arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 451 records · Page 25Linked to original sources

arXiv2025

ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development

The growing demand for Domain-Specific Architecture (DSA) has driven the development of Agile Hardware Development Methodology (AHDM). Hardware Construction Language (HCL) like Chisel offers high-level abstraction features, making it an ideal language for HCL-Based AHDM. While Large Language Models (LLMs) excel in code generation tasks, they still face challenges with Chisel generation, particularly regarding syntax correctness and design variability. Recent reasoning models have significantly enhanced code generation capabilities through test-time scaling techniques. However, we found that reasoning models without domain adaptation cannot bring substantial benefits to Chisel code generation tasks. This paper presents ChiseLLM, a solution comprising data processing and transformation, prompt-guided reasoning trace synthesis, and domain-adapted model training. We constructed high-quality datasets from public RTL code resources and guided the model to adopt structured thinking patterns through prompt enhancement methods. Experiments demonstrate that our ChiseLLM-7B and ChiseLLM-32B models improved syntax correctness by 18.85% and 26.32% respectively over base models, while increasing variability design ability by 47.58% compared to baseline reasoning models. Our datasets and models are publicly available, providing high-performance, cost-effective models for HCL-Based AHDM, and offering an effective baseline for future research. Github repository: https://github.com/observerw/ChiseLLM

cs.AI↗

arXiv2025

Discovery of a Low-mass Strong-lens System in SMACS J0723.3-7327

We report the discovery of an intriguing, low-mass galaxy-scale strong-lens system in the SMACS J0723.3-7327 galaxy cluster. By modeling James Webb Space Telescope imaging and Very Large Telescope Multi-Unit Spectroscopic Explorer spectroscopic data, we find that the lens is cluster member galaxy at $z=0.397$ with an Einstein radius of $0^{\prime \prime}.424$ $\pm$ $0^{\prime \prime}.012$, stellar mass of $M_* = (3.3 \pm 0.8) \times 10^{10} M_\odot$, half-light radius of $\sim 1$ kpc, and central stellar velocity dispersion of $140 \pm 6$ km s$^{-1}$. This lens galaxy is one of the few strong lens galaxies known to date that have stellar mass as low as $M_* \sim 10^{10.5} M_\odot$, offering an exceptional opportunity to peek into the population of low-mass galaxies that has largely remained unexplored in the context of strong-lensing studies. This strong lens system can also assist in assessing the systematic uncertainty in the lens modeling of cluster member galaxies.

astro-ph.GA↗

arXiv2025

Embodied Neuromorphic Control Applied on a 7-DOF Robotic Manipulator

The development of artificial intelligence towards real-time interaction with the environment is a key aspect of embodied intelligence and robotics. Inverse dynamics is a fundamental robotics problem, which maps from joint space to torque space of robotic systems. Traditional methods for solving it rely on direct physical modeling of robots which is difficult or even impossible due to nonlinearity and external disturbance. Recently, data-based model-learning algorithms are adopted to address this issue. However, they often require manual parameter tuning and high computational costs. Neuromorphic computing is inherently suitable to process spatiotemporal features in robot motion control at extremely low costs. However, current research is still in its infancy: existing works control only low-degree-of-freedom systems and lack performance quantification and comparison. In this paper, we propose a neuromorphic control framework to control 7 degree-of-freedom robotic manipulators. We use Spiking Neural Network to leverage the spatiotemporal continuity of the motion data to improve control accuracy, and eliminate manual parameters tuning. We validated the algorithm on two robotic platforms, which reduces torque prediction error by at least 60% and performs a target position tracking task successfully. This work advances embodied neuromorphic control by one step forward from proof of concept to applications in complex real-world tasks.

cs.RO↗

arXiv2025

Spike-Kal: A Spiking Neuron Network Assisted Kalman Filter

Kalman filtering can provide an optimal estimation of the system state from noisy observation data. This algorithm's performance depends on the accuracy of system modeling and noise statistical characteristics, which are usually challenging to obtain in practical applications. The powerful nonlinear modeling capabilities of deep learning, combined with its ability to extract features from large amounts of data automatically, offer new opportunities for improving the Kalman filter. This paper proposes a novel method that leverages the Spiking Neural Network to optimize the Kalman filter. Our approach aims to reduce the reliance on prior knowledge of system and observation noises, allowing for adaptation to varying statistical characteristics of time-varying noise. Furthermore, we investigate the potential of SNNs in improving the computational efficiency of the Kalman filter. In our method, we design an integration strategy between the SNN and the Kalman filter. The SNN is trained to directly approximate the optimal gain matrix from observation data, thereby alleviating the computational burden of complex matrix operations inherent in traditional Kalman filtering while maintaining the accuracy and robustness of state estimation. Its average error has been reduced by 18\%-65\% compared with other methods.

eess.SY↗

arXiv2025

Phenomenological study of the angle between jet axes in heavy-ion collisions

This paper presents a phenomenological study on the angle between the Standard and the Winner-Take-All (WTA) jet axes ($ΔR_{\rm axis}^{\rm WTA-Std}$) in high-energy nuclear collisions. The $p$+$p$ baseline is provided by the Pythia8 event generator. The in-medium jet propagation is simulated by the linear Boltzmann transport (LBT) model, which considers both the elastic and inelastic jet-medium interactions. Our theoretical results calculated by the LBT model show that the $ΔR_{\rm axis}^{\rm WTA-Std}$ distribution in Pb+Pb at $\sqrt{s}=5.02$ TeV is narrower than that in $p$+$p$, which agrees well with the recent ALICE measurements. The narrowing of $ΔR_{\rm axis}^{\rm WTA-Std}$ seems to violate the $p_T$-broadening nature of the jet quenching effect, usually explained by the influence of "selection bias". However, the physical details still need to be fully understood. Utilizing a matching-jet method to track the jet evolution in the QGP to remove the selection bias in the Monte Carlo simulations, we observe that the $ΔR_{\rm axis}^{\rm WTA-Std}$ distribution becomes broader due to the jet-medium interactions. At the same time, by rescaling the quark/gluon-jet fractions in Pb+Pb collisions to be the same as that in $p$+$p$, we find that the fraction change may not significantly influence the modification pattern of jet $ΔR_{\rm axis}^{\rm WTA-Std}$. On the other hand, the selected jet sample in A+A collisions has a significantly narrower initial $ΔR_{\rm axis}^{\rm WTA-Std}$ distribution than the $p$+$p$ baseline, and such a biased comparison between $p$+$p$ and A+A conceals the actual jet-broadening effect in the experimental measurements. The investigations presented in this paper will deepen our understanding of the relationship between the actual intra-jet modifications in the QGP and the experimental observations.

hep-ph↗

arXiv2025

Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization

Robot-assisted catheterization has garnered a good attention for its potentials in treating cardiovascular diseases. However, advancing surgeon-robot collaboration still requires further research, particularly on task-specific automation. For instance, automated tool segmentation can assist surgeons in visualizing and tracking of endovascular tools during cardiac procedures. While learning-based models have demonstrated state-of-the-art segmentation performances, generating ground-truth labels for fully-supervised methods is both labor-intensive time consuming, and costly. In this study, we propose a weakly-supervised learning method with multi-lateral pseudo labeling for tool segmentation in cardiovascular angiogram datasets. The method utilizes a modified U-Net architecture featuring one encoder and multiple laterally branched decoders. The decoders generate diverse pseudo labels under different perturbations, augmenting available partial labels. The pseudo labels are self-generated using a mixed loss function with shared consistency across the decoders. The weakly-supervised model was trained end-to-end and validated using partially annotated angiogram data from three cardiovascular catheterization procedures. Validation results show that the model could perform closer to fully-supervised models. Also, the proposed weakly-supervised multi-lateral method outperforms three well known methods used for weakly-supervised learning, offering the highest segmentation performance across the three angiogram datasets. Furthermore, numerous ablation studies confirmed the model's consistent performance under different parameters. Finally, the model was applied for tool segmentation in a robot-assisted catheterization experiments. The model enhanced visualization with high connectivity indices for guidewire and catheter, and a mean processing time of 35 ms per frame.

cs.CV↗

arXiv2025

Experimental Implementation of a Qubit-Efficient Variational Quantum Eigensolver with Analog Error Mitigation on a Superconducting Quantum Processor

We experimentally demonstrate a qubit-efficient variational quantum eigensolver (VQE) algorithm using a superconducting quantum processor, employing minimal quantum resources with only a transmon qubit coupled to a high-coherence photonic qubit. By leveraging matrix product states to compress the quantum state representation, we simulate an N + 1-spin circular Ising model with a transverse field. Furthermore, we develop an analog error mitigation approach through zero-noise extrapolation by introducing a precise noise injection technique for the transmon qubit. As a validation, we apply our error-mitigated qubit-efficient VQE in determining the ground state energies of a 4-spin Ising model. Our results demonstrate the feasibility of performing quantum algorithms with minimal quantum resources while effectively mitigating the impact of noise, offering a promising pathway to bridge the gap between theoretical advances and practical implementations on current noisy intermediate-scale quantum devices.

quant-ph↗

arXiv2025

LeakGuard: Detecting Memory Leaks Accurately and Scalably

Memory leaks are prevalent in various real-world software projects, thereby leading to serious attacks like denial-of-service. Though prior methods for detecting memory leaks made significant advance, they often suffer from low accuracy and weak scalability for testing large and complex programs. In this paper we present LeakGuard, a memory leak detection tool which provides satisfactory balance of accuracy and scalability. For accuracy, LeakGuard analyzes the behaviors of library and developer-defined memory allocation and deallocation functions in a path-sensitive manner and generates function summaries for them in a bottom-up approach. Additionally, we develop a pointer escape analysis technique to model the transfer of pointer ownership. For scalability, LeakGuard examines each function of interest independently by using its function summary and under-constrained symbolic execution technique, which effectively mitigates path explosion problem. Our extensive evaluation on 18 real-world software projects and standard benchmark datasets demonstrates that LeakGuard achieves significant advancements in multiple aspects: it exhibits superior MAD function identification capability compared to Goshawk, outperforms five state-of-the-art methods in defect detection accuracy, and successfully identifies 129 previously undetected memory leak bugs, all of which have been independently verified and confirmed by the respective development teams.

cs.CR↗

arXiv2025

AP-CAP: Advancing High-Quality Data Synthesis for Animal Pose Estimation via a Controllable Image Generation Pipeline

The task of 2D animal pose estimation plays a crucial role in advancing deep learning applications in animal behavior analysis and ecological research. Despite notable progress in some existing approaches, our study reveals that the scarcity of high-quality datasets remains a significant bottleneck, limiting the full potential of current methods. To address this challenge, we propose a novel Controllable Image Generation Pipeline for synthesizing animal pose estimation data, termed AP-CAP. Within this pipeline, we introduce a Multi-Modal Animal Image Generation Model capable of producing images with expected poses. To enhance the quality and diversity of the generated data, we further propose three innovative strategies: (1) Modality-Fusion-Based Animal Image Synthesis Strategy to integrate multi-source appearance representations, (2) Pose-Adjustment-Based Animal Image Synthesis Strategy to dynamically capture diverse pose variations, and (3) Caption-Enhancement-Based Animal Image Synthesis Strategy to enrich visual semantic understanding. Leveraging the proposed model and strategies, we create the MPCH Dataset (Modality-Pose-Caption Hybrid), the first hybrid dataset that innovatively combines synthetic and real data, establishing the largest-scale multi-source heterogeneous benchmark repository for animal pose estimation to date. Extensive experiments demonstrate the superiority of our method in improving both the performance and generalization capability of animal pose estimators.

cs.CV↗

arXiv2025

Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection

With the rapid growth of internet traffic, malicious network attacks have become increasingly frequent and sophisticated, posing significant threats to global cybersecurity. Traditional detection methods, including rule-based and machine learning-based approaches, struggle to accurately identify emerging threats, particularly in scenarios with limited samples. While recent advances in few-shot learning have partially addressed the data scarcity issue, existing methods still exhibit high false positive rates and lack the capability to effectively capture crucial local traffic patterns. In this paper, we propose HLoG, a novel hierarchical few-shot malicious traffic detection framework that leverages both local and global features extracted from network sessions. HLoG employs a sliding-window approach to segment sessions into phases, capturing fine-grained local interaction patterns through hierarchical bidirectional GRU encoding, while simultaneously modeling global contextual dependencies. We further design a session similarity assessment module that integrates local similarity with global self-attention-enhanced representations, achieving accurate and robust few-shot traffic classification. Comprehensive experiments on three meticulously reconstructed datasets demonstrate that HLoG significantly outperforms existing state-of-the-art methods. Particularly, HLoG achieves superior recall rates while substantially reducing false positives, highlighting its effectiveness and practical value in real-world cybersecurity applications.

cs.CR↗

arXiv2025

Simultaneously generating Brillouin microlaser and second harmonic within a lithium niobate microdisk

We report the simultaneous generation of second-harmonic generation (SHG) and Brillouin microlaser in a high-quality thin-film lithium niobate (TFLN) microdisk resonator. The microdisk is fabricated with ultrahigh-Q factor of 4X10(6) by photolithography-assisted chemo-mechanical etching, enabling significant cavity-enhancement effect for boosting nonlinear frequency conversion. Under 1559.632 nm pumping, Brillouin microlaser is demonstrated in the microdisk with Stokes Brillouin shift of 10 GHz, a low threshold of 1.81 mW, and a fundamental linewidth of 254.365 Hz. Meanwhile, efficient SHG is observed at 779.816 nm with an absolute conversion efficiency of 3.8% at pump level of 3.028 mW. The coexistence of these two nonlinear processes is enabled by the simultaneous confinement of the light and acoustic fileds for effect coupling in the microdisk, which enhances both optomechanical and second-order nonlinear interactions. This research provides new possibilities for integrated multi-frequency laser sources and multifunctional nonlinear photonic devices.

physics.optics↗

arXiv2025

ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation

Source-Free Domain Adaptation (SFDA) aims to train a target model without source data, and the key is to generate pseudo-labels using a pre-trained source model. However, we observe that the source model often produces highly uncertain pseudo-labels for hard samples, particularly those heavily affected by domain shifts, leading to these noisy pseudo-labels being introduced even before adaptation and further reinforced through parameter updates. Additionally, they continuously influence neighbor samples through propagation in the feature space.To eliminate the issue of noise accumulation, we propose a novel Progressive Curriculum Labeling (ElimPCL) method, which iteratively filters trustworthy pseudo-labeled samples based on prototype consistency to exclude high-noise samples from training. Furthermore, a Dual MixUP technique is designed in the feature space to enhance the separability of hard samples, thereby mitigating the interference of noisy samples on their neighbors.Extensive experiments validate the effectiveness of ElimPCL, achieving up to a 3.4% improvement on challenging tasks compared to state-of-the-art methods.

cs.CV↗

arXiv2025

Constraints on Velocity and Spin Dependent Exotic Interaction at the Millimeter Scale with a Diamagnetic-levitated Force Sensor

Light bosons, beyond the standard model and as prominent candidates for dark matter, can mediate velocity and spin dependent exotic interaction between electron spins and nucleons. At short ranges, it remains an open challenge to test this exotic interaction with high precision. Here, we present a method based on diamagnetic-levitated force sensor to detect the exotic interaction at the millimeter scale. Improved constraints for the coupling $g_A^e g_V^N$ are established, within the force range spanning from 0.15 mm to 1.5 mm. And the constraint $|g_A^e g_V^N| \leq 4.39 \times10^{-26}$ at $λ$ = 0.5 mm at the $95 \%$ confidence level, significantly surpasses previous results by more than three orders of magnitude. The diamagnetic levitation force measurement system developed here can also be repurposed to probe other exotic spin-dependent interactions, such as the exotic spin-spin interaction. This system provides a platform for the exploration of dark matter.

hep-ph↗

arXiv2025

Distributed Forgetting-factor Regret-based Online Optimization over Undirected Connected Networks

The evaluation of final-iteration tracking performance is a formidable obstacle in distributed online optimization algorithms. To address this issue, this paper proposes a novel evaluation metric named distributed forgetting-factor regret (DFFR). It incorporates a weight into the loss function at each iteration, which progressively reduces the weights of historical loss functions while enabling dynamic weights allocation across optimization horizon. Furthermore, we develop two distributed online optimization algorithms based on DFFR over undirected connected networks: the Distributed Online Gradient-free Algorithm for bandit-feedback problems and the Distributed Online Projection-free Algorithm for high-dimensional problems. Through theoretical analysis, we derive the upper bounds of DFFR for both algorithms and further prove that under mild conditions, DFFR either converges to zero or maintains a tight upper bound as iterations approach infinity. Experimental simulation demonstrates the effectiveness of the algorithms and the superior performance of DFFR.

eess.SY↗

arXiv2025

T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge

The deployment of Large Language Models (LLMs) on edge devices is increasingly important to enhance on-device intelligence. Weight quantization is crucial for reducing the memory footprint of LLMs on devices. However, low-bit LLMs necessitate mixed precision matrix multiplication (mpGEMM) of low precision weights and high precision activations during inference. Existing systems, lacking native support for mpGEMM, resort to dequantize weights for high precision computation. Such an indirect way can lead to a significant inference overhead. In this paper, we introduce T-MAC, an innovative lookup table(LUT)-based method designed for efficient low-bit LLM (i.e., weight-quantized LLM) inference on CPUs. T-MAC directly supports mpGEMM without dequantization, while simultaneously eliminating multiplications and reducing additions required. Specifically, T-MAC transforms the traditional data-type-centric multiplication to bit-wise table lookup, and enables a unified and scalable mpGEMM solution. Our LUT-based kernels scale linearly to the weight bit-width. Evaluated on low-bit Llama and BitNet models, T-MAC demonstrates up to 4x increase in throughput and 70% reduction in energy consumption compared to llama.cpp. For BitNet-b1.58-3B, T-MAC delivers a token generation throughput of 30 tokens/s with a single core and 71 tokens/s with eight cores on M2-Ultra, and 11 tokens/s on lower-end devices like Raspberry Pi 5, which significantly exceeds the adult average reading speed. T-MAC with LUT-based computing paradigm, paves the way for the practical deployment of low-bit LLMs on resource-constrained edge devices without compromising computational efficiency. The system is open-sourced at https://github.com/microsoft/T-MAC .

cs.DC↗

arXiv2025

Design of an Expression Recognition Solution Based on the Global Channel-Spatial Attention Mechanism and Proportional Criterion Fusion

Facial expression recognition is a challenging classification task that holds broad application prospects in the field of human-computer interaction. This paper aims to introduce the method we will adopt in the 8th Affective and Behavioral Analysis in the Wild (ABAW) Competition, which will be held during the Conference on Computer Vision and Pattern Recognition (CVPR) in 2025.First of all, we apply the frequency masking technique and the method of extracting data at equal time intervals to conduct targeted processing on the original videos. Then, based on the residual hybrid convolutional neural network and the multi-branch convolutional neural network respectively, we design feature extraction models for image and audio sequences. In particular, we propose a global channel-spatial attention mechanism to enhance the features initially extracted from both the audio and image modalities respectively.Finally, we adopt a decision fusion strategy based on the proportional criterion to fuse the classification results of the two single modalities, obtain an emotion probability vector, and output the final emotional classification. We also design a coarse - fine granularity loss function to optimize the performance of the entire network, which effectively improves the accuracy of facial expression recognition.In the facial expression recognition task of the 8th ABAW Competition, our method ranked third on the official validation set. This result fully confirms the effectiveness and competitiveness of the method we have proposed.

cs.CV↗

arXiv2025

ISLS: IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system's performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems.

eess.SP↗

arXiv2025

Robust Dataset Distillation by Matching Adversarial Trajectories

Dataset distillation synthesizes compact datasets that enable models to achieve performance comparable to training on the original large-scale datasets. However, existing distillation methods overlook the robustness of the model, resulting in models that are vulnerable to adversarial attacks when trained on distilled data. To address this limitation, we introduce the task of ``robust dataset distillation", a novel paradigm that embeds adversarial robustness into the synthetic datasets during the distillation process. We propose Matching Adversarial Trajectories (MAT), a method that integrates adversarial training into trajectory-based dataset distillation. MAT incorporates adversarial samples during trajectory generation to obtain robust training trajectories, which are then used to guide the distillation process. As experimentally demonstrated, even through natural training on our distilled dataset, models can achieve enhanced adversarial robustness while maintaining competitive accuracy compared to existing distillation methods. Our work highlights robust dataset distillation as a new and important research direction and provides a strong baseline for future research to bridge the gap between efficient training and adversarial robustness.

cs.CV↗