Robot navigation in dense crowds requires understanding social cues that humans naturally use, yet existing methods struggle with real-world complexity. We investigate two questions: (1) Where do pedestrians look when navigating crowds? and (2) Can eye tracking improve robot navigation? To answer, we introduce GazeNav, an egocentric dataset collected via wearable eye trackers, featuring synchronized video, gaze, and trajectories in crowded environments. Analysis reveals that the gaze of pedestrians is closely related to the semantic presence and movement of other individuals, exhibiting distinct attention patterns across navigation behaviors. Building on this, we propose Gaze2Nav, a modular framework that first predicts human gaze to infer socially salient pedestrians, then incorporates the semantic attention into motion planning alongside visual inputs. Our method achieves 87.6% salient pedestrian prediction accuracy and reduces trajectory error by 15.4% over state-of-the-art baselines. By aligning with human gaze, our framework improves both performance and interpretability, advancing toward human-like, socially intelligent robot navigation.

Human-in-the-loop (HIL) imitation learning enables agents to learn complex behaviors safely through real-time human intervention. However, existing methods struggle to efficiently leverage agent-generated data due to dynamically evolving trajectory distributions and imperfections caused by human intervention delays, often failing to faithfully imitate the human expert policy. In this work, we propose Faithful Dynamic Imitation Learning (FaithDaIL) to address these challenges. We formulate HIL imitation learning as an online non-convex problem and employ dynamic regret minimization to adapt to the shifting data distribution and track high-quality policy trajectories. To ensure faithful imitation of the human expert despite training on mixed agent and human data, we introduce an unbiased imitation objective and achieve it by weighting the behavior distribution relative to the human expert's as a proxy reward. Extensive experiments on MetaDrive and CARLA driving benchmarks demonstrate that FaithDaIL achieves state-of-the-art performance in safety and task success with significantly reduced human intervention data compared to prior HIL baselines.

Navigation through crowded human environments is challenging for social robots. While reinforcement learning has been adopted for its capacity to capture complex interactions, the training process often relies on simulators to replicate realistic crowd behaviors, ensuring cost-efficiency. Existing crowd simulation methods typically rely on either handcrafted rules, which may lead to overly aggressive navigation, or learning from human trajectory demonstrations, which can be challenging to generalize effectively. In this paper, we introduce a data-driven crowd simulation method called SocialGAIL, which leverages Generative Adversarial Imitation Learning (GAIL) to emulate real pedestrian navigation in crowded environments. SocialGAIL utilizes an attention-based graph neural network to encode observations and employs a generator-discriminator architecture to closely mimic pedestrian behavior. We propose a set of metrics to evaluate the faithfulness of crowd simulation. Experimental results demonstrate that SocialGAIL outperforms baseline methods in terms of goal-reaching, intermediate state faithfulness, trajectory faithfulness, and adherence to global trajectory patterns. The code of our approach is available at https://github.com/William-island/SocialGAIL.

Influenza poses a significant risk to public health, as evident by the 2009 H1N1 pandemic. Hospital emergency departments monitor infectious diseases such as influenza with surveillance systems based on arriving chief complaints. However, existing systems are too reliant on the completeness of data and are not acceptably accurate in a practical setting. To improve prediction accuracy, we propose a data cleaning process for data collected in hospital settings. Besides, we also propose a novel feature selection method called the Importance Contribution Index (ICI) which is based on orthogonal transformation. Various feature selection and pattern classification approaches are analyzed. The ICI and C4.5 decision tree are eventually adopted in the new surveillance system. Validation results have shown that the total accuracy has been improved by 7.1% in the enhanced system.

No abstract available.

With the flurry of research on P2P computing, many P2P technical challenges have emerged, one of which is how to efficiently locate desired resources. Advances have been made in this hot research field, where the pioneers are Pastry, CAN, Chord, and Tapestry. By using the functionality of a distributed hash table, they have achieved fair effectiveness. However they have many common limitations, such as ignoring the autonomous nature of peers, and just supporting weakly semantic functions. According to reality in the distributed network, we propose a content-based location mechanism, which not only keeps the autonomy of peers, but also supports approximate query and finer granularity of content sharing. Furthermore, this mechanism also facilitates P2P system to evolve dynamically. We have also used PeerIS, a P2P based information system used to verify it and obtained satisfactory results.

In this paper, we propose a control scheme with multiple neural network based feedforward adapters. Each individual neural network is trained with a particular setpoint value and a time sequence. We point out that a single neural network is not feasible to be used in the on-line batch operation. We have also shown that a linear feedforward adapter is practically impossible to implement. We utilize the feedforward neural network as a universal non-linear mapping to find a non-linear feedforward adapter. We construct a different learning error which requires some modification of the standard backpropagation learning algorithm to include the dynamics of the plant.