Theory and Algorithms Commons^™

The Dope Distance Is Sic: A Stable, Informative, And Computable Metric On Ordered Merge Trees, Jose Arbelo, Antonio Delgado, Charley Kirk, Zach Schlamowitz

Mathematics Summer Fellows

When analyzing time series data, it is often of interest to categorize them based on how different they are. We define a new dissimilarity measure between time series: Dynamic Ordered Persistence Editing (DOPE). DOPE satisfies metric properties, is stable to noise, is as informative as alternative approaches, and efficiently computable. Satisfying these properties simultaneously makes DOPE of interest to both theoreticians and data scientists alike.

2-Dimensional String Problems: Data Structures And Quantum Algorithms, Dhrumilkumar Patel

LSU Master's Theses

The field of stringology studies algorithms and data structures used for processing strings efficiently. The goal of this thesis is to investigate 2-dimensional (2D) variants of some fundamental string problems, including \textit{Exact Pattern Matching} and \textit{Longest Common Substring}.

In the 2D pattern matching problem, we are given a matrix $\M[1\dd n,1\dd n]$ that consists of $N = n \times n$ symbols drawn from an alphabet $\Sigma$ of size $\sigma$. The query consists of a $ m \times m$ square matrix $\PP[1\dd m, 1\dd m]$ drawn from the same alphabet, and the task is to find all the locations of $\PP$ …

A Nature-Inspired Approach For Scenario-Based Validation Of Autonomous Systems, Quentin Goss, Mustafa Akbas

Beyond: Undergraduate Research Journal

Scenario-based approaches are cost and time effective solutions to autonomous cyber-physical system testing to identify bugs before costly methods such as physical testing in a controlled or uncontrolled environment. Every bug in an autonomous cyber-physical system is a potential safety risk. This paper presents a scenario-based method for finding bugs and estimating boundaries of the bug profile. The method utilizes a nature-inspired approach adapting low discrepancy sampling with local search. Extensive simulations demonstrate the performance of the approach with various adaptations.

On The Total Set Chromatic Number Of Graphs, Mark Anthony C. Tolentino, Gerone Russel J. Eugenio, Mari-Jo P. Ruiz

Theory & Applications of Graphs

Given a vertex coloring c of a graph, the neighborhood color set of a vertex is defined to be the set of all of its neighbors’ colors. The coloring c is called a set coloring if any two adjacent vertices have different neighborhood color sets. The set chromatic number χ_s(G) of a graph G is the minimum number of colors required in a set coloring of G. In this work, we investigate a total analog of set colorings; that is, we study set colorings of the total graph of graphs. Given a graph G = (V, E) …

Intuitr: A Theorem Prover For Intuitionistic Propositional Logic, Erik Rauer

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

A constructive proof proves the existence of a mathematical object by giving the steps necessary to construct said object. Proofs of this type can be interpreted as an algorithm for creating such an object. Intuitionistic Propositional Logic (IPL) is a propositional logic system wherein all valid proofs are constructive. intuitR is a theorem prover for IPL, that is, it determines whether a given formula is valid in IPL or not. In this paper, we describe how intuitR determines the validity of a formula and review its performance. When compared on a benchmark set of problems, intuitR was determined to solve …

Filling Gaps On The Pareto Front In Multi- And Many-Objective Optimization, Richard Lussier

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Pareto fronts offer insight into the best found solutions of a given problem. Several algorithms have been developed to help maintain a well-distributed Pareto front and therefore offer a wide variety of solutions. However, in real-world problems, the Pareto front isn’t necessarily a continuous surface and may contain holes and/or discontinuous lines. These irregular areas on the Pareto front are considered gaps. These gaps can either be natural or artificial. In their research, Pellicer, Escudero, Alzueta, and Deb suggest a three-step procedure to find, validate, and fill these gaps. First, they developed an algorithm to generate gap points. Second, they …

Enhancing A Qubo Solver Via Data Driven Multi-Start And Its Application To Vehicle Routing Problem, Whei Yeap Suen, Matthieu Parizy, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Quadratic unconstrained binary optimization (QUBO) models have garnered growing interests as a strong alternative modelling framework for solving combinatorial optimization problems. A wide variety of optimization problems that are usually studied using conventional Operations Research approaches can be formulated as QUBO problems. However, QUBO solvers do not guarantee optimality when solving optimization problems. Instead, obtaining high quality solutions using QUBO solvers entails tuning of multiple parameters. Here in our work, we conjecture that the initial states adjustment method used in QUBO solvers can be improved, where careful tuning will yield overall better results. We propose a data-driven multi-start algorithm that …

Techniques To Enhance A Qubo Solver For Permutation-Based Combinatorial Optimization, Siong Thye Goh, Jianyuan Bo, Sabrish Gopalakrishnan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many combinatorial optimization problems can be formulated as a problem to determine the order of sequence or to find a corresponding mapping of the objects. We call such problems permutation-based optimization problems. Many such problems can be formulated as a quadratic unconstrained binary optimization (QUBO) or Ising model by introducing a penalty coefficient to the permutation constraint terms. While classical and quantum annealing approaches have been proposed to solve QUBOs to date, they face issues with optimality and feasibility. Here we treat a given QUBO solver as a black box and propose techniques to enhance its performance. First, to ensure …

Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem, Yunyun Niu, Jie Shao, Jianhua Xiao, Wen Song, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into …

Review Of Some Existing Qml Frameworks And Novel Hybrid Classical-Quantum Neural Networks Realising Binary Classification For The Noisy Datasets, N. Schetakis, D. Aghamalyan, Paul Robert Griffin, M. Boguslavsky

Research Collection School Of Computing and Information Systems

One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC-ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for …

Space-Efficient Algorithms And Verification Schemes For Graph Streams, Prantar Ghosh

Dartmouth College Ph.D Dissertations

Structured data-sets are often easy to represent using graphs. The prevalence of massive data-sets in the modern world gives rise to big graphs such as web graphs, social networks, biological networks, and citation graphs. Most of these graphs keep growing continuously and pose two major challenges in their processing: (a) it is infeasible to store them entirely in the memory of a regular server, and (b) even if stored entirely, it is incredibly inefficient to reread the whole graph every time a new query appears. Thus, a natural approach for efficiently processing and analyzing such graphs is reading them as …

Analysis Of A Quantum Attack On The Blum-Micali Pseudorandom Number Generator, Tingfei Feng

Mathematical Sciences Technical Reports (MSTR)

In 2012, Guedes, Assis, and Lula proposed a quantum attack on a pseudorandom number generator named the Blum-Micali Pseudorandom number generator. They claimed that the quantum attack can outperform classical attacks super-polynomially. However, this paper shows that the quantum attack cannot get the correct seed and provides another corrected algorithm that is in exponential time but still faster than the classical attack. Since the original classical attacks are in exponential time, the Blum-Micali pseudorandom number generator would be still quantum resistant.

Legislative Language For Success, Sanjana Gundala

Master's Theses

Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what …

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 …

Deep Learning For Anomaly Detection, Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe

Research Collection School Of Computing and Information Systems

A nomaly detection aims at identifying data points which are rare or significantly different from the majority of data points. Many techniques are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Deep-learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, including anomaly detection. To address this new trend, we organized this Special Issue on Deep Learning for Anomaly Detection to cover the …

Do-Gan: A Double Oracle Framework For Generative Adversarial Networks, Aye Phyu Phye Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a new approach to train Gen-erative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discrim-inator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend the double oracle framework to GANs. We first generalize the players' strategies as the trained models of generator and discriminator from the best response or-acles. We then compute the …

Learning To Solve Routing Problems Via Distributionally Robust Optimization, Jiang Yuan, Yaoxin Wu, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally robust optimization (group DRO) to tackle this issue, where we jointly optimize the weights for different groups of distributions and the parameters for the deep model in an interleaved manner during training. We also design a module based on convolutional neural network, which allows the deep model to learn more informative latent pattern among the nodes. We evaluate the proposed approach on two types of wellknown deep models including GCN …

Consensus Formation On Heterogeneous Networks, Edoardo Fadda, Junda He, Claudia J. Tessone, Paolo Barucca

Research Collection School Of Computing and Information Systems

Reaching consensus-a macroscopic state where the system constituents display the same microscopic state-is a necessity in multiple complex socio-technical and techno-economic systems: their correct functioning ultimately depends on it. In many distributed systems-of which blockchain-based applications are a paradigmatic example-the process of consensus formation is crucial not only for the emergence of a leading majority but for the very functioning of the system. We build a minimalistic network model of consensus formation on blockchain systems for quantifying how central nodes-with respect to their average distance to others-can leverage on their position to obtain competitive advantage in the consensus process. We …

Private Information Retrieval And Function Computation For Noncolluding Coded Databases, Sarah A. Obead

Dissertations

The rapid development of information and communication technologies has motivated many data-centric paradigms such as big data and cloud computing. The resulting paradigmatic shift to cloud/network-centric applications and the accessibility of information over public networking platforms has brought information privacy to the focal point of current research challenges. Motivated by the emerging privacy concerns, the problem of private information retrieval (PIR), a standard problem of information privacy that originated in theoretical computer science, has recently attracted much attention in the information theory and coding communities. The goal of PIR is to allow a user to download a message from a …

Missing Value Estimation Using Clustering And Deep Learning Within Multiple Imputation Framework, Manar D. Samad, Sakib Abrar, Norou Diawara

Computer Science Faculty Research

Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. Arguably the most popular imputation algorithm is multiple imputation by chained equations (MICE), which estimates missing values from linear conditioning on observed values. This paper proposes methods to improve both the imputation accuracy of MICE and the classification accuracy of imputed data by replacing MICE’s linear regressors with ensemble learning and deep neural networks (DNN). The imputation accuracy is further improved by characterizing individual samples with cluster labels (CISCL) obtained from the training data. Our extensive analyses of six tabular data …