Proportionality: A Way Forward for Resolving Religious Claims?
This essay gives an introduction to the idea of proportionality as a public law concept and argues that it is ideally suited to deciding conflicts between religious claims and the public interest or the rights of others. In particular it argues that a fact-specific resolution of claims can reduce societal tensions.
Should Religious Groups Be Exempt from Civil Rights Laws?
Should a private religious university lose its tax exempt status if it bans interracial dating? Should a religious school be able fire a pregnant married teacher because her continued work would violate the church's view that mothers of young children should not work outside the home? Should a religious social service agency, such as Catholic Charities, be exempt from a state regulation banning discrimination in the delivery of social services on the basis of sexual orientation? Should religious organizations be exempt from civil rights laws? This article argues that these questions raised difficult normative issues that have been answered practically by reference to the varying effects of historical social movements, producing the differential treatment of race, gender, and sexual orientation laws. The article explores avenues for negotiating solutions other than full exemptions or no exemptions. Besides the instrumental goal of solving - or avoiding - complex political and legal problems, this question of stance injects the dimensions of virtue ethics and value-added negotiation. In so doing the article proposes ways to pursue productive stances toward clashes over religious exemption claims is highly relevant to sustaining and replenishing both American pluralism and constitutional protections for minority groups.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.