Washington State University

About: Washington State University is a education organization based out in Pullman, Washington, United States. It is known for research contribution in the topics: Population & Gene. The organization has 26947 authors who have published 57736 publications receiving 2341509 citations. The organization is also known as: WSU & Wazzu.

A Survey of Unsupervised Deep Domain Adaptation

Abstract: Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.

Gaussian basis sets for use in correlated molecular calculations. VII. Valence, core-valence, and scalar relativistic basis sets for Li, Be, Na, and Mg

Abstract: Correlation consistent basis sets of double-ζ through quintuple-ζ quality for the alkali and alkaline earth metals Li, Be, Na, and Mg have been developed, including the valence (cc-pVnZ), augmented valence (aug-cc-pVnZ), core-valence (cc-pCVnZ), and weighted core-valence (cc-pwCVnZ) basis sets. The basis sets are also re-contracted for Douglas–Kroll scalar relativistic calculations and are found to be superior to non-relativistic basis sets in recovering scalar relativistic effects. CCSD(T) computations have been performed with these basis sets, and a series of properties have been examined, including atomic ionization potentials and electron affinities, optimized molecular geometries, harmonic vibrational frequencies, atomization energies, and enthalpies of formation for the molecules Li2, LiF, BeO, BeF, BeH2, BeF2, Na2, NaF, MgO, MgF, MgH2, and MgF2.

Quantitative relations between interaction parameter, miscibility and function in organic solar cells

Abstract: Although it is known that molecular interactions govern morphology formation and purity of mixed domains of conjugated polymer donors and small-molecule acceptors, and thus largely control the achievable performance of organic solar cells, quantifying interaction–function relations has remained elusive. Here, we first determine the temperature-dependent effective amorphous–amorphous interaction parameter, χaa(T), by mapping out the phase diagram of a model amorphous polymer:fullerene material system. We then establish a quantitative ‘constant-kink-saturation’ relation between χaa and the fill factor in organic solar cells that is verified in detail in a model system and delineated across numerous high- and low-performing materials systems, including fullerene and non-fullerene acceptors. Our experimental and computational data reveal that a high fill factor is obtained only when χaa is large enough to lead to strong phase separation. Our work outlines a basis for using various miscibility tests and future simulation methods that will significantly reduce or eliminate trial-and-error approaches to material synthesis and device fabrication of functional semiconducting blends and organic blends in general. This work reports a quantitative investigation of the interaction parameter and miscibility of donor and acceptor organic molecules and their relationship with the fill factor and photovoltaic performance of bulk-heterojunction organic solar cells.