Distribution-Based Categorization of Classifier Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples

Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%

HÉRNIA DE SPIEGEL – RELATO DE CASO

Introdução: A hérnia de Spiegel é uma entidade cirúrgica rara e benigna, geralmente associada ao esforço físico e com sintomas álgicos característicos mais comumente referidos pelo paciente, sendo menos comum estes pacientes cursarem com tumefação. As manifestações clínicas geralmente são pouco esclarecedoras e há um risco real de se transformar em uma emergência. É uma doença de difícil diagnóstico, não sendo incomum a necessidade de complementação com exame de imagem. Por cursarem com anel herniário estreito na maioria dos casos e o alto risco de estrangulamento, é de indicação cirúrgica absoluta. Pode ser abordada por via anterior, com ou sem inserção de tela, ou por via laparoscópica. Objetivo: Este artigo tem o objetivo de descrever um caso de uma hérnia rara, enfatizando os seus aspectos clínicos, diagnósticos, terapêuticos e o resultado obtido no pós-operatório. Relato de Caso: Paciente de 77 anos foi diagnosticado com hérnia de Spiegel com possível conteúdo intestinal em seu interior, e encaminhado ao centro cirúrgico. Foisubmetido à cirurgia onde foi visualizada uma hérnia projetando pela linha de Spiegel. Procedeu-se à redução do saco herniário, fechamento sem tensão do anel herniário e utilização de tela de polipropileno. O pós-operatório ocorreu sem complicações. Conclusão: Foi apresentado um caso de Hérnia de Spiegel, uma patologia considerada incomum na sociedade médica onde a abordagem cirúrgica foi eficaz

Innovation of Textiles through Natural By-Products and Wastes

Nowadays, the competitiveness of the textile industry and the consumers’ interest have been increasing the demand for innovative and functional textiles. Allied to this, sustainable developments are playing an increasingly important role in the textile industry. Such concerns led to a new development strategy based on the valorization of bio-based wastes and by-products of different industries, inserting this in the circular economy paradigm. These bio-based wastes and by-products come from several industries, as the agri-food industry. These resources present an enormous potential for valorization in the textile finish due to their intrinsic properties (antimicrobial, prebiotic, antioxidant activity, among others). This chapter will review the latest innovation and textile product development through different by-products and wastes, their main properties and characteristics and the advantages that they offer to the textile industry

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Educomunicação, Transformação Social e Desenvolvimento Sustentável

Esta publicação apresenta os principais trabalhos dos GTs do II Congresso Internacional de Comunicação e Educação nos temas Transformação social, com os artigos que abordam principalmente Educomunicação e/ou Mídia-Educação, no contexto de políticas de diversidade, inclusão e equidade; e, em Desenvolvimento Sustentável os artigos que abordam os avanços da relação comunicação/educação no contexto da educação ambiental e desenvolvimento sustentável

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost