RNAs play important roles in the central dogma of molecular biology, and are involved in multiple biology processes such as chromatin modification, transcriptional interference and translation initiation. The functions of RNAs, especially non-coding RNAs, are highly related to its secondary structures, therefore computational methods for RNA structure prediction are of...
In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
Polycyclic Aromatic Hydrocarbons (PAHs) have been studied for their carcinogenic toxicity. PAHs are formed by incomplete carbon combustion. Benzo[a]pyrene (BaP) is an IARC classified Class 1 Human carcinogenic PAH. New studies are conducted to gain a better understanding of PAHs and how to reduce exposure. Glutathione S-transferase M 1 (GSTM1...
Severe cases of COVID-19 have been associated with cytokine storms, leading to widespread inflammation, tissue damage, and organ failure. Pro-inflammatory cytokines have also been shown to play a role in liver diseases, including alcoholic liver disease. To investigate alcohol consumption as a risk factor for SARS-CoV-2 infections and COVID-19 severity,...
Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi-...
We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
This work presents a novel CCRW receiver that utilizes a window of variable width, for e˙ectively mitigating multipath and ambiguity in both civil and military positioning applica-tions using Global Navigation Satellite Systems (GNSS). This CCRW receiver incorporates a single stroboscopic window, whose width is iteratively reduced until the e˙ect of...
In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...
Labeling videos is costly, time-consuming and tedious. These costs can escalate in applications such as medical diagnosis or autonomous driving where we need domain expertise for annotation. Few-shot action recognition aims to solve this problem by annotation-efficient learning mechanisms.
This thesis presents MetaUVFS as the first Unsupervised Meta-learning algorithm for...
Enabling accurate and automated identification of wireless devices is critical for ensuring secure and authenticated data communication in large-scale networks such as IoT networks. In the aim of devising practical identification techniques that are immune to spoofing, hardware-driven RF fingerprinting using deep neural networks, which leverages the inevitable presence of...