arXiv subjects

Ying Wu

Publications and source records attributed to Ying Wu.

At least 19 recordsLinked to original sources

arXiv2026

3D Topologically Polarized Elastic Metamaterials Enable Asymmetric Energy Isolation at Low Frequencies

Topologically polarized elasticity has been extensively studied in lower-dimensions, yet its three-dimensional (3D) counterpart remains largely unexplored. Here, we demonstrate omnidirectional topological elasticity in 3D structures that incorporate bending stiffness, which elevates zero-frequency topological mechanical states into finite-frequency phononic modes. These modes are localized at a single boundary, creating a pronounced stiffness contrast in both static and finite-frequency dynamic regimes. This three-dimensional structure exhibits highly polarized mechanical behavior across all spatial dimensions, establishing omnidirectional asymmetric topological elasticity. Experimental and numerical results confirm robust, asymmetric energy isolation, arising from the interplay between bulk topological polarization and boundary-localized surface modes. Our findings establish a paradigm for 3D metamaterials, with promising applications in vibration shielding and directional wave manipulation.

cond-mat.soft↗

arXiv2026

Purcell effect and quantum Zeno effect suppressed self-discharging of quantum battery

Quantum batteries (QB), as an energy storage and transfer device, not only show obvious advantages compared to classical electrochemical batteries, but also have important applications in quantum information. Self-discharging is a central obstacle to storing useful work in open QB, especially when the charger itself provides an unavoidable loss channel. Here we show that such charger-induced loss can be converted into a protection mechanism by combining Purcell effect with quantum Zeno effect. We reveal that the virtual photon process and the Purcell effect can induce the strong coupling regime to the quantum Zeno regime, in which the stronger the dissipation of the charger, the weaker the self-discharging effect of the QB. As a result, the dissipation caused by the charger to the QB can be suppressed four orders of magnitude in our scheme. Meanwhile, the quantum Zeno effect induced by the Purcell effect can also avoid the energy backflow between the QB and the charger. Owing to the significantly suppressed dissipation, the stored energy of QB can be charged to a nearly full state and the stored energy is almost converted into extractable work, which greatly improves the energy conversion efficiency.

quant-ph↗

arXiv2026

Tripartite Interactions Induced Strongly Correlated Quantum Emissions

Efficient generation of multiquanta emission is crucial for quantum information processing but remains challenging due to its typical reliance on higher-order quantum processes. Here, we theoretically demonstrate strongly correlated photon-phonon emission enabled by direct tripartite interaction. This interaction facilitates the formation of high-order multiquanta states without more intermediate state transitions, thereby avoiding the suppressed transition rates associated with multiple sequential processes and substantially improving resonant transitions. As a result, high-efficiency strongly correlated even-quanta emission (e.g., two photons and two phonons) can be achieved in the presences of dissipation. Beyond that, we show that introducing two-photon dissipation enables strongly correlated odd-quanta emission (e.g., two photons and one phonon) in the tripartite interaction system by parity-protected suppression of single-photon loss and reconstruction of higher-order multiquanta processes. Our work extends multiquanta emission into the tripartite coupling regime and holds promising potential for applications in hybrid quantum networks.

quant-ph↗

arXiv2026

PetroBench: A Benchmark for Large Language Models in Petroleum Engineering

Large Language Models are increasingly applied in the petroleum industry, highlighting the need for a domain-specific evaluation framework. This study develops a benchmark for LLMs in petroleum engineering, including a three-stage process of data preprocessing, quality filtering, and multi-model validation. Using expert review, a standardized question bank with strong domain relevance and discriminative capability was constructed. The benchmark covers production, reservoir, and drilling engineering, with 1,200 questions across multiple-choice, true or false, term definition, and short-answer formats. Eight mainstream LLMs were evaluated under a unified API environment. Results show that models performed better on subjective than objective questions, indicating weaknesses in factual knowledge discrimination. The highest accuracies for multiple-choice and true or false questions were 65.3% and 74.3%, respectively. Gemini-3-Pro, Kimi-K2.5, and Claude-Opus-4.6-Thinking achieved the best overall scores of 72%-74%. Models performed best in production engineering and weakest in reservoir engineering. Chinese models showed advantages in multiple-choice questions, while international models performed slightly better in short-answer questions. The benchmark provides a reproducible and practical reference for evaluating and deploying LLMs in petroleum engineering.

cs.AI↗

arXiv2026

Feature Perturbation Pool-based Fusion Network for Unified Multi-Class Industrial Defect Detection

Multi-class defect detection constitutes a critical yet challenging task in industrial quality inspection, where existing approaches typically suffer from two fundamental limitations: (i) the necessity of training separate models for each defect category, resulting in substantial computational and memory overhead, and (ii) degraded robustness caused by inter-class feature perturbation when heterogeneous defect categories are jointly modeled. In this paper, we present FPFNet, a Feature Perturbation Pool-based Fusion Network that synergistically integrates a stochastic feature perturbation pool with a multi-layer feature fusion strategy to address these challenges within a unified detection framework. The feature perturbation pool enriches the training distribution by randomly injecting diverse noise patterns -- including Gaussian noise, F-Noise, and F-Drop -- into the extracted feature representations, thereby strengthening the model's robustness against domain shifts and unseen defect morphologies. Concurrently, the multi-layer feature fusion module aggregates hierarchical feature representations from both the encoder and decoder through residual connections and normalization, enabling the network to capture complex cross-scale relationships while preserving fine-grained spatial details essential for precise defect localization. Built upon the UniAD architecture~\cite{you2022unified}, our method achieves state-of-the-art performance on two widely adopted benchmarks: 97.17\% image-level AUROC and 96.93\% pixel-level AUROC on MVTec-AD, and 91.08\% image-level AUROC and 99.08\% pixel-level AUROC on VisA, surpassing existing methods by notable margins while introducing no additional learnable parameters or computational complexity.

cs.CV↗

arXiv2026

Tunable Asymmetric Acoustic Absorption in Ventilated Metasurfaces

Asymmetric sound absorption is essential for advanced acoustic manipulation. However, current frequency modulation and broadbanding highly depend on geometric reconfiguration, leading to inevitable structural complexity that impedes their practical applications. Here, we propose a tunable, highly efficient, asymmetric ventilated acoustic system comprising two heterogeneous resonators. Specifically, it couples a highly dissipative space-coiling resonator (SCR) as a dark mode for energy consumption, alongside a weakly damped Helmholtz resonator as a bright mode acting as a reflective soft boundary. Theoretical and numerical analyses reveal strong asymmetry within the deep-subwavelength region (with a resonator size of approximately \lambda/9.4), achieving 99% absorption for left-incident waves and 98% reflection for right-incident ones. Furthermore, the SCR introduces an interesting degree of freedom for acoustic tuning. Simply rotating the resonator induces a 92% absorption drop (~11 dB attenuation), functioning as an "Acoustic Switch". Moreover, this rotation significantly shifts the operating band. By parallel-coupling multi-angle isomorphic resonators, we achieve efficient broadband absorption (>0.8) from 325 to 375 Hz, offering an attractive paradigm for tunable acoustic metasurfaces and ventilated absorbers.

physics.app-ph↗

arXiv2026

Frequency-resolved N-photon correlations in the ultra-strong coupling regime

Frequency-resolved photon emission is central to applications from quantum information encoding to high-resolution spectroscopy, and then studying their correlations is therefore essential for revealing the underlying emission pathways and multiphoton statistics. Here, we investigate frequency-resolved N-photon correlations in an ultrastrongly coupled cavity QED system where a qubit interacts with a single-mode cavity. Owing to counter-rotating interactions, the eigenstates and energy spectrum are strongly modified, giving rise to rich spectral and statistical properties in the emitted frequency-resolved photons. Through frequency-selective detection, we reveal pronounced multiphoton antibunching, as well as multiphoton bunching originating from cascade transitions among dressed eigenstates. In particular, we show that parity symmetry plays a decisive role in shaping these correlations. The symmetry-breaking opens additional transition channels and dramatically enhances the generation of correlated photon pairs and even photon triplets of different frequencies. Our work extends frequency-resolved correlations to the ultra-strong coupling regime and demonstrates their potential as a sensitive probe of symmetry in light-matter interaction systems.

quant-ph↗

arXiv2026

SN 2024abfl: A Low-Luminosity Type IIP Supernova in NGC 2146 from a Low-Mass Red Supergiant Progenitor

Type IIP supernovae (SNe IIP) exhibit a significant diversity in their explosion properties, yet the physical mechanisms driving this diversity remain unknown. In this work, we present photometric and spectroscopic observations of SN 2024abfl, a SN IIP in NGC 2146 with a directly detected red supergiant (RSG) progenitor. We find it has a low plateau luminosity ($M_V \sim -15$ mag) and a relatively long plateau length ($\sim 126.5$ days). By fitting a semi-analytical model, we estimated a $^{56}$Ni mass of $\sim 0.009 M_\odot$, an initial kinetic energy of $\sim 0.42$ foe, an initial thermal energy of $\sim 0.03$ foe and an ejecta mass of $\sim 8.3 M_\odot$. The spectral evolution of SN 2024abfl is similar to those of other SNe IIP, except for much lower ejecta velocities at similar epochs. At later epochs, we find a relatively high-velocity H$\alpha$ absorption feature at $\sim -4000$ km s$^{-1}$, possibly due to a fast-moving plume of matter in the inner ejecta, and two emission features at $\pm 2000$ km s$^{-1}$, possibly caused by CSM interaction. We estimate the progenitor mass to be $\le 15 M_\odot$ based on nebular spectra. We conclude that SN 2024abfl is a low-luminosity SN IIP originating from a low-mass RSG progenitor.

astro-ph.HE↗

arXiv2025

Multiply-robust Estimator of Cumulative Incidence Function Difference for Right-Censored Competing Risks Data

In causal inference, estimating the average treatment effect is a central objective, and in the context of competing risks data, this effect can be quantified by the cause-specific cumulative incidence function (CIF) difference. While doubly robust estimators give a more robust way to estimate the causal effect from the observational study, they remain inconsistent if both models are misspecified. To improve the robustness, we develop a multiply robust estimator for the difference in cause-specific CIFs using right-censored competing risks data. The proposed framework integrates the pseudo-value approach, which transforms the censored, time-dependent CIF into a complete-data outcome, with the multiply robust estimation framework. By specifying multiple candidate models for both the propensity score and the outcome regression, the resulting estimator is consistent and asymptotically unbiased, provided that at least one of the multiple propensity score or outcome regression models is correctly specified. Simulation studies show our multiply robust estimator remains virtually unbiased and maintains nominal coverage rates under various model misspecification scenarios and a wide range of choices for the censoring rate. Finally, the proposed multiply robust model is illustrated using the Right Heart Catheterization dataset.

stat.ME↗

arXiv2025

Broadband Low-Frequency Near-Perfect Sound Absorber via Coupled Metasurfaces

We propose a simple yet effective method for low-frequency broadband acoustic absorption. The absorber consists of two concentric space-coiling resonators with distinct resonance frequencies, with the inner resonator characterized by a low-quality factor (Q) and the outer resonator by a high Q factor. The coupling between the two resonators enables efficient broadband absorption within a deep-subwavelength range exceeding 15 times the structural thickness. Numerical simulations, theoretical analysis, and experimental measurements demonstrate that highly efficient (greater than 80 percent) low-frequency broadband absorption is achieved in the range of 198-315 Hz, as well as a 58 percent fractional bandwidth spanning 183-334 Hz. Furthermore, with the outer dimension fixed, adjusting the parameters of the internal resonators enables flexible tuning of the absorption band across a broad frequency range. This work presents a powerful design methodology that eliminates the need for traditional complex spatially arranged multi-resonator assemblies. By employing a single class of resonant units, thin and efficient broadband absorbers can be achieved, offering various application prospects in the field of low-frequency sound absorption.

physics.optics↗

arXiv2025

Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference

This paper addresses the Kidnapped Robot Problem (KRP), a core localization challenge of relocalizing a robot in a known map without prior pose estimate upon localization loss or at SLAM initialization. For this purpose, a passive 2-D global relocalization framework is proposed. It estimates the global pose efficiently and reliably from a single LiDAR scan and an occupancy grid map while the robot remains stationary, thereby enhancing the long-term autonomy of mobile robots. The proposed framework casts global relocalization as a non-convex problem and solves it via the multi-hypothesis scheme with batched multi-stage inference and early termination, balancing completeness and efficiency. The Rapidly-exploring Random Tree (RRT), under traversability constraints, asymptotically covers the reachable space to generate sparse, uniformly distributed feasible positional hypotheses, fundamentally reducing the sampling space. The hypotheses are preliminarily ordered by the proposed Scan Mean Absolute Difference (SMAD), a coarse beam-error level metric that facilitates the early termination by prioritizing high-likelihood candidates. The SMAD computation is optimized for limited scan measurements. The Translation-Affinity Scan-to-Map Alignment Metric (TAM) is proposed for reliable orientation selection at hypothesized positions and accurate final global pose evaluation to mitigate degradation in conventional likelihood-field metrics under translational uncertainty induced by sparse hypotheses, as well as non-panoramic LiDAR scan and environmental changes. Real-world experiments on a resource-constrained mobile robot with non-panoramic LiDAR scans show that the proposed framework achieves competitive performance in success rate, robustness under measurement uncertainty, and computational efficiency.

cs.RO↗

arXiv2025

AutoScape: Geometry-Consistent Long-Horizon Scene Generation

This paper proposes AutoScape, a long-horizon driving scene generation framework. At its core is a novel RGB-D diffusion model that iteratively generates sparse, geometrically consistent keyframes, serving as reliable anchors for the scene's appearance and geometry. To maintain long-range geometric consistency, the model 1) jointly handles image and depth in a shared latent space, 2) explicitly conditions on the existing scene geometry (i.e., rendered point clouds) from previously generated keyframes, and 3) steers the sampling process with a warp-consistent guidance. Given high-quality RGB-D keyframes, a video diffusion model then interpolates between them to produce dense and coherent video frames. AutoScape generates realistic and geometrically consistent driving videos of over 20 seconds, improving the long-horizon FID and FVD scores over the prior state-of-the-art by 48.6\% and 43.0\%, respectively.

cs.CV↗

arXiv2025

Evolutionary Computation as Natural Generative AI

Generative AI (GenAI) has achieved remarkable success across a range of domains, but its capabilities remain constrained to statistical models of finite training sets and learning based on local gradient signals. This often results in artifacts that are more derivative than genuinely generative. In contrast, Evolutionary Computation (EC) offers a search-driven pathway to greater diversity and creativity, expanding generative capabilities by exploring uncharted solution spaces beyond the limits of available data. This work establishes a fundamental connection between EC and GenAI, redefining EC as Natural Generative AI (NatGenAI) -- a generative paradigm governed by exploratory search under natural selection. We demonstrate that classical EC with parent-centric operators mirrors conventional GenAI, while disruptive operators enable structured evolutionary leaps, often within just a few generations, to generate out-of-distribution artifacts. Moreover, the methods of evolutionary multitasking provide an unparalleled means of integrating disruptive EC (with cross-domain recombination of evolved features) and moderated selection mechanisms (allowing novel solutions to survive), thereby fostering sustained innovation. By reframing EC as NatGenAI, we emphasize structured disruption and selection pressure moderation as essential drivers of creativity. This perspective extends the generative paradigm beyond conventional boundaries and positions EC as crucial to advancing exploratory design, innovation, scientific discovery, and open-ended generation in the GenAI era.

cs.NE↗

arXiv2025

The Stellar Abundances and Galactic Evolution Survey (SAGES). IV. Surface Gravity Estimation and Giant-Dwarf Separation with the DDO51 Filter

Reliable estimation of stellar surface gravity (log $g$) for a large sample is crucial for evaluating stellar evolution models and understanding galactic structure; However, it is not easy to accomplish due to the difficulty in gathering a large spectroscopic data set. Photometric sky survey using a specific filter, on the other hand, can play a substantial role in the assessment of log $g$. The Stellar Abundances and Galactic Evolution Survey (SAGES) utilizes eight filters to provide accurate stellar parameters for $\sim10^{7}$ stars, with its DDO51 intermediate-band filter specifically designed for robust log $g$ determination. In this work, the observed SAGES $u_{\rm SC}$ and $v_{\rm SAGES}$ photometry, the synthetic photometry in $g$, $r$, $i$, and DDO51 bands derived from \textit{Gaia} XP spectra are employed to investigate the importance of the DDO51 filter in the determination of log $g$. We applied machine-learning-based extinction correction and employed XGBoost models, trained on stellar parameters from LAMOST, to predict log $g$ using photometric data. By comparing model predicted log $g$ with LAMOST values, we find that including DDO51 filter improve the accuracies of log $g$ estimates by 21.0\% (from 0.224\,dex to 0.177\,dex) overall, and by 26.5\% (from 0.302\,dex to 0.222\,dex ) for GK-type stars, as compared to those obtained without DDO51. The DDO51 filter is also validated to be particularly effective for metal-poor stars ([Fe/H]$<$-1.0), where it significantly mitigates systematic biases. Our findings highlight the diagnostic power of the SAGES DDO51 filter, providing enhanced stellar characterization vital for future in-depth studies of the Milky Way.

astro-ph.SR↗

arXiv2025

Cavity QED based on strongly localized modes: exponentially enhancing single-atom cooperativity

Large single-atom cooperativity in quantum systems is important for quantum information processing. Here, we propose to exponentially enhance the single-atom cooperativity parameter by exploiting the strongly localized effect of modes in cavity quantum electrodynamics (QED) systems. By increasing the wing width of a cavity with special geometry symmetry, the interference property allows us to exponentially improves the quality factor Q without altering the mode volume V for cavities supporting subwavelength light modes. This effectively overcomes the trade-off between Q and V in conventional subwavelength Fabry-Perot cavities. Consequently, we demonstrate the occurrence of ultra-long vacuum Rabi oscillations and the generation of strong photon blockade by enhancing the single-atom cooperativity parameter. This work offers a promising approach for advancing coherent manipulation and holds significant potential for applications in establishing longer-distance quantum communication networks, enhancing the precision and stability of quantum sensors, and improving the efficiency of quantum algorithms.

quant-ph↗

arXiv2025

Overlooked weak structural connections support human cognition under nonlinear connectome scaling

Human cognition depends on large scale communication constrained by white matter architecture. Although weak connections are abundant in mammalian connectomes, they have long been treated as noise and downweighted because of tractography uncertainty in the human brain, and their relevance to human cognition and large scale functional organization remains unresolved. Across multiple datasets and tractography pipelines, we show that, when tractography derived connectivity weights are interpreted through a nonlinear weighting framework, weak connections make measurable contributions to cognitive prediction, functional connectivity simulation, and structure-function coupling. These effects are selective: nonlinear weighting improves the prediction of general cognitive ability and memory more than that of crystallized intelligence or processing speed, consistent with the notion that weak connections preferentially expand the modal repertoire of brain networks to enhance both large scale integration and fine grained segregation, thereby supporting the functional balance essential for diverse cognitive abilities. Importantly, these effects are replicated in a reliability aware connectome generated by integrating two post tractography filtering methods, in which preserving weak links consistently outperforms conventional thresholding strategies. Finally, we show that weak connections contain functionally informative subsets organized along systems level and transcriptomic gradients. In particular, a specific class of weak connections, predominantly linking visual and motor systems with limbic regions and characterized by negative gene coexpression, exerts a disproportionately large influence on brain function.

q-bio.NC↗

arXiv2025

A Hybrid Prior Bayesian Method for Combining Domestic Real-World Data and Overseas Data in Global Drug Development

Background Hybrid clinical trial design integrates randomized controlled trials (RCTs) with real-world data (RWD) to enhance efficiency through dynamic incorporation of external data. Existing methods like the Meta-Analytic Predictive Prior (MAP) inadequately control data heterogeneity, adjust baseline discrepancies, or optimize dynamic borrowing proportions, introducing bias and limiting applications in bridging trials and multi-regional clinical trials (MRCTs). Objective This study proposes a novel hybrid Bayesian framework (EQPS-rMAP) to address heterogeneity and bias in multi-source data integration, validated through simulations and retrospective case analyses of risankizumab's efficacy in moderate-to-severe plaque psoriasis. Design and Methods EQPS-rMAP eliminates baseline covariate discrepancies via propensity score stratification, constructs stratum-specific MAP priors to dynamically adjust external data weights, and introduces equivalence probability weights to quantify data conflict risks. Performance was evaluated across six simulated scenarios (heterogeneity differences, baseline shifts) and real-world case analyses, comparing it with traditional methods (MAP, PSMAP, EBMAP) on estimation bias, type I error control, and sample size requirements. Results Simulations show EQPS-rMAP maintains estimation robustness under significant heterogeneity while reducing sample size demands and enhancing trial efficiency. Case analyses confirm superior external bias control and accuracy compared to conventional approaches. Conclusion and Significance EQPS-rMAP provides empirical evidence for hybrid clinical designs. By resolving baseline-heterogeneity conflicts through adaptive mechanisms, it enables reliable integration of external and real-world data in bridging trials, MRCTs, and post-marketing studies, broadening applicability without compromising rigor.

stat.ME↗

arXiv2025

The Mini-SiTian Array: Evaluation Camera System

The Mini-SiTian project, which is the pathfinder for the SiTian project, utilizes three 30 cm telescopes equipped with commercial CMOS cameras (ZWO ASI6200MM Pro) to simulate large-area time-domain survey. Due to the avoidance of the traditional mechanical shutter, the CMOS camera is favorable in time-domain survey projects. In the future, the SiTian telescope array will employ a two-by-two scientific-grade mosaic CMOS camera to survey a 10,000-degree square area every 30 minutes. Therefore, the performance of CMOS directly determines the detection capability of SiTian telescopes for transient sources, and a comprehensive understanding of the performance of CMOS cameras is crucial. In this research, laboratory testing was conducted to thoroughly evaluate three cameras by assessing several critical parameters, including bias stability, dark current, pixel anomalies, linearity, gain, and read noise. We find exceptional short-term bias stability with standard deviations below 0.02 ADU, negligible dark current of approximately 0.002 e$^{-}$ pixel$^{-1}$ s$^{-1}$ at $0^\circ\text{C}$, and excellent linearity with nonlinearity consistently below $\pm$ 0.5\%, and a small proportion (0.06\% to 0.08\%) of pixels with anomalous responses. Furthermore, our analysis demonstrates uniform gain values across all cameras, ranging from 0.252 to 0.255 e$^{-}$ ADU$^{-1}$, with low readout noise, measured to be below 1.6 e$^{-}$ using conventional methods. We also propose a novel method for pixel-level gain and read noise calculation for CMOS sensors, which revealed a narrow gain distribution and a low median read noise of 1.028 e$^-$ for one of the cameras. The laboratory testing of the ZWO ASI6200MM Pro cameras indicates their potential to meet the requirements of time-domain surveys for the Mini-SiTian project.

astro-ph.IM↗