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Full-Text Articles in Statistical Models
Sparse Representation Learning For Temporal Networks, Maxwell Mcneil
Sparse Representation Learning For Temporal Networks, Maxwell Mcneil
Electronic Theses & Dissertations (2024 - present)
Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …
Modeling A Sensor To Improve Its Efficacy, Nabin K. Malakar, Daniil Gladkov, Kevin H. Knuth
Modeling A Sensor To Improve Its Efficacy, Nabin K. Malakar, Daniil Gladkov, Kevin H. Knuth
Physics Faculty Scholarship
Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when employed within a Bayesian inferential framework. As a test bed we employ a robotic arm that is designed to autonomously take its own measurements using an inexpensive LEGO light sensor to estimate the position and radius of a white circle on a black field. The light sensor integrates the light arriving from a …