In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Abdelrahman Sayed Sayed to chat about his work on formal verification applied to autonomous vehicles. Could you tell us a bit about where you're studying and the broad topic of your research? My PhD topic is formal verification of neural ODE (ordinary differential equations) for safety evaluation in autonomous vehicles. Could you say something about formal verification and why it's such an important topic?

LICD has 577 features and labels. The dataset provides several ML research opportunities; we discuss two demonstration experiments.

Decision trees are widely used in many settings where interpretable models are preferred orrequired.

Department of Ships and Ocean Structures, SINTEF Ocean, Trondheim, Norway Abstract: In this paper, we present a Newton-Euler formulation of the equations of motion for underwater vehicles with an interntal moving mass actuator. Furthermore, the moving mass dynamics are expressed as an extension to the manoeuvring model for underwater vehicles, originally introduced by Fossen (1991). The influence of the moving mass is described in body-frame and included as states in both an additional kinematic equation and as part of the coupled rigid-body kinetics of the underwater vehicle. The Coriolis-centripetal effects are derived from Kirchhoff's equations and the hydrostatics are derived using first principals. The proposed Newton-Euler model is validated through simulation and compared with the traditional Hamiltonian internal moving mass actuator formulation.

We found that while training dataset size was a core determinant of alignment with human choices, contrastive training with multi-modal data (text and imagery) was a common feature of currently publicly available models that predicted human generalisation. Intrinsic dimensionality of representations had different effects on alignment for different model types. Lastly, we tested three sets of human-aligned representations and found no consistent improvements in predictive accuracy compared to the baselines.

Machine learning models and algorithms appear more and more frequently in systems that make decisions with an impact in our lives.