Integration Mechanisms for Heading Perception

Previous studies of heading perception suggest that human observers employ spatiotemporal pooling to accommodate noise in optic flow stimuli. Here, we investigated how spatial and temporal integration mechanisms are used for judgments of heading through a psychophysical experiment involving three different types of noise. Furthermore, we developed two ideal observer models to study the components of the spatial information used by observers when performing the heading task. In the psychophysical experiment, we applied three types of direction noise to optic flow stimuli to differentiate the involvement of spatial and temporal integration mechanisms. The results indicate that temporal integration mechanisms play a role in heading perception, though their contribution is weaker than that of the spatial integration mechanisms. To elucidate how observers process spatial information to extract heading from a noisy optic flow field, we compared psychophysical performance in response to random-walk direction noise with that of two ideal observer models (IOMs). One model relied on 2D screen-projected flow information (2D-IOM), while the other used environmental, i.e., 3D, flow information (3D-IOM). The results suggest that human observers compensate for the loss of information during the 2D retinal projection of the visual scene for modest amounts of noise. This suggests the likelihood of a 3D reconstruction during heading perception, which breaks down under extreme levels of noise

Random projection to preserve patient privacy

With the availability of accessible and widely used cloud services, it is natural that large components of healthcare systems migrate to them; for example, patient databases can be stored and processed in the cloud. Such cloud services provide enhanced flexibility and additional gains, such as availability, ease of data share, and so on. This trend poses serious threats regarding the privacy of the patients and the trust that an individual must put into the healthcare system itself. Thus, there is a strong need of privacy preservation, achieved through a variety of different approaches. In this paper, we study the application of a random projection-based approach to patient data as a means to achieve two goals: (1) provably mask the identity of users under some adversarial-attack settings, (2) preserve enough information to allow for aggregate data analysis and application of machine-learning techniques. As far as we know, such approaches have not been applied and tested on medical data. We analyze the tradeoff between the loss of accuracy on the outcome of machine-learning algorithms and the resilience against an adversary. We show that random projections proved to be strong against known input/output attacks while offering high quality data, as long as the projected space is smaller than the original space, and as long as the amount of leaked data available to the adversary is limited