Compactifying the Kerr-Schild Double Copy

We show that the classical double copy relationship for Kerr-Schild spacetimes can be dimensionally reduced to give a natural notion of the double copy for Kaluza-Klein theory with gravity coupled to a gauge field and a dilaton. Under dimensional reduction the Kerr-Schild (KS) ansatz becomes the stringy Kerr-Schild (sKS) ansatz introduced by Wu. This ansatz captures many Kaluza-Klein black hole solutions, including single-charge black holes arising in both gauged and ungauged supergravity theories. We identify the single copy gauge field and scalar field of an arbitrary sKS solution. We show that the boost-reduction procedure for generating charged black hole solutions can be generalized to any stationary KS seed, and used to combine the metric with the zeroth and single copies of that seed into a single sKS solution. Furthermore, we comment on multi-charge solutions that can be written in a multi-sKS form, proposing a double copy interpretation involving multiple single copy sectors.Comment: 20 page

Wiley encyclopedia of management

Definition of time-based competition as included in the Wiley Encyclopedia of Management.peer-reviewe

Exploring the contribution of motivation and experience in the post-pubescent own-gender bias in face recognition

The own-gender bias in face recognition has been hypothesised to be the result of extensive experience with own-gender faces, coupled with a motivation to process own-group faces more deeply than other-group faces. We test the effect of experience and motivation in four experiments employing standard old/new recognition paradigms. In Experiment 1, no own-gender recognition bias was observed following an attractiveness-rating encoding task regardless of school type (single- or mixed-sex). Experiment 2, which used a distinctiveness-rating encoding task, did find a significant own-gender bias for all groups of participants. Experiment 3 on adults found that the own-gender bias was not affected by self-reported contact with the other-gender, but the encoding task did moderate the size of the bias. Experiment 4 revealed that participants with an own-gender sexual orientation showed a stronger own-gender bias. These results indicate that motivational factors influence the own-gender bias whereas no evidence was found for perceptual experience

AI Loyalty: A New Paradigm for Aligning Stakeholder Interests

When we consult a doctor, lawyer, or financial advisor, we assume that they are acting in our best interests. But what should we assume when we interact with an artificial intelligence (AI) system? AI-driven personal assistants, such as Alexa and Siri, already serve as interfaces between consumers and information on the Web, and users routinely rely upon these and similar systems to take automated actions or provide information. Superficially, they may appear to be acting according to user interests, but many are designed with embedded conflicts of interests. To address this problem, we introduce the concept of AI loyalty. AI systems are loyal to the degree that they minimize and make transparent, conflicts of interest, and act in ways that prioritize the interests of users. Loyal AI products hold obvious appeal for the end-user and could promote the alignment of the long-term interests of AI developers and customers. To this end, we suggest criteria for assessing whether an AI system is acting in a manner that is loyal to the user, and argue that AI loyalty should be deliberately considered during the technological design process alongside other important values in AI ethics, such as fairness, accountability privacy, and equity