Machine learning regression on hyperspectral data to estimate multiple water parameters

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C) 2018 IEE

Cycle Accurate Energy and Throughput Estimation for Data Cache

Resource optimization in energy constrained real-time adaptive embedded systems highly depends on accurate energy and throughput estimates of processor peripherals. Such applications require lightweight, accurate mathematical models to profile energy and timing requirements on the go. This paper presents enhanced mathematical models for data cache energy and throughput estimation. The energy and throughput models were found to be within 95% accuracy of per instruction energy model of a processor, and a full system simulator?s timing model respectively. Furthermore, the possible application of these models in various scenarios is discussed in this paper

Superconductivity in striped and multi-Fermi-surface Hubbard models: From the cuprates to the pnictides

Single- and multi-band Hubbard models have been found to describe many of the complex phenomena that are observed in the cuprate and iron-based high-temperature superconductors. Simulations of these models therefore provide an ideal framework to study and understand the superconducting properties of these systems and the mechanisms responsible for them. Here we review recent dynamic cluster quantum Monte Carlo simulations of these models, which provide an unbiased view of the leading correlations in the system. In particular, we discuss what these simulations tell us about superconductivity in the homogeneous 2D single-orbital Hubbard model, and how charge stripes affect this behavior. We then describe recent simulations of a bilayer Hubbard model, which provides a simple model to study the type and nature of pairing in systems with multiple Fermi surfaces such as the iron-based superconductors.Comment: Published as part of Superstripes 2011 (Rome) conference proceeding

Systematic analysis of a spin-susceptibility representation of the pairing interaction in the 2D Hubbard model

A dynamic cluster quantum Monte Carlo algorithm is used to study a spin susceptibility representation of the pairing interaction for the two-dimensional Hubbard model with an on-site Coulomb interaction equal to the bandwidth for various doping levels. We find that the pairing interaction is well approximated by {3/2}\Ub(T)^2\chi(K-K') with an effective temperature and doping dependent coupling \Ub(T) and the numerically calculated spin susceptibility $\chi(K-K')$. We show that at low temperatures, \Ub may be accurately determined from a corresponding spin susceptibility based calculation of the single-particle self-energy. We conclude that the strength of the d-wave pairing interaction, characterized by the mean-field transition temperature, can be determined from a knowledge of the dressed spin susceptibility and the nodal quasiparticle spectral weight. This has important implications with respect to the questions of whether spin fluctuations are responsible for pairing in the high-T$_c$ cuprates.Comment: 5 pages, 5 figure

Biaxial order parameter in the homologous series of orthogonal bent-core smectic liquid crystals

The fundamental parameter of the uniaxial liquid crystalline state that governs nearly all of its physical properties is the primary orientational order parameter (S) for the long axes of molecules with respect to the director. The biaxial liquid crystals (LCs) possess biaxial order parameters depending on the phase symmetry of the system. In this paper we show that in the first approximation a biaxial orthogonal smectic phase can be described by two primary order parameters: S for the long axes and C for the ordering of the short axes of molecules. The temperature dependencies of S and C are obtained by the Haller's extrapolation technique through measurements of the optical birefringence and biaxiality on a nontilted polar antiferroelectric (Sm-APA) phase of a homologous series of LCs built from the bent-core achiral molecules. For such a biaxial smectic phase both S and C, particularly the temperature dependency of the latter, are being experimentally determined. Results show that S in the orthogonal smectic phase composed of bent cores is higher than in Sm-A calamatic LCs and C is also significantly large

Phase Diagram of the Hubbard Model: Beyond the Dynamical Mean Field

The Dynamical Cluster Approximation (DCA) is used to study non-local corrections to the dynamical mean field phase diagram of the two-dimensional Hubbard model. Regions of antiferromagnetic, d-wave superconducting, pseudo-gapped non-Fermi liquid, and Fermi liquid behaviors are found, in rough agreement with the generic phase diagram of the cuprates. The non-local fluctuations beyond the mean field both suppress the antiferromagnetism and mediate the superconductivity.Comment: 4 pages, 5 eps figures, submitted to PR