Dynamic charge distribution of pyrazine: Hartree-Fock-Slater calculation and comparison with X-ray diffraction results

The dynamic charge distribution of pyrazine (1,4-diazabenzene) has been calculated using a Hartree—Fock—Slater type wavefunction. The calculations were done in direct space employing a Taylor-series expansion. The calculated charge distribution shows approximate agreement with the results of an accurate X-ray diffraction experiment. A discussion of several factors affecting the agreement between experimental and theoretical results is given

Crystal structure and charge distribution of pyrazine: effects of extinction, thermal diffuse scattering and series termination

The crystal structure and electronic charge distribution of pyrazine (1,4-diazabenzene) has been determined at 184 K by X-ray methods. The structural results of Wheatley [Acta Cryst. (1957), 10, 182-187] have been confirmed. A clear indication of bonding effects is obtained. Neither positional and thermal parameters nor difference-Fourier maps are affected by extinction. The effect of thermal diffuse scattering (TDS) on positional parameters is also negligible. However, after correction for TDS, thermal parameters increase significantly. The difference-Fourier map is influenced by TDS as well as the inclusion of high-order Fourier terms

Molecular charge distribution of CO

The difference electron density of CO is studied by comparison of several calculations. It is shown that the Hartree-Fock-Slater and Hartree-Fock methods yield equally good charge-distributions and that the use of minimal basis sets should be avoided

More on Electric and Magnetic Fluxes in SU(2)

The free energies of static charges and center monopoles are given by their fluxes. While electric fluxes show the universal behaviour of the deconfinement transition, the monopole free energies vanish in the thermodynamic limit at all temperatures and are thus irrelevant for the transition. Magnetic fluxes may, however, be used to measure the topological susceptibility without cooling.Comment: 3 pages, LaTeX2e (ws-procs9x6.cls), 1 eps-figure, talk presented by L.v.S. at Quark Confinement and the Hadron Spectrum V, Gargnano, Italy, September 10-14, 200

The molecular structure of pyrazine as determined from gas-phase electron diffraction data

The structure of pyrazine (1,4 diazabenzene, C4H4N4) has been determined at 333 K by means of gas-phase electron diffraction. The r g parameters are as follows: r(C-C) = 1.339 ± 0.002 Å. r(C-N) = 1.403 ± 0.004 Å, r(C-H) = 1.115 ± 0.004 Å. C-C-N = 115.6 ± 0.4°, and C-C-H = 123.9 ± 0.6° (error limits are 2.5σ). At a 10% level the rα structure does not differ significantly from the structure in the solid state, so long as high order X-ray, results corrected for librational motion are used; otherwise significantly different results are found even at the 1% level. Calculated and observed mean square amplitudes compare favourably

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

Load-depth sensing of isotropic, linear viscoelastic materials using rigid axisymmetric indenters

An indentation experiment involves five variables: indenter shape, material behavior of the substrate, contact size, applied load and indentation depth. Only three variable are known afterwards, namely, indenter shape, plus load and depth as function of time. As the contact size is not measured and the determination of the material properties is the very aim of the test; two equations are needed to obtain a mathematically solvable system. For elastic materials, the contact size can always be eliminated once and for all in favor of the depth; a single relation between load, depth and material properties remains with the latter variable as unknown. For viscoelastic materials where hereditary integrals model the constitutive behavior, the relation between depth and contact size usually depends also on the (time-dependent) properties of the material. Solving the inverse problem, i.e., determining the material properties from the experimental data, therefore needs both equations. Extending Sneddon's analysis of the indentation problem for elastic materials to include viscoelastic materials, the two equations mentioned above are derived. To find the time dependence of the material properties the feasibility of Golden and Graham's method of decomposing hereditary integrals (J.M. Golden and G.A.C. Graham. Boundary value problems in linear viscoelasticity, Springer, 1988) is investigated and applied to a single load-unload process and to sinusoidally driven stationary state indentation processes.Comment: 116 pages, 29 figure

Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery

Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet.First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Science

LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas

Recently, privacy has a growing importance in several domains, especially in street-view images. The conventional way to achieve this is to automatically detect and blur sensitive information from these images. However, the processing cost of blurring increases with the ever-growing resolution of images. We propose a system that is cost-effective even after increasing the resolution by a factor of 2.5. The new system utilizes depth data obtained from LiDAR to significantly reduce the search space for detection, thereby reducing the processing cost. Besides this, we test several detectors after reducing the detection space and provide an alternative solution based on state-of-the-art deep learning detectors to the existing HoG-SVM-Deep system that is faster and has a higher performance.Comment: Accepted at Electronic Imaging 201