CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning [0.03%]
基于因果关系的类增量学习方法
Xinyu Zhou,Jing Yang,Xiaoli Ruan et al.
Xinyu Zhou et al.
Class-incremental learning (CIL) requires free learning of all previously learned tasks. This is desirable to achieve without task indexing when facing dynamic task sequences. However, problems such as unclear category discriminative bounda...
Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction [0.03%]
调和自动编码框架在磁性粒子成像重建中的多任务处理
Zechen Wei,Tao Zhu,Jiaxin Zhang et al.
Zechen Wei et al.
Magnetic particle imaging (MPI) is an innovative imaging modality offering high spatio-temporal resolution for reconstructing magnetic particle distributions. To achieve high-quality MPI images, traditional methods such as the X-space metho...
Wuyang Chen,Kele Xu,Qiya Song et al.
Wuyang Chen et al.
The integration of voice-encompassing both speech and nonverbal acoustics-with facial data in human-centric voice-face multimodal learning has emerged as a critical paradigm for understanding human-centered behavioral patterns. Unlike gener...
Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems [0.03%]
视觉辅助基础模型解决多任务车辆路径问题
Shuangchun Gui,Zhiguang Cao,Wen Song et al.
Shuangchun Gui et al.
Multitask vehicle routing problems (VRPs) play a critical role in enhancing efficiency across various industries and service sectors. These problems consist of multiple variants that optimize routing costs while meeting diverse customer con...
FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility [0.03%]
具有参数共享功能的FP3O：用于多智能体合作的近端策略优化方法
Lang Feng,Dong Xing,Junru Zhang et al.
Lang Feng et al.
Existing multiagent proximal policy optimization (PPO) algorithms come at the cost of limited generalizability on different parameter-sharing configurations [e.g., full, partial, and nonparameter sharing (NoPS)] when extending the theoretic...
Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images [0.03%]
多层次语义概念建模在多序列CMR图像心肌病理分割中的应用
Jinwei Dong,Lei Li,Liqin Huang et al.
Jinwei Dong et al.
Myocardial pathology segmentation (MyoPS) aims to accurately quantify myocardial scar and edema, which is critical for precise assessment of myocardial infarction (MI) severity. Recent deep learning (DL) methods have shown promising perform...
Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks [0.03%]
基于状态依赖型时滞的时变脉冲系统的稳定性及其在复杂网络中的应用
Weilian Liu,Xiaodi Li
Weilian Liu
This study investigates the stability of a set of time-varying impulsive systems incorporating state-dependent delay (SDD). The coexistence of SDD, time-varying parameters, and impulsive effects introduces strong nonlinear coupling between ...
Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks [0.03%]
基于权重和固有可塑性神经网络的自适应学习控制不确定系统
Jing He,Bing Zhou,Kai Zhao et al.
Jing He et al.
Although the "universal" approximation and learning capabilities of artificial neural networks (ANNs) are widely used for the control design of continuous nonlinear systems, two important issues regarding the mathematical model of ANNs and ...
TTP-SSFL: Test-Time Personalization Self-Supervised Federated Learning for Accelerating MR Image Reconstruction [0.03%]
基于测试时间个性化自监督的联邦学习加速MR图像重建
Chenghu Geng,Mingfeng Jiang,Dongsheng Ruan et al.
Chenghu Geng et al.
Federated learning (FL) has emerged as a promising paradigm for accelerating magnetic resonance (MR) image reconstruction while preserving data privacy in multicenter collaborations. However, existing FL-based reconstruction methods face tw...
ARKG: Adversarially Residual Knowledge Generalization to Open-Set Domain Adaptation [0.03%]
用于开放集领域适应的残差知识对抗性泛化（ARKG）
Reyhane Ghaffari,Mohammad Sadegh Helfroush,Kamran Kazemi et al.
Reyhane Ghaffari et al.
Open-set domain adaptation (OSDA) aims to bridge the gap between labeled source and unlabeled target domains while separating unknown data in the target domain. Recent works have addressed the OSDA setting with notable results, yet they hav...