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"TreeNet Based Fast Task Decomposition for Resource-Constrained Edge In" by Dong Lu, Yanlong Zhai et al.

Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common featur ....

Adaptation Models , Computational Modeling , Deep Learning , Edge Computing , Edge Intelligence , Odel Acceleration , Model Compression , Neural Networks , Resource Constrained , Task Analysis ,

Omicron surge: Oregon health authorities issue interim triage tool on how to prioritize care

Oregon health officials have provided the state's hospitals with an interim crisis care tool to help prioritize treatment if they reach a point when critical care beds, specialized equipment and other resources become scarce due to COVID-19 admissions.  ....

United States , White House , District Of Columbia , Dana Hargunani , Oregon Health Authority , Promoting Health Equity , Resource Constrained , Peter Graven , Oregon Health , Science University Office , Advanced Analytics ,