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 11 of 11 for “"dependency graphs"”.
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DepTracker : tracking JavaScript dependencies on the web
… page load times. Existing models of Web pages (dependency graphs) ignore the dynamic interactions of JavaScript objects along these critical paths. Current dependency graphs solely include the dependencies that arise from a Web object triggering a new HTTP request. This thesis presents …
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Parallel pipelined VLSI arrays for real-time image processing
… on embedding the algorithms, expressed by their dependency graphs, into two dimensional regularly connected processing arrays. Each node in a graph represents an operation which can be processed by an individual processor in the array. The embedding is performed such that data can be processed in …
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Audit trails in the Aeolus distributed security platform
… predecessors. Therefore, our audit trails form dependency graphs that capture the causal relationship among system events. These graphs can be used to reconstruct the chains of events leading to a given system state. Our results show that audit trail collection imposes a small overhead on system …
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Inter-flow consistency: novel SDN update abstraction for supporting inter-flow constraints
… propose an update scheduling algorithm based on dependency graphs, a data structure revealing dependency among different update operations and network elements, in order to guarantee two basic inter-flow consistency, spatial isolation and version isolation. Also, we implement a prototype system …
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Investigating and Recommending Co-Changed Entities for JavaScript Programs
… for each commit, we created one or more change dependency graphs (CDGs) to model the referencer-referencee relationship between co-changed entities. Next, we extracted the common subgraphs between CDGs to locate recurring co-change patterns between entities. Finally, based on those patterns, we …
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Reasoning about quantities in natural language
… propose graph representations called ""unit dependency graphs'', and show that these graph representations can be used to effectively incorporate dimensional analysis knowledge in quantitative reasoning. Next, we develop a general framework to incorporate any declarative knowledge into …
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus
… formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This graphical model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. I demonstrate the …
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Furthering Datalog in the pursuit of program analysis
… parallelising compilers that transform data dependency graphs. This definition is then lifted into the set domain, allowing for interface-boundary respecting commutativity specifications using semantic program properties, as source-code inline annotations. This system is then used to encode …
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From TPU to QPU: Bridging Fidelity Gaps Across Next-Generation Computing Systems
… representations of quantum circuits as dependency graphs and allowing detailed exploration of inter-gate relationships and noise impacts on fidelity. Crucially, the framework exposes a tunable knob that balances the runtime overhead of RL-driven optimization and the quality of the …
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Causal structure of networks of stochastic processes
… in the network, algorithms to identify the graphs in different scenarios and when only a subset of agents are observed. We demonstrate the utility of the methods by identifying causal influences between markets and causal flow of information between media sites. We study the statistical and …
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Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
Deep Neural Network (DNN) models are challenging to interpret because of their highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within safety critical applications, (2) makes it challenging to debug existing models, and (3) prevents us from extracting …