Solución de predicción de temperaturas usando datos de un simulador térmico

The industry of integrated circuits is experiencing a moment of fierce change. As is, the methods used in all stages implied in its design process. The present work presents a method to predict temperatures for System on Chip (SoC) chiplet part with quite simple power map and a single thermal interface material using Machine Learning (ML) and its offspring Deep Learning (DL). The SoC part is represented as a response surface of a 2D model geometry surface used for a set of experiments to determine the relevant factors for the temperature prediction. In addition to the experiment design, a deployment strategy to implement a continuous integration and deployment process to be used for the target organization is also proposed. The idea is to achieve the principle of productive ML that states that models should be constantly learning by automating new data ingestion into the training process to enhance model performance in each of the cycle updates. The project proposes a method to strengthen the established thermal processes of the target organization by using ML tools and provide an alternative to speed up thermal model analysis using new available techniques derived from ML and Deep Learning

The Political Economy of Indirect Control

This paper characterizes the efficient sequential equilibrium when a government uses indirect control to exert its authority. We develop a dynamic principal-agent model in which a principal (a government) delegates the prevention of a disturbance—such as riots, protests, terrorism, crime, or tax evasion—to an agent who has an advantage in accomplishing this task. Our setting is a standard dynamic principal-agent model with two additional features. First, the principal is allowed to exert direct control by intervening with an endogenously determined intensity of force which is costly to both players. Second, the principal suffers from limited commitment. Using recursive methods, we derive a fully analytical characterization of the likelihood, intensity, and duration of intervention. The first main insight from our model is that repeated and costly interventions are a feature of the efficient equilibrium. This is because they serve as a punishment to induce the agent into desired behavior. The second main insight is a detailed analysis of a fundamental tradeoff between the intensity and duration of intervention which is driven by the principal’s inability to commit. Finally, we derive sharp predictions regarding the impact of various factors on likelihood, intensity, and duration of intervention. We discuss these results in the context of some historical episodes.

Considerações sobre recuperação de áreas alteradas por atividades agropecuária e florestal na Amazônia brasileira.

bitstream/item/60886/1/CPATU-Doc83.pd

Conservação de recursos fitogenéticos em quintais agroflorestais em Mazagão, Amapá.

O objetivo do trabalho foi estudar quintais agroflorestais quanto à conservação de recursos genéticos vegetais em Mazagão. Foi realizado o levantamento da composição botânica de quatro quintais, sendo três em área de terra firme e um em várzea. O número de famílias variou de 13 a 31, com intervalo de 26 a 63 espécies. As espécies frutíferas foram as mais frequentes em todos os quintais estudados e quando somadas às madeiráveis e medicinais totalizavam cerca de 90% de toda a composição botânica encontrada

Bases técnicas e referenciais para o programa de restauração florestal do Pará: um bilhão de árvores para a Amazônia.

Bases conceituais e definições; Características do programa; Referencial para a estruturação do programa; Ações estruturantes; Monitoramento e avaliação do programa; Parcerias e articulações intersetoriais e interinstitucionais.bitstream/item/117238/1/Revista-para-Desenvolvimento-No.-2-Bilhao-de-Arvores.pd