10,268 research outputs found

    Uniqueness of bounded solutions for the homogeneous Landau equation with a Coulomb potential

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    We prove the uniqueness of bounded solutions for the spatially homogeneous Fokker-Planck-Landau equation with a Coulomb potential. Since the local (in time) existence of such solutions has been proved by Arsen'ev-Peskov (1977), we deduce a local well-posedness result. The stability with respect to the initial condition is also checked

    On the uniqueness for the spatially homogeneous Boltzmann equation with a strong angular singularity

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    We prove an inequality on the Wasserstein distance with quadratic cost between two solutions of the spatially homogeneous Boltzmann equation without angular cutoff, from which we deduce some uniqueness results. In particular, we obtain a local (in time) well-posedness result in the case of (possibly very) soft potentials. A global well-posedeness result is shown for all regularized hard and soft potentials without angular cutoff. Our uniqueness result seems to be the first one applying to a strong angular singularity, except in the special case of Maxwell molecules. Our proof relies on the ideas of Tanaka: we give a probabilistic interpretation of the Boltzmann equation in terms of a stochastic process. Then we show how to couple two such processes started with two different initial conditions, in such a way that they almost surely remain close to each other

    Quantitative lower bounds for the full Boltzmann equation, Part I: Periodic boundary conditions

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    We prove the appearance of an explicit lower bound on the solution to the full Boltzmann equation in the torus for a broad family of collision kernels including in particular long-range interaction models, under the assumption of some uniform bounds on some hydrodynamic quantities. This lower bound is independent of time and space. When the collision kernel satisfies Grad's cutoff assumption, the lower bound is a global Maxwellian and its asymptotic behavior in velocity is optimal, whereas for non-cutoff collision kernels the lower bound we obtain decreases exponentially but faster than the Maxwellian. Our results cover solutions constructed in a spatially homogeneous setting, as well as small-time or close-to-equilibrium solutions to the full Boltzmann equation in the torus. The constants are explicit and depend on the a priori bounds on the solution.Comment: 37 page

    Self-similar solutions with fat tails for Smoluchowski's coagulation equation with locally bounded kernels

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    The existence of self-similar solutions with fat tails for Smoluchowski's coagulation equation has so far only been established for the solvable and the diagonal kernel. In this paper we prove the existence of such self-similar solutions for continuous kernels KK that are homogeneous of degree γ∈[0,1)\gamma \in [0,1) and satisfy K(x,y)≀C(xÎł+yÎł)K(x,y) \leq C (x^{\gamma} + y^{\gamma}). More precisely, for any ρ∈(Îł,1)\rho \in (\gamma,1) we establish the existence of a continuous weak self-similar profile with decay x−(1+ρ)x^{-(1{+}\rho)} as x→∞x \to \infty

    Bi-defects of Nematic Surfactant Bilayers

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    We consider the effects of the coupling between the orientational order of the two monolayers in flat nematic bilayers. We show that the presence of a topological defect on one bilayer generates a nontrivial orientational texture on both monolayers. Therefore, one cannot consider isolated defects on one monolayer, but rather associated pairs of defects on either monolayer, which we call bi-defects. Bi-defects generally produce walls, such that the textures of the two monolayers are identical outside the walls, and different in their interior. We suggest some experimental conditions in which these structures could be observed.Comment: RevTeX, 4 pages, 3 figure

    The Generation of Magnetic Fields Through Driven Turbulence

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    We have tested the ability of driven turbulence to generate magnetic field structure from a weak uniform field using three dimensional numerical simulations of incompressible turbulence. We used a pseudo-spectral code with a numerical resolution of up to 1443144^3 collocation points. We find that the magnetic fields are amplified through field line stretching at a rate proportional to the difference between the velocity and the magnetic field strength times a constant. Equipartition between the kinetic and magnetic energy densities occurs at a scale somewhat smaller than the kinetic energy peak. Above the equipartition scale the velocity structure is, as expected, nearly isotropic. The magnetic field structure at these scales is uncertain, but the field correlation function is very weak. At the equipartition scale the magnetic fields show only a moderate degree of anisotropy, so that the typical radius of curvature of field lines is comparable to the typical perpendicular scale for field reversal. In other words, there are few field reversals within eddies at the equipartition scale, and no fine-grained series of reversals at smaller scales. At scales below the equipartition scale, both velocity and magnetic structures are anisotropic; the eddies are stretched along the local magnetic field lines, and the magnetic energy dominates the kinetic energy on the same scale by a factor which increases at higher wavenumbers. We do not show a scale-free inertial range, but the power spectra are a function of resolution and/or the imposed viscosity and resistivity. Our results are consistent with the emergence of a scale-free inertial range at higher Reynolds numbers.Comment: 14 pages (8 NEW figures), ApJ, in press (July 20, 2000?

    Gapped tunneling spectra in the normal state of Pr2−x_{2-x}Cex_xCuO4_4

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    We present tunneling data in the normal state of the electron doped cuprate superconductor Pr2−x_{2-x}Cex_xCuO4_4 for three different values of the doping xx. The normal state is obtained by applying a magnetic field greater than the upper critical field, Hc2H_{c2} for T<TcT < T_c. We observe an anomalous normal state gap near the Fermi level. From our analysis of the tunneling data we conclude that this is a feature of the normal state density of states. We discuss possible reasons for the formation of this gap and its implications for the nature of the charge carriers in the normal and the superconducting states of cuprate superconductors.Comment: 7 pages ReVTeX, 11 figures files included, submitted to PR

    Pharmacological augmentation of heavy ion cancer therapy

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    Asymptotics of self-similar solutions to coagulation equations with product kernel

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    We consider mass-conserving self-similar solutions for Smoluchowski's coagulation equation with kernel K(Ο,η)=(Οη)λK(\xi,\eta)= (\xi \eta)^{\lambda} with λ∈(0,1/2)\lambda \in (0,1/2). It is known that such self-similar solutions g(x)g(x) satisfy that x−1+2λg(x)x^{-1+2\lambda} g(x) is bounded above and below as x→0x \to 0. In this paper we describe in detail via formal asymptotics the qualitative behavior of a suitably rescaled function h(x)=hλx−1+2λg(x)h(x)=h_{\lambda} x^{-1+2\lambda} g(x) in the limit λ→0\lambda \to 0. It turns out that h∌1+Cxλ/2cos⁥(λlog⁥x)h \sim 1+ C x^{\lambda/2} \cos(\sqrt{\lambda} \log x) as x→0x \to 0. As xx becomes larger hh develops peaks of height 1/λ1/\lambda that are separated by large regions where hh is small. Finally, hh converges to zero exponentially fast as x→∞x \to \infty. Our analysis is based on different approximations of a nonlocal operator, that reduces the original equation in certain regimes to a system of ODE

    Prefix-Projection Global Constraint for Sequential Pattern Mining

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    Sequential pattern mining under constraints is a challenging data mining task. Many efficient ad hoc methods have been developed for mining sequential patterns, but they are all suffering from a lack of genericity. Recent works have investigated Constraint Programming (CP) methods, but they are not still effective because of their encoding. In this paper, we propose a global constraint based on the projected databases principle which remedies to this drawback. Experiments show that our approach clearly outperforms CP approaches and competes well with ad hoc methods on large datasets
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