A general purpose stabilised balloon platform

The development of a three axis stabilized balloon platform capable of being operated in three modes of increasing accuracy is discussed. The system relies on angular motion sensing for primary feedback with linear accelerometers, magnetometers, and a star sensor for positional information. When under primary control the system will acquire and stabilize on any accessible part of the celestial sphere. A video verification system is included to provide pointing confirmation. Under improved accuracy control, the star sensor is used to lock onto a target star

(2,m,n)-groups with Euler characteristic equal to −2asb-2^as^b

We study those $(2, m, n)$-groups which are almost simple and for which the absolute value of the Euler characteristic is a product of two prime powers. All such groups which are not isomorphic to $PSL_2 (q)$ or $PGL_2(q)$ are completely classified

Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent's action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent's policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach