Corrections to "A Fully Data-Driven Value Iteration for Stochastic LQR: Convergence, Robustness, and Stability" [0.03%]
“基于完全数据驱动的随机LQR值迭代”的更正：收敛性、鲁棒性和稳定性
Leilei Cui,Zhong-Ping Jiang,Petter N Kolm et al.
Leilei Cui et al.
In the above article, an error in [1, Th. 2].
GVA-BLS: Gaussian Vector Autoregression Broad Learning System Based on Randomly Distributed Embedding for Multistep-Ahead Prediction [0.03%]
基于随机分布嵌入的高斯向量自回归广义学习系统(GVA-BLS)的多步预测算法研究
Mingyuan Yu,Bowen Sun,Heshan Wang et al.
Mingyuan Yu et al.
As a model proficient in time series prediction, the recurrent broad learning system (RBLS) combines the advantages of traditional BLS and recurrent neural networks (RNNs). This combination enhances the learning of transient data and ensure...
A Novel DS-MADDPG Algorithm-Based Swarm-to-Swarm Interception Strategy [0.03%]
一种基于新型DS-MADDPG算法的群对群拦截策略
He Cai,Yibo Zhang,Youfeng Su et al.
He Cai et al.
This article studies a swarm-to-swarm interception problem, where a swarm of intercept uncrewed aerial vehicles (UAVs) attempt to intercept a swarm of target UAVs based on pure vision-feedback. The proposed interception strategy employs a h...
Safe Decision-Making via Adaptive Causal Representation for Autonomous Driving [0.03%]
基于自适应因果表征的自动驾驶安全决策方法研究
Chenyang Zhao,Haoen Huang,Depeng Li et al.
Chenyang Zhao et al.
Offline reinforcement learning (RL) is promising for autonomous driving, but as deployment conditions drift away from the offline training distribution, policies may encounter out-of-distribution (OOD) scenarios, such as unseen road geometr...
Contrastive Predictive Coding With Compression for Enhanced Channel State Feedback in Wireless Networks [0.03%]
基于压缩的对比预测编码在无线网络信道状态反馈中的应用研究
Ahmed Y Radwan,Fahad Syed Muhammad,Matthew Baker et al.
Ahmed Y Radwan et al.
Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current third Generation Partne...
ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion [0.03%]
各向异性结构感知和光谱重新校准的高光谱图像融合网络ASSR-Net
Qiya Song,Hongzhi Zhou,Lishan Tan et al.
Qiya Song et al.
Hyperspectral image fusion aims to reconstruct high-spatial-resolution hyperspectral images (HR-HSIs) by integrating complementary information from multisource inputs. Despite recent progress, existing methods still face two critical challe...
PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks [0.03%]
基于物理的多相一致性学习增强图神经网络中不平衡节点分类
Abdul Joseph Fofanah,Lian Wen,David Chen
Abdul Joseph Fofanah
Graph neural networks (GNNs) often struggle in class-imbalanced settings, where minority classes are underrepresented, and predictions are biased toward the majority. We propose PIMPC-GNN, a physics-informed multiphase consensus framework f...
Furqan Aziz
Furqan Aziz
Comparing graphs for structural similarity is one of the most important problems in graph analytics. However, due to the nonlinear nature of graphs, this problem is not straightforward to solve. Most existing graph comparison methods either...
LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation [0.03%]
基于状态空间模型的实时语义分割轻量级和准确平衡的L_ANet网络架构
Mingxi Zhuang,Shukai Liu,Guangming Wang et al.
Mingxi Zhuang et al.
Real-time semantic segmentation has extensive applications in practical fields such as autonomous driving. Despite the significant progress made by deep learning in semantic segmentation, the performance of real-time methods remains subopti...
MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction [0.03%]
基于增强依赖网络的多支股票行情预测模型
Che Liu,Zhi Zheng,Pengfei Luo et al.
Che Liu et al.
The stock movement prediction task has long been treated as one of the most crucial tasks for financial data mining. Unfortunately, prior arts may fail to capture the intricate nature of severe stock fluctuations, not to mention the difficu...