arXiv subjects

Ying Wang

Publications and source records attributed to Ying Wang.

At least 181 records · Page 10Linked to original sources

arXiv2025

A Physiological-Model-Based Neural Network Framework for Blood Pressure Estimation from Photoplethysmography Signals

Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg*s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased.

physics.med-ph↗

arXiv2025

FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking

Anti-UAV tracking poses significant challenges, including small target sizes, abrupt camera motion, and cluttered infrared backgrounds. Existing tracking paradigms can be broadly categorized into global- and local-based methods. Global-based trackers, such as SiamDT, achieve high accuracy by scanning the entire field of view but suffer from excessive computational overhead, limiting real-world deployment. In contrast, local-based methods, including OSTrack and ROMTrack, efficiently restrict the search region but struggle when targets undergo significant displacements due to abrupt camera motion. Through preliminary experiments, it is evident that a local tracker, when paired with adaptive search region adjustment, can significantly enhance tracking accuracy, narrowing the gap between local and global trackers. To address this challenge, we propose FocusTrack, a novel framework that dynamically refines the search region and strengthens feature representations, achieving an optimal balance between computational efficiency and tracking accuracy. Specifically, our Search Region Adjustment (SRA) strategy estimates the target presence probability and adaptively adjusts the field of view, ensuring the target remains within focus. Furthermore, to counteract feature degradation caused by varying search regions, the Attention-to-Mask (ATM) module is proposed. This module integrates hierarchical information, enriching the target representations with fine-grained details. Experimental results demonstrate that FocusTrack achieves state-of-the-art performance, obtaining 67.7% AUC on AntiUAV and 62.8% AUC on AntiUAV410, outperforming the baseline tracker by 8.5% and 9.1% AUC, respectively. In terms of efficiency, FocusTrack surpasses global-based trackers, requiring only 30G MACs and achieving 143 fps with FocusTrack (SRA) and 44 fps with the full version, both enabling real-time tracking.

cs.CV↗

arXiv2025

Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation

Despite recent advances in Large Language Models (LLMs) for code generation, the quality of LLM-generated code still faces significant challenges. One significant issue is code repetition, which refers to the model's tendency to generate structurally redundant code, resulting in inefficiencies and reduced readability. To address this, we conduct the first empirical study to investigate the prevalence and nature of repetition across 19 state-of-the-art code LLMs using three widely-used benchmarks. Our study includes both quantitative and qualitative analyses, revealing that repetition is pervasive and manifests at various granularities and extents, including character, statement, and block levels. We further summarize a taxonomy of 20 repetition patterns. Building on our findings, we propose DeRep, a rule-based technique designed to detect and mitigate repetition in generated code. We evaluate DeRep using both open-source benchmarks and in an industrial setting. Our results demonstrate that DeRep significantly outperforms baselines in reducing repetition (with an average improvements of 91.3%, 93.5%, and 79.9% in rep-3, rep-line, and sim-line metrics) and enhancing code quality (with a Pass@1 increase of 208.3% over greedy search). Furthermore, integrating DeRep improves the performance of existing repetition mitigation methods, with Pass@1 improvements ranging from 53.7% to 215.7%.

cs.SE↗

arXiv2025

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark

To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD) have received significant attention in recent years. Though those two lines of research indeed share the same objective, they have been studied independently in the community due to distinct evaluation setups, creating a gap that hinders the application and evaluation of methods from one to the other. To bridge the gap, in this work, we present a \underline{\textbf{U}}nified \underline{\textbf{B}}enchmark for unsupervised \underline{\textbf{G}}raph-level \underline{\textbf{O}}OD and anoma\underline{\textbf{L}}y \underline{\textbf{D}}etection (\ourmethod), a comprehensive evaluation framework that unifies GLAD and GLOD under the concept of generalized graph-level OOD detection. Our benchmark encompasses 35 datasets spanning four practical anomaly and OOD detection scenarios, facilitating the comparison of 18 representative GLAD/GLOD methods. We conduct multi-dimensional analyses to explore the effectiveness, OOD sensitivity spectrum, robustness, and efficiency of existing methods, shedding light on their strengths and limitations. Furthermore, we provide an open-source codebase (https://github.com/UB-GOLD/UB-GOLD) of \ourmethod to foster reproducible research and outline potential directions for future investigations based on our insights.

cs.LG↗

arXiv2025

Principal Component Stochastic Subspace Identification for Output-Only Modal Analysis

Stochastic Subspace Identification (SSI) is widely used in modal analysis of engineering structures, known for its numerical stability and high accuracy in modal parameter identification. SSI methods are generally classified into two types: Data-Driven (SSI-Data) and Covariance-Driven (SSI-Cov), which have been considered to originate from different theoretical foundations and computational principles. In contrast, this study demonstrates that SSI-Cov and SSI-Data converge to the same solution under the condition of infinite observations, by establishing a unified framework incorporating instrumental variable analysis. Further, a novel modal identification approach, Principal Component Stochastic Subspace Identification (PCSSI), is proposed based on this framework. This method employs Principal Component Analysis (PCA) to extract key components of the signal subspace and project the observed data onto this space, enhancing modal identification stability while significantly reducing computational complexity. Through 5000 Monte Carlo numerical simulations, the statistical analysis shows that PCSSI consistently outperforms traditional SSI methods in terms of numerical stability and noise reduction, demonstrating clear advantages over both SSI-Cov and SSI-Data. Its effectiveness is further validated using experimental data from a scaled bridge model. Compared to conventional SSI approaches, PCSSI demonstrates superior robustness under complex engineering conditions, especially when dealing with limited data or high noise levels, underscoring its strong potential for practical applications.

physics.comp-ph↗

arXiv2025

ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks

Quantum Neural Networks (QNNs) offer promising capabilities for complex data tasks, but are often constrained by limited qubit resources and high entanglement, which can hinder scalability and efficiency. In this paper, we introduce Adaptive Threshold Pruning (ATP), an encoding method that reduces entanglement and optimizes data complexity for efficient computations in QNNs. ATP dynamically prunes non-essential features in the data based on adaptive thresholds, effectively reducing quantum circuit requirements while preserving high performance. Extensive experiments across multiple datasets demonstrate that ATP reduces entanglement entropy and improves adversarial robustness when combined with adversarial training methods like FGSM. Our results highlight ATPs ability to balance computational efficiency and model resilience, achieving significant performance improvements with fewer resources, which will help make QNNs more feasible in practical, resource-constrained settings.

quant-ph↗

arXiv2025

Recursive Identification of Binary-Valued Systems under Uniform Persistent Excitations

This paper studies the control-oriented identification problem of set-valued moving average systems with uniform persistent excitations and observation noises. A stochastic approximation-based (SA-based) algorithm without projections or truncations is proposed. The algorithm overcomes the limitations of the existing empirical measurement method and the recursive projection method, where the former requires periodic inputs, and the latter requires projections to restrict the search region in a compact set.To analyze the convergence property of the algorithm, the distribution tail of the estimation error is proved to be exponentially convergent through an auxiliary stochastic process. Based on this key technique, the SA-based algorithm appears to be the first to reach the almost sure convergence rate of $ O(\sqrt{\ln\ln k/k}) $ theoretically in the non-periodic input case. Meanwhile, the mean square convergence is proved to have a rate of $ O(1/k) $, which is the best one even under accurate observations. A numerical example is given to demonstrate the effectiveness of the proposed algorithm and theoretical results.

eess.SY↗

arXiv2025

Hankel Determinants for Convolution of Power Series: An Extension of Cigler's Results

Cigler considered certain shifted Hankel determinants of convolution powers of Catalan numbers and conjectured identities for these determinants. Recently, Fulmek gave a bijective proof of Cigler's conjecture. Cigler then provided a computational proof. We extend Cigler's determinant identities to the convolution of general power series $F(x)$, where $F(x)$ satisfies a certain type of quadratic equation. As an application, we present the Hankel determinant identities of convolution powers of Motzkin numbers.

math.CO↗

arXiv2025

Distributed Estimation with Quantized Measurements and Communication over Markovian Switching Topologies

This paper addresses distributed parameter estimation in stochastic dynamic systems with quantized measurements, constrained by quantized communication and Markovian switching directed topologies. To enable accurate recovery of the original signal from quantized communication signal, a persistent excitation-compliant linear compression encoding method is introduced. Leveraging this encoding, this paper proposes an estimation-fusion type quantized distributed identification algorithm under a stochastic approximation framework. The algorithm operates in two phases: first, it estimates neighboring estimates using quantized communication information, then it creates a fusion estimate by combining these estimates through a consensus-based distributed stochastic approximation approach. To tackle the difficulty caused by the coupling between these two estimates, two combined Lyapunov functions are constructed to analyze the convergence performance. Specifically, the mean-square convergence of the estimates is established under a conditional expectation-type cooperative excitation condition and the union topology containing a spanning tree. Besides, the convergence rate is derived to match the step size's order under suitable step-size coefficients. Furthermore, the impact of communication uncertainties including stochastic communication noise and Markov-switching rate is analyzed on the convergence rate. A numerical example illustrates the theoretical findings and highlights the joint effect of sensors under quantized communication.

eess.SY↗

arXiv2025

One-bit consensus of controllable linear multi-agent systems with communication noises

This paper addresses the one-bit consensus of controllable linear multi-agent systems (MASs) with communication noises. A consensus algorithm consisting of a communication protocol and a consensus controller is designed. The communication protocol introduces a linear compression encoding function to achieve a one-bit data rate, thereby saving communication costs. The consensus controller with a stabilization term and a consensus term is proposed to ensure the consensus of a potentially unstable but controllable MAS. Specifically, in the consensus term, we adopt an estimation method to overcome the information loss caused by one-bit communications and a decay step to attenuate the effect of communication noise. Two combined Lyapunov functions are constructed to overcome the difficulty arising from the coupling of the control and estimation. By establishing similar iterative structures of these two functions, this paper shows that the MAS can achieve consensus in the mean square sense at the rate of the reciprocal of the iteration number under the case with a connected fixed topology. Moreover, the theoretical results are generalized to the case with jointly connected Markovian switching topologies by establishing a certain equivalence relationship between the Markovian switching topologies and a fixed topology. Two simulation examples are given to validate the algorithm.

eess.SY↗

arXiv2025

Exponential Speedup of the Janashia-Lagvilava Matrix Spectral Factorization Algorithm

Spectral factorization is a powerful mathematical tool with diverse applications in signal processing and beyond. The Janashia-Lagvilava method has emerged as a leading approach for matrix spectral factorization. In this paper, we extend a central equation of the method to the non-commutative case, enabling polynomial coefficients to be represented in block matrix form while preserving the equation's fundamental structure. This generalization results in an exponential speedup for high-dimensional matrices. Our approach addresses challenges in factorizing massive-dimensional matrices encountered in neural data analysis and other practical applications.

math.CV↗

arXiv2025

Integration of a GaAs-based nanomechanical phase shifter with quantum-dot single-photon sources

We demonstrate a small-footprint electromechanical phase shifter with a compact active length of $10\:μ\text{m}$ fabricated on a suspended GaAs membrane, offering versatile integration with quantum-dot single-photon sources. The phase shifter is based on a slot-mode waveguide, whose slot width can be controlled by electrostatic forces, enabling a large effective refractive index change $Δn_\text{eff} > 0.1$. We observe up to 3$π$ phase modulation with 10.6 \text{V} applied bias, and a figure of merit $V_πL = 5.7\cdot 10^{-3} \text{V}\cdot \text{cm}$. Integration with a Mach-Zehnder interferometer (MZI) further allows routing of single photons with up to 24 \text{dB} extinction ratio at cryogenic temperatures. This device enables advanced manipulation of quantum emitters and the realization of reconfigurable quantum photonic integrated circuits.

quant-ph↗

arXiv2025

Controlling emitter-field coupling in waveguides with a nanomechanical phase shifter

The ability to control light-matter interfaces with solid-state photon emitters is a major requirement for the development of quantum photonic integrated circuits. We demonstrate controllable coupling between a quantum dot and an optical mode in a dielectric waveguide using a nano-opto-electro-mechanical phase shifter and a photonic crystal mirror. By controlling the phase, we induce a virtual displacement of the mirror that modifies the local density of states at the position of the emitter, thereby enhancing or suppressing spontaneous emission without an optical cavity. We observe a broadband tuning of the spontaneous emission rate and a modulation of the intensity emitted by the quantum dot in the waveguide. The method reported here could be employed to optimize the emitter-field interaction between quantum dots in-operando, by maximizing a single-photon source generation rate or adjusting its lifetime, as well as a characterization tool for the direct measurement of emitter-photon cooperativity.

quant-ph↗

arXiv2025

Enantiomer-Selective Magnetoresistance in Chiral Gold Nanocrystals by Magnetic Control of Surface Potentials

Chiral nanomaterials offer intriguing possibilities for novel electronic and chemical applications. Here, we report the discovery of an enantiomer-selective magnetoresistance effect in chiral gold nanocrystals. Based on precise control of nanocrystal chiral morphology using amino acid-directed synthesis, we demonstrate that an external magnetic field can dramatically modulate resistance in an enantiomer-specific manner. For a given enantiomer, a magnetic field in one direction alters the resistance by dozens of times, while the opposite field direction leaves it unchanged. This asymmetric response reverses for the opposite enantiomer and are reproduced in both single nanocrystals by conduction atomic force microscopy and nanocrystal thin films in solid state devices. We attribute this phenomenon to a chirality-driven charge pumping effect, where the interplay between the chiral morphology and the magnetic field selectively modifies the surface potential. The magnitude and sign of the magnetoresistance can be further tuned by the surface chemistry of the nanocrystal, as demonstrated through sulfide treatment. Our findings reveal a new form of chirality-dependent magnetoresistance, distinct from previously known effects such as chirality-induced spin selectivity and electric magnetochiral anisotropy. The ability to remotely control surface potentials of chiral nanostructures using magnetic fields could enable novel approaches in catalysis, drug delivery, and nanoelectronics.

cond-mat.mes-hall↗

arXiv2025

Hankel determinants for convolution powers of Motzkin numbers

We evaluate the Hankel determinants of the convolution powers of Motzkin numbers for $r\leq 27$ by finding shifted periodic continued fractions, which arose in application of Sulanke and Xin's continued fraction method. We also conjecture some polynomial characterization of these determinants.

math.CO↗

arXiv2025

PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System

Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate output tokens. Several prior works focus on improving the performance of LLM decoding using parallelism techniques, such as batching and speculative decoding. State-of-the-art LLM decoding has both compute-bound and memory-bound kernels. Some prior works statically identify and map these different kernels to a heterogeneous architecture consisting of both processing-in-memory (PIM) units and computation-centric accelerators. We observe that characteristics of LLM decoding kernels (e.g., whether or not a kernel is memory-bound) can change dynamically due to parameter changes to meet user and/or system demands, making (1) static kernel mapping to PIM units and computation-centric accelerators suboptimal, and (2) one-size-fits-all approach of designing PIM units inefficient due to a large degree of heterogeneity even in memory-bound kernels. In this paper, we aim to accelerate LLM decoding while considering the dynamically changing characteristics of the kernels involved. We propose PAPI (PArallel Decoding with PIM), a PIM-enabled heterogeneous architecture that exploits dynamic scheduling of compute-bound or memory-bound kernels to suitable hardware units. PAPI has two key mechanisms: (1) online kernel characterization to dynamically schedule kernels to the most suitable hardware units at runtime and (2) a PIM-enabled heterogeneous computing system that harmoniously orchestrates both computation-centric processing units and hybrid PIM units with different computing capabilities. Our experimental results on three broadly-used LLMs show that PAPI achieves 1.8$\times$ and 11.1$\times$ speedups over a state-of-the-art heterogeneous LLM accelerator and a state-of-the-art PIM-only LLM accelerator, respectively.

cs.AR↗

arXiv2025

Resonant Energy Transfer and Collectively Driven Emitters in Waveguide QED

Waveguide quantum electrodynamics (QED) has opened a new frontier in quantum optics, which enables the radiative coupling of distantly located emitters via the spatially extended waveguide mode. This coupling leads to modified emission dynamics and previous work has reported the observation of increased intensity correlations (an antidip) when probing the resonance response of multiple emitters. However, the interference between independent emitters has been shown to lead to a similar response. Here, we directly observe resonant energy transfer between two distant quantum emitters by recording an antidip in the intensity correlations, $g^{(2)}(τ)$, while driving only one of the emitters. Under the condition that only a single emitter is driven, the antidip in photon coincidences is a distinctive signature of emitter-emitter coupling, which enables the transfer of energy from the driven to the undriven emitter. Interestingly, the observed mechanism is a long-range and waveguide-engineered version of resonant Förster transfer, which is responsible for the transport of energy between chlorophylls in the photosynthesis. Building on the established coupling, we demonstrate collective driving of the coupled emitter pair. Specifically, we control the relative driving phase and amplitude of the emitters and apply this collective excitation scheme to selectively populate the long-lived subradiant state. This results in suppressed emission, i.e. the peculiar situation where driving two emitters as opposed to one effectively reduces the probability of photon emission. Our work presents novel emission regimes and excitation schemes for a multi-emitter waveguide QED system. These can be exploited to deterministically generate emitter-emitter entanglement and advanced photonic states providing robustness against losses for photonic quantum computation and quantum communication.

quant-ph↗

arXiv2025

Make LLM Inference Affordable to Everyone: Augmenting GPU Memory with NDP-DIMM

The billion-scale Large Language Models (LLMs) need deployment on expensive server-grade GPUs with large-storage HBMs and abundant computation capability. As LLM-assisted services become popular, achieving cost-effective LLM inference on budget-friendly hardware becomes the trend. Extensive researches relocate LLM parameters from expensive GPUs to host memory. However, the restricted bandwidth between the host and GPU memory limits the inference performance. This work introduces Hermes, a budget-friendly system that leverages the near-data processing (NDP) within commodity DRAM DIMMs to enhance the performance of a single consumer-grade GPU, achieving efficient LLM inference. The inherent activation sparsity in LLMs naturally divides weight parameters into two categories, termed ``hot" and ``cold" neurons, respectively. Hot neurons, which consist of only approximately 20\% of all weight parameters, account for 80\% of the total computational load, while cold neurons make up the other 80\% of parameters but are responsible for just 20\% of the computational load. Therefore, we propose a heterogeneous computing strategy: mapping hot neurons to a single computation-efficient GPU, while offloading cold neurons to NDP-DIMMs, which offer large memory size but limited computation capabilities. Meanwhile, the dynamic nature of activation sparsity needs a real-time partition of hot/cold neurons and adaptive remapping of cold neurons across multiple NDP-DIMM modules. Therefore, we introduce a lightweight predictor optimizing real-time neuron partition and adjustment between GPU and NDP-DIMMs. We also utilize a window-based online scheduling mechanism to maintain load balance among NDP-DIMM modules. Hermes facilitates the deployment of LLaMA2-70B on consumer-grade hardware at 13.75 tokens/s and realizes an average 75.24$\times$ speedup over the state-of-the-art offloading-based inference system.

cs.AR↗