Nanodosimetric measurements with an avalanche confinement TEPC

This paper illustrates a tissue-equivalent proportional counter designed to have high gas gain and good energy resolution at nanometric simulated site sizes. Microdosimetric neutron and gamma spectra were measured in dimethyl ether and in propane-based tissue-equivalent gas mixture down to 35 nm. The comparison of experimental data with the results of Monte Carlo calculations shows a satisfactory agreement.     

Preliminary results on a dedicated silicon diode detector for proton dosimetry

The present work reports preliminary measurements on the behaviour of a new p-type stereotactic silicon diode. Hi-pSi, produced by Scanditronix and dedicated to proton dosimetry. Diode response was investigated in low-energy proton beams (26.7 MeV and 12 MeV nominal energy), mainly with attention to stability, linearity, dose rate and energy dependence of the detector response. Three different Hi-pSi diodes of the same type were investigated. The diode response was linear with dose and the standard deviation of repeated readings was less than 2.5%. A marked dependence on dose rate was observed for one of the diodes (a response increase of 47% in the 0.7-11 Gy x min(-1) range). After the dose rate and water to silicon mass collision stopping power ratio correction of the diode response in the depth dose measurements, the difference, at the Bragg peak, with respect to the reference chamber was about 4%, ascribed to poor knowledge of the materials in front of the sensitive volume. The diode response was also nearly independent of linear energy transfer (LET) in the 9.6-21.5 MeV effective energy range.     

Resilience: An Evolutionary Approach to Spatial Economic Systems

The concept of resilience has received a great deal of attention in the past decades. Starting from the first fundamental definitions offered by Holling, Pimms and Perrings in an economic-ecological modeling context, the present paper explores the evolution of the resilience concept—as well as related different measures—in both a continuous and discrete time setting.From this perspective, the paper explores the relevance of the resilience concept in socio-economic systems, by focussing the attention on the relationships among resilience, transition dynamics and lock-in effects, in particular in the light of the dynamics of technological innovation diffusion and adaptive behaviour of firms. In this framework we will describe an empirical application, in which the resilience and dynamics of the West-German labour market will be investigated. This empirical illustration is offered by making use of an algorithm constructed for detecting Lyapunov exponents, so as to classify the resilience among employment sectors in our case study.     

Non-curated distributed databases for experimental data and models in neuroscience

Neuroscience is generating vast amounts of highly diverse data which is of potential interest to researchers beyond the laboratories in which it is collected. In particular, quantitative neuroanatomical data is relevant to a wide variety of areas, including studies of development, aging, pathology and in biophysically oriented computational modelling. Moreover, the relatively discrete and well-defined nature of the data make it an ideal application for developing systems designed to facilitate data archiving, sharing and reuse. At present, the only widely used forms of dissemination are figures and tables in published papers which suffer from inaccessibility and the loss of machine readability. They may also present only an averaged or otherwise selected subset of the available data. Numerous database projects are in progress to address these shortcomings. They employ a variety of architectures and philosophies, each with its own merits and disadvantages. One axis on which they may be distinguished is the degree of top-down control, or curation, involved in data entry. Here we consider one extreme of this scale in which there is no curation, minimal standardization and a wide degree of freedom in the form of records used to document data. Such a scheme has advantages in the ease of database creation and in the equitable assignment of perceived intellectual property by keeping the control of data in the hands of the experts who collected it. It does, however, require a more sophisticated infrastructure than conventional databases since the software must be capable of organizing diverse and differently documented data sets in an effective way. Several components of a software system to provide this infrastructure are now in place. Examples are presented, showing how these tools can be used to archive and publish neuronal morphology data, and how they can give an integrated view of data stored at many different sites.     

Tight binding of the antitumor drug ditercalinium to quadruplex DNA

The structural selectivity of the DNA-binding antitumor drug ditercalinium was investigated by competition dialysis with a series of nineteen different DNA substrates. The 7H-pyridocarbazole dimer was found to bind to double-stranded DNA with a preference for GC-rich species but can in addition form stable complexes with triplex and quadruplex structures. The preferential interaction of the drug with four-stranded DNA structures was independently confirmed by electrospray mass spectrometry and a detailed analysis of the binding reaction was performed by surface plasmon resonance (SPR) spectroscopy. The BIAcore SPR study showed that the kinetic parameters for the interaction of ditercalinium with the human telomeric quadruplex sequence are comparable to those measured with a duplex sequence. Slow association and dissociation were observed with both the quadruplex and duplex structures. The newly discovered preferential binding of ditercalinium to the antiparallel quadruplex sequence d(AG(3)[T(2)AG(3)](3)) provides new perspectives for the design of drugs that can bind to human telomeres.     

Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained by mapping the input vector first in a nonlinear way to a high-dimensional kernel-induced feature space in which a linear large margin classifier is constructed. Practical expressions are formulated in the dual space in terms of the related kernel function, and the solution follows from a (convex) quadratic programming (QP) problem. In least-squares SVMs (LS-SVMs), the SVM problem formulation is modified by introducing a least-squares cost function and equality instead of inequality constraints, and the solution follows from a linear system in the dual space. Implicitly, the least-squares formulation corresponds to a regression formulation and is also related to kernel Fisher discriminant analysis. The least-squares regression formulation has advantages for deriving analytic expressions in a Bayesian evidence framework, in contrast to the classification formulations used, for example, in gaussian processes (GPs). The LS-SVM formulation has clear primal-dual interpretations, and without the bias term, one explicitly constructs a model that yields the same expressions as have been obtained with GPs for regression. In this article, the Bayesian evidence framework is combined with the LS-SVM classifier formulation. Starting from the feature space formulation, analytic expressions are obtained in the dual space on the different levels of Bayesian inference, while posterior class probabilities are obtained by marginalizing over the model parameters. Empirical results obtained on 10 public domain data sets show that the LS-SVM classifier designed within the Bayesian evidence framework consistently yields good generalization performances.     

Enhancing effective depth-of-field by image fusion using mathematical morphology

Reduced depth-of-field (DOF) poses a problem in the light optical imaging system, since the objects present outside this zone appear blurry in the recorded image. The effective DOF of the sensor may be enhanced considerably without compromising the quality of the image by fusing images captured with different focused regions. This paper presents an image fusion technique suitable for combining multifocus images of a scene. The method employs morphological filters to select sharply focused regions from various images and then combines them together to reconstruct the image in which all the regions are properly focused. A performance measure based on image gradients is used to compare the results obtained by the proposed method with those obtained by other image fusion techniques.     

Cross-cultural differences in recognizing affect from body posture

Conveyance and recognition of human emotion and affective expression is influenced by many factors, including culture. Within the user modeling field, it has become increasingly necessary to understand the role affect can play in personalizing interactive interfaces using embodied animated agents. However, little research within the computer science field aims at understanding cultural differences within this vein. Therefore, we conducted a study to evaluate if differences exist in the way various cultures perceive emotion from body posture. We used static posture images of affectively expressive avatars to conduct recognition experiments with subjects from three cultures. After analyzing the subjects' judgments using multivariate analysis, we grounded the identified differences into a set of low-level posture features. We then used Mixture Discriminant Analysis (MDA) and an unsupervised expectation maximization (EM) model to build separate cultural models for affective posture recognition. Our results could prove useful to aide designers in creating more effective affective avatars.     

Detecting change-points in multidimensional stochastic processes

A general test statistic for detecting change-points in multidimensional stochastic processes with unknown parameters is proposed. The test statistic is specialized to the case of detecting changes in sequences of covariance matrices. Large-sample distributional results are presented for the test statistic under the null hypothesis of no-change. The finite-sample properties of the test statistic are compared with two other test statistics proposed in the literature. Using a binary segmentation procedure, the potential of the various test statistics is investigated in a multidimensional setting both via simulations and the analysis of a real life example. In general, all test statistics become more effective as the dimension increases, avoiding the determination of too many “incorrect” change-point locations in a one-dimensional setting.