Latent Gaussian modeling and INLA: A review with focus on space-time applications

Bayesian hierarchical models with latent Gaussian layers have proven very flexible in capturing complex stochastic behavior and hierarchical structures in high-dimensional spatial and spatio-temporal data. Whereas simulation-based Bayesian inference through Markov Chain Monte Carlo may be hampered by slow convergence and numerical instabilities, the inferential framework of Integrated Nested Laplace Approximation (INLA) is capable to provide accurate and relatively fast analytical approximations to posterior quantities of interest. It heavily relies on the use of Gauss-Markov dependence structures to avoid the numerical bottleneck of high-dimensional nonsparse matrix computations. With a view towards space-time applications, we here review the principal theoretical concepts, model classes and inference tools within the INLA framework. Important elements to construct space-time models are certain spatial Mat\'ern-like Gauss-Markov random fields, obtained as approximate solutions to a stochastic partial differential equation. Efficient implementation of statistical inference tools for a large variety of models is available through the INLA package of the R software. To showcase the practical use of R-INLA and to illustrate its principal commands and syntax, a comprehensive simulation experiment is presented using simulated non Gaussian space-time count data with a first-order autoregressive dependence structure in time

New taxa of Epiphloeinae Kuwert (Cleridae) and Chaetosomatidae Crowson (Coleoptera: Cleroidea)

Twenty-one new taxa of Cleridae and one of Chaetosomatidae are described including four new genera: Acanthocollis, Decaphloeus, Megaphloeus, and Stegnoclava. Twenty new species are described: five species of Amboakis Opitz (A. ampla, A. antegalba, A. diffusa, A. demagna, A. waodani, one species of Epiphloeus Spinola (E. erwini), four species of Madoniella Pic (M. aspera, M. darlingtoni, M. divida, M. spilota), two species of Plocamocera Spinola (P. clinata, P. lena), seven species of Pyticeroides Kuwert (P. latisentis, P. moraquesi, P. parvoporis, P. pinnacerinis, P. pullis, P. turbosiris, P. ustulatis), and one species of Chaetosomatidae (Chaetosoma colossa)

Descriptions of new species of the New World genus Perilypus Spinola (Coleoptera: Cleridae: Clerinae)

Thirty-two new species of Perilypus Spinola (Coleoptera: Cleridae: Clerinae) are described; they are Perilypus ancorus, P. angustatus, P. aquilus, P. arenaceus, P. caligneus, P. cartagoensis, P. collatus, P. comosus, P. concisus, P. copanensis, P. copiosus, P. diutius, P. divaricatus, P. elimatus, P. flavoapicalis, P. galenae, P. hamus, P. hornito, P. infussus, P. iodus, P. lateralis, P. latissimus, P. licinus, P. limbus, P. miculus, P. odous, P. orophus, P. patulus, P. punctus, turnbowi, P. violaceus, and P. yasuniensis. Included in this work are 58 line drawings and 32 color habitus photographs of primary types. To facilitate species identification the species included herein are linked to a key to Perilypus species provided in a previous review of the genus

Balcus violaceus (Fabricius) : senior synonym of Balcus niger Sharp and B. signatus Broun (Coleoptera: Cleridae: Clerinae)

The elytra of Balcus signatus Broun (Coleoptera: Cleridae: Clerinae) from New Zealand have pale markings. Such markings, most prominently found in females, represent intraspecific variations of Balcus violaceus (Fabricius). Accordingly, Balcus signatus Brown is synonymized with Notoxus violaceus Fabricius, new synonymy. Four habitus figures of Balcus violaceus (Fabricius) are presented to display the range of elytral color variation in the species

Popular Ensemble Methods: An Empirical Study

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees

Automatic Accuracy Prediction for AMR Parsing

Abstract Meaning Representation (AMR) represents sentences as directed, acyclic and rooted graphs, aiming at capturing their meaning in a machine readable format. AMR parsing converts natural language sentences into such graphs. However, evaluating a parser on new data by means of comparison to manually created AMR graphs is very costly. Also, we would like to be able to detect parses of questionable quality, or preferring results of alternative systems by selecting the ones for which we can assess good quality. We propose AMR accuracy prediction as the task of predicting several metrics of correctness for an automatically generated AMR parse - in absence of the corresponding gold parse. We develop a neural end-to-end multi-output regression model and perform three case studies: firstly, we evaluate the model's capacity of predicting AMR parse accuracies and test whether it can reliably assign high scores to gold parses. Secondly, we perform parse selection based on predicted parse accuracies of candidate parses from alternative systems, with the aim of improving overall results. Finally, we predict system ranks for submissions from two AMR shared tasks on the basis of their predicted parse accuracy averages. All experiments are carried out across two different domains and show that our method is effective.Comment: accepted at *SEM 201

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file