Generalized Trust, External Sourcing, and Firm Performance in Economic Downturns

Going beyond prior research that has focused on dyadic, party-specific trust, this study investigates the importance of generalized trust, which is not specific to a counterparty and originates from a broader context. We analyze how generalized trust at the regional level affects the extent to which a firm relies on external suppliers and the performance effects of doing so. Furthermore, we assess how these relationships are impacted by an economic downturn. We exploit differences in generalized trust across 145 regions in 12 European countries and use data on more than a million small- and medium-sized enterprises (SMEs) before and during the Great Financial Crisis (from 2008 to 2010). Control variables are selected via a double-selection procedure based on machine learning. We find that firms in high generalized trust regions, compared with those in low generalized trust regions, source more externally (but do not reduce external sourcing less in an economic downturn) and benefit more from external sourcing during an economic downturn.

Radial greed algorithm with rectified chromaticity for anchorless region proposal applied in aerial surveillance

In aerial images, human figures are often rendered at low resolution and in relatively small sizes compared to other objects in the scene, or resemble likelihood to other non-human objects. The localization of trust regions for possible containment of the human figure becomes difficult and computationally exhaustive. The objective of this work is to develop an anchorless region proposal which can emphasize potential persons from other objects and the vegetative background in aerial images. Samples are taken from different angles, altitudes and environmental factors such as illumination. The original image is rendered in rectified color space to create a pseudo-segmented version where objects of close chromaticity are combined. The geometric features of segments formed are then calculated and subjected to Radial-Greed Algorithm where segments resembling human figures are selected as the proposed regions for classification. The proposed method achieved 96.76% less computational cost against brute sliding window method and hit rate of 95.96%. In addition, the proposed method achieved 98.32 % confidence level that it can hit target proposals at least 92% every time.

Trust and Local Bias

In this article, we examine the effect of social trust on local bias. Our evidence suggests that institutional investors located in high-trust regions of the United States exhibit lower local bias. Moreover, we find that high-trust investors are better diversified, suggesting that trust helps accomplish greater diversification. The results are not due to firm, demographic, or local economic characteristics. Additional analysis reveals that the documented informational advantage in local holdings exists only in low-trust regions. We show that this finding is consistent with a trust explanation.

Robust Nonlinear Newton Solver With Adaptive Interface-Localized Trust Regions

Summary The interplay of multiphase-flow effects and pressure/volume/temperature behavior encountered in reservoir simulations often provides strongly coupled nonlinear systems that are challenging to solve numerically. In a sequentially implicit method, many of the essential nonlinearities are associated with the transport equation, and convergence failure for the Newton solver is often caused by steps that pass inflection points and discontinuities in the fractional-flow functions. The industry-standard approach is to heuristically chop timesteps and/or dampen updates suggested by the Newton solver if these exceed a predefined limit. Alternatively, one can use trust regions (TRs) to determine safe updates that stay within regions that have the same curvature for numerical flux. This approach has previously been shown to give unconditional convergence for polymer- and waterflooding problems, also when property curves have kinks or near-discontinuous behavior. Although unconditionally convergent, this method tends to be overly restrictive. Herein, we show how the detection of oscillations in the Newton updates can be used to adaptively switch on and off TRs, resulting in a less-restrictive method better suited for realistic reservoir simulations. We demonstrate the performance of the method for a series of challenging test cases ranging from conceptual 2D setups to realistic (and publicly available) geomodels such as the Norne Field and the recent Olympus model from the Integrated Systems Approach for Petroleum Production (ISAPP) optimization challenge.