Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Sciences (6)
- Numerical Analysis and Scientific Computing (4)
- Artificial Intelligence and Robotics (3)
- Data Science (3)
- Statistics and Probability (3)
-
- Bioinformatics (2)
- Computational Biology (2)
- Earth Sciences (2)
- Genetics and Genomics (2)
- Glaciology (2)
- Life Sciences (2)
- Statistical Models (2)
- Applied Mathematics (1)
- Applied Statistics (1)
- Biostatistics (1)
- Climate (1)
- Ecology and Evolutionary Biology (1)
- Environmental Monitoring (1)
- Environmental Sciences (1)
- Hydrology (1)
- Natural Resources Management and Policy (1)
- Numerical Analysis and Computation (1)
- Oceanography and Atmospheric Sciences and Meteorology (1)
- Other Computer Sciences (1)
- Other Ecology and Evolutionary Biology (1)
- Other Genetics and Genomics (1)
- Partial Differential Equations (1)
- Software Engineering (1)
- Water Resource Management (1)
Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Applications Of Transfer Learning From Malicious To Vulnerable Binaries, Sean Patrick Mcnulty
Applications Of Transfer Learning From Malicious To Vulnerable Binaries, Sean Patrick Mcnulty
Graduate Student Theses, Dissertations, & Professional Papers
Malware detection and vulnerability detection are important cybersecurity tasks. Previous research has successfully applied a variety of machine learning methods to both. However, despite their potential synergies, previous research has yet to unite these two tasks. Given the recent success of transfer learning in many domains, such as language modeling and image recognition, this thesis investigated the use of transfer learning to improve vulnerability detection. Specifically, we pre-trained a series of models to detect malicious binaries and used the weights from those models to kickstart the detection of vulnerable binaries. In our study, we also investigated five different data representations …
A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan
A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan
Graduate Student Theses, Dissertations, & Professional Papers
Surrogate modeling is a new and expanding field in the world of deep learning, providing a computationally inexpensive way to approximate results from computationally demanding high-fidelity simulations. Ice sheet modeling is one of these computationally expensive models, the model used in this study currently requires between 10 and 20 minutes to complete one simulation. While this process is adequate for certain applications, the ability to use sampling approaches to perform statistical inference becomes infeasible. This issue can be overcome by using a surrogate model to approximate the ice sheet model, bringing the time to produce output down to a tenth …
Ensemble Protein Inference Evaluation, Kyle Lee Lucke
Ensemble Protein Inference Evaluation, Kyle Lee Lucke
Graduate Student Theses, Dissertations, & Professional Papers
The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …
Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii
Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii
Graduate Student Theses, Dissertations, & Professional Papers
Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that …
A Deep Learning Approach To Mapping Irrigation: U-Net Irrmapper, Thomas Henry Colligan Iv
A Deep Learning Approach To Mapping Irrigation: U-Net Irrmapper, Thomas Henry Colligan Iv
Graduate Student Theses, Dissertations, & Professional Papers
Accurate maps of irrigation are essential for understanding and managing water resources in light of a warming climate. We present a new method for mapping irrigation and apply it to the state of Montana over the years 2000-2019. The method is based on an ensemble of convolutional neural networks that only rely on raw Landsat surface reflectance data. The ensemble of networks method learns to mask clouds and ignore Landsat 7 scan-line failures without supervision, reducing the need for preprocessing data or feature engineering. Unlike other approaches to mapping irrigation, the method doesn't use other mapping products like the Cropland …
K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler
K-Mer Analysis Pipeline For Classification Of Dna Sequences From Metagenomic Samples, Russell Kaehler
Graduate Student Theses, Dissertations, & Professional Papers
Biological sequence datasets are increasing at a prodigious rate. The volume of data in these datasets surpasses what is observed in many other fields of science. New developments wherein metagenomic DNA from complex bacterial communities is recovered and sequenced are producing a new kind of data known as metagenomic data, which is comprised of DNA fragments from many genomes. Developing a utility to analyze such metagenomic data and predict the sample class from which it originated has many possible implications for ecological and medical applications. Within this document is a description of a series of analytical techniques used to process …
An Adaptive Hybrid Method For Link Prediction In Multi-Modal Directed Complex Networks Using The Graph Traversal Pattern, William Lyon
An Adaptive Hybrid Method For Link Prediction In Multi-Modal Directed Complex Networks Using The Graph Traversal Pattern, William Lyon
Graduate Student Theses, Dissertations, & Professional Papers
The paper examines the link prediction problem for directed multi-modal complex networks. Specically, a hybrid method combining collaborative filtering and Triadic Closeness methods is developed. The methods are applied to a sample of the GitHub network. Implementation details are discussed, with a focus on design of a scalable system for handilng large data sets. Finally, results of this new method are discussed with no significant improvement over current methods.