CM points and weight 3/2 modular forms.

The theta correspondence has been an important tool in the theory of automorphic forms with plentiful applications to arithmetic questions. In this paper, we consider a specific theta lift for an isotropic quadratic space V over Q of signature (1, 2). The theta kernel we employ associated to the lift has been constructed by Kudla-Millson (e.g., [29, 30]) in much greater generality for O(p, q) (U(p, q)) to realize generating series of cohomo-logical intersection numbers of certain, ’special ’ cycles in locally symmetric spaces of orthogonal (unitary) type as holomorphic Siegel (Hermitian) mod-ular forms. In our case for O(1, 2), the underlying locally symmetric space M is a modular curve, and the special cycles, parametrized by positive in-tegers N, are the classical CM points Z(N); i.e., quadratic irrationalities of discriminant −N in the upper half plane. We survey the results of [16] and of our joint work with Bruinier [12] on using this particular theta kernel to define lifts of various kinds of functions F on the underlying modular curve M. The theta lift is given b

Structured learning of assignment models for neuron reconstruction to minimize topological errors

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods.Peer ReviewedPostprint (author's final draft

Charge and momentum transfer in supercooled melts: Why should their relaxation times differ?

The steady state values of the viscosity and the intrinsic ionic-conductivity of quenched melts are computed, in terms of independently measurable quantities. The frequency dependence of the ac dielectric response is estimated. The discrepancy between the corresponding characteristic relaxation times is only apparent; it does not imply distinct mechanisms, but stems from the intrinsic barrier distribution for $\alpha$-relaxation in supercooled fluids and glasses. This type of intrinsic ``decoupling'' is argued not to exceed four orders in magnitude, for known glassformers. We explain the origin of the discrepancy between the stretching exponent $\beta$, as extracted from $\epsilon(\omega)$ and the dielectric modulus data. The actual width of the barrier distribution always grows with lowering the temperature. The contrary is an artifact of the large contribution of the dc-conductivity component to the modulus data. The methodology allows one to single out other contributions to the conductivity, as in ``superionic'' liquids or when charge carriers are delocalized, implying that in those systems, charge transfer does not require structural reconfiguration.Comment: submitted to J Chem Phy

Knowledge-based Model Building with KONWERK

Modeling a real world optimization problem in a form which can be processed by a machine (computer) is usually a very difficult and complex task. Therefore, building and verifying the model is often the most time consuming part of the whole process of solving a real world problem using methods of Operations Research. Software tools, which integrate representation methods developed in the field of Artificial Intelligence (AI) and methods of OR, can facilitate and speed up the process of model development. The paper introduces the idea of knowledge based modeling as a model development and representation technique facilitating the complex process of model building. We describe the KONWERK tool-box which combines hierarchical structured knowledge representation and object oriented methodology thus providing a framework for model building and application of different optimization methods. We want the reader to form an idea of the methodology of model development and knowledge representation with KONWERK and to understand the hierarchical structure of the knowledge base. The model of the Nitra River Case is used to describe and explain the modeling and knowledge representation with KONWERK. A given multicriteria model of the Nitra River Case was reimplemented using KONWERK within about three weeks and later enlarged by implementation of additional fairness criteria

Simple Lattice-Models of Ion Conduction: Counter Ion Model vs. Random Energy Model

The role of Coulomb interaction between the mobile particles in ionic conductors is still under debate. To clarify this aspect we perform Monte Carlo simulations on two simple lattice models (Counter Ion Model and Random Energy Model) which contain Coulomb interaction between the positively charged mobile particles, moving on a static disordered energy landscape. We find that the nature of static disorder plays an important role if one wishes to explore the impact of Coulomb interaction on the microscopic dynamics. This Coulomb type interaction impedes the dynamics in the Random Energy Model, but enhances dynamics in the Counter Ion Model in the relevant parameter range.Comment: To be published in Phys. Rev.

Mesoscopic non-equilibrium thermodynamics approach to non-Debye dielectric relaxation

Mesoscopic non-equilibrium thermodynamics is used to formulate a model describing non-homogeneous and non-Debye dielectric relaxation. The model is presented in terms of a Fokker-Planck equation for the probability distribution of non-interacting polar molecules in contact with a heat bath and in the presence of an external time-dependent electric field. Memory effects are introduced in the Fokker-Planck description through integral relations containing memory kernels, which in turn are used to establish a connection with fractional Fokker-Planck descriptions. The model is developed in terms of the evolution equations for the first two moments of the distribution function. These equations are solved by following a perturbative method from which the expressions for the complex susceptibilities are obtained as a functions of the frequency and the wave number. Different memory kernels are considered and used to compare with experiments of dielectric relaxation in glassy systems. For the case of Cole-Cole relaxation, we infer the distribution of relaxation times and its relation with an effective distribution of dipolar moments that can be attributed to different segmental motions of the polymer chains in a melt.Comment: 33 pages, 6 figure