A invalidação do termo de ajustamento de conduta

Tece considerações acerca da anulação do termo de ajustamento de conduta. Discute a natureza jurídica do ajustamento de conduta, tratando de seus requisitos de validade e de sua eficácia. Analisa posições doutrinárias e menciona a inaplicabilidade dos princípios do direito privado ao ajustamento de conduta

Does Cell Lineage in the Developing Cerebral Cortex Contribute to its Columnar Organization?

Since the pioneer work of Lorente de Nó, Ramón y Cajal, Brodmann, Mountcastle, Hubel and Wiesel and others, the cerebral cortex has been seen as a jigsaw of anatomic and functional modules involved in the processing of different sets of information. In fact, a columnar distribution of neurons displaying similar functional properties throughout the cerebral cortex has been observed by many researchers. Although it has been suggested that much of the anatomical substrate for such organization would be already specified at early developmental stages, before activity-dependent mechanisms could take place, it is still unclear whether gene expression in the ventricular zone (VZ) could play a role in the development of discrete functional units, such as minicolumns or columns. Cell lineage experiments using replication-incompetent retroviral vectors have shown that the progeny of a single neuroepithelial/radial glial cell in the dorsal telencephalon is organized into discrete radial clusters of sibling excitatory neurons, which have a higher propensity for developing chemical synapses with each other rather than with neighboring non-siblings. Here, we will discuss the possibility that the cell lineage of single neuroepithelial/radial glia cells could contribute for the columnar organization of the neocortex by generating radial columns of sibling, interconnected neurons. Borrowing some concepts from the studies on cell–cell recognition and transcription factor networks, we will also touch upon the potential molecular mechanisms involved in the establishment of sibling-neuron circuits

Administração pública democrática e o controle pelo Ministério Público

Versa sobre o princípio democrático da administração pública, tendo em vista o papel do Ministério Público na observância da exequibilidade do referido princípio no campo da gestão pública e a efetividade dos mecanismos de controle administrativo

END-TO-END PERFORMANCE ANALYSIS OF A RESOURCE ALLOCATION SERVICE

This dissertation is centered around the monitoring and control platform of the company Skyline Communications, the DataMiner. This platform has a specific module called SRM (Service and Resource Management). One of SRM’s many features is the capacity to make an advance reservation (booking) of resources of the client’s network. When a booking is created, there is a time interval/delay between the moment that a booking is requested and the moment that all necessary configurations for this booking actually start to be made at the Resource level. This delay is called SyncTime. The SyncTime is affected by the dynamics of the network (e.g, a sudden increase in the number of bookings made, at a given time). In order to guarantee the maximum possible quality of service to the client and ensure that the network dynamics will have minimal impact in the real-time delivery of the desired content, the SRM module must be able to estimate if a booking can be done in an acceptable SyncTime value. Given this problem, the main goal of this dissertation is to develop a machine learning based estimation/classification module that is capable of, based on the temporal state of the SRM module, make a prediction or classification of the SyncTime. Two approaches were considered: Classify the SyncTime based on classes or estimate the value of it. In order to test both approaches, we implemented several traditional machine learning methods as well as, several neural networks. Both approaches were tested using a dataset collected from a DataMiner cluster composed of three DataMiner agents using software developed in this dissertation. In order to collect the dataset, we considered several setups that captured the cluster in different network conditions. By comparing both approaches, the results suggested that classifying the predicted SyncTime using a classification model and classifying the predicted SyncTime of a estimation model are both equally good options. Furthermore, based on the results of all implementations, a prototype application was also developed. This application was fully developed in Python and it uses a Multilayer Perceptron in order to do the classification of the SyncTime of a booking, based on several inputs given by the user.Esta dissertação centra-se na plataforma de monitorização e controlo da empresa Skyline Communications, o DataMiner. Esta plataforma possui um módulo específico denominado de SRM (Service and Resource Management). Uma das características do SRM é a capacidade de fazer uma reserva antecipada (booking) dos recursos da rede de um cliente. Quando uma reserva é criada, existe um intervalo de tempo/atraso entre o momento em que uma reserva é solicitada e o momento em que todas as configurações necessárias para a mesma realmente começam a ser feitas ao nível de Recurso do DataMiner. Este atraso é chamado de SyncTime. O SyncTime é afetado pela dinâmica da rede (por exemplo, um aumento repentino no número de reservas feitas num determinado momento). De forma a assegurar a máxima qualidade de serviço possível ao cliente e garantir que a dinâmica da rede tenha o mínimo impacto na entrega em tempo real do conteúdo desejado, o módulo SRM deve ser capaz de estimar se uma reserva pode ser feita com um valor de SyncTime aceitável. Diante deste problema, o principal objetivo desta dissertação é desenvolver um módulo de regressão/classificação baseado em aprendizagem automática (machine learning) que seja capaz de fazer uma previsão do valor do SyncTime ou classificação do mesmo, com base no estado temporal do módulo SRM. Duas abordagens foram consideradas: Classificar o SyncTime com base em classes ou estimar o seu valor. Para testar as mesmas, implementou-se vários métodos tradicionais de machine learning, bem como várias redes neuronais. Ambas as abordagens foram testadas utilizando um conjunto de dados recolhido de um cluster composto por três agentes DataMiner usando software desenvolvido nesta dissertação. Para a recolha dos dados, considerou-se várias configurações que capturam o cluster em diferentes condições. Ao comparar ambas as abordagens, os resultados sugerem que classificar o SyncTime usando um modelo de classificação e classificar o valor do SyncTime previsto por um modelo de regressão são ambas boas opções. Com base nos resultados obtidos, foi criada ainda uma aplicação protótipo. Esta foi totalmente desenvolvida em Python e utiliza um Multilayer Perceptron para realizar a classificação do SyncTime de uma reserva, a partir dos dados introduzidos pelo utilizador

Financing Chain Associations Industry Speaks

industry associations, contribution systems, citriculture, Fundercitus, Agribusiness, Agricultural Finance, Industrial Organization, Marketing,

ARK: augmented reality kiosk

This paper aims at presenting a very first prototype of an Augmented Reality (AR) system that as been developed in recent months at our research group. The prototype adopts a kiosk format and allows users to directly interact with an AR environment using a conventional data glove. The most relevant feature of this environment is the use of a common monitor to display AR images, instead of employing specific Head-Mounted Displays. By integrating a half-silvered mirror and a black virtual hand, our solution solves the occlusion problem that normally occurs when a user interacts with a virtual environment displayed by a monitor or other projection system