Browsing by Author "Sreedharan, Sarath, committee member"

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Comparing memorability of gesture sets in an extended reality application
(Colorado State University. Libraries, 2024) Holen, Ethan J., author; Ortega, Francisco R., advisor; Sreedharan, Sarath, committee member; Rhodes, Matthew, committee member
In free-form gesture sets, memorability is an important yet often under-explored metric, despite evidence that the usability of interfaces improves when designed with more memorable input gestures. This study examines the memorability of three free-form gesture sets in the HoloLens 2: user-defined, elicitation-defined, and expert-defined. In addition, we examine gestures selected by the participants using common techniques from previous elicitation studies. We found that the user-defined gesture set was the most memorable, with an 88.57% recall rate. And was significantly more unforgettable than the expert-defined (72.73% recall) and the elicitation-defined (59.87% recall). This study also analyzed the user-defined gestures from this experiment. Although this was not an elicitation study, many of the methods commonly used in elicitation studies were used here. This analysis found a higher agreement rate when users were primed with a single gesture set before creating their own and a decrease in agreement when showing them two gesture sets beforehand. Given these results, we propose that designing systems with user-defined gestures will result in the most memorable sets; however, expert-defined gesture sets are also highly memorable and may better suit application design constraints.
Open Access
From neuro-inspired attention methods to generative diffusion: applications to weather and climate
(Colorado State University. Libraries, 2024) Stock, Jason, author; Anderson, Chuck, advisor; Ebert-Uphoff, Imme, committee member; Krishnaswamy, Nikhil, committee member; Sreedharan, Sarath, committee member
Machine learning presents new opportunities for addressing the complexities of atmospheric science, where high-dimensional, sparse, and variable data challenge traditional methods. This dissertation introduces a range of algorithms, motivated specifically by the intricacies of weather and climate applications. These challenges complement those that are fundamental in machine learning, such as extracting relevant features, generating high-quality imagery, and providing interpretable model predictions. To this end, we propose methods to integrate adaptive wavelets and spatial attention into neural networks, showing improvements on tasks with limited data. We design a memory-based model of sequential attention to expressively contextualize a subset of image regions. Additionally, we explore transformer models for image translation, with an emphasis on explainability, that overcome the limitations of convolutional networks. Lastly, we discover meaningful long-range dynamics in oscillatory data from an autoregressive generative diffusion model---a very different approach from the current physics-based models. These methods collectively improve predictive performance and deepen our understanding of both the underlying algorithmic and physical processes. The generality of most of these methods is demonstrated on synthetic data and classical vision tasks, but we place a particular emphasis on their impact in weather and climate modeling. Some notable examples include an application to estimate synthetic radar from satellite imagery, predicting the intensity of tropical cyclones, and modeling global climate variability from observational data for intraseasonal predictability. These approaches, however, are flexible and hold potential for adaptation across various application domains and data modalities.
Open Access
In pursuit of industrial like MAXSAT with reduced MAX-3SAT random generation
(Colorado State University. Libraries, 2024) Floyd, Noah R., author; Whitley, Darrell, advisor; Sreedharan, Sarath, committee member; Aristoff, David, committee member
In the modern landscape of MAXSAT, there are two broad classifications of problems: Random MAX-3SAT and Industrial SAT. Random MAX-3SAT problems by randomly sampling variables with a uniform probability and randomly assigning signs to the variable, one clause at a time. Industrial MAX-SAT consists of MAX-3SAT problems as encountered in the real world, and generally have a lower nonlinearity than random MAX-3SAT instances. One of the goals of recent research has been to figure out which rules and structures these industrial problems follow and how to replicate them randomly. This paper builds off of the paper" Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under Recombination", implementing its approach to MAX-3SAT clause generation and determining what it can reveal about industrial MAX-13SAT and random MAX-3SAT. This builds off of the transformation from SAT to MAX-SAT problems and hopes to create random MAXSAT problems that are more representative of industrial MAXSAT problems. All this would be in the pursuit of random MAX-3SAT that more accurately maps onto real-world MAX-3SAT instances so that more efficient MAX-3SAT solvers can be produced.
Open Access
SMOKE+: a video dataset for automated fine-grained assessment of smoke opacity
(Colorado State University. Libraries, 2024) Seefried, Ethan, author; Blanchard, Nathaniel, advisor; Sreedharan, Sarath, committee member; Roberts, Jacob, committee member
Computer vision has traditionally faced difficulties when applied to amorphous objects like smoke, owing to their ever-changing shape, texture, and dependence on background conditions. While recent advancements have enabled simple tasks such as smoke detection and basic classification (black or white), quantitative opacity estimation in line with the assessments made by certified professionals remains unexplored. To address this gap, I introduce the SMOKE+ dataset, which features opacity labels verified by three certified experts. My dataset encompasses five distinct testing days, two data collection sites in different regions, and a total of 13,632 labeled clips. Leveraging this data, we develop a state-of-the-art smoke opacity estimation method that employs a small number of Residual 3D blocks for efficient opacity estimation. Additionally I explore the use of MAMBA blocks in a video based architecture, exploiting their ability to handle spatial and temporal data in a linear fashion. Techniques developed during the SMOKE+ dataset creation were then refined and applied to a new dataset titled CSU101, designed for educational use in Computer Vision. In the future I intend to expand further into synthetic data, incorporating techniques into Unreal Engine or Unity to add accurate opacity labels.
Open Access
Theory and algorithms for w-stable ideals
(Colorado State University. Libraries, 2024) Ireland, Seth, author; Peterson, Chris, advisor; Cavalieri, Renzo, advisor; Gillespie, Maria, committee member; Sreedharan, Sarath, committee member
Strongly stable ideals are a class of monomial ideals which correspond to generic initial ideals in characteristic zero. Such ideals can be described completely by their Borel generators, a subset of the minimal monomial generators of the ideal. In [1], Francisco, Mermin, and Schweig develop formulas for the Hilbert series and Betti numbers of strongly stable ideals in terms of their Borel generators. In this thesis, a specialization of strongly stable ideals is presented which further restricts the subset of relevant generators. A choice of weight vector w ∈ Nn>0 restricts the set of strongly stable ideals to a subset designated as w-stable ideals. This restriction allows one to further compress the Borel generators to a subset termed the weighted Borel generators of the ideal. As in the non-weighted case, formulas for the Hilbert series and Betti numbers of strongly stable ideals can be expressed in terms of their weighted Borel generators. In computational support of this class of ideals, the new Macaulay2 package wStableIdeals.m2 has been developed and segments of its code support computations within the thesis. In a strengthening of combinatorial connections, strongly stable partitions are defined and shown to be in bijection with totally symmetric partitions.
Open Access
Using eye gaze to automatically identify familiarity
(Colorado State University. Libraries, 2024) Castillon, Iliana, author; Blanchard, Nathaniel, advisor; Sreedharan, Sarath, committee member; Cleary, Anne M., committee member
Understanding internal cognitive states, such as the sensation of familiarity, is crucial not only in the realm of human perception but also in enhancing interactions with artificial intelligence. One such state is the experience of familiarity, a fundamental aspect of human perception that often manifests as an intuitive recognition of faces or places. Automatically identifying cognitive experiences could pave the way for more nuance in human-AI interaction. While other works have shown the feasibility of automatically identifying other internal cognitive states like mind wandering using eye gaze features, the automatic detection of familiarity remains largely unexplored. In this work, we employed a paradigm from cognitive psychology to induce feelings of familiarity. Then, we trained machine learning models to automatically detect familiarity using eye gaze measurements, both in experiments with traditional computer use (e.g., eye tracker attached to monitor) and in virtual reality settings, in a participant independent manner. Familiarity was detected with a Cohen's kappa value, a measurement of accuracy corrected for random guessing, of 0.22 and 0.21, respectively. This work showcases the feasibility of automatically identifying feelings of familiarity and opens the door to exploring automated familiarity detection in other contexts, such as students engaged with a learning task while interacting with an intelligent tutoring system.