Re-localization due to finite response times in a nonlinear Anderson chain

We study a disordered nonlinear Schr\"odinger equation with an additional relaxation process having a finite response time $\tau$. Without the relaxation term, $\tau=0$, this model has been widely studied in the past and numerical simulations showed subdiffusive spreading of initially localized excitations. However, recently Caetano et al.\ (EPJ. B \textbf{80}, 2011) found that by introducing a response time $\tau > 0$, spreading is suppressed and any initially localized excitation will remain localized. Here, we explain the lack of subdiffusive spreading for $\tau>0$ by numerically analyzing the energy evolution. We find that in the presence of a relaxation process the energy drifts towards the band edge, which enforces the population of fewer and fewer localized modes and hence leads to re-localization. The explanation presented here is based on previous findings by the authors et al.\ (PRE \textbf{80}, 2009) on the energy dependence of thermalized states.Comment: 3 pages, 4 figure

Exciton G Factor Of Type-ii Inp Gaas Single Quantum Dots

We investigated the magneto-optical properties of type-II InP GaAs quantum dots using single-dot spectroscopy. The emission energy from individual dots presents a quadratic diamagnetic shift and a linear Zeeman splitting as a function of magnetic fields up to 10 T, as previously observed for type-I systems. We analyzed the in-plane localization of the carriers using the diamagnetic shift results. The values for the exciton g factor obtained for a large number of a InP GaAs dots are mainly constant, independent of the emission energy, and therefore, of the quantum dot dimensions. The result is attributed to the weak confinement of the holes in type-II InP GaAs quantum dots. © 2006 The American Physical Society.733Toda, Y., Shinomori, S., Suzuki, K., Arakawa, Y., (1998) Appl. Phys. Lett., 73, p. 517. , APPLAB 0003-6951 10.1063/1.121919Bayer, M., Kuther, A., Schäfer, F., Reithmaier, J.P., Forchel, A., (1999) Phys. Rev. B, 60, p. 8481. , PRBMDO. 0163-1829. 10.1103/PhysRevB.60.R8481Sugisaki, M., Ren, H.-W., Nishi, K., Sugou, S., Okuno, T., Masumoto, Y., (1998) Physica B, 256-258, p. 169. , PHYBE3 0921-4526Kotlyar, R., Reinecke, T.L., Bayer, M., Forchel, A., (2001) Phys. Rev. B, 63, p. 085310. , PRBMDO 0163-1829 10.1103/PhysRevB.63.085310Ribeiro, E., Govorov, A.O., Carvalho Jr., W., Medeiros-Ribeiro, G., (2004) Phys. Rev. Lett., 92, p. 126402. , PRLTAO 0031-9007 10.1103/PhysRevLett.92.126402Janssens, K.L., Partoens, B., Peeters, F.M., (2002) Phys. Rev. B, 66, p. 075314. , PRBMDO 0163-1829 10.1103/PhysRevB.66.075314Kalameitsev, A.B., Kovalev, V.M., Govorov, A.O., (1989) JETP Lett., 68, p. 669. , JTPLA2 0021-3640 10.1134/1.567926Sugisaki, M., Ren, H.W., Nair, S.V., Nishi, K., Masumoto, Y., (2002) Phys. Rev. B, 66, p. 235309. , PRBMDO 0163-1829 10.1103/PhysRevB.66.235309Godoy, M.P.F., Nakaema, M.K.K., Iikawa, F., Carvalho Jr., W., Ribeiro, E., Gobby, A.L., (2004) Rev. Sci. Instrum., 75, p. 1947. , RSINAK 0034-6748 10.1063/1.1753090Walck, S.N., Reinecke, T.L., (1998) Phys. Rev. B, 57, p. 9088. , PRBMDO 0163-1829 10.1103/PhysRevB.57.9088Laheld, U.E.H., Pedersen, F.B., Hemmer, P.C., (1993) Phys. Rev. B, 48, p. 4659. , PRBMDO 0163-1829 10.1103/PhysRevB.48.4659Bastard, G., Mendez, E.E., Chang, L.L., Esaki, L., (1982) Phys. Rev. B, 26, p. 1974. , PRBMDO 0163-1829 10.1103/PhysRevB.26.1974Nakaoka, T., Saito, T., Tatebayashi, J., Arakawa, Y., (2004) Phys. Rev. B, 70, p. 235337. , PRBMDO 0163-1829 10.1103/PhysRevB.70.235337Yugova, I.A., Ya. Gerlovin, I., Davydov, V.G., Ignatiev, I.V., Kozin, I.E., Ren, H.W., Sugisaki, M., Masumoto, Y., (2002) Phys. Rev. B, 66, p. 235309. , PRBMDO 0163-1829 10.1103/PhysRevB.66.235309Willmann, F., Suga, S., Dreybrodt, W., Cho, K., (1974) Solid State Commun., 14, p. 783. , SSCOA4 0038-1098Landi, S.M., Tribuzy, C.V.B., Souza, P.L., Butendeich, R., Bittencourt, A.C., Marques, G.E., (2003) Phys. Rev. B, 67, p. 085304. , PRBMDO 0163-1829 10.1103/PhysRevB.67.08530

Visual Network Analysis of Dynamic Metabolic Pathways

Abstract. We extend our previous work on the exploration of static metabolic networks to evolving, and therefore dynamic, pathways. We apply our visualization software to data from a simulation of early metabolism. Thereby, we show that our technique allows us to test and argue for or against different scenarios for the evolution of metabolic pathways. This supports a profound and efficient analysis of the structure and properties of the generated metabolic networks and its underlying components, while giving the user a vivid impression of the dynamics of the system. The analysis process is inspired by Ben Shneiderman’s mantra of information visualization. For the overview, user-defined diagrams give insight into topological changes of the graph as well as changes in the attribute set associated with the participating enzymes, substances and reactions. This way, “interesting features” in time as well as in space can be recognized. A linked view implementation enables the navigation into more detailed layers of perspective for in-depth analysis of individual network configuration