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Matrix factorization from a small number of observed entries has recently garnered much attention as the key ingredient of successful recommendation systems. One unresolved problem in this area is how to adapt current methods to handle changing user preferences over time. Recent proposals to address this issue are heuristic in nature and do not fully(More)
Wireless services in the unlicensed bands are proliferating but frequently face high interference from other devices due to a lack of coordination among heterogeneous technologies. In this paper we study how cognitive radio concepts enable systems to sense and predict interference patterns and adapt their spectrum access accordingly. This leads to a new(More)
The Weber–Fechner law states that perceived intensity is proportional to physical stimuli on a logarithmic scale. In this work, we formulate a Bayesian framework for the scaling of perception and find logarithmic and related scalings are optimal under expected relative error fidelity. Therefore, the Weber–Fechner law arises as being information(More)
We propose a new algorithm for estimation, prediction, and recommendation named the collaborative Kalman filter. Suited for use in collaborative filtering settings encountered in recommendation systems with significant temporal dynamics in user preferences, the approach extends probabilistic matrix factorization in time through a state-space model. This(More)
Streaming degradable content (such as video or audio) over a wireless channel presents new challenges to modern digital design, which was founded on the separation theorem of Shannon theory. Joint source-channel coding (JSCC) has recently received increasing interest to address the varying nature of the wireless channel conditions (under mobility or(More)
Quantization is an important but often ignored consideration in discussions about compressed sensing. This paper studies the design of quantizers for random measurements of sparse signals that are optimal with respect to mean-squared error of the lasso reconstruction. We utilize recent results in high-resolution functional scalar quantization and homotopy(More)
Distributed functional scalar quantization (DFSQ) theory provides optimality conditions and predicts performance of data acquisition systems in which a computation on acquired data is desired. We address two limitations of previous works: prohibitively expensive decoder design and a restriction to source distributions with bounded support. We show that a(More)
Quantizers for probabilistic sources are usually optimized for mean-squared error. In many applications, maintaining low relative error is a more suitable objective. This measure has previously been heuristically connected with the use of logarithmic companding in perceptual coding. We derive optimal companding quantizers for fixed rate and variable rate(More)