Ke Zhou

Weiwei Wu, Yueyang Li, Yuhu Shi et al.

Cross-subject EEG-based emotion recognition (EER) remains challenging due to strong inter-subject variability, which induces substantial distribution shifts in EEG signals, as well as the high complexity of emotion-related neural representations in both spatial organization and temporal evolution. Existing approaches typically improve spatial modeling, temporal modeling, or generalization strategies in isolation, which limits their ability to align representations across subjects while capturing multi-scale dynamics and suppressing subject-specific bias within a unified framework. To address these gaps, we propose a Region-aware Spatiotemporal Modeling framework with Collaborative Domain Generalization (RSM-CoDG) for cross-subject EEG emotion recognition. RSM-CoDG incorporates neuroscience priors derived from functional brain region partitioning to construct region-level spatial representations, thereby improving cross-subject comparability. It also employs multi-scale temporal modeling to characterize the dynamic evolution of emotion-evoked neural activity. In addition, the framework employs a collaborative domain generalization strategy, incorporating multidimensional constraints to reduce subject-specific bias in a fully unseen target subject setting, which enhances the generalization to unknown individuals. Extensive experimental results on SEED series datasets demonstrate that RSM-CoDG consistently outperforms existing competing methods, providing an effective approach for improving robustness. The source code is available at https://github.com/RyanLi-X/RSM-CoDG.

André Felipe Zanella, Linus W. Dietz, Sanja Šćepanović et al.

Urban parks support public health, but landscape architecture typically examines them through form and function. Prior equitable access research focused on park form, while functional studies relied on small-scale surveys, movement data, or broad usage metrics, missing specific activities and visit motivations. This gap limits our grasp of parks' functional diversity. We address this with a novel method refining mobile base station coverage via antenna azimuths to isolate park-specific traffic from surroundings. Using Paris as a case study, we process 492 million hourly per-app mobile records (35% market share) from 45 urban parks. We test the central-city hypothesis (multifunctional parks in dense, high-rent zones due to land constraints) and socio-spatial hypothesis (parks reflecting neighborhood routines and preferences). Results reveal parks' unique mobile traffic signatures, distinct from urban contexts and each other. Clustering by temporal and app patterns identifies three types: lunchbreak, cultural, and recreational parks, linked to health-promoting visitation motives. Central parks show diverse apps and peak usage; suburban recreational parks mirror local demographics, like income-aligned app preferences. This demonstrates mobile traffic's power as a proxy for urban green space activities, with key implications for park design, public health, and well-being strategies.

Yunchuan Guan, Yu Liu, Ke Zhou et al.

Recent advances in generative modeling enable neural networks to generate weights without relying on gradient-based optimization. However, current methods are limited by issues of over-coupling and long-horizon. The former tightly binds weight generation with task-specific objectives, thereby limiting the flexibility of the learned optimizer. The latter leads to inefficiency and low accuracy during inference, caused by the lack of local constraints. In this paper, we propose Lo-Hp, a decoupled two-stage weight generation framework that enhances flexibility through learning various optimization policies. It adopts a hybrid-policy sub-trajectory balance objective, which integrates on-policy and off-policy learning to capture local optimization policies. Theoretically, we demonstrate that learning solely local optimization policies can address the long-horizon issue while enhancing the generation of global optimal weights. In addition, we validate Lo-Hp's superior accuracy and inference efficiency in tasks that require frequent weight updates, such as transfer learning, few-shot learning, domain generalization, and large language model adaptation.

Yunchuan Guan, Yu Liu, Ke Zhou et al.

Diffusion-based algorithms have emerged as promising techniques for weight generation. However, existing solutions are limited by two challenges: generalizability and local target assignment. The former arises from the inherent lack of cross-task transferability in existing single-level optimization methods, limiting the model's performance on new tasks. The latter lies in existing research modeling only global optimal weights, neglecting the supervision signals in local target weights. Moreover, naively assigning local target weights causes local-global inconsistency. To address these issues, we propose Mc-Di, which integrates the diffusion algorithm with meta-learning for better generalizability. Furthermore, we extend the vanilla diffusion into a local consistency diffusion algorithm. Our theory and experiments demonstrate that it can learn from local targets while maintaining consistency with the global optima. We validate Mc-Di's superior accuracy and inference efficiency in tasks that require frequent weight updates, including transfer learning, few-shot learning, domain generalization, and large language model adaptation.