Physical Sciences and Mathematics Commons^™

On The Numerical Range Of Compact Operators, Montserrat Dabkowski

Master's Theses

One of the many characterizations of compact operators is as linear operators which
can be closely approximated by bounded finite rank operators (theorem 25). It is
well known that the numerical range of a bounded operator on a finite dimensional
Hilbert space is closed (theorem 54). In this thesis we explore how close to being
closed the numerical range of a compact operator is (theorem 56). We also describe
how limited the difference between the closure and the numerical range of a compact
operator can be (theorem 58). To aid in our exploration of the numerical range of
a compact …

Artist-Configurable Node-Based Approach To Generate Procedural Brush Stroke Textures For Digital Painting, Keavon Chambers

Master's Theses

Digital painting is the field of software designed to provide artists a virtual medium to emulate the experience and results of physical drawing. Several hardware and software components come together to form a whole workflow, ranging from the physical input devices, to the stroking process, to the texture content authorship. This thesis explores an artist-friendly approach to synthesize the textures that give life to digital brush strokes.

Most painting software provides a limited library of predefined brush textures. They aim to offer styles approximating physical media like paintbrushes, pencils, markers, and airbrushes. Often these are static bitmap textures that are …

Consensus Embedding For Multiple Networks: Computation And Applications, Mengzhen Li, Mehmet Koyutürk

Faculty Scholarship

Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a low-dimensional space such that the nodes that are “similar” with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be “multiplex” with multiple types of edges with different semantics. For such networks, computation of Consensus Embeddings based on the node embeddings of individual versions can be useful for various reasons, including privacy, efficiency, and effectiveness of analyses. Here, we systematically investigate the …

Mangrove Growth Promotion By Endophytic Actinobacteria And Seaweed Extract, Ameera Khalfan Salem Alkaabi

Theses

In this study, I aimed to determine the impact of the application of a commercial seaweed extract (SWE) bio-stimulant and endophytic actinobacterial isolates on growth performance and endogenous hormonal levels of mangroves. Therefore, I isolated endophytic plant growth promoting (PGP) actinobacteria (PGPA) from mangrove roots; and evaluated their potential as biological inoculants on mangrove seedlings under greenhouse and open-field nursery conditions. Seven salt tolerant isolates had the ability to produce different levels of in vitro plant growth regulators (PGRs) and 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase (ACCD), and to solubilize phosphorus. Accordingly, only one isolate, Streptomyces tubercidicus UAE1 (St), was …

Properties Of Certain Connected Graphs Related To Their Edge Metric Dimension, Sanabel Mahmoud Y. Bisharat

Theses

Metric dimension, resolving sets and edge metric dimension are very important invariants for the resolvability of graphs that have been studied and investigated intensively in the literature over the last decades. Their immense utilization in network topology, master mind games, robot navigation and representation of chemical compounds make their study very attractive. This thesis is concerned with the graph-theoretic properties of certain families of connected graphs related to their edge metric dimension. The main objective of this thesis is to study the comparison of metric dimension ver-sus edge metric dimension of certain families of graphs. The study investigates the relationship …

The Interactive Simulation Of Mars Dust Storms With The Mars General Circulation Model Marswrf At The Resolution Of 7.5°×9° (Latitude By Longitude), Khulood Hasan Alshehhi

Theses

The Mars dust cycle, including dust storms, has a great impact on the temperature, and the climate in general, on Mars. Dust particles in the atmosphere have a great capacity for absorbing/emitting infrared radiation resulting in large changes in the Martian climate. Local and regional dust storms occur in any Martian Year (MY), but global dust storm events (GDEs), which can cover the whole planet, occur, on average, once every 3-4 MYs. MarsWRF is a Mars version of the terrestrial numerical weather and climate model WRF (Weather Research and Forecasting Model) and part of the Planet WRF models for planetary …

A Multi-Criteria Decision-Making (Mcdm) Approach For Data-Driven Distance Learning Recommendations, Aysha Meshaal Alshamsi

Theses

Distance learning has been adopted as an alternative learning strategy to the dominant face-to-face teaching methodology. It has been largely implemented by many governments worldwide due to the spread of the COVID-19 pandemic and the implication in enforcing lockdown and social distancing. In emergency situations distance learning is referred to as Emergency Remote Teaching (ERT). Due to this dynamic, sudden shift, and scaling demand in distance learning, many challenges have been accentuated. These include technological adoption, student commitments, parent involvement, and teacher extra burden management, changes in the organization methodology, in addition to government development of new guidelines and regulations …

Fabrication And Characterization Of Nanostructured Hybrid Materials For Gas Sensing Applications, Husam H.D Altakroori

Theses

The rapid increase in environmental pollution has become a major concern, and its monitoring has evolved into a priority for human health. This fact has directed the researchers to make more efforts to find new techniques for the detection of gases hazardous to the environment and human health. With the tremendous advances in technology, gas-sensing devices have become popularly used in environmental applications to detect various toxic gases at very low concentrations.

This work aims at developing high-performance gas sensors with enhanced sensitivity, selectivity, low response time, and low operating temperature. The proposed sensors are fabricated based on the integration …

Asssembly And Deployment Of The Uaeu 256-Element Radio Array For Space Science, Ibrahim J.M. Alghoul

Theses

The scope of this project, a 256-element Radio Interferometer Array was assembled and deployed as part of the “UAEU Radio Astronomy Pathway Project”, which is jointly supported by the College of Science and the National Space Science & Technology Centre (NSSTC) at the UAE University. This antenna array was deployed at the NSSTC site, and it will serve as the central part of the ground-based radio observations facility to be utilized for multi-disciplinary space science research at the UAEU. The facility is capable of making low-frequency radio observations of astronomical sources and events, measurements for spacecraft tracking, atmospheric studies, planetary …

Training Thinner And Deeper Neural Networks: Jumpstart Regularization, Carles Riera, Camilo Rey, Thiago Serra, Eloi Puertas, Oriol Pujol

Faculty Conference Papers and Presentations

Neural networks are more expressive when they have multiple layers. In turn, conventional training methods are only successful if the depth does not lead to numerical issues such as exploding or vanishing gradients, which occur less frequently when the layers are sufficiently wide. However, increasing width to attain greater depth entails the use of heavier computational resources and leads to overparameterized models. These subsequent issues have been partially addressed by model compression methods such as quantization and pruning, some of which relying on normalization-based regularization of the loss function to make the effect of most parameters negligible. In this work, …

Statistical Effects Of Synthetic Template Mismatch With Model Deimos Observations, Christos Kakogiannis

Honors Theses

High-precision kinematics of individual stars in Milky Way satellite galaxies and globular clusters is crucial for studies of galaxy formation, cosmology, alternative gravity models, and dark matter. We tested a rigorous method that matches model DEIMOS observations of such targets with synthetic template spectra that deduces radial velocities and [Fe/H] abundances. We added noise to create a model spectrum with SNR = 30, fitted templates with different parameters to it, and calculated the
best-match velocity and the goodness of the fit. The best fits occur for templates that are close in parameter space to the model spectrum, and the fits …

Why Quantiles Are A Good Description Of Volatility In Economics: An Alternative Explanation, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich, Kittawit Autchariyapanitkul

Departmental Technical Reports (CS)

In econometrics, volatility of an investment is usually described by its Value-at-Risk (VaR), i.e., by an appropriate quantile of the corresponding probability distribution. The motivations for selecting VaR are largely empirical: VaR provides a more adequate description of what people intuitively perceive as risk. In this paper, we analyze this situation from the viewpoint of decision theory, and we show that this analysis naturally leads to the Value-at-Risk, i.e., to a quantile.

Interestingly, this analysis also naturally leads to an optimization problem related to quantile regression.

Hawthorne Effect: An Explanation Based On Decision Theory, Sofia Holguin, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

It is known that people feel better (and even work better) if someone pays attention to them; this is known as the Hawthorne effect. At first glance, it sounds counter-intuitive: this attention does not bring you any material benefits, so why would you feel better? If you are sick and someone gives you medicine, this will make you feel better, but if someone just pays attention, why does that make you feel better? In this paper, we use the general ideas of decision theory to explain this seemingly counterintuitive phenomenon.

Computational Paradox Of Deep Learning: A Qualitative Explanation, Jonatan Contreras, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

In general, the more unknowns in a problem, the more computational efforts is necessary to find all these unknowns. Interestingly, in state-of-the-art machine learning methods like deep learning, computations become easier when we increase the number of unknown parameters way beyond the number of equations. In this paper, we provide a qualitative explanation for this computational paradox.

Why Quantile Regression Works Well In Economics: A Partial Explanation, Olga Kosheleva, Vassilis G. Kaburlasos, Vladik Kreinovich, Roengchai Tansuchat

Departmental Technical Reports (CS)

To get a better picture of the future behavior of different economics-related quantities, we need to be able to predict not only their mean values, but also their distribution. For example, it is desirable not only to predict future average income, but also to predict the future distribution of income. One of the convenient ways to describe a probability distribution is by using alpha-quantiles such as medians (corresponding to alpha = 0.5), quartiles (corresponding to alpha = 0.25 and alpha = 0.75), etc. In principle, an alpha-quantile of the desired future quantity can depend on beta-quantiles of current distributions corresponding …

Invariance-Based Approach Explains Empirical Formulas From Pavement Engineering To Deep Learning, Edgar Daniel Rodriguez Velasquez, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many application areas, there are effective empirical formulas that need explanation. In this paper, we focus on two such challenges: neural networks, where a so-called softplus activation function is known to be very efficient, and pavement engineering, where there are empirical formulas describing the dependence of the pavement strength on the properties of the underlying soil. We show that similar scale-invariance ideas can explain both types of formulas -- and, in the case of pavement engineering, invariance ideas can lead to a new formula that combines the advantages of several known ones.

Multimodal Zero-Shot Hateful Meme Detection, Jiawen Zhu, Roy Ka-Wei Lee, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Facebook has recently launched the hateful meme detection challenge, which garnered much attention in academic and industry research communities. Researchers have proposed multimodal deep learning classification methods to perform hateful meme detection. While the proposed methods have yielded promising results, these classification methods are mostly supervised and heavily rely on labeled data that are not always available in the real-world setting. Therefore, this paper explores and aims to perform hateful meme detection in a zero-shot setting. Working towards this goal, we propose Target-Aware Multimodal Enhancement (TAME), which is a novel deep generative framework that can improve existing hateful meme classification …

Monolithic Ontological Methodology (Mom): An Effective Software Project Management Approach, Kamal Uddin Sarker, Aziz Deraman, Raza Hasan, Ali Abbas, Babar Shah, Abrar Ullah

All Works

Due to rapid changes in software applications, especially incorporating the demands of self-regulating technologies becomes a major challenge in software projects. This research focuses on technological, managerial, and procedural challenges, which are believed to be the most significant factors contributing to projects failure. To address these issues, this study proposes Monolithic Ontological Methodology (MOM) which addresses the weakness in the existing benchmark methodologies including PRINCE2, Extreme Programming, and Scrum in terms of project management, quality control, and stakeholder involvement. The MOM consists of seven phases and each phase has the required number of iterations until it is approved by management. …

The Smart In Smart Cities: A Framework For Image Classification Using Deep Learning, Rabiah Al-Qudah, Yaser Khamayseh, Monther Aldwairi, Sarfraz Khan

All Works

The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. …

Weakly-Supervised Tumor Purity Prediction From Frozen H&E Stained Slides, Matthew Brendel, Vanesa Getseva, Majd Al Assaad, Michael Sigouros, Alexandros Sigaras, Troy Kane, Pegah Khosravi, Juan Miguel Mosquera, Olivier Elemento, Iman Hajirasouliha

Publications and Research

Background

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive.

Methods

Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a digitally captured hematoxylin and eosin (H&E) stained histological slide, using several types of cancer from The Cancer Genome Atlas (TCGA) as a proof-of-concept.

Findings

Our model predicts …

Maximum Trapping Focal Length In Photophoretic Trap For 3d Imaging Systems, Jason M. Childers

Electrical Engineering

This product is a photophoretic trapping system which allows varying focal lengths to test which focal lengths are possible for trapping toner particles. This system establishes that there exists a maximum trapping distance limitation and is the first time the effect of focal length is studied in a photophoretic trapping system. Increasing photophoretic trapping focal length is necessary for improving this technology as a 3D display. The 3D imaging technology is realized by dragging a microscopic (micrometer-scale) particles with a laser beam to trace an image. This technology can display fully colored and high-resolution 3D images visible from almost any …

Toi-1696: A Nearby M4 Dwarf With A 3 R⊕ Planet In The Neptunian Desert, M. Mori, J. H. Livingston, J. De Leon, N. Narita, T. Hirano, A. Fukui, K. A. Collins, N. Fujita, Y. Hori, H. T. Ishikawa, K. Kawauchi, K. G. Stassun, N. Watanabe, S. Giacalone, R. Gore, A. Schroeder, C. D. Dressing, A. Bieryla, Eric L.N. Jensen, B. Massey, A. Shporer, M. Kuzuhara, D. Charbonneau, D. R. Ciardi, J. P. Doty, E. Esparza-Borges, H. Harakawa, K. Hodapp, M. Ikoma, K. Ikuta, K. Isogai, J. M. Jenkins, T. Kagetani, T. Kimura, T. Kodama, T. Kotani, V. Krishnamurthy, T. Kudo, S. Kurita, T. Kurokawa, N. Kusakabe, D. W. Latham, B. Mclean, F. Murgas, J. Nishikawa, T. Nishiumi, M. Omiya, H. P. Osborn, E. Palle, H. Parviainen, G. R. Ricker, S. Seager, T. Serizawa, H.-Y. Teng, Y. Terada, J. D. Twicken, A. Ueda, R. Vanderspek, S. Vievard, J. N. Winn, Y. Zou, M. Tamura

Physics & Astronomy Faculty Works

We present the discovery and validation of a temperate sub-Neptune around the nearby mid-M dwarf TIC 470381900 (TOI-1696), with a radius of 3.09 ± 0.11 R⊕ and an orbital period of 2.5 days, using a combination of Transiting Exoplanets Survey Satellite (TESS) and follow-up observations using ground-based telescopes. Joint analysis of multiband photometry from TESS, Multicolor Simultaneous Camera for studying Atmospheres of Transiting exoplanets (MuSCAT), MuSCAT3, Sinistro, and KeplerCam confirmed the transit signal to be achromatic as well as refined the orbital ephemeris. High-resolution imaging with Gemini/'Alopeke and high-resolution spectroscopy with the Subaru InfraRed Doppler (IRD) confirmed that there …

Where Is The Author: The Copyright Protection For Ai-Generated Works, Chieh Huang

Maurer Theses and Dissertations

The two groups of the human-or-machine questions, whether AI-generated works are copyrightable and whether AI-generated works have human authors, are revisiting the current copyright law with the emergence of AI-generated works. These revisiting questions reveal that the current authorship requirement fails to provide a clear and operable standard on evaluating a human contributor’s intellectual labor for creative output. Such a defect of the current authorship requirement has to be fixed to respond to the technological change of artificial intelligence and the burgeoning prevalence of AI- or advanced computer program-generated works.

This dissertation’s main goal is to fix the flaw …

Heating Fire Incidents In New York City, Merissa K. Lissade

Dissertations, Theses, and Capstone Projects

If you have ever had the Citizen app downloaded on your smart phone, then you know how many alerts you receive in a day living in New York City (NYC). Citizen is a mobile app that sends real-time safety alerts based on the location of its user. In my experience having the app, I have seen many notifications of fires caused by heaters during the winter. On the morning of January 9^th, 2022, I received a notification of an accidental blaze that took the lives of 17 people from the choking smoke of a 19-story residential building in …

Deep One-Class Classification Via Interpolated Gaussian Descriptor, Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their …

Desire: An Efficient Dynamic Cluster-Based Forest Indexing For Similarity Search In Multi-Metric Spaces, Yifan Zhu, Lu Chen, Yunjun Gao, Baihua Zheng, Pengfei Wang

Research Collection School Of Computing and Information Systems

Similarity search fnds similar objects for a given query object based on a certain similarity metric. Similarity search in metric spaces has attracted increasing attention, as the metric space can accommodate any type of data and support fexible distance metrics. However, a metric space only models a single data type with a specifc similarity metric. In contrast, a multi-metric space combines multiple metric spaces to simultaneously model a variety of data types and a collection of associated similarity metrics. Thus, a multi-metric space is capable of performing similarity search over any combination of metric spaces. Many studies focus on indexing …

Joint Pricing And Matching For City-Scale Ride Pooling, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Central to efficient ride-pooling are two challenges: (1) how to `price' customers' requests for rides, and (2) if the customer agrees to that price, how to best `match' these requests to drivers. While both of them are interdependent, each challenge's individual complexity has meant that, historically, they have been decoupled and studied individually. This paper creates a framework for batched pricing and matching in which pricing is seen as a meta-level optimisation over different possible matching decisions. Our key contributions are in developing a variant of the revenue-maximizing auction corresponding to the meta-level optimization problem, and then providing a scalable …

Faithful Extreme Rescaling Via Generative Prior Reciprocated Invertible Representations, Zhixuan Zhong, Liangyu Chai, Yang Zhou, Bailin Deng, Jia Pan, Shengfeng He

Research Collection School Of Computing and Information Systems

This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64×). Previous researches have leveraged the prior knowledge of a pretrained GAN model to generate high-quality upscaling results. However, they fail to produce pixel-accurate results due to the highly ambiguous extreme mapping process. We remedy this problem by introducing a reciprocated invertible image rescaling process, in which high-resolution information can be delicately embedded into an invertible low-resolution image and generative prior for a faithful HR reconstruction. In particular, the invertible LR features not …

Equilibrium And Dynamic Surface Tension Behavior In Colloidal Unimolecular Polymers (Cup), Ashish Zore, Peng Geng, Michael R. Van De Mark

Chemistry Faculty Research & Creative Works

Studies of the interfacial behavior of pure aqueous nanoparticles have been limited due tothe difficulty of making contaminant-free nanoparticles while also providing narrow size distribu-tion. Colloidal unimolecular polymers (CUPs) are a new type of single-chain nanoparticle with a particle size ranging from 3 to 9 nm, which can be produced free of surfactants and volatile organic contents (VOCs). CUP particles of different sizes and surface charges were made. The surface tension behavior of these CUP particles in water was studied using a maximum bubble pressure tensiometer. The equilibrium surface tension decreased with increasing concentration and the number of charges present …

Determining Knowledge From Student Performance Prediction Using Machine Learning, Wala El Rashied Mohamed

Theses

Recent years have seen a rapid development in the field of educational data mining (EDM), enhancing the ability to trace student knowledge. Data from intelligent tutoring systems (ITS) have been analyzed and interpreted by multiple researchers seeking to measure students’ knowledge as it evolves. Human nature, as well as other factors, makes it difficult to determine whether or not students are knowledgeable. This thesis sets out to examine the level of students’ knowledge by predicting their current and future academic performance based on records of their historical interactions. By restructuring data and considering a student perspective, we can gain insight …