How position, velocity, and temporal information combine in the prospective control of catching:data and model

The cerebral cortex contains circuitry for continuously computing properties of the environment and one's body, as well as relations among those properties. The success of complex perceptuomotor performances requires integrated, simultaneous use of such relational information. Ball catching is a good example as it involves reaching and grasping of visually pursued objects that move relative to the catcher. Although integrated neural control of catching has received sparse attention in the neuroscience literature, behavioral observations have led to the identification of control principles that may be embodied in the involved neural circuits. Here, we report a catching experiment that refines those principles via a novel manipulation. Visual field motion was used to perturb velocity information about balls traveling on various trajectories relative to a seated catcher, with various initial hand positions. The experiment produced evidence for a continuous, prospective catching strategy, in which hand movements are planned based on gaze-centered ball velocity and ball position information. Such a strategy was implemented in a new neural model, which suggests how position, velocity, and temporal information streams combine to shape catching movements. The model accurately reproduces the main and interaction effects found in the behavioral experiment and provides an interpretation of recently observed target motion-related activity in the motor cortex during interceptive reaching by monkeys. It functionally interprets a broad range of neurobiological and behavioral data, and thus contributes to a unified theory of the neural control of reaching to stationary and moving targets.

Hardware Acceleration of Background Modeling in the Compressed Domain

In intelligent video surveillance systems, scalability (of the number of simultaneous video streams) is important. Two key factors which hinder scalability are the time spent in decompressing the input video streams, and the limited computational power of the processor. This paper demonstrates how a combination of algorithmic and hardware techniques can overcome these limitations, and significantly increase the number of simultaneous streams. The techniques used are processing in the compressed domain, and exploitation of the multicore and vector processing capability of modern processors. The paper presents a system which performs background modeling, using a Mixture of Gaussians approach. This is an important first step in the segmentation of moving targets. The paper explores the effects of reducing the number of coefficients in the compressed domain, in terms of throughput speed and quality of the background modeling. The speedups achieved by exploiting compressed domain processing, multicore and vector processing are explored individually. Experiments show that a combination of all these techniques can give a speedup of 170 times on a single CPU compared to a purely serial, spatial domain implementation, with a slight gain in quality.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Intercepting beats in predesignated target zones

Moving to a rhythm necessitates precise timing between the movement of the chosen limb and the timing imposed by the beats. However, the temporal information specifying the moment when a beat will sound (the moment onto which one must synchronise one's movement) is not continuously provided by the acoustic array. Because of this informational void, the actors need some form of prospective information that will allow them to act sufficiently ahead of time in order to get their hand in the right place at the right time. In this acoustic interception study, where participants were asked to move between two targets in such a way that they arrived and stopped in the target zone at the same time as a beat sounded, we tested a model derived from tau-coupling theory (Lee DN (1998) Ecol Psychol 10:221-250). This model attempts to explain the form of a potential timing guide that specifies the duration of the inter-beat intervals and also describes how this informational guide can be used in the timing and guidance of movements. The results of our first experiment show that, for inter-beat intervals of less than 3 s, a large proportion of the movement (over 70%) can be explained by the proposed model. However, a second experiment, which augments the time between beats so that it surpasses 3 s, shows a marked decline in the percentage of information/movement coupling. A close analysis of the movement kinematics indicates a lack of control and anticipation in the participants' movements. The implications of these findings, in light of other research studies, are discussed.