Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 25 for “"attention networks"”.
-
Understanding tumor cell plasticity in spatial transcriptomics with graph attention networks and walk-based pseudotime analysis
… We introduce PlastiNet, which uses a graphical attention-based network to create a spatial aware embedding. The utility of our approach is validated in model systems, specifically in the brain and colon, where it successfully identifies biologically relevant neighborhoods and maps …
-
Identifying Attention Commonalities and Differences Between Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder
… examined one of Posner and Petersen’s (1990) attention networks (i.e., orienting – the ability to selectively focus on pertinent information) in children with Autism Spectrum Disorder (ASD) as compared to Attention-Deficit/Hyperactivity Disorder (AD/HD). The orienting processes of …
-
Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks
… for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep reinforcement …
-
Testing the "Interrupt Hypothesis": Does startle disrupt cognitive processing?
… and Protection Hypotheses by combining the Attention Networks Test (ANT), a combined flanker and cue reaction time task that measures the efficiency of several attentional networks, with the startle paradigm. Startle stimuli were presented in the interval between the onset of the visual cue …
-
Graph structures, random walks, and all that : learning graphs with jumping knowledge networks
… of drugs, community detection in social networks, and modeling interactions in physical systems. Recent deep learning approaches for graph representation learning, namely Graph Neural Networks (GNNs), follow a neighborhood aggregation procedure, where the representation vector of a node …
-
Midfrontal Theta Power and Attention in Middle Childhood
Middle childhood is a critical period of attentional development. Previous research has linked neural oscillations in the theta frequency band to controlled attentional and cognitive processes, which has been replicated in children and adults. The development of executive attention, which biases …
-
Accelerating graph attention network inference on CPUs with layer fusion
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
-
Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications
… By leveraging tree- and graph-based neural networks, this study pioneers a holistic approach that augments language understanding and processing capabilities. Through the fusion of structural and semantic-driven insights, this work tries to explore various NLP applications for two linguistic …
-
Aerobic Fitness and Attentional Control in Preadolescent Children
… the relationship between aerobic fitness and the attentional networks was assessed in forty-six preadolescent children. Neuroelectric (P3 amplitude and latency) and behavioral (accuracy, response time [RT]) indices of cognitive performance were assessed during the Attention Network Test (ANT). …
-
Comparison of Natural Language Processing Models for Depression Detection in Chatbot Dialogues
… recurrent unit (BGRU) models, (2) Hierarchical attention networks (HAN), and (3) Long-sequence Transformer models to accurately screen for depression in individuals. The models were all trained and tested on a common standard clinical dataset (DAICWoz) that is derived from clinical patient …
-
Machine learning based speech quality prediction
… deep learning architectures, such as CNNs, LSTM networks, and Transformer/self-attention networks were combined and compared. It was found that a network with CNN, Self-Attention, and a proposed attention-pooling delivers the best single-ended speech quality predictions on the considered dataset. …
-
Modulation of lateralised responses to primary affect
… by mapping subcortical and cortical emotional attention networks, specific to different variants of only negative affect (i.e. fear, sadness). How this subcortically originating lateralisation manifests into observable behaviour however still remains to be established. This research therefore …
-
Neuroimaging study of prenatal alcohol exposure effects on structural and functional connectivity in children
… regions and lower RSFC in 5 GM regions within 5 networks. Four of the 7 WM and 3 of the 5 GM regions also showed alterations in HE children, providing evidence that alterations in nonsyndromal children are less extensive and that some regions appear to be relatively spared. Alterations in DTI …
-
Physics-Informed Interpretable Attention-based Machine Learning for Jet Turbine Prediction
… of experimental. In this thesis, we utilize an attention-based neural network, the Temporal Fusion Transformer, on experimental data for prediction, allowing for interpretability and insight into model dynamics. We describe a series of experiments on different configurations of the model …
-
Enabling AI Copilots for Engineering Design With Parametric, Graph, And Component Inputs
… parametric data completion. By coupling Graph Attention Networks with a diffusion-based imputation mechanism, our method acts as a highly accurate and creative design auto-completion system for incomplete partial designs. On a dataset of 12,500 bicycles, this design imputation framework …
-
A functional imaging study of the relationship between the Default Mode Network and other control networks in the human brain
… to other large-scale cognitive control networks through functional connectivity analysis and analysis of combined electroencephalographic (EEG) recordings. Data utilised across a series of three experiments were obtained from combined EEG-functional Magnetic Resonance Imaging recordings …
-
Novel Algorithms for Understanding Online Reviews
… review understanding problem, which has gained attention from both industry and academia, and has found applications in many downstream tasks, such as recommendation, information retrieval and review summarization. In this dissertation, we aim to develop machine learning and natural language …
-
Decoding brains by paying attention: An attention-based fMRI task state decoding deep network architecture
… the effectiveness of linear, graph-based and attention-based methods for hierarchical classification. Furthermore, we propose a new attention-based network architecture which showcases superior performance to all of our baseline architectures without the use of handcrafted features on several …
Page 1 of 2