Abstract:
We present event distributions for the polymer translocation obtained by extensive Langevin dynamics simulations. Such distributions have not been reported previously and they provide new understanding of the stochastic characteristics of the process. We extract at a high length scale resolution distributions of polymer segments that continuously traverse through a nanoscale pore. The obtained log-normal distributions together with the characteristics of polymer translocation suggest that it is describable as a multiplicative stochastic process. In spite of its clear out-of-equilibrium nature the forced translocation is surprisingly similar to the unforced case. We find forms for the distributions almost unaltered with a common cut-off length. We show that the individual short-segment and short-time movements inside the pore give the scaling relations $\tau \sim N^\alpha$ and $\tau \sim f^{-\beta}$ for the polymer translocation.

Abstract:
Collective human behaviors are analyzed using the time series of word appearances in blogs. As expected, we confirm that the number of fluctuations is approximated by a Poisson distribution for very-low-frequency words. A non-trivial scaling roperty is confirmed for more-frequent words. We propose a simple model that shows that the fluctuations in the number of contributors is playing the central role in this non-Poissonian behavior.

Abstract:
We show that a microscopic definition of crystal defect, based on the effective mean single-particle potential energy, makes it possible to detect and visualize various types of local and extended crystal defects and develop an effective algorithm for tracking their time evolution.

Abstract:
Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10% of the edges.

Abstract:
We study the time dependent cross correlations of stock returns, i.e. we measure the correlation as the function of the time shift between pairs of stock return time series using tick-by-tick data. We find a weak but significant effect showing that in many cases the maximum correlation is at nonzero time shift indicating directions of influence between the companies. Due to the weakness of the effect and the shortness of the characteristic time (in the order of a few minutes) the effect is compatible with market efficiency. The interaction of companies defines a directed network of influence.

Abstract:
We present results from our simulations of biopolymer translocation in a solvent which explain the main experimental findings. The forced translocation can be described by simple force balance arguments for the relevant range of pore potentials in experiments and biological systems. Scaling of translocation time with polymer length varies with pore force and friction. Hydrodynamics affects this scaling and significantly reduces translocation times.

Abstract:
Dynamics of spreading of viscous non - volatile fluid droplets on surfaces is modelled using a solid - on - solid model, which is studied with Monte Carlo simulations. Tendency for dynamical layering and surface attraction are in part embedded into the effective dynamics of the model. This allows a description of the spreading process with a single parameter, which strongly influences the morphology of the droplets. The results qualitatively reproduce many experimentally observed density profiles for polymeric fluids, including rounded droplet shapes, and dynamical layering. PACS numbers: 68.10Gw, 05.70.Ln, 61.20.Ja.

Abstract:
We have studied the dynamics of spreading of viscous non-volatile fluids on surfaces by MC simulations of SOS models. We have concentrated on the complete wetting regime, with surface diffusion barriers neglected for simplicity. First, we have performed simulations for the standard SOS model. Formation of a single precursor layer, and a density profile with a spherical cap shaped center surrounded by Gaussian tails can be reproduced with this model. Dynamical layering (DL), however, only occurs with a very strongly attractive van der Waals type of substrate potential. To more realistically describe the spreading of viscous liquid droplets, we introduce a modified SOS model. In the new model, tendency for DL and the effect of the surface potential are in part embedded into the dynamics of the model. This allows a relatively simple description of the spreading under different conditions, with a temperature like parameter which strongly influences the droplet morphologies. Both rounded droplet shapes and DL can easily be reproduced with the model. Furthermore, the precursor width increases proportional to the square root of time, in accordance with experimental observations. PACS: 68.10.Gw, 05.70.Ln, 61.20.Ja.

Abstract:
The structure of the Cu(110) surface is studied at high temperatures using a combination of lattice-gas Monte Carlo and molecular dynamics methods with identical many-atom interactions derived from the effective medium theory. The anisotropic six-vertex model is used in the interpretation of the lattice-gas results. We find a clear roughening transition around T_R=1000K and T_R/T_M=0.81. Molecular dynamics reveals the clustering of surface defects as the atomistic mechanism of the transition and allows us to estimate characteristic time scales. For the system of size 50x50, the time scale of the local roughening at 1150 K of an initially smooth surface is of the order of 100 ps.

Abstract:
We present a numerical study of forced polymer translocation by using two separate pore models. Both of them have been extensively used in previous forced translocation studies. We show that variations in the pore model affect the forced translocation characteristics significantly in the biologically relevant pore force, i.e. driving force, range. Details of the model are shown to change even the obtained scaling relations, which is a strong indication of strongly out-of-equilibrium dynamics in the computational studies which have not yet succeeded in addressing the characteristics of the forced translocation for biopolymers at realistic length scale.