Lava flow morphology at an erupting andesitic stratovolcano: a satellite perspective on El Reventador, Ecuador

Lava flows pose a significant hazard to infrastructure and property located close to volcanoes, and understanding how flows advance is necessary to manage volcanic hazard during eruptions. Compared to low-silica basaltic flows, flows of andesite composition are infrequently erupted and so relatively few studies of their characteristics and behaviour exist. We use El Reventador, Ecuador as a target to investigate andesitic lava flow properties during a 4.5 year period of extrusive eruption between February 2012 and August 2016. We use satellite radar to map the dimensions of 43 lava flows and look at variations in their emplacement behaviour over time. We find that flows descend the north and south flanks of El Reventador, and were mostly emplaced during durations shorter than the satellite repeat interval of 24 days.Flows ranged in length from 0.3 to 1.7 km, and the length of these flows decreased over the observation period. We measure a decrease in flow volume with time that is correlated with a long-term exponential decrease in eruption rate, and propose that this behaviour is caused by temporary magma storage in the conduit acting as a melt capacitor between the magma reservoir and the surface. We use the dimensions of the flow levees and widths to estimate the flow yield strengths, which were of the order of 10-100 kPa. We observe that some flows were diverted by topographic obstacles, and compare measurements of decreased channel width and increased flow thickness at the obstacles with observations from laboratory experiments. Radar observations, such as those presented here, could be used to map and measure properties of evolving lava flow fields at other remote or difficult to monitor volcanoes

Do Douglas-fir branches and roots have juvenile wood?

We sampled boles, branches, and roots of four Douglas-fir (Pseudotsuga menziesii var. menziesii) trees to learn a) whether branches and roots have a zone of juvenile wood, defined as a zone with progressive changes in wood density and/or tracheid length from the pith outward at successively greater cambial ages, and if so, b) whether the radial patterns in branches and roots are similar to those in boles. Samples came from the following positions: bole, 30 cm aboveground; branch, 30 cm outward on the lowest live branch, and root, 1–3 m from the bole. Average sample cambial ages were bole, 60 years; branch, 30 years; and root, 48 years. Roots and branches had higher density wood than did the bole at the positions studied, and roots had the longest tracheids followed by the bole and then the branches. All three positions exhibited juvenile wood but with different radial patterns. All positions had their highest density near the pith, which was followed by a steep decline in the boles and a more gradual decline in the roots and branches. Boles and roots, but not branches, then showed an increase in density after this decline. Boles and branches had short tracheids near the pith followed by a gradual increase to an asymptotic value. In contrast, tracheid length in roots was relatively constant from pith to bark, although there was a dip of about 20% from about cambial ages 14 to 24. This study shows that the vascular cambium of this species is not constrained to produce one sole radial pattern of wood properties as it matures. The presence of different radial patterns in boles, branches, and roots supports the hypothesis that juvenile wood in the bole is an adaptive feature rather than an unavoidable developmental consequence. Further work should investigate more fully the different physiological or mechanical roles of the various wood types within the context of the position in the tree at which they occur.Keywords: root, branch, density, juvenile wood, specific gravity, tracheid length, Douglas-fi

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

Finding a short and accurate decision rule in disjunctive normal form by exhaustive search

Greedy approaches suffer from a restricted search space which could lead to suboptimal classifiers in terms of performance and classifier size. This study discusses exhaustive search as an alternative to greedy search for learning short and accurate decision rules. The Exhaustive Procedure for LOgic-Rule Extraction (EXPLORE) algorithm is presented, to induce decision rules in disjunctive normal form (DNF) in a systematic and efficient manner. We propose a method based on subsumption to reduce the number of values considered for instantiation in the literals, by taking into account the relational operator without loss of performance. Furthermore, we describe a branch-and-bound approach that makes optimal use of user-defined performance constraints. To improve the generalizability we use a validation set to determine the optimal length of the DNF rule. The performance and size of the DNF rules induced by EXPLORE are compared to those of eight well-known rule learners. Our results show that an exhaustive approach to rule learning in DNF results in significantly smaller classifiers than those of the other rule learners, while securing comparable or even better performance. Clearly, exhaustive search is computer-intensive and may not always be feasible. Nevertheless, based on this study, we believe that exhaustive search should be considered an alternative for greedy search in many problems

Finding related sentence pairs in MEDLINE

We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure

Using data mining for wine quality assessment

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physico- chemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certifi- cation step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a re- gression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its do- main. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selec- tion and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regres- sion and neural network methods. Such model is useful for understand- ing how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Background: Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences. Results: We present a mixture of experts approach to biomolecular sequence labeling that takes into account the global similarity between biomolecular sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian techniques to combine the predictions of the experts. We evaluate our approach on two biomolecular sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biomolecular sequence data. Conclusion: The mixture of experts model helps improve the performance of machine learning methods for identifying functionally important sites in biomolecular sequences.This is a proceeding from IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 10 (2009): S4, doi: 10.1186/1471-2105-10-S4-S4. Posted with permission.</p

Predicting self‐declared movie watching behavior using Facebook data and information‐fusion sensitivity analysis

The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self-reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self-declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self-reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information-fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time- and frequency-based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self-reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie-watchers and market their movies more efficiently

Adaptation-Based Programming in Haskell

We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.Comment: In Proceedings DSL 2011, arXiv:1109.032