Int J Numer Method Biomed Eng 2017 09 11;33(9). Epub 2017 Apr 11.

BCAM-Basque Center for Applied Mathematics, Mazarredo 14, Bilbao, E-48009, Spain.

In this paper, we address a 3D fluid-structure interaction benchmark problem that represents important characteristics of biomedical modeling. We present a goal-oriented adaptive finite element methodology for incompressible fluid-structure interaction based on a streamline diffusion-type stabilization of the balance equations for mass and momentum for the entire continuum in the domain, which is implemented in the Unicorn/FEniCS software framework. A phase marker function and its corresponding transport equation are introduced to select the constitutive law, where the mesh tracks the discontinuous fluid-structure interface. This results in a unified simulation method for fluids and structures. We present detailed results for the benchmark problem compared with experiments, together with a mesh convergence study.

IEEE Trans Cybern 2021 Feb 26;PP. Epub 2021 Feb 26.

The clique partitioning problem (CPP) of an edge-weighted complete graph is to partition the vertex set V into k disjoint subsets such that the sum of the edge weights within all cliques induced by the subsets is as large as possible. The problem has a number of practical applications in areas, such as data mining, engineering, and bioinformatics, and is, however, computationally challenging. To solve this NP-hard problem, we propose the first evolutionary algorithm that combines a dedicated merge-divide crossover operator to generate offspring solutions and an effective simulated annealing-based local optimization procedure to find high-quality local optima. Read More

IEEE Trans Pattern Anal Mach Intell 2021 Feb 23;PP. Epub 2021 Feb 23.

This paper studies instance-dependent Positive and Unlabeled (PU) classification, where whether a positive example will be labeled (indicated by s) is not only related to the class label y, but also depends on the observation x. Therefore, the labeling probability on positive examples is not uniform as previous works assumed, but is biased to some simple or critical data points. To depict the above dependency relationship, a graphical model is built in this paper which further leads to a maximization problem on the induced likelihood function regarding P(s,y|x). Read More

College of Information and Computer Sciences, University of Massachusetts Amherst.

We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the applications are to be deployed in a safety-critical domain such as healthcare. However, the output space of such structured prediction models is often too large to adapt binary or multi-class calibration methods directly. Read More

Department of Physics and Astronomy, Rice University, Houston, Texas 77005-1892, USA.

Single-reference methods such as Hartree-Fock-based coupled cluster theory are well known for their accuracy and efficiency for weakly correlated systems. For strongly correlated systems, more sophisticated methods are needed. Recent studies have revealed the potential of the antisymmetrized geminal power (AGP) as an excellent initial reference for the strong correlation problem. Read More

IEEE Trans Cybern 2021 Feb 19;PP. Epub 2021 Feb 19.

Multiview clustering has aroused increasing attention in recent years since real-world data are always comprised of multiple features or views. Despite the existing clustering methods having achieved promising performance, there still remain some challenges to be solved: 1) most existing methods are unscalable to large-scale datasets due to the high computational burden of eigendecomposition or graph construction and 2) most methods learn latent representations and cluster structures separately. Such a two-step learning scheme neglects the correlation between the two learning stages and may obtain a suboptimal clustering result. Read More