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CNN Seminar - Dr Katayoun Farrahi

When Mar 10, 2015
from 04:30 PM to 06:00 PM
Where Keynes Hall in King's College
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Dr Katayoun Farrahi

(Goldsmiths College, University of London)

Title: Epidemics over Mobile Phone Networks

 

Abstract:
Human interactions sensed ubiquitously by mobile phones can improve a significant number of public health problems, particularly helping to track the spread of disease. In this talk, we evaluate multiple avenues for the integration of high-resolution face to face Bluetooth-sensed interaction networks into epidemic models. Our goal is to evaluate the capacity of the different avenues of integration to track the spread of seasonal influenza on a real-world community of 72 individuals over a period of 17 weeks. The dataset considered contains real-time tracking of individual flu symptoms over the whole observation period, providing a concrete individualized source for evaluation. We present two different studies on this dataset. The first considers the standard SIR model simulated over real network dynamics with the overall goal of predicting the real infections over time. We obtain an error of less than 2 infected people on average when predicting the total number of individuals affected by the flu and a precision of approximately 30% when predicting exactly which individual will become infected at a given time. Our results indicate that high-resolution mobile phone data can increase the predictive power of even the simplest of epidemic models. The second study proposes a dual model for contact tracing, where an infection is spreading in the physical interpersonal network, which we assume can never be fully recovered, and contact tracing is occurring in a communication network which acts as a proxy for the first. Our results suggest that contact tracing via mobile phone communication may be a viable option for controlling contagious outbreaks.

CNN Seminar - Understanding Technology Pathway of Society from U.S. patents

When Feb 17, 2015
from 04:30 PM to 06:00 PM
Where Keynes Hall in King's College
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Title: Understanding Technology Pathway of Society from U.S. patents
Affiliation:
1) Institute for New Economic Thinking at the Oxford Martin School,
Walton Well Rd, OX2 6ED, Oxford
2) Mathematical Institute, University of Oxford, Oxford, United Kingdom
3) Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe NM 87501, USA


Abstract:
Technology’s advance is central to our understanding of economic growth and development. Furthermore, solutions for many of the planet’s most pressing challenges – economic recovery, poverty reduction, climate change, sustainability – require significant additions to society’s technological toolkit. Yet, our ability to quantitatively model and forecast technological change has been limited due to difficulties in defining units of analysis and in collecting comparative empirical data. Some inventions, namely patents, however, leave behind a documentary trail, enabling us to study the invention processes in a quantitative way. Here, we propose to develop a formal methodology to construct detailed technology "map" and its temporal evolution from large-scale U.S. Patent data spanning 220 years. We utilize the classification system consisting of codes as temporally consistent units of analysis (nodes). These technology codes are means to succinctly describe a patent’s technology capabilities. When codes appear together in a patent, therefore, we consider them inter-dependent to make a useable function and represent this inter-dependency by assigning a link to between them. These micro-scale invention activities add up to form networks in time where macro-scale structures emerge through temporal evolution of community structure. We observe and identify episodic change of structure over time corresponding to historic events and major breakthroughs, indicating the technology change is not gradual but serious of punctuated equilibriums.

CNN Seminar - (Dr Lucas Lacasa)

When Jan 27, 2015
from 04:30 PM to 06:00 PM
Where Keynes Hall in King's College
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(School of Mathematical Sciences, Queen Mary University London)

Title:
Time Series meet Network Science

Abstract:
In the last years, ideas and methods from network science have been applied to study the structure of time series and signals, thereby building a bridge between nonlinear dynamics, time series analysis and graph theory. In this talk I will focus on a particular approach, namely the family of visibility algorithms, and will give an overview of the main results that we have obtained recently. In particular, I will depict several case studies where concrete problems in nonlinear dynamics, stochastic processes and statistical physics can be easily mapped, via visibility algorithms, to the study of the topological properties of classes of visibility graphs. I will conclude with a summary of open problems and applications.

CNN Seminar - (Dr Naoki Masuda)

When Dec 02, 2014
from 05:15 PM to 07:00 PM
Where Keynes Hall in King's College
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(Department of Engineering Mathematics, University of Bristol)

 

Title: Dominance hierarchy networks of worker ants

 

Abstract: Group-living animals, from insects to mammals, often form dominance hierarchy, which is a directed network. The direction of the link represents aggression by one individual on the other subordinate individual. In small groups of animals, it has been long known that the hierarchy is often perfectly linear, allowing unique ranking of the individuals. However, perfect linearity is often violated in large groups. I present analysis of aggressive dominance hierarchy formed by worker ants as large directed networks and then discuss evolutionary implications of the results. I also present some results on generative models to account for the observed connectivity patterns, built on the assumption of heterogeneously distributed intrinsic strengths of individual nodes.

CNN Seminar - (Dr Thilo Gross)

When Nov 11, 2014
from 04:30 PM to 06:00 PM
Where Keynes Hall in King's College
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(Faculty of Engineering, University of Bristol)

 

Title: Localization of instabilities in complex networks.

 

Abstract: 

On several levels, humans depend on the functioning of complex networks, including the molecular networks in cells, ecological food webs and technical infrastructure nets. In such systems the complex interplay of distributed components can result in cascading failures.
Large complex networks are likely to be dynamically unstable and recent work showed that trying to stabilize the network can lead to large-scale failure. In this talk I will investigate both the propensity of networked systems to fail and the scale of the failure once it occurs.
I will show mathematically that naturally occurring networks have predominantly localised instabilities that are confined to small parts of the network, but attempts to stabilize the network can lead to a delocalisation, such that instabilities become less likely but affect a larger number of nodes. 
These results shed new light on the apparent stability of ecological food webs and the causes of systemic failure in artificial technical and organizational networks. Furthermore they have implications for the planning of interventions, such as vaccination campaigns against epidemic diseases.

CNN Seminar - (Dr Ioannis Psorakis)

When Oct 28, 2014
from 04:30 PM to 06:00 PM
Where Keynes Hall in King's College
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(Oxford Mathematical Institute and Thought Machine)

 

Title: Inferring social network structure in ecological systems from spatio-temporal data streams

 

Abstract:
"We propose a collection of analytical and computational methods for inferring the underlying social structure of a given animal population, observed only via timestamped occurrences of its members across a range of locations. We show that such data streams have a modular and temporally-focused structure, neither fully ordered nor completely random, with individuals appearing in “gathering events”. By exploiting such structure, we propose an appropriate mapping of those spatio-temporal data streams to a social network, based on the co-occurrences of individuals across gathering events. The methods proposed are applied on a data set of wild-bird visitations in order to explore bird sociality, reveal its internal organisation across a variety of different scales and provide insights into important biological processes relating to mating pair formation."
Please feel free to email us at cnninfo@hermes.cam.ac.uk for an opportunity to meet  the speaker in the afternoon before his talk - we will be happy to arrange this.

The growth of cities and neural networks

Imperial College Complexity & Networks seminar
When Mar 18, 2014
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room 611, Electrical & Electronic Engineering Building, Imperial College London, South Kensington Campus
Contact Name
Contact Phone +44 (0)20 7594 7574
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Speaker: Prof Vito Latora, School of Mathematical Sciences Queen Mary , University of London

Title: The growth of cities and neural networks

Abstract: Spatially embedded complex networks, such as nervous systems, the Internet, and transportation networks, generally have nontrivial topological patterns of connections combined with nearly minimal wiring costs. We report here the empirical analysis of two databases describing respectively: 200 years of evolution of the road network in a large area located north of Milan (Italy), and the growth of the nervous system of the C. elegans from the moment of fertilization to adulthood. We discuss the basic mechanisms that drive the evolution of such two spatial networks. 

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CNN Seminar - Harriet Keane (Oxford)

When Mar 04, 2014
from 04:30 PM to 05:30 PM
Where Keynes Hall in King's College
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Title: "Network Pharmacology for Parkinson's Disease"

Abstract:
Network pharmacology offers a new approach to identifying potential drug targets in diseases with complex aetiology. Parkinson’s disease (PD) is used as an exemplar of such a disease due to its sporadic nature and the involvement of multiple cellular pathways. PD is the second most common neurodegenerative disease with a prevalence of 5% at age 85. Despite this, there is currently no cure for PD or treatment capable of slowing disease progression. Many of the key features of PD can be reproduced using the neurotoxin MPP+, a complex I inhibitor that induces cytotoxicity via a programmed cell death (apoptotic) mechanism. Although key processes in MPP+ toxicity have been characterised, analysis of the underlying biological network can offer an insight into the interplay of these individual processes and pathways.

I will discuss the construction of protein-protein interaction networks (PPN) to model MPP+ induced cell death, using iRefIndex as a consolidated source of protein-protein interaction data. I analysed these networks to identify nodes whose deletion was expected to have a significant effect on MPP+ induced cell death and validated these predictions using our in vitro system. I further demonstrated that a partial rescue from MPP+ neurotoxicity can be achieved using the combined overexpression of four network targets. However, no single intervention is effective: we need multiple, targeted interventions to alter the biological outcome of the system. As well as the biological insights into the MPP+   model of PD, this was an exciting opportunity to directly test the approach of network pharmacology in vitro, and I will discuss the opportunities and challenges presented.

Effect of the interconnected network structure on the epidemic threshold

Imperial College Complexity & Networks seminar
When Mar 04, 2014
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room 611, Electrical & Electronic Engineering Building, Imperial College London, South Kensington Campus
Contact Name
Contact Phone +44 (0)20 7594 7574
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Speaker: Prof Huijuan Wang, Network Architecture and Services Group , Delft University of Technology

Title: Effect of the interconnected network structure on the epidemic threshold

Abstract: Modern network systems are becoming increasingly interconnected. Diseases and computer virus, for example, may spread across multiple species or communities. This motivated the recent studies on epidemic spreading in interconnected networks. New phenomena have been revealed. For example, in susceptible-infected-susceptible (SIS) model an endemic state may appear in the coupled networks even when an epidemic is unable to propagate in each network separately. However, the question of how the interconnected network structure affects the epidemic spreading has not been addressed. In this talk, we determine firstly a critical SIS epidemic threshold in two generic interconnected networks. Furthermore, we analytically express the perturbation approximation, lower and upper bounds of this epidemic threshold as a function of the properties of each component network and the interconnections in between. These approximation and bounds for the epidemic threshold are verified using numerical simulations and, thus, unveil how features of the two component networks and the interconnections affect the epidemic threshold, which provides essential insights essential insights into ways of designing interconnected networks to be robust against epidemics.

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How to analyse cultural change?

Imperial College Complexity & Networks Seminar
When Feb 18, 2014
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room 611, Electrical & Electronic Engineering Building, Imperial College London, South Kensington Campus
Contact Name
Contact Phone +44 (0)20 7594 7574
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Speaker: Dr Anne Kandler, AHRC CECD, Institute of Archaeology, University College London

Title: How to analyse cultural change?

Abstract: Cultural change is often quantified by changes in frequency of cultural traits over time. Based on those (observable) frequency patterns researchers aim to infer the nature of the underlying evolutionary processes and therefore to identify the (unobservable) causes of cultural change. Especially in archaeological and anthropological applications this inverse problem gains particular importance as occurrence or usage frequencies are often the only available information about past cultural traits or traditions and the forces affecting them. In this talk we start analyzing the described inference problem and discuss it in the context of the question of which learning strategies human populations should deploy to be well-adapted to changing environmental conditions. To do so we develop a mathematical framework which establishes a causal relationship between changes in frequency of different cultural traits and the considered underlying evolutionary processes (in our case learning strategies). Besides gaining theoretical insights into the question of which learning strategies lead to efficient adaptation processes in changing environments we focus on ‘reverse engineering’ conclusions about the learning strategies deployed in current or past population, given knowledge of the frequency change dynamic over space and time. Using appropriate statistical techniques we investigate under which conditions population-level characteristics such as frequency distributions of cultural variants  carry a signature of the underlying evolutionary processes and if this is the case how much information can be inferred from it. Importantly, we do not expect the existence of a unique relationship between observed frequency data and underlying evolutionary processes; to the contrary, we suspect that different processes can produce similar frequency pattern. However, our approach might help narrow down the range of possible processes that could have produced those observed frequency patterns, and thus still be instructive in the face of uncertainty. Rather than identifying a single evolutionary process that explains the data, we focus on excluding processes that cannot have produced the observed changes in frequencies. In the last part of the talk, we demonstrate the applicability of the developed framework to anthropological case studies.

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CNN Seminar - Dr Natasa Przulj (Imperial)

When Feb 11, 2014
from 04:30 PM to 05:30 PM
Where Munby Room in King's College
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Title: Mining real-world networks: from biology to economics

Abstract:
Comparing networks is important for understanding the organizational principles and tracking the dynamics in a broad spectrum of areas that involve many interacting objects. Examples include relationships between people, the global climate system, the world’s     economic system, bindings between bio-molecules in a cell, and much else. We introduce new network analysis, comparison and alignment methods, based on counts of local subnetworks, which are easy to compute and produce meaningful and robust results along a wide array of networks. We validate them on simple models, for which, unlike for real world, we know the answers and hence can check the validity of the methods. Also, we apply them to real-world networks from several domains and uncover new domain-specific knowledge. 

NOTE that the seminar will take place in the Munby Room in King's College (it's not far from Keynes Hall and there will be signs to direct you there). 

A theory of generalized entropies

Imperial College Complexity & Networks Seminar
When Feb 04, 2014
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room 611, Electrical & Electronic Engineering Building, Imperial College London, South Kensington Campus
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Speaker: Dr Rudolf A. Hanels, Section for Science of Complex Systems , Medical University of Vienna

Title: A theory of generalized entropies

Abstract: Generalized entropies have been introduced for describing characteristic non-exponential distribution functions that frequently appear in the context of non-extensive Systems. The controversy over the fundamental necessity of generalized entropies however can be decided in their favor. In contrast to equilibrium theories of systems the theory of generalized entropies has to distinguish carefully between so called “generalized extensive entropies” and the “entropy functionals” appearing in generalized MEPs. These MEPs however can be deduced from traditional maximum configuration arguments as they have been applied since Boltzmann. The key to understanding both notions of generalized entropies, for instance in (not necessarily stationary) systems with infinite memory (memory that starts with initialization of a process) is the way phase-space grows with system size and has little to do with observable distribution functions. In fact examples exist where systems requiring generalized entropies possess Gaussian distribution functions

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Bitcoin networks

University of Oxford Network Journal Club
When Jan 22, 2014
from 12:00 PM to 01:00 PM
Where Andrew Wiles Building, seminar room C5
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Speaker: Jonathan Levin
Title: Bitcoin networks
Abstract: Bitcoin is the world’s first decentralised payments system. All existing payments systems have central trusted authorities at the core that process transactions on its network, verifying against the classical security threats that exist: fraud and double spending. Bitcoin, in this way, marks a departure from traditional payment systems removing any trust lines between agents. The Bitcoin blockchain is a history of all past transactions on the network. This presentation will introduce the basic design features of Bitcoin, the currency and the protocol and will introduce the structure of the dataset that is available to researchers and some of the initial being carried out. It presents the first dataset of this kind where market interactions are made public and time stamped enabling both spatial and temporal network analyses of economic exchanges.

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CNN Seminar - Dr Mirco Musolesi (Birmingham)

When Jan 21, 2014
from 04:30 PM to 05:30 PM
Where Keynes Hall in King's College
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Title: Understanding and Exploiting Social and Mobility Information from Big Mobile Data

Abstract: Mobile phones are increasingly equipped with sensors, such as accelerometers, GPS receivers, and cameras, which can be used to sense and interpret people behaviour in real-time. Novel user-centered sensing applications can be built by exploiting the availability of these technologies. Moreover, data extracted from the sensors can also be used to model and predict people behaviour and movement patterns, providing a very rich set of multi-dimensional and linked data, which can be extremely useful, for instance, for the development of highly personalised applications, health interventions, and real-time support for policy-makers. 

In this talk I will discuss some of our recent projects in the area of large-scale scale data mining and modelling of mobile datasets with applications to human mobility prediction and epidemic spreading containment.  Indeed, the study of the interdependence of human movement and social ties of individuals is one of the most interesting research areas in computational social science. I will show how mobile phone data can be used to improve mobility prediction, by characterising  and exploiting the correlation between movements of friends and acquaintances. This can be seen as a process of discovering correlation patterns in networks of linked social and geographic data. I will also discuss how data from mobile operators can be effectively exploited to model epidemic spreading and devise effective containment strategies.


Bio: Dr. Mirco Musolesi is a Senior Lecturer at the School of Computer Science at the University of Birmingham. He received a PhD in Computer Science from University College London in 2007. Before joining Birmingham, he held research positions at Dartmouth College and Cambridge and a Lectureship at the University of St Andrews. His research interests lie at the interface of different areas, namely ubiquitous computing, large-scale data mining, and network science. More information about his research profile can be found at the following URL: http://www.cs.bham.ac.uk/~musolesm

Agent-based computing in economics and other social sciences: prospects and opportunities

CABDyN seminar series
When Jan 21, 2014
from 12:30 PM to 02:00 PM
Where Roy Griffiths Room (ARCO), Keble College
Contact Name
Contact Phone +44 1865 288785
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Speaker: Dr Robert L Axtell, who is an Associate Professor at George Mason University, Krasnow Institute for Advanced Study, where he is Departmental Chair of the Department of Computational Social Science. Rob Axtell works at the intersection of economics, behavioral game theory, and multi-agent systems computer science. His most recent research attempts to emerge a macroeconomy from tens of millions of interacting agents.

 

Title: Agent-based computing in economics and other social sciences: prospects and opportunities


Abstract: As a methodology, agent-based modeling and simulation is a youthful member of the social scientist's quiver of tools and techniques. The first systematic use of agents by social scientists began appearing some 20 years ago, largely driven by new ideas concerning 'complex systems' that were blossoming at the Santa Fe Institute. By every metric the field has exploded since then, with the number of publications, conferences, citations, and research expenditures all increasing exponentially. This rapid adoption has been driven by several factors, including the growth of computing power, increasing ease of software development as specialized frameworks appear, progressive availability of micro-data on individual people suitable for calibrating agent models, and advances in closely related fields, e.g., network science. However, as with any young methodology, there is today tremendous variance in the design, implementation, exercise, and ultimate use of agent computing across the social sciences, both positively (descriptively) and normatively (for policy purposes). In this talk I will describe some past successes of the approach, highlight important work that is underway, and flag apparent bottlenecks to the continuing expansion of agent approaches within the academic and policy communities.

 

I will advance three arguments: (1) Unlike our colleagues in the natural sciences, there is no way for social scientists to leverage all the capabilities of modern computing architectures numerically using equation-based models, since we typically do not use spatially-extended systems, and therefore the only way to fill up terabytes of memory is via large numbers of cognitively-rich agents; (2) The new availability of 'administratively comprehensive' data--ALL the data in relevant domains--implies a radically modified role for statistics and econometrics, as previous focus on sampling issues and moment methods are irrelevant when all distributions are known empirically; (3) The social sciences are inherently multi-level--behavior at the individual level accretes to produce aggregate phenomena that then feeds back to the agent level--and extant mathematical techniques for working across levels in the physical sciences and engineering (e.g., statistical mechanics, model aggregation theory) do not work well for social systems characterized by heterogeneous 'components', leaving computational approaches as the only way forward if we are really to understand how two people in a garage can grow into a $500 billion company and why 'novel' mortgages can create $20 trillion of financial losses.

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Multicanonical distribution: Statistical equilibrium of multiscale systems

Imperial College Complexity & Networks Seminar
When Jan 21, 2014
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room 611, Electrical & Electronic Engineering Building, Imperial College London, South Kensington Campus
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Contact Phone +44 (0)20 7594 7574
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Speaker: Prof. Giovani Vasconcelos Dept. of Physics Federal University of Pernambuco, Recife Brazil


Title: Multicanonical distribution: Statistical equilibrium of multiscale systems


Abstract: A multicanonical formalism is introduced to describe the statistical equilibrium of complex systems exhibiting a hierarchy of time and length scales, where the hierarchical structure is described as a set of nested ?internal heat reservoirs? with fluctuating ?temperatures.? The probability distribution of states at small scales is written as an appropriate averaging of the large-scale distribution (the Boltzmann-Gibbs distribution) over these effective internal degrees of freedom. For a large class of systems the multicanonical distribution is given explicitly in terms of generalized hypergeometric functions. As a concrete example, it is shown that generalized hypergeometric distributions describe remarkably well the statistics of acceleration measurements in Lagrangian turbulence.

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Unconsciously rational: optimal strategies in human mental searches in online auctions

Complexity & Networks seminar series, Imperial College London(s)
When Dec 10, 2013
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room, Imperial College London
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Title: Unconsciously rational: optimal strategies in human mental searches in online auctions

Speaker: Dr Andrea Baronchelli
School of Engineering and Mathematical Sciences, City University London

Abstract:

Characterizing how we explore abstract spaces is key to understand our (ir)rational behavior and decision making. While some light has been shed on the navigation of semantic networks, however, little is known about the mental exploration of metric spaces, such as the one dimensional line of numbers, prices, etc. Here we address this issue by investigating the behavior of  users exploring the “bid space” in online auctions. We find that they systematically perform Lévy flights, i.e., random walks whose step lengths follow a power-law distribution. Interestingly, this is the best strategy that can be adopted by a random searcher looking for a target in an unknown environment, and has been observed in the foraging patterns of many species. In the case of online auctions, we measure the power-law scaling over several decades, providing the neatest observation of Lévy flights reported so far. We also show that the histogram describing single individual exponents is well peaked, pointing out the existence of an almost universal behaviour. Furthermore, a simple model reveals that the observed exponents are nearly optimal, and represent a Nash equilibrium. We rationalize these findings through a simple evolutionary process, showing that the observed behavior is robust against invasion of alternative strategies. Our results show that humans share with the other animals universal patterns in general searching processes, and raise fundamental issues in cognitive, behavioural and evolutionary sciences.


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CNN Seminar - Dr Filippo Simini

When Dec 03, 2013
from 04:30 PM to 05:30 PM
Where Keynes Hall in King's College
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Title: Modelling human mobility and the origin of cities

Abstract: Human mobility and social interactions have always been subjects of interest to many disciplines, including sociology, geography, and economics. These topics are key to address real-life problems like the design optimal transportation systems for goods and individuals, the control and prevention of epidemic spreading, and the forecast of
social and economic consequences of migrations. Today, thanks to the availability of large and detailed datasets on people's communications and whereabouts, we are in the position to test the validity of classical theories and models, improve them, and develop a deeper understanding of the underlying mechanisms governing our society.
In this talk we introduce a decision-based stochastic model to estimate commuting flows, the radiation model, and discuss its connection with other spatial interaction models. We present some generalisations of the model, and analyse two scenarios in which we tested the model's performance using GSM and GPS data. Finally, we discuss the contribution of spatial interaction models towards the understanding of the urbanisation process.

Weighted projected networks: mapping hypergraphs to networks

CABDyN seminar series
When Nov 19, 2013
from 12:30 PM to 02:00 PM
Where Roy Griffiths Room (ARCO), Keble College, University of Oxford
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Speakers: Dr Eduardo López, Senior Research Fellow, CABDyN Complexity Centre and James Martin Fellow, Oxford Martin School

Title: Weighted projected networks: mapping hypergraphs to networks

Abstract: Many natural, technological, and social systems incorporate multiway interactions, yet are characterized and measured on the basis of weighted pairwise interactions. In this article, I propose a family of models in which pairwise interactions originate from multiway interactions, by starting from ensembles of hypergraphs and applying projections that generate ensembles of weighted projected networks. I calculate analytically the statistical properties of weighted projected networks, and suggest ways these could be used beyond theoretical studies. Projected weighted networks typically exhibit weight disorder along links even for very simple generating hypergraph ensembles. Also, as the size of a hypergraph changes, a signature of multiway interaction emerges on projected weighted networks that distinguishes them from fundamentally weighted pairwise networks. I find the percolation threshold and size of the largest component for hypergraphs of arbitrary uniform rank, translate the results into projected networks, and show that the transition is second order. This general approach to network formation has the potential to shed new light on our understanding of weighted networks.

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Serial order encoding, error prediction and information flow during sequential execution: application to music performance

Complexity & Networks seminar series, Imperial College London(s)
When Nov 19, 2013
from 12:00 PM to 01:00 PM
Where Gabor Seminar Room, Imperial College London
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Title: Serial order encoding, error prediction and information flow during sequential execution: application to music performance

Speaker: Dr. Maria Herrojo Ruiz

Department of Neurology with Chair in Experimental Neurology
Charité Universitätsmedizin Berlin

Abstract:

Sequential behaviour is widespread in humans and animals; it is exhibited in simple everyday tasks, such as in locomotion or the grooming behavior of animals, and in hierarchically organized domains, such as language or music performance. In this talk I will describe three lines of work investigating the acquisition and monitoring of simple sensorimotor and more complex musical sequences.  In the first part I will describe how approaches of assessing directionality between electrophysiological signals that are constrained by specific hypotheses can characterize predictive error mechanisms in skilled piano performance. In the second part I will present results from a multivariate pattern analysis of intracranial local field potential activity during explicit learning of piano sequences. The results highlight the hierarchical organization of serial order during encoding of movement sequences. In particular, they show that sequence boundaries are encoded in specific patterns of neuronal activity distributed across several frequency regions. Finally, I will relate these studies to a dynamic field theory-based model of sequence generation that we have expanded to investigate the emergence of serial order errors during sequence performance.

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