arXiv subjects

Yueyang Wu

Publications and source records attributed to Yueyang Wu.

6 recordsLinked to original sources

arXiv2026

Enhanced anomalous Nernst effect in Pr-doped kagome and honeycomb magnet LaCo$_5$

In this work, we report the successful synthesis of La$_{1-x}$Pr$_x$Co$_5$ ($x = 0.09, 0.21, 0.45$) single crystals. Magnetic field-dependent magnetization measurements reveal that Pr substitution induces negligible changes in the magnetic properties of LaCo$_5$, with the ferromagnetic ordering predominantly governed by the Co sublattices. Remarkably, the doped systems exhibit significant enhancements in the anomalous Nernst effect. At 300~K, the anomalous Nernst thermopower $S^A_{yx}$ reaches 6.5~$\mathrm{μV/K}$ in La$_{0.55}$Pr$_{0.45}$Co$_5$, corresponding to a $\sim$ 40 \% enhancement compared to the parent compound. This significant improvement can be predominantly attributed to Pr-doping-induced Fermi level modification, which directly leads to a redistribution of Berry curvature across the Fermi surface. This work highlights the effectiveness of Pr doping in boosting the anomalous Nernst effect of La$_{1-x}$Pr$_x$Co$_5$, offering a practical strategy to design advanced materials for room-temperature energy-harvesting technologies and high-efficiency thermal sensing devices.

cond-mat.mtrl-sci↗

arXiv2026

CERF: Communication-Efficient and Retraining-Free Collaborative Perception

Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability of individual agents. However, most existing methods rely on transmitting and fusing dense feature maps for collaboration, which incurs inevitable communication overhead and heterogeneity challenges, limiting their practicality for real-world deployment. To address these challenges, we propose CERF, a novel Communication-Efficient and Retraining-Free framework for open heterogeneous collaborative perception. In CERF, we introduce a new virtual modality (termed Poture), which is generated from the perception outputs of other agents, to augment the extracted Bird's Eye View (BEV) features of the ego agent. To mitigate transmission delays, we employ a Kalman-filter based tracker and a motion forecasting model to derive the current predictions from historical perception results. Extensive experiments demonstrate that CERF achieves performance comparable to mainstream intermediate-collaboration methods while reducing communication overhead by 95% across various downstream tasks. Furthermore, CERF enables seamless integration of unknown heterogeneous agents into the existing collaborative framework without additional retraining costs. Code is available at https://github.com/uestchjw/CERF.

cs.CV↗

arXiv2025

Mechanochemical feedback drives complex inertial dynamics in active solids

Active solids combine internal active driving with elasticity to realize states with nonequilibrium mechanics and autonomous motion. They are often studied in overdamped settings, e.g., in soft materials, and the role of inertia is less explored. We construct a model of a chemically active solid that incorporates mechanochemical feedback and show that, when feedback overwhelms mechanical damping, autonomous inertial dynamics can spontaneously emerge through sustained consumption of chemical fuel. By combining numerical simulations, analysis and dynamical systems approaches, we show how active feedback drives complex nonlinear dynamics on multiple time-scales, including limit cycles and chaos. Our results suggest design principles for creating ultrafast actuators and autonomous machines from soft, chemically-powered solids.

cond-mat.soft↗

arXiv2025

Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?

Collaborative perception enhances environmental awareness through inter-agent communication and is regarded as a promising solution to intelligent transportation systems. However, existing collaborative methods for Unmanned Aerial Vehicles (UAVs) overlook the unique characteristics of the UAV perspective, resulting in substantial communication overhead. To address this issue, we propose a novel communication-efficient collaborative perception framework based on late-intermediate fusion, dubbed LIF. The core concept is to exchange informative and compact detection results and shift the fusion stage to the feature representation level. In particular, we leverage vision-guided positional embedding (VPE) and box-based virtual augmented feature (BoBEV) to effectively integrate complementary information from various agents. Additionally, we innovatively introduce an uncertainty-driven communication mechanism that uses uncertainty evaluation to select high-quality and reliable shared areas. Experimental results demonstrate that our LIF achieves superior performance with minimal communication bandwidth, proving its effectiveness and practicality. Code and models are available at https://github.com/uestchjw/LIF.

cs.CV↗

arXiv2025

Volume-Wise Task fMRI Decoding with Deep Learning:Enhancing Temporal Resolution and Cognitive Function Analysis

In recent years,the application of deep learning in task functional Magnetic Resonance Imaging (tfMRI) decoding has led to significant advancements. However,most studies remain constrained by assumption of temporal stationarity in neural activity,resulting in predominantly block-wise analysis with limited temporal resolution on the order of tens of seconds. This limitation restricts the ability to decode cognitive functions in detail. To address these limitations, this study proposes a deep neural network designed for volume-wise identification of task states within tfMRI data,thereby overcoming the constraints of conventional methods. Evaluated on Human Connectome Project (HCP) motor and gambling tfMRI datasets,the model achieved impressive mean accuracy rates of 94.0% and 79.6%,respectively. These results demonstrate a substantial enhancement in temporal resolution,enabling more detailed exploration of cognitive processes. The study further employs visualization algorithms to investigate dynamic brain mappings during different tasks,marking a significant step forward in deep learning-based frame-level tfMRI decoding. This approach offers new methodologies and tools for examining dynamic changes in brain activities and understanding the underlying cognitive mechanisms.

cs.LG↗

arXiv2021

Attention module improves both performance and interpretability of 4D fMRI decoding neural network

Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in-depth interpretation of DNN channels. A 4D convolution operation was also included to extract temporo-spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the 7 different task benchmarks from the Human Connectome Project (HCP) dataset. The visualization analysis further illustrated the hierarchical emergence of task-specific masks with depth. Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively. Transfer learning also achieves good performance. A further visualization analysis shows that, after transfer learning, low-level attention masks remained similar to the source domain, whereas high-level attention masks changed adaptively. In conclusion, the proposed 4D model with attention module performed well and facilitated interpretation of DNNs, which is helpful for subsequent research.

eess.IV↗