The Social Consequences of the Crisis
A factor to be taken into consideration in evaluating the social consequences of the financial crisis and subsequent recession is the long-period trend to be seen towards increasing social inequality. In the case of Italy the data reveal symptoms of the social gap appearing even before the recent crisis, but growing inequality is by no means limited to Italy. Indeed, the trends towards inequality have led sociologists to abandon the image of confluence at the centre, putting in its place that of the social gap. The crisis of deregulation brings back to the fore the possibility of considering organic measures to relaunch the economy, and new agreements to promote social cohesion.
Comparing Greece and Portugal: Common employment trajectories but divergent outcomes?
The author investigates the implementation of labour policy reforms as part of the rescue programmes adopted in Greece and Portugal during the Great Recession. Both countries were strongly pressurized to reform their labour markets. However, while the content of policies was the same the changes then followed different pathways. Rather than considering path dependency and convergence as mutually exclusive, in this article the author contends that the concepts interact with each other and forge particular policy outcomes. Looking through the lens of a ‘contingent divergence’, it is argued that while the rescue programmes suggest a common policy trajectory of neoliberalism determining the content of policies, variation in the political ownership of the reform programmes and the subsequent disparity in reform implementation, government approaches to social dialogue, public policy choices and programme design faults have played their part in the diverse policy outcomes. Considering this, path dependence pressures account for divergence, while the common direction of the reforms defines the field within which the former takes place.
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.