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1.
Phys Rev E ; 103(4-1): 042302, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-34005909

RESUMO

We investigate the statistics of articulation points and bredges (bridge edges) in complex networks in which bonds are randomly removed in a percolation process. Because of the heterogeneous structure of a complex network, the probability of a node to be an articulation point or the probability of an edge to be a bredge will not be homogeneous across the network. We therefore analyze full distributions of articulation point probabilities as well as bredge probabilities, using a message-passing or cavity approach to the problem. Our methods allow us to obtain these distributions both for large single instances of networks and for ensembles of networks in the configuration model class in the thermodynamic limit, through a single unified approach. We also evaluate deconvolutions of these distributions according to degrees of the node or the degrees of both adjacent nodes in the case of bredges. We obtain closed form expressions for the large mean degree limit of Erdos-Rényi networks. Moreover, we reveal and are able to rationalize a significant amount of structure in the evolution of articulation point and bredge probabilities in response to random bond removal. We find that full distributions of articulation point and bredge probabilities in real networks and in their randomized counterparts may exhibit significant differences even where average articulation point and bredge probabilities do not. We argue that our results could be exploited in a variety of applications, including approaches to network dismantling or to vaccination and islanding strategies to prevent the spread of epidemics or of blackouts in process networks.

2.
Phys Rev E ; 103(1-1): 013001, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33601586

RESUMO

We suggest a geometrical mechanism for the ordering of slender filaments inside nonisotropic containers, using cortical microtubules in plant cells and the packing of viral genetic material inside capsids as concrete examples. We show analytically how the shape of the cell affects the ordering of phantom elastic rods that are not self-avoiding (i.e., self-crossing is allowed). We find that for oblate cells, the preferred orientation is along the equator, while for prolate spheroids with an aspect ratio close to 1, the orientation is along the principal (long axis). Surprisingly, at a high enough aspect ratio, a configurational phase transition occurs and the rods no longer point along the principal axis, but at an angle to it, due to high curvature at the poles. We discuss some of the possible effects of self-avoidance using energy considerations. These results are relevant to other packing problems as well, such as the spooling of filament in the industry or spider silk inside water droplets.

3.
Phys Rev E ; 102(5-2): 059904, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33327215

RESUMO

This corrects the article DOI: 10.1103/PhysRevE.90.050103.

4.
Phys Rev E ; 102(1-1): 012314, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32794990

RESUMO

A bredge (bridge-edge) in a network is an edge whose deletion would split the network component on which it resides into two separate components. Bredges are vulnerable links that play an important role in network collapse processes, which may result from node or link failures, attacks, or epidemics. Therefore, the abundance and properties of bredges affect the resilience of the network to these collapse scenarios. We present analytical results for the statistical properties of bredges in configuration model networks. Using a generating function approach based on the cavity method, we calculate the probability P[over ̂](e∈B) that a random edge e in a configuration model network with degree distribution P(k) is a bredge (B). We also calculate the joint degree distribution P[over ̂](k,k^{'}|B) of the end-nodes i and i^{'} of a random bredge. We examine the distinct properties of bredges on the giant component (GC) and on the finite tree components (FC) of the network. On the finite components all the edges are bredges and there are no degree-degree correlations. We calculate the probability P[over ̂](e∈B|GC) that a random edge on the giant component is a bredge. We also calculate the joint degree distribution P[over ̂](k,k^{'}|B,GC) of the end-nodes of bredges and the joint degree distribution P[over ̂](k,k^{'}|NB,GC) of the end-nodes of nonbredge edges on the giant component. Surprisingly, it is found that the degrees k and k^{'} of the end-nodes of bredges are correlated, while the degrees of the end-nodes of nonbredge edges are uncorrelated. We thus conclude that all the degree-degree correlations on the giant component are concentrated on the bredges. We calculate the covariance Γ(B,GC) of the joint degree distribution of end-nodes of bredges and show it is negative, namely bredges tend to connect high degree nodes to low degree nodes. We apply this analysis to ensembles of configuration model networks with degree distributions that follow a Poisson distribution (Erdos-Rényi networks), an exponential distribution and a power-law distribution (scale-free networks). The implications of these results are discussed in the context of common attack scenarios and network dismantling processes.

5.
Phys Rev E ; 101(6-1): 062308, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32688589

RESUMO

We present analytical results for the structural evolution of random networks undergoing contraction processes via generic node deletion scenarios, namely, random deletion, preferential deletion, and propagating deletion. Focusing on configuration model networks, which exhibit a given degree distribution P_{0}(k) and no correlations, we show using a rigorous argument that upon contraction the degree distributions of these networks converge towards a Poisson distribution. To this end, we use the relative entropy S_{t}=S[P_{t}(k)||π(k|〈K〉_{t})] of the degree distribution P_{t}(k) of the contracting network at time t with respect to the corresponding Poisson distribution π(k|〈K〉_{t}) with the same mean degree 〈K〉_{t} as a distance measure between P_{t}(k) and Poisson. The relative entropy is suitable as a distance measure since it satisfies S_{t}≥0 for any degree distribution P_{t}(k), while equality is obtained only for P_{t}(k)=π(k|〈K〉_{t}). We derive an equation for the time derivative dS_{t}/dt during network contraction and show that the relative entropy decreases monotonically to zero during the contraction process. We thus conclude that the degree distributions of contracting configuration model networks converge towards a Poisson distribution. Since the contracting networks remain uncorrelated, this means that their structures converge towards an Erdos-Rényi (ER) graph structure, substantiating earlier results obtained using direct integration of the master equation and computer simulations [Tishby et al., Phys. Rev. E 100, 032314 (2019)2470-004510.1103/PhysRevE.100.032314]. We demonstrate the convergence for configuration model networks with degenerate degree distributions (random regular graphs), exponential degree distributions, and power-law degree distributions (scale-free networks).

6.
Opt Express ; 27(24): 34530-34541, 2019 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-31878641

RESUMO

Superoscillating function is a band-limited function that is locally oscillating faster than its highest Fourier component. In this work, we study and implement methods to generate multi-lobe optical superoscillating beams, with nearly constant intensity and constant local frequency. We generated superoscillating patterns having up to 12 sub-wavelength oscillations, with local frequency of 20% to 40% above the band-limit. We then test the potential application of these beams to super-resolution structured illumination microscopy. By utilizing the Moiré effect on a fluorescent grating, we have demonstrated experimentally resolution improvement over the conventional sinusoidal illumination. Our simulations show that structured illumination microscopy with super oscillating multi-lobe beams can provide more than twofold improvement in resolution, with respect to the classical diffraction limit and for coherent or incoherent modalities.

7.
Phys Rev E ; 100(3-1): 032314, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31640068

RESUMO

In a highly influential paper twenty years ago, Barabási and Albert [Science 286, 509 (1999)SCIEAS0036-807510.1126/science.286.5439.509] showed that networks undergoing generic growth processes with preferential attachment evolve towards scale-free structures. In any finite system, the growth eventually stalls and is likely to be followed by a phase of network contraction due to node failures, attacks, or epidemics. Using the master equation formulation and computer simulations, we analyze the structural evolution of networks subjected to contraction processes via random, preferential, and propagating node deletions. We show that the contracting networks converge towards an Erdos-Rényi network structure whose mean degree continues to decrease as the contraction proceeds. This is manifested by the convergence of the degree distribution towards a Poisson distribution and the loss of degree-degree correlations.

8.
Phys Rev E ; 99(4-1): 042308, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31108666

RESUMO

We present a method for the construction of ensembles of random networks that consist of a single connected component with a given degree distribution. This approach extends the construction toolbox of random networks beyond the configuration model framework, in which one controls the degree distribution but not the number of components and their sizes. Unlike configuration model networks, which are completely uncorrelated, the resulting single-component networks exhibit degree-degree correlations. Moreover, they are found to be disassortative, namely, high-degree nodes tend to connect to low-degree nodes and vice versa. We demonstrate the method for single-component networks with ternary, exponential, and power-law degree distributions.

9.
Phys Rev E ; 99(1-1): 012146, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30780241

RESUMO

The Percus-Yevick theory for monodisperse hard spheres gives very good results for the pressure and structure factor of the system in a whole range of densities that lie within the liquid phase. However, the equation seems to lead to a very unacceptable result beyond that region. Namely, the Percus-Yevick theory predicts a smooth behavior of the pressure that diverges only when the volume fraction η approaches unity. Thus, within the theory there seems to be no indication for the termination of the liquid phase and the transition to a solid or to a glass. In the present article we study the Percus-Yevick hard-sphere pair distribution function, g_{2}(r), for various spatial dimensions. We find that beyond a certain critical volume fraction η_{c}, the pair distribution function, g_{2}(r), which should be positive definite, becomes negative at some distances. We also present an intriguing observation that the critical η_{c} values we find are consistent with volume fractions where onsets of random close packing (or maximally random jammed states) are reported in the literature for various dimensions. That observation is supported by an intuitive argument. This work may have important implications for other systems for which a Percus-Yevick theory exists.

10.
Phys Rev E ; 98(2-1): 022502, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-30253468

RESUMO

We study the shape and shape fluctuations of incompatible, positively curved ribbons, with a flat reference metric and a spherelike reference curvature. Such incompatible geometry is likely to occur in many self-assembled materials and other experimental systems. Ribbons of this geometry exhibit a sharp transition between a rigid ring and an anomalously soft spring as a function of their width. As a result, the temperature dependence of these ribbons' shape is unique, exhibiting a nonmonotonic dependence of the persistence and Kuhn lengths on the temperature and width. We map the possible configuration phase space and show the existence of three phases: At high temperatures it is the ideal chain phase, where the ribbon is well described by classical models (e.g., wormlike chain model). The second phase, for cold and narrow ribbons, is the plane ergodic phase; a ribbon in this phase might be thought of as made out of segments that gyrate within an oblate spheroid with extreme aspect ratio. The third phase, for cold, wide ribbons, is a direct result of the residual stress caused by the incompatibility, called the random structured phase. A ribbon in this phase behaves on large scales as an ideal chain. However, the segments of this chain are not straight; rather they may have different shapes, mainly helices (both left and right handed) of various pitches.

11.
Phys Rev E ; 98(1-1): 012301, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30110750

RESUMO

Networks that are fragmented into small disconnected components are prevalent in a large variety of systems. These include the secure communication networks of commercial enterprises, government agencies, and illicit organizations, as well as networks that suffered multiple failures, attacks, or epidemics. The structural and statistical properties of such networks resemble those of subcritical random networks, which consist of finite components, whose sizes are nonextensive. Surprisingly, such networks do not exhibit the small-world property that is typical in supercritical random networks, where the mean distance between pairs of nodes scales logarithmically with the network size. Unlike supercritical networks whose structure has been studied extensively, subcritical networks have attracted relatively little attention. A special feature of these networks is that the statistical and geometric properties vary between different components and depend on their sizes and topologies. The overall statistics of the network can be obtained by a summation over all the components with suitable weights. We use a topological expansion to perform a systematic analysis of the degree distribution and the distribution of shortest path lengths (DSPL) on components of given sizes and topologies in subcritical Erdos-Rényi (ER) networks. From this expansion we obtain an exact analytical expression for the DSPL of the entire subcritical network, in the asymptotic limit. The DSPL, which accounts for all the pairs of nodes that reside on the same finite component (FC), is found to follow a geometric distribution of the form P_{FC}(L=ℓ|L<∞)=(1-c)c^{ℓ-1}, where c<1 is the mean degree. Using computer simulations we calculate the DSPL in subcritical ER networks of increasing sizes and confirm the convergence to this asymptotic result. We also obtain exact asymptotic results for the mean distance, 〈L〉_{FC}, and for the standard deviation of the DSPL, σ_{L,FC}, and show that the simulation results converge to these asymptotic results. Using the duality relations between subcritical and supercritical ER networks, we obtain the DSPL on the nongiant components of ER networks above the percolation transition.

12.
Phys Rev E ; 97(4-1): 042318, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29758739

RESUMO

The microstructure of the giant component of the Erdos-Rényi network and other configuration model networks is analyzed using generating function methods. While configuration model networks are uncorrelated, the giant component exhibits a degree distribution which is different from the overall degree distribution of the network and includes degree-degree correlations of all orders. We present exact analytical results for the degree distributions as well as higher-order degree-degree correlations on the giant components of configuration model networks. We show that the degree-degree correlations are essential for the integrity of the giant component, in the sense that the degree distribution alone cannot guarantee that it will consist of a single connected component. To demonstrate the importance and broad applicability of these results, we apply them to the study of the distribution of shortest path lengths on the giant component, percolation on the giant component, and spectra of sparse matrices defined on the giant component. We show that by using the degree distribution on the giant component one obtains high quality results for these properties, which can be further improved by taking the degree-degree correlations into account. This suggests that many existing methods, currently used for the analysis of the whole network, can be adapted in a straightforward fashion to yield results conditioned on the giant component.

13.
Phys Rev E ; 96(3-1): 032301, 2017 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29347025

RESUMO

We present analytical results for the distribution of shortest path lengths (DSPL) in a network growth model which evolves by node duplication (ND). The model captures essential properties of the structure and growth dynamics of social networks, acquaintance networks, and scientific citation networks, where duplication mechanisms play a major role. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network, forming a link to the mother node, and with probability p to each one of its neighbors. The degree distribution of the resulting network turns out to follow a power-law distribution, thus the ND network is a scale-free network. To calculate the DSPL we derive a master equation for the time evolution of the probability P_{t}(L=ℓ), ℓ=1,2,⋯, where L is the distance between a pair of nodes and t is the time. Finding an exact analytical solution of the master equation, we obtain a closed form expression for P_{t}(L=ℓ). The mean distance 〈L〉_{t} and the diameter Δ_{t} are found to scale like lnt, namely, the ND network is a small-world network. The variance of the DSPL is also found to scale like lnt. Interestingly, the mean distance and the diameter exhibit properties of a small-world network, rather than the ultrasmall-world network behavior observed in other scale-free networks, in which 〈L〉_{t}∼lnlnt.

14.
Phys Rev E ; 96(6-1): 062307, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29347364

RESUMO

We present analytical results for the distribution of shortest cycle lengths (DSCL) in random networks. The approach is based on the relation between the DSCL and the distribution of shortest path lengths (DSPL). We apply this approach to configuration model networks, for which analytical results for the DSPL were obtained before. We first calculate the fraction of nodes in the network which reside on at least one cycle. Conditioning on being on a cycle, we provide the DSCL over ensembles of configuration model networks with degree distributions which follow a Poisson distribution (Erdos-Rényi network), degenerate distribution (random regular graph), and a power-law distribution (scale-free network). The mean and variance of the DSCL are calculated. The analytical results are found to be in very good agreement with the results of computer simulations.

15.
Phys Rev E ; 93(6): 062309, 2016 06.
Artigo em Inglês | MEDLINE | ID: mdl-27415282

RESUMO

We present analytical results for the distribution of shortest path lengths between random pairs of nodes in configuration model networks. The results, which are based on recursion equations, are shown to be in good agreement with numerical simulations for networks with degenerate, binomial, and power-law degree distributions. The mean, mode, and variance of the distribution of shortest path lengths are also evaluated. These results provide expressions for central measures and dispersion measures of the distribution of shortest path lengths in terms of moments of the degree distribution, illuminating the connection between the two distributions.

16.
Phys Rev Lett ; 116(7): 070601, 2016 Feb 19.
Artigo em Inglês | MEDLINE | ID: mdl-26943523

RESUMO

Using the weak-noise theory, we evaluate the probability distribution P(H,t) of large deviations of height H of the evolving surface height h(x,t) in the Kardar-Parisi-Zhang equation in one dimension when starting from a flat interface. We also determine the optimal history of the interface, conditioned on reaching the height H at time t. We argue that the tails of P behave, at arbitrary time t>0, and in a proper moving frame, as -lnP∼|H|^{5/2} and ∼|H|^{3/2}. The 3/2 tail coincides with the asymptotic of the Gaussian orthogonal ensemble Tracy-Widom distribution, previously observed at long times.

17.
Artigo em Inglês | MEDLINE | ID: mdl-26465481

RESUMO

The rupture of dry frictional interfaces occurs through the propagation of fronts breaking the contacts at the interface. Recent experiments have shown that the velocities of these rupture fronts range from quasistatic velocities proportional to the external loading rate to velocities larger than the shear wave speed. The way system parameters influence front speed is still poorly understood. Here we study steady-state rupture propagation in a one-dimensional (1D) spring-block model of an extended frictional interface for various friction laws. With the classical Amontons-Coulomb friction law, we derive a closed-form expression for the steady-state rupture velocity as a function of the interfacial shear stress just prior to rupture. We then consider an additional shear stiffness of the interface and show that the softer the interface, the slower the rupture fronts. We provide an approximate closed form expression for this effect. We finally show that adding a bulk viscosity on the relative motion of blocks accelerates steady-state rupture fronts and we give an approximate expression for this effect. We demonstrate that the 1D results are qualitatively valid in 2D. Our results provide insights into the qualitative role of various key parameters of a frictional interface on its rupture dynamics. They will be useful to better understand the many systems in which spring-block models have proved adequate, from friction to granular matter and earthquake dynamics.

18.
Phys Rev E Stat Nonlin Soft Matter Phys ; 90(5-1): 050103, 2014 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-25493721

RESUMO

We study the statistics of the condition number κ=λ_{max}/λ_{min} (the ratio between largest and smallest squared singular values) of N×M Gaussian random matrices. Using a Coulomb fluid technique, we derive analytically and for large N the cumulative P(κx) distributions of κ. We find that these distributions decay as P(κx)≈exp[-ßNΦ_{+}(x)], where ß is the Dyson index of the ensemble. The left and right rate functions Φ_{±}(x) are independent of ß and calculated exactly for any choice of the rectangularity parameter α=M/N-1>0. Interestingly, they show a weak nonanalytic behavior at their minimum 〈κ〉 (corresponding to the average condition number), a direct consequence of a phase transition in the associated Coulomb fluid problem. Matching the behavior of the rate functions around 〈κ〉, we determine exactly the scale of typical fluctuations ∼O(N^{-2/3}) and the tails of the limiting distribution of κ. The analytical results are in excellent agreement with numerical simulations.

19.
Phys Rev Lett ; 110(1): 014302, 2013 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-23383795

RESUMO

The dynamics and stability of brittle cracks are not yet fully understood. Here we use the Willis-Movchan 3D linear perturbation formalism [J. Mech. Phys. Solids 45, 591 (1997)] to study the out-of-plane stability of planar crack fronts in the framework of linear elastic fracture mechanics. We discuss a minimal scenario in which linearly unstable crack front corrugations might emerge above a critical front propagation speed. We calculate this speed as a function of Poisson's ratio and show that corrugations propagate along the crack front at nearly the Rayleigh wave speed. Finally, we hypothesize about a possible relation between such corrugations and the long-standing problem of crack branching.

20.
Phys Rev Lett ; 107(12): 125701, 2011 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-22026776

RESUMO

In this Letter, we derive exponent inequalities relating the dynamic exponent z to the steady state exponent Γ for a general class of stochastically driven dynamical systems. We begin by deriving a general exact inequality, relating the response function and the correlation function, from which the various exponent inequalities emanate. We then distinguish between two classes of dynamical systems and obtain different and complementary inequalities relating z and Γ. The consequences of those inequalities for a wide set of dynamical problems, including critical dynamics and Kardar-Parisi-Zhang-like problems, are discussed.

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