Atoll research bulletin.

no.531-542 (2005

Marine Protected Areas: Country Case Studies on Policy, Governance and Institutional Issues

This document presents case studies of the policy, governance and institutional issues of marine protected areas (MPAs) in South America (Northeastern)-Brazil; India, Palau and Senegal. It is the first of four in a global series of case studies on MPAs. An initial volume provides a synthesis and analysis of all the studies. The set of global MPA case studies was designed to close a deficit in information on the governance of MPAs and spatial management tools, within both fisheries management and biodiversity conservation contexts. The studies examine governance opportunities in and constraints on the use of spatial management measures at the national level. They were also designed to inform implementation of the FAO Technical Guidelines on marine protected areas (MPAs) and fisheries, which were developed to provide information and guidance on the use of MPAs in the context of fisheries

A Metapopulation Model for Chikungunya Including Populations Mobility on a Large-Scale Network

In this work we study the influence of populations mobility on the spread of a vector-borne disease. We focus on the chikungunya epidemic event that occurred in 2005-2006 on the R\'eunion Island, Indian Ocean, France, and validate our models with real epidemic data from the event. We propose a metapopulation model to represent both a high-resolution patch model of the island with realistic population densities and also mobility models for humans (based on real-motion data) and mosquitoes. In this metapopulation network, two models are coupled: one for the dynamics of the mosquito population and one for the transmission of the disease. A high-resolution numerical model is created out from real geographical, demographical and mobility data. The Island is modeled with an 18 000-nodes metapopulation network. Numerical results show the impact of the geographical environment and populations' mobility on the spread of the disease. The model is finally validated against real epidemic data from the R\'eunion event.Comment: Accepted in Journal of Theoretical biolog

Roosevelt Wild Life Bulletin

https://digitalcommons.esf.edu/rwlsbulletin/1002/thumbnail.jp

Exploring the possibilities of obtaining CNN-quality classification models without using convolutional neural networks

In this thesis, we pursue the success of Convolutional Neural Networks for image classification tasks. We explore the possibilities of achieving state-of-the-art performance without explicitly using CNNs on 2D grayscale images. We propose a Binary Patch Convolution (BPC) framework based on binarized patches from each group of images in a supervised task, eliminating the kernel learning process of CNNs. The binarized patches act as activations of different shapes and are applied using convolution. One of the key aspects of the framework is that it maintains a direct relation between the convolution kernels and the original images. Therefore, we can present a method to measure information content in a feature map for observing relations between different groups. We discuss and test different strategies for selecting groups of images to extract patches from while evaluating their effect on classification accuracy. The practical implementation of the BPC framework allows for many convolution kernels to be evaluated, positively impacting the framework’s performance. Ultimately, the proposed framework can extract pertinent features for classification and can be combined with any classifier. The framework is tested on the MNIST and Fashion-MNIST datasets and achieves competitive accuracy, even outperforming related work. We also discuss challenges and future work applicable to the framework. Furthermore, we have attempted to capture trends in the error of images by proposing an iterative variant of singular value bases classification. The proposed method fails to capture a generalizable error trend; thus, we have recognized that it is a challenging task for images. The process has given valuable insight into how to approach image classification problems. On top of that, we have examined the effects of negative transfer inherent in an original problem. Our experiments show that models trained on all groups in the data (global) are outperformed by models trained on different combinations of subgroups (local). Our proposed approaches for minimizing negative transfer within a task effectively increase classification accuracy. However, they are infeasible to deploy in practical scenarios due to the computation time introduced. The results are meant to motivate research toward within-task minimization of negative transfer, primarily since the existing research is focused on doing so in transfer learning