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We consider a system of non-interacting particles on a line with initial positions distributed uniformly with density $\rho$ on the negative half-line. We consider two different models: (i) each particle performs independent Brownian motion with stochastic resetting to its initial position with rate $r$ and (ii) each particle performs run and tumble motion, and with rate $r$ its position gets reset to its initial value and simultaneously its velocity gets randomised. We study the effects of resetting on the distribution $P(Q,t)$ of the integrated particle current $Q$ up to time $t$ through the origin (from left to right). We study both the annealed and the quenched current distributions and in both cases, we find that resetting induces a stationary limiting distribution of the current at long times. However, we show that the approach to the stationary state of the current distribution in the annealed and the quenched cases are drastically different for both models. In the annealed case, the whole distribution $P_{\rm an}(Q,t)$ approaches its stationary limit uniformly for all $Q$. In contrast, the quenched distribution $P_{\rm qu}(Q,t)$ attains its stationary form for $Q Q_{\rm crit}(t)$. We show that $Q_{\rm crit}(t)$ increases linearly with $t$ for large $t$. On the scale where $Q \sim Q_{\rm crit}(t)$, we show that $P_{\rm qu}(Q,t)$ has an unusual large deviation form with a rate function that has a third-order phase transition at the critical point. We have computed the associated rate functions analytically for both models. Using an importance sampling method that allows to probe probabilities as tiny as $10^{-14000}$, we were able to compute numerically this non-analytic rate function for the resetting Brownian dynamics and found excellent agreement with our analytical prediction., Comment: 26 pages, 6 figures

The maximum-weight matching problem and the behavior of its energy landscape is numerically investigated. We apply a perturbation method adapted from the analysis of spin glasses. This gives inside into the complexity of the energy landscape of different ensembles. Erd\"os-Renyi graphs and ring graphs with randomly added edges are considered and two types of distributions for the random edge weighs are used. For maximum-weight matching, fast and scalable algorithms exist, such that we can study large graphs of more than $10^5$ nodes. Our results show that the structure of the energy landscape for standard ensembles of matching is simple, comparable to the energy landscape of a ferromagnet. Nonetheless, for some of the here presented ensembles our results allow for the presence of complex energy landscapes in the spirit of Replica-Symmetry Breaking., Comment: 9 pages, 5 figures

Supplemental Material – for Differential Effects of Speech and Language Therapy and rTMS in Chronic Versus Subacute Post-stroke Aphasia: Results of the NORTHSTAR-CA Trial by Anna Zumbansen, Heike Kneifel, Latifa Lazzouni, Anja Ophey, Sandra E. Black, Joyce L. Chen, Dylan Edwards, Thomas Funck, Alexander Erich Hartmann, Wolf-Dieter Heiss, Franziska Hildesheim, Sylvain Lanthier, Paul Lespérance, George Mochizuki, Caroline Paquette, Elizabet Rochon, Ilona Rubi-Fessen, Jennie Valles, Susan Wortman-Jutt and Alexander Thiel; on behalf of the NORTHSTAR-study group in Neurorehabilitation and Neural Repair

We numerically study the dynamics of the SIR disease model on small-world networks by using a large-deviation approach. This allows us to obtain the probability density function of the total fraction of infected nodes and of the maximum fraction of simultaneously infected nodes down to very small probability densities like $10^{-2500}$. We analyze the structure of the disease dynamics and observed three regimes in all probability density functions, which correspond to quick mild, quick extremely severe and sustained severe dynamical evolutions, respectively. Furthermore, the mathematical rate functions of the densities are investigated. The results indicate that the so called large-deviation property hold for the SIR model. Finally, we measured correlations with other quantities like the duration of an outbreak or the peak position of the fraction of infections, also in the rare regions which are not accessible by standard simulation techniques.

Supplemental material, sj-pdf-1-eso-10.1177_2396987320934935 for Non-invasive brain stimulation as add-on therapy for subacute post-stroke aphasia: a randomized trial (NORTHSTAR) by Anna Zumbansen, Sandra E Black, Joyce L Chen, Dylan J Edwards, Alexander Hartmann, Wolf-Dieter Heiss, Sylvain Lanthier, Paul Lesperance, George Mochizuki, Caroline Paquette, Elizabeth A Rochon, Ilona Rubi-Fessen, Jennie Valles, Heike Kneifel, Susan Wortman-Jutt, Alexander Thiel and on behalf of the NORTHSTAR-study group in European Stroke Journal

We study the entropy $S$ of longest increasing subsequences (LIS), i.e., the logarithm of the number of distinct LIS. We consider two ensembles of sequences, namely random permutations of integers and sequences drawn i.i.d.\ from a limited number of distinct integers. Using sophisticated algorithms, we are able to exactly count the number of LIS for each given sequence. Furthermore, we are not only measuring averages and variances for the considered ensembles of sequences, but we sample very large parts of the probability distribution $p(S)$ with very high precision. Especially, we are able to observe the tails of extremely rare events which occur with probabilities smaller than $10^{-600}$. We show that the distribution of the entropy of the LIS is approximately Gaussian with deviations in the far tails, which might vanish in the limit of long sequences. Further we propose a large-deviation rate function which fits best to our observed data., Comment: 10 pages, 8 figures

Supplemental material, sj-pdf-1-eso-10.1177_2396987320934935 for Non-invasive brain stimulation as add-on therapy for subacute post-stroke aphasia: a randomized trial (NORTHSTAR) by Anna Zumbansen, Sandra E Black, Joyce L Chen, Dylan J Edwards, Alexander Hartmann, Wolf-Dieter Heiss, Sylvain Lanthier, Paul Lesperance, George Mochizuki, Caroline Paquette, Elizabeth A Rochon, Ilona Rubi-Fessen, Jennie Valles, Heike Kneifel, Susan Wortman-Jutt, Alexander Thiel and on behalf of the NORTHSTAR-study group in European Stroke Journal

We study numerically the behavior of RNA secondary structures under influence of a varying external force. This allows to measure the work $W$ during the resulting fast unfolding and refolding processes. Here, we investigate a medium-size hairpin structure. Using a sophisticated large-deviation algorithm, we are able to measure work distributions with high precision down to probabilities as small as $10^{-46}$. Due to this precision and by comparison with exact free-energy calculations we are able to verify the theorems of Crooks and Jarzynski. Furthermore, we analyze force-extension curves and the configurations of the secondary structures during unfolding and refolding for typical equilibrium processes and non-equilibrium processes, conditioned to selected values of the measured work $W$, typical and rare ones. We find that the non-equilibrium processes where the work values are close to those which are most relevant for applying Crooks and Jarzynski theorems, respectively, are most and quite similar to the equilibrium processes. Thus, a similarity of equilibrium and non-equilibrium behavior with respect to a mere scalar variable, which occurs with a very small probability but can be generated in a controlled but non-targeted way, is related to a high similarity for the set of configurations sampled along the full dynamical trajectory., Comment: 12 pages, 9 figures

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 properties of such networks resemble those of subcritical random networks, which consist of finite components, whose sizes are non-extensive. 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 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-Renyi (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_{\rm FC}(L=\ell|L, 57 pages, 11 figures, 7 tables

We study the distribution of diameters d of Erd��s-R��nyi random graphs with average connectivity c. The diameter d is the maximum among all shortest distances between pairs of nodes in a graph and an important quantity for all dynamic processes taking place on graphs. Here we study the distribution P(d) numerically for various values of c, in the non-percolating and the percolating regime. Using large-deviations techniques, we are able to reach small probabilities like 10^{-100} which allow us to obtain the distribution over basically the full range of the support, for graphs up to N=1000 nodes. For values c1 the distribution is more complex and no complete analytical results are available. For this parameter range, P(d) exhibits an inflection point, which we found to be related to a structural change of the graphs. For all values of c, we determined the finite-size rate function Phi(d/N) and were able to extrapolate numerically to N->infinity, indicating that the large deviation principle holds., 9 figures

Data clustering is an approach to seek for structure in sets of complex data, i.e., sets of "objects". The main objective is to identify groups of objects which are similar to each other, e.g., for classification. Here, an introduction to clustering is given and three basic approaches are introduced: the k-means algorithm, neighbour-based clustering, and an agglomerative clustering method. For all cases, C source code examples are given, allowing for an easy implementation., Comment: 22 pages. All source code in anc directory included. Section 8.5.6 of book: A.K. Hartmann, Big Practical Guide to Computer Simulations, World-Scientifc, Singapore (2015)

We study the the non-equilibrium ageing behaviour of the +/-J Edwards-Anderson model in three dimensions for samples of size up to N=128^3 and for up to 10^8 Monte Carlo sweeps. In particular we are interested in the change of the ageing when crossing from the spin-glass phase to the ferromagnetic phase. The necessary long simulation times are reached by employing a CUDA-based GPU implementation, which allows for single-spin flip times as small as 8ps. We measure typical spin glass correlation functions in space and time to determine the growing length scale and extract the constituting exponents. We observe a clear signature of the disorder-driven equilibrium transition in the non-equilibrium behavior., Comment: 8 pages, 8 figures

We study the correlated-disorder driven zero-temperature phase transition of the Random-Field Ising Magnet using exact numerical ground-state calculations for cubic lattices. We consider correlations of the quenched disorder decaying proportional to r^a, where r is the distance between two lattice sites and a, 9 pages, 13 figures

The disorder-driven phase transition of the RFIM is observed using exact ground-state computer simulations for hyper cubic lattices in d=5,6,7 dimensions. Finite-size scaling analyses are used to calculate the critical point and the critical exponents of the specific heat, magnetization, susceptibility and of the correlation length. For dimensions d=6,7 which are larger or equal to the assumed upper critical dimension, d_u=6, mean-field behaviour is found, i.e. alpha=0, beta=1/2, gamma=1, nu=1/2. For the analysis of the numerical data, it appears to be necessary to include recently proposed corrections to scaling at and beyond the upper critical dimension., 8 pages and 13 figures; A consise summary of this work can be found in the papercore database at http://www.papercore.org/Ahrens2010

Ground states of three-dimensional EA Ising spin glasses are calculated for sizes up to 14^3 using a combination of a genetic algorithm and cluster-exact approximation. For each realization several independent ground states are obtained. Then, by applying ballistic search and T=0 Monte-Carlo simulations, it is ensured that each ground state appears with the same probability. Consequently, the results represent the true T=0 thermodynamic behavior. The distribution P(|q|) of overlaps is evaluated. For increasing size the width of P(|q|) and the fraction of the distribution below q_0=0.5 converge to zero. This indicates that for the infinite system P(|q|) is a delta function, in contrast to previous results. Thus, the ground-state behavior is dominated by few large clusters of similar ground states., Comment: 7 pages revtex, 6 figures, 27 references

The genetic cluster-exact approximation algorithm is an efficient method to calculate ground states of EA spin glasses. The method can be used to study ground-state landscapes by calculating many independent ground states for each realization of the disorder. The algorithm is analyzed with respect to the statistics of the ground states and the valleys of the energy landscape. Furthermore, the distribution inside each valley is evaluated. It is shown that the algorithm does not lead to a true T=0 thermodynamic distribution, i.e. each ground state has not the same frequency of occurrence when performing many runs. An extension of the technique is outlined, which guarantees that each ground states occurs with the same probability., Comment: 7 pages, 7 figures, 28 references; update of references

Large numbers of ground states of the three-dimensional $\pm J$ random-bond Ising model are calculated for sizes up to $14^3$ using a combination of a genetic algorithm and Cluster-Exact Approximation. Several quantities are calculated as function of the concentration $p$ of the antiferromagnetic bonds. The critical concentration where the ferromagnetic order disappears is determined using the Binder cumulant of the magnetization. A value of $p_c=0.222\pm 0.005$ is obtained. From the finite-size behavior of the Binder cumulant and the magnetization critical exponents $\nu=1.1 \pm 0.3$ and $\beta=0.2 \pm 0.1$ are calculated., Comment: 8 pages, 11 figures, revtex

Ground states of 3d EA Ising spin glasses are calculated for sizes up to $14^3$ using a combination of genetic algorithms and cluster-exact approximation . The distribution $P(|q|)$ of overlaps is calculated. For increasing size the width of $P(|q|)$ converges to a nonzero value, indicating that many pure ground states exist for short range Ising spin glasses., Comment: 4 pages, 3 figures, 2 tables, 16 references