Grammar-based Representation and Identification of Dynamical Systems

In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure and model parameter estimation problem, a new representation of dynamical systems is proposed. The proposed representation is based on Tree Adjoining Grammar, a formalism that was developed from linguistic considerations. Using the proposed representation, the identification problem can be interpreted as a multi-objective optimization problem and we propose a Evolutionary Algorithm-based approach to solve the problem. A benchmark example is used to demonstrate the proposed approach. The results were found to be comparable to that obtained by state-of-the-art non-linear system identification methods, without making use of knowledge of the system description.Comment: Submitted to European Control Conference (ECC) 201

Differentiable Genetic Programming

We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions. On several problems of increasing complexity we find that dCGP is able to find the exact form of the symbolic expression as well as the constants values. We also demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems, presenting, in both cases, results that confirm the efficacy of our approach

RNAiFold2T: Constraint Programming design of thermo-IRES switches

Motivation: RNA thermometers (RNATs) are cis-regulatory ele- ments that change secondary structure upon temperature shift. Often involved in the regulation of heat shock, cold shock and virulence genes, RNATs constitute an interesting potential resource in synthetic biology, where engineered RNATs could prove to be useful tools in biosensors and conditional gene regulation. Results: Solving the 2-temperature inverse folding problem is critical for RNAT engineering. Here we introduce RNAiFold2T, the first Constraint Programming (CP) and Large Neighborhood Search (LNS) algorithms to solve this problem. Benchmarking tests of RNAiFold2T against existent programs (adaptive walk and genetic algorithm) inverse folding show that our software generates two orders of magnitude more solutions, thus allow- ing ample exploration of the space of solutions. Subsequently, solutions can be prioritized by computing various measures, including probability of target structure in the ensemble, melting temperature, etc. Using this strategy, we rationally designed two thermosensor internal ribosome entry site (thermo-IRES) elements, whose normalized cap-independent transla- tion efficiency is approximately 50% greater at 42?C than 30?C, when tested in reticulocyte lysates. Translation efficiency is lower than that of the wild-type IRES element, which on the other hand is fully resistant to temperature shift-up. This appears to be the first purely computational design of functional RNA thermoswitches, and certainly the first purely computational design of functional thermo-IRES elements. Availability: RNAiFold2T is publicly available as as part of the new re- lease RNAiFold3.0 at https://github.com/clotelab/RNAiFold and http: //bioinformatics.bc.edu/clotelab/RNAiFold, which latter has a web server as well. The software is written in C++ and uses OR-Tools CP search engine.Comment: 24 pages, 5 figures, Intelligent Systems for Molecular Biology (ISMB 2016), to appear in journal Bioinformatics 201

Exploring a New ExpAce: The Complementarities between Experimental Economics and Agent-based Computational Economics

What is the relationship, if any, between Experimental Economics and Agent-based Computational Economics? Experimental Economics (EXP) investigates individual behaviour (and the emergence of aggregate regularities) by means of human subject experiments. Agent-based Computational Economics (ACE), on the other hand, studies the relationships between the micro and the macro level with the aid of artificial experiments. Note that the way ACE makes use of experiments to formulate theories is indeed similar to the way EXP does. The question we want to address is whether they can complement and integrate with each other. What can Agent-based computational Economics give to, and take from, Experimental Economics? Can they help and sustain each other, and ultimately gain space out of their restricted respective niches of practitioners? We believe that the answer to all these questions is yes: there can be and there should be profitable “contaminations” in both directions, of which we provide a first comprehensive discussion.Experimental Economics, Agent-based Computational Economics, Agent-Based Models, Simulation.

Knowledge transformers : a link between learning and creativity

The purpose of this paper is to investigate whether knowledge transformers which are featured in the learning process, are also present in the creative process. This is achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers

Knowledge transformers : a link between learning and creativity

The purpose of this paper is to investigate whether knowledge transformers that are featured in the learning process are also present in the creative process. First, this was achieved by reviewing accounts of inventions and discoveries with the view of explaining them in terms of knowledge transformers. Second, this was achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers

Artificial Identity: Representations of Robots and Cyborgs in Contemporary Anglo-American Science Fiction Films

The ‘human condition’ has traditionally been an area of study addressed primarily by philosophers concerned with the mind/body problem, rather than studied as a neuroscientific conundrum. However, contemporary developments in science and technology that afford us a greater knowledge of the human brain have resulted in an increased scientific focus on consciousness, emotion and personhood. This thesis argues that such explorations into consciousness and emotion as prerequisites of ‘artificial identity’ have entered the domain of contemporary cinema through the representations of robots and cyborgs. Despite the capacity for transhumanist practice and the creation of artificially intelligent automata that these developments have made possible, blurring the line between organic human and mechanical robots, it remains common for no distinctions to be made between the terms ‘human’ and ‘person’, which are used interchangeably to describe a member of the human race. Philosopher Daniel C. Dennett, though, has proposed a series of criteria for personhood that challenge the assumption that only humans can be considered persons. The application of his criteria to a series of key texts that highlight the relationships between humans and representations of automata - I, Robot (2004, Dir. Alex Proyas), Terminator Salvation (2009, Dir. McG) and Bicentennial Man (1999, Dir. Chris Columbus) – is central to this thesis. It explores the extent to which the representations of robots and cyborgs can be considered persons within utopian and dystopian narratives that have, at their core, a view of artificial identity as desirable or as nightmare. In conjunction with Dennett, the theories of neurologist and neuroscientist Antonio Damasio are applied, which explore both the biological means by which emotional (rather than solely physical) feelings are generated in humans, and the capacity of humans to simulate emotion. As Damasio argues that many of the central operations of the human central nervous and visceral systems are reducible to fundamental physics, the suggestion is that robots, too, could also ‘experience’ consciousness and emotion, being as they are very simplistic versions of humans. As such, the application of these theories suggests that the representations of robots and cyborgs in the key texts could be considered persons