Exponential ergodicity of the jump-diffusion CIR process

In this paper we study the jump-diffusion CIR process (shorted as JCIR), which is an extension of the classical CIR model. The jumps of the JCIR are introduced with the help of a pure-jump L\'evy process $(J_t, t \ge 0)$. Under some suitable conditions on the L\'evy measure of $(J_t, t \ge 0)$, we derive a lower bound for the transition densities of the JCIR process. We also find some sufficient condition guaranteeing the existence of a Forster-Lyapunov function for the JCIR process, which allows us to prove its exponential ergodicity.Comment: 14 page

Preparatory cortical and spinal settings to counteract anticipated and non-anticipated perturbations

Little is known about how the central nervous system prepares postural responses differently in anticipated compared to non-anticipated perturbations. To investigate this, participants were exposed to translational and rotational perturbations presented in a blocked (anticipated) and a random (non-anticipated) design. The preparatory setting (‘central set’) was measured by H-reflexes, motor-evoked potentials (MEPs), and short-interval intracortical inhibition (SICI) shortly before perturbation onset in the soleus of 15 healthy adults. Additionally, the behavioral consequences of differential preparatory settings were analyzed by comparing the short- (SLR), medium- (MLR), and long-latency response (LLR) of the soleus after anticipated and non-anticipated rotations and translations. H-reflexes elicited before perturbation were different between conditions (p=0.023) with larger amplitudes in anticipated translations compared to anticipated rotations (37.0%; p=0.048). Reduced SICI was found in the three conditions containing perturbations compared to static standing (p0.001). Muscular responses assessed after perturbations remained unchanged for the SLR and MLR, whereas the LLR was decreased in anticipated rotations (−36.2%; p=0.002) and increased in anticipated translations (16.7%; p=0.046) compared to the corresponding non-anticipated perturbation. As the SLR and MLR are organized at the spinal and the LLR at the cortical level, the preparatory setting seems to mainly influence cortically mediated postural responses. However, the modulation of the H- reflex before anticipated perturbations indicates that supraspinal centers adjusted Ia- afferent transmission for the soleus in a perturbation-specific manner. Intracortical inhibition was also modulated but differentiates to a lesser extent only between perturbation conditions and unperturbed stance

Yield Curve Shapes and the Asymptotic Short Rate Distribution in Affine One-Factor Models

We consider a model for interest rates, where the short rate is given by a time-homogenous, one-dimensional affine process in the sense of Duffie, Filipovic and Schachermayer. We show that in such a model yield curves can only be normal, inverse or humped (i.e. endowed with a single local maximum). Each case can be characterized by simple conditions on the present short rate. We give conditions under which the short rate process will converge to a limit distribution and describe the limit distribution in terms of its cumulant generating function. We apply our results to the Vasicek model, the CIR model, a CIR model with added jumps and a model of Ornstein-Uhlenbeck type

Heavy Quark Production and PDF's Subgroup Report

We present a status report of a variety of projects related to heavy quark production and parton distributions for the Tevatron Run II.Comment: Latex. 8 pages, 7 eps figures. Contribution to the Physics at Run II Workshops: QCD and Weak Boson Physic

Magnetoelastic coupling in triangular lattice antiferromagnet CuCrS2

CuCrS2 is a triangular lattice Heisenberg antiferromagnet with a rhombohedral crystal structure. We report on neutron and synchrotron powder diffraction results which reveal a monoclinic lattice distortion at the magnetic transition and verify a magnetoelastic coupling. CuCrS2 is therefore an interesting material to study the influence of magnetism on the relief of geometrical frustration.Comment: 6 pages, 6 figures, 1 tabl

Web Service Discovery in a Semantically Extended UDDI Registry: the Case of FUSION

Service-oriented computing is being adopted at an unprecedented rate, making the effectiveness of automated service discovery an increasingly important challenge. UDDI has emerged as a de facto industry standard and fundamental building block within SOA infrastructures. Nevertheless, conventional UDDI registries lack means to provide unambiguous, semantically rich representations of Web service capabilities, and the logic inference power required for facilitating automated service discovery. To overcome this important limitation, a number of approaches have been proposed towards augmenting Web service discovery with semantics. This paper discusses the benefits of semantically extending Web service descriptions and UDDI registries, and presents an overview of the approach put forward in project FUSION, towards semantically-enhanced publication and discovery of services based on SAWSDL

Guidance of sentinel lymph node biopsy decisions in patients with T1-T2 melanoma using gene expression profiling.

AIM: Can gene expression profiling be used to identify patients with T1-T2 melanoma at low risk for sentinel lymph node (SLN) positivity? PATIENTS & METHODS: Bioinformatics modeling determined a population in which a 31-gene expression profile test predicted \u3c5% SLN positivity. Multicenter, prospectively-tested (n = 1421) and retrospective (n = 690) cohorts were used for validation and outcomes, respectively. RESULTS: Patients 55-64 years and ≥65 years with a class 1A (low-risk) profile had SLN positivity rates of 4.9% and 1.6%. Class 2B (high-risk) patients had SLN positivity rates of 30.8% and 11.9%. Melanoma-specific survival was 99.3% for patients ≥55 years with class 1A, T1-T2 tumors and 55.0% for class 2B, SLN-positive, T1-T2 tumors. CONCLUSION: The 31-gene expression profile test identifies patients who could potentially avoid SLN biopsy

Generation and validation

Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this paper, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modelingmolecular dynamics with a MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that a MSM reproduces the kinetics of the molecular dynamics data

On small-noise equations with degenerate limiting system arising from volatility models

The one-dimensional SDE with non Lipschitz diffusion coefficient $dX_{t} = b(X_{t})dt + \sigma X_{t}^{\gamma} dB_{t}, \ X_{0}=x, \ \gamma<1$ is widely studied in mathematical finance. Several works have proposed asymptotic analysis of densities and implied volatilities in models involving instances of this equation, based on a careful implementation of saddle-point methods and (essentially) the explicit knowledge of Fourier transforms. Recent research on tail asymptotics for heat kernels [J-D. Deuschel, P.~Friz, A.~Jacquier, and S.~Violante. Marginal density expansions for diffusions and stochastic volatility, part II: Applications. 2013, arxiv:1305.6765] suggests to work with the rescaled variable $X^{\varepsilon}:=\varepsilon^{1/(1-\gamma)} X$: while allowing to turn a space asymptotic problem into a small-$\varepsilon$ problem with fixed terminal point, the process $X^{\varepsilon}$ satisfies a SDE in Wentzell--Freidlin form (i.e. with driving noise $\varepsilon dB$). We prove a pathwise large deviation principle for the process $X^{\varepsilon}$ as $\varepsilon \to 0$. As it will become clear, the limiting ODE governing the large deviations admits infinitely many solutions, a non-standard situation in the Wentzell--Freidlin theory. As for applications, the $\varepsilon$-scaling allows to derive exact log-asymptotics for path functionals of the process: while on the one hand the resulting formulae are confirmed by the CIR-CEV benchmarks, on the other hand the large deviation approach (i) applies to equations with a more general drift term and (ii) potentially opens the way to heat kernel analysis for higher-dimensional diffusions involving such an SDE as a component.Comment: 21 pages, 1 figur