11,694 research outputs found
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
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