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Presentation Slides for "Convolutional Neural Networks for Video Intra Prediction Using Cross-component Adaptation"

Modern medical science demands sophisticated signal representation methods in order to cope with the increasing amount of data. Important criteria for these methods are mainly low computational and storage costs, whereas the underlying mathematical model should still be interpretable and meaningful for the data analyst. One of the most promising models fulfilling these criteria is based on Hermite functions, however having some important limitations for specific biomedical wave shapes.

We introduce a novel discrete signal processing framework, called discrete-lattice SP, for signals indexed by a finite lattice. A lattice is a partially ordered set that supports a meet (or join) operation that returns the greatest element below two given elements. Discrete-lattice SP chooses the meet as shift operation and derives associated notion of (meet-invariant) convolution, Fourier transform, frequency response, and a convolution theorem. Examples of lattices include sets of sets that are closed under intersection and trees.

The spectral method is an important approach for signal esti- mation that is often used as an initialization to iterative methods as well as a stand-alone estimator, where the signal is estimated by the top eigenvector of certain carefully-constructed data matrix. A re- cent line of work has characterized the asymptotic behavior of such data matrices used in spectral methods, which reveals an interesting phase transition phenomenon: there exists a critical sampling thresh- old below which the estimate of the spectral method is uninforma- tive.

In many signal processing tasks, one seeks to recover an r- column matrix object X ∈ Cn×r from a set of nonnegative quadratic measurements up to orthonormal transforms. Example applications include coherence retrieval in optical imaging and co- variance sketching for high-dimensional streaming data. To this end, efficient nonconvex optimization methods are quite appealing, due to their computational efficiency and scalability to large-scale problems.

Squared-loss mutual information (SMI) is a surrogate of Shannon mutual information that is more advantageous for estimation. On the other hand, the coherence matrix of a pair of random vectors, a power-normalized version of the sample cross-covariance matrix, is a well-known second-order statistic found in the core of fundamental signal processing problems, such as canonical correlation analysis (CCA).

In this paper, we consider interference channel model in which transmissions from multiple users are partially correlated. This correlation arises in wireless sensor network (WSN) scenarios and temporally correlated models. Considering this model, two minimum mean squared error (MSE) based precoding methods are derived. With these formulations, an iterative convergent procedure is formulated similar to a typical interference alignment (IA) algorithm. Simulations show that the second method provides the best sum rates for different correlation values.

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network.

A method for feedforward active noise control (ANC) over a spatial region is proposed. Conventional multipoint ANC aims to reduce the noise at multiple discrete positions; therefore, the noise reduction in the region between these points cannot be guaranteed. Recent studies revealed the possibility of spatial ANC, i.e., noise control in a continuous target region.