Abstract
The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
Gemini Observatory has been awarded from the National Science Foundation a major fund to build a new state-of-the-art Multi Conjugate Adaptive Optics facility for Gemini North on Maunakea called GNAO. The current baseline system will use two lasers each split in two to create an artificial constellation of four laser guide star to measure the distortions caused by the atmosphere. At least two deformable mirror conjugated to 0km and the main altitude layer above Maunakea will be used to correct these distortions. The facility will be designed to feed future instrumentation, initially a near infrared imager and potentially a visiting 4-arm multi object adaptive optics IFU spectrograph.1 In this paper I will present the main characteristics of this exciting facility, its promises and its challenges. I will also present its conceptual design and results of trade studies conducted within the team and the Gemini Adaptive Optics Working Group. The expected first light is for October 2024.

Abstract
Faster Internet connections are breaking most of the geographic barriers. At the same time, the huge digital content that have been generated in last years is motivating new forms of digital piracy. We know that piracy of copyrighted digital material has a huge impact on countries' economy, being a major issue for the whole society and not only for content creators. The purpose of this paper is to investigate digital piracy intention. For that purpose, we have expanded the framework of the theory of planned behavior using the utility theory, the deterrence theory and other relevant constructs. Using data from students of a Portuguese university and high school, a sample of 590 questionnaires has been collected. Two models were developed and analyzed using structural equation modeling. The first considers the full sample (Full Model), while the second considers only those who had pirated (Pirate Model). The pirate model confirmed the existence of a significant and strong relation between past behavior and intention toward digital piracy.

Abstract
The enhancement of tubular structures such as vessels in medical images has been addressed in the past, aiming for easier extraction and or visualization of such structures by professionals. Some literature methodologies propose vesselness measures whose design is motivated by local properties of vascular networks and how these influence the eigenvalues of the Hessian matrix. However, past work fails to combine properly the scale-space and neighborhood information, thus leading to the proposal of suboptimal vesselness measures. In this paper, we show that a shallow convolutional neural network is able to learn more optimal embedding spaces from the eigenvalue analysis at different scales, thus leading to a stronger vessel enhancement. Additionally, we also show that such a system maintains one of the biggest advantages of Hessian-based vesselness measures, which is the robustness to data with varying statistics. © 2019, Springer Nature Switzerland AG.

Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. Starting from a particular lock-free hash map data structure, named Lock-Free Hash Tries (LFHT), we focus on solving the problem of memory reclamation without losing the lock-freedom property. We propose an approach that explores the characteristics of the LFHT structure in order to achieve efficient memory reclamation with low and well-defined memory bounds. Experimental results show that our approach obtains better results when compared with other state-of-the-art memory reclamation methods and provides a competitive and scalable hash map implementation, if compared to lock-based implementations.