large quasi-equilibrium lattice heating as the origin of the nonlinear electronic response. The phonon-free electronic amplitude $A_{\rm elec}$ reveals a fluence-reversal fingerprint: with cooling from the hybridization-crossover regime, the response evolves from weak-linear behavior to Rothwarf-Taylor-like bottleneck suppression and finally to anomalous high-fluence enhancement at the lowest temperatures. This reversal cannot be accounted for by a rigid fixed-gap bottleneck alone and instead identifies an ultrafast optical signature of photoinduced redistribution of a fragile Kondo-hybridized electronic response.

cond-mat.str-el↗
arXiv2026

Causal Inference with a Hidden Treatment

In many causal inference settings, the treatment of interest is not directly observed; instead, one or more error-prone proxy measurements are available, creating a fundamental identification challenge. Building on identification methods for hidden treatments with proxies, we develop a general semiparametric framework for causal effect estimation that accommodates several common estimands. Hidden treatment models differ fundamentally from classical missing-data settings, where semiparametric theory relies on positivity requiring a nonzero probability of observing a complete case for each treatment value. Here this assumption fails by design because the true treatment is never observed, creating new challenges for semiparametric characterization and efficient estimation. To overcome these challenges, we develop a new semiparametric characterization for hidden treatment models by deriving a formal mapping between the orthogonal complement to the nuisance tangent space, which contains all influence functions for a causal functional in the oracle full-data model, and its counterpart in the observed hidden treatment model. This mapping gives closed-form observed-data influence functions and yields the semiparametric efficiency bound. It also leads to semiparametric efficient estimators with a new form of multiple robustness or mixed bias property, enabling inference with nonparametric nuisance estimators. A further challenge is that some nuisance functions depend on the hidden treatment, preventing direct use of standard nonparametric regression methods. We introduce an iterative estimation algorithm and establish its large-sample properties. Simulations demonstrate the finite-sample performance of the proposed estimators, and an application estimates the causal effect of Alzheimer's disease on hippocampal volume using data from the Alzheimer's Disease Neuroimaging Initiative.

stat.ME↗
arXiv2026

DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation

Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky. Offline reinforcement learning and, more recently, Transformer-based sequence modeling have shown promise for learning bidding policies from logged data, but their unimodal and purely parametric formulations often collapse multiple effective bidding strategies into suboptimal averaged actions and perform unreliably under sparse or long-tail traffic. To mitigate these limitations, we propose DRIVE (Distributional and Retrieval-Augmented Bidding with Value Evaluation), a unified Transformer-based framework that decouples candidate action generation from decision making for offline auto-bidding. DRIVE combines distributional action modeling, retrieval-augmented candidate generation from high-quality historical decisions, and value-based evaluation to select the most promising bid at inference time. Extensive experiments on AuctionNet and additional offline reinforcement learning benchmarks demonstrate that DRIVE consistently improves bidding performance and generalizes well across multiple Transformer-based methods.

cs.LG↗
arXiv2026

VICX: Generalizable Robot Manipulation via Video Generation and In-Context Operator Network

Generalizable robot manipulation requires not only task-level reasoning over unseen scenes, but also reliable grounding of visual plans into embodiment-specific execution. To bridge this gap, we propose VICX (Video generation and In-Context eXecution), a decoupled closed-loop manipulation framework. In VICX, a frozen video generation model produces vision-language-conditioned high-level visual plans, while a Video-to-Trajectory In-Context Operator Network (V2T-ICON) serves as the task-agnostic interface that grounds these plans into executable robot-state trajectories. To improve execution generalization, V2T-ICON operates on segmentation-extracted arm-only frame observations and uses retrieved image-state pairs as in-context prompts, allowing a robust and generalizable visual-to-state mapping at inference time without parameter updates. Experiments on Meta-World show that VICX supports cross-task generalization, closed-loop self-correction, and cross-embodiment transfer, demonstrating dual generalization across both task semantics and robot execution. The project webpage can be found here: https://scaling-group.github.io/vicx/.

cs.RO↗
arXiv2026

A Survey of 3D Reconstruction with Event Cameras

Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes. Compared to traditional frame-based cameras, event cameras produce sparse yet temporally dense data streams, enabling robust and accurate 3D reconstruction even under challenging conditions such as high-speed motion, low illumination, and extreme dynamic range scenarios. These capabilities offer substantial promise for transformative applications across various fields, including autonomous driving, robotics, aerial navigation, and immersive virtual reality. In this survey, we present the first comprehensive review exclusively dedicated to event-based 3D reconstruction. Existing approaches are systematically categorised based on input modality into stereo, monocular, and multimodal systems, and further classified according to reconstruction methodologies, including geometry-based techniques, deep learning approaches, and neural rendering techniques such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). Within each category, methods are chronologically organised to highlight the evolution of key concepts and advancements. Furthermore, we provide a detailed summary of publicly available datasets specifically suited to event-based reconstruction tasks. Finally, we discuss significant open challenges in dataset availability, standardised evaluation, effective representation, and dynamic scene reconstruction, outlining insightful directions for future research. This survey aims to serve as an essential reference and provides a clear and motivating roadmap toward advancing the state of the art in event-driven 3D reconstruction.

cs.CV↗
arXiv2026

Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data

Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly when outcomes are rare. While g-formula-based methods such as the iterative conditional expectation (ICE) estimator provide a principled framework for longitudinal causal inference, they become computationally expensive, especially when bootstrap-based variance estimation is required. In addition, outcome rarity at each time point induces severe class imbalance, leading to instability and convergence issues in logistic regression and related models. To address these challenges, we propose a principled subsampling and reweighting strategy for longitudinal survival data that can be applied to a range of existing causal effect estimators in this setting, including the ICE estimator. The proposed method substantially reduces computational burden while preserving consistency and improving estimation stability in rare-outcome settings. We evaluate the method through simulations and validate it using a large-scale EHR cohort study on social and behavioral determinants of health (SBDH) and suicide risk, demonstrating its effectiveness for modeling rare outcomes in longitudinal data.

stat.ME↗
arXiv2026

Electronic Nematicity Revealed by Polarized Ultrafast Spectroscopy in Bilayer La$_3$Ni$_2$O$_7$

We report a polarized ultrafast pump-probe study of the normal-state electronic dynamics in bilayer La$_3$Ni$_2$O$_7$ and trilayer La$_4$Ni$_3$O$_{10}$ single crystals at ambient pressure. While both nickelates exhibit density-wave (DW) transitions accompanied by the opening of a quasiparticle relaxation bottleneck, their electronic responses display strikingly different symmetry properties. La4Ni3O10 maintains an isotropic optical response across the entire temperature range. In contrast, La$_3$Ni$_2$O$_7$ exhibits a pronounced twofold ($C_2$) anisotropy in its lowtemperature electronic dynamics. This electronic nematicity, evident in both the relaxation dynamics and the polarization-dependent effective bottleneck energy scales, is strongly modified below 115 K, suggesting coupling or competition with a secondary DW-like instability reported by complementary probes. The presence of macroscopic electronic anisotropy in the bilayer system, and its absence in the trilayer system, suggests a possible relation between electronic nematic correlations and the superconducting normal state in a$_3$Ni$_2$O$_7$ that deserves further exploration.

cond-mat.str-el↗
arXiv2026

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse

Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for parameter-modifying methods. Existing approaches mitigate this issue through heuristic constraints on parameter updates, yet the mechanisms underlying such degradation remain insufficiently understood. In this work, we present a spectral analysis of sequential knowledge editing and show that a model's general abilities are closely associated with dominant singular directions of pretrained weight matrices. These directions are highly sensitive to perturbations and are progressively disrupted by repeated edits, closely tracking the collapse in both editing efficacy and general performance. Building on this insight, we propose REVIVE, a plug-and-play framework that stabilizes sequential editing by explicitly preserving the dominant singular subspace. REVIVE represents parameter updates in the spectral basis of the original weights and filters components that would interfere with the protected region. Extensive experiments across multiple models and benchmarks show that REVIVE consistently improves editing efficacy while substantially preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits.

cs.CL↗
arXiv2026

EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT

Chest computed tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric imaging increasingly exceed what can be addressed by scan-level prediction alone. Clinically useful AI for CT must not only recognize disease across the whole volume, but also localize abnormalities and provide interpretable visual evidence. Existing vision-language foundation models typically compress scans and reports into global image-text representations, limiting their ability to preserve spatial evidence and support clinically meaningful interpretation. Here we developed EXACT, an explainable anomaly-aware foundation model for three-dimensional chest CT that learns spatially resolved representations from paired clinical scans and radiology reports. EXACT was pre-trained on 25,692 CT-reports pairs using anatomy-aware weak supervision, jointly learning organ segmentation and multi-instance anomaly localization without manual voxel-level annotations. The resulting organ-specific anomaly-aware maps assign each voxel a disease-specific anomaly score confined to its corresponding anatomy, jointly encoding lesion extent and organ-level context. In retrospective multinational and multi-center evaluations, EXACT showed broad and consistent improvements across clinically relevant CT tasks, spanning multi-disease diagnosis, zero-shot anomaly localization, downstream adaptation, and visually grounded report generation, outperforming existing three-dimensional medical foundation models. By transforming routine clinical CT scans and free-text reports into explainable voxel-level representations, EXACT establishes a scalable paradigm for trustworthy volumetric medical AI.

cs.CV↗
arXiv2026

Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation

Person-Job Fit (PJF) is a critical component for online recruitment. Existing approaches face several challenges, particularly in handling low-quality job descriptions and similar candidate-job pairs, which impair model performance. To address these challenges, this paper proposes a large language model (LLM) based method with two novel techniques: (1) LLM-based data augmentation, which polishes and rewrites low-quality job descriptions by leveraging chain-of-thought (COT) prompts, and (2) category-aware Mixture of Experts (MoE) that assists in identifying similar candidate-job pairs. This MoE module incorporates category embeddings to dynamically assign weights to the experts and learns more distinguishable patterns for similar candidate-job pairs. We perform offline evaluations and online A/B tests on our recruitment platform. Our method relatively surpasses existing methods by 2.40% in AUC and 7.46% in GAUC, and boosts click-through conversion rate (CTCVR) by 19.4% in online tests, saving millions of CNY in external headhunting expenses.

cs.AI↗
arXiv2026

Moving Light Adaptive Colonoscopy Reconstruction via Illumination-Attenuation-Aware 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) enables real-time view synthesis in colonoscopy but assumes static illumination, making it incompatible with the strong photometric variations caused by the moving light source and camera. This mismatch leads existing methods to compensate for illumination attenuation with structure-violating Gaussians, degrading geometric fidelity. Prior work considers only distance-based attenuation and overlooks the physical characteristics of colonscopic lighting. In this paper, we propose ColIAGS, an improved 3DGS framework for colonoscopy. To mimic realistic appearance under varying illumination, we introduce a lighting model with two types of illumination attenuation factors. To satisfy this lighting model's approximation and effectively integrate it into the 3DGS framework, we design Improved Geometry Modeling to strengthen geometry details and Improved Appearance Modeling to implicitly optimize illumination attenuation solutions. Experimental results on standard benchmarks demonstrate that ColIAGS supports both high-quality novel-view synthesis and accurate geometry reconstruction, outperforming state-of-the-art methods in rendering fidelity and Depth MSE. Our code is available at https://github.com/haowang020110/ColIAGS.

cs.CV↗
arXiv2026

DOne: Decoupling Structure and Rendering for High-Fidelity Design-to-Code Generation

While Vision Language Models (VLMs) have shown promise in Design-to-Code generation, they suffer from a "holistic bottleneck-failing to reconcile high-level structural hierarchy with fine-grained visual details, often resulting in layout distortions or generic placeholders. To bridge this gap, we propose DOne, an end-to-end framework that decouples structure understanding from element rendering. DOne introduces (1) a learned layout segmentation module to decompose complex designs, avoiding the limitations of heuristic cropping; (2) a specialized hybrid element retriever to handle the extreme aspect ratios and densities of UI components; and (3) a schema-guided generation paradigm that bridges layout and code. To rigorously assess performance, we introduce HiFi2Code, a benchmark featuring significantly higher layout complexity than existing datasets. Extensive evaluations on the HiFi2Code demonstrate that DOne outperforms exiting methods in both high-level visual similarity (e.g., over 10% in GPT Score) and fine-grained element alignment. Human evaluations confirm a 3 times productivity gain with higher visual fidelity.

cs.CV↗
arXiv2026

Pressure-Induced Structural and Magnetic Evolution in Layered Antiferromagnet YbMn$_2$Sb$_2$

Electronic states under pressure exhibit unconventional spin and charge dynamics that provide a powerful route to uncover exotic phases in quantum materials. Here, we present the structural, magnetic, and electronic evolution of YbMn$_2$Sb$_2$ under pressure. Single-crystal X-ray diffraction reveals a pressure-induced structural transition from the space group trigonal $P\bar{3}m1$ to the monoclinic $P2_1$/$m$ phase near 3.5 GPa, which remains stable up to 10 GPa. Magnetization measurements display an anomalously weak net magnetic moment and the absence of Curie-Weiss behavior up to 400 K, suggesting the formation of short-range Mn moment pairs that cancel macroscopically and subsequently evolve into long-range order upon cooling. Temperature-dependent resistivity shows semiconducting behavior with a transition at ~119 K at ambient pressure, while pressure induces a dramatic suppression of resistance and the emergence of metallic-like temperature dependence, stabilized beyond 5 GPa. This pressure-driven semiconductor-metal transition is consistent with our density functional theory calculations, confirming the closing of the band gap under compression. Neutron diffraction under pressure identifies an incommensurate magnetic structure with antiparallel correlations between paired spins. Together, these results demonstrate how pressure-driven structural tuning and competing exchange interactions stabilize unconventional magnetic states in this low-dimensional magnetic semiconductor.

cond-mat.mtrl-sci↗
arXiv2026

QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image

Recent methods for pathology report generation from whole-slide image (WSI) are capable of producing slide-level diagnostic descriptions but fail to ground fine-grained statements in localized visual evidence. Furthermore, they lack control over which diagnostic details to include and how to verify them. Inspired by emerging agentic analysis paradigms and the diagnostic workflow of pathologists,who selectively examine multiple fields of view, we propose QCAgent, an agentic framework for quality-controllable WSI report generation. The core innovations of this framework are as follows: (i) it incorporates a customized critique mechanism guided by a user-defined checklist specifying required diagnostic details and constraints; (ii) it re-identifies informative regions in the WSI based on the critique feedback and text-patch semantic retrieval, a process that iteratively enriches and reconciles the report. Experiments demonstrate that by making report requirements explicitly prompt-defined, constraint-aware, and verifiable through evidence-grounded refinement, QCAgent enables controllable generation of clinically meaningful and high-coverage pathology reports from WSI.

cs.CV↗
arXiv2026

The distinction of time-reversal-like degeneracy by electronic transport in a new compound

We report the discovery of a new compound, Ce$_3$MgBi$_5$, and reveal the hidden time-reversal-like degenerate states within it. Ce$_3$MgBi$_5$ is an antiferromagnet with the distorted kagome lattice of Ce atoms, in which several fractional magnetization plateaus emerge with the increase of magnetic field. At the 1/2 magnetization plateau, obvious hysteresis has been observed in the magnetoresistance and Hall resistivity during the rise and fall of the magnetic field. However, hysteresis vanishes in the corresponding measurements of magnetization, indicating the existence of degenerate states with the same net magnetization but different electronic transport properties. The degenerate states can be connected by the time-reversal-like operation. In addition, by comparing with HoAgGe, it is suggested that the special crystal structure in Ce$_3$MgBi$_5$ may have a shielding effect on the time-reversal-like operation, thereby affecting the distinction of degenerate states. Our work establishes Ce$_3$MgBi$_5$ as an example of utilizing electronic transport properties to identify and distinguish hidden symmetries in frustrated magnetic systems.

cond-mat.str-el↗
arXiv2026

Optimal Harvesting in Stream Networks: Maximizing Biomass and Yield

In this study, we develop a metapopulation model framework to identify optimal harvesting strategies for a population in a stream network. We consider two distinct optimization objectives: maximization of total biomass and maximization of total yield, under the constraint of a fixed total harvesting effort. We examine in detail the special case of a two-patch network and fully characterize the optimal strategies for each objective. We show that when the population growth rate exceeds a critical threshold, a single harvesting strategy can simultaneously maximize both objectives. For general $n$-patch networks with homogeneous growth rates across patches, we focus on the regime of large growth rates and demonstrate that the optimal harvesting strategy selects patches according to their intraspecific competition rates and an effective net flow metric determined by network connectivity parameters.

math.DS↗