Control of Networked Multiagent Systems with Uncertain Graph Topologies

Multiagent systems consist of agents that locally exchange information through a physical network subject to a graph topology. Current control methods for networked multiagent systems assume the knowledge of graph topologies in order to design distributed control laws for achieving desired global system behaviors. However, this assumption may not be valid for situations where graph topologies are subject to uncertainties either due to changes in the physical network or the presence of modeling errors especially for multiagent systems involving a large number of interacting agents. Motivating from this standpoint, this paper studies distributed control of networked multiagent systems with uncertain graph topologies. The proposed framework involves a controller architecture that has an ability to adapt its feed- back gains in response to system variations. Specifically, we analytically show that the proposed controller drives the trajectories of a networked multiagent system subject to a graph topology with time-varying uncertainties to a close neighborhood of the trajectories of a given reference model having a desired graph topology. As a special case, we also show that a networked multi-agent system subject to a graph topology with constant uncertainties asymptotically converges to the trajectories of a given reference model. Although the main result of this paper is presented in the context of average consensus problem, the proposed framework can be used for many other problems related to networked multiagent systems with uncertain graph topologies.Comment: 14 pages, 2 figure

Generalized Sparse Discriminant Analysis for Event-Related Potential Classification

A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work we propose a penalized version of the sparse discriminant analysis (SDA), called generalized sparse discriminant analysis (GSDA), for binary classification. This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The GSDA method is designed to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that, on one hand, GSDA outperforms standard SDA in the sense of classification performance, sparsity and required computing time, and, on the other hand, it also yields better overall performances, compared to well-known ERP classification algorithms, for single-trial ERP classification when insufficient training samples are available. Hence, GSDA constitute a potential useful method for reducing the calibration times in ERP-based BCI systems.Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentin

FIN 301 Principles of Financial Management

Course syllabus for FIN 301 Principles of Financial Management Course description: Deals with theory and practice of the financial management function in planning, raising, and directing the efficient allocation of funds within the firm. Prerequisites: ACCT 301 and STAT 361. Recommend students have background in algebra and familiarity with graphing techniques

Specialization and trade-offs in plant-feeding insects

The immense diversity of life on Earth has been attributed to the partitioning of available resources into ecological niches, but it is not obvious what determines the niche size of each species. For example, most plant-feeding insects consume only one or a few closely-related host-plant species despite the advantages of having a broader diet. Many researchers have therefore suggested that the evolution of broad diets in plant-feeding insects must be constrained by genetic trade-offs between adaptations to alternative host-plants. Despite its intuitive feel, however, little empirical evidence in support of the trade-off hypothesis has emerged from decades of experimental studies comparing individual performance on alternative hosts within insect populations. Here I use a broader approach to evaluate the role of trade-offs in driving ecological specialization in plant-feeding insects. By collecting host-use data for thousands of insect species and fitting those data into long-term evolutionary models, I investigate whether trade-off constraints have left observable signatures in the present ecological niches of existing species. Chapter 1 focuses on a single family of insects, the armored scales (Hempitera: Diaspididae), revealing that positive correlations between evolutionary changes in host performance best fit the observed patterns of diaspidid presence and absence on nearly all focal host taxa, suggesting that adaptations to particular hosts enhance rather than reduce performance on other hosts. In chapter 2, I uncovered a complex network of evolutionary interactions between caterpillar adaptations to eleven host-plant orders, indicating that different host-use trade-offs act over long- and short-term evolutionary timescales. In contrast, host-use patterns of true bugs revealed a total lack of trade-offs for the same host-plant orders over both timescales. Chapter 3 turns to armored scale insects again, this time those that we collected in systematic surveys across a large diversity of trees in two tropical rainforest habitats. Using each insect species’ abundance on each tree as a proxy for host-plant performance, we found no evidence for performance trade-offs on alternative hosts despite apparent host-use specialization. Overall, these results suggest that the extreme specialization of plant-feeding insects arises from long-term, potentially nonadaptive evolutionary processes rather than simple genetic trade-offs

Biomechanical and Neural Factors Associated with Gait Dysfunction and Freezing in People with Parkinson Disease

Parkinson disease: PD) is a progressive neurological disorder with no known cure, affecting one million Americans. Half of those with PD experience freezing of gait: FOG), manifested as an inability to complete effective stepping. Gait dysfunction and FOG are associated with falls, severe injury, and reduced quality of life, and are among the most disabling and distressing symptoms of PD. The causes of FOG and gait dysfunction are not well understood. Further, FOG is notoriously difficult to elicit in a laboratory setting, making efforts to track or identify individuals at risk for freezing difficult. An important first step in determining the mechanism of gait dysfunction and FOG is to identify factors associated with these symptoms. Therefore, the overall goal of this project was to better understand how pathologies of movement and brain function are associated with gait dysfunction and FOG. To this end we conducted three experiments: chapters 2-4). In experiment 1: chapter 2), we assessed the relationship between coordination of steps and freezing of gait. Results suggested that individuals with PD who freeze exhibit worse coordination than those who do not freeze, and further, that tasks related to freezing: turning and backward walking) resulted in worse coordination than forward walking. Finally, there was a significant positive correlation between freezing severity and global coordination of steps. These results together support the hypothesized relationship between coordination of steps and freezing. In experiment 2: chapter 3), we investigated neural signals associated with gait dysfunction: measured via blood oxygen level dependent [BOLD] signal) in those with PD compared to healthy adults. We found that during complex gait tasks, those with PD activated the supplementary motor area more than healthy adults. In addition, we observed reduced activity in the globus pallidus in people with PD. Finally, PD exhibited consistent positive correlations between a measure of gait function: overground walking velocity) and brain activation such that those with higher brain activity exhibited better gait function. In experiment 3: chapter 4), we investigated the neural underpinnings of freezing of gait. Specifically, we looked at gait imagery in those with PD who do experience freezing: freezers) and those who do not: non-freezers). We found those who experience freezing exhibited reduced BOLD signal in the cerebellar locomotor region, suggesting dysfunctional activity in this region may play a role in freezing. BOLD response within freezer and non-freezer groups were not consistently correlated to functional gait measures such as overground gait speed or freezing severity. Together these results better elucidate how pathologies of movement: i.e. coordination of steps) and neural function are related to gait dysfunction and freezing. Specifically, we found that coordination of steps and activity of the cerebellar locomotor regions may be related to freezing. Further, altered activation of the globus pallidus may be related to gait dysfunction in those with PD, and generally, larger BOLD response is correlated to improved overground gait function

The Theoretical and Empirical Implications of Money in Asset Pricing Models With Special Reference to the Equity Premium.

This dissertation examines the role of money, monetary uncertainty, and monetary policy for the pricing of financial assets. The existing literature is extended in several ways. First, closed-form solutions for asset returns are obtained, thereby allowing analytical results to be derived. Second, nominal equity prices and returns are computed, thereby allowing problems associated with estimating inflation to be avoided. Furthermore, shifts in the money stock that result from changes in monetary regime are distinguished from those that result from changes in monetary uncertainty. The introduction of money leads to qualitative predictions about asset prices that differ from those obtained in a nonmonetary economy. Results indicate that the equity premium puzzle remains intact even after the introduction of monetary factors. It is shown that real and nominal equity returns are quantitatively insensitive to changes in monetary uncertainty. Nominal returns are sensitive to shifts in monetary regime, but real returns are not. Furthermore, the correlation coefficient between real equity returns and inflation is sensitive to changes in both monetary uncertainty and the monetary regime