Towards heterotic computing with droplets in a fully automated droplet-maker platform

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations

Some Thoughts on the Teaching of Mathematics -- ten years later

I describe some deep-seated problems in higher mathematical education, and give some ideas for their solution -- I advocate a move away from the traditional introduction of mathematics through calculus, and towards computation and discrete mathematics.Comment: 10 pages, to appear in Notices of the AM

Implementing PRISMA/DB in an OOPL

PRISMA/DB is implemented in a parallel object-oriented language to gain insight in the usage of parallelism. This environment allows us to experiment with parallelism by simply changing the allocation of objects to the processors of the PRISMA machine. These objects are obtained by a strictly modular design of PRISMA/DB. Communication between the objects is required to cooperatively handle the various tasks, but it limits the potential for parallelism. From this approach, we hope to gain a better understanding of parallelism, which can be used to enhance the performance of PRISMA/DB.\ud The work reported in this document was conducted as part of the PRISMA project, a joint effort with Philips Research Eindhoven, partially supported by the Dutch "Stimuleringsprojectteam Informaticaonderzoek (SPIN)

A Multi-Engine Approach to Answer Set Programming

Answer Set Programming (ASP) is a truly-declarative programming paradigm proposed in the area of non-monotonic reasoning and logic programming, that has been recently employed in many applications. The development of efficient ASP systems is, thus, crucial. Having in mind the task of improving the solving methods for ASP, there are two usual ways to reach this goal: $(i)$ extending state-of-the-art techniques and ASP solvers, or $(ii)$ designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the "best" available solver on a per-instance basis. In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, we apply classification methods that, given the features of the instances in a {\sl training} set and the solvers' performance on these instances, inductively learn algorithm selection strategies to be applied to a {\sl test} set. We report the results of a number of experiments considering solvers and different training and test sets of instances taken from the ones submitted to the "System Track" of the 3rd ASP Competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve more instances compared with any solver that entered the 3rd ASP Competition. (To appear in Theory and Practice of Logic Programming (TPLP).)Comment: 26 pages, 8 figure

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

The Relativity of Existence

Despite the success of modern physics in formulating mathematical theories that can predict the outcome of experiments, we have made remarkably little progress towards answering the most fundamental question of: why is there a universe at all, as opposed to nothingness? In this paper, it is shown that this seemingly mind-boggling question has a simple logical answer if we accept that existence in the universe is nothing more than mathematical existence relative to the axioms of our universe. This premise is not baseless; it is shown here that there are indeed several independent strong logical arguments for why we should believe that mathematical existence is the only kind of existence. Moreover, it is shown that, under this premise, the answers to many other puzzling questions about our universe come almost immediately. Among these questions are: why is the universe apparently fine-tuned to be able to support life? Why are the laws of physics so elegant? Why do we have three dimensions of space and one of time, with approximate locality and causality at macroscopic scales? How can the universe be non-local and non-causal at the quantum scale? How can the laws of quantum mechanics rely on true randomness