Although Aitchison’s [Aitchison, J., 1986. The Statistical Analysis of Compositional Data, Chapman and Hall, London] method of logratio transformation of compositional data is widely used in various domains, it is limited by the assumption of a strict non-negativity of the components and the requirement of special treatments in practice of the zero components. We propose a dimension-reduction a...

Functional data analysis (FDA) deals with samples where a whole function is observed for each individual. A particular case is when the observed functions are densities, that is also an example of infinite dimensional compositional data. We focus on the dimensionality reduction problem for this particular type of data. Several methods are considered here: functional principal components analysi...

Neuroevolution has come a long way over the last decade. Lots of interesting and successful new methods and algorithms have been presented, with great improvements that make the field become very promising. Concretely, HyperNEAT has shown a great potential for evolving large scale neural networks, by discovering geometric regularities, thus being suitable for evolving complex controllers. Howev...

The reduction in transportation costs when travelling with a low-cost airline (LCA) seems to have modified the composition of the trip budget. An understanding of expenditure composition when comparing LCA and legacy airline travellers is vital for destination marketers. Using micro official statistics data for air travellers to Spain in 2010 and the compositional data analysis (CODA) methodolo...

Promotion is a major strategy in Research article introductions (RAIs) to attract reader's attention and gain recognition. Building on previous researches that suggest promotion RAIs could be realized by different kinds of linguistic elements rhetoric structures, this study collected 100 SCI the discipline computer science as data explored interpersonal metafunction structure modal adjuncts. Me...

This paper proposes to integrate a feature pursuit learning process into a greedy bottom-up learning scheme. The algorithm combines the benefits of bottom-up and top-down approaches for learning hierarchical models: It allows to induce the hierarchical structure of objects in an unsupervised manner, while avoiding a hard decision on the activation of parts. We follow the principle of compositio...

Soundness of compositional reasoning rules depends on computational models and sometimes is rather involved. Since it is tedious to establish new rules, verifiers are forced to mould verification problems into a handful of proof rules available to them. In this paper, a syntactic approach to establishing soundness of proof rules in automated compositional reasoning is shown. Not only can our wo...

Stochastic Process Algebras (SPA) have been proposed as compositional specification formalisms for quantitative models. Here we apply these compositional features to SPAs extended by rewards. State space reduction of performability models can be achieved based on the behaviour-preserving notion of Markov Reward Bisimulation. For a framework extended by immediate actions we develop a new equival...

Nucleotide sequences from a variety of sources display compositional bias (e.g., Bernardi et al. 1985; Jukes and Bhushan 1986; Sueoka 1988; Crozier and Crozier 1993; Dowton and Austin 1995). This bias in the proportion of the four nucleotides in a particular DNA fragment was originally thought to arise from mutational bias in the interconversion of adenine-thymine (AT) and guanine-cytosine (GC)...

Compositional reasoning about any behavioral property of a system depends, first, on the ability to express that property for both individual components and systems constructed from them. Expressiveness problems arise when considering compositional reasoning about performance in the presence of complex user-defined types (as opposed to simpler built-in types). There are interesting implications...