The evolution of major cannabinoids and terpenes during the growth of Cannabis sativa plants was studied. In this work, seven different plants were selected: three each from chemotypes I and III and one from chemotype II. Fifty clones of each mother plant were grown indoors under controlled conditions. Every week, three plants from each variety were cut and dried, and the leaves and flowers were analyzed separately. Eight major cannabinoids were analyzed via HPLC-DAD, and 28 terpenes were quantified using GC-FID and verified via GC-MS. The chemotypes of the plants, as defined by the tetrahydrocannabinolic acid/cannabidiolic acid (THCA/CBDA) ratio, were clear from the beginning and stable during growth. The concentrations of the major cannabinoids and terpenes were determined, and different patterns were found among the chemotypes. In particular, the plants from chemotypes II and III needed more time to reach peak production of THCA, CBDA, and monoterpenes. Differences in the cannabigerolic acid development among the different chemotypes and between monoterpene and sesquiterpene evolution patterns were also observed. Plants of different chemotypes were clearly differentiated by their terpene content, and characteristic terpenes of each chemotype were identified.

A number of applications have been recently proposed based on deep networks and interplanetary trajectories. These approaches often rely on the availability of a large number of optimal trajectories to learn from. In this paper we introduce a new method to quickly create millions of optimal spacecraft trajectories from a single nominal trajectory. Apart from the generation of the nominal trajectory, no additional optimal control problems need to be solved as all the trajectories, by construction, satisfy Pontryagin's minimum principle and the relevant transversality conditions. We then consider deep feed forward neural networks and benchmark three learning methods on the created dataset: policy imitation, value function learning and value function gradient learning. Our results are shown for the case of the interplanetary trajectory optimization problem of reaching Venus orbit, with the nominal trajectory starting from the Earth. We find that both policy imitation and value function gradient learning are able to learn the optimal state feedback, while in the case of value function learning the optimal policy is not captured, only the final value of the optimal propellant mass is.

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We present a novel desktop-based system for high-quality facial capture including geometry and facial appearance. The proposed acquisition system is highly practical and scalable, consisting purely of commodity components. The setup consists of a set of displays for controlled illumination for reflectance capture, in conjunction with multiview acquisition of facial geometry. We additionally present a novel set of modulated binary illumination patterns for efficient acquisition of reflectance and photometric normals using our setup, with diffuse-specular separation. We demonstrate high-quality results with two different variants of the capture setup -one entirely consisting of portable mobile devices targeting static facial capture, and the other consisting of desktop LCD displays targeting both static and dynamic facial capture.