arXiv subjects

Henrik Jeldtoft Jensen

Publications and source records attributed to Henrik Jeldtoft Jensen.

At least 55 records · Page 3Linked to original sources

arXiv2015

Revisiting Interval Graphs for Network Science

The vertices of an interval graph represent intervals over a real line where overlapping intervals denote that their corresponding vertices are adjacent. This implies that the vertices are measurable by a metric and there exists a linear structure in the system. The generalization is an embedding of a graph onto a multi-dimensional Euclidean space and it was used by scientists to study the multi-relational complexity of ecology. However the research went out of fashion in the 1980s and was not revisited when Network Science recently expressed interests with multi-relational networks known as multiplexes. This paper studies interval graphs from the perspective of Network Science.

physics.soc-ph↗

arXiv2014

Forecasting transitions in systems with high dimensional stochastic complex dynamics: A Linear Stability Analysis of the Tangled Nature Model

We propose a new procedure to monitor and forecast the onset of transitions in high dimensional complex systems. We describe our procedure by an application to the Tangled Nature model of evolutionary ecology. The quasi-stable configurations of the full stochastic dynamics are taken as input for a stability analysis by means of the deterministic mean field equations. Numerical analysis of the high dimensional stability matrix allows us to identify unstable directions associated with eigenvalues with positive real part. The overlap of the instantaneous configuration vector of the full stochastic system with the eigenvectors of the unstable directions of the deterministic mean field approximation is found to be a good early-warning of the transitions occurring intermittently.

nlin.AO↗

arXiv2014

The causal inference of cortical neural networks during music improvisations

In this paper, we present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional mutual information from mixed embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain neural networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found musicians responding differently to listeners when playing music in different conditions.

q-bio.NC↗

arXiv2014

Comparison of Communities Detection Algorithms for Multiplex

Multiplex is a set of graphs on the same vertex set, i.e. $\{G(V,E_1),\ldots,G(V,E_m)\}$. It is a generalized graph to model multiple relationships with parallel edges between vertices. This paper is a literature review of existing communities detection algorithms for multiplex and a comparative analysis of them.

physics.soc-ph↗

arXiv2014

Quantifying 'causality' in complex systems: Understanding Transfer Entropy

'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of `causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.

cond-mat.stat-mech↗

arXiv2014

Why free markets die: An evolutionary perspective

Company mergers and acquisitions are often perceived to act as catalysts for corporate growth in free markets systems: it is conventional wisdom that those activities lead to better and more efficient markets. However, the broad adoption of this perception into corporate strategy is prone to result in a less diverse and more unstable environment, dominated by either very large or very small niche entities. We show here that ancestry, i.e. the cumulative history of mergers, is the key characteristic that encapsulates the diverse range of drivers behind mergers and acquisitions, across a range of industries and geographies. A long-term growth analysis reveals that entities which have been party to fewer mergers tend to grow faster than more highly acquisitive businesses.

q-fin.GN↗

arXiv2013

Stability of the Centrality of Unions of Networks on the Same Vertex Set

Let $G^1(V,E_1)$ and $G^2(V,E_2)$ be two networks on the same vertex set $V$ and consider the union of edges $G(V, E_1 \cup E_2)$. This paper studies the stability of the Degree, Betweenness and Eigenvector Centrality of the resultant network, $G(V, E_1 \cup E_2)$. Specifically assume $v^1_{max}$ and $v^c_{max}$ are the highest centrality vertices of $G^1(V,E_1)$ and $G(V, E_1 \cup E_2)$ respectively, we want to find $Pr(v^1_{max} = v^c_{max})$.

nlin.AO↗

arXiv2013

Edge Union of Networks on the Same Vertex Set

Random networks generators like Erdoes-Renyi, Watts-Strogatz and Barabasi-Albert models are used as models to study real-world networks. Let G^1(V,E_1) and G^2(V,E_2) be two such networks on the same vertex set V. This paper studies the degree distribution and cluster coefficient of the resultant networks, G(V, E_1 U E_2).

physics.soc-ph↗

arXiv2013

Ecosystems perspective on financial networks: diagnostic tools

The economical world consists of a highly interconnected and interdependent network of firms. Here we develop temporal and structural network tools to analyze the state of the economy. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dynamics surrounding the crash in 2008, is seen as a key emergent feature arising naturally from the evolutionary and adaptive dynamics inherent to the financial markets. Similarly, collusion amongst construction firms in a number of regions in Japan in the 2000s can be identified with the formation of clusters of anomalous highly connected companies.

q-fin.RM↗

arXiv2012

Doppler synchronization of pulsating phases by time delay

Synchronization by exchange of pulses is a widespread phenomenon, observed in flashing fireflies, applauding audiences and the neuronal network of the brain. Hitherto the focus has been on integrate-and-fire oscillators. Here we consider entirely analytic time evolution. Oscillators exchange narrow but finite pulses. For any non-zero time lag between the oscillators complete synchronization occurs for any number of oscillators arranged in interaction networks whose adjacency matrix fulfils some simple conditions. The time to synchronization decreases with increasing time lag.

math-ph↗

arXiv2012

Entangled Economy: an ecosystems approach to modeling systemic level dynamics

We present a model of an economy inspired by individual based model approaches in evolutionary ecology. We demonstrate that evolutionary dynamics in a space of companies interconnected through a correlated interaction matrix produces time dependencies of the total size of the economy total number of companies, companies age and capital distribution that compares well with statistics for USA. We discuss the relevance of our modeling framework to policy making.

q-fin.GN↗

arXiv2011

Self-Organized Criticality, effective dynamics and the universality class of the Deterministic Lattice Gas

We show that the Deterministic Lattice Gas (DLG) (Phys. Rev. Lett. 26, 3103 (1990)) model of Self-Organized Criticality (SOC) despite of its deterministic micro dynamics belongs to the Manna universality class of absorbing state phase transitions. This finding is consistent with our observation that an effective stochastic term is generated in the DLG at large length scales, whereby the macro dynamics of the DLG appears closer to the other stochastic SOC models of the Manna class.

cond-mat.stat-mech↗

arXiv2011

Connecting the Micro-dynamics to the Emergent Macro-variables: Self-Organized Criticality and Absorbing Phase Transitions in the Deterministic Lattice Gas

We reinvestigate the Deterministic Lattice Gas introduced as a paradigmatic model of the 1/f spectra (Phys. Rev. Lett. V26, 3103 (1990)) arising according to the Self-Organized Criticality scenario. We demonstrate that the density fluctuations exhibit an unexpected dependence on systems size and relate the finding to effective Langevin equations. The low density behavior is controlled by the critical properties of the gas at the absorbing state phase transition. We also show that the Deterministic Lattice Gas is in the Manna universality class of absorbing state phase transitions. This is in contrast to expectations in the literature which suggested that the entirely deterministic nature of the dynamics would put the model in a different universality class. To our knowledge this is the first fully deterministic member of the Manna universality class.

cond-mat.stat-mech↗

arXiv2010

Self-similar correlation function in brain resting-state fMRI

Adaptive behavior, cognition and emotion are the result of a bewildering variety of brain spatiotemporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain's 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e., they are functionally integrated. Here, we investigate these large-scale dynamical properties by inspecting the character of the spatiotemporal correlations of brain resting-state activity. In physical systems, these correlations in space and time are captured by measuring the correlation coefficient between a signal recorded at two different points in space at two different times. We show that this two-point correlation function extracted from resting-state fMRI data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/f frequency behavior of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. This dynamical property may be seen as an important marker of brain well-being both in health and disease.

q-bio.NC↗

arXiv2010

Tempo and Mode of Evolution in the Tangled Nature Model

We study the Tangled Nature model of macro evolution and demonstrate that the co-evolutionary dynamics produces an increasingly correlated core of well occupied types. At the same time the entire configuration of types becomes increasing de-correlated. This finding is related to ecosystem evolution. The systems level dynamics of the model is subordinated to intermittent transitions between meta-stable states. We improve on previous studies of the statistics of the transition times and show that the fluctuations in the offspring probability decreases with number of transitions. The longtime adaptation, as seen by an increasing population size is demonstrated to be related to the convexity of the offspring probability. We explain how the models behaviour is a mathematical reflection of Darwin's concept of adaptation of profitable variations.

cond-mat.stat-mech↗

arXiv2010

From gene regulatory networks to population dynamics: robustness, diversity and their role in progression to cancer

The aim of this paper is to discuss the role of robustness and diversity in population dynamics in particular to some properties of the multi-step from healthy tissue to fully malignant tumours. Recent evidence shows that diversity within the cell population of a neoplasm, a pre-tumoural lession that can develop into a fully malignant tumour, is the best predictor for its evolving into a tumour. By studying the dynamics of a population described by a multi-type, population-size limited branching process in terms of the evolutionary formalism, we show some general principles regarding the probability of a resident population to being invaded by a mutant population in terms of the number of types present in the population and their resilience. We show that, although diversity in the mutant population poses a barrier for the emergence of the initial (benign) lession, under appropiate conditions, namely, the phenotypes in the mutant population being more resilient than those of the resident population, a more variable noeplastic population is more likely to be invaded by a more malignant one. Analysis of a model of gene regulatory networks suggest possible mechanisms giving rise to mutants with increased phenotypic diversity and robustness. We then go on to show how these results may help us to interpret some recent data regarding the evolution of Barrett's oesophagus into throat cancer.

q-bio.PE↗

arXiv2010

Quiescence: a mechanism for escaping the effects of drug on cell populations

We point out that a simple and generic strategy to lower the risk for extinction consists in the developing a dormant stage in which the organism is unable to multiply but may die. The dormant organism is protected against the poisonous environment. The result is to increase the survival probability of the entire population by introducing a type of zero reproductive fitness. This is possible, because the reservoir of dormant individuals act as a buffer that can cushion fatal fluctuations in the number of births and deaths which without the dormant population would have driven the entire population to extinction.

q-bio.PE↗

arXiv2009

Logarithmically slow onset of synchronization

Here we investigate specifically the transient of a synchronizing system, considering synchronization as a relaxation phenomenon. The stepwise establishment of synchronization is studied in the system of dynamically coupled maps introduced by Ito & Kaneko (Phys. Rev. Lett., 88, 028701, 2001 & Phys. Rev. E, 67, 046226, 2003), where the plasticity of dynamical couplings might be relevant in the context of neuroscience. We show the occurrence of logarithmically slow dynamics in the transient of a fully deterministic dynamical system.

cond-mat.stat-mech↗