{"id":5373,"date":"2025-09-13T18:09:41","date_gmt":"2025-09-13T21:09:41","guid":{"rendered":"https:\/\/sibgrapi.sbc.org.br\/2025\/?page_id=5373"},"modified":"2025-09-13T18:43:25","modified_gmt":"2025-09-13T21:43:25","slug":"accepted-tutorials","status":"publish","type":"page","link":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/accepted-tutorials\/","title":{"rendered":"accepted tutorials"},"content":{"rendered":"\t\t
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accepted tutorials<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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1. Tiny Titans: Efficient Large Vision, Language and Multimodal Models through Pruning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 09:00 am
Duration: 3h
Level: Intermediate<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Notable progress in solving complex reasoning tasks relies on large models. Unfortunately, developing these models demands substantial computational resources and energy consumption. Hence, the industry pushes the most significant advances in state-of-the-art models and draws the attention of the scientific community to the environmental impact of AI (GreenAI). Pruning emerges as an effective mechanism to address the capacity-computational cost dilemma by eliminating structures (weights, neurons or layers) from deep models. This tutorial introduces theoretical and technical foundations within this promising, active and exciting field. It delves into pruning techniques as a pillar of GreenAI and a foundation for the next wave of efficient large vision, language, and multimodal models. Our tutorial also covers how existing forms of pruning impact efficiency gains, guiding participants to make informed choices for their scenario and infrastructure. Specifically, we equip participants with the basics and key recipes to effectively apply pruning in practical computer vision scenarios. Given the calls for efficient general-purpose AI models, we believe our tutorial serves as a valuable tool for practitioners.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Carolina Tavares<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Leandro Mugnain<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Gustavo Nascimento<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Ian Pons<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Keith Ogawa<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Guilherme Stern<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Lucas Libanio<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Aline Paes<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal Fluminense<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Anna Helena Reali<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Artur Jordao<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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2. Rank-based Unsupervised Similarity Learning: Framework and Applications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 09:00 am
Duration: 3h
Level: Intermediate<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Multimedia data collections have grown exponentially due to advances in acquisition and sharing technologies, creating a pressing need for robust similarity learning methods that do not depend on costly annotations. Deep features extracted by convolutional and transformer networks provide powerful embeddings, but often lie on complex manifolds that simple pairwise comparisons fail to capture. Unsupervised similarity learning addresses this gap by post-processing these embeddings with rank-based strategies that leverage the ordering of neighbors and manifold geometry through different approaches such as graph and hypergraph constructions. This tutorial discusses foundational concepts in contextual similarity and presents a comprehensive overview of rank-based methods for unsupervised distance and similarity learning. It introduces the Unsupervised Distance Learning Framework (UDLF) along with its Python wrapper pyUDLF and the UDLFWeb interface to automate and facilitate experimentation and visualization of results. We show how these tools enhance performance in image retrieval, classification, and clustering tasks and discuss future directions for extending rank-based methods to other modalities and scaling to large datasets.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Italo de Matos Saldanha<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Vinicius Atsushi Sato Kawai<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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UNESP<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Bionda Rozi<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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UNESP<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Gustavo Rosseto Leticio<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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UNESP<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Lucas Pascotti Valem<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Daniel Carlos Guimar\u00e3es Pedronette<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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UNESP<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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3. The Flow of Creation: A Tour of Flow Matching for Visuals<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 09:00 am
Duration: 3h
Level: Advanced<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Flow Matching has recently emerged as an efficient alternative to the generative method paradigms. Here we aim to provide researchers and practitioners with both the theoretical and the practical aspects of this technique. We delve into the continuous formulation, where a neural network learns a vector field to transform noise into data via an ordinary differential equation, and also explore its discrete counterpart. The paper covers the entire workflow, from the core mathematical concepts and training objectives to sampling procedures, including classifier-free guidance and conditioning generation. By showcasing a diverse range of applications\u2014from image synthesis and human motion generation to computational biology and robotics\u2014this work equips readers with the essential knowledge to apply and innovate with the versatile and computationally efficient flow matching framework.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Manolo Canales Cuba<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal do ABC<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Vinicius Mel\u00edcio<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal do ABC<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Jo\u00e3o Paulo Gois<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal do ABC<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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4. Getting Started with Semantic Segmentation in PyTorch Using SMP<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 01:00 pm
Duration: 3h
Level: Intermediate<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Semantic segmentation is a core task in computer vision, essential for applications requiring detailed scene understanding, such as medical imaging, precision agriculture, and remote sensing. Recent advances in deep learning have significantly enhanced segmentation performance, particularly through encoder-decoder architectures combined with transfer learning. This tutorial provides a practical introduction to semantic segmentation using the Segmentation Models PyTorch (SMP) library, a widely adopted framework that integrates state-of-the-art architectures with pretrained encoders in an accessible interface. We offer a comprehensive overview of key concepts, supported model architectures, loss functions, evaluation metrics, and training strategies, emphasizing transparency and flexibility through native PyTorch implementations. To reinforce the concepts, we present two case studies: binary segmentation with the 38-Cloud dataset and multiclass segmentation with the DeepGlobe dataset. Both illustrate real-world applications, model configuration, preprocessing, and performance evaluation. All tutorial materials, including source code and reproducible experiments, will be made publicly available. The goal is to equip participants with practical knowledge to design, train, and evaluate semantic segmentation models effectively in a variety of domains.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Joao Mari<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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UFV<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Leandro Silva<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Federal University of Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Mauricio Escarpinati<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Federal University of Uberl\u00e2ndia<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Andr\u00e9 Backes<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Federal University of S\u00e3o Carlos<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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5. Regularization for Inverse Problems and Machine Learning: A Tutorial<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Duration: 3h
Time: 01:00 pm
Level: Intermediate<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In this tutorial, we explore different paradigms for solving image processing tasks such as denoising and deblurring. These tasks can be interpreted as ill-posed inverse problems, grounded in physical-mathematical models that require regularization to produce meaningful solutions. Alternatively, they can be framed as supervised regression tasks within a machine learning context, where regularization techniques are used to improve generalization to unseen data. We compare these two perspectives, highlighting their similarities and differences\u2014particularly in how regularization is understood and applied in each framework.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Roberto Gutierrez Beraldo<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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CECS\/UFABC<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Leonardo Alves Ferreira<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Samsung<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Ricardo Suyama<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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6. Symmetry Shape Analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 01:00 pm
Duration: 3h
Level: Intermediate<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Symmetry is a fundamental and pervasive property found in both natural and man-made objects, playing a key role in aesthetics, structure, and function. In computational domains, symmetry serves as a powerful cue for data compression, structure inference, and shape understanding. This work presents a comprehensive overview of symmetry analysis in 3D shapes, with a particular focus on computational methods for symmetry detection and their applications in diverse fields such as CAD, computer vision, medicine, archaeology, and 3D modeling. We provide formal definitions of exact, approximate, and partial symmetries in the context of rigid transformations, and we survey five major categories of detection approaches: transformation-based, correspondence-based, voting-based, optimization-based, and learning-based methods. Special emphasis is placed on recent deep learning techniques, which have significantly advanced the state of the art yet face challenges in generalization and robustness. Finally, we identify key open problems and future directions, including the need for richer and more varied datasets, better generalization of learning-based models, effective formulations for symmetry detection in incomplete data, and the integration of symmetry priors in generative modeling. Our analysis highlights both the progress and the limitations of current methods and aims to guide future research toward more principled and capable symmetry-aware systems.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Ivan Sipiran<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Department of Computer Science, University of Chile<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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7. From Volume Rendering to 3D Gaussian Splatting: Theory and Applications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 01:00 pm
Duration: 3h
Level: Advanced<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Deep learning has transformed medical image analysis. However, building effective models for volumetric data, such as CT, MRI, and PET scans, presents a new set of challenges. These include high computational costs, limited annotated datasets, and the need for architectures that can process volumetric data directly. This survey provides a comprehensive overview of recent advances in deep neural network architectures specifically designed for 3D medical imaging. We analyze the progression from early 3D CNNs to hybrid and Transformer-based models, highlighting their structural innovations and applicability. In addition, we review training strategies such as supervised pretraining, self-supervised learning, and the development of general-purpose 3D foundational models. Also, a comprehensive overview of 3D medical imaging datasets and their associated tasks is presented in the supplementary materials, highlighting the diverse clinical objectives that deep learning models can support across healthcare and diagnostic applications. A dedicated section addresses the emerging field of explainability in volumetric contexts, emphasizing the limitations of adapting 2D explainability-focused tools and the importance of 3D-native explanation frameworks. We conclude by outlining the key trends in the field, including the growing availability of multiple and functionally diverse datasets; novel architectural designs tailored for volumetric learning; the transition from convolutional to attention-based models; increased reliance on transfer learning from both labeled and unlabeled data; and the emergence of 3D-native XAI methods that provide clinically meaningful and trustworthy explanations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Vitor Matias<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade de S\u00e3o Paulo<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Daniel Perazzo<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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IMPA<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Vin\u00edcius da Silva<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Tecgraf PUC-Rio<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Alberto Raposo<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Tecgraf PUC-Rio<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Luiz Velho<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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IMPA<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Afonso Paiva<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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ICMC-USP<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Tiago Novello<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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IMPA<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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8. Strategies for Deep Learning in Volumetric Medical Imaging: A Survey<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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September 30th, 2025
Time: 09:00 am (morning session), 01:00 pm (afternoon session)
Duration: 6h
Level: Advanced<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The problem of 3D reconstruction from posed images is undergoing a fundamental transformation, driven by continuous advances in 3D Gaussian Splatting (3DGS). By modeling scenes explicitly as collections of 3D Gaussians, 3DGS enables efficient rasterization through volumetric splatting, offering thus a seamless integration with common graphics pipelines. Despite its real-time rendering capabilities for novel view synthesis, 3DGS suffers from a high memory footprint, the tendency to bake lighting effects directly into its representation, and limited support for secondary-ray effects. This tutorial provides a concise yet comprehensive overview of the 3DGS pipeline, starting from its splatting formulation and then exploring the main efforts in addressing its limitations. Finally, we survey a range of applications that leverage 3DGS for surface reconstruction, avatar modeling, animation, and content generation — highlighting its efficient rendering and suitability for feed-forward pipelines.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Jo\u00e3o Vitor Silva de Oliveira<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal de Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Danilo Vieira<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal de Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Mateus Silva<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal de Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Daniel Fernandes<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal de Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Marcos Ribeiro<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal de Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Hugo Oliveira<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Universidade Federal de Vi\u00e7osa<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

accepted tutorials 1. Tiny Titans: Efficient Large Vision, Language and Multimodal Models through Pruning September 30th, 2025Time: 09:00 am Duration: […]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-5373","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/pages\/5373","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/comments?post=5373"}],"version-history":[{"count":17,"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/pages\/5373\/revisions"}],"predecessor-version":[{"id":5422,"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/pages\/5373\/revisions\/5422"}],"wp:attachment":[{"href":"https:\/\/sibgrapi.sbc.org.br\/2025\/index.php\/wp-json\/wp\/v2\/media?parent=5373"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}