Synthesis of extended uridine phosphonates derived from an allosteric P2Y2 receptor ligand

In this study we report the synthesis of C5/C6-fused uridine phosphonates that are structurally related to earlier reported allosteric P2Y2 receptor ligands. A silyl-Hilbert-Johnson reaction of six quinazoline-2,4-(1H,3H)-dione-like base moieties with a suitable ribofuranosephosphonate afforded the desired analogues after full deprotection. In contrast to the parent 5-(4-fluoropheny)uridine phosphonate, the present extended-base uridine phosphonates essentially failed to modulate the P2Y2 receptor

Improving numerical reasoning capabilities of inductive logic programming systems

Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAG method to evaluate learning performance. We have found these extensions essential to improve on results mer statistical-based algorithms for time series forecasting used in the empirical evaluation study

Electronic g-factor and Magneto-transport in InSb Quantum Wells

High mobility InSb quantum wells with tunable carrier densities are investigated by transport experiments in magnetic fields tilted with respect to the sample normal. We employ the coincidence method and the temperature dependence of the Shubnikov-de Haas oscillations and find a value for the effective g-factor of $\mid g^{\ast}\mid$ =35$\pm$4 and a value for the effective mass of $m^*\approx0.017 m_0$, where $m_0$ is the electron mass in vacuum. Our measurements are performed in a magnetic field and a density range where the enhancement mechanism of the effective g-factor can be neglected. Accordingly, the obtained effective g-factor and the effective mass can be quantitatively explained in a single particle picture. Additionally, we explore the magneto-transport up to magnetic fields of 35 T and do not find features related to the fractional quantum Hall effect.Comment: 18 Pages, 5 Figure

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Relational Learning Using Constrained Confidence-Rated Boosting

Abstract. In propositional learning, boosting has been a very popular technique for increasing the accuracy of classification learners. In first-order learning, on the other hand, surprisingly little attention has been paid to boosting, perhaps due to the fact that simple forms of boosting lead to loss of comprehensibility and are too slow when used with standard ILP learners. In this paper, we show how both concerns can be addressed by using a recently proposed technique of constrained confidencerated boosting and a fast weak ILP learner. We give a detailed description of our algorithm and show on two standard benchmark problems that indeed such a weak learner can be boosted to perform comparably to state-of-the-art ILP systems while maintaining acceptable comprehensibility and obtaining short run-times

Scaling Boosting by Margin-based Inclusion of Features and Relations

Boosting is well known to increase the accuracy of propositional and multi-relational classification learners. However, the base learner’s efficiency vitally determines boosting’s efficiency since the complexity of the underlying learner is amplified by iterated calls of the learner in the boosting framework. The idea of restricting the learner to smaller feature subsets in order to increase efficiency is widely used. Surprisingly, little attention has been paid so far to exploiting characteristics of boosting itself to include features based on the current learning progress. In this paper, we show that the dynamics inherent to boosting offer ideal means to maximize the efficiency of the learning process. We describe how to utilize the training examples’ margins—which are known to be maximized by boosting—to reduce learning times without a deterioration of the learning quality. We suggest to stepwise include features in the learning process in response to a slowdown in the improvement of the margins. Experimental results show that this approach significantly reduces the learning time while maintaining or even improving the predictive accuracy of the underlying fully equipped learner