Optimal Estimation of a Classical Force with a Damped Oscillator in the non-Markovian Bath

We solve the optimal quantum limit of probing a classical force exactly by a damped oscillator initially prepared in the factorized squeezed state. The memory effects of the thermal bath on the oscillator evolution are investigated. We show that the optimal force sensitivity obtained by the quantum estimation theory approaches to zero for the non-Markovian bath, whereas approaches to a finite non-zero value for the Markovian bath as the energy of the damped oscillator goes to infinity.Comment: 5 pages, 4 figure

An optimal nonlinear guidance logic for the trajectory tracking of supercavitating vehicles

Supercavitating vehicles (SV) are a class of high-speed autonomous underwater vessels. They present a great challenge in designing the guidance law in comparison with the traditional autonomous underwater vehicles. This is due to the fact that their constraints and working environment are much more complex. To tackle the above challenge, an optimal nonlinear midcourse guidance logic is proposed by considering the wake terminal guidance and the remote target attack tasks. The proposed guidance logic is optimized by using an efficient genetic algorithm to obtain its optimal parameters. The results from our simulation case study suggest that the proposed guidance logic can meet both the motion requirements and navigation constraints while effectively cooperating with the wake terminal guidance. Moreover, it has potential in reducing the energy consumption to significantly improve the overall vehicle energy efficiency

Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201