Exceptionally large room-temperature ferroelectric polarization in the novel PbNiO3 multiferroic oxide

We present a study based on several advanced First-Principles methods, of the recently synthesized PbNiO3 [J. Am. Chem. Soc 133, 16920 (2011)], a rhombohedral antiferromagnetic insulator which crystallizes in the highly distorted R3c crystal structure. We find this compound electrically polarized, with a very large electric polarization of about 100 (\muC/cm)^2, thus even exceeding the polarization of well-known BiFeO3. PbNiO3 is a proper ferroelectric, with polarization driven by large Pb-O polar displacements along the [111] direction. Contrarily to naive expectations, a definite ionic charge of 4+ for Pb ion can not be assigned, and in fact the large Pb 6s-O 2p hybridization drives the ferroelectric distortion through a lone-pair mechanism similar to that of other Pb- and Bi-based multiferroic

Diagrammatic Monte Carlo study of the Fr\"ohlich polaron dispersion in 2D and 3D

We present results for the solution of the large polaron Fr\"ohlich Hamiltonian in 3-dimensions (3D) and 2-dimensions (2D) obtained via the Diagrammatic Monte Carlo (DMC) method. Our implementation is based on the approach by Mishchenko [A.S. Mishchenko et al., Phys. Rev. B 62, 6317 (2000)]. Polaron ground state energies and effective polaron masses are successfully benchmarked with data obtained using Feynman's path integral formalism. By comparing 3D and 2D data, we verify the analytically exact scaling relations for energies and effective masses from 3D$\to$2D, which provides a stringent test for the quality of DMC predictions. The accuracy of our results is further proven by providing values for the exactly known coefficients in weak- and strong coupling expansions. Moreover, we compute polaron dispersion curves which are validated with analytically known lower and upper limits in the small coupling regime and verify the first order expansion results for larger couplings, thus disproving previous critiques on the apparent incompatibility of DMC with analytical results and furnishing useful reference for a wide range of coupling strengths

An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm

Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, are used to prove the effectiveness of the proposed approach. The coprocessor natively supports the entire set of CGA operations including both basic operations (products, sums/differences, and unary operations) and complex operations as rigid body motion operations (reflections, rotations, translations, and dilations). The coprocessor prototype is implemented on the Xilinx ML510 development platform as a complete system-on-chip (SoC), integrating both a PowerPC processing core and a CGA coprocessing core on the same Xilinx Virtex-5 FPGA chip. Experimental results show speedups of 78x and 246x for inverse kinematics and grasping algorithms, respectively, with respect to the execution on the PowerPC processor

Rock-salt SnS and SnSe: Native Topological Crystalline Insulators

Unlike time-reversal topological insulators, surface metallic states with Dirac cone dispersion in the recently discovered topological crystalline insulators (TCIs) are protected by crystal symmetry. To date, TCI behaviors have been observed in SnTe and the related alloys Pb$_{1-x}$Sn$_{x}$Se/Te, which incorporate heavy elements with large spin-orbit coupling (SOC). Here, by combining first-principles and {\it ab initio} tight-binding calculations, we report the formation of a TCI in the relatively lighter rock-salt SnS and SnSe. This TCI is characterized by an even number of Dirac cones at the high-symmetry (001), (110) and (111) surfaces, which are protected by the reflection symmetry with respect to the ($\bar{1}$10) mirror plane. We find that both SnS and SnSe have an intrinsically inverted band structure and the SOC is necessary only to open the bulk band gap. The bulk band gap evolution upon volume expansion reveals a topological transition from an ambient pressure TCI to a topologically trivial insulator. Our results indicate that the SOC alone is not sufficient to drive the topological transition.Comment: 5 pages, 5 figure

Polymeric forms of carbon in dense lithium carbide

The immense interest in carbon nanomaterials continues to stimulate intense research activities aimed to realize carbon nanowires, since linear chains of carbon atoms are expected to display novel and technologically relevant optical, electrical and mechanical properties. Although various allotropes of carbon (e.g., diamond, nanotubes, graphene, etc.) are among the best known materials, it remains challenging to stabilize carbon in the one-dimensional form because of the difficulty to suitably saturate the dangling bonds of carbon. Here, we show through first-principles calculations that ordered polymeric carbon chains can be stabilized in solid Li$_2$C$_2$ under moderate pressure. This pressure-induced phase (above 5 GPa) consists of parallel arrays of twofold zigzag carbon chains embedded in lithium cages, which display a metallic character due to the formation of partially occupied carbon lone-pair states in \emph{sp}$^2$-like hybrids. It is found that this phase remains the most favorable one in a wide range of pressure. At extreme pressure (larger the 215 GPa) a structural and electronic phase transition towards an insulating single-bonded threefold-coordinated carbon network is predicted.Comment: 10 pages, 6 figure

Implementation and evaluation of medical imaging techniques based on conformal geometric algebra

Medical imaging tasks, such as segmentation, 3D modeling, and registration of medical images, involve complex geometric problems, usually solved by standard linear algebra and matrix calculations. In the last few decades, conformal geometric algebra (CGA) has emerged as a new approach to geometric computing that offers a simple and efficient representation of geometric objects and transformations. However, the practical use of CGA-based methods for big data image processing in medical imaging requires fast and efficient implementations of CGA operations to meet both real-time processing constraints and accuracy requirements. The purpose of this study is to present a novel implementation of CGA-based medical imaging techniques that makes them effective and practically usable. The paper exploits a new simplified formulation of CGA operators that allows significantly reduced execution times while maintaining the needed result precision. We have exploited this novel CGA formulation to re-design a suite of medical imaging automatic methods, including image segmentation, 3D reconstruction and registration. Experimental tests show that the re-formulated CGA-based methods lead to both higher precision results and reduced computation times, which makes them suitable for big data image processing applications. The segmentation algorithm provides the Dice index, sensitivity and specificity values of 98.14%, 98.05% and 97.73%, respectively, while the order of magnitude of the errors measured for the registration methods is 10-5

Structural and vibrational properties of two-dimensional MnxOy\rm Mn_xO_y nanolayers on Pd(100)

Using different experimental techniques combined with density functional based theoretical methods we have explored the formation of interface-stabilized manganese oxide structures grown on Pd(100) at (sub)monolayer coverage. Amongst the multitude of phases experimentally observed we focus our attention on four structures which can be classified into two distinct regimes, characterized by different building blocks. Two oxygen-rich phases are described in terms of MnO(111)-like O-Mn-O trilayers, whereas the other two have a lower oxygen content and are based on a MnO(100)-like monolayer structure. The excellent agreement between calculated and experimental scanning tunneling microscopy images and vibrational electron energy loss spectra allows for a detailed atomic description of the explored models.Comment: 14 pages, 11 figure

Scattering on two Aharonov-Bohm vortices with opposite fluxes

The scattering of an incident plane wave on two Aharonov-Bohm vortices with opposite fluxes is considered in detail. The presence of the vortices imposes non-trivial boundary conditions for the partial waves on a cut joining the two vortices. These conditions result in an infinite system of equations for scattering amplitudes between incoming and outgoing partial waves, which can be solved numerically. The main focus of the paper is the analytic determination of the scattering amplitude in two limits, the small flux limit and the limit of small vortex separation. In the latter limit the dominant contribution comes from the S-wave amplitude. Calculating it, however, still requires solving an infinite system of equations, which is achieved by the Riemann-Hilbert method. The results agree well with the numerical calculations

Water level forecasting through fuzzy logic and artificial neural network approaches

In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. <P style='line-height: 20px;'> All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the <I>same input and output variables</I>. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. <P style='line-height: 20px;'> The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy). It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach