Physical Sciences and Mathematics Commons^™

Cross-Lingual Adaptation For Recipe Retrieval With Mixup, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Cross-modal recipe retrieval has attracted research attention in recent years, thanks to the availability of large-scale paired data for training. Nevertheless, obtaining adequate recipe-image pairs covering the majority of cuisines for supervised learning is difficult if not impossible. By transferring knowledge learnt from a data-rich cuisine to a data-scarce cuisine, domain adaptation sheds light on this practical problem. Nevertheless, existing works assume recipes in source and target domains are mostly originated from the same cuisine and written in the same language. This paper studies unsupervised domain adaptation for image-to-recipe retrieval, where recipes in source and target domains are in different …

Reinforcement Learning-Based Interactive Video Search, Zhixin Ma, Jiaxin Wu, Zhijian Hou, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Despite the rapid progress in text-to-video search due to the advancement of cross-modal representation learning, the existing techniques still fall short in helping users to rapidly identify the search targets. Particularly, in the situation that a system suggests a long list of similar candidates, the user needs to painstakingly inspect every search result. The experience is frustrated with repeated watching of similar clips, and more frustratingly, the search targets may be overlooked due to mental tiredness. This paper explores reinforcement learning-based (RL) searching to relieve the user from the burden of brute force inspection. Specifically, the system maintains a graph …

Learnings From A Pilot Hybrid Question Answering System: Cqas: Case Study Based On A Singapore Government Agency's Customer Service Centre, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

The Singapore Government first released their digital government blueprint in 2018 with the key message for all their agencies to be "digital to the core and served with heart". With this push, agencies are moving towards human-centric digital services, especially for individual citizens. During COVID-19, Singapore government agencies introduced many COVID-19 digital initiatives resulting in more incoming inquiries from citizens to respective agencies. This surge in inquiries created the challenge on the agencies' end to meet service level agreements. One widely adopted solution is the use of chatbot technology that directly interfaces with the customer. However, several organisations have faced …

Generative Flows With Invertible Attentions, Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc Van Gool

Research Collection School Of Computing and Information Systems

Flow-based generative models have shown an excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations. Yet, learning attentions in generative flows remains understudied, while it has made breakthroughs in other domains. To fill the gap, this paper introduces two types of invertible attention mechanisms, i.e., map-based and transformer-based attentions, for both unconditional and conditional generative flows. The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows. The masked scheme allows for invertible attention modules with tractable Jacobian determinants, …

Officers: Operational Framework For Intelligent Crime-And-Emergency Response Scheduling, Jonathan David Chase, Siong Thye Goh, Tran Phong, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was …

A Self-Consistent Model For Brown Dwarf Populations, R. E. Ryan Jr., P. Thorman, C. Aganze, A. J. Burgasser, S. H. Cohen, N. P. Hathi, Benne Holwerda, N. Pirzkal, R. A. Windhorst

Faculty and Staff Scholarship

We present a self-consistent model of the Milky Way to reproduce the observed distributions (spectral type, absolute J-band magnitude, effective temperature) and total velocity dispersion of brown dwarfs. For our model, we adopt parametric forms for the star formation history and initial-mass function, published evolutionary models, and theoretical age–velocity relations. Using standard Markov Chain Monte Carlo methods, we derive a power-law index of the initial-mass function of α = −0.71 ± 0.11, which is an improvement over previous studies. We consider a gamma-function form for the star formation history, though we find that this complex model is only slightly …

On Three-Dimensional Rotating Viscoelastic Jets In The Giesekus Model, Daniel N. Riahi, Saulo Orizaga

School of Mathematical & Statistical Sciences Faculty Publications

We investigate three-dimensional nonlinear rotating viscoelastic curved jets in the presence of gravity force. Applying the Giesekus model for the viscoelastic stress parts of the jet flow system and using perturbation methods with a consistent scaling, a relatively simple system of equations with realistic three-dimensional centerlines is developed. We determine numerically the relevant solution quantities of the model in terms of the radius, speed, tensile force, stretching rate, strain rate and the jet centerline versus arc length and for different parameter values associated with gravity, viscosity, rotation, surface tension and viscoelasticity. Considering the jet flow system in full 3-dimensions and …

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 …

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 …

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 …

Emissions Baseline Report For The Agriculture Sector In Western Australia, Mandy Curnow, Christophe D'Abbadie, Robert French, Jackie Bucat, Dario Stefanelli, Neil Lantzke, Valeria Almeida Lima, Jess Austen, Lee Chester, Kate Pritchett, Millie Brady, Dylan Bennett

Climate Science Published Reports

The Intergovernmental Panel on Climate Change Sixth Assessment Report found that climate impacts are appearing earlier and are more severe than expected. The world faces multiple unavoidable climate hazards over the next two decades with global warming of 1.5ºC and accelerated action is required to avoid mortality and loss of biodiversity and infrastructure.

Achieving Climate Resilience requires both climate adaptation and mitigation of Greenhouse Gases which are accelerating climate change. Western Australia is committed to building resilience of the agriculture industry and as part of that is focussed on reducing greenhouse gas (GHG) emissions at both an industry and government …

First Joint Observation By The Underground Gravitational-Wave Detector Kagra With Geo 600, R. Abbott, H. Abe, Fausto Acernese, K. Ackley, N. Adhikari, Michael G. Benjamin, Teviet Creighton, Mario C. Diaz, Francisco Llamas, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

We report the results of the first joint observation of the KAGRA detector with GEO 600. KAGRA is a cryogenic and underground gravitational-wave detector consisting of a laser interferometer with 3 km arms, located in Kamioka, Gifu, Japan. GEO 600 is a British–German laser interferometer with 600 m arms, located near Hannover, Germany. GEO 600 and KAGRA performed a joint observing run from April 7 to 20, 2020. We present the results of the joint analysis of the GEO–KAGRA data for transient gravitational-wave signals, including the coalescence of neutron-star binaries and generic unmodeled transients. We also perform dedicated searches for …

Suppressing Disorder-Order Phase Transition Of Gd2zr2o7 Pyrochlore By Dy3+ Doping And Their Impact On Luminescence, Santosh K. Gupta, S. Nigam, Jose P. Zuniga, Yuanbing Mao

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

The functionality of the Gd2Zr2O7 matrix is dictated by ordering at the cationic lattice (pyrochlore or defective fluorite) and associated local manifestations. In this work, we have synthesized nano and bulk undoped Gd2Zr2O7 (GZO) and Gd2Zr2O7:Dy3+ (GZOD) by molten salt synthesis adopting a crystal engineering approach and investigated their impact on photoluminescence properties. DFT calculations along with the higher intensity of yellow emission in the bulk GZOD confirm that the presence of Dy3+ is energetically favorable in defect fluorite structure compared to ordered pyrochlore counterpart. Increase/adjustments in the Zr–O bond distances in Dy3+ surroundings facilitate the release of local strain/stress …

A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz

Faculty Publications

The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this …

Runtime Energy Savings Based On Machine Learning Models For Multicore Applications, Vaibhav Sundriyal, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.

Coded Distributed Function Computation, Pedro J. Soto

Dissertations, Theses, and Capstone Projects

A ubiquitous problem in computer science research is the optimization of computation on large data sets. Such computations are usually too large to be performed on one machine and therefore the task needs to be distributed amongst a network of machines. However, a common problem within distributed computing is the mitigation of delays caused by faulty machines. This can be performed by the use of coding theory to optimize the amount of redundancy needed to handle such faults. This problem differs from classical coding theory since it is concerned with the dynamic coded computation on data rather than just statically …

Why, New York City? Gauging The Quality Of Life Through The Thoughts Of Tweeters, Sheryl Williams

Dissertations, Theses, and Capstone Projects

As a resource for social data, Twitter’s platform has been used to measure the quality of life through sentiment analysis. This capstone project explores another methodological technique—querying Twitter data around specific keyword terms to determine dominant topics, word patterns, and sentiment leanings in a geographical area. Focusing on New York City and Los Angeles for comparative analysis, the keyword term “why” will be used to build a Python analysis around topic modeling and sentiment analysis. Using this approach, the analysis reveals social and cultural differences, the overall sentiment of tweets, and subjects of interest to tweeters.

GitHub Repository for all …

Collective Behavior Of Dissipatively-Coupled Photonic Oscillator Networks, Jiajie Ding

Dissertations, Theses, and Capstone Projects

This thesis discusses the collective behavior of networks formed by photonic oscillators. The key motivations for doing this research are the interest in developing high power laser arrays on photonic chips and performing optical computing.

A Comparison Of Machine Learning Techniques For Validating Students’ Proficiency In Mathematics, Alexander Avdeev

Dissertations, Theses, and Capstone Projects

A principal goal of this project was to compare several machine learning (ML) algorithms to explore and validate math proficiency classifications based on standardized test scores. The data used in these analyses came from the 6th-grade students’ mathematics assessment records of the New York State Education Department’s Testing Program (NYSTP). Our approach was to test a number of competing machine learning (ML) algorithms for classifying students’ as proficient based on their test scores and other demographic information. Our samples were drawn from the 2016 test-taking cohort of 6th-grade students (N=156,800). Five classifiers including multinominal logistic regression (MLR), XGBoost, Tree-As, Lagrangian …

Symmetry-Inspired Analysis Of Biological Networks, Ian Leifer

Dissertations, Theses, and Capstone Projects

The description of a complex system like gene regulation of a cell or a brain of an animal in terms of the dynamics of each individual element is an insurmountable task due to the complexity of interactions and the scores of associated parameters. Recent decades brought about the description of these systems that employs network models. In such models the entire system is represented by a graph encapsulating a set of independently functioning objects and their interactions. This creates a level of abstraction that makes the analysis of such large scale system possible. Common practice is to draw conclusions about …

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 …

Biomimetic And Medical Applications Of Hollow Nanoscale Structures, Justin Fang

Dissertations, Theses, and Capstone Projects

Materials whose structure incorporates nanoscale void spaces have multiple possible uses, whether in a bulk form or as individual particles, due to the combination of high surface area ratios and nanoscale material properties. This thesis will explore a few of these possibilities, concentrating on potential biomimetic and biomedical applications, for two materials: metal- organic frameworks and superparamagnetic iron oxide nanocages.

Metal-organic frameworks consist of metal ions such as Cu2+ which have highly porous lattice structures allowing them to absorb and release guest molecules such as peptides like diphenylalanine; this stored chemical energy can be turned into kinetic energy and used …

The Zariski-Riemann Space As A Universal Model For The Birational Geometry Of A Function Field, Giovan Battista Pignatti Morano Di Custoza

Dissertations, Theses, and Capstone Projects

Given a function field $K$ over an algebraically closed field $k$, we propose to use the Zariski-Riemann space $\ZR (K/k)$ of valuation rings as a universal model that governs the birational geometry of the field extension $K/k$. More specifically, we find an exact correspondence between ad-hoc collections of open subsets of $\ZR (K/k)$ ordered by quasi-refinements and the category of normal models of $K/k$ with morphisms the birational maps. We then introduce suitable Grothendieck topologies and we develop a sheaf theory on $\ZR (K/k)$ which induces, locally at once, the sheaf theory of each normal model. Conversely, given a sheaf …

Exploring The Effectiveness Of Multiple-Exemplar Training For Visual Analysis Of Ab-Design Graphs, Verena S. Bethke

Dissertations, Theses, and Capstone Projects

In behavior analysis, data are usually analyzed using visual analysis of the graphed data. There are a wide range of methods used to visually analyze data, from a basic ‘textbook’ style approach to the use of visual aids, decision-rubrics, and computer-based approaches. In the literature, there have been some comparisons of the efficacy of different approaches. Visual analysis as a behavior can be taught using a variety of methods, independent of how the skill itself is to be performed. Teaching methods include lecture, online instruction, and equivalence-based instruction. There is not much research on the teaching of visual analysis specifically, …

Thickened Surfaces, Checkerboard Surfaces, And Quantum Link Invariants, Joseph W. Boninger

Dissertations, Theses, and Capstone Projects

This dissertation has two parts, each motivated by an open problem related to the Jones polynomial. The first part addresses the Volume Conjecture of Kashaev, Murakami, and Murakami. We define a polynomial invariant, J^T_n, of links in the thickened torus, which we call the nth toroidal colored Jones polynomial, and we show J^T_n satisfies many properties of the original colored Jones polynomial. Most significantly, J^T_n exhibits volume conjecture behavior. We prove a volume conjecture for the 2-by-2 square weave, and provide computational evidence for other links. We also give two equivalent constructions …

Phase Transitions, Critical Phenomena, And Correlation Functions In The 2d Ising Model And Its Applications To Quantum Dynamics: A Tensor Network Approach, Sankhya Basu

Dissertations, Theses, and Capstone Projects

This thesis explores several aspects of the 2D Ising Model at both real and complex temperatures utilizing tensor network algorithms. We briefly discuss the importance of tensor networks in the context of forming efficient representations of wavefunctions and partition functions for quantum and classical many-body systems respectively, followed by a brief review of the tensor network renormalization algorithms to compute the one point and two point correlation functions. We use the Tensor Renormalization Group (TRG) to study critical phenomena and examine feasibility of accurate estimations of universal critical data for three critical points for three critical points in two dimensions …

A Multi-Task Gaussian Process Self-Attention Neural Network For Real-Time Prediction Of The Need For Mechanical Ventilators In Covid-19 Patients, Xiaoqian Jiang

Faculty, Staff and Student Publications

OBJECTIVE: The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality.

METHODS: We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task …

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 …

The Design, Synthesis, And Characterization Of Copper-Based Metal-Organic Frameworks For Their Investigation Against Cancer And Their Effect As Anti-Microbial Agents, Sandy Elmehrath

Dissertations

A wide range of nanomaterials have been developed for biomedical applications, such as drug delivery, biomedical imaging, and sensors. Nanomaterials can include nanoparticles (NPs) and nanofibers with various dimensions that are both natural and synthetic. A successful nanomaterial, for use in biological applications, is characterized by its biocompatibility, biodegradability, intrinsic high surfaces area, high interconnected porosity, and functionality. These features were achieved with the development of metal-organic framework (MOF) nanostructures. MOFs are assemblies of metal ions and organic linkers that are built into different geometries and can exist in all dimensions (up to 3-D). The choice of linkers with well-defined …

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 …