Statistical Shape Analysis using Kernel PCA

©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.641417DOI:10.1117/12.641417Presented at Image Processing Algorithms and Systems, Neural Networks, and Machine Learning, 16-18 January 2006, San Jose, California, USA.Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional feature space F. However, many applications like kernel PCA (principal component analysis) can be used more effectively if a pre-image of the projection in the feature space is available. In this paper, we propose a novel method to reconstruct a unique approximate pre-image of a feature vector and apply it for statistical shape analysis. We provide some experimental results to demonstrate the advantages of kernel PCA over linear PCA for shape learning, which include, but are not limited to, ability to learn and distinguish multiple geometries of shapes and robustness to occlusions

Something from (almost) nothing: Buildup of object memory from forgettable single fixations

We can recognize thousands of individual objects in scores of familiar settings, and yet we see most of them only through occasional glances that are quickly forgotten. How do we come to recognize any of these objects? Here, we show that when objects are presented intermittently for durations of single fixations, the originally fleeting memories become gradually stabilized, such that, after just eight separated fixations, recognition memory after half an hour is as good as during an immediate memory test. However, with still shorter presentation durations, memories take more exposures to stabilize. Our results thus suggest that repeated glances suffice to remember the objects of our environment

Network adaptation improves temporal representation of naturalistic stimuli in drosophila eye: II Mechanisms

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information

Measurements of μμ pairs from open heavy flavor and Drell-Yan in p+p collisions at s =200 GeV

PHENIX reports differential cross sections of μμ pairs from semileptonic heavy-flavor decays and the Drell-Yan production mechanism measured in p+p collisions at s=200 GeV at forward and backward rapidity (1.2<|η|<2.2). The μμ pairs from cc, bb, and Drell-Yan are separated using a template fit to unlike- and like-sign muon pair spectra in mass and pT. The azimuthal opening angle correlation between the muons from cc and bb decays and the pair-pT distributions are compared to distributions generated using pythia and powheg models, which both include next-to-leading order processes. The measured distributions for pairs from cc are consistent with pythia calculations. The cc data present narrower azimuthal correlations and softer pT distributions compared to distributions generated from powheg. The bb data are well described by both models. The extrapolated total cross section for bottom production is 3.75±0.24(stat)±0.500.35(syst)±0.45(global) [μb], which is consistent with previous measurements at the Relativistic Heavy Ion Collider in the same system at the same collision energy and is approximately a factor of 2 higher than the central value calculated with theoretical models. The measured Drell-Yan cross section is in good agreement with next-to-leading-order quantum-chromodynamics calculations

Soft branes in supersymmetry-breaking backgrounds

We revisit the analysis of effective field theories resulting from non-supersymmetric perturbations to supersymmetric flux compactifications of the type-IIB superstring with an eye towards those resulting from the backreaction of a small number of anti-D3-branes. Independently of the background, we show that the low-energy Lagrangian describing the fluctuations of a stack of probe D3-branes exhibits soft supersymmetry breaking, despite perturbations to marginal operators that were not fully considered in some previous treatments. We take this as an indication that the breaking of supersymmetry by anti-D3-branes or other sources may be spontaneous rather than explicit. In support of this, we consider the action of an anti-D3-brane probing an otherwise supersymmetric configuration and identify a candidate for the corresponding goldstino.Comment: 36+5 pages. References added, minor typos correcte

Pseudorapidity Dependence of Particle Production and Elliptic Flow in Asymmetric Nuclear Collisions of p+Al, p+Au, d+Au, and ^{3}He+Au at sqrt[s_{NN}]=200 GeV.

Asymmetric nuclear collisions of p+Al, p+Au, d+Au, and ^{3}He+Au at sqrt[s_{NN}]=200  GeV provide an excellent laboratory for understanding particle production, as well as exploring interactions among these particles after their initial creation in the collision. We present measurements of charged hadron production dN_{ch}/dη in all such collision systems over a broad pseudorapidity range and as a function of collision multiplicity. A simple wounded quark model is remarkably successful at describing the full data set. We also measure the elliptic flow v_{2} over a similarly broad pseudorapidity range. These measurements provide key constraints on models of particle emission and their translation into flow

Anticipation of guilt for everyday moral transgressions : the role of the anterior insula and the influence of interpersonal psychopathic traits

Psychopathy is a personality disorder characterised by atypical moral behaviour likely rooted in atypical affective/motivational processing, as opposed to an inability to judge the wrongness of an action. Guilt is a moral emotion believed to play a crucial role in adherence to moral and social norms, but the mechanisms by which guilt (or lack thereof) may influence behaviour in individuals with high levels of psychopathic traits are unclear. We measured neural responses during the anticipation of guilt about committing potential everyday moral transgressions, and tested the extent to which these varied with psychopathic traits. We found a significant interaction between the degree to which anticipated guilt was modulated in the anterior insula and interpersonal psychopathic traits: anterior insula modulation of anticipated guilt was weaker in individuals with higher levels of these traits. Data from a second sample confirmed that this pattern of findings was specific to the modulation of anticipated guilt and not related to the perceived wrongness of the transgression. These results suggest a central role for the anterior insula in coding the anticipation of guilt regarding potential moral transgressions and advance our understanding of the neurocognitive mechanisms that may underlie propensity to antisocial behaviour

Waiting time distribution in public health care: empirics and theory

Excessive waiting times for elective surgery have been a long-standing concern in many national healthcare systems in the OECD. How do the hospital admission patterns that generate waiting lists affect different patients? What are the hospitals characteristics that determine waiting times? By developing a model of healthcare provision and analysing empirically the entire waiting time distribution we attempt to shed some light on those issues. We first build a theoretical model that describes the optimal waiting time distribution for capacity constraint hospitals. Secondly, employing duration analysis, we obtain empirical representations of that distribution across hospitals in the UK from 1997–2005. We observe important differences on the ‘scale’ and on the ‘shape’ of admission rates. Scale refers to how quickly patients are treated and shape represents trade-offs across duration-treatment profiles. By fitting the theoretical to the empirical distributions we estimate the main structural parameters of the model and are able to closely identify the main drivers of these empirical differences. We find that the level of resources allocated to elective surgery (budget and physical capacity), which determines how constrained the hospital is, explains differences in scale. Changes in benefits and costs structures of healthcare provision, which relate, respectively, to the desire to prioritise patients by duration and the reduction in costs due to delayed treatment, determine the shape, affecting short and long duration patients differently