Petri nets for modelling metabolic pathways: a survey

In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net

On the parameterised complexity of learning patterns

10.1007/978-1-4471-2155-8-35Computer and Information Sciences II - 26th International Symposium on Computer and Information Sciences, ISCIS 2011277-28

On the amount of nonconstructivity in learning formal languages from positive data

10.1007/978-3-642-29952-0_41Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)7287 LNCS423-43

Algorithmic Identification of Probabilities Is Hard

International audienceSuppose that we are given an infinite binary sequence which is random for a Bernoulli measure of parameter p. By the law of large numbers, the frequency of zeros in the sequence tends to p, and thus we can get better and better approximations of p as we read the sequence. We study in this paper a similar question, but from the viewpoint of inductive inference. We suppose now that p is a computable real, and one asks for more: as we are reading more and more bits of our random sequence, we have to eventually guess the exact parameter p (in the form of its Turing code). Can one do such a thing uniformly for all sequences that are random for computable Bernoulli measures, or even for a 'large enough' fraction of them? In this paper, we give a negative answer to this question. In fact, we prove a very general negative result which extends far beyond the class of Bernoulli measures

Incremental Concept Learning for Bounded Data Mining

Information and Computation152174-110INFC

An Exergetic Life Cycle Assessment for Improving Hydrogen Production by Steam Methane Reforming

As some cognitive research suggests, in the process of learning languages, in addition to overt explicit negative evidence, a child often receives covert explicit evidence in form of corrected or rephrased sentences. In this paper, we suggest one approach to formalization of overt and covert evidence within the framework of one-shot learners via subset and membership queries to a teacher (oracle). We compare and explore general capabilities of our models, as well as complexity advantages of learnability models of one type over models of other types, where complexity is measured in terms of number of queries. In particular, we establish that “correcting ” positive examples give sometimes more power to a learner than just negative (counter)examples and access to full positive data