<abstract><p>Unsupervised domain adaptation (UDA) is an emerging research topic in the field of machine learning and pattern recognition, which aims to help unlabeled target by transferring knowledge from source domain. To perform UDA, a variety methods have been proposed, most concentrate on scenario single (1S1T). However, real applications, usually with multiple domains are invol...

Biogeography Based Optimization (BBO) is a recently introduced optimization technique based on science of biogeography, i. e. , study of distribution of biological species over space and time. In BBO, potential solutions of a problem are grouped in integer vectors known as habitats. Feature, i. e. , Suitability Index Variable (SIV), sharing among various habitats is made to occur with migration...

We demonstrate the directional emission of individual GaAs nanowires by coupling this emission to Yagi-Uda optical antennas. In particular, we have replaced the resonant metallic feed element of the nanoantenna by an individual nanowire and measured with the microscope the photoluminescence of the hybrid structure as a function of the emission angle by imaging the back focal plane of the object...

Abstract—This paper describes a wideband double-dipole Yagi-Uda antenna fed by a microstrip-slot coplanar stripline transition. The conventional dipole driver of a Yagi-Uda antenna is replaced by two parallel dipoles with different lengths to achieve multi-resonances, and a small, tapered ground plane is used to allow flexibility in the placement of a pair of reflectors for effective reflection...

The enzymatic method for cholesterol determination can use either an endpoint or a kinetic method. Not much is known concerning the properties (K(m) and V(max)) of the commercial enzyme for the kinetic method. We measured the K(m) and V(max) of Brevibacterium, Streptomyces, Pseudomonas fluorescens, and Cellulomonas cholesterol oxidase. Brevibacterium gave the highest K(m) value (230.3 x 10(-4) ...

Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a labeled source domain an unlabeled target domain. UDA has been extensively studied in the computer vision literature. Deep networks have shown be vulnerable adversarial attacks. However, very little focus is devoted improving robustness of deep models, causing serious concerns about model reliability. Adversarial Trai...

Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled similar yet shifted target domain. Though achieving good performance, these are inapplicable for Multivariate Time-Series (MTS) data. MTS data collected from multiple sensors, each of which follows various distributions. However,...

In unsupervised domain adaptation (UDA), a classifier for the target is trained with massive true-label data from source and unlabeled domain. However, collecting in can be expensive sometimes impractical. Compared to true label (TL), complementary (CL) specifies class that pattern does not belong to, hence, CLs would less laborious than TLs. this article, we propose novel setting where compose...

The 0-1,4-glycanase Cex of the gram-positive bacterium Cellulomonas fimi is a glycoprotein comprising a C-terminal cellulose-binding domain connected to an N-terminal catalytic domain by a linker containing only prolyl and threonyl (PT) residues. Cex is also glycosylated by Streptomyces lividans. The glycosylation of Cex produced in both C. fimi and S. lividans protects the enzyme from proteoly...