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.
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Showing 1 to 20 of 36 for “"temporal dependencies"”.
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that …
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Temporally Correct Algorithms for Transaction Concurrency Control in Distributed Databases
Many activities are comprised of temporally dependent events that must be executed in a specific chronological order. Supportive software applications must preserve these temporal dependencies. Whenever the processing of this type of an application includes transactions submitted to a database that …
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On the Use of Independent Component Analysis & Functional Network Connectivity Analysis: Evaluation on Two Distinct Large-Scale Psychopathology Studies
… of analysis allows us to delve further into the temporal dependencies among components or 'regions' within the brain. In this thesis, we investigate the implementation of group ICA and FNC analysis on two large-scale psychopathology studies -— the first from a multi-site study involving the …
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Gesture Recognition in Tennis Biomechanics
… tennis ball trajectory. In attempt to learn temporal dependencies within a tennis swing, we implemented gate-augmented RNNs. This study compared the RNN to two gated models; gated recurrent units (GRU), and long short-term memory (LSTM) units. We observed similar classification performance …
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Recurrent neural networks in cognitive and vision neuroscience
… neural networks on tasks requiring long-term temporal dependencies, which are critical components of cognitive functions such as working memory and decision-making. By introducing specialized skip-connections to promote the emergence of task-relevant dynamics, we enable these networks to …
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New Approaches to Synthetic Tabular Data Generation
… variables, diverse attribute types, functional dependencies across columns, and temporal dependencies across rows. We aim to explore how to generate higher-quality synthetic tabular data through the following subproblems: (1) auto-regressive DNNs for synthetic table generation (STG), (2) large …
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Spatial-Temporal Multivariate Time Series Forecasting Using Graph Neural Networks, with an Application to Traffic Speed Prediction
… models that effectively capture spatial and temporal dependencies inherent in the data. This dissertation introduces a novel architecture based on Graph Neural Networks that addresses the challenges of time series prediction, while also providing unique insights through decomposition of model …
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Data-Driven Decoding of Quantum Surface Codes using Recurrent Neural Networks
… are investigated as their are able to capture temporal dependencies and model sequential data. GRU and LSTM models are trained using the data generated with the help of the Stim simulator for quantum circuits that are based on experimental setups. The models are trained to classify error …
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Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
… is a recent surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed …
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Assessing Broadband and Spectral Irradiance Variability for Solar Nowcasting Using Statistical Analysis and Machine Learning
… the first- and second-order spectral and temporal dependencies of irradiance time series within the context of stationarity. The temporal structures indicate that solar irradiance processes are at best weakly stationary, and the implications for forecasting are discussed. The results of …
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Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications
… commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new …
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Simultaneous SNV calling and Phylogenetic Inference for Single-cell Sequencing Data
… of a tree. The phylogenetic tree captures the temporal dependencies across the genomes and provides an important constraint that allows to distinguish true mutations from error that masquerades as mutation. However, this approach of simultaneously identifying mutations while accounting for the …
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Advanced space-time integration for knowledge discovery in human mobility studies
… fast-growing volume of and interest in spatio-temporal mobility data, there is also an increasing need for new methods of analyzing this kind of data. Particularly, considerable effort has been made to characterize human activity-travel patterns from the spatio-temporal mobility data. However, …
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Proactively Handling Failures in Extreme-Scale Big Data Storage: A Data Driven Approach
… Big Data storage systems and there are complex dependencies among them, we propose a data-driven approach by characterizing an extreme-scale storage system that is composed of 8 storage clusters with 462,578 data drives over a one-year period, which adds up to 857,183,442 data drive hours. …
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Evaluation, Interpretation, and Maintenance of Machine Learning Models for IT Operations
… Since operational data instances often exhibit temporal dependencies, the use of improper model evaluation methods can lead to performance overestimation. Insufficient model maintenance on AIOps solutions incorporated in the production environment can also lead to future performance degradation. …
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An assessment of the onset of summer rainy season in Southern Africa - case study of Botswana
… to analyze this data. The ESA for DBN models temporal dependencies among the weather parameters and climate indices using Direct Acyclic Graphs (DAG). This innovative DBN technology, ESA, reveals more detailed information from complex models. It reveals what is currently happening over time in …
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Automated Detection And Quantification Of Pain Using Electroencephalography
… to help improve chronic pain treatment. A temporally pain-specific biomarker using EEG was developed using EEG microstates to evaluate their specificity to pain compared to rest and two non-rest conditions evoking similar responses. Multifractal analyses on the microstate sequence showed …
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Dynamic graph neural network framework for real-time multi-modal data analysis and predictive modeling
… to capture the intricate interactions among temporal, spatial, and domain-specific knowledge, particularly as these factors evolve dynamically, while also accounting for the complexities of multi-modal data in real time, with current GNN architectures often falling short in leveraging …
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Meta-learning representations with relational structure
… priors in model architectures. Intuitive dependencies within data, such as pixels mostly contributing to the context of their neighbours, may be formalised and embedded to improve generalisation and allow models with great capacity to avoid overfitting. Meta-learning has also been applied …
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Evaluating the Impact of GPU Frequency Tuning and Power Capping on Performance and Efficiency
… LSTM effectively han- dles workloads with temporal dependencies, and RF provides robust performance for diverse benchmarks. This predictive framework not only enables real-time optimization but also as- sists in developing energy-efficient scheduling strategies for future HPC systems. …
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