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In this paper, we propose a procedure to accelerate the resolution of the well-known ``Elastic-Net'' problem. Our procedure is based on the (partial) identification of the solution support and the reformulation of the original problem into a problem of reduced dimension. The identification of the support leverages the novel concept of ``safe relaxing'' where one aims to identify non-zero coefficients of the solution.

In this paper, we propose a procedure to accelerate the resolution of the well-known ``Elastic-Net'' problem. Our procedure is based on the (partial) identification of the solution support and the reformulation of the original problem into a problem of reduced dimension. The identification of the support leverages the novel concept of ``safe relaxing'' where one aims to identify non-zero coefficients of the solution.

In this work, we propose a novel approach for multi-modal emotion recognition from conversations using speech and text. The audio representations are learned jointly with a learnable audio front-end (LEAF) model feeding to a CNN based classifier. The text representations are derived from pre-trained bidirectional encoder representations from transformer (BERT) along with a gated recurrent network (GRU). Both the textual and audio representations are separately processed using a bidirectional GRU network with self-attention.

We present a novel screening methodology to safely discard irrelevant nodes within a generic branch-and-bound (BnB) algorithm solving the l0-penalized least-squares problem. Our contribution is a set of two simple tests to detect sets of feasible vectors that cannot yield optimal solutions. This allows to prune nodes of the BnB search tree, thus reducing the overall optimization time. One cornerstone of our contribution is a nesting property between tests at different nodes that allows to implement them with a low computational cost.

We present a novel screening methodology to safely discard irrelevant nodes within a generic branch-and-bound (BnB) algorithm solving the l0-penalized least-squares problem. Our contribution is a set of two simple tests to detect sets of feasible vectors that cannot yield optimal solutions. This allows to prune nodes of the BnB search tree, thus reducing the overall optimization time. One cornerstone of our contribution is a nesting property between tests at different nodes that allows to implement them with a low computational cost.

A spatial signal is monitored by a large-scale sensor network. We propose a novel method to identify areas where the signal behaves interestingly, anomalously, or simply differently from what is expected. The sensors pre-process their measurements locally and transmit a local summary statistic to a fusion center or a cloud. This saves bandwidth and energy. The fusion center or cloud computes a spatially varying empirical Bayes prior on the signal’s spatial behavior. The spatial domain is modeled as a fine discrete grid.