Thermodynamically Consistent Diffuse Interface Models for Incompressible Two-Phase Flows with Different Densities

A new diffuse interface model for a two-phase flow of two incompressible fluids with different densities is introduced using methods from rational continuum mechanics. The model fulfills local and global dissipation inequalities and is also generalized to situations with a soluble species. Using the method of matched asymptotic expansions we derive various sharp interface models in the limit when the interfacial thickness tends to zero. Depending on the scaling of the mobility in the diffusion equation we either derive classical sharp interface models or models where bulk or surface diffusion is possible in the limit. In the two latter cases the classical Gibbs-Thomson equation has to be modified to include kinetic terms. Finally, we show that all sharp interface models fulfill natural energy inequalities.Comment: 34 page

Lossless Analog Compression

We establish the fundamental limits of lossless analog compression by considering the recovery of arbitrary m-dimensional real random vectors x from the noiseless linear measurements y=Ax with n x m measurement matrix A. Our theory is inspired by the groundbreaking work of Wu and Verdu (2010) on almost lossless analog compression, but applies to the nonasymptotic, i.e., fixed-m case, and considers zero error probability. Specifically, our achievability result states that, for almost all A, the random vector x can be recovered with zero error probability provided that n > K(x), where K(x) is given by the infimum of the lower modified Minkowski dimension over all support sets U of x. We then particularize this achievability result to the class of s-rectifiable random vectors as introduced in Koliander et al. (2016); these are random vectors of absolutely continuous distribution---with respect to the s-dimensional Hausdorff measure---supported on countable unions of s-dimensional differentiable submanifolds of the m-dimensional real coordinate space. Countable unions of differentiable submanifolds include essentially all signal models used in the compressed sensing literature. Specifically, we prove that, for almost all A, s-rectifiable random vectors x can be recovered with zero error probability from n>s linear measurements. This threshold is, however, found not to be tight as exemplified by the construction of an s-rectifiable random vector that can be recovered with zero error probability from n<s linear measurements. This leads us to the introduction of the new class of s-analytic random vectors, which admit a strong converse in the sense of n greater than or equal to s being necessary for recovery with probability of error smaller than one. The central conceptual tools in the development of our theory are geometric measure theory and the theory of real analytic functions

Lossless Linear Analog Compression

We establish the fundamental limits of lossless linear analog compression by considering the recovery of random vectors ${\boldsymbol{\mathsf{x}}}\in{\mathbb R}^m$ from the noiseless linear measurements ${\boldsymbol{\mathsf{y}}}=\boldsymbol{A}{\boldsymbol{\mathsf{x}}}$ with measurement matrix $\boldsymbol{A}\in{\mathbb R}^{n\times m}$. Specifically, for a random vector ${\boldsymbol{\mathsf{x}}}\in{\mathbb R}^m$ of arbitrary distribution we show that ${\boldsymbol{\mathsf{x}}}$ can be recovered with zero error probability from $n>\inf\underline{\operatorname{dim}}_\mathrm{MB}(U)$ linear measurements, where $\underline{\operatorname{dim}}_\mathrm{MB}(\cdot)$ denotes the lower modified Minkowski dimension and the infimum is over all sets $U\subseteq{\mathbb R}^{m}$ with $\mathbb{P}[{\boldsymbol{\mathsf{x}}}\in U]=1$. This achievability statement holds for Lebesgue almost all measurement matrices $\boldsymbol{A}$. We then show that $s$-rectifiable random vectors---a stochastic generalization of $s$-sparse vectors---can be recovered with zero error probability from $n>s$ linear measurements. From classical compressed sensing theory we would expect $n\geq s$ to be necessary for successful recovery of ${\boldsymbol{\mathsf{x}}}$. Surprisingly, certain classes of $s$-rectifiable random vectors can be recovered from fewer than $s$ measurements. Imposing an additional regularity condition on the distribution of $s$-rectifiable random vectors ${\boldsymbol{\mathsf{x}}}$, we do get the expected converse result of $s$ measurements being necessary. The resulting class of random vectors appears to be new and will be referred to as $s$-analytic random vectors

Lossy Compression of General Random Variables

This paper is concerned with the lossy compression of general random variables, specifically with rate-distortion theory and quantization of random variables taking values in general measurable spaces such as, e.g., manifolds and fractal sets. Manifold structures are prevalent in data science, e.g., in compressed sensing, machine learning, image processing, and handwritten digit recognition. Fractal sets find application in image compression and in the modeling of Ethernet traffic. Our main contributions are bounds on the rate-distortion function and the quantization error. These bounds are very general and essentially only require the existence of reference measures satisfying certain regularity conditions in terms of small ball probabilities. To illustrate the wide applicability of our results, we particularize them to random variables taking values in i) manifolds, namely, hyperspheres and Grassmannians, and ii) self-similar sets characterized by iterated function systems satisfying the weak separation property

Rotational barriers in perylene fluorescent dyes

Rotational barriers in N-substituted perylene dyes have been determined. Phenyl substituents with tert-butyl groups in the o-position give rigid systems, whereas secondary alkyl groups cause low rotational barriers. In spite of that, fluorescent quantum yields are high in both cases. Conformations in solution are discussed

Directly decomposable ideals and congruence kernels of commutative semirings

As pointed out in the monographs by J. S. Golan and by W. Kuich and A. Salomaa on semirings, ideals play an important role despite the fact that they need not be congruence kernels as in the case of rings. Hence, having two commutative semirings S1 and S2, one can ask whether an ideal I of their direct product S = S1 x S2 can be expressed in the form I1 x I2 where Ij is an ideal of Sj for j=1,2. Of course, the converse is elementary, namely if Ij is an ideal of Sj for j=1,2 then I1 x I2 is an ideal of S1 x S2. Having a congruence on a commutative semiring S, its 0-class is an ideal of S, but not every ideal is of this form. Hence, the lattice Id S of all ideals of S and the lattice Ker S of all congruence kernels (i.e. 0-classes of congruences) of S need not be equal. Furthermore, we show that the mapping which assigns to every congruence its kernel need not be a homomorphism from Con S onto Ker S. Moreover, the question arises when a congruence kernel of the direct product S1 x S2 of two commutative semirings can be expressed as a direct product of the corresponding kernels on the factors. In the paper we present necessary and sufficient conditions for such direct decompositions both for ideals and for congruence kernels of commutative semirings. We also provide sufficient conditions for varieties of commutative semirings to have directly decomposable kernels

Das Alter der Sinterkalke vom Solbad Laer i.T.W.

Aus 4 Profilen durch den Laerer Sinterkalk wurden 33 Proben pollenanalytisch untersucht. Zwei Zähltabellen geben für jede einzelne dieser Proben den Gehalt an Pollen und Sporen. Zwei Diagramme stellen die Ergebnisse dieser qualitativen und quantitativen Analysen graphisch dar. Neben den Pollenkörnern der gebräuchlichen 11 Baumarten wurden 24 verschiedene Nichtbaumpollen-Gruppen ausgewertet. Als ältester Zeitabschnitt ließ sich die mindestens 10000 Jahre alte sog. "Jüngste Dryaszeit" feststellen, gekennzeichnet durch eine subarktische Tundra. Die darauf folgenden Zeitabschnitte Präboreal, Boreal, Atlantikum, Subboreal und Subatlantikum konnten in einem oder mehreren der Profile gefaßt werden. In günstig gelagerten Fällen wurden darüber hinaus Beziehungen angedeutet, die zwischen der Bildung dieses Sinterkalklagers und der Besiedlung durch den Menschen bestehen

Completion of Matrices with Low Description Complexity

We propose a theory for matrix completion that goes beyond the low-rank structure commonly considered in the literature and applies to general matrices of low description complexity. Specifically, complexity of the sets of matrices encompassed by the theory is measured in terms of Hausdorff and upper Minkowski dimensions. Our goal is the characterization of the number of linear measurements, with an emphasis on rank-$1$ measurements, needed for the existence of an algorithm that yields reconstruction, either perfect, with probability 1, or with arbitrarily small probability of error, depending on the setup. Concretely, we show that matrices taken from a set $\mathcal{U}$ such that $\mathcal{U}-\mathcal{U}$ has Hausdorff dimension $s$ can be recovered from $k>s$ measurements, and random matrices supported on a set $\mathcal{U}$ of Hausdorff dimension $s$ can be recovered with probability 1 from $k>s$ measurements. What is more, we establish the existence of recovery mappings that are robust against additive perturbations or noise in the measurements. Concretely, we show that there are $\beta$-H\"older continuous mappings recovering matrices taken from a set of upper Minkowski dimension $s$ from $k>2s/(1-\beta)$ measurements and, with arbitrarily small probability of error, random matrices supported on a set of upper Minkowski dimension $s$ from $k>s/(1-\beta)$ measurements. The numerous concrete examples we consider include low-rank matrices, sparse matrices, QR decompositions with sparse R-components, and matrices of fractal nature