Assessing the Rental Value of Residential Properties: An Abductive Learning Networks Approach

This paper attempts to estimate rental value of residential properties using Abductive Learning Networks (ALN), and artificial intelligence technique. The results indicate that the ALN model provides an accurate estimation of rents with only seven input variables, while other multivariate statistical techniques do not. The ALN model automatically selects the best network structure, node types and coefficients, and therefore it simplifies the maintenance of the model. Once the final model is synthesized, the ALN model becomes very compact, rapidly executable and cost-effective.

Subwavelength localization and toroidal dipole moment of spoof surface plasmon polaritons

We experimentally and theoretically demonstrate subwavelength scale localization of spoof surface plasmon polaritons at a point defect in a two-dimensional groove metal array. An analytical expression for dispersion relation of spoof surface plasmon polaritons substantiates the existence of a band gap where a defect mode can be introduced. A waveguide coupling method allows us to excite localized spoof surface plasmon polariton modes and measure their resonance frequencies. Numerical calculations confirm that localized modes can have a very small modal volume and a high Q factor both of which are essential in enhancing light-matter interactions. Interestingly, we find that the localized spoof surface plasmon polariton has a significant toroidal dipole moment, which is responsible for the high Q factor, as well as an electric quadrupole moment. In addition, the dispersion properties of spoof surface plasmon polaritons are analyzed using a modal expansion method and numerical calculations

Electron-hole asymmetry in Co- and Mn-doped SrFe2As2

Phase diagram of electron and hole-doped SrFe2As2 single crystals is investigated using Co and Mn substitution at the Fe-sites. We found that the spin-density-wave state is suppressed by both dopants, but the superconducting phase appears only for Co (electron)-doping, not for Mn (hole)-doping. Absence of the superconductivity by Mn-doping is in sharp contrast to the hole-doped system with K-substitution at the Sr sites. Distinct structural change, in particular the increase of the Fe-As distance by Mn-doping is important to have a magnetic and semiconducting ground state as confirmed by first principles calculations. The absence of electron-hole symmetry in the Fe-site-doped SrFe2As2 suggests that the occurrence of high-Tc superconductivity is sensitive to the structural modification rather than the charge doping.Comment: 7 pages, 6 figure

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

Performance Evaluation of Finite-Life Real Estate Investment Trusts

This study analyzes the investment performance of real estate investment trusts, comparing the finite-life trusts (FREIT) with traditional REITs and stock returns. The results indicate that the FREITs performed more poorly than the REITs, with both the FREITs and REITs underperforming the market index over the period studied. It was also found that while portfolio risk diversification benefits may exist for the REITs and FREITs, it is not clear that the reduced risk is warranted by the large reduction in returns. Finally, this research shows that little total or unanticipated inflation hedging capability exists for the REITs or FREITs over the period studied, although anticipated inflation hedging capabilities were found.

Interplay between Fermi surface topology and ordering in URu2_{2}Si2_2 revealed through abrupt Hall coefficient changes in strong magnetic fields

Temperature- and field-dependent measurements of the Hall effect of pure and 4 % Rh-doped URu$_{2}$Si$_{2}$ reveal low density (0.03 hole/U) high mobility carriers to be unique to the `hidden order' phase and consistent with an itinerant density-wave order parameter. The Fermi surface undergoes a series of abrupt changes as the magnetic field is increased. When combined with existing de Haas-van Alphen data, the Hall data expose a strong interplay between the stability of the `hidden order,' the degree of polarization of the Fermi liquid and the Fermi surface topology.Comment: 4 pages, 4 figures, Accepted to Phys. Rev. Let

Orbital selective Fermi surface shifts and mechanism of high Tc_c superconductivity in correlated AFeAs (A=Li,Na)

Based on the dynamical mean field theory (DMFT) and angle resolved photoemission spectroscopy (ARPES), we have investigated the mechanism of high $T_c$ superconductivity in stoichiometric LiFeAs. The calculated spectrum is in excellent agreement with the observed ARPES measurement. The Fermi surface (FS) nesting, which is predicted in the conventional density functional theory method, is suppressed due to the orbital-dependent correlation effect with the DMFT method. We have shown that such marginal breakdown of the FS nesting is an essential condition to the spin-fluctuation mediated superconductivity, while the good FS nesting in NaFeAs induces a spin density wave ground state. Our results indicate that fully charge self-consistent description of the correlation effect is crucial in the description of the FS nesting-driven instabilities.Comment: 5 pages, 4 figures, supporting informatio