62,117 research outputs found
Complex Networks from Simple Rewrite Systems
Complex networks are all around us, and they can be generated by simple
mechanisms. Understanding what kinds of networks can be produced by following
simple rules is therefore of great importance. We investigate this issue by
studying the dynamics of extremely simple systems where are `writer' moves
around a network, and modifies it in a way that depends upon the writer's
surroundings. Each vertex in the network has three edges incident upon it,
which are colored red, blue and green. This edge coloring is done to provide a
way for the writer to orient its movement. We explore the dynamics of a space
of 3888 of these `colored trinet automata' systems. We find a large variety of
behaviour, ranging from the very simple to the very complex. We also discover
simple rules that generate forms which are remarkably similar to a wide range
of natural objects. We study our systems using simulations (with appropriate
visualization techniques) and analyze selected rules mathematically. We arrive
at an empirical classification scheme which reveals a lot about the kinds of
dynamics and networks that can be generated by these systems
Ten virtues of structured graphs
This paper extends the invited talk by the first author about the virtues
of structured graphs. The motivation behind the talk and this paper relies on our
experience on the development of ADR, a formal approach for the design of styleconformant,
reconfigurable software systems. ADR is based on hierarchical graphs
with interfaces and it has been conceived in the attempt of reconciling software architectures
and process calculi by means of graphical methods. We have tried to
write an ADR agnostic paper where we raise some drawbacks of flat, unstructured
graphs for the design and analysis of software systems and we argue that hierarchical,
structured graphs can alleviate such drawbacks
Compressing Recurrent Neural Network with Tensor Train
Recurrent Neural Network (RNN) are a popular choice for modeling temporal and
sequential tasks and achieve many state-of-the-art performance on various
complex problems. However, most of the state-of-the-art RNNs have millions of
parameters and require many computational resources for training and predicting
new data. This paper proposes an alternative RNN model to reduce the number of
parameters significantly by representing the weight parameters based on Tensor
Train (TT) format. In this paper, we implement the TT-format representation for
several RNN architectures such as simple RNN and Gated Recurrent Unit (GRU). We
compare and evaluate our proposed RNN model with uncompressed RNN model on
sequence classification and sequence prediction tasks. Our proposed RNNs with
TT-format are able to preserve the performance while reducing the number of RNN
parameters significantly up to 40 times smaller.Comment: Accepted at IJCNN 201
Cell division and migration in a 'genotype' for neural networks
Much research has been dedicated recently to applying genetic algorithms to populations of
neural networks. However, while in real organisms the inherited genotype maps in complex
ways into the resulting phenotype, in most of this research the development process that
creates the individual phenotype is ignored. In this paper we present a model of neural
development which includes cell division and cell migration in addition to axonal growth and
branching. This reflects, in a very simplified way, what happens in the ontogeny of real
organisms. The development process of our artificial organisms shows successive phases of
functional differentiation and specialization. In addition, we find that mutations that affect
different phases of development have very different evolutionary consequences. A single
change in the early stages of cell division/migration can have huge effects on the phenotype
while changes in later stages have usually a less drammatic impact. Sometimes changes that
affect the first developental stages may be retained producing sudden changes in evolutionary
history
Runtime verification for biochemical programs
The biochemical paradigm is well-suited for modelling autonomous systems and new programming languages are emerging from this approach. However, in order to validate such programs, we need to define precisely their semantics and to provide verification techniques. In this paper, we consider a higher-order biochemical calculus that models the structure of system states and its dynamics thanks to rewriting abstractions, namely rules and strategies. We extend this calculus with a runtime verification technique in order to perform automatic discovery of property satisfaction failure. The property specification language is a subclass of LTL safety and liveness properties
Ecological Modelling with the Calculus of Wrapped Compartments
The Calculus of Wrapped Compartments is a framework based on stochastic
multiset rewriting in a compartmentalised setting originally developed for the
modelling and analysis of biological interactions. In this paper, we propose to
use this calculus for the description of ecological systems and we provide the
modelling guidelines to encode within the calculus some of the main
interactions leading ecosystems evolution. As a case study, we model the
distribution of height of Croton wagneri, a shrub constituting the endemic
predominant species of the dry ecosystem in southern Ecuador. In particular, we
consider the plant at different altitude gradients (i.e. at different
temperature conditions), to study how it adapts under the effects of global
climate change.Comment: A preliminary version of this paper has been presented in CMC13 (LNCS
7762, pp 358-377, 2013
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