@conference {
	title = {A Systems Biology Approach to Understanding Alzheimer{\textquoteright}s Disease},
	author = {Kyrtsos, C Rose., Baras, John S.},
	year = {2010},
	month = {04/2010},
	abstract = {Alzheimer{\textquoteright}s disease (AD) is the most prevalent form of dementia affecting the elderly. AD has devastating effects on the individual, leading to neuronal and synaptic loss, neuroinflammation, dysfunction of the blood-brain barrier and deposition of amyloid beta plaques in the brain parenchyma and cerebral blood vessels. Due to the inherent difficulty studying the many possible initiating factors of AD with in vivo models, we have chosen to approach this problem from a quantitative perspective initially to determine what key factors may initiate AD pathogenesis. We have developed a multi-scale systems biology model that describes the interactions between different components of the brain{\textquoteright}s biological network at the tissue, cellular and transcriptional levels. Neurons have been modeled using a modified McCulloch-Pitts neural network that accounts for cell death and synapse loss due to varying amyloid beta and cholesterol levels. Microglia have been modeled in both the ramified and activated states using the Langevin equations of motion. The spatial-temporal distribution of beta amyloid has been modeled using the convection-reaction-diffusion equation with stochastic generation rates. Transport of beta amyloid by LRP-1 across the blood-brain barrier has also been modeled. Future generations of the model will take into account experimental results, as well as include models for plaque development in the basement membrane of cerebral blood vessels.},
	address = {College Park, Maryland},
	booktitle = {2010 Southern Biomedical Engineering Conference},
	organization = {University of Maryland}
}
@conference {
	title = {Convergence Results for Ant Routing Algorithms via Stochastic Approximation and Optimization},
	author = {Purkayastha, Punyaslok., Baras, John S.},
	year = {2010},
	month = {04/2010},
	abstract = {In this paper, we provide convergence results for an Ant Routing (ARA) Algorithm for wireline, packet switched communication networks, that are acyclic. Such algorithms are inspired by the foraging behavior of ants in nature. We consider an ARA algorithm proposed by Bean and Costa [2]. The algorithm has the virtues of being adaptive and distributed, and can provide a multipath routing solution. We consider a scenario where there are multiple incoming data traffic streams that are to be routed to their destinations via the network. Ant packets, which are nothing but probe packets, are used to estimate the path delays in the network. The node routing tables, which consist of routing probabilities for the outgoing links, are updated based on these delay estimates. In contrast to the available analytical studies in the literature, the link delays in our model are stochastic, time-varying, and dependent on the link traffic. The evolution of the delay estimates and the routing probabilities are described by a set of stochastic iterative equations. In doing so, we take into account the distributed and asynchronous nature of the algorithm operation. Using methods from the theory of stochastic approximations, we show that the evolution of the delay estimates can be closely tracked by a deterministic ODE (Ordinary Differential Equation) system, when the step-size of the delay estimation scheme is small. We study the equilibrium behavior of the ODE in order to obtain the equilibrium behavior of the algorithm. We also provide illustrative simulation results.},
	address = {Stockholm, Sweden},
	booktitle = {Proceddings of the 13th International Conference on Hybrid Systems: Computation and Control (HSCC {\textquoteright}10)},
	pages = {pp. 201-210}
}
@conference {
	title = {A Trust Based Distributed Kalman Filtering Approach for Mode Estimation in Power Systems},
	author = {Jiang, Tao., Matei, Ion., Baras, John S.},
	year = {2010},
	month = {04/2010},
	abstract = {We consider distributed mode estimation in power systems. The measurements are observed by PMUs (Power Management Units). We introduce a novel model of trust, using weights on the graph links and nodes that represent the networked PMUs. We describe two algorithms that intergarte distributed Kalman filtering with these trust weights. We consider two interpretations of these trust weights as information accuracy and reliability. We show that by appropriate use of these weights from the distributed estimation algorithm avoids using information from untrusted PMUs. Simulation experiments further demonstrate the behavior of these algorithms.},
	address = {Stockholm, Sweden},
	booktitle = {First Workshop on Secure Control Systems (SCS) as part of CPSWeek 2010},
	pages = {pp. 1-6}
}
