Synwalk: community detection via random walk modelling

Complex systems, abstractly represented as networks, are ubiquitous in everyday life. Analyzing and understanding these systems requires, among others, tools for community detection. As no single best community detection algorithm can exist, robustness across a wide variety of problem settings is desirable. In this work, we present Synwalk, a random walk-based community detection method. Synwalk builds upon a solid theoretical basis and detects communities by synthesizing the random walk induced by the given network from a class of candidate random walks. We thoroughly validate the effectiveness of our approach on synthetic and empirical networks, respectively, and compare Synwalk’s performance with the performance of Infomap and Walktrap (also random walk-based), Louvain (based on modularity maximization) and stochastic block model inference. Our results indicate that Synwalk performs robustly on networks with varying mixing parameters and degree distributions. We outperform Infomap on networks with high mixing parameter, and Infomap and Walktrap on networks with many small communities and low average degree. Our work has a potential to inspire further development of community detection via synthesis of random walks and we provide concrete ideas for future research.

Simultaneous determination of CKM angle γ and charm mixing parameters

A combination of measurements sensitive to the CP violation angle γ of the Cabibbo-Kobayashi-Maskawa unitarity triangle and to the charm mixing parameters that describe oscillations between D0 and $$ \overline{D} $$ D ¯ 0 mesons is performed. Results from the charm and beauty sectors, based on data collected with the LHCb detector at CERN’s Large Hadron Collider, are combined for the first time. This method provides an improvement on the precision of the charm mixing parameter y by a factor of two with respect to the current world average. The charm mixing parameters are determined to be $$ x=\left({0.400}_{-0.053}^{+0.052}\right)\% $$ x = 0.400 − 0.053 + 0.052 % and y = $$ \left({0.630}_{-0.030}^{+0.033}\right)\% $$ 0.630 − 0.030 + 0.033 % . The angle γ is found to be γ = $$ \left({65.4}_{-4.2}^{+3.8}\right){}^{\circ} $$ 65.4 − 4.2 + 3.8 ° and is the most precise determination from a single experiment.

Dark photon bounds in the dark EFT

Dark photons are massive abelian gauge bosons that interact with ordinary photons via a kinetic mixing with the hypercharge field strength tensor. This theory is probed by a variety of different experiments and limits are set on a combination of the dark photon mass and kinetic mixing parameter. These limits can however be strongly modified by the presence of additional heavy degrees of freedom. Using the framework of dark effective field theory, we study how robust are the current experimental bounds when these new states are present. We focus in particular on the possible existence of a dark dipole interaction between the Standard Model leptons and the dark photon. We show that, under certain assumptions, the presence of a dark dipole modifies existing supernovæ bounds for cut-off scales up to $$ \mathcal{O} $$ O (10–100 TeV). On the other hand, terrestrial experiments, such as LSND and E137, can probe cut-off scales up to $$ \mathcal{O} $$ O (3 TeV). For the latter experiment we highlight that the bound may extend down to vanishing kinetic mixing.

Compton-like dark photon production in electron-nucleus collisions

The Compton-like production of massive dark photons is investigated in ultrarelativistic electron-ion collisions considering the kinetic mixing between the dark photon and the Standard Model photon. The quasi-real photons in the heavy ion are described by the EPA approximation and the model is employed to calculate the integrated cross section and event rates as a function of the dark photon mass, mγ′, and mixing parameter, ε. Predictions are shown for electron-ion colliders (EICs) in the mass range 100 ≤ mγ′ ≤ 500 MeV. Numerical results are provided within the kinematic coverage of the planned machines Electron-ion collider in China (EicC), A Polarized Electron-Ion Collider at Jefferson Lab (JLEIC), Electron Ion Collider/USA (EIC), Large Hadron Electron Collider (LHeC) and Future Circular Collider (FCC-eA). It complements existing search strategies for dark photons in the considered mass interval.

Distributed Mechanism for Detecting Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs

In recent decades, distributed consensus-based algorithms for data aggregation have been gaining in importance in wireless sensor networks since their implementation as a complementary mechanism can ensure sensor-measured values with high reliability and optimized energy consumption in spite of imprecise sensor readings. In the presented article, we address the average consensus algorithm over bipartite regular graphs, where the application of the maximum-degree weights causes the divergence of the algorithm. We provide a spectral analysis of the algorithm, propose a distributed mechanism to detect whether a graph is bipartite regular, and identify how to reconfigure the algorithm so that the convergence of the average consensus algorithm is guaranteed over bipartite regular graphs. More specifically, we identify in the article that only the largest and the smallest eigenvalues of the weight matrix are located on the unit circle; the sum of all the inner states is preserved at each iteration despite the algorithm divergence; and the inner states oscillate between two values close to the arithmetic means determined by the initial inner states from each disjoint subset. The proposed mechanism utilizes the first-order forward and backward finite-difference of the inner states (more specifically, five conditions are proposed) to detect whether a graph is bipartite regular or not. Subsequently, the mixing parameter of the algorithm can be reconfigured the way it is identified in this study whereby the convergence of the algorithm is ensured in bipartite regular graphs. In the experimental part, we tested our mechanism over randomly generated bipartite regular graphs, random graphs, and random geometric graphs with various parameters, thereby identifying its very high detection rate and proving that the algorithm can estimate the arithmetic mean with high precision (like in error-free scenarios) after the suggested reconfiguration.

Extreme Value Analysis for Mixture Models with Heavy-Tailed Impurity

This paper deals with the extreme value analysis for the triangular arrays which appear when some parameters of the mixture model vary as the number of observations grows. When the mixing parameter is small, it is natural to associate one of the components with “an impurity” (in the case of regularly varying distribution, “heavy-tailed impurity”), which “pollutes” another component. We show that the set of possible limit distributions is much more diverse than in the classical Fisher–Tippett–Gnedenko theorem, and provide the numerical examples showing the efficiency of the proposed model for studying the maximal values of the stock returns.

Local quantum uncertainty versus negativity through Gisin states

We investigate the pairwise quantum correlations in standard Gisin states and in Gisin states based on bipartite spin-coherent states by employing quantum negativity and quantum local uncertainty as bona fide quantum correlations measures. Gisin states are defined as mixtures of separable mixed states and some pure entangled ones. We compare the behavior of the two quantifiers of Gisin states and we find that both measures exhibit a sudden change in terms of the mixing parameter. Furthermore, we show that entangled Gisin states contain nonclassical correlations that are captured by the local quantum uncertainty and cannot be revealed by the negativity quantifier.

Influence through mixing: hotspots as benchmarks for basic black-hole behaviour

Effective theories are being developed for fields outside black holes, often with an unusual open-system feel due to the influence of large number of degrees of freedom that lie out of reach beyond the horizon. What is often difficult when interpreting such theories is the absence of comparisons to simpler systems that share these features. We propose here such a simple model, involving a single external scalar field that mixes in a limited region of space with a ‘hotspot’ containing a large number of hot internal degrees of freedom. Since the model is at heart gaussian it can be solved explicitly, and we do so for the mode functions and correlation functions for the external field once the hotspot fields are traced out. We compare with calculations that work perturbatively in the mixing parameter, and by doing so can precisely identify its domain of validity. We also show how renormalization-group EFT methods can allow some perturbative contributions to be resummed beyond leading order, verifying the result using the exact expression.

Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov’s accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm’s computational complexity in future research.

Comparison of three-dimensional flow mixing via pulsation and dynamical stirring: application to the mixing of parallel streams at different temperatures

Purpose This paper aims to compare the performance of flow pulsation versus flow stirring in the context of mixing of a passive scalar at moderate Reynolds numbers in confined flows. This comparison has been undertaken in two limits: diffusion can be neglected as compared to convection (very large Peclet) and diffusion and convection effects are comparable. The comparison was performed both in terms of global parameters: pumping power and mixing efficiency and local flow topology. Design/methodology/approach The study has been addressed by setting up a common conceptual three-dimensional problem that consisted of the mixing of two parallel streams in a square section channel past a square section prism. Stirring and pulsation frequencies and amplitudes were changed and combined at an inlet Reynolds number of 200. The numerical model was solved using a finite volume formulation by adapting a series of open-source OpenFOAM computational fluid dynamic (CFD) libraries. For cases with flow pulsation, the icoFoam solver for laminar incompressible transient flows was used. For cases with stirring, the icoDyMFoam solver, which uses the arbitrary Lagrangian–Eulerian method for the description of the moving dynamical mesh, was used to model the prism motion. At the local flow topology level, a new method was proposed to analyze mixing. Time evolution of folding and wrinkling of sheets made up of virtual particles that travel along streak lines was quantified by generating lower rank projections of the sheets onto the spaces spanned by the main eigenvectors of an appropriate space-temporal data decomposition. Findings In the limit when convection is dominant, the results showed the superior performance of stirring versus flow pulsation both in terms of mixing and required pumping power. In the cases with finite Peclet, the mixing parameters by stirring and flow pulsation were comparable, but pulsation required larger pumping power than stirring. For some precise synchronization of stirring and pulsation, the mixing parameter reached its maximum, although at the expense of higher pumping power. At the local flow topology level, the new method proposed to quantify mixing has been found to correlate well with the global mixing parameter. Originality/value A new systematic comparative study of two methods, stirring and pulsation, to achieve mixing of passive scalars in the mini scale for confined flows has been presented. The main value, apart from the conclusions, is that both methods have been tested against the same flow configuration, which allows for a self-consistent comparison. Of particular interest is the fact that it has been found that accurate synchronization of both methods yields mixing parameters higher than those associated to both methods taken separately. This suggests that it is possible to synchronize mixing methods of a different nature to achieve optimum designs. The new theoretical method that has been proposed to understand the mixing performance at the local level has shown promising results, and it is the intention of the authors to test its validity in a broader range of flow parameters. All these findings could be taken as potential guidelines for the design of mixing processes in the mini scale in the process industry.