A memory-efficient graph representation based on a probabilistic data structure, a Bloom filter, that allows us to efficiently store assembly graphs in as little as 4 bits per k-mer, albeit inexactly, is introduced, which reduces the overall memory requirements for de novo assembly of metagenomes.
It is shown that zero-determinant strategies with an informational advantage over other players that allows them to recognize each other can be evolutionarily stable (and able to exploit other players), however, such an advantage is bound to be short-lived as opposing strategies evolve to counteract the recognition.
It is shown that a relatively simple perceptual constraint—predator confusion—could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey.
It is found that for evolved complex networks as well as for the yeast protein–protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules.
It is argued here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, a formal definition of representation is given based on information theory, and R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success.
This work uses Hamiltonian dynamics of models of the Ising type to describe populations of cooperating and defecting players to show that the equilibrium fraction of cooperators is given by the expectation value of a thermal observable akin to a magnetization.
This chapter introduces the basic concepts of data preprocessing, which can substantially improve the overall quality of the patterns mined and/or the time required for the actual mining.
It is suggested that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory.
The results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks ("brains") with many concepts, leading to an increase in their internal complexity.