There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models. While OpenSim provides useful tools to analyse human movement, it is not fast enough to be routinely used for emerging research directions, e.g., learning and simulating motor control through deep neural networks and Reinforcement Learning (RL). We propose a framework for converting OpenSim models to MuJoCo, the de facto simulator in machine learning research, which itself lacks accurate musculo-skeletal human models. We show that with a few simple approximations of anatomical details, an OpenSim model can be automatically converted to a MuJoCo version that runs up to 600 times faster. We also demonstrate an approach to computationally optimize MuJoCo model parameters so that forward simulations of both simulators produce similar results.

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In this paper we present a new RGB-D dataset a captured with the Kinect sensor. The dataset is composed of typical children's toys and contains a total of 449 RGB-D images alongside with their annotated ground truth images. Compared to existing RBG-D object segmentation datasets, the objects in our proposed dataset have more complex shapes and less texture. The images are also crowded and thus highly occluded. Three state-of-the-art segmentation methods are benchmarked using the dataset. These methods attack the problem of object segmentation from different starting points, providing a comprehensive view on the properties of the proposed dataset as well as the state-of-the-art performance. The results are mostly satisfactory but there remains plenty of room for improvement. This novel dataset thus poses the next challenge in the area of RGB-D object segmentation.

Simulation-based methods are developing rapidly, changing theory and practice in applied fields.• Simulations help create and validate new HCI theory, making design and engineering more predictable, and improving safety and accessibility.• Emulation of user behaviour with generative models tests our understanding of an interactive system.• Simulation-based intelligence can be created by directly embedding models in intelligent interactive systems.• Model-based evaluation provides insights into usability before user-testing, saving money, time, and discomfort.