65,491 research outputs found
Structured additive regression for multicategorical space-time data: A mixed model approach
In many practical situations, simple regression models suffer from the fact that the dependence of responses on covariates can not be sufficiently described by a purely parametric predictor. For example effects of continuous covariates may be nonlinear or complex interactions between covariates may be present. A specific problem of space-time data is that observations are in general spatially and/or temporally correlated. Moreover, unobserved heterogeneity between individuals or units may be present. While, in recent years, there has been a lot of work in this area dealing with univariate response models, only limited attention has been given to models for multicategorical space-time data. We propose a general class of structured additive regression models (STAR) for multicategorical responses, allowing for a flexible semiparametric predictor. This class includes models for multinomial responses with unordered categories as well as models for ordinal responses. Non-linear effects of continuous covariates, time trends and interactions between continuous covariates are modelled through Bayesian versions of penalized splines and flexible seasonal components. Spatial effects can be estimated based on Markov random fields, stationary Gaussian random fields or two-dimensional penalized splines. We present our approach from a Bayesian perspective, allowing to treat all functions and effects within a unified general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference is performed on the basis of a multicategorical linear mixed model representation. This can be viewed as posterior mode estimation and is closely related to penalized likelihood estimation in a frequentist setting. Variance components, corresponding to inverse smoothing parameters, are then estimated by using restricted maximum likelihood. Numerically efficient algorithms allow computations even for fairly large data sets. As a typical example we present results on an analysis of data from a forest health survey
Indexing Metric Spaces for Exact Similarity Search
With the continued digitalization of societal processes, we are seeing an
explosion in available data. This is referred to as big data. In a research
setting, three aspects of the data are often viewed as the main sources of
challenges when attempting to enable value creation from big data: volume,
velocity and variety. Many studies address volume or velocity, while much fewer
studies concern the variety. Metric space is ideal for addressing variety
because it can accommodate any type of data as long as its associated distance
notion satisfies the triangle inequality. To accelerate search in metric space,
a collection of indexing techniques for metric data have been proposed.
However, existing surveys each offers only a narrow coverage, and no
comprehensive empirical study of those techniques exists. We offer a survey of
all the existing metric indexes that can support exact similarity search, by i)
summarizing all the existing partitioning, pruning and validation techniques
used for metric indexes, ii) providing the time and storage complexity analysis
on the index construction, and iii) report on a comprehensive empirical
comparison of their similarity query processing performance. Here, empirical
comparisons are used to evaluate the index performance during search as it is
hard to see the complexity analysis differences on the similarity query
processing and the query performance depends on the pruning and validation
abilities related to the data distribution. This article aims at revealing
different strengths and weaknesses of different indexing techniques in order to
offer guidance on selecting an appropriate indexing technique for a given
setting, and directing the future research for metric indexes
Turing machines can be efficiently simulated by the General Purpose Analog Computer
The Church-Turing thesis states that any sufficiently powerful computational
model which captures the notion of algorithm is computationally equivalent to
the Turing machine. This equivalence usually holds both at a computability
level and at a computational complexity level modulo polynomial reductions.
However, the situation is less clear in what concerns models of computation
using real numbers, and no analog of the Church-Turing thesis exists for this
case. Recently it was shown that some models of computation with real numbers
were equivalent from a computability perspective. In particular it was shown
that Shannon's General Purpose Analog Computer (GPAC) is equivalent to
Computable Analysis. However, little is known about what happens at a
computational complexity level. In this paper we shed some light on the
connections between this two models, from a computational complexity level, by
showing that, modulo polynomial reductions, computations of Turing machines can
be simulated by GPACs, without the need of using more (space) resources than
those used in the original Turing computation, as long as we are talking about
bounded computations. In other words, computations done by the GPAC are as
space-efficient as computations done in the context of Computable Analysis
Alternative implementations of a fractional order control algorithm on FPGAs
Traditionally, microprocessor and digital signal processors have been used extensively in controlling simple processes, such as direct current motors. The Field Programmable Gate Arrays (FPGA) are currently emerging as an alternative to the previously used devices in controlling all sorts of processes. The fractional order proportional-integrative control algorithm has the advantage of enhancing the closed loop performance as compared to traditional proportional-integrative controllers, but the implementation requires a higher number of computations. Implementations of control algorithms on FPGAs are nowadays much faster than implementations on microprocessors. This allows for a more accurate digital realization of the fractional order controller. The paper presents nine alternative implementations of such control algorithm on two different FPGA targets. The experimental results, considering DC motor speed control, show that double, fixed-point and integer data representation may be used efficiently for control purposes
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