Synchronization of Coupled Boolean Phase Oscillators

We design, characterize, and couple Boolean phase oscillators that include state-dependent feedback delay. The state-dependent delay allows us to realize an adjustable coupling strength, even though only Boolean signals are exchanged. Specifically, increasing the coupling strength via the range of state-dependent delay leads to larger locking ranges in uni- and bi-directional coupling of oscillators in both experiment and numerical simulation with a piecewise switching model. In the unidirectional coupling scheme, we unveil asymmetric triangular-shaped locking regions (Arnold tongues) that appear at multiples of the natural frequency of the oscillators. This extends observations of a single locking region reported in previous studies. In the bidirectional coupling scheme, we map out a symmetric locking region in the parameter space of frequency detuning and coupling strength. Because of large scalability of our setup, our observations constitute a first step towards realizing large-scale networks of coupled oscillators to address fundamental questions on the dynamical properties of networks in a new experimental setting.Comment: 8 pages, 8 figure

Multirhythmicity in an optoelectronic oscillator with large delay

An optoelectronic oscillator exhibiting a large delay in its feedback loop is studied both experimentally and theoretically. We show that multiple square-wave oscillations may coexist for the same values of the parameters (multirhythmicity). Depending on the sign of the phase shift, these regimes admit either periods close to an integer fraction of the delay or periods close to an odd integer fraction of twice the delay. These periodic solutions emerge from successive Hopf bifurcation points and stabilize at a finite amplitude following a scenario similar to Eckhaus instability in spatially extended systems. We find quantitative agreements between experiments and numerical simulations. The linear stability of the square-waves is substantiated analytically by determining stable fixed points of a map.Comment: 14 pages, 7 figure

Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation

Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. Therefore, to obtain an accurate registration it is important to have an accurate estimation of the true underlying probability distribution. Within the statistics literature, many methods have been proposed for finding the 'optimal' probability density, with the aim of improving the estimation by means of optimal histogram bin size selection. This provokes the common question of how many bins should actually be used when constructing a histogram. There is no definitive answer to this. This question itself has received little attention in the MI literature, and yet this issue is critical to the effectiveness of the algorithm. The purpose of this paper is to highlight this fundamental element of the MI algorithm. We present a comprehensive study that introduces methods from statistics literature and incorporates these for image registration. We demonstrate this work for registration of multi-modal retinal images: colour fundus photographs and scanning laser ophthalmoscope images. The registration of these modalities offers significant enhancement to early glaucoma detection, however traditional registration techniques fail to perform sufficiently well. We find that adaptive probability density estimation heavily impacts on registration accuracy and runtime, improving over traditional binning techniques. © 2013 Elsevier Ltd

Synchronization of coupled neural oscillators with heterogeneous delays

We investigate the effects of heterogeneous delays in the coupling of two excitable neural systems. Depending upon the coupling strengths and the time delays in the mutual and self-coupling, the compound system exhibits different types of synchronized oscillations of variable period. We analyze this synchronization based on the interplay of the different time delays and support the numerical results by analytical findings. In addition, we elaborate on bursting-like dynamics with two competing timescales on the basis of the autocorrelation function.Comment: 18 pages, 14 figure

Excitability in autonomous Boolean networks

We demonstrate theoretically and experimentally that excitable systems can be built with autonomous Boolean networks. Their experimental implementation is realized with asynchronous logic gates on a reconfigurabe chip. When these excitable systems are assembled into time-delay networks, their dynamics display nanosecond time-scale spike synchronization patterns that are controllable in period and phase.Comment: 6 pages, 5 figures, accepted in Europhysics Letters (epljournal.edpsciences.org

SARS-CoV-2 Seroprevalence and Vaccine Correlate of Protection Standardization

In the COVID-19 pandemic, there was great interest in population seroprevalence estimationof individuals with antibodies against SARS-CoV-2 and in evaluation of antibodies as surrogatemarkers for vaccine efficacy. In the first paper, methods for estimation of seroprevalencefrom surveys which can have selection bias and serologic tests which can have measurementerror are presented. These challenges are addressed with the leveraging of auxiliary datafrom target populations, e.g., population census data, and of validation laboratory studies offalse positive and false negative rates. Direct standardization is used for the development ofnonparametric and parametric seroprevalence estimators. The estimators are proven consistentand asymptotically normal. Simulation studies demonstrate performance across a variety ofselection bias and misclassification error scenarios. The proposed methods are applied toSARS-CoV-2 seroprevalence studies in New York City, Belgium, and North Carolina. Drawing a simple comparison of COVID-19 vaccine trial efficacy estimates is problematicwithout considering factors affecting the trial context and design, including characteristics ofa study’s population (Rapaka et al., 2022). A meta-analytic paradigm for surrogate endpointevaluation entails estimating an association between the treatment effects on the surrogateand clinical endpoints, respectively, using data from multiple clinical trials. This approachcan be used to estimate the association between vaccine induced anti-SARS-CoV-2 antibodiesand vaccine efficacy against symptomatic COVID-19 illnesss. In the second paper, multiplevaccine trials are standardized to a common target population. Meta-analytic causal associationparameters, estimators, and the asymptotic distributions of the estimators are considered. A hypothesis test of an implication of a conditional exchangeability assumption is proposed.Simulation studies demonstrate the methods in scenarios motivated by data from several U.S.government Phase 3 SARS-CoV-2 vaccine trials. When data are fused across data sets, often the random variables are assumed to be independentbut not identically distributed, as in the preceding chapters. However, standard estimatingequation theory assumes an independent and identically distributed set up. In the third paper,the consistency and asymptotic normality of estimating equation estimators when data areindependent but not identically distributed is considered. Regularity conditions for consistencyand asymptotic normality in the non-iid setting are presented and examples for application ofthe estimating equation theory to data fusion estimators are provided.Doctor of Philosoph

Between images and built form: Automating the recognition of standardised building components using deep learning

Building on the richness of recent contributions in the field, this paper presents a state-of-the-art CNN analysis method for automatingthe recognition of standardised building components in modern heritage buildings. At the turn of the twentieth century manufacturedbuilding components became widely advertised for specification by architects. Consequently, a form of standardisation across varioustypologies began to take place. During this era of rapid economic and industrialised growth, many forms of public building wereerected. This paper seeks to demonstrate a method for informing the recognition of such elements using deep learning to recognise'families' of elements across a range of buildings in order to retrieve and recognise their technical specifications from the contemporarytrade literature. The method is illustrated through the case of Carnegie Public Libraries in the UK, which provides a unique butubiquitous platform from which to explore the potential for the automated recognition of manufactured standard architecturalcomponents. The aim of enhancing this knowledge base is to use the degree to which these were standardised originally as a means toinform and so support their ongoing care but also that of many other contemporary buildings. Although these libraries are numerous,they are maintained at a local level and as such, their shared challenges for maintenance remain unknown to one another. Additionally,this paper presents a methodology to indirectly retrieve useful indicators and semantics, relating to emerging HBIM families, byapplying deep learning to a varied range of architectural imagery