A common rule for integration and suppression of luminance contrast across eyes, space, time, and pattern

Visual perception begins by dissecting the retinal image into millions of small patches for local analyses by local receptive fields. However, image structures extend well beyond these receptive fields and so further processes must be involved in sewing the image fragments back together to derive representations of higher order (more global) structures. To investigate the integration process, we also need to understand the opposite process of suppression. To investigate both processes together, we measured triplets of dipper functions for targets and pedestals involving interdigitated stimulus pairs (A, B). Previous work has shown that summation and suppression operate over the full contrast range for the domains of ocularity and space. Here, we extend that work to include orientation and time domains. Temporal stimuli were 15-Hz counter-phase sine-wave gratings, where A and B were the positive and negative phases of the oscillation, respectively. For orientation, we used orthogonally oriented contrast patches (A, B) whose sum was an isotropic difference of Gaussians. Results from all four domains could be understood within a common framework in which summation operates separately within the numerator and denominator of a contrast gain control equation. This simple arrangement of summation and counter-suppression achieves integration of various stimulus attributes without distorting the underlying contrast code.

Mixture density network training by computation in parameter space

Training Mixture Density Network (MDN) configurations within the NETLAB framework takes time due to the nature of the computation of the error function and the gradient of the error function. By optimising the computation of these functions, so that gradient information is computed in parameter space, training time is decreased by at least a factor of sixty for the example given. Decreased training time increases the spectrum of problems to which MDNs can be practically applied making the MDN framework an attractive method to the applied problem solver.

The use of flash and pattern evoked fields in the diagnosis of Alzheimer's disease

Subjects with Alzheimer's disease (AD) exhibit normal visually evoked potentials (VEP) to pattern reversal stimuli but a delayed P2 flash response. The pattern response may originate in the primary visual cortex via the geniculo-calcarine pathway while the flash P2 may originate in the association areas via the cholinergic-tectal pathway. We now show: a) that the pathology of AD is more prominent in the visual association areas B18/19 than in B17 and b) that the magnetic signal to flash and pattern may originate from B18/19 and B17 respectively.

Performing in a night-time leisure venue: a visual analysis of erotic dance

This article analyses a range of different meanings attached to images of erotic dance, with a particular focus on the 'impression management' (Goffman 1959) enacted by dancers. It presents a visual analysis of the work of a female erotic performer in a lesbian erotic dance venue in the UK. Still photographs, along with observational data and interviews, convey the complexity and skill of an erotic dancer's diverse gendered and sexualised performances. The visual data highlights the extensive 'aesthetic labour' (Nickson et al. 2001) and 'emotional labour' (Hochschild 1983) the dancer must put in to constructing her work 'self'. However, a more ambitious use of the visual is identified: the dancer's own use of images of her work. This use of the visual by dancers themselves highlights a more complex 'impression management' strategy undertaken by a dancer and brings into question the separation of 'real' and 'work' 'selves' in erotic dance. © Sociological Research Online, 1996-2012.

Comments on multiple oscillatory solutions in systems with time-delay feedback

A complex Ginzburg-Landau equation subjected to local and global time-delay feedback terms is considered. In particular, multiple oscillatory solutions and their properties are studied. We present novel results regarding the disappearance of limit cycle solutions, derive analytical criteria for frequency degeneration, amplitude degeneration, frequency extrema. Furthermore, we discuss the influence of the phase shift parameter and show analytically that the stabilization of the steady state and the decay of all oscillations (amplitude death) cannot happen for global feedback only. Finally, we explain the onset of traveling wave patterns close to the regime of amplitude death.

Significant quantum effects in hydrogen activation

Dissociation of molecular hydrogen is an important step in a wide variety of chemical, biological, and physical processes. Due to the light mass of hydrogen, it is recognized that quantum effects are often important to its reactivity. However, understanding how quantum effects impact the reactivity of hydrogen is still in its infancy. Here, we examine this issue using a well-defined Pd/Cu(111) alloy that allows the activation of hydrogen and deuterium molecules to be examined at individual Pd atom surface sites over a wide range of temperatures. Experiments comparing the uptake of hydrogen and deuterium as a function of temperature reveal completely different behavior of the two species. The rate of hydrogen activation increases at lower sample temperature, whereas deuterium activation slows as the temperature is lowered. Density functional theory simulations in which quantum nuclear effects are accounted for reveal that tunneling through the dissociation barrier is prevalent for H2 up to ∼190 K and for D2 up to ∼140 K. Kinetic Monte Carlo simulations indicate that the effective barrier to H2 dissociation is so low that hydrogen uptake on the surface is limited merely by thermodynamics, whereas the D2 dissociation process is controlled by kinetics. These data illustrate the complexity and inherent quantum nature of this ubiquitous and seemingly simple chemical process. Examining these effects in other systems with a similar range of approaches may uncover temperature regimes where quantum effects can be harnessed, yielding greater control of bond-breaking processes at surfaces and uncovering useful chemistries such as selective bond activation or isotope separation.

Generalized KPCA by adaptive rules in feature space

Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA). IRKPCA works well in dealing with outliers, and can be carried out in an iterative manner, which makes it suitable to process incremental input data. As in the traditional robust PCA (RPCA), a binary field is employed for characterizing the outlier process, and the optimization problem is formulated as maximizing marginal distribution of a Gibbs distribution. In this paper, this optimization problem is solved by stochastic gradient descent techniques. In IRKPCA, the outlier process is in a high-dimensional feature space, and therefore kernel trick is used. IRKPCA can be regarded as a kernelized version of RPCA and a robust form of kernel Hebbian algorithm. Experimental results on synthetic data demonstrate the effectiveness of IRKPCA. © 2010 Taylor & Francis.

An edge-based matching kernel through discrete-time quantum walks

In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the line graph structure are twofold. First, for a graph, its directed line graph is a dual representation and each vertex of the line graph represents a corresponding edge in the original graph. Second, we show that the discrete-time quantum walk can be seen as a walk on the line graph and the state space of the walk is the vertex set of the line graph, i.e., the state space of the walk is the edges of the original graph. As a result, the directed line graph provides an elegant way of developing new edge-based matching kernel based on discrete-time quantum walks. For a pair of graphs, we compute the h-layer depth-based representation for each vertex of their directed line graphs by computing entropic signatures (computed from discrete-time quantum walks on the line graphs) on the family of K-layer expansion subgraphs rooted at the vertex, i.e., we compute the depth-based representations for edges of the original graphs through their directed line graphs. Based on the new representations, we define an edge-based matching method for the pair of graphs by aligning the h-layer depth-based representations computed through the directed line graphs. The new edge-based matching kernel is thus computed by counting the number of matched vertices identified by the matching method on the directed line graphs. Experiments on standard graph datasets demonstrate the effectiveness of our new kernel.

A quantum Jensen-Shannon graph kernel using the continuous-time quantum walk

In this paper, we use the quantum Jensen-Shannon divergence as a means to establish the similarity between a pair of graphs and to develop a novel graph kernel. In quantum theory, the quantum Jensen-Shannon divergence is defined as a distance measure between quantum states. In order to compute the quantum Jensen-Shannon divergence between a pair of graphs, we first need to associate a density operator with each of them. Hence, we decide to simulate the evolution of a continuous-time quantum walk on each graph and we propose a way to associate a suitable quantum state with it. With the density operator of this quantum state to hand, the graph kernel is defined as a function of the quantum Jensen-Shannon divergence between the graph density operators. We evaluate the performance of our kernel on several standard graph datasets from bioinformatics. We use the Principle Component Analysis (PCA) on the kernel matrix to embed the graphs into a feature space for classification. The experimental results demonstrate the effectiveness of the proposed approach. © 2013 Springer-Verlag.