Learning from the past with experiment databases

Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior

Comparing predictions made by a prediction model, clinical score, and physicians Pediatric asthma exacerbations in the emergency department

Background: Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. Objectives: First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. Design: A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2, data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Measurements: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results: In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant. Conclusion: Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy

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

Growth modes of nanoparticle superlattice thin films

We report about the fabrication and characterization of iron oxide nanoparticle thin film superlattices. The formation into different film morphologies is controlled by tuning the particle plus solvent-to-substrate interaction. It turns out that the wetting vs. dewetting properties of the solvent before the self-assembly process during solvent evaporation plays a major role to determine the resulting film morphology. In addition to layerwise growth also three-dimensional mesocrystalline growth is evidenced. The understanding of the mechanisms ruling nanoparticle self-assembly represents an important step toward the fabrication of novel materials with tailored optical, magnetic or electrical transport properties

Nanostructuring of the conjugated polyelectrolyte poly[9,9-bis(4-sulfonylbutoxyphenyl) fluorene-2,7-diyl-2,2 '- bithiophene] in liquid crystalline C12E4 in bulk water and aligned thin films

Peer reviewe

Solvent content in thin spin-coated polystyrene homopolymer films

The solvent content of thin polystyrene (PS) films, spin-coated from protonated and deuterated toluene onto silicon substrates, is investigated. Neutron reflectometry (NR) is used to probe the total remaining solvent inside the PS films in a nondestructive and noninvasive way. In freshly prepared films, the investigated parameters are the molecular weight of PS and the total film thickness. Moreover, the effect of postproduction treatment by annealing at temperatures below and above the glass transition of PS as well as long-term storage over 2 years are examined to deduce the reduction of the remaining solvent. The remaining solvent content increases with increasing molecular weight and with increasing film thickness. An enrichment of toluene at the Si/polymer interface is found. Under the different annealing and storage conditions tested, the remaining solvent is not totally removed. The observed behavior is discussed in the framework of polymer thin films and compared with results obtained by alternative experimental approaches

Morphology and photoluminescence study of titania nanoparticles

Titania nanoparticles are prepared by sol–gel chemistry with a poly(ethylene oxide) methyl ether methacrylate-block-poly(dimethylsiloxane)-block-poly(ethylene oxide) methyl ether methacrylate triblock copolymer acting as the templating agent. The sol–gel components—hydrochloric acid, titanium tetraisopropoxide, and triblock copolymer—are varied to investigate their effect on the resulting titania morphology. An increased titania precursor or polymer content yields smaller primary titania structures. Microbeam grazing incidence small-angle X-ray scattering measurements, which are analyzed with a unified fit model, reveal information about the titania structure sizes. These small structures could not be observed via the used microscopy techniques. The interplay among the sol–gel components via our triblock copolymer results in different sized titania nanoparticles with higher packing densities. Smaller sized titania particles, (∼13–20 nm in diameter) in the range of exciton diffusion length, are formed by 2% by weight polymer and show good crystallinity with less surface defects and high oxygen vacancies