5 citations · 8 across the 9 of their papers we have counts for
10 papers
Where Does EEG Help Wearable Freezing of Gait Detection?
Chuntian Chi, Lifang He, Huajie Shao +1
Freezing of gait (FoG) detectors built on inertial measurement units (IMUs) are accurate while the legs move, but they become unreliable when the legs are nearly still, which is ex…
MANIGUARD: A Benchmark and Data Suite for Specification-Grounded Safety Evaluation and Improvement of Robotic Manipulation
Yiyan Peng, Philip Wang, Simon Sinong Zhan +11
Foundation-model policies for robotic manipulation are advancing rapidly on task success, but rigorous evaluation of whether they succeed safely is still lacking. We introduce Mani…
ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment
Hongjue Zhao, Haosen Sun, Jiangtao Kong +8
Activation steering, or representation engineering, offers a lightweight approach to align large language models (LLMs) by manipulating their internal activations at inference time…
Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo +5
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical ga…
SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents
Simon Sinong Zhan, Philip Wang, Yao Liu +13
We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents. SENTINEL is the first to provide multi-level safety eva…
FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation
Chuntian Chi, John Clapham, Leslie Cloud +4
Freezing-of-Gait (FoG) affects over 50% of mid-to-late stage Parkinson's disease (PD) patients, significantly impairing patients' mobility independence and reducing quality of life…
P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Wei Fan, JinYi Yoon, Xiaochang Li +2
Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a m…
Measuring Copyright Risks of Large Language Model via Partial Information Probing
Weijie Zhao, Huajie Shao, Zhaozhuo Xu +2
Exploring the data sources used to train Large Language Models (LLMs) is a crucial direction in investigating potential copyright infringement by these models. While this approach…
NetBench: A Large-Scale and Comprehensive Network Traffic Benchmark Dataset for Foundation Models
Chen Qian, Xiaochang Li, Qineng Wang +2
In computer networking, network traffic refers to the amount of data transmitted in the form of packets between internetworked computers or Cyber-Physical Systems. Monitoring and a…
Lens: A Knowledge-Guided Foundation Model for Network Traffic
Xiaochang Li, Chen Qian, Qineng Wang +7
Network traffic refers to the amount of data being sent and received over the Internet or any system that connects computers. Analyzing network traffic is vital for security and ma…