GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point cloud classification, namely, GSV-NET, which uses Gaussian Supervector and enhancing region representation. GSV-NET extracts and combines both global and regional features of the 3D point cloud to further enhance the information of the point cloud features for the 3D point cloud classification. Firstly, we input the Gaussian Supervector description into a 3D wide-inception convolution neural network (CNN) structure to define the global feature. Secondly, we convert the regions of the 3D point cloud into color representation and capture region features with a 2D wide-inception network. These extracted features are inputs of a 1D CNN architecture. We evaluate the proposed framework on the point cloud dataset: ModelNet and the LiDAR dataset: Sydney. The ModelNet dataset was developed by Princeton University (New Jersey, United States), while the Sydney dataset was created by the University of Sydney (Sydney, Australia). Based on our numerical results, our framework achieves more accuracy than the state-of-the-art approaches.

Corrupción, oligarquía y republicanismo plebeyo

Reseña: Vergara, Camila (2020) <em>Systemic Corruption: Constitutional Ideas for an Anti-Oligarchic Republic. </em>Princeton: Princeton University Press.

REGIONAL BIAS IN LATE ANTIQUE AND EARLY MEDIEVAL COIN FINDS AND ITS EFFECTS ON DATA: THREE CASE STUDIES

This paper discusses the role of bias and uncertainty in the FLAME project (Framing the Late Antique and Early Medieval Economy) at Princeton University. FLAME is a large Digital Humanities project focused on collecting and storing data on coin minting and circulation in west Afro-Eurasia from 325 to 750 CE, roughly coinciding with the period of transition between the late antique and early medieval periods. The overarching goal is historical – that is, we wish to be able to say something new about how the world of late antiquity and the medieval period really was. However, in the process of building this database, and its accompanying online tools, we have also observed that the data is difficult and problematic. This paper, then, is an account of some of these historiographical and methodological issues in the form of three case studies (Britain, France, and Ukraine) and a short discussion of strategies that FLAME employs to communicate these biases to users, who benefit from a transparent discussion of messiness and difficulty in the data. The paper proceeds in seven sections, of which the first is an introduction. Section Two presents basic technical details of the project, such as its database implementation (MySQL) and its online visualization systems (ArcGIS), access to which can be found at https://flame.princeton.edu. Section Three discusses the historiographic questions at stake, distinguishing between Primary Bias (inherent in materials themselves) and Secondary Bias (particular to national and political contexts). Section Four, Five, and Six are each devoted to a separate case study: Britain, France, and Ukraine. Each discusses FLAME's data on that region and briefly touches upon contextual factors that may bias regional data. Thus, Section Four discusses Britain, with much analysis focused on the role of the Portable Antiquities Scheme in incentivizing reporting of found antiquities, and its effects on coin data. Section Five discusses France, where FLAME records many coin finds, but from a limited time period (primarily from Merovingian states). Section Six discusses the situation in Ukraine, where we were helped by existing scholarly resources (such as the coin inventories of Kropotkin), but where cultural heritage preservation suffers from weak state enforcement and where much scholarship suffers from spotty recording practices, and often outright theft of national treasures, going back to the imperial Russian period. Section Seven concludes the paper, noting that such methodological and second-order discussion of bias is a critical desideratum for the Digital Humanities as it matures into its second decade.

As cidades falam? (Tradução)

Tradução do artigo da Robert S. Lynd Professor of Sociology na Universidade de Columbia, Saskia Sassen, para a publicação Public Culture, Durham, v. 25, n. 2, de julho de 2013. Sassen (5 de janeiro de 1947) é uma socióloga holandesa-americana conhecida por suas análises da globalização e da migração humana internacional e seus impactos no contexto urbano. Publicou 12 livros, juntos traduzidos para mais de 20 idiomas, como The Global City (Princeton University Press, [1991] 2. ed., 2001), no qual desenvolveu o termo “cidade global”. Ela recebeu diversos prêmios e menções, incluindo 12 doutorados honoris causa, e foi selecionada como uma das principais pensadoras globais em diversas listas. Mais recentemente, recebeu o Prémio Príncipe de Astúrias 2013 nas Ciências Sociais e foi nomeada Membro Estrangeiro da Real Academia das Ciências da Holanda. A Revista Prumo agradece a generosidade da autora em ceder a autorização para tradução e publicação deste artigo.