Observation of Wigner cusps in a metallic carbon nanotube

Previous gate-dependent conductance measurements of metallic carbon nanotubes have revealed unexplainable conductance suppressions, occurring at two different gate voltages. These were previously attributed to the gate-dependency of contact resistance. Our gate-dependent conductivity measurements on a metallic nanotube with known chirality show that these bimodal conductance suppressions are the manifestations of Wigner cusps, often seen in atomic and nuclear physics experiments.Comment: 6 pages, 3 figure

Ring-Exchange Interaction Effects on Magnons in Dirac Magnet CoTiO3_3

In magnetically ordered materials with localized electrons, the fundamental magnetic interactions are due to exchange of electrons [1-3]. Typically, only the interaction between pairs of electrons' spins is considered to explain the nature of the ground state and its excitations, whereas three-, four-, and six-spin interactions are ignored. When these higher order processes occur in a loop they are called cyclic or ring exchange. The ring-exchange interaction is required to explain low temperature behavior in bulk and thin films of solid $^3$He [4-8]. It also plays a crucial role in the quantum magnet La$_2$CuO$_4$ [9,10]. Here, we use a combination of time domain THz (TDTS) and magneto-Raman spectroscopies to measure the low energy magnetic excitations in CoTiO$_3$, a proposed Dirac topological magnon material [11,12] where the origin of the energy gap in the magnon spectrum at the Brillouin zone center remains unclear. We measured the magnetic field dependence of the energies of the two lowest energy magnons and determine that the gap opens due to the ring-exchange interaction between the six spins in a hexagon. This interaction also explains the selection rules of the THz magnon absorption. Finally, we clarify that topological surface magnons are not expected in CoTiO$_3$. Our study demonstrates the power of combining TDTS and Raman spectroscopies with theory to identify the microscopic origins of the magnetic excitations in quantum magnets.Comment: 7 pages, 4 figures in main text, 26 pages and 11 figures in supplemen

Pricing reverse mortgages in Spain

[EN] In Spain, as in other European countries, the continuous ageing of the population creates a need for long-term care services and their financing. However, in Spain the development of this kind of services is still embryonic. The aim of this article is to obtain a calculation method for reverse mortgages in Spain based on the fit and projection of dynamic tables for Spanish mortality, using the Lee and Carter model. Mortality and life expectancy for the next 20 years are predicted using the fitted model, and confidence intervals are obtained from the prediction errors of parameters for the mortality index of the model. The last part of the article illustrates an application of the results to calculate the reverse mortgage model promoted by the Spanish Instituto de Crédito Oficial (Spanish State Financial Agency), for which the authors have developed a computer application.The authors are indebted to Jose Garrido, whose suggestions improved the original manuscript, and to the anonymous referee for his/her valuable comments. This work was partially supported by grants from the MEyC (Ministerio de Educacio´n y Ciencia, Spain), projects MTM2010- 14961 and MTM2008-05152.Debón Aucejo, AM.; Montes, F.; Sala, R. (2013). Pricing reverse mortgages in Spain. European Actuarial Journal. 3:23-43. https://doi.org/10.1007/s13385-013-0071-yS23433Blay-Berrueta D (2007) Sistemas de cofinaciaciación de la dependencia: seguro privado frente a hipoteca inversa. Cuadernos de la Fundación, Fundación Mapfre Estudios, Madrid.Booth H (2006) Demographic forecasting: 1980 to 2005 in review. Int J Forecast 22(3):547–582Booth H, Hyndman R, Tickle L, de Jong P (2006) Lee–Carter mortality forecasting: a multi-country comparison of variants and extensions. Demogr Res 15(9):289–310Booth H, Maindonald J, Smith L (2002) Applying Lee–Carter under conditions of variable mortality decline. Popul Stud 56(3):325–336Booth H, Tickle L (2003) The future aged: new projections of Australia’s ederly population. Popul Stud 22(4):38–44Brouhns N, Denuit M, Keilegom IV (2005) Bootstrapping Poisson log-bilinear model for mortality forecasting. Scand Actuar J 2005(3):212–224Brouhns N, Denuit M, Vermunt J (2002) A Poisson log-bilinear regression approach to the construction of projected lifetables. Insur Math Econ 31(3):373–393Carter L, Lee R (1992) Modeling and forecasting US sex differentials in mortality. Int J Forecast 8(3):393–411Carter L, Prkawetz A (2001) Examining structural shifs in mortality using the Lee–Carter method. Mpidr wp 2001–2007, Center for Demography and Ecology Information, University of Wisconsin-Madison.Chinloy P, Megbolugbe I (1994) Reverse mortgages: contracting and crossover. J Am Real Estate Urban Econ Assoc 22(2):367–386Coale A, Guo G (1989) Revisited regional model life tables at very low levels of mortality. Popul Index 55:613–643Coale A, Kisker E (1990) Defects in data old age mortality in the United States: New procedures for calculating approximately accurate mortality schedules and lifes tables at the highest ages. Asian Pac Popul Forum 4:1–31Cossette H, Delwarde A, Denuit M, Guillot F, Étienne M (2007) Pension plan valuation and mortality projection: a case study with mortality data. N Am Actuar J 11(2):1–34.Costa-Font J (2009) Ageing in place? exploring elderly people’s housing preferences in Spain. Urban Stud 46(2):295–316Costa-Font J (2013) Housing-related well-being in older people: the impact of environmental and financial influences. Urban Stud 50(4):657–673Currie I, Kirkby J, Durban M, Eilers P (2004) Smooth Lee–Carter models and beyond. In: Workshop on Lee–Carter Methods, http://www.ma.hw.ac.uk/~iain/workshop/workshop.html . Accessed 4 Mar 2005Czado C, Delwarde A, Denuit M (2005) Bayesian Poisson log-bilinear mortality projections. Insur Math Econ 36(3):260–284D’Amato V, Haberman S, Piscopo G, Russolillo M (2012) Modelling dependent data for longevity projections. Insur Math Econ 51(3):694–701Davidoff T (2012) Can ‘high costs’ justify weak demand for the home equity conversion mortgage? Technical report, available at SSRNDavidoff T, Welke G (2007) Selection and moral hazard in the reverse mortgage market. Technical report, Haas School of Business, UC BerkeleyDebón A, Montes F, Mateu J, Porcu E, Bevilacqua M (2008) Modelling residuals dependence in dymanic life tables. Comput Stat Data Anal 52(3):3128–3147Debón A, Montes F, Puig F (2008) Modelling and forecasting mortality in Spain. Eur J Oper Res 189(3):624–637Debón A, Montes F, Sala R (2009) Tablas de mortalidad dinámicas. Una aplicación a la hipoteca inversa en España. Fundación ICO. Publicaciones de la Universitat de Valéncia, ValenciaDebón A, Montes F, Martínez-Ruiz F (2011) Statistical methods to compare mortality for a group with non-divergent populations: an application to Spanish regions. Eur Actuar J 1:291–308Delwarde A, Denuit M, Eilers P (2007) Smoothing the Lee–Carter and poisson log-bilinear models for mortality forecasting: a penalized log-likelihood approach. Stat Modell 7(1):29–48Denuit M (2007) Distribution of the random future life expectancies in log-bilinear mortality projections models. Lifetime Data Anal 13(3):381–397Denuit M, Goderniaux A (2004) Closing and projecting lifetables using log-linear models. Mitteilungen. der Schweizerischen Aktuarvereingung 1:29–49Felipe A, Guillén M, Pérez-Marín A (2002) Recent mortality trends in the Spanish population. Br Actuar J 8(4):757–786.Forfar D, McCutcheon J, Wilkie A (1988) On graduation by mathematical formula. J Inst Actuar 115(459):1–149Guillen M, Vidiella-i-Anguera A (2005) Forecasting Spanish natural life expectancy. Risk Anal 25(5):1161–1170Heligman L, Pollard J (1980) The age pattern of mortality. J Inst Actuar 107:49–80Herranz-Gonzalez R (2006) Hipoteca inversa y figuras afines. Informes Portal Mayores 49, IMSERSO, Madrid, http://www.imsersomayores.csic.es/documentos/documentos/herranz-hipoteca-01.pdfHoriuchi S, Wilmoth J (1998) Decelaration in the age pattern of mortality at older ages. Demography 35:391–412Hyndman RJ (2008) Forecast: forecasting functions for time series. R package version 1.11Koissi M, Shapiro A, Hgns G (2006) Evaluating and extending the Lee–Carter model for mortality forecasting confidence interval. Insur Math Econ 38(1):1–20Kutty N (1998) The scope for poverty alleviation among elderly home-owners in the United States through reverse mortgages. Urban Stud 35(1):113–129Lee R (2000) The Lee–Carter method for forecasting mortality, with various extensions and applications. N Am Actuar J 4(1):80–91Lee R, Carter L (1992) Modelling and forecasting US mortality. J Am Stat Assoc 87(419):659–671Lee R, Nault F (1993) Modeling and forecasting provincial mortality in Canada. Montreal world congress of the International Union for Scientific Study of PopulationLee R, Rofman R (1994) Modelación y Proyección de la mortalidad en Chile. Notas Poblacin 22(59):182–213Li N, Lee R (2005) Coherent mortality forecast for a group of populations: an extension of the Lee–Carter method. Demography 42(3):575–593Li S-H, Hardy M, Tan K (2009) Uncertainty in mortality forecasting: an extensin to the classical Lee–Carter approach. Astin Bull 31:137–164Lindbergson M (2001) Mortality among the elderly in Sweden. Scan Actuar J 1:79–94Liu X, Braun WJ (2010) Investigating mortality uncertainty using the block bootstrap. J Probab Stat 2010:385–399McNown R, Rogers A (1989) Forecasting mortality: a parametrized time series aproach. Demography 26(4):645–660McNown R, Rogers A (1992) Forecasting cause-specific mortality using time series methods. Int J Forecast 8(3):413–432Miceli T, Sirmans C (1994) Reverse mortgages and borrower maintenance risk. J Am Real Estate Urban Econ Assoc 22(2):433–450Pedroza C (2006) A bayesian forecasting model: predicting US male mortality. Biostatistics 7(4):530–550Renshaw A, Haberman S (2003) Lee–Carter mortality forecasting: a parallel generalized linear modelling aproach for England and Wales mortality projections. J R Stat Soc C 52(1):119–137Renshaw A, Haberman S (2003) Lee–Carter mortality forecasting with age specific enhancement. Insur Math Econ 33(2):255–272Renshaw A, Haberman S (2003) On the forecasting of mortality reduction factors. Insur Math Econ 32(3):379–401Renshaw A, Haberman S (2006) A cohort-based extension to the Lee–Carter model for mortality reduction factors. Insur Math Econ 38(3):556–570Renshaw A, Haberman S (2008) On simulation-based approaches to risk measurement in mortality with specific reference to poisson Lee–Carter modelling. Insur Math Econ 42(2):797–816Shiller R, Weiss A (2000) Moral hazard in home equity conversion. Real Estate Econ 28(1):1–31Skarr D (2008) Financial planner’s guide to the FHA insured home equity conversion mortgage. J Financ Plan 21(5):68–75Sánchez-Álvarez I, Lpez-Ares S, Quiroga-García R (2007) Diseño de hipotecas inversas en el mercado español. Proyecto 205/05 3, Instituto de Mayores y Servicios SocialesTaffin C (2006) La hipoteca inversa o vitalicia. Informes externos, Asociación Hipotecaria EspañolaThatcher A, Kannisto V, Andreev K (2002) The survivor ratio method for estimating numbers at high ages. Demogr Res 6(1):1–18Thatcher A, Kannisto V, Vaupel J (1998) The force of mortality at ages 80 to 120. Odense University Press, OdenseTuljapurkar S, Li N, Boe C (2000) A universal pattern of mortality decline in the G7 countries. Nature 405(6788):789–792Wang L, Valdez E, Piggott J (2008) Securization of longevity risk in reverse mortgages. N Am Actuar J 12(4):345–370Wilmoth J (1993) Computational methods for fitting and extrapolating the Lee–Carter model of mortality change. Technical report, Departament of Demography, University of California, BerkeleyWilmoth J (1996) Health and mortality among elderly populations, chapter mortality projections for Japan: a comparison of four methods. Oxford University Press, Oxford, pp 266–28

Incorporating household structure into a discrete event simulation model of tuberculosis and HIV

Human immunodeficiency virus (HIV) increases the risks of developing tuberculosis (TB) disease followinginfection, and speeds up disease progression. This has had a devastating effect on TB epidemics in sub-SaharanAfrica, where incidence rates have more than trebled in the past twenty years. Current control methods for TBdisease have failed to keep pace with this growth in TB, and there is an urgent need to find TB control strategiesthat are effective in high-HIV prevalent settings. This paper describes a discrete-event simulation model ofendemic TB that includes the effects of HIV and of household structure on the transmission dynamics of TB.Incorporating a social structure allows us to compare the effectiveness of contact-tracing interventions withtargeted case-finding at high risk groups. We describe the modeling of the household structure in some detail, asthis has applications to the modeling of other infectious diseases