This paper describes the GIBIS team experience in the Predicting Media Interestingness Task at MediaEval 2017. In this task, the teams were required to develop an approach to predict whether images or videos are interesting or not. Our proposal relies on late fusion with rank aggregation methods for combining ranking models learned with different features and by different learning-to-rank algor...

Background Geographic variations in management and outcomes of individuals supported by continuous-flow left ventricular assist devices (CF-LVAD) between the United States (US) Europe (EU) is largely unknown. Methods We created a retrospective, multinational registry 524 patients who received CF-LVAD (either HVAD or Heartmate II) January 2008 April 2017. Follow up spanned from date implant to p...

The structure at in nity of a matrix pencil can be obtained by rank determination of Toeplitz matrices. We show that the generic rank of these matrices equals the structural rank. Thus the Toeplitz matrix approach is also suited for investigations of structure matrix pencils. Computational results underline the e ciency of this approach.

For a given matrix H which has d singular values larger than ε, an expression for all rank-d approximants Ĥ such that (H− Ĥ) has 2-norm less than ε is derived. These approximants have minimal rank, and the set includes the usual ‘truncated SVD’ low-rank approximation. The main step in the procedure is a generalized Schur algorithm, which requires only O(1/2 m 2n) operations (for an m × n matrix...

In this paper we compare pronoun resolution algorithms and introduce a centering algorithm (LeftRight Centering) that adheres to the constraints and rules of centering theory and is an alternative to Brennan, Friedman, and Pollard's (1987) algorithm. We then use the Left-Right Centering algorithm to see if two psycholinguistic claims on Cf-list ranking will actually improve pronoun resolution a...

We present a distributed stochastic gradient descent algorithm for performing low-rank matrix factorization on streaming data. Low-rank matrix factorization is often used as a technique for collaborative filtering. As opposed to recent algorithms that perform matrix factorization in parallel on a batch of training examples [4], our algorithm operates on a stream of incoming examples. We experim...

(i) We provide a framework for online conflict-free coloring (CF-coloring) any hypergraph. We use this framework to obtain an efficient randomized online algorithm for CF-coloring any k-degenerate hypergraph. Our algorithm uses O(k log n) colors with high probability and this bound is asymptotically optimal for any constant k. Moreover, our algorithm uses O(k log k log n) random bits with high ...

Motivated by numerous models of representing trust and distrust within a graph ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. An efficient algorithm is given for computing Dirichlet PageRank vectors subject to Dirichlet boundary conditions on a subset of nodes. We then give several algorithms for various trust-based ranking pro...

We present an efficient algorithm for computing the vertex ranking number of an asteroidal triple-free graph. lts running time is bounded by a polynomial in the number of vertices and the number of minimal separators of the input graph.