Hie-Tae Moon

Coherence resonance in small-world networks of excitable cells

Abstract We investigate the effect of small-world networks on the phenomenon of coherence resonance of Hodgkin–Huxley neurons. It is found that increasing the randomness p of the network topology leads to an enhancement of temporal coherence and of spatial synchronization of the phenomenon. Especially, it is found that (1) spatial synchronization increases as characteristic path length shortens and (2) firing frequency increases as clustering coefficient decreases.

Group dynamics of the Japanese market

We investigated the network structures of the Japanese stock market using the minimum spanning tree. We defined a grouping coefficient to test the validity of the conventional grouping by industrial categories, and found a decreasing in trend for the coefficient. This phenomenon supports the increasing external influences on the market due to globalization. To reduce this influence, we used S&P500 index as the international market and removed its correlation with every stock. We found stronger a grouping in this measurement when compared to the original analysis, which agrees with our assumption that the international market influences to the Japanese market.

Effect of spatially correlated noise on coherence resonance in a network of excitable cells.

We study the effect of spatially correlated noise on coherence resonance (CR) in a Watts-Strogatz small-world network of Fitz Hugh-Nagumo neurons, where the noise correlation decays exponentially with distance between neurons. It is found that CR is considerably improved just by a small fraction of long-range connections for an intermediate coupling strength. For other coupling strengths, an abrupt change in CR occurs following the drastic fracture of the clustered structures in the network. Our study shows that spatially correlated noise plays a significant role in the phenomenon of CR reinforcing the role of the clustered structure of the system.

Optical Modeling and Analysis of Organic Solar Cells with Coherent Multilayers and Incoherent Glass Substrate Using Generalized Transfer Matrix Method

We present optical modeling and physical analysis results of thin-film organic solar cells (OSCs) based on a generalized transfer matrix method, which can calculate, with a simple matrix form, the mixed coherent and incoherent interaction of an incoherent glass substrate with other coherent layers. The spatial distribution of the electric field intensity, power density, and power dissipation are calculated in both coherent and incoherent layers with respect to the optical spacer thickness. By decomposing the power density and power dissipation into forward-propagating, backward-propagating, and their interference components, we demonstrate that the dependence of the spacer thickness on the total device reflectance plays an important role in determining the light absorption efficiency of the OSC.

Immunization dynamics on a two-layer network model

We introduced a two-layer network model for the study of the immunization dynamics in epidemics. Spreading of an epidemic is modeled as an excitatory process in a Watts–Strogatz small-world network (infection layer) while immunization by prevention of the disease as a dynamic process in a Barabasi–Albert scale-free network (prevention layer). It is shown that prevention indeed turns periodic rages of an epidemic into small fluctuations, and in a certain situation, actually plays an adverse role and helps the disease survive. We argue that the presence of two different characteristic time scales contributes to the immunization dynamics observed.

Volatility return intervals analysis of the Japanese market

Abstract.We investigate scaling and memory effects in return intervals between price volatilities above a certain threshold q for the Japanese stock market using daily and intraday data sets. We find that the distribution of return intervals can be approximated by a scaling function that depends only on the ratio between the return interval τ and its mean 〈τ〉. We also find memory effects such that a large (or small) return interval follows a large (or small) interval by investigating the conditional distribution and mean return interval. The results are similar to previous studies of other markets and indicate that similar statistical features appear in different financial markets. We also compare our results between the period before and after the big crash at the end of 1989. We find that scaling and memory effects of the return intervals show similar features although the statistical properties of the returns are different.

Complexity analysis of the stock market

We study the complexity of the stock market by constructing e-machines of Standard and Poors 500 index from February 1983 to April 2006 and by measuring the statistical complexities. It is found that both the statistical complexity and the number of causal states of constructed e-machines have decreased for last 20 years and that the average memory length needed to predict the future optimally has become shorter. These results support that the information is delivered to the economic agents and applied to the market prices more rapidly in year 2006 than in year 1983.

Topological determinants of protein unfolding rates

For proteins that fold by two‐state kinetics, the folding and unfolding processes are believed to be closely related to their native structures. In particular, folding and unfolding rates are influenced by the native structures of proteins. Thus, we focus on finding important topological quantities from a protein structure that determine its unfolding rate. After constructing graphs from protein native structures, we investigate the relationships between unfolding rates and various topological quantities of the graphs. First, we find that the correlation between the unfolding rate and the contact order is not as prominent as in the case of the folding rate and the contact order. Next, we investigate the correlation between the unfolding rate and the clustering coefficient of the graph of a protein native structure, and observe no correlation between them. Finally, we find that a newly introduced quantity, the impact of edge removal per residue, has a good overall correlation with protein unfolding rates. The impact of edge removal is defined as the ratio of the change of the average path length to the edge removal probability. From these facts, we conclude that the protein unfolding process is closely related to the protein native structure. Proteins 2005.

Pattern formation in a two-dimensional array of oscillators with phase-shifted coupling

We investigate the dynamics of a two-dimensional array of oscillators with phase-shifted coupling. Each oscillator is allowed to interact with its neighbors within a finite radius. The system exhibits various patterns including squarelike pinwheels, (anti)spirals with phase-randomized cores, and antiferro patterns embedded in (anti)spirals. We consider the symmetry properties of the system to explain the observed behaviors, and estimate the wavelengths of the patterns by linear analysis. Finally, we point out the implications of our work for biological neural networks.

Soliton-kink interactions in a generalized nonlinear Schrödinger system

Abstract The interactions between the solitons and kinks in a nonintegrable system are studied within the context of the generic cubic-quintic nonlinear Schrodinger equation, in which the bright soliton, dark soliton, kink and anti-kink solutions have been known. The structures have interesting relations with each other: they can coexist, and the kinks (and anti-kinks) play a role of domain walls dividing the space into self-focusing and self-defocusing regions. Numerical analysis shows that the bright (dark) solitons can be transformed into the dark (bright) ones upon collisions with the kinks, directly showing the reciprocity between the two classes of solitons.