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Yuxi Wang

Publications and source records attributed to Yuxi Wang.

At least 19 recordsLinked to original sources

Long-Wave Spectral Instability of Shear Layers for the Compressible Euler Equations

We study the long-wave spectral instability of the two-dimensional compressible Euler equations around smooth monotone shear layers. We construct decaying half-line solutions of the compressible Rayleigh equation through a long-wave expansion and derive a second-order expansion of the matching Wronskian. For every fixed Mach number $m>0$, we prove the existence of unstable modes for sufficiently small wavenumbers. For $m<\sqrt2$, this holds for a class of profiles, with $c_i$ tending to a positive constant as $\alpha\to0$. At $m=\sqrt{2}$, the profile $U_s(Y)=\tanh Y$ admits an unstable mode with $c\to0$ and $c_i$ of order $\alpha^{1/3}$. For $m>\sqrt2$, the same profile remains unstable, with $c\to c_*(m)\in(0,1)$ and $c_i>0$ of order $\alpha$. The corresponding temporal growth rates are of order $\alpha$, $\alpha^{4/3}$ and $\alpha^2$, respectively, showing a change in the long-wave instability scaling at $m=\sqrt2$. In the zero-thickness limit, the supercritical unstable eigenvalue approaches the real axis, consistently with the stability results for supersonic compressible vortex sheets in \cite{CS1,CS2}.

math.AP

ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits

As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. This makes early-stage estimation of parasitic capacitance and resistance important for parasitic-aware design exploration before full physical implementation. However, progress on GNN-based parasitic modeling has been hindered by the lack of public, high-fidelity RC benchmarks that support reproducible evaluation. To address this gap, we introduce ParasGB, the first open-source benchmark suite for pre-layout parasitic parameter prediction on circuit graphs. ParasGB provides large-scale, heterogeneous RC networks extracted with commercial EDA tools from tape-out-proven designs, together with a unified evaluation protocol covering node-level ground capacitance, edge-level resistance, and edge-level coupling capacitance. Within this framework, we benchmark diverse GNN architectures using a standardized training pipeline and expose challenges such as extreme label imbalance, long-tailed parasitic distributions, and strong structural heterogeneity. By establishing a physically grounded and standardized benchmark for early-stage parasitic prediction, ParasGB provides an open platform for reproducible research on circuit graph learning and parasitic-aware model development. All datasets, preprocessing scripts, and configurations are publicly available in our code repository https://github.com/ShenShan123/ParasGB.git.

cs.LG

Neutral curves and traveling waves in plane Poiseuille flow

We study the spectrum of the Orr--Sommerfeld operator associated with the incompressible plane Poiseuille flow in the high-Reynolds-number regime. In the Tollmien--Schlichting eigenvalue region, we prove the existence and uniqueness of the lower and upper branches of the neutral curve. For each sufficiently small fixed wavenumber $\alpha$, the viscosities corresponding to the lower and upper neutral branches satisfy $\nu\sim |\alpha|^7$ and $\nu\sim |\alpha|^{11}$, respectively. Equivalently, for each sufficiently small viscosity $\nu$, the corresponding lower and upper neutral wavenumbers satisfy $\alpha^2\sim \nu^{2/7}$ and $\alpha^2\sim \nu^{2/11}$, respectively. We also establish the simplicity of the neutral eigenvalues and verify the transversal crossing condition on both neutral branches. The proof is based on a boundary-adapted version of the Rayleigh--Airy iteration scheme, together with precise expansions and refined estimates for the correction terms and their parameter derivatives. These spectral results verify the assumptions required in the classical Hopf bifurcation framework of Joseph--Sattinger \cite{JS1972} and Iooss \cite{Iooss1972}, and hence yield traveling-wave solutions bifurcating from the plane Poiseuille flow at the neutral points.

math.AP

Intrinsically low thermal conductivity of stoichiometric lithium niobate:Experimental measurement and microscopic origin

With the rapid development of integrated electro-optic and nonlinear optical devices based on lithium niobate (LiNbO$_3$, LN), thermal management is becoming a critical area of focus. However, experimental measurement of thermal transport in stoichiometric LiNbO$_3$ (sLN) remains scarce, and the intrinsic microscopic mechanisms remain to be established. Here, we combine the laser pump-probe technique of frequency-domain thermoreflectance (FDTR) with state-of-the-art machine-learned atomistic simulations to comprehensively investigate thermal transport in sLN. The measured and simulated room-temperature thermal conductivity ($\kappa$) values of sLN agree well, which are orders-of-magnitude lower than that of many classic and emerging semiconductors such as silicon. Furthermore, the temperature-dependent $\kappa$ exhibits a $T^{-\alpha}$ scaling with $\alpha$ near unity, suggesting that thermal transport is dominated by intrinsic phonon-phonon scattering. By comparing sLN with cubic boron arsenide (cBAs) which serves as an ultrahigh-$\kappa$ benchmark, we reveal that harmonic properties are not responsible for the low $\kappa$ of sLN, which feature phonon heat capacity and group velocities that are either higher than or comparable to those in cBAs. Instead, the low $\kappa$ originates from substantially stronger anharmonicity and larger scattering phase space. These two factors collectively suppress phonon lifetimes by 1-2 orders of magnitude, leading to a maximum phonon mean free path of approximately 140 nm. As a result, notable size effects emerge in thin-film sLN below 1 $\mu$m, with $\kappa$ dropping to half the bulk value at 10 nm. Altogether, our findings establish a fundamental understanding of thermal transport in sLN and provide atomistic insights for thermal management in advanced lithium niobate technologies.

cond-mat.mtrl-sci

The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction

4D hand motion reconstruction from egocentric video is bottlenecked by clear limitations of existing methods: image-based pipelines depend on a detector that fails under heavy occlusion, while video-based methods rely on temporal modules learned only from scarce hand-pose annotations, a narrow signal insufficient to model motion dynamics, occlusion reasoning, and hand-object interaction. These capabilities, however, are exactly what video generative models must implicitly acquire when trained to synthesize coherent video at internet scale. Motivated by this, we present ViDiHand, which leverages the representations of a pretrained video diffusion model to reconstruct 4D two-hand pose. We adapt it via a hand-overlay rendering objective that specializes its features for hands while preserving its world priors. A decoder then recovers metric-scale pose from the adapted features. The whole pipeline operates directly on full frames--no detector, no infiller, and no test-time optimization. On ARCTIC, HOT3D, and HOI4D, ViDiHand substantially outperforms prior methods, establishing video diffusion models as a powerful new foundation for hand motion reconstruction and a promising route to scalable in-the-wild data collection for embodied AI. Project page: https://vidihand.github.io.

cs.CV

Ocean4D: Generative Underwater 4D Reconstruction via Medium-Aware Video Diffusion

Underwater 4D reconstruction remains challenging due to the coupling between degraded light transport in participating media and dynamic water variations. Most existing Methods are developed under in-air assumptions and do not explicitly account for underwater absorption and backscatter. Additionally, near-static assumptions make these approaches sensitive to drifting particles and dynamic distractors , leading to unstable geometry and inconsistent cross-view results. To address these issues, we propose a generative framework for underwater 4D reconstruction, named Ocean4D, which is built on two complementary components. Specifically, 4D-GCC constructs 4D geometrically consistent conditioning with improved cross-frame coverage, while the Medium-Aware Block performs implicit medium-aware denoising in the latent diffusion process to stabilize underwater appearance under absorption and scattering. Given a monocular video and target cameras, our method generates videos along the target trajectories while preserving global structure and cross-view consistency. Extensive experiments on both dynamic and static underwater benchmarks demonstrate state-of-the-art performance on underwater reconstruction.

cs.CV

DiffPC: Diffusion-Based Projector Photometric Compensation

Projector photometric compensation corrects color distortions introduced by surface texture, reflection, and ambient lighting. Existing deep learning-based methods usually require professional scene-specific data collection and lack consideration for perceptual quality. To address this limitation, we present a diffusion-based photometric compensation method that reconstructs compensation images under photometric and content-aware guidance. Specifically, we first model the photometric distortions introduced during projection as environment-dependent additive noise, thereby reformulating the photometric compensation problem as a denoising task with physical constraints. Next, we introduce a diffusion model, which generates compensation images by following an additive trajectory to iteratively remove the noise. Finally, to accurately estimate the noise at each timestep, by analyzing the factors that contribute to distortions in the physical process of projection and capturing, we design a noise estimation network that incorporates features of both photometry-aware and content conditions. Experiments show that our method achieves superior visual performance in unknown scenarios, thereby exhibiting significant practical advantages over prior methods.

cs.MM

VoxWatermark: A Large-Scale Benchmark for Audio Watermark Detection under Perturbations

With the rapid deployment of speech generation systems in open environments, providing verifiable source attribution and copyright accountability for audio content has become critical. A gap in current research is the lack of a unified benchmark that systematically compares different watermark injection methods under realistic distribution shifts. To address this, we build VoxWatermark by applying 10 watermarking methods (4 neural and 6 traditional) with unified injection and annotation on multilingual, multi-source corpora, and introducing no-box, black-box, and white-box perturbations to simulate real recording and transmission conditions. Based on this benchmark, we propose AudioWMD as a robust baseline detector for large-scale, multi-method, cross-distribution settings. Results show that injection-method diversity and distribution shifts affect detection stability, while validating the effectiveness and scalability of AudioWMD. Dataset and code are publicly available.

eess.AS

BotDirector: Robot Storytelling Across the Symmetrical Reality with Multi-modal Interactions

Robot storytelling offers a unique blend of technological innovation and creative expression that engages children in unprecedented ways. However, the technical aspects are often too complicated for children. We propose an interactive system that facilitates robot storytelling with tangible and natural language interactions. Children arrange the playground with their own stuff and create narratives with an LLM agent. The created narratives are transformed into a motion sequence based on the map and characters, and the motions are executed by self-navigating swarm robots. This system enhances robot storytelling with flexible scenarios, enabling young children to create robot dramas with everyday objects.

cs.RO

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction

Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existing evaluations focusing almost exclusively on accuracy preservation. However, in safety-critical applications, a model's ability to reliably quantify its own uncertainty is equally important. We ask: does compression preserve this ability? To answer this question, we benchmark 12 LLMs under various compression configurations across five NLP tasks, using conformal prediction to provide a rigorous, distribution-free measure of uncertainty. Our experiments reveal that: (I) compression frequently decouples accuracy from uncertainty; (II) larger models absorb compression-induced uncertainty far more effectively than smaller ones; and (III) uncertainty inflation is often threshold-like rather than gradual. These results suggest that accuracy-only evaluation is insufficient for assessing the deployment readiness of compressed LLMs, and that uncertainty-aware benchmarking should be a standard component of model compression pipelines.

cs.AI

Some Large Language Models Exhibit Consistent Risk Attitudes

As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test whether large language models (LLMs) exhibit systematic and consistent risk attitudes under uncertainty. We introduce a cross-domain framework that decouples contextual risk belief from categorical decision, and apply it to six representative LLMs and 100 human participants across spatial navigation, clinical triage, and financial allocation tasks. Using regression models, we extract each agents belief-to-decision mapping and quantify risk sensitivity and risk attitude bias. We find that most tested LLMs exhibit (i) robust intra-task consistency, indicating stable mappings from contextual belief to risk decision within a fixed task domain; (ii) cross-domain rank-order stability, preserving relative risk posture across tasks; and (iii) a convergence toward a restricted risk-attitude distribution relative to the broader human baseline. These results reveal risk attitude as a stable and previously uncharacterized dimension of LLM behavior, establishing a foundation for evaluating and aligning AI systems in open-ended decision-making and motivating further investigation into the origins of these intrinsic behavioral dispositions.

cs.AI

Extinction Distributions in Nearby Star-resolved Galaxies. II. M33

Extinction maps are essential for tracing interstellar dust and enabling accurate stellar population studies in galaxies. Here, a high-resolution extinction distribution of nearby galaxy M33 is constructed by fitting multiband color indexes of the individually resolved red giant branch (RGB) stars from the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey. Achieving an angular resolution of approximately 6$^{\prime\prime}$ ($\sim$ 24.4 pc), the extinction map reveals the intricate and heterogeneous distribution of dust throughout the entire disk of M33, with distinct delineation of spiral arms, inter-arm regions, and compact dust clouds. In addition, it exhibits strong spatial correspondence with the distributions of total hydrogen, H I, and CO, underscoring the reliability of the extinction map for tracing both diffuse and dense components of the interstellar medium. The derived $V$-band extinction reaches up to 2.5 mag per pixel, with a mean value of about 1.05 mag. Beyond providing new insights into the dust structure of M33, the extinction map offers a robust foundation for accurate extinction corrections and will support future studies, including upcoming observations with the Chinese Space Station Telescope.

astro-ph.GA

Intrinsically ultralow thermal conductivity in all-inorganic superatomic bulk crystals

Superatomic compounds, composed of atomic clusters interwoven by weak chemical bonds exhibit large anharmonicity vibrations, are excellent candidates for ultralow thermal conductivity (\k{appa}) materials. However, growing bulk superatomic single crystals is challenging due to complex chemical composition and chemical bonds, and studies on their intrinsic thermal property are scarce. Here, we grew high-quality superatomic single crystals of Re6Se8Te7 and Re6Te15, both of which are narrow band gap semiconductors that change into metals under external physical pressure. At room-temperature, the \k{appa} are 0.32 W m-1 K-1 and 0.53 W m-1 K-1 in Re6Se8Te7 and Re6Te15, respectively, ranking among the lowest value reported in all-inorganic bulk crystals. It is mainly attributed to the large Grüneisen parameter (1.93) and low average sound speed (< 1482 m/s), which are due to soft Te7 nets weakly embedded among the rigid Re6Se8 (Re6Te8) quasi-cubic clusters. The appearance of boson peak, i. e., hump of C(T)/T3, verifies the existence of disordered phonon transports. Besides, the temperature dependence of \k{appa} can be described by classic Debye-Callaway model. Notably, above 350 K, the \k{appa} values of Re6Se8Te7 and Re6Te15 are remarkably close to the upper limit derived from glassy-like diffusion model. This finding sets the superatomic compounds as a promising family for searching ultralow-\k{appa} and energy management materials.

cond-mat.mtrl-sci

A gentle tutorial on Bock's algorithm for minimum directed spanning trees with a structured reformulation

Bock's 1971 algorithm is an exact primal--dual method for the minimum-cost arborescence problem, but its Algol presentation obscures the interaction of its maintained arrays and label-directed control flow. We provide a self-contained tutorial comprising the original listing, a line-mapped explanation, a circuit-forming three-node example, and a complete trace of Bock's ten-node instance. We also present a structured reformulation that replaces temporary span-label changes with explicit component and trace state. Local tightness and contraction-progress results, together with an operational-correspondence theorem, establish that the reformulation preserves Bock's candidate choices, transfers, and final solution.

cs.CL

Hand2World: Autoregressive Egocentric Interaction Generation via Free-Space Hand Gestures

Egocentric interactive world models are essential for augmented reality and embodied AI, where visual generation must respond to user input with low latency, geometric consistency, and long-term stability. We study egocentric interaction generation from a single scene image under free-space hand gestures, aiming to synthesize photorealistic videos in which hands enter the scene, interact with objects, and induce plausible world dynamics under head motion. This setting introduces fundamental challenges, including distribution shift between free-space gestures and contact-heavy training data, ambiguity between hand motion and camera motion in monocular views, and the need for arbitrary-length video generation. We present Hand2World, a unified autoregressive framework that addresses these challenges through occlusion-invariant hand conditioning based on projected 3D hand meshes, allowing visibility and occlusion to be inferred from scene context rather than encoded in the control signal. To stabilize egocentric viewpoint changes, we inject explicit camera geometry via per-pixel Plücker-ray embeddings, disentangling camera motion from hand motion and preventing background drift. We further develop a fully automated monocular annotation pipeline and distill a bidirectional diffusion model into a causal generator, enabling arbitrary-length synthesis. Experiments on three egocentric interaction benchmarks show substantial improvements in perceptual quality and 3D consistency while supporting camera control and long-horizon interactive generation.

cs.CV

Instability of shear flows with neutral embedded eigenvalues

We study the linear stability of a class of monotone shear flows. When the associated Rayleigh operator possesses a neutral embedded eigenvalue, we show that solutions of the linearized system may exhibit arbitrarily large growth in both the $L^\infty$ and $L^2$ norms. Moreover, when the embedded eigenvalue is multiple, we prove that the instability becomes stronger and explicitly construct solutions that grow linearly in time. This instability originates from the non-normality of the Rayleigh operator.

math.AP

Polarizing Antiresonant Hollow-Core Fiber

Achieving robust single-polarization guidance in hollow-core fibers has remained a longstanding challenge, limiting their integration into precision photonic systems. Here, we report the first experimental realization of a low-loss, polarization filtering antiresonant hollow-core fiber (AR-HCF). Conventional AR-HCFs inherently support degenerate orthogonal polarization modes, making them vulnerable to polarization drift under environmental perturbations. Our dual-ring fiber design introduces polarization-selective resonant coupling to lossy cladding modes, enabling strong polarization filtering without compromising transmission efficiency. The fiber achieves a polarization extinction ratio exceeding 21 dB and a propagation loss as low as 0.15 dB m^-1 over a 10 m fiber length. The design is scalable across wavelength bands and maintains polarization discrimination under mechanical bending, making it highly suitable for applications in fiber-based gyroscopes, quantum optics, and polarization-sensitive nonlinear interactions. This work represents a significant step toward monolithic, polarization-selective hollow-core fiber systems.

physics.optics

Variation of the 2175 Å extinction feature in Andromeda galaxy

Extinction curves contain key information on interstellar dust composition and size distribution, with the 2175 Å bump being the most prominent feature. We analyze 20 sightlines toward M31 using HST/STIS UV spectroscopy combined with multi-band photometry to characterize this feature. The extinction curves show substantial diversity, from MW-like shapes to flatter profiles with $R_V$ reaching up to $\sim5.8$. The strength of the 2175 Å feature varies widely, including two sightlines where the bump is essentially absent. The bump central wavelength spans a broader range than previously reported, while its width remains consistent with earlier studies. A moderate positive correlation is found between bump strength ($c_3$) and width ($γ$). We derive an average UV extinction curve toward M31 with $R_V \approx 3.53$. These results provide new constraints on dust properties and their spatial variations in this galaxy.

astro-ph.GA