Temporal Network Prediction and Interpretation

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods...

MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like...

Steering Micro-Robotic Swarm by Dynamic Actuating Fields

We present a general solution for steering microrobotic<br/>swarm by dynamic actuating fields. In our approach, the<br/>motion of micro-robots is controlled by changing the actuating<br/>direction of a field applied to them. The time-series sequence<br/>of actuating field’s directions can be computed automatically.<br/>Given a target position in...

Stochasticity, nonlinearity and forecasting of streamflow processes

Abstract not available