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Modeling multi-cellular systems using sub-cellular elements

We introduce a model for describing the dynamics of large numbers of interacting cells. The fundamental dynamical variables in the model are sub-cellular elements, which interact with each other through phenomenological intra- and inter-cellular potentials. Advantages of the model include i) adaptive cell-shape dynamics, ii) flexible accommodation of additional intra-cellular biology, and iii) the absence of an underlying grid. We present here a detailed description of the model, and use successive mean-field approximations to connect it to more coarse-grained approaches, such as discrete cell-based algorithms and coupled partial differential equations. We also discuss efficient algorithms for encoding the model, and give an example of a simulation of an epithelial sheet. Given the biological flexibility of the model, we propose that it can be used effectively for modeling a range of multi-cellular processes, such as tumor dynamics and embryogenesis.Comment: 20 pages, 4 figure

Component sizes in networks with arbitrary degree distributions

We give an exact solution for the complete distribution of component sizes in random networks with arbitrary degree distributions. The solution tells us the probability that a randomly chosen node belongs to a component of size s, for any s. We apply our results to networks with the three most commonly studied degree distributions -- Poisson, exponential, and power-law -- as well as to the calculation of cluster sizes for bond percolation on networks, which correspond to the sizes of outbreaks of SIR epidemic processes on the same networks. For the particular case of the power-law degree distribution, we show that the component size distribution itself follows a power law everywhere below the phase transition at which a giant component forms, but takes an exponential form when a giant component is present.Comment: 5 pages, 1 figur

Optimization in Gradient Networks

Gradient networks can be used to model the dominant structure of complex networks. Previous works have focused on random gradient networks. Here we study gradient networks that minimize jamming on substrate networks with scale-free and Erd\H{o}s-R\'enyi structure. We introduce structural correlations and strongly reduce congestion occurring on the network by using a Monte Carlo optimization scheme. This optimization alters the degree distribution and other structural properties of the resulting gradient networks. These results are expected to be relevant for transport and other dynamical processes in real network systems.Comment: 5 pages, 4 figure

Globalisation and insecurity

We construct a simple model of the e¤ect of increased interna-tional openness on risk bearing in an environment in which the onlyrisk-sharing institutions are self-enforcing agreements. We showhow increased openness can weaken long-term relationships, andhence risk sharing, by increasing the e¤ectiveness of the market,much as some critics of globalization have argued. However, theharm thereby done is tempered by the fact that in order to havesuch a negative indirect e¤ect, openness must have a direct e¤ectthat reduces risk. It is shown that on balance, globalization reducesrisk and raises welfare for those in small countries, but increases riskand reduces welfare for those in large countries. We construct a simple model of the e¤ect of increased interna-tional openness on risk bearing in an environment in which the onlyrisk-sharing institutions are self-enforcing agreements. We showhow increased openness can weaken long-term relationships, andhence risk sharing, by increasing the e¤ectiveness of the market,much as some critics of globalization have argued. However, theharm thereby done is tempered by the fact that in order to havesuch a negative indirect e¤ect, openness must have a direct e¤ectthat reduces risk. It is shown that on balance, globalization reducesrisk and raises welfare for those in small countries, but increases riskand reduces welfare for those in large countries

Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech