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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Generating Contextual Text Embeddings For Emergency Department Chief Complaints Using Bert, David Chang
Generating Contextual Text Embeddings For Emergency Department Chief Complaints Using Bert, David Chang
Yale Day of Data
We applied BERT, a state-of-the-art natural language processing model, on chief complaint data from the Yale Emergency Department to map free-text notes to structured chief complaint categories.
Smiler: Consistent And Usable Saliency Model Implementations, Toni Kunic, Calden Wloka, John K. Tsotsos
Smiler: Consistent And Usable Saliency Model Implementations, Toni Kunic, Calden Wloka, John K. Tsotsos
MODVIS Workshop
The Saliency Model Implementation Library for Experimental Research (SMILER) is a new software package which provides an open, standardized, and extensible framework for maintaining and executing computational saliency models. This work drastically reduces the human effort required to apply saliency algorithms to new tasks and datasets, while also ensuring consistency and procedural correctness for results and conclusions produced by different parties. At its launch SMILER already includes twenty three saliency models (fourteen models based in MATLAB and nine supported through containerization), and the open design of SMILER encourages this number to grow with future contributions from the community. The project …
Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos
Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos
MODVIS Workshop
No abstract provided.
Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan
Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan
Creative Activity and Research Day - CARD
With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …
Autonomous Watercraft Simulation And Programming, Nicholas J. Savino
Autonomous Watercraft Simulation And Programming, Nicholas J. Savino
Student Scholar Showcase
Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades, advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats, and an increasing popularity of self-driving cars. We predicted the motion of an autonomous vehicle using simulations in Python. The simulation models the motion of a small scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as …
Wearing The Inside Out: Using Long Short-Term Memory Networks And Wearable Data To Identify Human Emotions, Carlos Aguirre, Maria Fernanda De La Torre
Wearing The Inside Out: Using Long Short-Term Memory Networks And Wearable Data To Identify Human Emotions, Carlos Aguirre, Maria Fernanda De La Torre
Kansas State University Undergraduate Research Conference
Studying emotions may sound unusual in computer science, a field based on quantifiable data and rationality. Contrary to belief, studies have shown any decision is highly dependent on emotional input. To improve human-computer interaction, it is crucial to improve our understanding of human emotions and teach machines to identify them. With large amounts of information streaming available from our environment, identifying our current emotional state becomes challenging, even at the individual self-level. This project aims to identify indicative emotional temporal data from wearable devices. Using brain activity data from an EEG and smart watches that record data, such as heart-beat, …
A Formal Approach To Circle Formation In Multi-Agent Systems, Rui Yang
A Formal Approach To Circle Formation In Multi-Agent Systems, Rui Yang
Computer Science Graduate Research Workshop
No abstract provided.
Conflict Resolution Using Α-Shapes For Distributed Robotic Sampling Of Ambient Phenomena In Initially Unknown Environments, Brad Woosley
Conflict Resolution Using Α-Shapes For Distributed Robotic Sampling Of Ambient Phenomena In Initially Unknown Environments, Brad Woosley
Computer Science Graduate Research Workshop
No abstract provided.
Processing Narratives By Means Of Action Languages, Craig Olson
Processing Narratives By Means Of Action Languages, Craig Olson
Computer Science Graduate Research Workshop
No abstract provided.
Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra
Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra
Computer Science Graduate Research Workshop
No abstract provided.
Machine Learning Techniques For Predicting Mobility-Related Perception Errors In Astronauts, Steven Belcher
Machine Learning Techniques For Predicting Mobility-Related Perception Errors In Astronauts, Steven Belcher
Computer Science Graduate Research Workshop
No abstract provided.
A Technique For Improving Classification Accuracy Of Highly Imbalanced And Sparse Datasets, Sindhura Bonthu
A Technique For Improving Classification Accuracy Of Highly Imbalanced And Sparse Datasets, Sindhura Bonthu
Computer Science Graduate Research Workshop
No abstract provided.
Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine
Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine
Undergraduate Research and Scholarship Symposium
Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …
Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.
Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.
SDSU Data Science Symposium
Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …
Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard
Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard
Yale Day of Data
In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim …