Zobrazeno 11 - 20
of 2 106
pro vyhledávání: '"Ferrés, A"'
This study leverages mobile phone data for 5.4 million users to unveil the complex dynamics of internal migration and daily mobility in Santiago de Chile during the global COVID-19 pandemic, with a focus on socioeconomic differentials. Major findings
Externí odkaz:
http://arxiv.org/abs/2309.11062
Autor:
Fobi, Simone, Cardona, Manuel, Collins, Elliott, Robinson, Caleb, Ortiz, Anthony, Sederholm, Tina, Dodhia, Rahul, Ferres, Juan Lavista
This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a singl
Externí odkaz:
http://arxiv.org/abs/2307.11921
Autor:
Robinson, Caleb, Nsutezo, Simone Fobi, Ortiz, Anthony, Sederholm, Tina, Dodhia, Rahul, Birge, Cameron, Richards, Kasie, Pitcher, Kris, Duarte, Paulo, Ferres, Juan M. Lavista
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage assessments in an automated manne
Externí odkaz:
http://arxiv.org/abs/2306.12589
Autor:
Roman, Anthony Cintron, Xu, Kevin, Smith, Arfon, Vega, Jehu Torres, Robinson, Caleb, Ferres, Juan M Lavista
GitHub is the world's largest platform for collaborative software development, with over 100 million users. GitHub is also used extensively for open data collaboration, hosting more than 800 million open data files, totaling 142 terabytes of data. Th
Externí odkaz:
http://arxiv.org/abs/2306.06191
Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery. A common benchmark case is to evaluate SS
Externí odkaz:
http://arxiv.org/abs/2305.13456
Autor:
Meyer, Lucas Augusto, Romero, Sergio, Bertoli, Gabriele, Burt, Tom, Weinert, Alex, Ferres, Juan Lavista
This study investigates the effectiveness of multifactor authentication (MFA) in protecting commercial accounts from unauthorized access, with an additional focus on accounts with known credential leaks. We employ the benchmark-multiplier method, cou
Externí odkaz:
http://arxiv.org/abs/2305.00945
As the need for efficient digital circuits is ever growing in the industry, the design of such systems remains daunting, requiring both expertise and time. In an attempt to close the gap between software development and hardware design, powerful feat
Externí odkaz:
http://arxiv.org/abs/2302.12702
Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task. However, so far, no research has applied similar approaches to the Lexical Simplification (LS) task. In this paper, we present ConLS, a Con
Externí odkaz:
http://arxiv.org/abs/2302.02900
Autor:
Saggion, Horacio, Štajner, Sanja, Ferrés, Daniel, Sheang, Kim Cheng, Shardlow, Matthew, North, Kai, Zampieri, Marcos
We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022. The task called the Natura
Externí odkaz:
http://arxiv.org/abs/2302.02888
Autor:
Nasir, Md, Sederholm, Tina, Sharma, Anshu, Mallu, Sundeep Reddy, Ghatage, Sumedh Ranjan, Dodhia, Rahul, Ferres, Juan Lavista
Vulnerability and risk assessment of neighborhoods is essential for effective disaster preparedness. Existing traditional systems, due to dependency on time-consuming and cost-intensive field surveying, do not provide a scalable way to decipher warni
Externí odkaz:
http://arxiv.org/abs/2211.11636