Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 3 of 3
Filtering by
- Creators: Daliri, Ayoub
- Creators: Scaglione, Anna
Description
Our ability to understand networks is important to many applications, from the analysis and modeling of biological networks to analyzing social networks. Unveiling network dynamics allows us to make predictions and decisions. Moreover, network dynamics models have inspired new ideas for computational methods involving multi-agent cooperation, offering effective solutions for optimization tasks. This dissertation presents new theoretical results on network inference and multi-agent optimization, split into two parts -
The first part deals with modeling and identification of network dynamics. I study two types of network dynamics arising from social and gene networks. Based on the network dynamics, the proposed network identification method works like a `network RADAR', meaning that interaction strengths between agents are inferred by injecting `signal' into the network and observing the resultant reverberation. In social networks, this is accomplished by stubborn agents whose opinions do not change throughout a discussion. In gene networks, genes are suppressed to create desired perturbations. The steady-states under these perturbations are characterized. In contrast to the common assumption of full rank input, I take a laxer assumption where low-rank input is used, to better model the empirical network data. Importantly, a network is proven to be identifiable from low rank data of rank that grows proportional to the network's sparsity. The proposed method is applied to synthetic and empirical data, and is shown to offer superior performance compared to prior work. The second part is concerned with algorithms on networks. I develop three consensus-based algorithms for multi-agent optimization. The first method is a decentralized Frank-Wolfe (DeFW) algorithm. The main advantage of DeFW lies on its projection-free nature, where we can replace the costly projection step in traditional algorithms by a low-cost linear optimization step. I prove the convergence rates of DeFW for convex and non-convex problems. I also develop two consensus-based alternating optimization algorithms --- one for least square problems and one for non-convex problems. These algorithms exploit the problem structure for faster convergence and their efficacy is demonstrated by numerical simulations.
I conclude this dissertation by describing future research directions.
The first part deals with modeling and identification of network dynamics. I study two types of network dynamics arising from social and gene networks. Based on the network dynamics, the proposed network identification method works like a `network RADAR', meaning that interaction strengths between agents are inferred by injecting `signal' into the network and observing the resultant reverberation. In social networks, this is accomplished by stubborn agents whose opinions do not change throughout a discussion. In gene networks, genes are suppressed to create desired perturbations. The steady-states under these perturbations are characterized. In contrast to the common assumption of full rank input, I take a laxer assumption where low-rank input is used, to better model the empirical network data. Importantly, a network is proven to be identifiable from low rank data of rank that grows proportional to the network's sparsity. The proposed method is applied to synthetic and empirical data, and is shown to offer superior performance compared to prior work. The second part is concerned with algorithms on networks. I develop three consensus-based algorithms for multi-agent optimization. The first method is a decentralized Frank-Wolfe (DeFW) algorithm. The main advantage of DeFW lies on its projection-free nature, where we can replace the costly projection step in traditional algorithms by a low-cost linear optimization step. I prove the convergence rates of DeFW for convex and non-convex problems. I also develop two consensus-based alternating optimization algorithms --- one for least square problems and one for non-convex problems. These algorithms exploit the problem structure for faster convergence and their efficacy is demonstrated by numerical simulations.
I conclude this dissertation by describing future research directions.
ContributorsWai, Hoi To (Author) / Scaglione, Anna (Thesis advisor) / Berisha, Visar (Committee member) / Nedich, Angelia (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2017
Description
Previous studies have shown that experimentally implemented formant perturbations result in production of compensatory responses in the opposite direction of the perturbations. In this study, we investigated how participants adapt to a) auditory perturbations that shift formants to a specific point in the vowel space and hence remove variability of formants (focused perturbations), and b) auditory perturbations that preserve the natural variability of formants (uniform perturbations). We examined whether the degree of adaptation to focused perturbations was different from adaptation to uniform adaptations. We found that adaptation magnitude of the first formant (F1) was smaller in response to focused perturbations. However, F1 adaptation was initially moved in the same direction as the perturbation, and after several trials the F1 adaptation changed its course toward the opposite direction of the perturbation. We also found that adaptation of the second formant (F2) was smaller in response to focused perturbations than F2 responses to uniform perturbations. Overall, these results suggest that formant variability is an important component of speech, and that our central nervous system takes into account such variability to produce more accurate speech output.
ContributorsDittman, Jonathan William (Author) / Daliri, Ayoub (Thesis director) / Berisha, Visar (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Parkinson’s Disease is one of the most complicated and abundantneurodegenerative diseases in the world. Previous analysis of Parkinson’s disease has
identified both speech and gait deficits throughout progression of the disease. There has
been minimal research looking into the correlation between both the speech and gait
deficits in those diagnosed with Parkinson’s. There is high indication that there is a
correlation between the two given the similar pathology and origins of both deficits. This
exploratory study aims to establish correlation between both the gait and speech deficits
in those diagnosed with Parkinson’s disease. Using previously identified motor and
speech measurements and tasks, I conducted a correlational study of individuals with
Parkinson’s disease at baseline. There were correlations between multiple speech and gait
variability outcomes. The expected correlations ranged from average harmonics-to-noise
ratio values against anticipatory postural adjustments-lateral peak distance to average
shimmer values against anticipatory postural adjustments-lateral peak distance. There
were also unexpected outcomes that ranged from F2 variability against the average
number of steps in a turn to intensity variability against step duration variability. I also
analyzed the speech changes over 1 year as a secondary outcome of the study. Finally, I
found that averages and variabilities increased over 1 year regarding speech primary
outcomes. This study serves as a basis for further treatment that may be able to
simultaneously treat both speech and gait deficits in those diagnosed with Parkinson’s.
The exploratory study also indicates multiple targets for further investigation to better
understand cohesive and compensatory mechanisms.
ContributorsBelnavis, Alexander Salvador (Author) / Peterson, Daniel (Thesis advisor) / Daliri, Ayoub (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2022