High harmonic spectra computed using time-dependent Kohn-Sham theory with Gaussian orbitals and a complex absorbing potential

High harmonic spectra for H2 are simulated by solving the time-dependent Kohn-Sham equation in the presence of a strong laser field, using an atom-centered Gaussian representation of the orbitals and a complex absorbing potential to mitigate artifacts associated with the finite extent of the basis functions, such as spurious reflection of the outgoing electronic wave packet. Interference between the outgoing and reflected waves manifests in the Fourier transform of the time-dependent dipole moment function and leads to peak broadening in the high harmonic spectrum as well as the appearance of spurious peaks at energies well above the cutoff energy at which the harmonic progression is expected terminate. We demonstrate that well-resolved spectra can be obtained through the use of an atom-centered absorbing potential. As compared to grid-based algorithms for solving the time-dependent Kohn-Sham equations, the present approach is more readily extendible to larger polyatomic molecules.

Analysis of the Transformation of Random Signals and Noise Using Poly-Gaussian Models

Analysis performed transformation of random signals and noise in linear and nonlinear systems based on the use of poly-Gaussian models and multidimensional PDF of the output paths of information-measuring and radio systems. The classification of elements of these systems, as well as expressions describing the input action and output response of the system are given. It is shown that the analysis of information-measuring and systems can be carried out using poly-Gaussian models. The analysis is carried out with a series connection of a linear system and a nonlinear element, a series connection of a nonlinear element and a linear system, as well as with a parallel connection of the named links. The output response in all cases will be a mixture of a poly-Gaussian distribution with a number of components. An example of the analysis of signal transmission through an intermediate frequency amplifier and a linear detector against a background of non-Gaussian noise is given. The resulting probability density distribution of the sum of the signal and non-Gaussian noise at the output of the detector will be poly-Rice. The multidimensional probability distribution density of the output processes of the nonlinear signal envelope detector is also obtained. The results of modeling the found distribution densities are presented. It is shown that the use of the poly-Gaussian representation of signals and noise, as well as the impulse response of the system, makes it possible to effectively analyze inertial systems in the time domain.

Interpretable and effective hashing via Bernoulli variational auto-encoders

Due to the rapid increase in the amount of data generated in many fields of science and engineering, information retrieval methods tailored to large-scale datasets have become increasingly important in the last years. Semantic hashing is an emerging technique for this purpose that works on the idea of representing complex data objects, like images and text, using similarity-preserving binary codes that are then used for indexing and search. In this paper, we investigate a hashing algorithm that uses a deep variational auto-encoder to learn and predict the codes. Unlike previous approaches of this type, that learn a continuous (Gaussian) representation and then project the embedding to obtain hash codes, our method employs Bernoulli latent variables in both the training and the prediction stage. Constraining the model to use a binary encoding allow us to obtain a more interpretable representation for hashing: each factor in the generative model represents a bit that should help to reconstruct and thus identify the input pattern. Interestingly, we found that the binary constraint does not lead to a loss but an increase of search accuracy. We argue that continuous formulations learn a representation that can significantly differ from the code used for search. Minding this gap in the design of the auto-encoder can translate into more accurate retrieval results. Extensive experiments on seven datasets involving image data and text data illustrate these findings and demonstrate the advantages of our approach.

Gaussian Representations of K-Means Clusters: Case Study of Educational Process Mining of UCI

The information of data patterns can help determining analytical direction, choosing right tools for analysis, and validating inferential results. However, this argument might not be helpful because of diverse patterns. To disclose inside information, a learning approach about data clustering is proposed by integrating K-means and Gaussian representation from data science. It gets insight of similar and dominant distribution through iterative learning. Its core technique lies in the design of data representation which can carry similarity and dominance characteristics from samples to K-learning. For illustration, it is applied in the educational process mining of UCI. Its results can provide strategic information for educational activities.

Development and evaluation of pollen source methodologies for the Victorian Grass Pollen Emissions Module VGPEM1.0

We present the first representation of grass pollen in a 3-D dispersion model in Australia, tested using observations from eight counting sites in Victoria. The region's population has high rates of allergic rhinitis and asthma, and this has been linked to the high incidence of grass pollen allergy. Despite this, grass pollen dispersion in the Australian atmosphere has not been studied previously, and its source strength is untested. We describe 10 pollen emission source methodologies examining the strengths of different immediate and seasonal timing functions, and the spatial distribution of the sources. The timing function assumes a smooth seasonal term, modulated by an hourly meteorological function. A simple Gaussian representation of the pollen season worked well (average r=0.54), but lacked the spatial and temporal variation that the satellite-derived enhanced vegetation index (EVI) can provide. However, poor results were obtained using the EVI gradient (average r=0.35), which provides the timing when grass turns from maximum greenness to a drying and flowering period; this is due to noise in the spatial and temporal variability from this combined spatial and seasonal term. Better results were obtained using statistical methods that combine elements of the EVI dataset, a smooth seasonal term and instantaneous variation based on historical grass pollen observations (average r=0.69). The seasonal magnitude is inferred from the maximum winter-time EVI, whereas the timing of the season peak is based on the day of the year when the EVI falls to 0.05 below its winter maximum. Measurements are vital to monitor changes in the pollen season, and the new pollen measurement sites in the Victorian network should be maintained.

Development and evaluation of pollen source methodologies for the Victorian Grass Pollen Emissions Module VGPEM1.0

We present the first representation of grass pollen in a 3D dispersion model anywhere in Australia, tested using observations from eight counting sites in Victoria. The region's population has high rates of allergic rhinitis and asthma, and this has been linked to the high incidence of grass pollen allergy. Despite this, grass pollen dispersion in the Australian atmosphere has not been studied previously, and its source strength is untested. We describe ten pollen emission source methodologies examining the strengths of different immediate and seasonal timing functions, and spatial distribution of the sources. The timing function assumes a smooth seasonal term, modulated by an hourly meteorological function. A simple Gaussian representation of the pollen season worked well (average r = 0.54), but lacks the spatial and temporal variation that the satellite-derived Enhanced Vegetation Index (EVI) data can provide. However poor results were obtained using the EVI gradient (average r = 0.35), which gives the timing when grass turns from maximum greenness to a drying and flowering period; this is due to the greater spatial and temporal variability from this combined spatial and seasonal term. Better results were obtained using statistical methods that combine elements of the EVI dataset, a smooth seasonal term and instantaneous variation based on historical grass pollen observations (average r = 0.69). The seasonal magnitude is inferred from the maximum winter-time EVI, while the timing of the peak of the season was based on the day of the year when the EVI falls to 0.05 below its winter maximum. Measurements are vital to monitor changes in the pollen season, and the new pollen measurement sites in the Victorian network should be maintained.