Does Colour Filling-In Account for Colour Perception in Natural Images?

It is popular to attribute the appearance of extended colour fields to a process of filling-in from the differential colour signals at colour edges, where one colour transitions to another. We ask whether such a process can account for the appearance of extended colour fields in natural images. Some form of colour filling-in must underlie the equiluminant colour Craik-O’Brien-Cornsweet effect and the Water colour Effect, but these effects are too weak to account for the appearance of extended colour fields in natural images. Moreover, the graded colour disappearance effect reported as evidence for colour filling-in does not work under natural viewing conditions. We demonstrate that natural images do not look very colourful when their colour is restricted to edge transitions . Moreover, purely chromatic images with maximally graded (‘edgeless’) transitions look fully colourful. Consequently, we conclude that colour filling-in makes no more than a minor contribution to the appearance of extended colour regions in natural images

Warren McCulloch and the British cyberneticians

Warren McCulloch was a significant influence on a number of British cyberneticians, as some British pioneers in this area were on him. He interacted regularly with most of the main figures on the British cybernetics scene, forming close friendships and collaborations with several, as well as mentoring others. Many of these interactions stemmed from a 1949 visit to London during which he gave the opening talk at the inaugural meeting of the Ratio Club, a gathering of brilliant, mainly young, British scientists working in areas related to cybernetics. This paper traces some of these relationships and interaction

Proteome analysis of the hyaluronic acid-producing bacterium, Streptococcus zooepidemicus

Background: Streptococcus equi subsp. zooepidemicus (S. zooepidemicus) is a commensal of horses and an opportunistic pathogen in many animals and humans. Some strains produce copious amounts of hyaluronic acid, making S. zooepidemicus an important industrial microorganism for the production of this valuable biopolymer used in the pharmaceutical and cosmetic industry. Encapsulation by hyaluronic acid is considered an important virulence factor in other streptococci, though the importance in S. zooepidemicus remains poorly understood. Proteomics may provide a better understanding of virulence factors in S. zooepidemicus, facilitate the design of better diagnostics and treatments, and guide engineering of superior production strains

The evolution of representation in simple cognitive networks

Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether or not they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks---an artificial neural network and a network of hidden Markov gates---to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation, and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system, and should be predictive of an agent's long-term adaptive success.Comment: 36 pages, 10 figures, one Tabl

Controlled Dynamics of Interfaces in a Vibrated Granular Layer

We present experimental study of a topological excitation, {\it interface}, in a vertically vibrated layer of granular material. We show that these interfaces, separating regions of granular material oscillation with opposite phases, can be shifted and controlled by a very small amount of an additional subharmonic signal, mixed with the harmonic driving signal. The speed and the direction of interface motion depends sensitively on the phase and the amplitude of the subharmonic driving.Comment: 4 pages, 6 figures, RevTe

Exact Solutions for Domain Walls in Coupled Complex Ginzburg - Landau Equations

The complex Ginzburg Landau equation (CGLE) is a ubiquitous model for the evolution of slowly varying wave packets in nonlinear dissipative media. A front (shock) is a transient layer between a plane-wave state and a zero background. We report exact solutions for domain walls, i.e., pairs of fronts with opposite polarities, in a system of two coupled CGLEs, which describe transient layers between semi-infinite domains occupied by each component in the absence of the other one. For this purpose, a modified Hirota bilinear operator, first proposed by Bekki and Nozaki, is employed. A novel factorization procedure is applied to reduce the intermediate calculations considerably. The ensuing system of equations for the amplitudes and frequencies is solved by means of computer-assisted algebra. Exact solutions for mutually-locked front pairs of opposite polarities, with one or several free parameters, are thus generated. The signs of the cubic gain/loss, linear amplification/attenuation, and velocity of the coupled-front complex can be adjusted in a variety of configurations. Numerical simulations are performed to study the stability properties of such fronts.Comment: Journal of the Physical Society of Japan, in pres

Solution structure and novel insights into the determinants of the receptor specificity of human relaxin-3

Relaxin- 3 is the most recently discovered member of the relaxin family of peptide hormones. In contrast to relaxin- 1 and - 2, whose main functions are associated with pregnancy, relaxin- 3 is involved in neuropeptide signaling in the brain. Here, we report the solution structure of human relaxin- 3, the first structure of a relaxin family member to be solved by NMR methods. Overall, relaxin- 3 adopts an insulin- like fold, but the structure differs crucially from the crystal structure of human relaxin- 2 near the B- chain terminus. In particular, the B- chain C terminus folds back, allowing Trp(B27) to interact with the hydrophobic-core. This interaction partly blocks the conserved RXXXRXXI motif identified as a determinant for the interaction with the relaxin receptor LGR7 and may account for the lower affinity of relaxin- 3 relative to relaxin for this receptor. This structural feature is likely important for the activation of its endogenous receptor, GPCR135

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Surface gravity waves in deep fluid at vertical shear flows

Special features of surface gravity waves in deep fluid flow with constant vertical shear of velocity is studied. It is found that the mean flow velocity shear leads to non-trivial modification of surface gravity wave modes dispersive characteristics. Moreover, the shear induces generation of surface gravity waves by internal vortex mode perturbations. The performed analytical and numerical study provides, that surface gravity waves are effectively generated by the internal perturbations at high shear rates. The generation is different for the waves propagating in the different directions. Generation of surface gravity waves propagating along the main flow considerably exceeds the generation of surface gravity waves in the opposite direction for relatively small shear rates, whereas the later wave is generated more effectively for the high shear rates. From the mathematical point of view the wave generation is caused by non self-adjointness of the linear operators that describe the shear flow.Comment: JETP, accepte