Stable p-wave resonant two-dimensional Fermi-Bose dimers

We consider two-dimensional weakly-bound heterospecies molecules formed in a Fermi-Bose mixture with attractive Fermi-Bose and repulsive Bose-Bose interactions. Bosonic exchanges lead to an intermolecular attraction, which can be controlled and tuned to a p-wave resonance. Such attractive fermionic molecules can be realized in quasi-two-dimensional ultracold isotopic or heteronuclear mixtures. We show that they are stable with respect to the recombination to deeply-bound molecular states and with respect to the formation of higher-order clusters (trimers, tetramers, etc.

Use of investment project implementation mechanism under production sharing agreement for the development of oil and gas

Strengthening the investment attractiveness of economic systems of Russia’s mineral raw material specialization by means of oil and gas project implementation under a production sharing agreement. Purpose of the article is formation of conceptual approaches to the use of the mechanism for implementing investment projects under a product sharing agreement in the development of oil and gas bearing regions of Russia. The techniques and methods of system analysis are used for understanding the procedural and institutional, economic and financial nature of the proposed mechanism for implementing investment projects under a PSA model in the development of oil and gas bearing territories of Russia.peer-reviewe

Cross-Shape Graph Convolutional Networks

We present a method that processes 3D point clouds by performing graph convolution operations across shapes. In this manner, point descriptors are learned by allowing interaction and propagation of feature representations within a shape collection. To enable this form of non-local, cross-shape graph convolution, our method learns a pairwise point attention mechanism indicating the degree of interaction between points on different shapes. Our method also learns to create a graph over shapes of an input collection whose edges connect shapes deemed as useful for performing cross-shape convolution. The edges are also equipped with learned weights indicating the compatibility of each shape pair for cross-shape convolution. Our experiments demonstrate that this interaction and propagation of point representations across shapes make them more discriminative. In particular, our results show significantly improved performance for 3D point cloud semantic segmentation compared to conventional approaches, especially in cases with the limited number of training examples