76 research outputs found
Predictive Modelling Approach to Data-Driven Computational Preventive Medicine
This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus.
Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance.
In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics
The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining
Signatures of dissipative quantum chaos
Understanding the far-from-equilibrium dynamics of dissipative quantum
systems, where dissipation and decoherence coexist with unitary dynamics, is an
enormous challenge with immense rewards. Often, the only realistic approach is
to forgo a detailed microscopic description and search for signatures of
universal behavior shared by collections of many distinct, yet sufficiently
similar, complex systems. Quantum chaos provides a powerful statistical
framework for addressing this question, relying on symmetries to obtain
information not accessible otherwise. This thesis examines how to reconcile
chaos with dissipation, proceeding along two complementary lines. In Part I, we
apply non-Hermitian random matrix theory to open quantum systems with Markovian
dissipation and discuss the relaxation timescales and steady states of three
representative examples of increasing physical relevance: single-particle
Lindbladians and Kraus maps, open free fermions, and dissipative
Sachdev-Ye-Kitaev (SYK) models. In Part II, we investigate the symmetries,
correlations, and universality of many-body open quantum systems, classifying
several models of dissipative quantum matter. From a theoretical viewpoint,
this thesis lays out a generic framework for the study of the universal
properties of realistic, chaotic, and dissipative quantum systems. From a
practical viewpoint, it provides the concrete building blocks of dynamical
dissipative evolution constrained by symmetry, with potential technological
impact on the fabrication of complex quantum structures.
(Full abstract in the thesis.)Comment: PhD Thesis, University of Lisbon (2023). 264 pages, 54 figures.
Partial overlap with arXiv:1905.02155, arXiv:1910.12784, arXiv:2007.04326,
arXiv:2011.06565, arXiv:2104.07647, arXiv:2110.03444, arXiv:2112.12109,
arXiv:2210.07959, arXiv:2210.01695, arXiv:2211.01650, arXiv:2212.00474, and
arXiv:2305.0966
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
2021-2022, University of Memphis bulletin
University of Memphis bulletin containing the graduate catalog for 2021-2022.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1441/thumbnail.jp
2019-2020, University of Memphis bulletin
University of Memphis bulletin containing the graduate catalog for 2019-2020.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1439/thumbnail.jp
Recommended from our members
Homogeneous vector capsules and their application to sufficient and complete data
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCapsules (vector-valued neurons) have recently become a more active area of research
in neural networks. However, existing formulations have several drawbacks including
the large number of trainable parameters that they require as well as the reliance on
routing mechanisms between layers of capsules.
The primary aim of this project is to demonstrate the benefits of a new formulation
of capsules called Homogeneous Vector Capsules (HVCs) that overcome these
drawbacks.
Using HVCs, new state-of-the-art accuracies for the MNIST dataset are established
for multiple individual models as well as multiple ensembles.
This work additionally presents a dataset consisting of high-resolution images of
13 micro-PCBs captured in various rotations and perspectives relative to the camera,
with each sample labeled for PCB type, rotation category, and perspective categories.
Experiments performed and elucidated in this work examine classification accuracy of
rotations and perspectives that were not trained on as well as the ability to artificially
generate missing rotations and perspectives during training. The results of these
experiments include showing that using HVCs is superior to using fully connected
layers.
This work also showed that certain training samples are more informative of class
membership than others. These samples can be identified prior to training by analyzing
their position in reduced dimensional space relative to the classes’ centroids in that
space. And a definition and calculation both for class density and dataset completeness
based on the distribution of data in the reduced dimensional space has been put forth.
Experimentation using the dataset completeness calculation shows that those datasets
that meet a certain completeness threshold can be trained on a subset of the total
dataset, based on each class’s density, while improving upon or maintaining validation
accuracy
2018-2019, University of Memphis bulletin
University of Memphis bulletin containing the graduate catalog for 2018-2019.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1438/thumbnail.jp
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
Tools and Algorithms for the Construction and Analysis of Systems
This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems
2020-2021, University of Memphis bulletin
University of Memphis bulletin containing the graduate catalog for 2020-2021.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1440/thumbnail.jp
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