The Politics of Gun Control and India's Internal Security
In the past two decades, security has come to be one of the most contested and debated concepts within the domain of international relations. Its state-centred contours interpreted narrowly as security of territory from external aggression, or as protection of national interests in foreign policy or as global security from the threat of nuclear holocaust, have today made way for a new security understanding that includes economic, environmental, cultural sectors and new security referents—societies, nonstate actors, individuals. This conceptual widening has been complemented at a practical level by a growing focus on conflicts and security challenges that stem from within states and which trace their roots to non-traditional sources such as drugs, terrorism, small arms, cyber war and trafficking in human beings.
Youth violence in Boston: Gun markets, serious youth offenders, and a use-reduction strategy
Since the mid-1980s, there has been a dramatic increase in youth gun violence.1 In most areas, juveniles and many other youth are legally prohibited from purchasing firearms, especially handguns. As a result, many firearms utilized in youth crimes are obtained through active and pervasive illicit gun markets. There is currently very little being done to address these illicit markets,2 while at the same time the capacity of police departments to design and implement creative new operational strategies (through "community" and "problem-solving" policing) is increasing. Approaches focusing on illicit firearms markets thus seem a logical and potentially productive strategy. Finally, while enforcement and prevention efforts have so far paid relatively small dividends in reducing youth gun violence, attacking illicit gun markets is an idea which at least has not yet failed.3
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.