Human spontaneous gaze patterns in viewing of faces of different species

Human studies have reported clear differences in perceptual and neural processing of faces of different species, implying the contribution of visual experience to face perception. Can these differences be manifested in our eye scanning patterns while extracting salient facial information? Here we systematically compared non-pet owners’ gaze patterns while exploring human, monkey, dog and cat faces in a passive viewing task. Our analysis revealed that the faces of different species induced similar patterns of fixation distribution between left and right hemi-face, and among key local facial features with the eyes attracting the highest proportion of fixations and viewing times, followed by the nose and then the mouth. Only the proportion of fixation directed at the mouth region was species-dependent and could be differentiated at the earliest stage of face viewing. It seems that our spontaneous eye scanning patterns associated with face exploration were mainly constrained by general facial configurations; the species affiliation of the inspected faces had limited impact on gaze allocation, at least under free viewing conditions

On the Survival of Overconfident Traders in a Competitive Securities Market

Recent research has proposed several ways in which overconfident traders can persist in competition with rational traders. This paper offers an additional reason: overconfident traders do better than purely rational traders at exploiting mispricing caused by liquidity or noise traders. We examine both the static profitability of overconfident versus rational trading strategies, and the dynamic evolution of a population of overconfident, rational and noise traders. Replication of overconfident and rational types is assumed to be increasing in the recent profitability of their strategies. The main result is that the long-run steady-state equilibrium always involves overconfident traders as a substantial positive fraction of the population.Survivorship, Natural Selection, Overconfident Traders, Noise traders

Quantum Griffiths Singularities in the Transverse-Field Ising Spin Glass

We report a Monte Carlo study of the effects of {\it fluctuations} in the bond distribution of Ising spin glasses in a transverse magnetic field, in the {\it paramagnetic phase} in the $T\to 0$ limit. Rare, strong fluctuations give rise to Griffiths singularities, which can dominate the zero-temperature behavior of these quantum systems, as originally demonstrated by McCoy for one-dimensional ($d=1$) systems. Our simulations are done on a square lattice in $d=2$ and a cubic lattice in $d=3$, for a gaussian distribution of nearest neighbor (only) bonds. In $d=2$, where the {\it linear} susceptibility was found to diverge at the critical transverse field strength $\Gamma_c$ for the order-disorder phase transition at T=0, the average {\it nonlinear} susceptibility $\chi_{nl}$ diverges in the paramagnetic phase for $\Gamma$ well above $\Gamma_c$, as is also demonstrated in the accompanying paper by Rieger and Young. In $d=3$, the linear susceptibility remains finite at $\Gamma_c$, and while Griffiths singularity effects are certainly observable in the paramagnetic phase, the nonlinear susceptibility appears to diverge only rather close to $\Gamma_c$. These results show that Griffiths singularities remain persistent in dimensions above one (where they are known to be strong), though their magnitude decreases monotonically with increasing dimensionality (there being no Griffiths singularities in the limit of infinite dimensionality).Comment: 20 pages, REVTEX, 6 eps figures included using the epsf macros; to appear in Phys. Rev.

Detecting complex events in user-generated video using concept classifiers

Automatic detection of complex events in user-generated videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed byconstructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events