Universality classes in directed sandpile models

We perform large scale numerical simulations of a directed version of the two-state stochastic sandpile model. Numerical results show that this stochastic model defines a new universality class with respect to the Abelian directed sandpile. The physical origin of the different critical behavior has to be ascribed to the presence of multiple topplings in the stochastic model. These results provide new insights onto the long debated question of universality in abelian and stochastic sandpiles.Comment: 5 pages, RevTex, includes 9 EPS figures. Minor english corrections. One reference adde

Advances in human-computer interactions: methods, algorithms, and applications

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Visual working memory in immersive visualization: a change detection experiment and an image-computable model

Visual working memory (VWM) is a cognitive mechanism essential for interacting with the environment and accomplishing ongoing tasks, as it allows fast processing of visual inputs at the expense of the amount of information that can be stored. A better understanding of its functioning would be beneficial to research fields such as simulation and training in immersive Virtual Reality or information visualization and computer graphics. The current work focuses on the design and implementation of a paradigm for evaluating VWM in immersive visualization and of a novel image-based computational model for mimicking the human behavioral data of VWM. We evaluated the VWM at the variation of four conditions: set size, spatial layout, visual angle (VA) subtending stimuli presentation space, and observation time. We adopted a full factorial design and analysed participants' performances in the change detection experiment. The analysis of hit rates and false alarm rates confirms the existence of a limit of VWM capacity of around 7 & PLUSMN; 2 items, as found in the literature based on the use of 2D videos and images. Only VA and observation time influence performances (p<0.0001). Indeed, with VA enlargement, participants need more time to have a complete overview of the presented stimuli. Moreover, we show that our model has a high level of agreement with the human data, r>0.88 (p<0.05)

Near-optimal combination of disparity across a log-polar scaled visual field

The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment