In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: In many applications spanning from sensor to social networks, transportation systems, gene regulatory networks or big data, the signals of interest are defined over the vertices of a graph. The aim of this paper is to propose a least mean square (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the performance of the proposed method. Furthermore, to cope with the case where the bandwidth is not known beforehand, we propose a method that performs a sparse online estimation of the signal support in the (graph) frequency domain, which enables online adaptation of the graph sampling strategy. Finally, we apply the proposed method to build the power spatial density cartography of a given operational region in a cognitive network environment.

A: Least Mean Squares Estimation of Graph Signals
****
Q: Recent years have witnessed increasing interest in the potential benefits of 'intelligent' autonomous machines such as robots. Honda's Asimo humanoid robot, iRobot's Roomba robot vacuum cleaner and Google's driverless cars have fired the imagination of the general public, and social media buzz with speculation about a utopian world of helpful robot assistants or the coming robot apocalypse! However, there is a long way to go before autonomous systems reach the level of capabilities required for even the simplest of tasks involving human-robot interaction especially if it involves communicative behaviour such as speech and language. Of course the field of Artificial Intelligence (AI) has made great strides in these areas, and has moved on from abstract high-level rule-based paradigms to embodied architectures whose operations are grounded in real physical environments. What is still missing, however, is an overarching theory of intelligent communicative behaviour that informs system-level design decisions in order to provide a more coherent approach to system integration. This chapter introduces the beginnings of such a framework inspired by the principles of Perceptual Control Theory (PCT). In particular, it is observed that PCT has hitherto tended to view perceptual processes as a relatively straightforward series of transformations from sensation to perception, and has overlooked the potential of powerful generative model-based solutions that have emerged in practical fields such as visual or auditory scene analysis. Starting from first principles, a sequence of arguments is presented which not only shows how these ideas might be integrated into PCT, but which also extend PCT towards a remarkably symmetric architecture for a needs-driven communicative agent. It is concluded that, if behaviour is the control of perception (the central tenet of PCT), then perception (at least for communicative agents) is the simulation of behaviour.

A: PCT and Beyond: Towards a Computational Framework for 'Intelligent' Communicative Systems
****
Q: In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agent's environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agent's. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agent's state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning.

A:
Cross-Domain Perceptual Reward Functions
****