Matching Items (57)
Filtering by
- Genre: Doctoral Dissertation
- Creators: Xue, Guoliang
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
An exploratory qualitative study was conducted using a grounded theory approach (Charmaz, 2006) to examine the subjective experiences of mindfulness. Semi-structured interviews were conducted with 10 individuals who practice mindfulness on a regular basis. Data analysis revealed domains related to the experience, conditions, strategies and consequences related to the practice of mindfulness. The following main themes emerged: subjective experience, mechanism of practice, use of metaphors, contextual influences, and shift in perception. An emerging theoretical model related to the experience of mindfulness is also proposed. Implications, limitations and suggestions for future research are discussed.
ContributorsMejia, Araceli (Author) / Kurpius, Sharon (Thesis advisor) / Arciniega, Miguel (Committee member) / Kinnier, Richard (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing.
ContributorsYang, Lei (Author) / Zhang, Junshan (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Xue, Guoliang (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2012
Description
The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.
ContributorsZhang, Rui (Author) / Zhang, Yanchao (Thesis advisor) / Duman, Tolga Mete (Committee member) / Xue, Guoliang (Committee member) / Zhang, Junshan (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
Communication networks, both wired and wireless, are expected to have a certain level of fault-tolerance capability.These networks are also expected to ensure a graceful degradation in performance when some of the network components fail. Traditional studies on fault tolerance in communication networks, for the most part, make no assumptions regarding the location of node/link faults, i.e., the faulty nodes and links may be close to each other or far from each other. However, in many real life scenarios, there exists a strong spatial correlation among the faulty nodes and links. Such failures are often encountered in disaster situations, e.g., natural calamities or enemy attacks. In presence of such region-based faults, many of traditional network analysis and fault-tolerant metrics, that are valid under non-spatially correlated faults, are no longer applicable. To this effect, the main thrust of this research is design and analysis of robust networks in presence of such region-based faults. One important finding of this research is that if some prior knowledge is available on the maximum size of the region that might be affected due to a region-based fault, this piece of knowledge can be effectively utilized for resource efficient design of networks. It has been shown in this dissertation that in some scenarios, effective utilization of this knowledge may result in substantial saving is transmission power in wireless networks. In this dissertation, the impact of region-based faults on the connectivity of wireless networks has been studied and a new metric, region-based connectivity, is proposed to measure the fault-tolerance capability of a network. In addition, novel metrics, such as the region-based component decomposition number(RBCDN) and region-based largest component size(RBLCS) have been proposed to capture the network state, when a region-based fault disconnects the network. Finally, this dissertation presents efficient resource allocation techniques that ensure tolerance against region-based faults, in distributed file storage networks and data center networks.
ContributorsBanerjee, Sujogya (Author) / Sen, Arunabha (Thesis advisor) / Xue, Guoliang (Committee member) / Richa, Andrea (Committee member) / Hurlbert, Glenn (Committee member) / Arizona State University (Publisher)
Created2013
Description
While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by many individual entities. These entities make their decisions independently and selfishly, and maybe cooperate with a small group of other entities only when this form of coalition yields a better return. The interaction among multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. In this dissertation, we are interested in modeling, analyzing, addressing network problems caused by the selfish behavior of network entities. First, we study how the selfish behavior of network entities affects the system performance while users are competing for limited resource. For this resource allocation domain, we aim to study the selfish routing problem in networks with fair queuing on links, the relay assignment problem in cooperative networks, and the channel allocation problem in wireless networks. Another important aspect of this dissertation is the study of designing efficient mechanisms to incentivize network entities to achieve certain system objective. For this incentive mechanism domain, we aim to motivate wireless devices to serve as relays for cooperative communication, and to recruit smartphones for crowdsourcing. In addition, we apply different game theoretic approaches to problems in security and privacy domain. For this domain, we aim to analyze how a user could defend against a smart jammer, who can quickly learn about the user's transmission power. We also design mechanisms to encourage mobile phone users to participate in location privacy protection, in order to achieve k-anonymity.
ContributorsYang, Dejun (Author) / Xue, Guoliang (Thesis advisor) / Richa, Andrea (Committee member) / Sen, Arunabha (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid advancement of wireless technology has instigated the broad deployment of wireless networks. Different types of networks have been developed, including wireless sensor networks, mobile ad hoc networks, wireless local area networks, and cellular networks. These networks have different structures and applications, and require different control algorithms. The focus of this thesis is to design scheduling and power control algorithms in wireless networks, and analyze their performances. In this thesis, we first study the multicast capacity of wireless ad hoc networks. Gupta and Kumar studied the scaling law of the unicast capacity of wireless ad hoc networks. They derived the order of the unicast throughput, as the number of nodes in the network goes to infinity. In our work, we characterize the scaling of the multicast capacity of large-scale MANETs under a delay constraint D. We first derive an upper bound on the multicast throughput, and then propose a lower bound on the multicast capacity by proposing a joint coding-scheduling algorithm that achieves a throughput within logarithmic factor of the upper bound. We then study the power control problem in ad-hoc wireless networks. We propose a distributed power control algorithm based on the Gibbs sampler, and prove that the algorithm is throughput optimal. Finally, we consider the scheduling algorithm in collocated wireless networks with flow-level dynamics. Specifically, we study the delay performance of workload-based scheduling algorithm with SRPT as a tie-breaking rule. We demonstrate the superior flow-level delay performance of the proposed algorithm using simulations.
ContributorsZhou, Shan (Author) / Ying, Lei (Thesis advisor) / Zhang, Yanchao (Committee member) / Zhang, Junshan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2013
Description
We are expecting hundreds of cores per chip in the near future. However, scaling the memory architecture in manycore architectures becomes a major challenge. Cache coherence provides a single image of memory at any time in execution to all the cores, yet coherent cache architectures are believed will not scale to hundreds and thousands of cores. In addition, caches and coherence logic already take 20-50% of the total power consumption of the processor and 30-60% of die area. Therefore, a more scalable architecture is needed for manycore architectures. Software Managed Manycore (SMM) architectures emerge as a solution. They have scalable memory design in which each core has direct access to only its local scratchpad memory, and any data transfers to/from other memories must be done explicitly in the application using Direct Memory Access (DMA) commands. Lack of automatic memory management in the hardware makes such architectures extremely power-efficient, but they also become difficult to program. If the code/data of the task mapped onto a core cannot fit in the local scratchpad memory, then DMA calls must be added to bring in the code/data before it is required, and it may need to be evicted after its use. However, doing this adds a lot of complexity to the programmer's job. Now programmers must worry about data management, on top of worrying about the functional correctness of the program - which is already quite complex. This dissertation presents a comprehensive compiler and runtime integration to automatically manage the code and data of each task in the limited local memory of the core. We firstly developed a Complete Circular Stack Management. It manages stack frames between the local memory and the main memory, and addresses the stack pointer problem as well. Though it works, we found we could further optimize the management for most cases. Thus a Smart Stack Data Management (SSDM) is provided. In this work, we formulate the stack data management problem and propose a greedy algorithm for the same. Later on, we propose a general cost estimation algorithm, based on which CMSM heuristic for code mapping problem is developed. Finally, heap data is dynamic in nature and therefore it is hard to manage it. We provide two schemes to manage unlimited amount of heap data in constant sized region in the local memory. In addition to those separate schemes for different kinds of data, we also provide a memory partition methodology.
ContributorsBai, Ke (Author) / Shrivastava, Aviral (Thesis advisor) / Chatha, Karamvir (Committee member) / Xue, Guoliang (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014