UMBC College of Engineering and Information Technology
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Grounded Language Learning: Where Robotics and NLP Meet
Grounded language acquisition is concerned with learning the meaning of language as it applies to the physical world. As robots become more capable and ubiquitous, there is an increasing need for non-specialists to interact ... -
Learning to Understand Non-Categorical Physical Language for Human Robot Interactions
Learning the meaning of language with respect to the physical world in which a robot operates is a necessary step for shared autonomy systems in which natural language is part of a user-specific, customizable interface. ... -
A Manifold Alignment Approach to Grounded Language Learning
As robots become advanced and affordable enough to have in our daily lives, the next question is: How do we make using these machines as intuitive as possible? Language offers an approachable and relatively accessible ... -
Planning with Abstract Learned Models While Learning Transferable Subtasks
(AAAI, 2020-03-04)We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this ... -
Share to Seek: The Effects of Disease Complexity on Health Information–Seeking Behavior
(JMIR, 2021-03-24)Background: Web-based question and answer (Q&A) sites have emerged as an alternative source for serving individuals’ health information needs. Although a number of studies have analyzed user-generated content in web-based ... -
Measuring deadtime and double-counts in a non-paralyzable scintillating neutron detector using arrival time statistics
(Elsevier, 2021-03-26)A 6LiF:ZnS(Ag) based cold neutron detector with wavelength shifting (WLS) fibers and SiPM photodetector was developed at the NIST Center for Neutron Research for the CANDoR instrument (Chromatic Analysis Neutron Diffractometer ... -
Convolutional LSTM for Planetary Boundary Layer Height (PBLH) Prediction
(AAAI, 2021-03-22)We describe new work that uses deep learning to learn temporal changes in Planetary Boundary Layer Height (PBLH). This work is performed in conjunction with a deep edge detection method that identifies edges in imagery ... -
LSTMs for Inferring Planetary Boundary Layer Height (PBLH)
(AAAI, 2021-03-22)In this paper, we describe new work which is part of a larger study to understand how machine learning could be used to augment existing methods for calculating and estimating the Planetary Boundary Layer Height (PBLH). ... -
On Finer Control of Information Flow in LSTMs
(Springer Nature, 2019-01-18)Since its inception in 1995, the Long Short-Term Memory (LSTM) architecture for recurrent neural networks has shown promising performance, sometimes state-of-art, for various tasks. Aiming at achieving constant error flow ... -
Origins Space Telescope: trades and decisions leading to the baseline mission concept
(SPIE, 2021-03-13)The Origins Space Telescope will trace the history of our origins from the time dust and heavy elements permanently altered the cosmic landscape to present-day life. How did galaxies evolve from the earliest galactic systems ... -
Event Representation with Sequential, Semi-Supervised Discrete Variables
(2020-10-16)Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential, ... -
Knowledge Graph Inference using Tensor Embedding
(2020-09-12)Axiom based inference provides a clear and consistent way of reasoning to add more information to a knowledge graph. However, constructing a set of axioms is expensive and requires domain expertise, time, and money. It is ... -
Image segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciences
(UMBC HPCF, 2020)Dust and sandstorms originating from Earth’s major arid and semi-arid desert areas can significantly affect the climate system and health. Many existing methods use heuristic rules to classify on a pixel-level regarding ... -
Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
(UMBC HPCF, 2020)The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in ... -
Evaluation of Tropical Cloud Simulations between CMIP6 Models and Satellite Observations CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
(UMBC HPCF, 2020)In this project, we look at the Global Climate Models (GCM) of CMIP6 (6th generation of Coupled Model Intercomparison Project). We analyze the cloud parameterizations of three CMIP6 models, namely, NASA-GISS-E2.1-G, ... -
Studying Anomalous Discrepancies between MODIS and CALIOP Cloud Observations CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
(UMBC HPCF, 2020)When examining collocated data from the A-Train satellite constellation, there are a notable number of clouds that CALIOP identifies as transparent but MODIS paradoxically reports as having a high cloud optical thickness ... -
Team-Based Online Multidisciplinary Education on Big Data + High-Performance Computing + Atmospheric Sciences
(UMBC HPCF, 2020)Given the context of many institutions moving to online instruction due to the COVID-19 pandemic in 2020, we share our experiences of an online team-based multidisciplinary education program on big data + high performance ... -
An introduction to physics-based animation
(Association for Computing Machinery, 2018-08)Physics-based animation has emerged as a core area of computer graphics finding widespread application in the film and video game industries as well as in areas such as virtual surgery, virtual reality, and training simulations. ...
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