Christopher Street March

General note: Title and date provided by Bettye Lane.

Christopher Street March

General note: Title and date provided by Bettye Lane.

Christopher Street March

General note: Title and date provided by Bettye Lane.

Christopher Street (Children of Gays)

General note: Title and date provided by Bettye Lane.

Christopher Street

General note: Title and date provided by Bettye Lane.

Women Against Pornography march down 42nd Street

General note: Title and date provided by Bettye Lane.

Christopher Street

General note: Title and date provided by Bettye Lane.

Women demonstrate against street harassment

General note: Title and date provided by Bettye Lane.

Women demonstrate against street harassment

General note: Title and date provided by Bettye Lane.

Christopher Street March

General note: Title and date provided by Bettye Lane.

Anti-war demonstrators sit in street in front of ITT on Park Avenue

General note: Title and date provided by Bettye Lane.

Italian-American Civil Rights League sign at the Fiesta of Maria S.S. della Grazie on Mulberry Street

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Copy-paste data augmentation for domain transfer on traffic signs

City streets carry a lot of information that can be exploited to improve the quality of the services the citizens receive. For example, autonomous vehicles need to act accordingly to all the element that are nearby the vehicle itself, like pedestrians, traffic signs and other vehicles. It is also possible to use such information for smart city applications, for example to predict and analyze the traffic or pedestrian flows. Among all the objects that it is possible to find in a street, traffic signs are very important because of the information they carry. This information can in fact be exploited both for autonomous driving and for smart city applications. Deep learning and, more generally, machine learning models however need huge quantities to learn. Even though modern models are very good at gener- alizing, the more samples the model has, the better it can generalize between different samples. Creating these datasets organically, namely with real pictures, is a very tedious task because of the wide variety of signs available in the whole world and especially because of all the possible light, orientation conditions and con- ditions in general in which they can appear. In addition to that, it may not be easy to collect enough samples for all the possible traffic signs available, cause some of them may be very rare to find. Instead of collecting pictures manually, it is possible to exploit data aug- mentation techniques to create synthetic datasets containing the signs that are needed. Creating this data synthetically allows to control the distribution and the conditions of the signs in the datasets, improving the quality and quantity of training data that is going to be used. This thesis work is about using copy-paste data augmentation to create synthetic data for the traffic sign recognition task.

Street children and their relationship with the police

Background: This paper explores and analyses the experiences of school-age street children. It specifically addresses the relationship of the street children who live on the streets of Sao Paulo (a large Brazilian metropolis), in relation to their experiences, with the policemen. Methods: The paper is a secondary analysis of date previously collected in 1999. The data were collected through individual semi-structured interviews, with 14 school-age children frequenting two city public refuges, with their legal guardians` consent. The text from transcribed interviews was organized according to the validity norms of `thematic analysis`, a technique of contents analysis method. The decomposing and reconstructing process of that analysis gave rise to thematic categories (among which `the police category`) that represented the reconstruction of the difficulties faced by the children in their development. Results and discussion: The children portrayed the police as an enemy, a fearful figure and one of the most agonizing street experiences. Rarely did the police have a positive image to them. According to the children, police violence occurs in three forms: through systematic police persecution in an effort to remove the children from the streets against their will; actions that had the deliberate intent to humiliate them with verbal or physical aggression; and through alleged sexual abuse, revealed by the children in a veiled manner. The authority that is supposedly intended to protect them is portrayed as one of the most feared social agents. Conclusion: The reported hostile behaviour of the policemen shows the state of vulnerability of those children living on the street. This situation must be focused like a health problem because it causes injury to development of children. Nurses can help them through organizing assistance to children in situation of personal and social risk in the school nursing and health institution.