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Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On th ...
The wireless multiple-unicast problem is considered over a layered network, where the rates of transmission are limited by the relaying and interference effect. The deterministic model is used to capture the broadcasting and multiple access effects. The ca ...
As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed com ...
The wireless multiple-unicast problem is considered over a layered network, where the rates of transmission are limited by the relaying and interference effect. The deterministic model introduced in 131 is used to capture the broadcasting and multiple acce ...
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We introduce a new theoretical model of ad hoc mobile computing in which agents have severely restricted memory, highly unpredictable movement and no initial knowledge of the system. Each agent’s memory can store O(1) bits, plus a unique identifier, and O(1 ...
We examine the problem of learning a set of parameters from a distributed dataset. We assume the datasets are collected by agents over a distributed ad-hoc network, and that the communication of the actual raw data is prohibitive due to either privacy cons ...
The creation of wide-area, real-time monitoring systems for the road network has the potential to achieve a step change in both our understanding of the evolution of congestion and forecasting/information to minimise its economic consequences. While such c ...
We propose a fully-distributed stochastic-gradient strategy based on diffusion adaptation techniques. We show that, for strongly convex risk functions, the excess-risk at every node decays at the rate of O(1/Ni), where N is the number of learners and i is ...
The reason for using distributed constraint satisfaction algorithms is often to allow agents to find a solution while revealing as little as possible about their variables and constraints. So far, most algorithms for DisCSP do not guarantee privacy of this ...
Recent developments in information and communication technologies have been profound and life-changing. Most people are now equipped with smart phones with high computation power and communication capabilities. These devices can efficiently run multiple so ...
