Using Alternating Minimization and Convexified Carleman Weighted Objective Functional for a Time-Domain Inverse Scattering Problem

This paper considers a 1D time-domain inverse scattering problem for the Helmholtz equation in which penetrable scatterers are to be determined from boundary measurements of the scattering data. It is formulated as a coefficient identification problem for a wave equation. Using the Laplace transform, the inverse problem is converted into an overdetermined nonlinear system of partial differential equations. To solve this system, a Carleman weighted objective functional, which is proved to be strictly convex in an arbitrary set in a Hilbert space, is constructed. An alternating minimization algorithm is used to minimize the Carleman weighted objective functional. Numerical results are presented to illustrate the performance of the proposed algorithm. © 2023 by the author

Mathematical modeling and inverse problems in applications

Mathematical models, based on ordinary or partial differential equations, are widely used to describe physical/chemical/biological processes and can be found in several applications: nondestructive testing, subsurface imaging, defense, medicine, environmental sciences, etc

How Digital Natives Learn and Thrive in the Digital Age: Evidence from an Emerging Economy

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students

Experimental Evaluation of Geopolymer Concrete Strength Using Sea Sand and Sea Water in Mixture

This paper presents the experimental strength evaluation of geopolymer concrete and ordinary concrete using sea sand and seawater in the mixture. A series of 30 cubic samples with a 150 mm side length and 12 rectangular specimens with a dimension of 100 × 100 × 400 mm (width × thickness × length) were cast and tested in this study. Specimens were divided equally into two groups. The first group of specimens was cast using geopolymer as the main binder (GPC), while the second group of samples was made using ordinary Portland Cement (OPC). While the compression tests were performed for specimens in two groups at the ages of 3, 7, 28, 60, and 120 days, the tensile tests were only performed for specimens at 7 and 28 days. The testing results revealed that the compression strength of GPC specimens using sea sand and seawater was significantly higher than that of OPC samples using the same type of salted sand and water. Besides, the use of sea sand and seawater for replacing river sand and fresh water in the production of GPC is feasible in terms of compressive strength since GPC produces a higher compressive strength than that of conventional concrete. Doi: 10.28991/CEJ-2022-08-08-03 Full Text: PD

LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss

Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.Comment: Accepted to EASE'2