Relaxational behavior of the infinite-range Ising spin-glass in a transverse field

We study the zero-temperature behavior of the infinite-ranged Ising spin glass in a transverse field. Using spin summation techniques and Monte Carlo methods we characterize the zero-temperature quantum transition. Our results are well compatible with a value $\nu=\frac{1}{4}$ for the correlation length exponent, $z=4$ for the dynamical exponent and an algebraic decay $t^{-1}$ for the imaginary-time correlation function. The zero-temperature relaxation of the energy in the presence of the transverse field shows that the system monotonically reaches the ground state energy due to tunneling processes and displays strong glassy effects.Comment: 15 pages + 5 Figures, Revte

Quantum critical effects in mean-field glassy systems

We consider the effects of quantum fluctuations in mean-field quantum spin-glass models with pairwise interactions. We examine the nature of the quantum glass transition at zero temperature in a transverse field. In models (such as the random orthogonal model) where the classical phase transition is discontinuous an analysis using the static approximation reveals that the transition becomes continuous at zero temperature

A Discrete Model for Color Naming

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map.</p

[Beating frequency of motile cilia lining the third cerebral ventricle is finely tuned by the hypothalamic peptide MCH]

International audienc

Laparoscopic colic resection in the elderly: a comparative study

The purpose of this study was to evalute any relative benefits for laparoscopic colectomy in patients over 70 years old compared with under 70 years ol

A New Paradigm for Geometric AccuracyPrediction in Medical Image Segmentation

Safety and accuracy are two important keywords when deal-ing with life-critical systems. In particular, in medical image processingthese two aspects have to be taken into careful attention since it represents the first step in the process that starts with the image acquisition and proceeds to the diagnosis step and therapy definition. Therefore it is important to analyze the possible inaccuracy sources that can be found in this step, since they will affect the accuracy of the whole system. In literature there are several techniques for the safety analysis and accuracy evaluation of complex systems, however most of the proposed approaches in the field of medical image processing only face the problem of defining different metrics that can accurately assess the accuracy of imaging processing techniques from a purely geometrical and quantitative point of view.In this paper we introduce a different approach to the problem of segmentation accuracy assessment based on the analysis of the critical aspects in the segmentation workflow that can affect the accuracy of the overall system, according to the specific clinical problem under investigation.We present a proof of feasibility of our approach by combining the use of Petri Nets for the modeling of the workflow of segmentation procedures in two different clinical scenarios: accuracy evaluation of manual segmentations performed by non-experts (skin tumors) and of a semi-automatic system (liver lesions).Results show that it is feasible to correlate the qualitative analysis withthe quantitative measures: in this way it is possible to predict the inaccuracy of the segmentation results and to optimize the different steps of the system before even acquiring and processing the data

A Discrete Model for Color Naming

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many di\ufb00erent disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identi\ufb01ed by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly de\ufb01ned by \ufb01tting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the di\ufb00erent categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELABcolor space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate themembership values of any other point in the color space. Model validation is performed both directly, through the comparison ofthe predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), andindirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in bothcases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semanticallymeaningful color-based segmentation map