Kleber Mendonça Filho wants his films to reclaim lost history. For Kleber Mendonça Filho, filmmaking is an act of both provocation and preservation. Mendonça was born in 1968, in the early years of a ruthless military dictatorship--a time when cinema, like much else, was harshly constrained. His mother, Joselice Jucá, was a historian who studied Brazil's abolitionist movement, and she taught him that filling gaps in the cultural memory was a way to expose concealed truths. His relationship with film is inextricably linked with his home town, Recife--a port city where attractive beaches and high-rise developments coexist with sprawling favelas and rampant crime. In his youth, Mendonça was fascinated by the city's grand cinema palaces. He carried a Super 8 camera to the tops of marquees and shot dizzying images; he spent hours in projection booths, learning the mechanics of how films reached the screen. Over time, Mendonça watched those theatres fall into decline, an experience that he likened to being aboard a ship as it wrecked. But even as Recife lost its allure, he made the city a fixture of his films--a way of vindicating its place in history. His first narrative feature, "Neighboring Sounds," takes place on a street where he lived as a child, a setting that he spent years documenting. Later, he made "Pictures of Ghosts," a documentary about Recife told largely through its cinemas.

--This paper presents a multitask learning approach based on long-short-term memory (LSTM) networks for the joint prediction of arboviral outbreaks and case counts of dengue, chikungunya, and Zika in Recife, Brazil. Leveraging historical public health data from DataSUS (2017-2023), the proposed model concurrently performs binary classification (outbreak detection) and regression (case forecasting) tasks. A sliding window strategy was adopted to construct temporal features using varying input lengths (60, 90, and 120 days), with hyperparameter optimization carried out using Keras T uner . Model evaluation used time series cross-validation for robustness and a held-out test from 2023 for generalization assessment. The results show that longer windows improve dengue regression accuracy, while classification performance peaked at intermediate windows, suggesting an optimal trade-off between sequence length and generalization. The multitask architecture delivers competitive performance across diseases and tasks, demonstrating the feasibility and advantages of unified modeling strategies for scalable epidemic forecasting in data-limited public health scenarios.

In the late 1990s, Recife, on Brazil's northeastern coast, was in decline. Its picturesque historic center, made up of 17th century colonial buildings with Dutch, Portuguese, and French influences, had plunged into neglect, reflecting a deep economic crisis worsened by deindustrialization. Many young people were fleeing the city for opportunities in the commercial centers of São Paulo and Rio de Janeiro, or heading overseas. In 2000, a group of businesspeople, government officials, and academics came up with a vision to regenerate Recife's historic center by building a new technology district. With 33 million reais ($6.8 million) raised from the privatization of the local electricity company, they created Porto Digital, a nonprofit organization with the mission of turning Recife into a hub for technology and the creative industries.

To enable data-driven scenario planning, we take the flows is, therefore, a requisite to better plan urban areas. In this first steps in leveraging the Geo-contextual Multitask Embedding context, an important task is to study hypothetical scenarios in Learner (GMEL) model, previously proposed in Liu et al. [16], as our which possible future changes are evaluated. For instance, how the base model for predicting commuting flows based on geographic increase in residential units or transportation modes in a neighborhood information (e.g., infrastructure, land use, transportation). Commuting will change the commuting flows to or from that region? In flows are defined as flows between a workers' residence this paper, we propose to leverage GMEL, a recently introduced location and a workplace location. While major cities have the resources graph neural network model, to evaluate changes in commuting to collect and process high-resolution land use data, other flows taking into account different land use and infrastructure scenarios.

This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS) [1], a traditional league in the Latin American Robotics Competition (LARC). In the VSSS league, two teams of three small robots play against each other. We propose a simulated environment in which continuous or discrete control policies can be trained, and a Sim-to-Real method to allow using the obtained policies to control a robot in the real world. The results show that the learned policies display a broad repertoire of behaviors which are difficult to specify by hand. This approach, called VSSS-RL, was able to beat the human-designed policy for the striker of the team ranked 3rd place in the 2018 LARC, in 1-vs-1 matches.