Revolution through Competition?

Competition, in the form of survival of the fittest, has been around far longer than humans, but humans are unique in setting up artificial competitions and awarding prizes for success. The Olympic Games is perhaps the oldest competition that continues to this day (albeit with a hiatus of two millennia), though no doubt it wasn't the first. Technological competitions are more recent but not young.     

Pressure dependence of intersubband transitions in HgTe/Hg<Subscript>0.3</Subscript>Cd<Subscript>0.7</Subscript>Te superlattices

The optical absorption coefficient of HgTe/Hg_0.3Cd_0.7Te superlattices (SLs) and its pressure dependence has been investigated at hydrostatic pressures up to 30 kbar at room temperature. The corresponding intersubband transition energies result from a comparison of experimental and theoretical absorption coefficients. The latter is based on the band structure, which is calculated using Kane’s four-band (8×8 k · p) model together with the envelope function approximation. The experimental linear pressure coefficients of the H1 — E1 and H1 — L1 intersubband transitions are in good agreement with the theoretical values, e.g., 7.15±0.3 meV/kbar and 6.2±0.3 meV/kbar compared to 7.4 and 6.4 meV/kbar, respectively. this is in stark contrast to the pressure dependence of ≤1 meV/kbar of the photoluminescence (PL) peaks of a similar SL reported in the literature. Consequently, we conclude that the reported PL peaks are not due to intersubband transitions and that the k · p model correctly reproduces the electronic band structure and its pressure dependence of HgTe/Hg_1−xCd_xTe SLs.     

Fast learning of relational kernels

We develop a general theoretical framework for statistical logical learning with kernels based on dynamic propositionalization, where structure learning corresponds to inferring a suitable kernel on logical objects, and parameter learning corresponds to function learning in the resulting reproducing kernel Hilbert space. In particular, we study the case where structure learning is performed by a simple FOIL-like algorithm, and propose alternative scoring functions for guiding the search process. We present an empirical evaluation on several data sets in the single-task as well as in the multi-task setting.     

Basic concepts and taxonomy of dependable and secure computing

This paper gives the main definitions relating to dependability, a generic concept including a special case of such attributes as reliability, availability, safety, integrity, maintainability, etc. Security brings in concerns for confidentiality, in addition to availability and integrity. Basic definitions are given first. They are then commented upon, and supplemented by additional definitions, which address the threats to dependability and security (faults, errors, failures), their attributes, and the means for their achievement (fault prevention, fault tolerance, fault removal, fault forecasting). The aim is to explicate a set of general concepts, of relevance across a wide range of situations and, therefore, helping communication and cooperation among a number of scientific and technical communities, including ones that are concentrating on particular types of system, of system failures, or of causes of system failures.     

Logistic Model Trees

Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, there has been work on combining these two schemes into model trees, i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm to several other state-of-the-art learning schemes on 36 benchmark UCI datasets, and show that it produces accurate and compact classifiers.Editor Johannes FürnkranzThis is an extended version of a paper that appeared in the Proceedings of the 14th European Conference on Machine Learning (Landwehr et al., 2003).