arXiv subjects

Yang Xia

Publications and source records attributed to Yang Xia.

At least 19 recordsLinked to original sources

arXiv2026

Vibration patterns and reed - pipe coupling in the free reeds of the hulusi

The hulusi is a free-reed instrument from Yunnan, Western China, known for its unique sound. Its reed and frame are cut from a single brass sheet in a symmetric shape, the free end narrower than the clamped end. Its free reeds are coupled to tubes that determine the instrument's pitch during normal playing. With reduced playing pressure, the reed frequency takes over the instrument sound. We report point-by-point laser Doppler vibrometry on the reeds of six handmade hulusi, two each in the keys of F, B$\flat$ and C, measured at 29 points on every reed or tongue over nine fingerings and two supply pressures. Steady-state spectra carry two components: a tube-dominant frequency $f_\mathrm{tube}$, locked to the air-column resonance and carrying a complete harmonic series, and a reed-dominant frequency $f_\mathrm{reed}$, which is not an integer multiple of it. Their difference $Δ= f_\mathrm{tube} - f_\mathrm{reed}$ runs from 38 to 523~Hz across the dataset, from about three semitones to more than an octave. The reed-dominant frequency rises with the pressure at the reed, by $28.0 \pm 10.3$ Hz/kPa once $f_\mathrm{tube}$ is controlled for. At low pressure supply the oscillation loses its lock to the air column, falls silent for about 100~ms, and returns at the reed frequency within 0.1 Hz. The spatial patterns are forced oscillation shapes of one limit cycle at its fundamental and harmonics rather than eigenmodes: the measured frequencies stand in strict 1:2:3 ratios, far from the bending ratios of a cantilever. The reed-dominant pattern is pure bending and holds across fingerings and supply pressures, whereas the tube-dominant pattern breaks up near the oscillation threshold.

physics.flu-dyn↗

arXiv2026

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.

cs.AI↗

arXiv2026

Timbre Analysis of the Hulusi, a Southwestern Chinese Free-Reed Instrument, using Machine Learning

The hulusi is a wind instrument that was invented in Yunnan Province, China, and has become tremendously popular in recent years. It consists of a mouthpiece, a gourd, and three bamboo tubes, all with free reeds made of copper. The main bamboo tube in the middle has seven finger holes. In this instrument, the pipe length, not the free reed's eigenfrequency, determines the instrument's pitch, unlike, for example, with the Western accordion or the blues harp. In this study, a machine learning model implemented in the COMSAR framework (https://github.com/ifsm) was used to investigate the timbre characteristics of the \emph{hulusi} to cluster different instruments and pitches. The measured \emph{hulusi} pitches C, B, A, G, and F were analyzed according to seven psychoacoustic features, among which only the spectral centroid, sharpness, and fractal correlation dimension are shown to form pitch clusters. These timbre features were used to train Kohonen self-organizing maps (SOMs) for clustering. Brightness and sharpness analysis revealed that the highest pitches were less bright and less sharp than mid- and low-range pitches were. Furthermore, the fractal correlation dimension, which mainly determines the chaoticity of the initial transients, was the best-clustering timbre feature for the hulusi, with the highest pitches showing the least chaoticity. This result is supported by defining a cluster quality index for the SOMs.

cs.SD↗

arXiv2026

sp-DBA: a general framework for adaptive transform-domain computation

Transform-domain methods simplify analysis and computation, making them central to scientific computing and signal processing. However, existing adaptive strategies often introduce new data structures or require substantial workflow redesign, limiting efficient execution on massively parallel hardware. Here we present spectral dynamic block activation (sp-DBA), an adaptive acceleration framework for transform-domain workflows. sp-DBA dynamically activates transform-domain computation blocks as a calculation evolves, concentrating computation where it is needed while retaining numerical accuracy and optimized transform operations. Across representative workflows in materials science, biology, and optical communications, the demonstrated implementations achieve speedups of up to 28.1-fold for transform-domain updates and up to 8.4-fold overall acceleration; strong- and weak-scaling tests on up to eight GPUs show that the local adaptive update remains effective within distributed FFT workflows. By introducing execution-level adaptivity into mature transform-domain workflows without rebuilding existing solvers, sp-DBA extends the scale, duration, and complexity of scientific simulations and signal-processing calculations on modern parallel systems.

cs.CE↗

arXiv2026

From the First to Subsequent Pulses: Evolution of Discharge inside a Preformed Bubble in Water

The evolution of pulsed discharge behavior inside a preformed air bubble in water from the first to subsequent pulses was experimentally investigated using a synchronized needle to bubble system. A positive nanosecond high-voltage pulsed power supply, together with a pulse valve and ICCD imaging, was employed to generate reproducible preformed bubbles and to record the corresponding discharge development with good temporal synchronization. The results show that, although the preformed bubbles exhibit good repeatability in size and morphology under identical conditions, the first-pulse discharge inside the bubble remains highly stochastic. The first discharge is predominantly corona-like and is not significantly affected by bubble size once the electrode is covered by the bubble. By varying the pulse width, the discharge inside the bubble was observed to evolve progressively from corona-like emission to streamer discharge, accompanied by increasing instability of the bubble interface. At sufficiently large pulse width and pulse number, bubble wrinkling and even rupture were induced. The effect of solution conductivity was also examined. Increasing conductivity significantly enhanced discharge intensity, enlarged the luminous region, and promoted streamer propagation along the inner bubble surface. At sufficiently high conductivity, the first pulse already produced strong discharge and rapid bubble rupture. In addition, the current amplitude and the energy dissipated per pulse increased with conductivity and pulse number. These results demonstrate that the discharge evolution inside a preformed bubble is jointly governed by pulse history, pulse width, and solution conductivity, and that residual effects from previous pulses play an important role in the transition from the first pulse to subsequent discharges.

physics.plasm-ph↗

arXiv2026

Cross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentation

Domain-generalized LiDAR semantic segmentation (LSS) seeks to train models on source-domain point clouds that generalize reliably to multiple unseen target domains, which is essential for real-world LiDAR applications. However, existing approaches assume similar acquisition views (e.g., vehicle-mounted) and struggle in cross-view scenarios, where observations differ substantially due to viewpoint-dependent structural incompleteness and non-uniform point density. Accordingly, we formulate cross-view domain generalization for LiDAR semantic segmentation and propose a novel framework, termed CVGC (Cross-View Geometric Consistency). Specifically, we introduce a cross-view geometric augmentation module that models viewpoint-induced variations in visibility and sampling density, generating multiple cross-view observations of the same scene. Subsequently, a geometric consistency module enforces consistent semantic and occupancy predictions across geometrically augmented point clouds of the same scene. Extensive experiments on six public LiDAR datasets establish the first systematic evaluation of cross-view domain generalization for LiDAR semantic segmentation, demonstrating that CVGC consistently outperforms state-of-the-art methods when generalizing from a single source domain to multiple target domains with heterogeneous acquisition viewpoints. The source code will be publicly available at https://github.com/KintomZi/CVGC-DG

cs.CV↗

arXiv2025

IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting

Recent advances in multimodal large language models (MLLMs) have led to impressive progress across various benchmarks. However, their capability in understanding infrared images remains unexplored. To address this gap, we introduce IF-Bench, the first high-quality benchmark designed for evaluating multimodal understanding of infrared images. IF-Bench consists of 499 images sourced from 23 infrared datasets and 680 carefully curated visual question-answer pairs, covering 10 essential dimensions of image understanding. Based on this benchmark, we systematically evaluate over 40 open-source and closed-source MLLMs, employing cyclic evaluation, bilingual assessment, and hybrid judgment strategies to enhance the reliability of the results. Our analysis reveals how model scale, architecture, and inference paradigms affect infrared image comprehension, providing valuable insights for this area. Furthermore, we propose a training-free generative visual prompting (GenViP) method, which leverages advanced image editing models to translate infrared images into semantically and spatially aligned RGB counterparts, thereby mitigating domain distribution shifts. Extensive experiments demonstrate that our method consistently yields significant performance improvements across a wide range of MLLMs. The benchmark and code are available at https://github.com/casiatao/IF-Bench.

cs.CV↗

arXiv2025

Cyber-Resilient Fault Diagnosis Methodology in Inverter-Based Resource-Dominated Microgrids with Single-Point Measurement

Cyber-attacks jeopardize the safe operation of inverter-based resource-dominated microgrids (IBR-dominated microgrids). At the same time, existing diagnostic methods either depend on expensive multi-point instrumentation or stringent modeling assumptions that are untenable under single-point measurement constraints. This paper proposes a Fractional-Order Memory-Enhanced Attack-Diagnosis Scheme (FO-MADS) that achieves timely fault localization and cyber-resilient fault diagnosis using only one VPQ (voltage, active power, reactive power) measurement point. FO-MADS first constructs a dual fractional-order feature library by jointly applying Caputo and Grünwald-Letnikov derivatives, thereby amplifying micro-perturbations and slow drifts in the VPQ signal. A two-stage hierarchical classifier then pinpoints the affected inverter and isolates the faulty IGBT switch, effectively alleviating class imbalance. Robustness is further strengthened through Progressive Memory-Replay Adversarial Training (PMR-AT), whose attack-aware loss is dynamically re-weighted via Online Hard Example Mining (OHEM) to prioritize the most challenging samples. Experiments on a four-inverter IBR-dominated microgrid testbed comprising 1 normal and 24 fault classes under four attack scenarios demonstrate diagnostic accuracies of 96.6% (bias), 94.0% (noise), 92.8% (data replacement), and 95.7% (replay), while sustaining 96.7% under attack-free conditions. These results establish FO-MADS as a cost-effective and readily deployable solution that markedly enhances the cyber-physical resilience of IBR-dominated microgrids.

eess.SY↗

arXiv2023

A Spatial-channel-temporal-fused Attention for Spiking Neural Networks

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic process for selecting salient regions in biological vision systems. Although visual attention mechanisms have achieved great success in computer vision applications, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing accumulated historical spatial-channel information in the present study. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and two other SNN models with degenerated attention modules, but also achieves competitive accuracy with existing state-of-the-art methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability when faced with incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that incorporating appropriate cognitive mechanisms of the brain may provide a promising approach to elevate the capabilities of SNNs.

cs.CV↗

arXiv2023

A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning~(STL-SNNs) achieve significantly superior performance on various static and neuromorphic datasets than SNNs trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.

cs.NE↗

arXiv2022

Transport through Quantum Anomalous Hall Bilayers with Lattice Mismatch

We theoretically investigate quantum transport properties of quantum anomalous Hall bilayers, with arbitrary ratio of lattice constants, i.e., with lattice mismatch. In the simplest case of ratio 1 (but with different model parameters in two layers), the inter-layer coupling results in resonant traversing between forward propagating waves in two layers. In the case of generic ratios, there is a quantized conductance plateau originated from two Chern numbers associated with two layers. However, the phase boundary of this quantization plateau consists of a fractal transitional region (instead of a clear transition line) of interpenetrating edge states (with quantized conductance) and bulk states (with unquantized conductance). We attribute these bulk states as mismatch induced in-gap bulk states. Different from in-gap localized states induced by random disorder, these in-gap bulk states are extended in the limit of vanishing random disorder. However, the detailed fine structure of this transitional region is sensitive to disorder, lattice structure, sample size, and even the configuration of leads connecting to it, due to the bulk and topologically trivial nature of these in-gap bulk states.

cond-mat.mes-hall↗

arXiv2021

APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, such as financial fraud detection. Therefore, we propose Asynchronous Propagation Attention Network, an asynchronous continuous time dynamic graph algorithm for real-time temporal graph embedding. Traditional graph models usually execute two serial operations: first graph computation and then model inference. We decouple model inference and graph computation step so that the heavy graph query operations will not damage the speed of model inference. Extensive experiments demonstrate that the proposed method can achieve competitive performance and 8.7 times inference speed improvement in the meantime.

cs.AI↗

arXiv2020

Chebyshev Polynomial Method to Landauer-Büttiker Formula of Quantum Transport in Nanostructures

Landauer-Büttiker formula describes the electronic quantum transports in nanostructures and molecules. It will be numerically demanding for simulations of complex or large size systems due to, for example, matrix inversion calculations. Recently, Chebyshev polynomial method has attracted intense interests in numerical simulations of quantum systems due to the high efficiency in parallelization, because the only matrix operation it involves is just the product of sparse matrices and vectors. Many progresses have been made on the Chebyshev polynomial representations of physical quantities for isolated or bulk quantum structures. Here we present the Chebyshev polynomial method to the typical electronic scattering problem, the Landauer-Büttiker formula for the conductance of quantum transports in nanostructures. We first describe the full algorithm based on the standard bath kernel polynomial method (KPM). Then, we present two simple butefficient improvements. One of them has a time consumption remarkably less than the direct matrix calculation without KPM. Some typical examples are also presented to illustrate the numerical effectiveness.

cond-mat.mes-hall↗

arXiv2020

Numerical Study of Disorder on the Orbital Magnetization in Two Dimensions

The modern theory of orbital magnetization (OM) was developed by using Wannier function method, which has a formalism similar with the Berry phase. In this manuscript, we perform a numerical study on the fate of the OM under disorder, by using this method on the Haldane model in two dimensions, which can be tuned between a normal insulator or a Chern insulator at half filling. The effects of increasing disorder on OM for both cases are simulated. Energy renormalization shifts are observed in the weak disorder regime and topologically trivial case, which was predicted by a self-consistent T-matrix approximation. Besides this, two other phenomena can be seen. One is the localization trend of the band orbital magnetization. The other is the remarkable contribution from topological chiral states arising from nonzero Chern number or large value of integrated Berry curvature. If the fermi energy is fixed at the gap center of the clean system, there is an enhancement of |M| at the intermediate disorder, for both cases of normal and Chern insulators, which can be attributed to the disorder induced topological metal state before localization.

cond-mat.mes-hall↗

arXiv2019

Observation of Rydberg exciton polaritons and their condensate in a perovskite cavity

The condensation of half-light half-matter exciton polaritons in semiconductor optical cavities is a striking example of macroscopic quantum coherence in a solid state platform. Quantum coherence is possible only when there are strong interactions between the exciton polaritons provided by their excitonic constituents. Rydberg excitons with high principle value exhibit strong dipole-dipole interactions in cold atoms. However, polaritons with the excitonic constituent that is an excited state, namely Rydberg exciton polaritons (REPs), have not yet been experimentally observed. Here, for the first time, we observe the formation of REPs in a single crystal CsPbBr3 perovskite cavity without any external fields. These polaritons exhibit strong nonlinear behavior that leads to a coherent polariton condensate with a prominent blue shift. Furthermore, the REPs in CsPbBr3 are highly anisotropic and have a large extinction ratio, arising from the perovskite's orthorhombic crystal structure. Our observation not only sheds light on the importance of many-body physics in coherent polariton systems involving higher-order excited states, but also paves the way for exploring these coherent interactions for solid state quantum optical information processing.

cond-mat.mtrl-sci↗

arXiv2019

Localization Trajectory and Chern-Simons axion coupling for Bilayer Quantum Anomalous Hall Systems

Quantum anomalous Hall (QAH) multilayers provide a platform of topological materials with high Chern numbers. We investigate the localization routes of bilayer QAH systems with Chern number C = 2 under strong disorder, by numerical simulations on their quantum transport properties and the Chern-Simons axion coupling. Compared to the single layer counterpart with C = 2, the localization trajectories present much richer behaviors, for example, the existence of the stable intermediate state with C = 1 can be tuned by model parameters. This state was always unstable in the single layer case. Furthermore, the two parameter scaling trajectories also exhibit multiple patterns, some of which were not captured by the standard Pruisken picture. During the process towards localization, the Chern-Simons axion coupling shows a surprisingly remarkable peak which is even higher and sharper in the large size limit. Therefore the disordered bilayer QAH system can be a good candidate for this nontrivial magnetoelectric coupling mediated by orbital motions.

cond-mat.mes-hall↗

arXiv2019

Room-temperature giant Stark effect of single photon emitter in van der Waals material

Single photon emitters (SPEs) are critical building blocks needed for quantum science and technology. For practical applications, large-scale room-temperature solid-state platforms are required. Color centers in layered hexagonal boron nitride (hBN) have recently been found to be ultra-bright and stable SPEs at room temperature. Yet, to scale up solid-state quantum information processing, large tuning range of single photon energy is demanded for wavelength division multiplexing quantum key distribution, where indistinguishability is not required, and for indistinguishable single-photon production from multi-emitters. Stark effect can tune the single photon energy by an electric field, which however, has been achieved only at cryogenic temperature so far. Here we report the first room-temperature Stark effect of SPEs by exploiting hBN color centers. Surprisingly, we observe a giant Stark shift of single photon more than 30 meV, about one order of magnitude greater than previously reported in color center emitters. Moreover, for the first time, the orientation of the electric permanent dipole moment in the solid-state SPE is determined via angle-resolved Stark effect, revealing the intrinsic broken symmetries at such a color center. The remarkable Stark shift discovered here and the significant advance in understanding its atomic structure pave a way towards the scalable solid-state on-chip quantum communication and computation at room temperature.

cond-mat.mes-hall↗

arXiv2019

Dynamical Behaviors of the Tumor-immune System in a Stochastic Environment

This paper investigates dynamic behaviors of the tumor-immune system perturbed by environmental noise. The model describes the response of the cytotoxic T lymphocyte (CTL) to the growth of an immunogenic tumour. The main methods are stochastic Lyapunov analysis, comparison theorem for stochastic differential equations (SDEs) and strong ergodicity theorem. Firstly, we prove the existence and uniqueness of the global positive solution for the tumor-immune system. Then we go a further step to study the boundaries of moments for tumor cells and effector cells and the asymptotic behavior in the boundary equilibrium points. Furthermore, we discuss the existence and uniqueness of stationary distribution and stochastic permanence of the tumor-immune system. Finally, we give several examples and numerical simulations to verify our results.

math.PR↗