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 this paper, we address the problem of embedded feature selection for ranking on top of the list problems. We pose this problem as a regularized empirical risk minimization with p-norm push loss function (p = ) and sparsity inducing regularizers. We leverage the issues related to this challenging optimization problem by considering an alternating direction method of multipliers algorithm which is built upon proximal operators of the loss function and the regularizer. Our main technical contribution is thus to provide a numerical scheme for computing the infinite push loss function proximal operator. Experimental results on toy, DNA microarray and BCI problems show how our novel algorithm compares favorably to competitors for ranking on top while using fewer variables in the scoring function.

A: Sparse Support Vector Infinite Push
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Q: Problem solving in Answer Set Programming consists of two steps, a first grounding phase, systematically replacing all variables by terms, and a second solving phase computing the stable models of the obtained ground program. An intricate part of both phases is the treatment of aggregates, which are popular language constructs that allow for expressing properties over sets. In this paper, we elaborate upon the treatment of aggregates during grounding in gringo series 4. Consequently, our approach is applicable to grounding based on semi-naive database evaluation techniques. In particular, we provide a series of algorithms detailing the treatment of recursive aggregates and illustrate this by a running example.

A: Grounding Recursive Aggregates: Preliminary Report
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Q: A method is presented for the rhythmic pars<lb>ing problem: Given a sequence of observed<lb>musical note onset times, we simultaneously<lb>estimate the corresponding notated rhythm<lb>and tempo process. A graphical model is<lb>developed that<lb>represents the evolution of<lb>tempo and rhythm and relates these hid<lb>den quantities to an observable performance.<lb>The rhythm variables are discrete and the<lb>tempo and observation variables are contin<lb>uous. We show how to compute the<lb>glob<lb>ally most likely configuration of the tempo<lb>and rhythm variables given an observation of<lb>note onset times. Preliminary experiments<lb>are presented on a small data set. A gen<lb>eralization to computing MAP estimates for<lb>arbitrary conditional Gaussian distributions<lb>is outlined.

A:
A Mixed Graphical Model for Rhythmic Parsing
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