arXiv subjects

Kevin E. Bassler

Publications and source records attributed to Kevin E. Bassler.

At least 37 records · Page 2Linked to original sources

arXiv2016

Effects of single- and multi-substituted Zn ions in doped-122 type iron-based superconductors

Recent experiments on Zn-substituted 122-type iron-based superconductors (FeSCs) at electron- and hole- doped region provide us with a testing ground for understanding the effect of Zn impurities in these systems. Our first-principle calculations of the electronic structure reveal that the Zn 3d orbitals are far below the Fermi level and chemically inactive, while the Zn 4s-orbital is partially occupied and its wave function overlapping with those 3d-orbitals of neighboring Fe-ions. This suggests that the impurity effect is originating in the Zn 4s-orbital, not its 3d-orbitals. Employing a phenomenological two-orbital lattice model for 122-FeSCs and the self-consistent Bogoliubov-de Gennes equations, we study how the Zn-impurities suppress the superconductivity in electron- and hole- doped compounds. Our obtained results qualitatively agree with the experimental measurements.

cond-mat.supr-con↗

arXiv2015

Quantifying randomness in real networks

Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the $dk$-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks---the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain---and find that many important local and global structural properties of these networks are closely reproduced by $dk$-random graphs whose degree distributions, degree correlations, and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate $dk$-random graphs.

physics.soc-ph↗

arXiv2015

Exact sampling of graphs with prescribed degree correlations

Many real-world networks exhibit correlations between the node degrees. For instance, in social networks nodes tend to connect to nodes of similar degree. Conversely, in biological and technological networks, high-degree nodes tend to be linked with low-degree nodes. Degree correlations also affect the dynamics of processes supported by a network structure, such as the spread of opinions or epidemics. The proper modelling of these systems, i.e., without uncontrolled biases, requires the sampling of networks with a specified set of constraints. We present a solution to the sampling problem when the constraints imposed are the degree correlations. In particular, we develop an efficient and exact method to construct and sample graphs with a specified joint-degree matrix, which is a matrix providing the number of edges between all the sets of nodes of a given degree, for all degrees, thus completely specifying all pairwise degree correlations, and additionally, the degree sequence itself. Our algorithm always produces independent samples without backtracking. The complexity of the graph construction algorithm is O(NM) where N is the number of nodes and M is the number of edges.

cs.DM↗

arXiv2015

Networks with preferred degree: A mini-review and some new results

Since their inception about a decade ago, dynamic networks which adapt to the state of the nodes have attracted much attention. One simple case of such an adaptive dynamics is a model of social networks in which individuals are typically comfortable with a certain number of contacts, i.e., preferred degrees. This paper is partly a review of earlier work of single homogeneous systems and ones with two interacting networks, and partly a presentation of some new results. In general, the dynamics does not obey detailed balance and the stationary distributions are not known analytically. A particular limit of the latter is a system of extreme introverts and extroverts - the XIE model. Remarkably, in this case, the detailed balance condition is satisfied, the exact distribution and an effective Hamiltonian can be found explicitly. Further, the model exhibits a phase transition in which the total number of links in the system - a macroscopically interesting quantity, displays an extreme Thouless effect. We show that in the limit of large populations and away from the transition, the model reduces to one with non-interacting agents of the majority subgroup. We determine the nature of fluctuations near the transition. We also introduce variants of the model where the agents show preferential attachment or detachment. There are significant changes to the degree distributions in the steady state, some of which can be understood by theoretical arguments and some remain to be explored. Many intriguing questions are posed, providing some food for thought and avenues for future research.

cond-mat.stat-mech↗

arXiv2015

A unified description of superconducting pairing symmetry in electron-doped Fe-based-122 compounds

The pairing symmetry is examined in highly electron-doped Ba(Fe$_{1-x}$Co$_x$As)$_2$ and A$_y$Fe$_2$Se$_2$ (with A=K, Cs) compounds, with similar crystallographic and electronic band structures. Starting from a phenomenological two-orbital model, we consider nearest-neighbor and next-nearest-neighbor intraorbital pairing interactions on the Fe square lattice. In this model, we find a unified description of the evolution from $s_\pm$-wave pairing ($2.0 < n \lesssim 2.4$) to $d$-wave pairing ($2.4 \lesssim n \lesssim 2.5$) as a function of electron filling. In the crossover region a novel time-reversal symmetry breaking state with $s_\pm+id$ pairing symmetry emerges. This minimal model offers an overall picture of the evolution of superconductivity with electron doping for both $s_\pm$-wave [Ba(Fe$_{1-x}$Co$_x$As)$_2$] and $d$-wave [A$_y$Fe$_2$Se$_2$] pairing, as long as the dopants only play the role of a charge reservoir. However, the situation is more complicated for Ba(Fe$_{1-x}$Co$_x$As)$_2$. A real-space study further shows that when the impurity scattering effects of Co dopants are taken into account, the superconductivity is completely suppressed for $n > 2.4$. This preempts any observation of $d$-wave pairing in this compound, in contrast to A$_y$Fe$_2$Se$_2$.

cond-mat.supr-con↗

arXiv2014

Fast and accurate determination of modularity and its effect size

We present a fast spectral algorithm for community detection in complex networks. Our method searches for the partition with the maximum value of the modularity via the interplay of several refinement steps that include both agglomeration and division. We validate the accuracy of the algorithm by applying it to several real-world benchmark networks. On all these, our algorithm performs as well or better than any other known polynomial scheme. This allows us to extensively study the modularity distribution in ensembles of Erdős-Rényi networks, producing theoretical predictions for means and variances inclusive of finite-size corrections. Our work provides a way to accurately estimate the effect size of modularity, providing a $z$-score measure of it and enabling a more informative comparison of networks with different numbers of nodes and links.

physics.soc-ph↗

arXiv2014

Mesoscopic structures and the Laplacian spectra of random geometric graphs

We investigate the Laplacian spectra of random geometric graphs (RGGs). The spectra are found to consist of both a discrete and a continuous part. The discrete part is a collection of Dirac delta peaks at integer values roughly centered around the mean degree. The peaks are mainly due to the existence of mesoscopic structures that occur far more abundantly in RGGs than in non-spatial networks. The probability of certain mesoscopic structures is analytically calculated for one-dimensional RGGs and they are shown to produce integer-valued eigenvalues that comprise a significant fraction of the spectrum, even in the large network limit. A phenomenon reminiscent of Bose-Einstein condensation in the appearance of zero eigenvalues is also found.

cond-mat.stat-mech↗

arXiv2014

Modeling interacting dynamic networks: III. Extraordinary properties in a population of extreme introverts and extroverts

Recently, we introduced dynamic networks with preferred degrees, showing that interesting properties are present in a single, homogeneous system as well as one with two interacting networks. While simulations are readily performed, analytic studies are challenging, due mainly to the lack of detailed balance in the dynamics. Here, we consider the two-community case in a special limit: a system of extreme introverts and extroverts - the XIE model. Surprising phenomena appear, even in this minimal model, where the only control parameters are the numbers of each subgroup: $N_{I,E}$. Specifically, an extraordinary transition emerges when $N_I$ crosses $N_E$. For example, the fraction of total number of I-E links jumps from $\thicksim 0$ to $\thicksim 1$. In a $N_I=N_E$ system, this fraction performs a pure random walk so that its distribution displays a flat plateau across most of $[0,1]$, with the edges vanishing as $(N_{I,E})^{-0.38}$ for large systems. Thus, we believe the XIE model exhibits an extreme Thouless effect. For this limiting model, we show that detailed balance is restored and explicitly find the microscopic steady-state distribution. We then use a mean-field approach to find analytic expressions for the degree distributions that are in reasonably good agreement with simulations, provided $N_I$ is not too close to $N_{E}$.

physics.soc-ph↗

arXiv2014

Symmetry in Critical Random Boolean Network Dynamics

Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce a novel approach to the analysis of heterogeneous complex systems.

cond-mat.stat-mech↗

arXiv2012

Magnetic and superconducting structures near twin boundaries in low doped Fe-pnictides

The effects of twin boundaries (TBs) on the complex interaction between magnetism and superconductivity in slightly electron-doped Ba(Ca)(FeAs)$_2$ superconductors are investigated. The spatial distributions of the magnetic, superconducting and charge density orders near two different types of TBs are calculated. We find that TBs corresponding to a 90$^\circ$ lattice rotation in the a-b plane enable magnetic domain walls to form with only a small effective Coulomb interaction between valance electrons, and that superconductivity is enhanced at such TBs. Contrastingly, we find that superconductivity is suppressed at TBs corresponding to an asymmetrical placement of As atoms with respect to the Fe atoms in the a-b plane.

cond-mat.supr-con↗

arXiv2012

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.

q-bio.QM↗

arXiv2011

Collectively optimal routing for congested traffic limited by link capacity

We show that the capacity of a complex network that models a city street grid to support congested traffic can be optimized by using routes that collectively minimize the maximum ratio of betweenness to capacity in any link. Networks with a heterogeneous distribution of link capacities and with a heterogeneous transport load are considered. We find that overall traffic congestion and average travel times can be significantly reduced by a judicious use of slower, smaller capacity links.

physics.soc-ph↗

arXiv2011

All scale-free networks are sparse

We study the realizability of scale free-networks with a given degree sequence, showing that the fraction of realizable sequences undergoes two first-order transitions at the values 0 and 2 of the power-law exponent. We substantiate this finding by analytical reasoning and by a numerical method, proposed here, based on extreme value arguments, which can be applied to any given degree distribution. Our results reveal a fundamental reason why large scale-free networks without constraints on minimum and maximum degree must be sparse.

physics.soc-ph↗

arXiv2011

Canalization in the Critical States of Highly Connected Networks of Competing Boolean Nodes

Canalization is a classic concept in Developmental Biology that is thought to be an important feature of evolving systems. In a Boolean network it is a form of network robustness in which a subset of the input signals control the behavior of a node regardless of the remaining input. It has been shown that Boolean networks can become canalized if they evolve through a frustrated competition between nodes. This was demonstrated for large networks in which each node had K=3 inputs. Those networks evolve to a critical steady-state at the boarder of two phases of dynamical behavior. Moreover, the evolution of these networks was shown to be associated with the symmetry of the evolutionary dynamics. We extend these results to the more highly connected K>3 cases and show that similar canalized critical steady states emerge with the same associated dynamical symmetry, but only if the evolutionary dynamics is biased toward homogeneous Boolean functions.

q-bio.MN↗

arXiv2010

Phase Diagram for a 2-D Two-Temperature Diffusive XY Model

Using Monte Carlo simulations, we determine the phase diagram of a diffusive two-temperature XY model. When the two temperatures are equal the system becomes the equilibrium XY model with the continuous Kosterlitz-Thouless (KT) vortex-antivortex unbinding phase transition. When the two temperatures are unequal the system is driven by an energy flow through the system from the higher temperature heat-bath to the lower temperature one and reaches a far-from-equilibrium steady state. We show that the nonequilibrium phase diagram contains three phases: A homogenous disordered phase and two phases with long range, spin-wave order. Two critical lines, representing continuous phase transitions from a homogenous disordered phase to two phases of long range order, meet at the equilibrium the KT point. The shape of the nonequilibrium critical lines as they approach the KT point is described by a crossover exponent of phi = 2.52 \pm 0.05. Finally, we suggest that the transition between the two phases with long-range order is first-order, making the KT-point where all three phases meet a bicritical point.

cond-mat.stat-mech↗

arXiv2010

Anomalous ordering in inhomogeneously strained materials

We study a continuous quasi-two-dimensional order-disorder phase transition that occurs in a simple model of a material that is inhomogeneously strained due to the presence of dislocation lines. Performing Monte Carlo simulations of different system sizes and using finite size scaling, we measure critical exponents describing the transition of beta=0.18\pm0.02, gamma=1.0\pm0.1, and alpha=0.10\pm0.02. Comparable exponents have been reported in a variety of physical systems. These systems undergo a range of different types of phase transitions, including structural transitions, exciton percolation, and magnetic ordering. In particular, similar exponents have been found to describe the development of magnetic order at the onset of the pseudogap transition in high-temperature superconductors. Their common universal critical exponents suggest that the essential physics of the transition in all of these physical systems is the same as in our model. We argue that the nature of the transition in our model is related to surface transitions, although our model has no free surface.

cond-mat.stat-mech↗

arXiv2010

Depth-dependent ordering, two-length-scale phenomena and crossover behavior in a crystal featuring a skin-layer with defects

Structural defects in a crystal are responsible for the "two length-scale" behavior, in which a sharp central peak is superimposed over a broad peak in critical diffuse X-ray scattering. We have previously measured the scaling behavior of the central peak by scattering from a near-surface region of a V2H crystal, which has a first-order transition in the bulk. As the temperature is lowered toward the critical temperature, a crossover in critical behavior is seen, with the temperature range nearest to the critical point being characterized by mean field exponents. Near the transition, a small two-phase coexistence region is observed. The values of transition and crossover temperatures decay with depth. An explanation of these experimental results is here proposed by means of a theory in which edge dislocations in the near-surface region occur in walls oriented in the two directions normal to the surface. The strain caused by the dislocation lines causes the ordering in the crystal to occur as growth of roughly cylindrically shaped regions. After the regions have reached a certain size, the crossover in the critical behavior occurs, and mean field behavior prevails. At a still lower temperature, the rest of the material between the cylindrical regions orders via a weak first-order transition.

cond-mat.mtrl-sci↗

arXiv2010

Efficient and exact sampling of simple graphs with given arbitrary degree sequence

Uniform sampling from graphical realizations of a given degree sequence is a fundamental component in simulation-based measurements of network observables, with applications ranging from epidemics, through social networks to Internet modeling. Existing graph sampling methods are either link-swap based (Markov-Chain Monte Carlo algorithms) or stub-matching based (the Configuration Model). Both types are ill-controlled, with typically unknown mixing times for link-swap methods and uncontrolled rejections for the Configuration Model. Here we propose an efficient, polynomial time algorithm that generates statistically independent graph samples with a given, arbitrary, degree sequence. The algorithm provides a weight associated with each sample, allowing the observable to be measured either uniformly over the graph ensemble, or, alternatively, with a desired distribution. Unlike other algorithms, this method always produces a sample, without back-tracking or rejections. Using a central limit theorem-based reasoning, we argue, that for large N, and for degree sequences admitting many realizations, the sample weights are expected to have a lognormal distribution. As examples, we apply our algorithm to generate networks with degree sequences drawn from power-law distributions and from binomial distributions.

physics.soc-ph↗