Network Flow Optimization for Restoration of Images

The network flow optimization approach is offered for restoration of grayscale and color images corrupted by noise. The Ising models are used as a statistical background of the proposed method. The new multiresolution network flow minimum cut algorithm, which is especially efficient in identification of the maximum a posteriori estimates of corrupted images, is presented. The algorithm is able to compute the MAP estimates of large size images and can be used in a concurrent mode. We also describe the efficient solutions of the problem of integer minimization of two energy functions for the Ising models of gray-scale and color images

The Battle between Age and Frequency

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Navigation of brain networks

Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45%-60% reductions in navigation performance. Specifically, we found that brain networks cannot be progressively rewired (randomized or clusterized) to result in topologies with significantly improved navigation performance. Navigation was also found to: i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks; and, ii) explain significant variation in functional connectivity. Unlike prevalently studied communication strategies in connectomics, navigation does not mandate biologically unrealistic assumptions about global knowledge of network topology. We conclude that the wiring and spatial embedding of brain networks is conducive to effective decentralized communication. Graph-theoretic studies of the connectome should consider measures of network efficiency and centrality that are consistent with decentralized models of neural communication

A Remarkable Example of Bubble Nucleation Suppression

Suppression of cavitation is a relatively common goal of fluid engineers and therefore examples of bubble nucleation suppression in other technological contexts are useful in suggesting ways in which such suppression might be achieved. In this paper we describe a remarkable example of bubble nucleation suppression achieved by a combination of the elimination of nucleation sites and the reduction of bubble growth time. The context is the invention of a device that allows the injection of aqueous solutions highly supersaturated with oxygen into the bloodstream without the formation of significant gaseous oxygen bubbles

Injection of Highly Supersaturated Oxygen Solutions without Nucleation

It is possible to inject highly supersaturated aqueous solutions of gas through a small capillary into an aqueous environment without the formation of significant gas bubbles. Such a technique has considerable potential therapeutic value in the treatment, for example, of heart attacks and strokes. The present paper is the second in a series (see Brereton et al. [1]) investigating the basic phenomenon behind this surprising effect. Recent experiments clearly demonstrate that the nucleation, when it does occur, results from heterogeneous nucleation on the interior surface of the distal end of the capillary. This paper describes the effects of the treatment of this interior surface on the nucleation processes and the results of high speed video observations of the phenomena. A heterogeneous nucleation model is presented which is in accord with the experimental observations