112 citations · 116 across the 10 of their papers we have counts for
10 papers
Structural Rationale Distillation via Reasoning Space Compression
Jialin Yang, Jiankun Wang, Jiajun Wu +3
When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who…
Bitcoin Cross-Chain Bridge: A Taxonomy and Its Promise in Artificial Intelligence of Things
Guojun Tang, Carylyne Chan, Ning Nan +5
Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentral…
Dual-stage and Lightweight Patient Chart Summarization for Emergency Physicians
Jiajun Wu, Swaleh Zaidi, Braden Teitge +4
Electronic health records (EHRs) contain extensive unstructured clinical data that can overwhelm emergency physicians trying to identify critical information. We present a two-stag…
Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
Zainab Saad, Jialin Yang, Henry Leung +1
The growing reliance on large-scale data centers to run resource-intensive workloads has significantly increased the global carbon footprint, underscoring the need for sustainable…
SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data
Hanzhe Wei, Jiajun Wu, Jialin Yang +2
Time series anomaly detection plays a crucial role in a wide range of fields, such as healthcare and internet traffic monitoring. The emergence of large language models (LLMs) offe…
A Survey on Task Scheduling in Carbon-Aware Container Orchestration
Jialin Yang, Zainab Saad, Jiajun Wu +3
The soaring energy demands of large-scale software ecosystems and cloud data centers, accelerated by the intensive training and deployment of large language models, have driven ene…
Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks
Fan Dong, Henry Leung, Steve Drew
Federated learning offers a compelling solution to the challenges of networking and data privacy within aerial and space networks by utilizing vast private edge data and computing…
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
Ali Abbasi, Fan Dong, Xin Wang +3
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process. Cl…
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
Fan Dong, Ali Abbasi, Henry Leung +3
Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private e…
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Jiajun Wu, Steve Drew, Fan Dong +2
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effec…