An Universal Image Attractiveness Ranking Framework

We propose a new framework to rank image attractiveness using a novel pairwise deep network trained with a large set of side-by-side multi-labeled image pairs from a web image index. The judges only provide relative ranking between two images without the need to directly assign an absolute score, or rate any predefined image attribute, thus making the rating more intuitive and accurate. We investigate a deep attractiveness rank net (DARN), a combination of deep convolutional neural network and rank net, to directly learn an attractiveness score mean and variance for each image and the underlying criteria the judges use to label each pair. The extension of this model (DARN-V2) is able to adapt to individual judge's personal preference. We also show the attractiveness of search results are significantly improved by using this attractiveness information in a real commercial search engine. We evaluate our model against other state-of-the-art models on our side-by-side web test data and another public aesthetic data set. With much less judgments (1M vs 50M), our model outperforms on side-by-side labeled data, and is comparable on data labeled by absolute score.Comment: Accepted by 2019 Winter Conference on Application of Computer Vision (WACV

Deliberation Model for On-Device Spoken Language Understanding

We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU), where a streaming automatic speech recognition (ASR) model produces the first-pass hypothesis and a second-pass natural language understanding (NLU) component generates the semantic parse by conditioning on both ASR's text and audio embeddings. By formulating E2E SLU as a generalized decoder, our system is able to support complex compositional semantic structures. Furthermore, the sharing of parameters between ASR and NLU makes the system especially suitable for resource-constrained (on-device) environments; our proposed approach consistently outperforms strong pipeline NLU baselines by 0.82% to 1.34% across various operating points on the spoken version of the TOPv2 dataset. We demonstrate that the fusion of text and audio features, coupled with the system's ability to rewrite the first-pass hypothesis, makes our approach more robust to ASR errors. Finally, we show that our approach can significantly reduce the degradation when moving from natural speech to synthetic speech training, but more work is required to make text-to-speech (TTS) a viable solution for scaling up E2E SLU.Comment: Submitted to INTERSPEECH 202

Advances in Molecular Quantum Chemistry Contained in the Q-Chem 4 Program Package

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube

Efficiency evaluation of a specialist in the field of optical design during education and work

Our goal is to evaluate the efficiency of a specialist in the field of optical design. The classification of specialists is given depending on their level of education and work experience in the field of optical design. The analysis of factors that determine the efficiency of the specialist is performed, on the basis of which the equation for calculating efficiency is derived. As an example, we present empirical data for calculating the efficiency of some specialists

Multi-Criteria Optimization of Refinery

Vector optimization of refinery is discussed. As the target function, a multi-level hierarchy criterion convolution is used. This convolution is formed following the recurrent procedure based on a scalar invariant representing a linear combination of Hölder norms: the first order norm and the sup-norm. The issue of matrix representation of the suggested invariant has been analyzed; the procedure of building the respective matrix structure has been developed. The approach has been implemented in a respective package of application software programs

Multi-Criteria Optimization of Refinery

Vector optimization of refinery is discussed. As the target function, a multi-level hierarchy criterion convolution is used. This convolution is formed following the recurrent procedure based on a scalar invariant representing a linear combination of Hölder norms: the first order norm and the sup-norm. The issue of matrix representation of the suggested invariant has been analyzed; the procedure of building the respective matrix structure has been developed. The approach has been implemented in a respective package of application software programs

Method of searching for global extremum of a continuous function on a simplex

A non-convex problem of mathematical programming is considered, which permissible region is a simplex. A two-stage algorithm is proposed for approximate solution of the problem. The region of global optimum is determined using the Ψ-transform method at the first stage; local “fine-tuning” of the solution is performed at the second stage. The Ψ-transform was modified taking into account the special features of the problem under consideration. Ψ-function is determined according to the results of statistical tests implemented using the generator of random points uniformly distributed over the simplex. The proposed method of reflection of regular simplexes is used for fine-tuning of the solution. An example of application of the developed algorithm for solving the problem of optimization of component composition of the hydrocarbon mixture is presented