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Articles 1  30 of 1798
FullText Articles in Theory and Algorithms
Time Evolution Is A Source Of Bias In The Wolf Algorithm For Largest Lyapunov Exponents, Kolby Brink, Tyler Wiles, Nicholas Stergiou, Aaron Likens
Time Evolution Is A Source Of Bias In The Wolf Algorithm For Largest Lyapunov Exponents, Kolby Brink, Tyler Wiles, Nicholas Stergiou, Aaron Likens
UNO Student Research and Creative Activity Fair
Human movement is inherently variable by nature. One of the most common analytical tools for assessing movement variability is the largest Lyapunov exponent (LyE) which quantifies the rate of trajectory divergence or convergence in an ndimensional state space. One popular method for assessing LyE is the Wolf algorithm. Many studies have investigated how Wolf’s calculation of the LyE changes due to sampling frequency, filtering, data normalization, and stride normalization. However, a surprisingly understudied parameter needed for LyE computation is evolution time. The purpose of this study is to investigate how the LyE changes as a function of evolution time …
Session11: SkipGcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle
Session11: SkipGcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle
SDSU Data Science Symposium
Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
Faculty Scholarship at Penn Carey Law
Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machinelearning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …
Combinatorics Syllabus, Tugce Ozdemir
Combinatorics Syllabus, Tugce Ozdemir
Open Educational Resources
No abstract provided.
Cooperative Deep Q Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan
Cooperative Deep Q Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan
Publications
In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dualneural network (NN)driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the actionvalue function in the presence of image inputs. In particular, we develop a temporal difference (TD) errordriven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …
PatchWise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal
PatchWise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal
Mechanical & Aerospace Engineering Faculty Publications
This paper expands the authors’ prior work[1], which focuses on developing a convolutional neural network (CNN) model capable of mapping timeaveraged, unsteady Reynold’saveraged NavierStokes (URANS) simulations to higher resolution results informed by timeaveraged detached eddy simulations (DES). The authors present improvements over the prior CNN autoencoder model that result from hyperparameter optimization, increased data set augmentation through the adoption of a patchwise training approach, and the predictions of primitive variables rather than vorticity magnitude. The training of the CNN model developed in this study uses the same URANS and DES simulations of a transonic flow around several NACA 4digit airfoils …
Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, JrShin Li
Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, JrShin 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 …
Survey On Sentiment Analysis: Evolution Of Research Methods And Topics, Jingfeng Cui, Zhaoxia Wang, SengBeng Ho, Erik Cambria
Survey On Sentiment Analysis: Evolution Of Research Methods And Topics, Jingfeng Cui, Zhaoxia Wang, SengBeng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates …
Crosshair Optimizer, Jason Torrence
Crosshair Optimizer, Jason Torrence
All Master's Theses
Metaheuristic optimization algorithms are heuristics that are capable of creating a "good enough'' solution to a computationally complex problem. Algorithms in this area of study are focused on the process of exploration and exploitation: exploration of the solution space and exploitation of the results that have been found during that exploration, with most resources going toward the former half of the process. The novel Crosshair optimizer developed in this thesis seeks to take advantage of the latter, exploiting the best possible result as much as possible by directly searching the area around that best result with a stochastic approach. This …
Advances In The Automatic Detection Of Optimization Opportunities In Computer Programs, Delaram Talaashrafi
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 highperformance software, the programmer should develop optimized algorithms to maximize the system’s resource utilization. However, designing such algorithms is challenging and timeconsuming. 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 …
ContextAware Collaborative NeuroSymbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli
ContextAware Collaborative NeuroSymbolic 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, contextaware, multimodal fusion for realtime, robust decisionmaking 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 stateoftheart ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting largescale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertaintyquantified neurosymbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multimodal and multivantage sensor data. The approach features …
Improving Adjacency List Storage Methods For Polypeptide Similarity Analysis, Arianna Swensen
Improving Adjacency List Storage Methods For Polypeptide Similarity Analysis, Arianna Swensen
Honors Theses
Protein design is a complex biomolecular and computational problem. Working on increasingly large protein folding problems requires an improvement in current analysis methods available. This work first discusses various methods of protein design, including de novo protein design, which is the primary focus of this thesis. Then, a new approach utilizing a B+ tree to effectively store and query a graph of keys and vertices is proposed in order to store the number of times two polypeptides are considered to be similar. This approach is found to have a reduction in time complexity from current mapping methods and thus provides …
Obstacles In Learning Algorithm RunTime Complexity Analysis, Bailey Licht
Obstacles In Learning Algorithm RunTime Complexity Analysis, Bailey Licht
Theses/Capstones/Creative Projects
Algorithm runtime 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 runtime complexity analysis if these missing stages and learning styles are addressed.
An Efficient AnnealingAssisted Differential Evolution For MultiParameter Adaptive Latent Factor Analysis, Qing Li, Guansong Pang, Mingsheng Shang
An Efficient AnnealingAssisted Differential Evolution For MultiParameter Adaptive Latent Factor Analysis, Qing Li, Guansong Pang, Mingsheng Shang
Research Collection School Of Computing and Information Systems
A highdimensional 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 multiparameters 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 annealingassisted differential evolution for multiparameter adaptive latent factor analysis …
Three Contributions To The Theory And Practice Of Optimizing Compilers, Linxiao Wang
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 …
MorphologicallyAware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw
MorphologicallyAware 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.
MetaComplementing The Semantics Of Short Texts In Neural Topic Models, Ce Zhang, Hady Wirawan Lauw
MetaComplementing 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 cooccurrences 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 cooccurrences, 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 MetaComplement Topic Model, which improves topic quality …
Hyperspectral Unmixing: A Theoretical Aspect And Applications To Crism Data Processing, Yuki Itoh
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, Raghavendra K. Addanki
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 lowcost 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 SarsCov2 Transmission, Daniel Maxin, Spencer Gannon
An Algorithm For Indoor SarsCov2 Transmission, Daniel Maxin, Spencer Gannon
Journal of Mind and Medical Sciences
We propose a computer modeling approach for SARSCoV2 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, Yulu Jin, Youdan Xiao
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 ProblemSolving And The Artifacts Of Health And Medicine, Michael W. Raphael
Artificial Intelligence And The Situational Rationality Of Diagnosis: Human ProblemSolving And The Artifacts Of Health And Medicine, Michael W. Raphael
Publications and Research
What is the problemsolving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problemsolving for medical sociology regarding the limits of automation for decisionbased 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 problemsolving process in doctorpatient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problemsolving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …
Fellowship Application Sample, John Dowd
Fellowship Application Sample, John Dowd
ICS Fellow Applications
No abstract provided.
Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, Julian Gouffray
Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, Julian Gouffray
James Madison Undergraduate Research Journal (JMURJ)
This paper examines how the Bitcoinaltcoin 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 Grangercausality 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 …
CovInception: Covid19 Detection Tool Using Chest XRay, Aswini Thota, Ololade Awodipe, Rashmi Patel
CovInception: Covid19 Detection Tool Using Chest XRay, Aswini Thota, Ololade Awodipe, Rashmi Patel
SMU Data Science Review
Since the pandemic started, researchers have been trying to find a way to detect COVID19 which is a costeffective, fast, and reliable way to keep the economy viable and running. This research details how chest Xray 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 firstline test for COVID19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pretrained algorithms were finetuned and applied to the images to train the model and the best model obtained was finetuned InceptionV3 model …
Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler
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 …
On The Cryptographic Deniability Of The Signal Protocol, Nihal Vatandas
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 nontrivial 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, Yitzchak Novick
An Analysis Of The Friendship Paradox And Derived Sampling Methods, Yitzchak Novick
Dissertations, Theses, and Capstone Projects
The friendship paradox (FP) is the famous samplingbias phenomenon that leads to the seemingly paradoxical truth that, on average, people’s friends have more friends than they do. Among the many farreaching 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 …
Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng
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 NPhard. To solve FAST effectively and efficiently, we first present an …
Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, Adib Sedig
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 …