FABIO JOSE AYRES
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Relatório de pesquisa A judicialização de benefícios previdenciários e assistenciais(2020) NATALIA PIRES DE VASCONCELOS; Arguelles, Diego Werneck; Lima, Rafael Scavone Bellem de; FABIO JOSE AYRES; HEDIBERT FREITAS LOPES; Carlotti, Danilo; Wang, Henrique Yu Jiunn; Funari, Helena; PAULO FURQUIM DE AZEVEDO; Queirós, Danielly; Colares, Elisa; Stemler, Igor; Mota, Isabely; Monteiro, Alexander; Bittencourt, Cristianna; Amorim, Pedro; Marques, Ricardo; Rosa, Thatiane; Ferreira, Carlos Vinicius Ribeiro; Pereira, Filipe; Borges, Davi; Amorim, Pedro; Barbão, Jaqueline; VANESSA BOARATIDecisões administrativas na área de previdência social são objeto frequente de demandas judiciais. A magnitude dessa judicialização é grande o suficiente para afetar não só a política previdenciária, mas o funcionamento do próprio Judiciário, visto que se trata de um dos tipos de demanda que mais congestiona as cortes brasileiras. Esta pesquisa se dedica a esse tema, tendo como principal objetivo o de (i) investigar as causas da revisão judicial de decisões administrativas do Instituto Nacional do Seguro Social (INSS) referentes à concessão ou revisão de benefícios previdenciários ou assistenciais, bem como (ii) apontar propostas de políticas para mitigar os custos associados ao elevado nível de litigância nessa área. Sendo um fenômeno de representatividade nacional, esta pesquisa também investiga as heterogeneidades regionais e os diferentes padrões de concessão administrativa e judicial de benefícios previdenciários e assistenciais.Artigo Científico Well-Connected Communities in Real-World and Synthetic Networks(2023) Park, Minhyuk; Tabatabaee, Yasamin; Ramavarapu, Vikram; Liu, Baqiao; Pailodi, Vidya Kamath; Ramachandran, Rajiv; Korobskiy, Dmitriy; FABIO JOSE AYRES; Chacko, George; Warnow, TandyIntegral to the problem of detecting communities through graph clustering is the expectation that they are "well connected". In this respect, we examine five different community detection approaches optimizing different criteria: the Leiden algorithm optimizing the Constant Potts Model, the Leiden algorithm optimizing modularity, Iterative K-Core Clustering (IKC), Infomap, and Markov Clustering (MCL). Surprisingly, all these methods produce, to varying extents, communities that fail even a mild requirement for well connectedness. To remediate clusters that are not well connected, we have developed the "Connectivity Modifier" (CM), which, at the cost of coverage, iteratively removes small edge cuts and re-clusters until all communities produced are well connected. Results from real-world and synthetic networks illustrate a tradeoff users make between well connected clusters and coverage, and raise questions about the "clusterability" of networks and models of community structure.Trabalho de Evento Unsupervised Improvement of Audio-Text Cross-Modal Representations(2023) Wang, Zhepei; Subakan, Cem; Subramani, Krishna; Wu, Junkai; TIAGO FERNANDES TAVARES; FABIO JOSE AYRES; Smaragdis, ParisRecent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional training approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, learning such representations requires a large amount of human-annotated audio-text pairs. In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We explore domain-unspecific and domain-specific curation methods to create audio-text pairs that we use to further improve the model. We also show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance on downstream sound event classification or acoustic scene classification tasks.