Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data are typically collected from diverse sources. The multiple sources are different not only from the target but also from each other, thus, domain adaptater should not be modeled in the same way. Moreover, those sources...

Microscopic images from multiple modalities can produce plentiful experimental information. In practice, biological or physical constraints under a given observation period may prevent researchers acquiring enough microscopic scanning. Recent studies demonstrate that image synthesis is one of the popular approaches to release such constraints. Nonetheless, most existing only translate source do...

The notion of trust, as required for secure operations over the Internet, is important for ascertaining the source of received messages. How can we measure the degree of trust in authenticating the source? Knowledge in the domain is not established, so knowledge engineering becomes knowledge generation rather than mere acquisition. Special techniques are required, and special features of KBS so...

BACKGROUND There is little or no information available on the impact of funding by the food industry on trial outcomes and methodological quality of synbiotics, probiotics and prebiotics research in infants. The objective of this study was to compare the methodological quality, outcomes of food industry sponsored trials versus non industry sponsored trials, with regards to supplementation of sy...

Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This paper considers transfer learning with Markov logic networks (MLNs), a powerful formalism for learning in relational domains. We present a complete MLN transfer system that first autonomously maps the predicates in the so...

In a classical transfer learning setting, we have sufficient fully labeled data from the source domain (or the training domain) where we fully observe the data points X, and all corresponding labels Y tr are known. On the other hand, we are given data points, X, from the target domain (or the test domain), but few or none of the corresponding labels, Y , are given. The source and the target dom...

We present a spectral element algorithm and open-source code for computing the fractional Laplacian defined by eigenfunction expansion on finite 2D/3D complex domains with both homogeneous nonhomogeneous boundaries. demonstrate scalability of large clusters constructing based computed eigenvalues eigenfunctions using up to thousands CPUs. To accuracy this eigen-based approach factional Laplacia...