To Compute, or not to Compute
Christof Teuscher, LANL, USA
This half-day tutorial will provide a comprehensive, unbiased, and down-to-earth overview on theoretical and practical aspects of classical and non-classical computation in abstract, artificial, and natural systems. By the end of the tutorial, participants should know: what computation is, what it isn’t, and what its limits and applications are; what the relevant differences between theoretical, artificial, and natural computational systems are; how to use the concept of computation for abstract and real machines, models, organisms, and how to reason about their computational power.
Reaction-Diffusion Computers
Andrew Adamatzky, Benjamin De Lacy Costello, Tetsuya Asai
This half-day tutorial introduces computation with travelling waves in reaction-diffusion non-linear media. A reaction-diffusion computer is a massively parallel computing device, where micro-volumes of the chemical medium act as elementary few-bit processors; and chemical species diffuse and react in parallel. In the reaction-diffusion computer both the data and the results of the computation are encoded as concentration profiles of the reagents, or local disturbances of concentrations, whilst the computation per se is performed via the spreading and interaction of waves caused by the local disturbances. The tutorial brings out results of decade-long studies in designing experimental and simulated prototypes of reaction-diffusion computing devices for image processing, path planning, robot navigation, computational geometry, logics and artificial intelligence.
More information can be found here
Biomolecular Automata
Natasha Jonoska, University of South Florida, USA, and Darko Stefanovic, University of New Mexico, USA
This 2 hour tutorial comprises two one hour lectures on the design and implementation of finite state automata machines using DNA and other biomolecules.
Randomness
Cristian S. Calude, University of Auckland, New Zealand
This three-hour tutorial will discuss three types of randomness, software-generated randomness (pseudo-randomness), quantum randomness and algorithmic randomness, and will present various probabilistic computations using with these types of randomness. Typical questions include: Is quantum randomness algorithmic random? Are there finite tests capable of distinguishing pseudo-randomness from quantum randomness? Are there reliable methods to produce large strings of quantum random bits? How accurate are Monte-Carlo tests in which the sequence of coin tosses is replaced by a sequence of quantum random strings? Do we need randomness to trespass the Turing barrier?
Brain Signal Analysis
José del R. Millán
This tutorial will introduce a new and fast-evolving field of direct interaction between the human neural system and machines aiming to augment human capabilities by enabling people (especially disabled) to
communicate and control devices by mere “thinking”. The course will cover different kinds of physiological signals (from muscular activity to brain waves) and the algorithms for recognizing the subject’s intent
as well as a variety of emotional/cognitive states.
Quantum Computing
Viv Kendon, University of Leeds
Quantum computing is the exploitation of quantum mechanical effects to produce qualitatively more efficient computation. The simplest example is to replace "bits" with "qubits" (quantum bits) that can be in a superposition of zero and one at the same time. This allows for parallel processing across many input values without duplicating the processing unit. However, quantum mechanics only allows the extraction of a single (randomly selected) one out of the superposition of results, and this places a severe restriction on how quantum algorithms can be constructed. Nonetheless, a number of important quantum algorithms have been proposed, which, along with error correction to insulate the quantum computer from external noise, provide the incentive for a flourishing programme to develop practical quantum computing devices. As well as this digital method of quantum computing, there is the original suggestion from Richard Feynman for quantum simulation, in which the Hilbert space (phase space) of the physical system under study is mapped directly onto the Hilbert space of the quantum computer. One may also make use of quantum parameters that take continuous values, in more direct analogy with analog computing.
The first hour of this two hour tutorial will outline the basics of quantum computing, covering the theory and brief mention of the most promising experimental implementations. The second hour will discuss the likely error rates and accuracy required for useful quantum computation in real devices, for digital, simulation and analog models of quantum computing.
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