Extracting topological features from dynamical measures in networks of Kuramoto oscillators

The Kuramoto model for an ensemble of coupled oscillators provides a paradigmatic example of non-equilibrium transitions between an incoherent and a synchronized state. Here we analyze populations of almost identical oscillators in arbitrary interaction networks. Our aim is to extract topological features of the connectivity pattern from purely dynamical measures, based on the fact that in a heterogeneous network the global dynamics is not only affected by the distribution of the natural frequencies, but also by the location of the different values. In order to perform a quantitative study we focused on a very simple frequency distribution considering that all the frequencies are equal but one, that of the pacemaker node. We then analyze the dynamical behavior of the system at the transition point and slightly above it, as well as very far from the critical point, when it is in a highly incoherent state. The gathered topological information ranges from local features, such as the single node connectivity, to the hierarchical structure of functional clusters, and even to the entire adjacency matrix.Comment: 11 pages, 10 figure

Globalising assessment: an ethnography of literacy assessment, camels and fast food in the Mongolian Gobi

What happens when standardised literacy assessments travel globally? The paper presents an ethnographic account of adult literacy assessment events in rural Mongolia. It examines the dynamics of literacy assessment in terms of the movement and re-contextualisation of test items as they travel globally and are received locally by Mongolian respondents. The analysis of literacy assessment events is informed by Goodwin’s ‘participation framework’ on language as embodied and situated interactive phenomena and by Actor Network Theory. Actor Network Theory (ANT) is applied to examine literacy assessment events as processes of translation shaped by an ‘assemblage’ of human and non-human actors (including the assessment texts)

Division of Giardia isolates from humans into two genetically distinct assemblages by electrophoretic analysis of enzyme encoded at 27 loci in comparison with Giardia muris

Giardia that infect humans are known to be heterogeneous but they are assigned currently to a single species, Giardia intestinalis (syn. G. lamblia). The genetic differences that exist within G. intestinalis have not yet been assessed quantitatively and neither have they been compared in magnitude with those that exist between G. intestinalis and species that are morphologically similar (G. duodenalis) or morphologically distinct (e.g. G. muris). In this study, 60 Australian isolates of G. intestinalis were analysed electrophoretically at 27 enzyme loci and compared with G. muris and a feline isolate of G. duodenalis. Isolates of G. intestinalis were distinct genetically from both G. muris (approximately 80% fixed allelic differences) and the feline G. duodenalis isolate (approximately 75% fixed allelic differences). The G. intestinalis isolates were extremely heterogeneous but they fell into 2 major genetic assemblages, separated by fixed allelic differences at approximately 60% of loci examined. The magnitude of the genetic differences between the G. intestinalis assemblages approached the level that distinguished the G. duodenalis isolate from the morphologically distinct G. muris. This raises important questions about the evolutionary relationships of the assemblages with Homo sapiens, the possibility of ancient or contemporary transmission from animal hosts to humans and the biogeographical origins of the two clusters.G. Mayrhofer, R. H. Andrews, P. L. Ey and N. B. Chilto

Impact of Unexpected Events, Shocking News and Rumours on Foreign Exchange Market Dynamics

We analyze the dynamical response of the world's financial community to various types of unexpected events, including the 9/11 terrorist attacks as they unfolded on a minute-by-minute basis. We find that there are various 'species' of news, characterized by how quickly the news get absorbed, how much meaning and importance is assigned to it by the community, and what subsequent actions are then taken. For example, the response to the unfolding events of 9/11 shows a gradual collective understanding of what was happening, rather than an immediate realization. For news items which are not simple economic statements, and hence whose implications are not immediately obvious, we uncover periods of collective discovery during which collective opinions seem to oscillate in a remarkably synchronized way. In the case of a rumour, our findings also provide a concrete example of contagion in inter-connected communities. Practical applications of this work include the possibility of producing selective newsfeeds for specific communities, based on their likely impact

On morphological hierarchical representations for image processing and spatial data clustering

Hierarchical data representations in the context of classi cation and data clustering were put forward during the fties. Recently, hierarchical image representations have gained renewed interest for segmentation purposes. In this paper, we briefly survey fundamental results on hierarchical clustering and then detail recent paradigms developed for the hierarchical representation of images in the framework of mathematical morphology: constrained connectivity and ultrametric watersheds. Constrained connectivity can be viewed as a way to constrain an initial hierarchy in such a way that a set of desired constraints are satis ed. The framework of ultrametric watersheds provides a generic scheme for computing any hierarchical connected clustering, in particular when such a hierarchy is constrained. The suitability of this framework for solving practical problems is illustrated with applications in remote sensing

Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency

Bostonia: The Boston University Alumni Magazine. Volume 10

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computervision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the SupportVectorMachines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the SupportVectorMachines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies

Character Analysis In The Banisteriopsis Campestris Complex (Malpighiaceae), Using Spatial Auto‐Correlation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149728/1/tax02450.pd