Phylogenetic Reconstruction, Morphological Diversification and Generic Delimitation of Disepalum (Annonaceae)

Taxonomic delimitation of Disepalum (Annonaceae) is contentious, with some researchers favoring a narrow circumscription following segregation of the genus Enicosanthellum. We reconstruct the phylogeny of Disepalum and related taxa based on four chloroplast and two nuclear DNA regions as a framework for clarifying taxonomic delimitation and assessing evolutionary transitions in key morphological characters. Maximum parsimony, maximum likelihood and Bayesian methods resulted in a consistent, well-resolved and strongly supported topology. Disepalum s.l. is monophyletic and strongly supported, with Disepalum s. str. and Enicosanthellum retrieved as sister groups. Although this topology is consistent with both taxonomic delimitations, the distribution of morphological synapomorphies provides greater support for the inclusion of Enicosanthellum within Disepalum s.l. We propose a novel infrageneric classification with two subgenera. Subgen. Disepalum (= Disepalum s. str.) is supported by numerous synapomorphies, including the reduction of the calyx to two sepals and connation of petals. Subgen. Enicosanthellum lacks obvious morphological synapomorphies, but possesses several diagnostic characters (symplesiomorphies), including a trimerous calyx and free petals in two whorls. We evaluate changes in petal morphology in relation to hypotheses of the genetic control of floral development and suggest that the compression of two petal whorls into one and the associated fusion of contiguous petals may be associated with the loss of the pollination chamber, which in turn may be associated with a shift in primary pollinator. We also suggest that the formation of pollen octads may be selectively advantageous when pollinator visits are infrequent, although this would only be applicable if multiple ovules could be fertilized by each octad; since the flowers are apocarpous, this would require an extragynoecial compitum to enable intercarpellary growth of pollen tubes. We furthermore infer that the monocarp fruit stalks are likely to have evolved independently from those in other Annonaceae genera and may facilitate effective dispersal by providing a color contrast within the fruit.published_or_final_versio

Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning.

Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported

Optimal Prediction for Prefetching in the Worst Case

AMS subject classi cations. 68Q25, 68T05, 68P20, 68N25, 60J20 PII. S0097539794261817Response time delays caused by I/O are a major problem in many systems and database applications. Prefetching and cache replacement methods are attracting renewed attention because of their success in avoiding costly I/Os. Prefetching can be looked upon as a type of online sequential prediction, where the predictions must be accurate as well as made in a computationally e cient way. Unlike other online problems, prefetching cannot admit a competitive analysis, since the optimal o ine prefetcher incurs no cost when it knows the future page requests. Previous analytical work on prefetching [J. Assoc. Comput. Mach., 143 (1996), pp. 771{793] consisted of modeling the user as a probabilistic Markov source. In this paper, we look at the much stronger form of worst-case analysis and derive a randomized algorithm for pure prefetching. We compare our algorithm for every page request sequence with the important class of nite state prefetchers, making no assumptions as to how the sequence of page requests is generated. We prove analytically that the fault rate of our online prefetching algorithm converges almost surely for every page request sequence to the fault rate of the optimal nite state prefetcher for the sequence. This analysis model can be looked upon as a generalization of the com- petitive framework, in that it compares an online algorithm in a worst-case manner over all sequences with a powerful yet nonclairvoyant opponent. We simultaneously achieve the computational goal of implementing our prefetcher in optimal constant expected time per prefetched page using the optimal dynamic discrete random variate generator of Matias, Vitter, and Ni [Proc. 4th Annual SIAM/ACM Symposium on Discrete Algorithms, Austin, TX, January 1993]

Optimal Prediction for Prefetching in the Worst Case

This is the published version. Copyright © 1998 Society for Industrial and Applied MathematicsResponse time delays caused by I/O are a major problem in many systems and database applications. Prefetching and cache replacement methods are attracting renewed attention because of their success in avoiding costly I/Os. Prefetching can be looked upon as a type of online sequential prediction, where the predictions must be accurate as well as made in a computationally efficient way. Unlike other online problems, prefetching cannot admit a competitive analysis, since the optimal offline prefetcher incurs no cost when it knows the future page requests. Previous analytical work on prefetching [. Vitter Krishnan 1991.] [J. Assoc. Comput. Mach., 143 (1996), pp. 771--793] consisted of modeling the user as a probabilistic Markov source. In this paper, we look at the much stronger form of worst-case analysis and derive a randomized algorithm for pure prefetching. We compare our algorithm for every page request sequence with the important class of finite state prefetchers, making no assumptions as to how the sequence of page requests is generated. We prove analytically that the fault rate of our online prefetching algorithm converges almost surely for every page request sequence to the fault rate of the optimal finite state prefetcher for the sequence. This analysis model can be looked upon as a generalization of the competitive framework, in that it compares an online algorithm in a worst-case manner over all sequences with a powerful yet nonclairvoyant opponent. We simultaneously achieve the computational goal of implementing our prefetcher in optimal constant expected time per prefetched page using the optimal dynamic discrete random variate generator of [. Matias Matias, Vitter, and Ni [Proc. 4th Annual SIAM/ACM Symposium on Discrete Algorithms, Austin, TX, January 1993]

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area