An adaptive ANOVA stochastic Galerkin method for partial differential equations with random inputs

It is known that standard stochastic Galerkin methods encounter challenges when solving partial differential equations with high dimensional random inputs, which are typically caused by the large number of stochastic basis functions required. It becomes crucial to properly choose effective basis functions, such that the dimension of the stochastic approximation space can be reduced. In this work, we focus on the stochastic Galerkin approximation associated with generalized polynomial chaos (gPC), and explore the gPC expansion based on the analysis of variance (ANOVA) decomposition. A concise form of the gPC expansion is presented for each component function of the ANOVA expansion, and an adaptive ANOVA procedure is proposed to construct the overall stochastic Galerkin system. Numerical results demonstrate the efficiency of our proposed adaptive ANOVA stochastic Galerkin method

Interval-Valued Intuitionistic Fuzzy Einstein Geometric Choquet Integral Operator and Its Application to Multiattribute Group Decision-Making

With respect to the multiattribute decision-making (MADM) problem in which the attributes have interdependent or interactive phenomena under the interval-valued intuitionistic fuzzy environment, we propose a group decision-making approach based on the interval-valued intuitionistic fuzzy Einstein geometric Choquet integral operator (IVIFEGC). Firstly, the Einstein operational laws and some basic principle on interval-valued intuitionistic fuzzy sets are introduced. Then, the IVIFEGC is developed and some desirable properties of the operator are studied. Further, an approach to multiattribute group decision-making with interval-valued intuitionistic fuzzy information is developed, where the attributes have interdependent phenomena. Finally, an illustrative example is used to illustrate the developed approach

Selling the Data Product: Pricing Strategies and Welfare Implications

This paper examines the pricing and welfare implications of data as a factor of production with a stylized economic model. We introduce a generalized framework that specifies two types of data: 1) public data pricing, which maximizes social welfare, and 2) commercial data pricing, which maximizes the profit. The model reveals two takeaways: first, two prices may converge in the data economy. It is due to that data come from citizens and may be used to create value back to them. Therefore, a profit- seeking data seller might find it optimal to extend the user base, which is in line with the interest of the welfare maximizer. Second, the pricing gap between optimal prices does not change monotonically with the improvement of data quality. These findings shed new light on the current and future of data product operations, particularly in the understudied public sectors

Caldesmon regulates the motility of vascular smooth muscle cells by modulating the actin cytoskeleton stability

<p>Abstract</p> <p>Background</p> <p>Migration of vascular smooth muscle cells (SMCs) from the media to intima constitutes a critical step in the development of proliferative vascular diseases. To elucidate the regulatory mechanism of vacular SMC motility, the roles of caldesmon (CaD) and its phosphorylation were investigated.</p> <p>Methods</p> <p>We have performed Transwell migration assays, immunofluorescence microscopy, traction microscopy and cell rounding assays using A7r5 cells transfected with EGFP (control), EGFP-wtCaD or phosphomimetic CaD mutants, including EGFP-A1A2 (the two PAK sites Ser452 and Ser482 converted to Ala), EGFP-A3A4 (the two Erk sites Ser497 and Ser527 converted to Ala), EGFP-A1234 (both PAK- and Erk-sites converted to Ala) and EGFP-D1234 (both PAK- and Erk-sites converted to Asp).</p> <p>Results</p> <p>We found that cells transfected with wtCaD, A1A2 or A3A4 mutants of CaD migrated at a rate approximately 50% more slowly than those EGFP-transfected cells. The migration activity for A1234 cells was only about 13% of control cells. Thus it seems both MAPK and PAK contribute to the motility of A7r5 cells and the effects are comparable and additive. The A1234 mutant also gave rise to highest strain energy and lowest rate of cell rounding. The migratory and contractile properties of these cells are consistent with stabilized actin cytoskeletal structures. Indeed, the A1234 mutant cells exhibited most robust stress fibers, whereas cells transfected with wtCaD or A3A4 (and A1A2) had moderately reinforced actin cytoskeleton. The control cells (transfected with EGFP alone) exhibited actin cytoskeleton that was similar to that in untransfected cells, and also migrated at about the same speed as the untransfected cells.</p> <p>Conclusions</p> <p>These results suggest that both the expression level and the level of MAPK- and/or PAK-mediated phosphorylation of CaD play key roles in regulating the cell motility by modulating the actin cytoskeleton stability in dedifferentiated vascular SMCs such as A7r5.</p

Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey

Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming at locating the oriented objects of numerous predefined object categories. Recently, deep learning based methods have achieved remarkable performance in detecting oriented objects in optical remote sensing imagery. However, a thorough review of the literature in remote sensing has not yet emerged. Therefore, we give a comprehensive survey of recent advances and cover many aspects of oriented object detection, including problem definition, commonly used datasets, evaluation protocols, detection frameworks, oriented object representations, and feature representations. Besides, the state-of-the-art methods are analyzed and discussed. We finally discuss future research directions to put forward some useful research guidance. We believe that this survey shall be valuable to researchers across academia and industr