Dynamic Function Learning Through Control Of Ensemble Systems, 2023 Washington University in St. Louis

#### Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, Jr-Shin Li

*Publications*

Learning tasks involving function approximation are preva- lent in numerous domains of science and engineering. The underlying idea is to design a learning algorithm that gener- ates a sequence of functions converging to the desired target function with arbitrary accuracy by using the available data samples. In this paper, we present a novel interpretation of iterative function learning through the lens of ensemble dy- namical systems, with an emphasis on establishing the equiv- alence between convergence of function learning algorithms and asymptotic behavior of ensemble systems. In particular, given a set of observation data in a function learning task, we …

Advances In The Automatic Detection Of Optimization Opportunities In Computer Programs, 2022 The University of Western Ontario

#### Advances In The Automatic Detection Of Optimization Opportunities In Computer Programs, Delaram Talaashrafi

*Electronic Thesis and Dissertation Repository*

Massively parallel and heterogeneous systems together with their APIs have been used for various applications. To achieve high-performance software, the programmer should develop optimized algorithms to maximize the system’s resource utilization. However, designing such algorithms is challenging and time-consuming. Therefore, optimizing compilers are developed to take part in the programmer’s optimization burden. Developing effective optimizing compilers is an active area of research. Specifically, because loop nests are usually the hot spots in a program, their optimization has been the main subject of many optimization algorithms. This thesis aims to improve the scope and applicability of performance optimization algorithms used in …

Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, 2022 Army Cyber Institute, U.S. Military Academy

#### Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

*ACI Journal Articles*

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …

Obstacles In Learning Algorithm Run-Time Complexity Analysis, 2022 University of Nebraska at Omaha

#### Obstacles In Learning Algorithm Run-Time Complexity Analysis, Bailey Licht

*Theses/Capstones/Creative Projects*

Algorithm run-time complexity analysis is an important topic in data structures and algorithms courses, but it is also a topic that many students struggle with. Commonly cited difficulties include the necessary mathematical background knowledge, the abstract nature of the topic, and the presentation style of the material. Analyzing the subject of algorithm analysis using multiple learning theories shows that course materials often leave out key steps in the learning process and neglect certain learning styles. Students can be more successful at learning algorithm run-time complexity analysis if these missing stages and learning styles are addressed.

An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, 2022 Chongqing University of Post and Telecommunications

#### An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, Qing Li, Guansong Pang, Mingsheng Shang

*Research Collection School Of Computing and Information Systems*

A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis …

Three Contributions To The Theory And Practice Of Optimizing Compilers, 2022 The University of Western Ontario

#### Three Contributions To The Theory And Practice Of Optimizing Compilers, Linxiao Wang

*Electronic Thesis and Dissertation Repository*

The theory and practice of optimizing compilers gather techniques that, from input computer programs, aim at generating code making the best use of modern computer hardware. On the theory side, this thesis contributes new results and algorithms in polyhedral geometry. On the practical side, this thesis contributes techniques for the tuning of parameters of programs targeting GPUs. We detailed these two fronts of our work below.

Consider a convex polyhedral set P given by a system of linear inequalities A*x <= b, where A is an integer matrix and b is an integer vector. We are interested in the integer hull P_{I} of P which is the smallest convex polyhedral set that contains all the integer points in P. In Chapter …

Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, 2022 Singapore Management University

#### Meta-Complementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw

*Research Collection School Of Computing and Information Systems*

Topic models infer latent topic distributions based on observed word co-occurrences in a text corpus. While typically a corpus contains documents of variable lengths, most previous topic models treat documents of different lengths uniformly, assuming that each document is sufficiently informative. However, shorter documents may have only a few word co-occurrences, resulting in inferior topic quality. Some other previous works assume that all documents are short, and leverage external auxiliary data, e.g., pretrained word embeddings and document connectivity. Orthogonal to existing works, we remedy this problem within the corpus itself by proposing a Meta-Complement Topic Model, which improves topic quality …

Morphologically-Aware Vocabulary Reduction Of Word Embeddings, 2022 Singapore Management University

#### Morphologically-Aware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw

*Research Collection School Of Computing and Information Systems*

We propose SubText, a compression mechanism via vocabulary reduction. The crux is to judiciously select a subset of word embeddings which support the reconstruction of the remaining word embeddings based on their form alone. The proposed algorithm considers the preservation of the original embeddings, as well as a word’s relationship to other words that are morphologically or semantically similar. Comprehensive evaluation of the compressed vocabulary reveals SubText’s efficacy on diverse tasks over traditional vocabulary reduction techniques, as validated on English, as well as a collection of inflected languages.

Hyperspectral Unmixing: A Theoretical Aspect And Applications To Crism Data Processing, 2022 University of Massachusetts Amherst

#### Hyperspectral Unmixing: A Theoretical Aspect And Applications To Crism Data Processing, Yuki Itoh

*Doctoral Dissertations*

Hyperspectral imaging has been deployed in earth and planetary remote sensing, and has contributed the development of new methods for monitoring the earth environment and new discoveries in planetary science. It has given scientists and engineers a new way to observe the surface of earth and planetary bodies by measuring the spectroscopic spectrum at a pixel scale.

Hyperspectal images require complex processing before practical use. One of the important goals of hyperspectral imaging is to obtain the images of reflectance spectrum. A raw image obtained by hyperspectral remote sensing usually undergoes conversion to a physical quantity representing the intensity of …

Combinatorial Algorithms For Graph Discovery And Experimental Design, 2022 University of Massachusetts Amherst

#### Combinatorial Algorithms For Graph Discovery And Experimental Design, Raghavendra K. Addanki

*Doctoral Dissertations*

In this thesis, we study the design and analysis of algorithms for discovering the structure and properties of an unknown graph, with applications in two different domains: causal inference and sublinear graph algorithms. In both these domains, graph discovery is possible using restricted forms of *experiments*, and our objective is to design low-cost experiments.

First, we describe efficient experimental approaches to the *causal discovery* problem, which in its simplest form, asks us to identify the causal relations (edges of the unknown graph) between variables (vertices of the unknown graph) of a given system. For causal discovery, we study algorithms …

An Algorithm For Indoor Sars-Cov-2 Transmission, 2022 Valparaiso University

#### An Algorithm For Indoor Sars-Cov-2 Transmission, Daniel Maxin, Spencer Gannon

*Journal of Mind and Medical Sciences*

We propose a computer modeling approach for SARS-CoV-2 transmission that can be preferable to a purely mathematical framework. It is illustrated its functionality in a specific case of indoor transmission. Based on literature, we assume that infection is due to aerosols with viral particles that persist and accumulate for hours in the air even after the persons who produced them left the space. We incorporate also restricted opening hours as a mitigation measure and one possible behavioral change in response to this measure. It is shown via several examples how this algorithmic modeling approach can be used to run various …

Path Choice Of Algorithm Intellectual Property Protection, 2022 Law School, Beijing Normal University, Beijing 100875, China

#### Path Choice Of Algorithm Intellectual Property Protection, Yulu Jin, Youdan Xiao

*Bulletin of Chinese Academy of Sciences (Chinese Version)*

Protection of algorithm by intellectual property is a powerful way to stimulate innovation and regulate the risk of the algorithm. Algorithm that can be protected by intellectual property right is the program algorithm, which is compiled in computer language, in the form of coded instruction sequence, run by the computer and produce independent rational value results. The article is combed out that there are drawbacks to the traditional path of IP to protect program algorithms:it has conflict between program algorithm and copyright law system; the trade secret path is at odds with program algorithmic governance; and program algorithm can hardly …

Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, 2022 CUNY Graduate Center

#### Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael

*Publications and Research*

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …

Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, 2022 James Madison University

#### Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, Julian Gouffray

*James Madison Undergraduate Research Journal (JMURJ)*

This paper examines how the Bitcoin-altcoin return relationship has evolved in periods between 2015 and 2020. To understand this relation, we observe data on the cryptocurrency Bitcoin and prominent altcoins Ethereum, Litecoin, Ripple, Stellar, and Monero, which collectively represent over 90% of the market throughout the observed period. We employ a vector autoregressive model (VAR) to produce forecast error variance decompositions, orthogonal impulse response functions, and Granger-causality tests. We find evidence that Bitcoin return variation has increasingly explained altcoin returns and that market inefficiency increased between 2017 and 2020, as shown by increased Granger causality between Bitcoin and altcoins. These …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, 2022 Southern Methodist University

#### Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

*SMU Data Science Review*

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, 2022 Southern Methodist University

#### Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

*SMU Data Science Review*

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three …

Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, 2022 Wuhan University

#### Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

*Research Collection School Of Computing and Information Systems*

Reorganizing bus frequencies to cater for actual travel demands can significantly save the cost of the public transport system. This paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an …

On The Cryptographic Deniability Of The Signal Protocol, 2022 The Graduate Center, City University of New York

#### On The Cryptographic Deniability Of The Signal Protocol, Nihal Vatandas

*Dissertations, Theses, and Capstone Projects*

Offline deniability is the ability to a posteriori deny having participated in a particular communication session. This property has been widely assumed for the Signal messaging application, yet no formal proof has appeared in the literature. In this work, we present the first formal study of the offline deniability of the Signal protocol. Our analysis shows that building a deniability proof for Signal is non-trivial and requires strong assumptions on the underlying mathematical groups where the protocol is run.

To do so, we study various implicitly authenticated key exchange protocols, including MQV, HMQV, and 3DH/X3DH, the latter being the core …

An Analysis Of The Friendship Paradox And Derived Sampling Methods, 2022 The Graduate Center, City University of New York

#### An Analysis Of The Friendship Paradox And Derived Sampling Methods, Yitzchak Novick

*Dissertations, Theses, and Capstone Projects*

The friendship paradox (FP) is the famous sampling-bias phenomenon that leads to the seemingly paradoxical truth that, on average, people’s friends have more friends than they do. Among the many far-reaching research findings the FP inspired is a sampling method that samples neighbors of vertices in a graph in order to acquire random vertices that are of higher expected degree than average.

Our research examines the friendship paradox on a local level. We seek to quantify the impact of the FP on an individual vertex by defining the vertex’s “friendship index”, a measure of the extent to which the phenomenon …

Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, 2022 Western University

#### Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, Adib Sedig

*Undergraduate Student Research Internships Conference*

This project is primarily inspired by three papers: Colin Camerer and Xiaomin Li’s (2019 working paper)—*Using Visual Salience in Empirical Game Theory*, Ryan Oprea’s (2020)—*What Makes a Rule Complex?*, and Caplin et. al.’s (2011)—*Search and Satisficing*. Over the summer, I worked towards constructing a model of choice for the visual coordination game that can model player behavior more accurately than traditional game theoretic predictions. It attempts to do so by incorporating a degree of bias towards salience into a cellular automaton search algorithm and utilizing it alongside a sequential search mechanism of satisficing. This …