arXiv subjects

Yongsheng Huang

Publications and source records attributed to Yongsheng Huang.

At least 19 recordsLinked to original sources

arXiv2026

Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks

Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter-temporal self-distillation, implicitly assuming that per-timestep predictions should be treated equally. In practice, SNN predictions vary and evolve over time, and intermediate timesteps need not all be individually correct even when the final aggregated output is correct. Under such conditions, effective distillation should not force every timestep toward the same supervision target, but instead provide corrective guidance to erroneous timesteps while preserving useful temporal dynamics. To address this issue, we propose Selective Alignment Knowledge Distillation (SeAl-KD), which selectively aligns class-level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter-timestep similarity. Extensive experiments on static image and neuromorphic event-based datasets demonstrate consistent improvements over existing distillation methods. The code is available at https://github.com/KaiSUN1/SeAl

cs.LG↗

arXiv2026

Single-frame super-resolution via Sparse Point Optimization

Fluorescence microscopy is essential in biological and medical research, providing critical insights into cellular structures. However, limited by optical diffraction and background noise, a substantial amount of hidden information is still unexploited. To address these challenges, we introduce a novel computational method, termed Sparse Point Optimization Theory (SPOT), which accurately localizes fluorescent emitters by solving an optimization problem. Our results demonstrate that SPOT successfully resolves 30 nm fluorescent line pairs, reveals structural details beyond the diffraction limit in both Airyscan and structured illumination microscopy, and outperforms established algorithms in single-molecule localization tasks. This generic method effectively pushes the resolution limit in the presence of noise, and holds great promise for advancing fluorescence microscopy and analysis in cell biology.

physics.bio-ph↗

arXiv2026

MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks

Spiking Neural Networks (SNNs) currently face a critical bottleneck: while individual neurons exhibit dynamic biological properties, their macro-scopic architectures remain confined within conventional connectivity patterns that are static and hierarchical. This discrepancy between neuron-level dynamics and network-level fixed connectivity eliminates critical brain-like lateral interactions, limiting adaptability in changing environments. To address this, we propose MorphSNN, a backbone framework inspired by biological non-synaptic diffusion and structural plasticity. Specifically, we introduce a Graph Diffusion (GD)mechanism to facilitate efficient undirected signal propagation, complementing the feedforward hierarchy. Furthermore, it incorporates a Spatio-Temporal Structural Plasticity (STSP) mechanism, endowing the network with the capability for instance-specific, dynamic topological reorganization, thereby overcoming the limitations of fixed topologies. Experiments demonstrate that MorphSNN achieves state-of-the-art accuracy on static and neuromorphic datasets; for instance, it reaches 83.35% accuracy on N-Caltech101 with only 5 timesteps. More importantly, its self-evolving topology functions as an intrinsic distribution fingerprint, enabling superior Out-of- Distribution (OOD) detection without auxiliary training. The code is available at anonymous.4open.science/r/MorphSNN-B0BC.

cs.NE↗

arXiv2026

We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification

The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep learning. However, existing studies face challenges in domain incremental learning. In this paper, we propose a lightweight and robust dual-causal disentanglement framework (DualCD) to enhance the robustness of models under domain incremental scenarios, which can be seamlessly integrated into time series classification models. Specifically, DualCD first introduces a temporal feature disentanglement module to capture class-causal features and spurious features. The causal features can offer sufficient predictive power to support the classifier in domain incremental learning settings. To accurately capture these causal features, we further design a dual-causal intervention mechanism to eliminate the influence of both intra-class and inter-class confounding features. This mechanism constructs variant samples by combining the current class's causal features with intra-class spurious features and with causal features from other classes. The causal intervention loss encourages the model to accurately predict the labels of these variant samples based solely on the causal features. Extensive experiments on multiple datasets and models demonstrate that DualCD effectively improves performance in domain incremental scenarios. We summarize our rich experiments into a comprehensive benchmark to facilitate research in domain incremental time series classification.

cs.LG↗

arXiv2025

CogniSNN: An Exploration to Random Graph Architecture based Spiking Neural Networks with Enhanced Depth-Scalability and Path-Plasticity

Currently, most spiking neural networks (SNNs) still mimic the chain-like hierarchical architecture in traditional artificial neural networks (ANNs). This method significantly differs from random connections between neurons found in biological brains, limiting the ability to model the evolving mechanisms of neural pathways in biological neural systems, particularly in terms of dynamic depth-scalability and adaptive path-plasticity. This paper develops a new modeling paradigm for SNNs with random graph architecture (RGA), termed Cognition-aware SNN (CogniSNN). Furthermore, we model the depth-scalability and path-plasticity in CogniSNN by introducing a modified spiking residual neural node (ResNode) to counteract network degradation in deeper graph pathways, as well as a critical path-based algorithm that enables CogniSNN to perform path reusability on new tasks leveraging the features of the data and the RGA learned in old tasks. Experiments show that the performance of CogniSNN with redesigned ResNode is comparable, even superior, to current state-of-the-art SNNs on neuromorphic datasets. The critical path-based approach effectively achieves path reuse capability while maintaining expected performance in learning new tasks that are similar to or distinct from the old ones. This study showcases the potential of RGA-based SNNs and paves a new path for modeling the fusion of computational neuroscience and deep intelligent agents. The code is available at github.com/Yongsheng124/CogniSNN.

cs.NE↗

arXiv2025

CogniSNN: Enabling Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability with Random Graph Architectures in Spiking Neural Networks

Spiking neural networks (SNNs), regarded as the third generation of artificial neural networks, are expected to bridge the gap between artificial intelligence and computational neuroscience. However, most mainstream SNN research directly adopts the rigid, chain-like hierarchical architecture of traditional artificial neural networks (ANNs), ignoring key structural characteristics of the brain. Biological neurons are stochastically interconnected, forming complex neural pathways that exhibit Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability. In this paper, we introduce a new SNN paradigm, named Cognition-aware SNN (CogniSNN), by incorporating Random Graph Architecture (RGA). Furthermore, we address the issues of network degradation and dimensional mismatch in deep pathways by introducing an improved pure spiking residual mechanism alongside an adaptive pooling strategy. Then, we design a Key Pathway-based Learning without Forgetting (KP-LwF) approach, which selectively reuses critical neural pathways while retaining historical knowledge, enabling efficient multi-task transfer. Finally, we propose a Dynamic Growth Learning (DGL) algorithm that allows neurons and synapses to grow dynamically along the internal temporal dimension. Extensive experiments demonstrate that CogniSNN achieves performance comparable to, or even surpassing, current state-of-the-art SNNs on neuromorphic datasets and Tiny-ImageNet. The Pathway-Reusability enhances the network's continuous learning capability across different scenarios, while the dynamic growth algorithm improves robustness against interference and mitigates the fixed-timestep constraints during neuromorphic chip deployment. This work demonstrates the potential of SNNs with random graph structures in advancing brain-inspired intelligence and lays the foundation for their practical application on neuromorphic hardware.

cs.NE↗

arXiv2025

Estimation of the Inverse Compton Scattering Background in MeV Gamma-Gamma Collider

The MeV Gamma-Gamma Collider would provide a direct experimental platform for elastic light-by-light scattering ($γγ\to γγ$) and the Breit-Wheeler process with two real photons ($γγ\to e^+e^-$). A Monte Carlo code, the Genie Background Evaluation Tool (GBET), has been developed to fully simulate two successive inverse Compton scatterings in the linear regime, including $e^- + \text{laser} \to e^-+ γ$ and $e^- + γ\rightarrow e^- + γ$. GBET overcomes the inherent information loss in traditional luminosity-spectrum-based chain simulations, preserving full particle-level information and achieving higher physical fidelity. The effectiveness of the code is verified by benchmarking against the simulation results of CAIN. GBET shows that the event rate of background photons generated by the first inverse Compton scattering is $6.24 \times 10^7$/s, with energies below 18 eV; the second inverse Compton scattering generates background electrons at 51.99/s and photons at 0.99/s, both with energies below 11 MeV. In addition, Møller scattering contributes background electrons at 0.56/s with energies around 200 MeV. The count rates of background electrons and positrons originating from the Breit-Wheeler process are 1312.2/s and 1314.3/s, respectively, with energy distributions ranging from 511 to 720 keV.

physics.ins-det↗

arXiv2025

TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting

Although Transformers excel in natural language processing, their extension to time series forecasting remains challenging due to insufficient consideration of the differences between textual and temporal modalities. In this paper, we develop a novel Transformer architecture designed for time series data, aiming to maximize its representational capacity. We identify two key but often overlooked characteristics of time series: (1) unidirectional influence from the past to the future, and (2) the phenomenon of decaying influence over time. These characteristics are introduced to enhance the attention mechanism of Transformers. We propose TimeFormer, whose core innovation is a self-attention mechanism with two modulation terms (MoSA), designed to capture these temporal priors of time series under the constraints of the Hawkes process and causal masking. Additionally, TimeFormer introduces a framework based on multi-scale and subsequence analysis to capture semantic dependencies at different temporal scales, enriching the temporal dependencies. Extensive experiments conducted on multiple real-world datasets show that TimeFormer significantly outperforms state-of-the-art methods, achieving up to a 7.45% reduction in MSE compared to the best baseline and setting new benchmarks on 94.04\% of evaluation metrics. Moreover, we demonstrate that the MoSA mechanism can be broadly applied to enhance the performance of other Transformer-based models.

cs.LG↗

arXiv2025

Cascading nuclear excitation of $^{235}\text{U}$ via inelastic electron scattering in laser-irradiated clusters

Nuclear excitation induced by lasers holds broad application prospects in precision metrology and nuclear energy, such as nuclear batteries and nuclear clocks. In the present work, the nuclear excitation via inelastic electron scattering (NEIES) mechanism in $^{235}\text{U}$ is investigated by combining the Dirac-Hartree-Fock-Slater method for theoretical calculations with 2D3V particle-in-cell (PIC) simulations for numerical modeling. The excitation cross-sections of $^{235}\text{U}$ from the ground state to excited states is evaluated and an efficient indirect excitation scheme for generating $^{235}\text{U}$ isomers is proposed by first exciting the nuclei from ground-state to high-energy excited states and then decays to the isomeric state. The calculations show that laser-tuned multi-channel NEIES can boost isomeric state accumulation efficiency by eleven orders of magnitude over direct excitation. Additionally, PIC simulations reveal that laser polarization significantly alters high-energy electron distributions. At identical intensity, p-polarized lasers generate seven times more electrons with energies sufficient for exciting $^{235}\text{U}$ to the second excited state (and beyond to the fourth state) compared to s-polarization. This work offers a practical and feasible way for efficient isomeric state generation, supporting experimental tests of laser-driven nuclear excitation in sub-TW lasers.

nucl-th↗

arXiv2025

DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification

Real-world time series typically exhibit complex temporal variations, making the time series classification task notably challenging. Recent advancements have demonstrated the potential of multi-scale analysis approaches, which provide an effective solution for capturing these complex temporal patterns. However, existing multi-scale analysis-based time series prediction methods fail to eliminate redundant scale-shared features across multi-scale time series, resulting in the model over- or under-focusing on scale-shared features. To address this issue, we propose a novel end-to-end Disentangled Multi-Scale framework for Time Series classification (DisMS-TS). The core idea of DisMS-TS is to eliminate redundant shared features in multi-scale time series, thereby improving prediction performance. Specifically, we propose a temporal disentanglement module to capture scale-shared and scale-specific temporal representations, respectively. Subsequently, to effectively learn both scale-shared and scale-specific temporal representations, we introduce two regularization terms that ensure the consistency of scale-shared representations and the disparity of scale-specific representations across all temporal scales. Extensive experiments conducted on multiple datasets validate the superiority of DisMS-TS over its competitive baselines, with the accuracy improvement up to 9.71%.

cs.AI↗

arXiv2025

A Comprehensive Monte Carlo Simulation Tool on Electron Transport in Noble Gases and Liquids

For the particle detectors based on noble gases or liquids, it is essential to understand the transport dynamic and the properties of the electrons. We report the development of a tool for electron transport in noble gases He, Ne, Ar, Kr, or Xe, and liquids Ar, Kr, or Xe. The simulation, implemented in C++ and MATLAB, is based on electron-atom collisions, including elastic scattering, excitation and ionization. We validate the program through assessing the electron's swarm parameters, specifically the drift velocity and the diffusion coefficient. For electron transport in liquids, two models are discussed and both are used for the construction of the Monte Carlo framework based on the Cohen Leker theory. The results demonstrate the effectiveness and accuracy of the simulation tool, which offers a valuable support for detector design and data analysis.

physics.ins-det↗

arXiv2025

Topology-aware Microservice Architecture in Edge Networks: Deployment Optimization and Implementation

As a ubiquitous deployment paradigm, integrating microservice architecture (MSA) into edge networks promises to enhance the flexibility and scalability of services. However, it also presents significant challenges stemming from dispersed node locations and intricate network topologies. In this paper, we have proposed a topology-aware MSA characterized by a three-tier network traffic model encompassing the service, microservices, and edge node layers. This model meticulously characterizes the complex dependencies between edge network topologies and microservices, mapping microservice deployment onto link traffic to accurately estimate communication delay. Building upon this model, we have formulated a weighted sum communication delay optimization problem considering different types of services. Then, a novel topology-aware and individual-adaptive microservices deployment (TAIA-MD) scheme is proposed to solve the problem efficiently, which accurately senses the network topology and incorporates an individual-adaptive mechanism in a genetic algorithm to accelerate the convergence and avoid local optima. Extensive simulations show that, compared to the existing deployment schemes, TAIA-MD improves the communication delay performance by approximately 30% to 60% and effectively enhances the overall network performance. Furthermore, we implement the TAIA-MD scheme on a practical microservice physical platform. The experimental results demonstrate that TAIA-MD achieves superior robustness in withstanding link failures and network fluctuations.

cs.NI↗

arXiv2025

DMSA: A Decentralized Microservice Architecture for Edge Networks

The dispersed node locations and complex topologies of edge networks, combined with intricate dynamic microservice dependencies, render traditional centralized microservice architectures (MSAs) unsuitable. In this paper, we propose a decentralized microservice architecture (DMSA), which delegates scheduling functions from the control plane to edge nodes. DMSA redesigns and implements three core modules of microservice discovery, monitoring, and scheduling for edge networks to achieve precise awareness of instance deployments, low monitoring overhead and measurement errors, and accurate dynamic scheduling, respectively. Particularly, DMSA has customized a microservice scheduling scheme that leverages multi-port listening and zero-copy forwarding to guarantee high data forwarding efficiency. Moreover, a dynamic weighted multi-level load balancing algorithm is proposed to adjust scheduling dynamically with consideration of reliability, priority, and response delay. Finally, we have implemented a physical verification platform for DMSA. Extensive empirical results demonstrate that compared to state-of-the-art and traditional scheduling schemes, DMSA effectively counteracts link failures and network fluctuations, improving the service response delay and execution success rate by approximately $60\% \sim 75\%$ and $10\%\sim15\%$, respectively.

cs.NI↗

arXiv2024

Network-Aware Reliability Modeling and Optimization for Microservice Placement

Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 29% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 62% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 21% compared to the SRP algorithm with the shared backup mechanism.

cs.NI↗

arXiv2024

Stable radiation field positron acceleration in a micro-tube

Nowadays, there is a desperate need for an ultra-acceleration-gradient method for antimatter particles, which holds great significance in exploring the origin of matter, CP violation, astrophysics, and medical physics. Compared to traditional accelerators with low gradients and a limited acceleration region for positrons in laser-driven charge separation fields, we propose an innovative high-gradient positron acceleration mechanism with implementation advantages. Injecting a relativistic electron beam into a dense plasma micro-tube generates a stable and periodic high-intensity mid-infrared radiation (mid-IR) field, reaching tens of GV/m. This field, propagating synchronously with the electron beam, achieves a 1 GeV energy gain for the positron bunch within 140 picoseconds with a minimal energy spread-approximately 1.56% during a stable phase. By utilizing continuous mid-IR, the efficiency of energy transfer from the electron beam to either a single positron bunch or three positron bunches simultaneously could reach up to 20% and 40%, respectively. This acceleration scheme can achieve cascaded acceleration for a single positron bunch and series acceleration for multiple positron bunches in a continuous, stable, and efficient manner.

physics.plasm-ph↗

arXiv2024

Research progress on advanced positron acceleration

Plasma wakefield acceleration (PWFA) is a promising method for reducing the scale and cost of future electron-positron collider experiments by using shorter plasma sections to enhance beam energy. While electron acceleration has already achieved breakthroughs in theory and experimentation, generating high-quality positron beams in plasma presents greater challenges, such as controlling emittance and energy spread, improving energy conversion efficiency, and generating positron sources. In this paper, we have summarized the research progress on advanced positron acceleration schemes, including particle beam-driven wakefield acceleration, laser-driven wakefield acceleration, radiation-based acceleration, hollow plasma channels, among others. The strengths and weaknesses of these approaches are analyzed, and the future outlook is discussed to drive experimental advancements.

physics.acc-ph↗

arXiv2023

High energy beam energy measurement with microwave-electron Compton backscattering

The uncertainty of the energy measurement of the electron beam on circular electron positron collider (CEPC) must be smaller than 10$\mathrm{MeV}$ to make sure the accurate measurement of the mass of the Higgs boson. In order to simplify the energy measurement system, a new method is proposed by fitting the Compton edge of the energy distribution of the gamma ray from a microwave-electron Compton scattering. With our method, the uncertainty of the energy measurement is 6$\mathrm{MeV}$ for the electron energy of $120\mathrm{GeV}$ in the Higgs mode. In this system, the energy resolution of the gamma detection needs to reach $10^{-4}$. Therefore, only the high-purity germanium (HPGe) detector can meet the critical requirement. In a head-on collision mode, the initial photons should be microwave photons with the wavelength of 3.04 centimeters. A cylindrical resonant cavity with selected ${TM_{010}}$ mode is used to transmit microwaves. After the microwave-electron Compton backscattering, the scattered photons and the synchrotron-radiation background transmit a shielding structure and then are detected by a HPGe detector at the end of the beam line of the synchrotron-radiation applications. The hole radius in the side wall of the cavity is about $1.5\mathrm{mm}$ to allow the electron beam passing through. The results of the computer simulation technology (CST) software shows that the influence of the hole radius on the cavity field is negligible. The change of the resonance frequency can be easily corrected by fine-tuning the cavity size.

hep-ex↗

arXiv2023

The linear and nonlinear inverse Compton scattering between microwaves and electrons in a resonant cavity

The new scheme of the energy measurement of the extremely high energy electron beam with the inverse Compton scattering between electrons and microwave photons requires the precise calculation of the interaction cross section of electrons and microwave photons in a resonant cavity. In the local space of the cavity, the electromagnetic field is expressed by Bessel functions. Although Bessel functions can form a complete set of orthogonal basis, it is difficult to quantify them directly as fundamental wave functions. Fortunately, with the Fourier expansion of Bessel functions, the local electromagnetic field can be considered as the superposition of a series of plane waves. Therefore, with corresponding corrections of the cross section formula of the classical Compton scattering, the cross section of the linear or nonlinear microwave Compton scattering in the local space can be described accurately. As an important application of our results in astrophysics, corresponding ground verification devices can be designed to perform experimental verifications on the prediction of the Sunyaev-Zeldovich (SZ) effect of the cosmic microwave background radiation. Our results could also provide a new way to generate wave sources with strong practical value, such as the terahertz waves, the ultra-violet (EUV) waves, or the mid-infrared beams.

physics.plasm-ph↗