32 citations · 42 across the 16 of their papers we have counts for
18 papers
DynamicDx: Evaluating Evidence Acquisition in Video-Based Diagnosis
Jiahui Li, Yutong Guo, Nan Yang +3
Diagnosing a patient from video requires more than recognizing the sign: a vision-language model must turn what it sees into hypotheses, questions and tests. DynamicDx evaluates ea…
Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography
Yuanyuan Zhang, Yida Zhang, Jiahui Li +6
Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variation…
EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses
Jiahui Li, Ruili Fang, Zishuai Liu +5
Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence. Existing diagnosis-prediction benchmarks are poorly suited to this setting: they re…
LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition
Zishuai Liu, Ruili Fang, Jin Lu +1
Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living. In realistic deployments, however, sensor event…
Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign
Jiahui Li, Yida Zhang, Zixuan Zeng +10
Accurate peak detection across diverse cardiac physiological signals, including the Electrocardiogram (ECG), Photoplethysmogram (PPG), Ballistocardiogram (BCG), and Bodyseismograph…
DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition
Jiahui Li, Ruili Fang, Zishuai Liu +3
Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems treat beats as isolated local in…
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Taida Li, Yujun Yan, Fei Dou +2
Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This s…
SIGMA: Semantic Identifier Grouping for Molecular Autoregression
Xinyu Wang, Fei Dou, Jinbo Bi +1
Autoregressive molecular models assign probability to molecular serializations even though chemical identity is invariant to serialization. Equivalent serializations can therefore…
LARV: Data-Free Layer-wise Adaptive Rescaling Veneer for Model Merging
Xinyu Wang, Ke Deng, Fei Dou +2
Model merging aims to combine multiple fine-tuned models into a single multi-task model without access to training data. Existing task-vector merging methods such as TIES, TSV-M, a…
Achieving Fine-grained Cross-modal Understanding through Brain-inspired Hierarchical Representation Learning
Weihang You, Hanqi Jiang, Yi Pan +3
Understanding neural responses to visual stimuli remains challenging due to the inherent complexity of brain representations and the modality gap between neural data and visual inp…
MARAuder's Map: Motion-Aware Real-time Activity Recognition with Layout-Based Trajectories
Zishuai Liu, Weihang You, Jin Lu +1
Ambient sensor-based human activity recognition (HAR) in smart homes remains challenging due to the need for real-time inference, spatially grounded reasoning, and context-aware te…
CARE: Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams
Junhao Zhao, Zishuai Liu, Ruili Fang +3
The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained b…
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
Haoran Lu, Luyang Fang, Ruidong Zhang +47
Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment…
ADLGen: Synthesizing Symbolic, Event-Triggered Sensor Sequences for Human Activity Modeling
Weihang You, Hanqi Jiang, Zishuai Liu +4
Real world collection of Activities of Daily Living data is challenging due to privacy concerns, costly deployment and labeling, and the inherent sparsity and imbalance of human be…
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization
Huaqin Zhao, Jiaxi Li, Yi Pan +7
Fine-tuning large language models (LLMs) poses significant memory challenges, as the back-propagation process demands extensive resources, especially with growing model sizes. Rece…
A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education
Ehsan Latif, Yifan Zhou, Shuchen Guo +24
As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce…
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Fei Dou, Jin Ye, Geng Yuan +22
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrig…
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
Ziba Parsons, Fei Dou, Houyi Du +2
This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to t…