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—This paper considers the problem of network coding for multiple unicast connections in networks represented by directed acyclic graphs. The concept of interference alignment, traditionally used in interference networks, is extended to analyze the performance of linear network coding in this setup and to provide a systematic code design approach. It is… (More)

—We consider the problem of network coding across three unicast sessions over a directed acyclic graph, where each sender and the receiver is connected to the network via a single edge of unit capacity. We adapt a precoding-based interference alignment technique, originally developed for the wireless interference channel, to find a solution to this… (More)

—We study infection spreading on large static networks when the spread is assisted by a small number of additional virtually mobile agents. For networks which are " spatially constrained " , we show that the spread of infection can be significantly sped up even by a few virtually mobile agents acting randomly. More specifically, for general networks with… (More)

—We study epidemic spreading processes in large networks, when the spread is assisted by a small number of external agents: infection sources with bounded spreading power, but whose movement is unrestricted vis-` a-vis the underlying network topology. For networks which are 'spatially constrained', we show that the spread of infection can be significantly… (More)

—This paper considers the problem of compressive sensing over a finite alphabet, where the finite alphabet may be inherent to the nature of the data or a result of quantization. There are multiple examples of finite alphabet based static as well as time-series data with inherent sparse structure; and quantizing real values is an essential step while… (More)

—This paper presents a rate distortion approach to Markov graph learning. It provides lower bounds on the number of samples required for any algorithm to learn the Markov graph structure of a probability distribution, up to edit distance. We first prove a general result for any probability distribution, and then specialize it for Ising and Gaussian models.… (More)

—We consider the problem of learning the underlying graph structure of discrete Markov networks based on power-law graphs, generated using the configuration model. We translate the learning problem into an equivalent channel coding problem and obtain necessary conditions for solvability in terms of problem parameters. In particular, we relate the exponent… (More)

—We consider the problem of linear network coding over communication networks, representable by directed acyclic graphs, with multiple groupcast sessions: the network comprises of multiple destination nodes, each desiring messages from multiple sources. We adopt an interference alignment perspective, providing new insights into designing practical network… (More)

With the increase in simulation of urban environments for the purpose of planning, modelling vehicular traffic has become important. Since empirical evidence on traffic flow is relatively sparse, models are being increasingly used for planning urban roads and environments. In this paper, a simple, explicit model is proposed to approximate the speed versus… (More)