1,786 research outputs found
Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks
The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a âself-modellingâ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other âactive linkingâ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks
Block Coordinate Descent for Sparse NMF
Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data
analysis. An important variant is the sparse NMF problem which arises when we
explicitly require the learnt features to be sparse. A natural measure of
sparsity is the L norm, however its optimization is NP-hard. Mixed norms,
such as L/L measure, have been shown to model sparsity robustly, based
on intuitive attributes that such measures need to satisfy. This is in contrast
to computationally cheaper alternatives such as the plain L norm. However,
present algorithms designed for optimizing the mixed norm L/L are slow
and other formulations for sparse NMF have been proposed such as those based on
L and L norms. Our proposed algorithm allows us to solve the mixed norm
sparsity constraints while not sacrificing computation time. We present
experimental evidence on real-world datasets that shows our new algorithm
performs an order of magnitude faster compared to the current state-of-the-art
solvers optimizing the mixed norm and is suitable for large-scale datasets
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SAT competition series, these parameters are ignored: solvers are
run using a single default parameter setting (supplied by the authors) for all
benchmark instances in a given track. While this competition format rewards
solvers with robust default settings, it does not reflect the situation faced
by a practitioner who only cares about performance on one particular
application and can invest some time into tuning solver parameters for this
application. The new Configurable SAT Solver Competition (CSSC) compares
solvers in this latter setting, scoring each solver by the performance it
achieved after a fully automated configuration step. This article describes the
CSSC in more detail, and reports the results obtained in its two instantiations
so far, CSSC 2013 and 2014
Engineering Resilient Collective Adaptive Systems by Self-Stabilisation
Collective adaptive systems are an emerging class of networked computational
systems, particularly suited in application domains such as smart cities,
complex sensor networks, and the Internet of Things. These systems tend to
feature large scale, heterogeneity of communication model (including
opportunistic peer-to-peer wireless interaction), and require inherent
self-adaptiveness properties to address unforeseen changes in operating
conditions. In this context, it is extremely difficult (if not seemingly
intractable) to engineer reusable pieces of distributed behaviour so as to make
them provably correct and smoothly composable.
Building on the field calculus, a computational model (and associated
toolchain) capturing the notion of aggregate network-level computation, we
address this problem with an engineering methodology coupling formal theory and
computer simulation. On the one hand, functional properties are addressed by
identifying the largest-to-date field calculus fragment generating
self-stabilising behaviour, guaranteed to eventually attain a correct and
stable final state despite any transient perturbation in state or topology, and
including highly reusable building blocks for information spreading,
aggregation, and time evolution. On the other hand, dynamical properties are
addressed by simulation, empirically evaluating the different performances that
can be obtained by switching between implementations of building blocks with
provably equivalent functional properties. Overall, our methodology sheds light
on how to identify core building blocks of collective behaviour, and how to
select implementations that improve system performance while leaving overall
system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio
Doctor of Philosophy
dissertationOne of the fundamental building blocks of many computational sciences is the construction and use of a discretized, geometric representation of a problem domain, often referred to as a mesh. Such a discretization enables an otherwise complex domain to be represented simply, and computation to be performed over that domain with a finite number of basis elements. As mesh generation techniques have become more sophisticated over the years, focus has largely shifted to quality mesh generation techniques that guarantee or empirically generate numerically well-behaved elements. In this dissertation, the two complementary meshing subproblems of vertex placement and element creation are analyzed, both separately and together. First, a dynamic particle system achieves adaptivity over domains by inferring feature size through a new information passing algorithm. Second, a new tetrahedral algorithm is constructed that carefully combines lattice-based stenciling and mesh warping to produce guaranteed quality meshes on multimaterial volumetric domains. Finally, the ideas of lattice cleaving and dynamic particle systems are merged into a unified framework for producing guaranteed quality, unstructured and adaptive meshing of multimaterial volumetric domains
- âŚ