arXiv subjects

Ruoyu Zhang

Publications and source records attributed to Ruoyu Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Rapid Adaptation of SpO2 Estimation to Wearable Devices via Transfer Learning on Low-Sampling-Rate PPG

Blood oxygen saturation (SpO2) is a vital marker for healthcare monitoring. Traditional SpO2 estimation methods often rely on complex clinical calibration, making them unsuitable for low-power, wearable applications. In this paper, we propose a transfer learning-based framework for the rapid adaptation of SpO2 estimation to energy-efficient wearable devices using low-sampling-rate (25Hz) dual-channel photoplethysmography (PPG). We first pretrain a bidirectional Long Short-Term Memory (BiLSTM) model with self-attention on a public clinical dataset, then fine-tune it using data collected from our wearable We-Be band and an FDA-approved reference pulse oximeter. Experimental results show that our approach achieves a mean absolute error (MAE) of 2.967% on the public dataset and 2.624% on the private dataset, significantly outperforming traditional calibration and non-transferred machine learning baselines. Moreover, using 25Hz PPG reduces power consumption by 40% compared to 100Hz, excluding baseline draw. Our method also attains an MAE of 3.284% in instantaneous SpO2 prediction, effectively capturing rapid fluctuations. These results demonstrate the rapid adaptation of accurate, low-power SpO2 monitoring on wearable devices without the need for clinical calibration.

eess.SP↗

arXiv2025

Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning

Accurate and generalizable blood pressure (BP) estimation is vital for the early detection and management of cardiovascular diseases. In this study, we enforce subject-level data splitting on a public multi-wavelength photoplethysmography (PPG) dataset and propose a generalizable BP estimation framework based on curriculum-adversarial learning. Our approach combines curriculum learning, which transitions from hypertension classification to BP regression, with domain-adversarial training that confuses subject identity to encourage the learning of subject-invariant features. Experiments show that multi-channel fusion consistently outperforms single-channel models. On the four-wavelength PPG dataset, our method achieves strong performance under strict subject-level splitting, with mean absolute errors (MAE) of 14.2mmHg for systolic blood pressure (SBP) and 6.4mmHg for diastolic blood pressure (DBP). Additionally, ablation studies validate the effectiveness of both the curriculum and adversarial components. These results highlight the potential of leveraging complementary information in multi-wavelength PPG and curriculum-adversarial strategies for accurate and robust BP estimation.

eess.SP↗

arXiv2025

Know Me by My Pulse: Toward Practical Continuous Authentication on Wearable Devices via Wrist-Worn PPG

Biometric authentication using physiological signals offers a promising path toward secure and user-friendly access control in wearable devices. While electrocardiogram (ECG) signals have shown high discriminability, their intrusive sensing requirements and discontinuous acquisition limit practicality. Photoplethysmography (PPG), on the other hand, enables continuous, non-intrusive authentication with seamless integration into wrist-worn wearable devices. However, most prior work relies on high-frequency PPG (e.g., 75 - 500 Hz) and complex deep models, which incur significant energy and computational overhead, impeding deployment in power-constrained real-world systems. In this paper, we present the first real-world implementation and evaluation of a continuous authentication system on a smartwatch, We-Be Band, using low-frequency (25 Hz) multi-channel PPG signals. Our method employs a Bi-LSTM with attention mechanism to extract identity-specific features from short (4 s) windows of 4-channel PPG. Through extensive evaluations on both public datasets (PTTPPG) and our We-Be Dataset (26 subjects), we demonstrate strong classification performance with an average test accuracy of 88.11%, macro F1-score of 0.88, False Acceptance Rate (FAR) of 0.48%, False Rejection Rate (FRR) of 11.77%, and Equal Error Rate (EER) of 2.76%. Our 25 Hz system reduces sensor power consumption by 53% compared to 512 Hz and 19% compared to 128 Hz setups without compromising performance. We find that sampling at 25 Hz preserves authentication accuracy, whereas performance drops sharply at 20 Hz while offering only trivial additional power savings, underscoring 25 Hz as the practical lower bound. Additionally, we find that models trained exclusively on resting data fail under motion, while activity-diverse training improves robustness across physiological states.

cs.CR↗

arXiv2025

Hyperon Pair Production at BESIII

This poster presents recent experimental findings from the BESIII experiment, including two categories of studies. Firstly, it reports the studies on the hyperon pair production in the processes $e^+e^- \to Λ\barΛ$, $Σ^+\barΣ^-$, $Σ^0\barΣ^0$, $Ξ^0\barΞ^0$ and $Ξ^-\barΞ^+$. The Born cross sections for these processes are measured at center-of-mass energies ranging from $3.5$ to $4.9$ GeV. For the first time, evidence for the decays $ψ(3770)\toΛ\barΛ$ and $Ξ^-\barΞ^+$ is found. Secondly, it reports the measurements of the Born cross sections of multi-body decays involving hyperon final states. The evidence of $ψ(4160)\to K^-\barΞ^+Λ+c.c.$ is found. These results offer new perspectives on hyperon production in $e^+e^-$ annihilation and the decay mechanism of vector charmonium(-like) states, contributing to our understanding of hadron dynamics.

hep-ex↗

arXiv2025

DeepSeek-V3 Technical Report

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

cs.CL↗

arXiv2025

TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs; however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs' inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs' contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models.

cs.CV↗

arXiv2025

Terahertz Integrated Sensing and Communication-Empowered UAVs in 6G: A Transceiver Design Perspective

Due to their high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are expected to play a pivotal role in not only communication, but also sensing. Especially by exploiting the ultra-wide bandwidth of terahertz (THz) bands, integrated sensing and communication (ISAC)-empowered UAV has been a promising technology of 6G space-air-ground integrated networks. In this article, we systematically investigate the key techniques and essential obstacles for THz-ISAC-empowered UAV from a transceiver design perspective, with the highlight of its major challenges and key technologies. Specifically, we discuss the THz-ISAC-UAV wireless propagation environment, based on which several channel characteristics for communication and sensing are revealed. We point out the transceiver payload design peculiarities for THz-ISAC-UAV from the perspective of antenna design, radio frequency front-end, and baseband signal processing. To deal with the specificities faced by the payload, we shed light on three key technologies, i.e., hybrid beamforming for ultra-massive MIMO-ISAC, power-efficient THz-ISAC waveform design, as well as communication and sensing channel state information acquisition, and extensively elaborate their concepts and key issues. More importantly, future research directions and associated open problems are presented, which may unleash the full potential of THz-ISAC-UAV for 6G wireless networks.

eess.SP↗

arXiv2025

DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling

Graph convolutional networks (GCNs) have emerged as a powerful tool for skeleton-based action and gesture recognition, thanks to their ability to model spatial and temporal dependencies in skeleton data. However, existing GCN-based methods face critical limitations: (1) they lack effective spatio-temporal topology modeling that captures dynamic variations in skeletal motion, and (2) they struggle to model multiscale structural relationships beyond local joint connectivity. To address these issues, we propose a novel framework called Dynamic Spatial-Temporal Semantic Awareness Graph Convolutional Network (DSTSA-GCN). DSTSA-GCN introduces three key modules: Group Channel-wise Graph Convolution (GC-GC), Group Temporal-wise Graph Convolution (GT-GC), and Multi-Scale Temporal Convolution (MS-TCN). GC-GC and GT-GC operate in parallel to independently model channel-specific and frame-specific correlations, enabling robust topology learning that accounts for temporal variations. Additionally, both modules employ a grouping strategy to adaptively capture multiscale structural relationships. Complementing this, MS-TCN enhances temporal modeling through group-wise temporal convolutions with diverse receptive fields. Extensive experiments demonstrate that DSTSA-GCN significantly improves the topology modeling capabilities of GCNs, achieving state-of-the-art performance on benchmark datasets for gesture and action recognition, including SHREC17 Track, DHG-14\/28, NTU-RGB+D, and NTU-RGB+D-120.

cs.CV↗

arXiv2025

Channel Estimation for Movable-Antenna MIMO Systems Via Tensor Decomposition

In this letter, we investigate the channel estimation problem for MIMO wireless communication systems with movable antennas (MAs) at both the transmitter (Tx) and receiver (Rx). To achieve high channel estimation accuracy with low pilot training overhead, we propose a tensor decomposition-based method for estimating the parameters of multi-path channel components, including their azimuth and elevation angles, as well as complex gain coefficients, thereby reconstructing the wireless channel between any pair of Tx and Rx MA positions in the Tx and Rx regions. First, we introduce a two-stage Tx-Rx successive antenna movement pattern for pilot training, such that the received pilot signals in both stages can be expressed as a third-order tensor. Then, we obtain the factor matrices of the tensor via the canonical polyadic decomposition, and thereby estimate the angle/gain parameters for enabling the channel reconstruction between arbitrary Tx/Rx MA positions. In addition, we analyze the uniqueness condition of the tensor decomposition, which ensures the complete channel reconstruction between the whole Tx and Rx regions based on the channel measurements at only a finite number of Tx/Rx MA positions. Finally, simulation results are presented to evaluate the proposed tensor decomposition-based method as compared to existing methods, in terms of channel estimation accuracy and pilot overhead.

eess.SP↗

arXiv2024

GESH-Net: Graph-Enhanced Spherical Harmonic Convolutional Networks for Cortical Surface Registration

Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. Researching cortical surface registration based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. (2)By integrating the attention mechanism, a graph-enhenced module is introduced into the registration network, using the graph attention module to help the network learn global features of cortical surface data, enhancing the learning ability of the network. The results show that the graph attention module effectively enhances the network's ability to extract global features, and its registration results have significant advantages over other methods.

cs.CV↗

arXiv2024

DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure

While speculative decoding has recently appeared as a promising direction for accelerating the inference of large language models (LLMs), the speedup and scalability are strongly bounded by the token acceptance rate. Prevalent methods usually organize predicted tokens as independent chains or fixed token trees, which fails to generalize to diverse query distributions. In this paper, we propose DySpec, a faster speculative decoding algorithm with a novel dynamic token tree structure. We begin by bridging the draft distribution and acceptance rate from intuitive and empirical clues, and successfully show that the two variables are strongly correlated. Based on this, we employ a greedy strategy to dynamically expand the token tree at run time. Theoretically, we show that our method can achieve optimal results under mild assumptions. Empirically, DySpec yields a higher acceptance rate and speedup than fixed trees. DySpec can drastically improve the throughput and reduce the latency of token generation across various data distribution and model sizes, which significantly outperforms strong competitors, including Specinfer and Sequoia. Under low temperature setting, DySpec can improve the throughput up to 9.1$\times$ and reduce the latency up to 9.4$\times$ on Llama2-70B. Under high temperature setting, DySpec can also improve the throughput up to 6.21$\times$, despite the increasing difficulty of speculating more than one token per step for draft model.

cs.LG↗

arXiv2024

APEX: Attention on Personality based Emotion ReXgnition Framework

Automated emotion recognition has applications in various fields, such as human-machine interaction, healthcare, security, education, and emotion-aware recommendation/feedback systems. Developing methods to analyze human emotions accurately is essential to enable such diverse applications. Multiple studies have been conducted to explore the possibility of using physiological signals and machine-learning techniques to evaluate human emotions. Furthermore, internal factors such as personality have been considered and involved in emotion recognition. However, integrating personality that is user specific within traditional machine-learning methods that use user-agnostic large data sets has become a critical problem. This study proposes the APEX: attention on personality-based emotion recognition framework, in which multiple weak classifiers are trained on physiological signals of each participant's data, and the classification results are reweighed based on the personality correlations between corresponding subjects and test subjects. Experiments have been conducted on the ASCERTAIN dataset, and the results show that the proposed framework outperforms existing studies.

eess.SY↗

arXiv2024

Advanced Energy-Efficient System for Precision Electrodermal Activity Monitoring in Stress Detection

This paper presents a novel Electrodermal Activity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on enhancing the accuracy and efficiency of EDA measurements, a reliable indicator of stress. Traditional EDA monitoring solutions often grapple with trade-offs between sensor placement, cost, and power consumption, leading to compromised data accuracy. Our innovative design incorporates an adaptive gain mechanism, catering to the broad dynamic range and high-resolution needs of EDA data analysis. The performance of our system was extensively tested through simulations and a custom Printed Circuit Board (PCB), achieving an error rate below 1\% and maintaining power consumption at a mere 700$μ$A under a 3.7V power supply. This research contributes significantly to the field of wearable health technology, offering a robust and efficient solution for long-term stress monitoring.

eess.SY↗

arXiv2024

Leveraging Large Language Model as Simulated Patients for Clinical Education

Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice. However, the high cost of training and hiring qualified SPs, along with the heavy workload and potential risks they face in consistently portraying actual patients, limit students' access to this type of clinical training. Consequently, the integration of computer program-based simulated patients has emerged as a valuable educational tool in recent years. With the rapid development of Large Language Models (LLMs), their exceptional capabilities in conversational artificial intelligence and role-playing have been demonstrated, making them a feasible option for implementing Virtual Simulated Patient (VSP). In this paper, we present an integrated model-agnostic framework called CureFun that harnesses the potential of LLMs in clinical medical education. This framework facilitates natural conversations between students and simulated patients, evaluates their dialogue, and provides suggestions to enhance students' clinical inquiry skills. Through comprehensive evaluations, our approach demonstrates more authentic and professional SP-scenario dialogue flows compared to other LLM-based chatbots, thus proving its proficiency in simulating patients. Additionally, leveraging CureFun's evaluation ability, we assess several medical LLMs and discuss the possibilities and limitations of using LLMs as virtual doctors from the perspective of their diagnostic abilities.

cs.CL↗

arXiv2024

Large Language Models for Code Analysis: Do LLMs Really Do Their Job?

Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into harnessing LLMs for code analysis purposes. However, the existing body of literature falls short in delivering a systematic evaluation and assessment of LLMs' effectiveness in code analysis, particularly in the context of obfuscated code. This paper seeks to bridge this gap by offering a comprehensive evaluation of LLMs' capabilities in performing code analysis tasks. Additionally, it presents real-world case studies that employ LLMs for code analysis. Our findings indicate that LLMs can indeed serve as valuable tools for automating code analysis, albeit with certain limitations. Through meticulous exploration, this research contributes to a deeper understanding of the potential and constraints associated with utilizing LLMs in code analysis, paving the way for enhanced applications in this critical domain.

cs.SE↗

arXiv2024

Achieving quantized transport in Floquet topological insulators via energy filters

Due to photon-assisted transport processes, chiral edge modes induced by periodic driving do not directly mediate quantized transport. Here we show how narrow bandwidth "energy filters" can restore quantization by suppressing photon assisted transport through Floquet sidebands. We derive a Floquet Landauer type equation to describe transport through such an energy-filtered setup, and show how the filter can be integrated out to yield a sharply energy-dependent renormalized system-lead coupling. We show analytically and through numerical simulations that a nearly quantized conductance can be achieved in both off-resonantly and resonantly induced quasienergy gaps when filters are introduced. The conductance approaches the appropriate quantized value on each plateau with increasing system and filter size. We introduce a "Floquet distribution function" and show both analytically and numerically that it approaches the equilibrium Fermi-Dirac form when narrow-band filters are introduced, highlighting the mechanism that restores quantized transport.

cond-mat.mes-hall↗

arXiv2024

FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

Transformer-based Large Language Models (LLMs) have made a significant impact on various domains. However, LLMs' efficiency suffers from both heavy computation and memory overheads. Compression techniques like sparsification and quantization are commonly used to mitigate the gap between LLM's computation/memory overheads and hardware capacity. However, existing GPU and transformer-based accelerators cannot efficiently process compressed LLMs, due to the following unresolved challenges: low computational efficiency, underutilized memory bandwidth, and large compilation overheads. This paper proposes FlightLLM, enabling efficient LLMs inference with a complete mapping flow on FPGAs. In FlightLLM, we highlight an innovative solution that the computation and memory overhead of LLMs can be solved by utilizing FPGA-specific resources (e.g., DSP48 and heterogeneous memory hierarchy). We propose a configurable sparse DSP chain to support different sparsity patterns with high computation efficiency. Second, we propose an always-on-chip decode scheme to boost memory bandwidth with mixed-precision support. Finally, to make FlightLLM available for real-world LLMs, we propose a length adaptive compilation method to reduce the compilation overhead. Implemented on the Xilinx Alveo U280 FPGA, FlightLLM achieves 6.0$\times$ higher energy efficiency and 1.8$\times$ better cost efficiency against commercial GPUs (e.g., NVIDIA V100S) on modern LLMs (e.g., LLaMA2-7B) using vLLM and SmoothQuant under the batch size of one. FlightLLM beats NVIDIA A100 GPU with 1.2$\times$ higher throughput using the latest Versal VHK158 FPGA.

cs.AR↗

arXiv2024

Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde

cs.IT↗