Institute for Scientific Interchange Foundation
Institute for Scientific Interchange Foundation
Banchi L.,Institute for Scientific Interchange Foundation |
Giorda P.,Institute for Scientific Interchange Foundation |
Zanardi P.,University of Southern California |
Zanardi P.,National University of Singapore
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2014
A general framework for analyzing the recently discovered phase transitions in the steady state of dissipation-driven open quantum systems is still lacking. To fill this gap, we extend the so-called fidelity approach to quantum phase transitions to open systems whose steady state is a Gaussian fermionic state. We endow the manifold of correlation matrices of steady states with a metric tensor g measuring the distinguishability distance between solutions corresponding to a different set of control parameters. The phase diagram can then be mapped out in terms of the scaling behavior of g and connections with the Liouvillean gap and the model correlation functions unveiled. We argue that the fidelity approach, thanks to its differential-geometric and information-theoretic nature, provides insights into dissipative quantum critical phenomena as well as a general and powerful strategy to explore them. © 2014 American Physical Society.
Sirbu A.,Dublin City University |
Sirbu A.,Institute for Scientific Interchange Foundation |
Kerr G.,German Cancer Research Center |
Crane M.,Dublin City University |
Ruskin H.J.,Dublin City University
PLoS ONE | Year: 2012
With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter's disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset. © 2012 Sîrbu et al.
Liu S.,Zhejiang University |
Liu S.,Northeastern University |
Perra N.,Northeastern University |
Karsai M.,Northeastern University |
And 2 more authors.
Physical Review Letters | Year: 2014
The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set. © 2014 American Physical Society.
Pastor-Satorras R.,Polytechnic University of Catalonia |
Castellano C.,CNR Institute for Complex Systems |
Castellano C.,University of Rome La Sapienza |
Van Mieghem P.,Technical University of Delft |
And 2 more authors.
Reviews of Modern Physics | Year: 2015
In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported. © 2015 American Physical Society. © 2015 American Physical Society.
Perra N.,Northeastern University |
Baronchelli A.,Northeastern University |
Mocanu D.,Northeastern University |
Goncalves B.,Northeastern University |
And 3 more authors.
Physical Review Letters | Year: 2012
The random walk process underlies the description of a large number of real-world phenomena. Here we provide the study of random walk processes in time-varying networks in the regime of time-scale mixing, i.e., when the network connectivity pattern and the random walk process dynamics are unfolding on the same time scale. We consider a model for time-varying networks created from the activity potential of the nodes and derive solutions of the asymptotic behavior of random walks and the mean first passage time in undirected and directed networks. Our findings show striking differences with respect to the well-known results obtained in quenched and annealed networks, emphasizing the effects of dynamical connectivity patterns in the definition of proper strategies for search, retrieval, and diffusion processes in time-varying networks. © 2012 American Physical Society.
Zapperi S.,CNR Institute for Energetics and Interphases |
Zapperi S.,Institute for Scientific Interchange Foundation |
Mahadevan L.,Harvard University
Biophysical Journal | Year: 2011
The intermittent transition between slow growth and rapid shrinkage in polymeric assemblies is termed "dynamic instability", a feature observed in a variety of biochemically distinct assemblies including microtubules, actin, and their bacterial analogs. The existence of this labile phase of a polymer has many functional consequences in cytoskeletal dynamics, and its repeated appearance suggests that it is relatively easy to evolve. Here, we consider the minimal ingredients for the existence of dynamic instability by considering a single polymorphic filament that grows by binding to a substrate, undergoes a conformation change, and may unbind as a consequence of the residual strains induced by this change. We identify two parameters that control the phase space of possibilities for the filament: a structural mechanical parameter that characterizes the ratio of the bond strengths along the filament to those with the substrate (or equivalently the ratio of longitudinal to lateral interactions in an assembly), and a kinetic parameter that characterizes the ratio of timescales for growth and conformation change. In the deterministic limit, these parameters serve to demarcate a region of uninterrupted growth from that of collapse. However, in the presence of disorder in either the structural or the kinetic parameter the growth and collapse phases can coexist where the filament can grow slowly, shrink rapidly, and transition between these phases, thus exhibiting dynamic instability. We exhibit the window for the existence of dynamic instability in a phase diagram that allows us to quantify the evolvability of this labile phase. © 2011 Biophysical Society.
Kadar Z.,Institute for Scientific Interchange Foundation |
Zimboras Z.,Institute for Scientific Interchange Foundation
Physical Review A - Atomic, Molecular, and Optical Physics | Year: 2010
We investigate the entanglement entropy of a block of L sites in quasifree translation-invariant spin chains concentrating on the effect of reflection-symmetry breaking. The Majorana two-point functions corresponding to the Jordan-Wigner transformed fermionic modes are determined in the most general case; from these, it follows that reflection symmetry in the ground state can only be broken if the model is quantum critical. The large L asymptotics of the entropy are calculated analytically for general gauge-invariant models, which have, until now, been done only for the reflection-symmetric sector. Analytical results are also derived for certain nongauge-invariant models (e.g., for the Ising model with Dzyaloshinskii-Moriya interaction). We also study numerically finite chains of length N with a nonreflection-symmetric Hamiltonian and report that the reflection symmetry of the entropy of the first L spins is violated but the reflection-symmetric Calabrese-Cardy formula is recovered asymptotically. Furthermore, for noncritical reflection-symmetry-breaking Hamiltonians, we find an anomaly in the behavior of the saturation entropy as we approach the critical line. The paper also provides a concise but extensive review of the block-entropy asymptotics in translation-invariant quasifree spin chains with an analysis of the nearest-neighbor case and the enumeration of the yet unsolved parts of the quasifree landscape. © 2010 The American Physical Society.
Schollnberger H.,University of Salzburg |
Schollnberger H.,Helmholtz Center for Environmental Research |
Beerenwinkel N.,ETH Zurich |
Hoogenveen R.,National Institute for Public Health and the Environment RIVM |
And 2 more authors.
Cancer Research | Year: 2010
Carcinogenesis is the result of mutations and subsequent clonal expansions of mutated, selectively advantageous cells. To investigate the relative contributions of mutation versus cell selection in tumorigenesis, we compared two mathematical models of carcinogenesis in two different cancer types: lung and colon. One approach is based on a population genetics model, the Wright-Fisher process, whereas the other approach is the two-stage clonal expansion model. We compared the dynamics of tumorigenesis predicted by the two models in terms of the time period until the first malignant cell appears, which will subsequently form a tumor. The mean waiting time to cancer has been calculated approximately for the evolutionary colon cancer model. Here, we derive new analytic approximations to the median waiting time for the two-stage lung cancer model and for a multistage approximation to the Wright-Fisher process. Both equations show that the waiting time to cancer is dominated by the selective advantage per mutation and the net clonal expansion rate, respectively, whereas the mutation rate has less effect. Our comparisons support the idea that the main driving force in lung and colon carcinogenesis is Darwinian cell selection. 2010 AACR.
News Article | November 3, 2016
Getting urban planning right is no mean feat. It requires understanding how and when people travel between different places. This knowledge, in turn, helps in dimensioning roads and motorways and in scaling the capacity of utilities, such as power grids or mobile phone towers. Now, physicists at the Institute for Scientific Interchange Foundation in Turin, Italy, have exploited the geolocalisation data from millions of users of the photo sharing site Flickr to show how it is possible to predict crowd movements. Mariano Beiró and colleagues have combined this data with existing theoretical models explaining the movement of people. In a study published in EPJ Data Science, they show that their approach can help improve predictions concerning the nature of travel of large crowds of people between two places. Previously, social scientists proposed some theoretical models to explain how the flows of people or goods between cities are related to population distribution, economic development or travel distance. Thanks to geolocalisation, these last 50 years of static modelling can now be refined. In this study, the authors have thus combined them with actual digital crumbs left by social media users showing how people travel in the USA at different scales of distance. To do so, they used a hybrid algorithm based on machine learning and capable of integrating patterns by processing available data to infer results when data is not available. Beiró and colleagues thus showed how predictions of how many people fly between airports, or commute between two counties, can be improved. Future research could extend these methods to real-time predictions of the collective flows of people, as data becomes more widely and more rapidly accessible. This will help avoid potential traffic congestion or resource shortages due to an emergency situation, for example. References: Mariano G. Beiró, André Panisson, Michele Tizzoni, Ciro Cattuto (2016), Predicting human mobility through the assimilation of social media traces into mobility models, EPJ Data Science, 5:30, DOI 10.1140/epjds/s13688-016-0092-2
News Article | November 3, 2016
Getting urban planning right is no mean feat. It requires understanding how and when people travel between different places. This knowledge, in turn, helps in dimensioning roads and motorways and in scaling the capacity of utilities, such as power grids or mobile phone towers. Now, physicists at the Institute for Scientific Interchange Foundation in Turin, Italy, have exploited the geolocalisation data from millions of users of the photo sharing site Flickr to show how it is possible to predict crowd movements. Mariano Beiró and colleagues have combined this data with existing theoretical models explaining the movement of people. In a study published in EPJ Data Science, they show that their approach can help improve predictions concerning the nature of travel of large crowds of people between two places. Previously, social scientists proposed some theoretical models to explain how the flows of people or goods between cities are related to population distribution, economic development or travel distance. Thanks to geolocalisation, these last 50 years of static modelling can now be refined. In this study, the authors have thus combined them with actual digital crumbs left by social media users showing how people travel in the USA at different scales of distance. To do so, they used a hybrid algorithm based on machine learning and capable of integrating patterns by processing available data to infer results when data is not available. Beiró and colleagues thus showed how predictions of how many people fly between airports, or commute between two counties, can be improved. Future research could extend these methods to real-time predictions of the collective flows of people, as data becomes more widely and more rapidly accessible. This will help avoid potential traffic congestion or resource shortages due to an emergency situation, for example. Explore further: An algorithm for taxi sharing More information: Mariano G Beiró et al, Predicting human mobility through the assimilation of social media traces into mobility models, EPJ Data Science (2016). DOI: 10.1140/epjds/s13688-016-0092-2