Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Italy's various Middle Ages

Questo saggio sottolinea la multivocalit\ue0 del richiamo al Medioevo nell'Italia dell'Ottocento e dei primi decenni del Novecent

Approximate Relational Reasoning by Stochastic Propositionalization

For many real-world applications it is important to choose the right representation language. While the setting of First Order Logic (FOL) is the most suitable one to model the multi-relational data of real and complex domains, on the other hand it puts the question of the computational complexity of the knowledge induction process. A way of tackling the complexity of such real domains, in which a lot of relationships are required to model the objects involved, is to use a method that reformulates a multi-relational learning task into an attribute-value one. In this chapter we present an approximate reasoning method able to keep low the complexity of a relational problem by using a stochastic inference procedure. The complexity of the relational language is decreased by means of a propositionalization technique, while the NP-completeness of the deduction is tackled using an approximate query evaluation. The proposed approximate reasoning technique has been used to solve the problem of relational rule induction as well as the task of relational clustering. An anytime algorithm has been used for the induction, implemented by a population based method, able to efficiently extract knowledge from relational data, while the clustering task, both unsupervised and supervised, has been solved using a Partition Around Medoid (PAM) clustering algorithm. The validity of the proposed techniques has been proved making an empirical evaluation on real-world datasets

Wax-incorporated Emulsion Gel Beads of Calcium Pectinate for Intragastric Floating Drug Delivery

The purpose of this study was to prepare wax-incorporated pectin-based emulsion gel beads using a modified emulsion-gelation method. The waxes in pectin–olive oil mixtures containing a model drug, metronidazole, were hot-melted, homogenized and then extruded into calcium chloride solution. The beads formed were separated, washed with distilled water and dried for 12 h. The influence of various types and amounts of wax on floating and drug release behavior of emulsion gel beads of calcium pectinate was investigated. The drug-loaded gel beads were found to float on simulated gastric fluid if the sufficient amount of oil was used. Incorporation of wax into the emulsion gel beads affected the drug release. Water-soluble wax (i.e. polyethylene glycol) increased the drug release while other water-insoluble waxes (i.e. glyceryl monostearate, stearyl alcohol, carnauba wax, spermaceti wax and white wax) significantly retarded the drug release. Different waxes had a slight effect on the drug release. However, the increased amount of incorporated wax in the formulations significantly sustained the drug release while the beads remained floating. The results suggest that wax-incorporated emulsion gel beads could be used as a carrier for intragastric floating drug delivery