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- Member of: ASU Electronic Theses and Dissertations
Description
Let $G=(V,E)$ be a graph. A \emph{list assignment} $L$ for $G$ is a function from
$V$ to subsets of the natural numbers. An $L$-\emph{coloring} is a function $f$
with domain $V$ such that $f(v)\in L(v)$ for all vertices $v\in V$ and $f(x)\ne f(y)$
whenever $xy\in E$. If $|L(v)|=t$ for all $v\in V$ then $L$ is a $t$-\emph{list
assignment}. The graph $G$ is $t$-choosable if for every $t$-list assignment $L$
there is an $L$-coloring. The least $t$ such that $G$ is $t$-choosable is called
the list chromatic number of $G$, and is denoted by $\ch(G)$. The complete multipartite
graph with $k$ parts, each of size $s$ is denoted by $K_{s*k}$. Erd\H{o}s et al.
suggested the problem of determining $\ensuremath{\ch(K_{s*k})}$, and showed that
$\ch(K_{2*k})=k$. Alon gave bounds of the form $\Theta(k\log s)$. Kierstead proved
the exact bound $\ch(K_{3*k})=\lceil\frac{4k-1}{3}\rceil$. Here it is proved that
$\ch(K_{4*k})=\lceil\frac{3k-1}{2}\rceil$.
An online version of the list coloring problem was introduced independently by Schauz
and Zhu. It can be formulated as a game between two players, Alice and Bob. Alice
designs lists of colors for all vertices, but does not tell Bob, and is allowed to
change her mind about unrevealed colors as the game progresses. On her $i$-th turn
Alice reveals all vertices with $i$ in their list. On his $i$-th turn Bob decides,
irrevocably, which (independent set) of these vertices to color with $i$. For a
function $l$ from $V$ to the natural numbers, Bob wins the $l$-\emph{game} if
eventually he colors every vertex $v$ before $v$ has had $l(v)+1$ colors of its
list revealed by Alice; otherwise Alice wins. The graph $G$ is $l$-\emph{online
choosable} or \emph{$l$-paintable} if Bob has a strategy to win the $l$-game. If
$l(v)=t$ for all $v\in V$ and $G$ is $l$-paintable, then $G$ is t-paintable.
The \emph{online list chromatic number }of $G$ is the least $t$ such that $G$
is $t$-paintable, and is denoted by $\ensuremath{\ch^{\mathrm{OL}}(G)}$. Evidently,
$\ch^{\mathrm{OL}}(G)\geq\ch(G)$. Zhu conjectured that the gap $\ch^{\mathrm{OL}}(G)-\ch(G)$
can be arbitrarily large. However there are only a few known examples with this gap
equal to one, and none with larger gap. This conjecture is explored in this thesis.
One of the obstacles is that there are not many graphs whose exact list coloring
number is known. This is one of the motivations for establishing new cases of Erd\H{o}s'
problem. Here new examples of graphs with gap one are found, and related technical
results are developed as tools for attacking Zhu's conjecture.
The square $G^{2}$ of a graph $G$ is formed by adding edges between all vertices
at distance $2$. It was conjectured that every graph $G$ satisfies $\chi(G^{2})=\ch(G^{2})$.
This was recently disproved for specially constructed graphs. Here it is shown that
a graph arising naturally in the theory of cellular networks is also a counterexample.
$V$ to subsets of the natural numbers. An $L$-\emph{coloring} is a function $f$
with domain $V$ such that $f(v)\in L(v)$ for all vertices $v\in V$ and $f(x)\ne f(y)$
whenever $xy\in E$. If $|L(v)|=t$ for all $v\in V$ then $L$ is a $t$-\emph{list
assignment}. The graph $G$ is $t$-choosable if for every $t$-list assignment $L$
there is an $L$-coloring. The least $t$ such that $G$ is $t$-choosable is called
the list chromatic number of $G$, and is denoted by $\ch(G)$. The complete multipartite
graph with $k$ parts, each of size $s$ is denoted by $K_{s*k}$. Erd\H{o}s et al.
suggested the problem of determining $\ensuremath{\ch(K_{s*k})}$, and showed that
$\ch(K_{2*k})=k$. Alon gave bounds of the form $\Theta(k\log s)$. Kierstead proved
the exact bound $\ch(K_{3*k})=\lceil\frac{4k-1}{3}\rceil$. Here it is proved that
$\ch(K_{4*k})=\lceil\frac{3k-1}{2}\rceil$.
An online version of the list coloring problem was introduced independently by Schauz
and Zhu. It can be formulated as a game between two players, Alice and Bob. Alice
designs lists of colors for all vertices, but does not tell Bob, and is allowed to
change her mind about unrevealed colors as the game progresses. On her $i$-th turn
Alice reveals all vertices with $i$ in their list. On his $i$-th turn Bob decides,
irrevocably, which (independent set) of these vertices to color with $i$. For a
function $l$ from $V$ to the natural numbers, Bob wins the $l$-\emph{game} if
eventually he colors every vertex $v$ before $v$ has had $l(v)+1$ colors of its
list revealed by Alice; otherwise Alice wins. The graph $G$ is $l$-\emph{online
choosable} or \emph{$l$-paintable} if Bob has a strategy to win the $l$-game. If
$l(v)=t$ for all $v\in V$ and $G$ is $l$-paintable, then $G$ is t-paintable.
The \emph{online list chromatic number }of $G$ is the least $t$ such that $G$
is $t$-paintable, and is denoted by $\ensuremath{\ch^{\mathrm{OL}}(G)}$. Evidently,
$\ch^{\mathrm{OL}}(G)\geq\ch(G)$. Zhu conjectured that the gap $\ch^{\mathrm{OL}}(G)-\ch(G)$
can be arbitrarily large. However there are only a few known examples with this gap
equal to one, and none with larger gap. This conjecture is explored in this thesis.
One of the obstacles is that there are not many graphs whose exact list coloring
number is known. This is one of the motivations for establishing new cases of Erd\H{o}s'
problem. Here new examples of graphs with gap one are found, and related technical
results are developed as tools for attacking Zhu's conjecture.
The square $G^{2}$ of a graph $G$ is formed by adding edges between all vertices
at distance $2$. It was conjectured that every graph $G$ satisfies $\chi(G^{2})=\ch(G^{2})$.
This was recently disproved for specially constructed graphs. Here it is shown that
a graph arising naturally in the theory of cellular networks is also a counterexample.
ContributorsWang, Ran (Author) / Kierstead, H.A. (Thesis advisor) / Colbourn, Charles (Committee member) / Czygrinow, Andrzej (Committee member) / Fishel, Susanna (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
One of the most remarkable outcomes resulting from the evolution of the web into Web 2.0, has been the propelling of blogging into a widely adopted and globally accepted phenomenon. While the unprecedented growth of the Blogosphere has added diversity and enriched the media, it has also added complexity. To cope with the relentless expansion, many enthusiastic bloggers have embarked on voluntarily writing, tagging, labeling, and cataloguing their posts in hopes of reaching the widest possible audience. Unbeknown to them, this reaching-for-others process triggers the generation of a new kind of collective wisdom, a result of shared collaboration, and the exchange of ideas, purpose, and objectives, through the formation of associations, links, and relations. Mastering an understanding of the Blogosphere can greatly help facilitate the needs of the ever growing number of these users, as well as producers, service providers, and advertisers into facilitation of the categorization and navigation of this vast environment. This work explores a novel method to leverage the collective wisdom from the infused label space for blog search and discovery. The work demonstrates that the wisdom space can provide a most unique and desirable framework to which to discover the highly sought after background information that could aid in the building of classifiers. This work incorporates this insight into the construction of a better clustering of blogs which boosts the performance of classifiers for identifying more relevant labels for blogs, and offers a mechanism that can be incorporated into replacing spurious labels and mislabels in a multi-labeled space.
ContributorsGalan, Magdiel F (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Complex systems are pervasive in science and engineering. Some examples include complex engineered networks such as the internet, the power grid, and transportation networks. The complexity of such systems arises not just from their size, but also from their structure, operation (including control and management), evolution over time, and that people are involved in their design and operation. Our understanding of such systems is limited because their behaviour cannot be characterized using traditional techniques of modelling and analysis.
As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant on a response of a system. However, traditional approaches for screening are ineffective for complex systems because of the size of the experimental design. Consequently, the factors considered are often restricted, but this automatically restricts the interactions that may be identified as well. Alternatively, the designs are restricted to only identify main effects, but this then fails to consider any possible interactions of the factors.
To address this problem, a specific combinatorial design termed a locating array is proposed as a screening design for complex systems. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs.
As a proof-of-concept, a locating array is applied to screen for main effects and low-order interactions on the response of average transport control protocol (TCP) throughput in a simulation model of a mobile ad hoc network (MANET). A MANET is a collection of mobile wireless nodes that self-organize without the aid of any centralized control or fixed infrastructure. The full-factorial design for the MANET considered is infeasible (with over 10^{43} design points) yet a locating array has only 421 design points.
In conjunction with the locating array, a ``heavy hitters'' algorithm is developed to identify the influential main effects and two-way interactions, correcting for the non-normal distribution of the average throughput, and uneven coverage of terms in the locating array. The significance of the identified main effects and interactions is validated independently using the statistical software JMP.
The statistical characteristics used to evaluate traditional screening designs are also applied to locating arrays.
These include the matrix of covariance, fraction of design space, and aliasing, among others. The results lend additional support to the use of locating arrays as screening designs.
The use of locating arrays as screening designs for complex engineered systems is promising as they yield useful models. This facilitates quantitative evaluation of architectures and protocols and contributes to our understanding of complex engineered networks.
As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant on a response of a system. However, traditional approaches for screening are ineffective for complex systems because of the size of the experimental design. Consequently, the factors considered are often restricted, but this automatically restricts the interactions that may be identified as well. Alternatively, the designs are restricted to only identify main effects, but this then fails to consider any possible interactions of the factors.
To address this problem, a specific combinatorial design termed a locating array is proposed as a screening design for complex systems. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs.
As a proof-of-concept, a locating array is applied to screen for main effects and low-order interactions on the response of average transport control protocol (TCP) throughput in a simulation model of a mobile ad hoc network (MANET). A MANET is a collection of mobile wireless nodes that self-organize without the aid of any centralized control or fixed infrastructure. The full-factorial design for the MANET considered is infeasible (with over 10^{43} design points) yet a locating array has only 421 design points.
In conjunction with the locating array, a ``heavy hitters'' algorithm is developed to identify the influential main effects and two-way interactions, correcting for the non-normal distribution of the average throughput, and uneven coverage of terms in the locating array. The significance of the identified main effects and interactions is validated independently using the statistical software JMP.
The statistical characteristics used to evaluate traditional screening designs are also applied to locating arrays.
These include the matrix of covariance, fraction of design space, and aliasing, among others. The results lend additional support to the use of locating arrays as screening designs.
The use of locating arrays as screening designs for complex engineered systems is promising as they yield useful models. This facilitates quantitative evaluation of architectures and protocols and contributes to our understanding of complex engineered networks.
ContributorsAldaco-Gastelum, Abraham Netzahualcoyotl (Author) / Syrotiuk, Violet R. (Thesis advisor) / Colbourn, Charles J. (Committee member) / Sen, Arunabha (Committee member) / Montgomery, Douglas C. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Drosophila melanogaster, as an important model organism, is used to explore the mechanism which governs cell differentiation and embryonic development. Understanding the mechanism will help to reveal the effects of genes on other species or even human beings. Currently, digital camera techniques make high quality Drosophila gene expression imaging possible. On the other hand, due to the advances in biology, gene expression images which can reveal spatiotemporal patterns are generated in a high-throughput pace. Thus, an automated and efficient system that can analyze gene expression will become a necessary tool for investigating the gene functions, interactions and developmental processes. One investigation method is to compare the expression patterns of different developmental stages. Recently, however, the expression patterns are manually annotated with rough stage ranges. The work of annotation requires professional knowledge from experienced biologists. Hence, how to transfer the domain knowledge in biology into an automated system which can automatically annotate the patterns provides a challenging problem for computer scientists. In this thesis, the problem of stage annotation for Drosophila embryo is modeled in the machine learning framework. Three sparse learning algorithms and one ensemble algorithm are used to attack the problem. The sparse algorithms are Lasso, group Lasso and sparse group Lasso. The ensemble algorithm is based on a voting method. Besides that the proposed algorithms can annotate the patterns to stages instead of stage ranges with high accuracy; the decimal stage annotation algorithm presents a novel way to annotate the patterns to decimal stages. In addition, some analysis on the algorithm performance are made and corresponding explanations are given. Finally, with the proposed system, all the lateral view BDGP and FlyFish images are annotated and several interesting applications of decimal stage value are revealed.
ContributorsPan, Cheng (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Farin, Gerald (Committee member) / Arizona State University (Publisher)
Created2012
Description
Text classification, in the artificial intelligence domain, is an activity in which text documents are automatically classified into predefined categories using machine learning techniques. An example of this is classifying uncategorized news articles into different predefined categories such as "Business", "Politics", "Education", "Technology" , etc. In this thesis, supervised machine learning approach is followed, in which a module is first trained with pre-classified training data and then class of test data is predicted. Good feature extraction is an important step in the machine learning approach and hence the main component of this text classifier is semantic triplet based features in addition to traditional features like standard keyword based features and statistical features based on shallow-parsing (such as density of POS tags and named entities). Triplet {Subject, Verb, Object} in a sentence is defined as a relation between subject and object, the relation being the predicate (verb). Triplet extraction process, is a 5 step process which takes input corpus as a web text document(s), each consisting of one or many paragraphs, from RSS feeds to lists of extremist website. Input corpus feeds into the "Pronoun Resolution" step, which uses an heuristic approach to identify the noun phrases referenced by the pronouns. The next step "SRL Parser" is a shallow semantic parser and converts the incoming pronoun resolved paragraphs into annotated predicate argument format. The output of SRL parser is processed by "Triplet Extractor" algorithm which forms the triplet in the form {Subject, Verb, Object}. Generalization and reduction of triplet features is the next step. Reduced feature representation reduces computing time, yields better discriminatory behavior and handles curse of dimensionality phenomena. For training and testing, a ten- fold cross validation approach is followed. In each round SVM classifier is trained with 90% of labeled (training) data and in the testing phase, classes of remaining 10% unlabeled (testing) data are predicted. Concluding, this paper proposes a model with semantic triplet based features for story classification. The effectiveness of the model is demonstrated against other traditional features used in the literature for text classification tasks.
ContributorsKarad, Ravi Chandravadan (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steven (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nowadays, wireless communications and networks have been widely used in our daily lives. One of the most important topics related to networking research is using optimization tools to improve the utilization of network resources. In this dissertation, we concentrate on optimization for resource-constrained wireless networks, and study two fundamental resource-allocation problems: 1) distributed routing optimization and 2) anypath routing optimization. The study on the distributed routing optimization problem is composed of two main thrusts, targeted at understanding distributed routing and resource optimization for multihop wireless networks. The first thrust is dedicated to understanding the impact of full-duplex transmission on wireless network resource optimization. We propose two provably good distributed algorithms to optimize the resources in a full-duplex wireless network. We prove their optimality and also provide network status analysis using dual space information. The second thrust is dedicated to understanding the influence of network entity load constraints on network resource allocation and routing computation. We propose a provably good distributed algorithm to allocate wireless resources. In addition, we propose a new subgradient optimization framework, which can provide findgrained convergence, optimality, and dual space information at each iteration. This framework can provide a useful theoretical foundation for many networking optimization problems. The study on the anypath routing optimization problem is composed of two main thrusts. The first thrust is dedicated to understanding the computational complexity of multi-constrained anypath routing and designing approximate solutions. We prove that this problem is NP-hard when the number of constraints is larger than one. We present two polynomial time K-approximation algorithms. One is a centralized algorithm while the other one is a distributed algorithm. For the second thrust, we study directional anypath routing and present a cross-layer design of MAC and routing. For the MAC layer, we present a directional anycast MAC. For the routing layer, we propose two polynomial time routing algorithms to compute directional anypaths based on two antenna models, and prove their ptimality based on the packet delivery ratio metric.
ContributorsFang, Xi (Author) / Xue, Guoliang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ye, Jieping (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The overall contribution of the Minerva Initiative at ASU is to map social organizations in a multidimensional space that provides a measure of their radical or counter radical influence over the demographics of a nation. This tool serves as a simple content management system to store and track project resources like documents, images, videos and web links. It provides centralized and secure access to email conversations among project team members. Conversations are categorized into one of the seven pre-defined categories. Each category is associated with a certain set of keywords and we follow a frequency based approach for matching email conversations with the categories. The interface is hosted as a web application which can be accessed by the project team.
ContributorsNair, Apurva Aravindakshan (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023