Human Dimensions of Marine Fisheries: Using GIS to Illustrate Land-Sea Connections in the Northeast U.S. Herring, Clupea harengus, Fishery

Geographic Information Systems can help improve ocean literacy and inform our understanding of the human dimensions of marine resource use. This paper describes a pilot project where GIS is used to illustrate the connections between fish stocks and the social, cultural, and economic components of the fishery on land. This method of presenting and merging qualitative and quantitative data represents a new approach to assist fishery managers, participants, policy-makers, and other stakeholders in visualizing an often confusing and poorly understood web of interactions. The Atlantic herring fishery serves as a case study and maps from this pilot project are presented and methods reviewed

Convergence of a linearly transformed particle method for aggregation equations

We study a linearly transformed particle method for the aggregation equation with smooth or singular interaction forces. For the smooth interaction forces, we provide convergence estimates in $L^1$ and $L^\infty$ norms depending on the regularity of the initial data. Moreover, we give convergence estimates in bounded Lipschitz distance for measure valued solutions. For singular interaction forces, we establish the convergence of the error between the approximated and exact flows up to the existence time of the solutions in $L^1 \cap L^p$ norm

Uniform convergence of a linearly transformed particle method for the Vlasov-Poisson system

International audienceA particle method with linear transformation of the particle shape functions is studied for the 1d-1v Vlasov-Poisson equation, and a priori error estimates are proven which show that the approximated densities converge in the uniform norm. When compared to standard fixed-shape particle methods, the present approach can be seen as a way to gain one order in the convergence rate of the particle trajectories at the cost of linearly transforming each particle shape. It also allows to compute strongly convergent densities with particles that overlap in a bounded way

The Neural Representation Benchmark and its Evaluation on Brain and Machine

A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible to directly test representational learning algorithms directly against the representations contained in neural systems. Here, we propose a new benchmark for visual representations on which we have directly tested the neural representation in multiple visual cortical areas in macaque (utilizing data from [Majaj et al., 2012]), and on which any computer vision algorithm that produces a feature space can be tested. The benchmark measures the effectiveness of the neural or machine representation by computing the classification loss on the ordered eigendecomposition of a kernel matrix [Montavon et al., 2011]. In our analysis we find that the neural representation in visual area IT is superior to visual area V4. In our analysis of representational learning algorithms, we find that three-layer models approach the representational performance of V4 and the algorithm in [Le et al., 2012] surpasses the performance of V4. Impressively, we find that a recent supervised algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of IT for an intermediate level of image variation difficulty, and surpasses IT at a higher difficulty level. We believe this result represents a major milestone: it is the first learning algorithm we have found that exceeds our current estimate of IT representation performance. We hope that this benchmark will assist the community in matching the representational performance of visual cortex and will serve as an initial rallying point for further correspondence between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected in this versio

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

The primate visual system achieves remarkable visual object recognition performance even in brief presentations and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations such as the amount of noise, the number of neural recording sites, and the number trials, and computational limitations such as the complexity of the decoding classifier and the number of classifier training examples. In this work we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353

PLANEJAMENTO E ESTRATÉGIA EM INSTITUIÇÕES ENSINO SUPERIOR PRIVADAS: UMA BUSCA POR VANTAGEM COMPETIVA

Planejamento e estratégia são pontos chave para quaisquer organização com o intúito de se destacar frente ao seu mercado de atuação. Portanto, não seria diferente para o mercado acadêmico e para as intituições universitárias privadas, a necessidade de uma atenção à estas etapas de gestão administrativa do negócio. Diante deste contexto, o artigo teve como objetivo apresentar uma análise sobre o planejamento estratégico para as instituições universitárias privadas com o auxílio de duas metodologias mundialmente conhecidas: Matriz SWOT e As 5 Forças de Porter. Em relação ao método, a pesquisa foi classificada como descritiva e explicativa, com uma abordagem qualitativa, sendo ainda classificada como bibliográfica e documental. Os resultados demonstram o quão necessário é o planejamento estratégico para as Instituições de Ensino Superior (IES) visto a exposição aos fatores do ambiente interno e externo, bem como, à agressiva concorrência em busca de uma fatia do setor acadêmico no mercado nacional e internacional

Optimization of the 2PRU-1PRS Parallel Manipulator Based on Workspace and Power Consumption Criteria

In the last few years, parallel manipulators are being increasingly studied and used for different applications. The performance of parallel manipulators is very sensitive to the geometric parameters, so it is essential to optimize them in order to obtain the desired function. We propose two optimization algorithms that consider the size and regularity of the workspace. The first one obtains the geometric parameters combination that results in the biggest and most regular workspace. The second method analyzes the geometric parameters combinations that result in an acceptable size of the workspace—even if it is not the biggest one—and finds out which ones result in the lowest power consumption. Even if the results vary depending on the application and trajectories studied, the proposed methodology can be followed to any type of parallel manipulator, application or trajectory. In this work we focus on the dimension optimization of the geometric parameters of the 2PRU-1PRS Multi-Axial Shaking Table (MAST) for automobile pieces testing purposes.This research was funded by the Regional Government of the Basque Country (IT949-16) and the Science and Innovation Ministry of the Spanish Government (PID2019-105262RB-I00)