C. L. Philip Chen

Adaptive-Slack Prescribed-Performance Control: Achieving Safe Trajectory Tracking in Cluttered Environments via Reinforcement Learning.

Achieving high-precision trajectory tracking while ensuring collision avoidance presents a fundamental conflict for autonomous systems operating in cluttered environments. Traditional prescribed-performance control (PPC) imposes rigid shrinking funnels that may become infeasible during avoidance maneuvers, whereas soft...

Share First, Route What Remains: A Unified Framework for Token-Adaptive MoE Computation

Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts. Shared-expert designs preserve reusable knowledge, fine-grained methods vary computation within experts, and dynamic routers adapt the number of active experts. Yet these decisions are usually made independently, over...

Neuro-SPO: Physically-Grounded and Affect-Aligned EEG-to-Keyword Decoding

The Neural Spectral-Physical Operator (Neuro-SPO), a robust framework that reframes EEG-to-Keyword decoding as a continuous representation learning problem, and introduces a Structure-Regularized Optimization strategy, employing the Affective Modulation Alignment and Topology-Preserving Ranking Objective to rectify the...

Reinforcement learning control for switched nonlinear systems with limited-time state constraints

Structured Multi-Evidence Learning for Hyperspectral Image Clustering

The hyperspectral images (HSIs) record subtle spectral responses of land-cover materials over a large number of contiguous bands, providing rich information for unsupervised land-cover analysis. However, local environmental variations, spectral ambiguity, and band redundancy often make the underlying clustering structu...

Adaptive Progressive Optimization Ensemble Approach for High-Dimensional Imbalanced Data Classification

High-dimensional imbalanced data presents the problems of massive invalid features and class imbalance, making it arduous for classifiers to gain respectable outcomes. Compared to the individual classifier, classifier ensemble has great potential to elevate the performance. In this paper, an adaptive progressive optimi...

G1Stack: A Learning-Assisted Scheduler Framework for Interactive and Batch Workload Co-Location With High Resource Efficiency

Interactive services typically over-provision CPU resources to meet Service Level Objectives (SLOs) for tail latency amidst workload fluctuations. This inefficiency motivates emerging research into workload co-location, where batch jobs are hosted alongside interactive services to harvest underutilized resources. Howev...

Decision Boundary Drift: A Security, Privacy, and Trust Risk of Continual Learning for Agentic AI in Edge Networks

Continual learning (CL) is a key paradigm that enables intelligent agents to operate autonomously in edge networks over the long term. However, continuous model updates can lead to catastrophic forgetting and representation instability in edge deployment scenarios, which may further induce Decision Boundary Drift (DBD)...

Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms

A role-of-learning taxonomy is proposed that categorizes existing methods according to how learning participates in the planning pipeline, including direct policy learning, learning-augmented classical planning, hybrid planning, and training enhancement methods.

Large model-assisted video summarization via global entity unification and robust importance scoring

A summarization pipeline around Global Entity Unification and Robust Importance Scoring is built, but unlike earlier efforts, each object is traced across frames and attached consistent identifiers to it, and Fragmented, isolated descriptions become a single, object-aware text corpus that unifies the storyline.