The net profitability of airline alliances using referential dollars

This study revises a previous research in which we analysed the net profitability of airline alliances but did not control for the impact of inflation on such profitability. Using the same methodology, 15 international airlines as subjects and their net financial results for a period of 11 years as primary research variables, we now compared the performance of airlines before and after joining their respective alliances using referential dollars (i.e., constant dollars with 2010 as base year) instead of nominal dollars. The results showed a similar deterioration in short-term net profits after joining an alliance as the previous study did, and a similar behaviour of statistics tests. Thus, the conclusion then achieved still stand after this revision

Relative Entropy and Torsion Coupling

We evaluate the relative entropy on a ball region near the UV fixed point of a holographic conformal field theory deformed by a fermionic operator of nonzero vacuum expectation value. The positivity of the relative entropy considered here is implied by the expected monotonicity of decrease of quantum entanglement under RG flow. The calculations are done in the perturbative framework of Einstein-Cartan gravity in four-dimensional asymptotic anti-de Sitter space with a postulated standard bilinear coupling between axial fermion current and torsion. By requiring positivity of relative entropy, our result yields a constraint on axial current-torsion coupling, fermion mass and equation of state.Comment: 31 pages; match the version accepted by PR

Detecting Visual Relationships with Deep Relational Networks

Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. "ride") or each distinct visual phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships. On two large datasets, the proposed method achieves substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape