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  • Journal article
    Cofré R, Videla L, Rosas F, 2019,

    An introduction to the non-equilibrium steady states of maximum entropy spike trains

    , Entropy, Vol: 21, Pages: 1-28, ISSN: 1099-4300

    Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.

  • Journal article
    Rosas FE, Mediano PAM, Gastpar M, Jensen HJet al., 2019,

    Quantifying high-order interdependencies via multivariate extensions of the mutual information

    , Physical Review E, Vol: 100, ISSN: 2470-0045

    This paper introduces a model-agnostic approach to study statistical synergy, a form of emergence in which patterns at large scales are not traceable from lower scales. Our framework leverages various multivariate extensions of Shannon's mutual information, and introduces the O-information as a metric that is capable of characterizing synergy- and redundancy-dominated systems. The O-information is a symmetric quantity, and can assess intrinsic properties of a system without dividing its parts into “predictors” and “targets.” We develop key analytical properties of the O-information, and study how it relates to other metrics of high-order interactions from the statistical mechanics and neuroscience literature. Finally, as a proof of concept, we present an exploration on the relevance of statistical synergy in Baroque music scores.

  • Journal article
    Yao Q, Evans TS, Christensen K, Evans TS, Plato ADKet al., 2019,

    How the network properties of shareholders vary with investor type and country

    , PLoS One, Vol: 14, Pages: 1-19, ISSN: 1932-6203

    We construct two examples of shareholder networks in which shareholders are connected if they have shares in the same company. We do this for the shareholders in Turkish companies and we compare this against the network formed from the shareholdings in Dutch companies. We analyse the properties of these two networks in terms of the different types of shareholder. We create a suitable randomised version of these networks to enable us to find significant features in our networks. For that we find the roles played by different types of shareholder in these networks, and also show how these roles differ in the two countries we study.

  • Journal article
    Viegas EM, Goto H, Takayasu H, Takayasu M, Jensen HJet al., 2019,

    Assembling real networks from synthetic and unstructured subsets: the corporate reporting case

    , Scientific Reports, Vol: 9, ISSN: 2045-2322

    The analysis of interfirm business transaction networks provides invaluable insight into the trading dynamics and economicstructure of countries. However, there is a general scarcity of data available recording real, accurate and extensive informationfor these types of networks. As a result, and in common with other types of network studies - such as protein interactions forinstance - research tends to rely on partial and incomplete datasets, i.e. subsets, with less certain conclusions. Hereh, wemake use of unstructured financial and corporate reporting data in Japan as the base source to construct a financial reportingnetwork, which is then compared and contrasted to the wider real business transaction network. The comparative analysisbetween these two rich datasets - the proxy, partially derived network and the real, complete network at macro as well as localstructural levels - provides an enhanced understanding of the non trivial relationships between partial sampled subsets andfully formed networks. Furthermore, we present an elemental agent based pruning algorithm that reconciles and preserves keystructural differences between these two networks, which may serve as an embryonic generic framework of potentially wideruse to network research, enabling enhanced extrapolation of conclusions from partial data or subsets.

  • Journal article
    Cofre R, Herzog R, Corcoran D, Rosas Fet al., 2019,

    A comparison of the maximum entropy principle across biological spatial scales

    Despite their obvious differences, biological systems at different scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are usually obscured by the parcelled terminology employed by various scientific sub-disciplines. To explore these commonalities, this papers a comparative study of diverse applications of the maximum entropy principle, ranging from amino acids up to societies. By presenting these studies under a common language, this paper establishes a unified view over seemingly highly heterogeneous biological scenarios.

  • Journal article
    Azari MM, Rosas F, Pollin S, 2019,

    Cellular connectivity for UAVs: Network modeling, performance analysis, and design guidelines

    , IEEE Transactions on Wireless Communications, Vol: 18, Pages: 3366-3381, ISSN: 1536-1276

    The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network-level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased load-dependent co-channel interference received from neighboring ground base stations (BSs) is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 m can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network-based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase the coverage from 23% to 89% and throughput from 3.5 to 5.8 b/s/Hz, outperforming ground UEs. Furthermore, our findings reveal that depending on the UAV altitude and its antenna configuration, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of microcells limit the UAV performance. Although UAV usage has the potential to increase the area spectral efficiency (ASE) of cellular networks with a moderate number of cells, they might hamper the development of future ultradense networks.

  • Journal article
    Rosas De Andraca FE, Faggian M, Ginelli F, Levnajic Zet al., 2019,

    Synchronization in time-varying random networks with vanishing connectivity

    , Scientific Reports, Vol: 9, Pages: 1-11, ISSN: 2045-2322

    A sufficiently connected topology linking the constituent units of a complex system is usually seen as a prerequisite forthe emergence of collective phenomena such as synchronization. We present a random network of heterogeneous phaseoscillators in which the links mediating the interactions are constantly rearranged with a characteristic timescale and, possibly,an extremely low instantaneous connectivity. We show that with strong coupling and sufficiently fast rewiring the networkreaches partial synchronization even in the vanishing connectivity limit. In particular, we provide an approximate analyticalargument, based on the comparison between the different characteristic timescales of our system in the low connectivityregime, which is able to predict the transition to synchronization threshold with satisfactory precision beyond the formal fastrewiring limit. We interpret our results as a qualitative mechanism for emergence of consensus in social communities. Inparticular, our result suggest that groups of individuals are capable of aligning their opinions under extremely sparse exchangesof views, which is reminiscent of fast communications that take place in the modern social media. Our results may also berelevant to characterize the onset of collective behavior in engineered systems of mobile units with limited wireless capabilities

  • Journal article
    Vasiliauskaite V, Evans TS, 2019,

    Social success of perfumes

    , PLoS ONE, Vol: 14, ISSN: 1932-6203

    We study data on perfumes and their odour descriptors-notes-to understand how note compositions, called accords, influence successful fragrance formulas. We obtain accords which tend to be present in perfumes that receive significantly more customer ratings. Our findings show that the most popular notes and the most over-represented accords are different to those that have the strongest effect to the perfume ratings. We also used network centrality to understand which notes have the highest potential to enhance note compositions. We find that large degree notes, such as musk and vanilla as well as generically-named notes, e.g. floral notes, are amongst the notes that enhance accords the most. This work presents a framework which would be a timely tool for perfumers to explore a multidimensional space of scent compositions.

  • Journal article
    Patel VM, Panzarasa P, Ashrafian H, Evans TS, Kirresh A, Sevdalis N, Darzi A, Athanasiou Tet al., 2019,

    Collaborative patterns, authorship practices and scientific success in biomedical research: a network analysis.

    , Journal of the Royal Society of Medicine, Vol: 112, Pages: 245-257, ISSN: 1758-1095

    OBJECTIVE: To investigate the relationship between biomedical researchers' collaborative and authorship practices and scientific success. DESIGN: Longitudinal quantitative analysis of individual researchers' careers over a nine-year period. SETTING: A leading biomedical research institution in the United Kingdom. PARTICIPANTS: Five hundred and twenty-five biomedical researchers who were in employment on 31 December 2009. MAIN OUTCOME MEASURES: We constructed the co-authorship network in which nodes are the researchers, and links are established between any two researchers if they co-authored one or more articles. For each researcher, we recorded the position held in the co-authorship network and in the bylines of all articles published in each three-year interval and calculated the number of citations these articles accrued until January 2013. We estimated maximum likelihood negative binomial panel regression models. RESULTS: Our analysis suggests that collaboration sustained success, yet excessive co-authorship did not. Last positions in non-alphabetised bylines were beneficial for higher academic ranks but not for junior ones. A professor could witness a 20.57% increase in the expected citation count if last-listed non-alphabetically in one additional publication; yet, a lecturer suffered from a 13.04% reduction. First positions in alphabetised bylines were positively associated with performance for junior academics only. A lecturer could experience a 8.78% increase in the expected citation count if first-listed alphabetically in one additional publication. While junior researchers amplified success when brokering among otherwise disconnected collaborators, senior researchers prospered from socially cohesive networks, rich in third-party relationships. CONCLUSIONS: These results help biomedical scientists shape successful careers and research institutions develop effective assessment and recruitment policies that will ultimately sustain the quality of biomedical r

  • Journal article
    Rassouli B, Rosas FE, Gunduz D, 2019,

    Data disclosure under perfect sample privacy

    Perfect data privacy seems to be in fundamental opposition to the economicaland scientific opportunities associated with extensive data exchange. Defyingthis intuition, this paper develops a framework that allows the disclosure ofcollective properties of datasets without compromising the privacy ofindividual data samples. We present an algorithm to build an optimal disclosurestrategy/mapping, and discuss it fundamental limits on finite andasymptotically large datasets. Furthermore, we present explicit expressions tothe asymptotic performance of this scheme in some scenarios, and study caseswhere our approach attains maximal efficiency. We finally discuss suboptimalschemes to provide sample privacy guarantees to large datasets with a reducedcomputational cost.

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