arXiv subjects

Duo Chen

Publications and source records attributed to Duo Chen.

16 recordsLinked to original sources

arXiv2026

Reconstructing Spiking Neural Networks Using a Single Neuron with Autapses

Spiking neural networks (SNNs) are promising for neuromorphic computing, but high-performing models still rely on dense multilayer architectures with substantial communication and state-storage costs. Inspired by autapses, we propose time-delayed autapse SNN (TDA-SNN), a framework that reconstructs SNNs with a single leaky integrate-and-fire neuron and a prototype-learning-based training strategy. By reorganizing internal temporal states, TDA-SNN can realize reservoir, multilayer perceptron, and convolution-like spiking architectures within a unified framework. Experiments on sequential, event-based, and image benchmarks show competitive performance in reservoir and MLP settings, while convolutional results reveal a clear space--time trade-off. Compared with standard SNNs, TDA-SNN greatly reduces neuron count and state memory while increasing per-neuron information capacity, at the cost of additional temporal latency in extreme single-neuron settings. These findings highlight the potential of temporally multiplexed single-neuron models as compact computational units for brain-inspired computing.

cs.NE↗

arXiv2026

Optically Activated Superconductivity in MgB2 via Electroluminescent GaP Inhomogeneous Phase

Experimental results demonstrate a viable strategy for tuning the superconducting properties of MgB2 through the incorporation of an electroluminescent inhomogeneous phase, revealing an interfacial light-phonon-electron synergistic mechanism that enhances superconductivity in conventional phonon-mediated systems. By introducing GaP electroluminescent inhomogeneous phases into MgB2 and activating their emission in situ through the application of a bias current during measurements, it is experimentally observed that the localized optical field and electromagnetic near field generated at the interface can effectively couple with the E2g phonon mode of the Mg-B layers, thereby significantly enhancing the electron-phonon interaction. As the emission intensity of the inhomogeneous phase increases, the interface light-field-driven mechanism markedly enhances the electron-phonon coupling constant lambda and leads to a gradual increase in the superconducting transition temperature Tc (with a maximum enhancement of approximately 1.4 K), enabling a tunable enhancement of the superconducting pairing channel in MgB2 without altering its primary chemical composition. In addition, the nanoscale dispersed distribution of the GaP inhomogeneous phase is expected to induce fine-scale defects that act as effective pinning centers and promote densification, resulting in an increase of the critical current density by approximately 69% at 20 K in the self-field and an enhancement of Hirr by about 31.5%. These results indicate that the electroluminescent inhomogeneous phase can synergistically enhance the superconducting performance of MgB2 through two mechanisms: "in situ near-field-enhanced pairing" and "structural pinning-assisted flux optimization", thereby providing a new design strategy for constructing superconducting material systems that can be activated by internal optical fields.

cond-mat.supr-con↗

arXiv2026

Explainable Interictal Epileptiform Discharge Detection Method Based on Scalp EEG and Retrieval-Augmented Generation

The detection of interictal epileptiform discharge (IED) is crucial for the diagnosis of epilepsy, but automated methods often lack interpretability. This study proposes IED-RAG, an explainable multimodal framework for joint IED detection and report generation. Our approach employs a dual-encoder to extract electrophysiological and semantic features, aligned via contrastive learning in a shared EEG-text embedding space. During inference, clinically relevant EEG-text pairs are retrieved from a vector database as explicit evidence to condition a large language model (LLM) for the generation of evidence-based reports. Evaluated on a private dataset from Wuhan Children's Hospital and the public TUH EEG Events Corpus (TUEV), the framework achieved balanced accuracies of 89.17\% and 71.38\%, with BLEU scores of 89.61\% and 64.14\%, respectively. The results demonstrate that retrieval of explicit evidence enhances both diagnostic performance and clinical interpretability compared to standard black-box methods.

eess.SP↗

arXiv2025

Luminescence-Induced Tunable Superconductivity in BSCCO via GaP Quantum Dots

The enhancement of superconducting properties in high-temperature copper-oxide superconductor B(P)SCCO remains a hot research topic in the field of superconducting materials. Building on previous research, here we introduce GaP quantum dots as an heterophase into the B(P)SCCO superconductor, aiming to enhance its superconductivity through the luminescent properties of GaP quantum dots. The experimental results demonstrate that the introduction of GaP quantum dots into B(P)SCCO generates significant tunable superconducting effects, leading to enhanced critical transition temperature (Tc), critical current density (Jc), and Meissner field (Hc) of B(P)SCCO with increasing luminescent intensity of the GaP quantum dots. The enhancement effect induced by GaP quantum dots exhibits a positive correlation with luminescent intensity, meaning samples with the addition of GaP quantum dots exhibiting higher luminescent intensity show elevated Tc, Jc, and Hc values. Unlike impurity effects, a distinct critical concentration dependency is observed. Notably, this GaP quantum dot modification strategy is not only effective in conventional superconductors but also applicable to high-temperature oxide superconductors.

cond-mat.supr-con↗

arXiv2025

NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration

Spiking neural networks (SNNs) are artificial neural networks based on simulated biological neurons and have attracted much attention in recent artificial intelligence technology studies. The dendrites in biological neurons have efficient information processing ability and computational power; however, the neurons of SNNs rarely match the complex structure of the dendrites. Inspired by the nonlinear structure and highly sparse properties of neuronal dendrites, in this study, we propose an efficient, lightweight SNN method with nonlinear pruning and dendritic integration (NSPDI-SNN). In this method, we introduce nonlinear dendritic integration (NDI) to improve the representation of the spatiotemporal information of neurons. We implement heterogeneous state transition ratios of dendritic spines and construct a new and flexible nonlinear synaptic pruning (NSP) method to achieve the high sparsity of SNN. We conducted systematic experiments on three benchmark datasets (DVS128 Gesture, CIFAR10-DVS, and CIFAR10) and extended the evaluation to two complex tasks (speech recognition and reinforcement learning-based maze navigation task). Across all tasks, NSPDI-SNN consistently achieved high sparsity with minimal performance degradation. In particular, our method achieved the best experimental results on all three event stream datasets. Further analysis showed that NSPDI significantly improved the efficiency of synaptic information transfer as sparsity increased. In conclusion, our results indicate that the complex structure and nonlinear computation of neuronal dendrites provide a promising approach for developing efficient SNN methods.

q-bio.NC↗

arXiv2025

Toward Efficient Spiking Transformers: Synapse Pruning Meets Synergistic Learning-Based Compensation

As a foundational architecture of artificial intelligence models, Transformer has been recently adapted to spiking neural networks with promising performance across various tasks. However, existing spiking Transformer(ST)-based models require a substantial number of parameters and incur high computational costs, thus limiting their deployment in resource-constrained environments. To address these challenges, we propose combining synapse pruning with a synergistic learning-based compensation strategy to derive lightweight ST-based models. Specifically, two types of tailored pruning strategies are introduced to reduce redundancy in the weight matrices of ST blocks: an unstructured $\mathrm{L_{1}P}$ method to induce sparse representations, and a structured DSP method to induce low-rank representations. In addition, we propose an enhanced spiking neuron model, termed the synergistic leaky integrate-and-fire (sLIF) neuron, to effectively compensate for model pruning through synergistic learning between synaptic and intrinsic plasticity mechanisms. Extensive experiments on benchmark datasets demonstrate that the proposed methods significantly reduce model size and computational overhead while maintaining competitive performance. These results validate the effectiveness of the proposed pruning and compensation strategies in constructing efficient and high-performing ST-based models.

cs.LG↗

arXiv2025

A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders

Recent developed graph-based methods for diagnosing brain disorders using functional connectivity highly rely on predefined brain atlases, but overlook the rich information embedded within atlases and the confounding effects of site and phenotype variability. To address these challenges, we propose a two-stage Brain-to-Population Graph Learning (B2P-GL) framework that integrates the semantic similarity of brain regions and condition-based population graph modeling. In the first stage, termed brain representation learning, we leverage brain atlas knowledge from GPT-4 to enrich the graph representation and refine the brain graph through an adaptive node reassignment graph attention network. In the second stage, termed population disorder diagnosis, phenotypic data is incorporated into population graph construction and feature fusion to mitigate confounding effects and enhance diagnosis performance. Experiments on the ABIDE I, ADHD-200, and Rest-meta-MDD datasets show that B2P-GL outperforms state-of-the-art methods in prediction accuracy while enhancing interpretability. Overall, our proposed framework offers a reliable and personalized approach to brain disorder diagnosis, advancing clinical applicability.

cs.LG↗

arXiv2025

The coexistence of possible magnetic and chiral rotation in $^{129}\mathrm{Cs}$ and $^{131}\mathrm{La}$: a microscopic investigation

A microscopic investigation of the rotational properties in $^{129}\mathrm{Cs}$ and $^{131}\mathrm{La}$ was carried out using the three-dimensional tilted axis cranking covariant density functional theory (3DTAC-CDFT). The calculations reveal the coexistence of magnetic and chiral rotation built on identical qusiparticle configurations $\pi {h}_{11/2}^{1}\otimes \nu {h}_{11/2}^{-2}$ in $^{129}\mathrm{Cs}$ and $^{131}\mathrm{La}$, establishing a new type of shape coexistence. The calculations predict the deformation parameters for magnetic rotation in $^{129}\mathrm{Cs}$ ($\beta\approx0.23$, $\gamma \approx41^\circ$) and $^{131}\mathrm{La}$ ($\beta \approx0.25,$ $\gamma \approx42^\circ$), along with those for possible chiral rotation in $^{129}\mathrm{Cs}$ ($\beta \approx0.20,$ $\gamma \approx29^\circ$) and in $^{131}\mathrm{La}$ ($\beta \approx0.20,$ $\gamma \approx27^\circ$). The energy spectra, the relation between the spin and the rotational frequency, and the reduced $M1$ and $E2$ transition probabilities are obtained with the various configurations. The experimental characteristics of band B8 in $^{129}\mathrm{Cs}$ and band 13 in $^{131}\mathrm{La}$ are well reproduced. Moreover, a distinctive rotational mode transition is uncovered in this work, progressing from the principal-axis rotation to the planar rotation and finally to the chiral rotation, as the rotational frequency evolves through different regimes.

nucl-th↗

arXiv2025

Enhancing Subsequent Video Retrieval via Vision-Language Models (VLMs)

The rapid growth of video content demands efficient and precise retrieval systems. While vision-language models (VLMs) excel in representation learning, they often struggle with adaptive, time-sensitive video retrieval. This paper introduces a novel framework that combines vector similarity search with graph-based data structures. By leveraging VLM embeddings for initial retrieval and modeling contextual relationships among video segments, our approach enables adaptive query refinement and improves retrieval accuracy. Experiments demonstrate its precision, scalability, and robustness, offering an effective solution for interactive video retrieval in dynamic environments.

cs.CV↗

arXiv2025

ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation

Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network's ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.

cs.CV↗

arXiv2025

Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data's non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel's features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance.

cs.LG↗

arXiv2023

Green light GaN p-n junction luminescent particles enhance the superconducting properties of B(P)SCCO Smart Meta-Superconductors (SMSCs)

Superconducting materials exhibit unique physical properties and hold great scientific value and vast industrial application prospects. However, due to limitations such as critical temperature (TC) and critical current density (JC), the large-scale application of superconducting materials remains challenging. Chemical doping has been a commonly used method to enhance the superconductivity of B(P)SCCO. However, satisfactory enhancement results have been difficult to achieve. In this study, we introduced green light GaN p-n junction particles as inhomogeneous phases into B(P)SCCO polycrystalline particles to form a smart meta-superconductors (SMSCs) structure. Based on the electroluminescence properties of the p-n junction, the Cooper pairs were stimulated and strengthened to enhance the superconductivity of B(P)SCCO. Experimental results demonstrate that the introduction of inhomogeneous phases can indeed enhance the critical temperature TC, critical current density JC, and complete diamagnetism (Meissner effect) of B(P)SCCO superconductors. Moreover, When the particle size of raw material of B(P)SCCO is reduced from 30{\mu}m to 5{\mu}m, the grain size of the sintered samples also decreases, and the optimal doping concentration of the inhomogeneous phases increases from 0.15 wt.% to 0.2 wt.%, further improving the enhancement of superconductivity.

cond-mat.supr-con↗

arXiv2023

Green-light p-n Junction Particle Inhomogeneous Phase Enhancement of MgB2 Smart Meta-Superconductor

Improving the critical temperature (TC), critical magnetic field (HC), and critical current (JC) of superconducting materials has always been one of the most significant challenges in the field of superconductivity, but progress has been slow over the years. Based on the concept of injecting energy to enhance electron pairing states, in this study, we have employed a solid-state sintering method to fabricate a series of smart meta-superconductors (SMSCs) consisting of p-n junction nanostructures with a wavelength of 550 nm, doped within an MgB2 matrix. Experimental results demonstrate that compared to pure MgB2 samples, the critical transition temperature (TC) has increased by 1.2 K, the critical current (JC) has increased by 52.8%, and the Meissner effect (HC) shows significant improvement in its diamagnetic properties. This phenomenon of enhanced superconducting performance can be explained by the coupling between superconducting electrons and evanescent waves.

cond-mat.supr-con↗

arXiv2023

The evolution of the chiral symmetry in cesium isotopes

Following the reports of candidate chiral doublet bands observed in cesium isotopes, the possible chiral candidates and the evolution of three-dimensional rotation in $^{120-134}{\textrm{Cs}}$ are investigated within the microscopic three-dimensional tilted axis cranking covariant density functional theory (3DTAC-CDFT). By investigating the evolution of the polar angle $\theta$ and azimuth angle $\varphi$ as a function of rotational frequency $\hbar\omega$, the transition from the planar rotation to the chiral rotation has been found in $^{121-133}{\textrm{Cs}}$. The corresponding critical rotational frequency $\omega_{\textrm{crit}}$ of the appearance of chiral aplanar rotation decreases as neutron number increases, which can be attributed to the neutrons in $(gd)$ and $(sd)$ shells having smaller angular momentum components along both the short and long axes, and larger components along medium axis, respectively. In comparison, only planar rotation has been obtained in $^{120,134}{\textrm{Cs}}$. With these interpretations, the obtained $I\sim\hbar\omega$ and energy spectra as well as $B(M1)/B(E2)$ values show reasonable agreement with the available experimental data. In addition, the evolution of quadrupole deformation $\beta$ and triaxial deformation $\gamma$ are also discussed.

nucl-th↗

arXiv2020

TSception: A Deep Learning Framework for Emotion Detection Using EEG

In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously. The temporal learner consists of multi-scale 1D convolutional kernels whose lengths are related to the sampling rate of the EEG signal, which learns multiple temporal and frequency representations. The spatial learner takes advantage of the asymmetry property of emotion responses at the frontal brain area to learn the discriminative representations from the left and right hemispheres of the brain. In our study, a system is designed to study the emotional arousal in an immersive virtual reality (VR) environment. EEG data were collected from 18 healthy subjects using this system to evaluate the performance of the proposed deep learning network for the classification of low and high emotional arousal states. The proposed method is compared with SVM, EEGNet, and LSTM. TSception achieves a high classification accuracy of 86.03%, which outperforms the prior methods significantly (p<0.05). The code is available at https://github.com/deepBrains/TSception

eess.SP↗

arXiv2019

Highly Conductive Co3Se4 Embedded in N-doped 3D Interconnected Carbonaceous Networks for Enhanced Lithium and Sodium Storage

Traditional cobalt selenides as active materials in lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) would suffer from drastic volume expansions and large stacking effects, leading to a low cycling stability. In this work, we utilized a facile template method for preparing Co3Se4@N-CN (CSNC) that encapsulated Co3Se4 nanoparticles into 3D interconnected nitrogen-doped carbon network (N-CN). Satisfactorily, it possesses excellent cycling stability with enhanced lithium and sodium energy storage capacity. As an anode material in LIBs, CSNC exhibited a prominent reversible discharge performance of 1313.5 mAh g-1 after 100 cycles at 0.1 A g-1 and 835.6 mAh g-1 after 500 cycles at 1.0 A g-1. Interestingly, according to the analysis from cyclic voltammetry, the in-situ generated Se might provide extra capacity that leaded to a rising trend of capacity. When utilized as an anode in SIBs, CSNC delivered an outstanding capacity of 448.7 mAh g-1 after 100 cycles at 0.1 A g-1 and could retain 328.9 mAh g-1 (77.2% of that of 0.1 A g-1) even at a high current density of 5.0 A g-1. The results demonstrate that CSNC is a superior anode material in LIBs and SIBs with great promise. More importantly, this strategy opens up an effective avenue for the design of transition metal selenide/carbonaceous composites for advanced battery storage systems.

physics.app-ph↗