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EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series
We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers; (ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e. g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time.
OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices
Recent advances in Internet-of-Things (IoT) technologies have sparked significant interest towards developing learning-based sensing applications on embedded edge devices.
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
These components collaboratively tackle the trade-offs in on-device DRL training, improving timing and algorithm performance while minimizing the risk of out-of-memory (OOM) errors.
