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An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong Wen Deng, Chaoyang Zhang
An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong Wen Deng, Chaoyang Zhang
Michigan Tech Publications
Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang
Michigan Tech Publications
The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …
Jamming Detection And Classification In Ofdm-Based Uavs Via Feature- And Spectrogram-Tailored Machine Learning, Y. Li, J. Pawlak, J. Price, K. Al Shamaileh, Q. Niyaz, S. Paheding, V. Devabhaktuni
Jamming Detection And Classification In Ofdm-Based Uavs Via Feature- And Spectrogram-Tailored Machine Learning, Y. Li, J. Pawlak, J. Price, K. Al Shamaileh, Q. Niyaz, S. Paheding, V. Devabhaktuni
Michigan Tech Publications
In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), …
Synthetic Augmentation Methods For Object Detection In Overhead Imagery, Nicholas R. Hamilton
Synthetic Augmentation Methods For Object Detection In Overhead Imagery, Nicholas R. Hamilton
Dissertations, Master's Theses and Master's Reports
The multidisciplinary area of geospatial intelligence (GEOINT) is continually changing and becoming more complex. From efforts to automate portions of GEOINT using machine learning, which augment the analyst and improve exploitation, to optimizing the growing number of sources and variables, there is no denying that the strategies involved in this collection method are rapidly progressing. The unique and inherent complexities involved in imagery analysis from an overhead perspective--—e.g., target resolution, imaging band(s), and imaging angle--—test the ability of even the most developed and novel machine learning techniques. To support advancement in the application of object detection in overhead imagery, we …
Toward Deep Learning Emulators For Modeling The Large-Scale Structure Of The Universe, Neerav Kaushal
Toward Deep Learning Emulators For Modeling The Large-Scale Structure Of The Universe, Neerav Kaushal
Dissertations, Master's Theses and Master's Reports
Multi-billion dollar cosmological surveys are being conducted almost every decade in today’s era of precision cosmology. These surveys scan vast swaths of sky and generate tons of observational data. In order to extract meaningful information from this data and test these observations against theory, rigorous theoretical predictions are needed. In the absence of an analytic method, cosmological simulations become the most widely used tool to provide these predictions in order to test against the observations. They can be used to study covariance matrices, generate mock galaxy catalogs and provide ready-to-use snapshots for detailed redshift analyses. But cosmological simulations of matter …