Supervised Domain Adaptation using Graph Embedding

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.Comment: 7 pages, 3 figures, 3 table

Ursinus College Alumni Journal, July 1957

Bachelor\u27s degrees awarded to 135 • Letters to the editor • Ursinus evaluated • Note from the Admissions Office • Commencement 1957 • Ursinus astronomers record celestial event • The faculty cornered • York County alumni meet • April election results • New York alumni honor H. Dean Steward \u2707 • H. Ober Hess \u2733 speaks at college • History Department advises saving of documents • \u27Id al-Jalus, Ta\u27izz, Yemen, 1953 • May Day festivities • Washington alumni meet • Seventeen annual prizes awarded at commencement • Ursinus Woman\u27s Club • Mrs. William U. Helfferich feted • 1957 track season • 1957 varsity baseball • 1957 varsity tennis • 1957 top athletes • Women\u27s basketball • Blazer award • Memorial trophy for Ken Walker • Tennis and badminton undefeated season • News about ourselves • Weddings • Births • Necrologyhttps://digitalcommons.ursinus.edu/alumnijournal/1059/thumbnail.jp

Modeling the natural gas knocking behaviour using gas-phase infrared spectra and multivariate calibration

[Abstract] To assess the knocking properties of natural gas (NG) when it is used as fuel for vehicles is vital to optimize the design and functioning of their motors. Analytical efforts in this field are needed as the engines used to define it empirically are not available anymore, and existent mathematical algorithms yield different accuracy. The hybridization of gas-phase infrared spectrometry and partial least squares multivariate regression is presented first time to address the determination of the methane number (MN) of NG samples. It circumvents the need for the previous knowledge of the NG composition required to apply dedicated equations. The use of true NG samples to develop the models is also quite new in the field. Proof-of-concept studies were made with synthetic spectra and, then, a collection of liquefied NG samples for which MN values were computed by the National Physics Laboratory algorithm (NPL) from their sample composition were used to develop operative models. Additional validation was made with a collection of synthetic standard mixtures prepared for two European projects (EMRP LNG II and EMPIR LNG III) whose service methane numbers (SMN) were measured with an engine. The FTIR-PLS approach yielded statistically unbiased predictions with average standard errors around 0.4% MN when compared to the NPL-MN and SMN values, and standard deviations of the means ca. 1% MN. The approach is fast, cost effective as it involves standard instrumentation, and can be considered compliant with the green chemistry principles.This work is part of the EMPIR 16ENG09 project ‘Metrological support for LNG and LBG as transport fuel (LNG III)’. This project has received funding from the EMPIR programme co-financed by the Participant States and from the European Union's Horizon 2020 Research and Innovation programme. The authors from TU Braunschweig would like to thank IAV, Mahle, MAN Truck & Bus and Motortech for their support in preparing the test engine. The Group of Applied Analytical Chemistry of the University of A Coruña acknowledges Mestrelab, Reganosa and Naturgy for hiring its services for FTIR method developmentFinanciado para publicación en acceso aberto: Universidade da Coruña/CISU

Similarity Considerations on Wall Heat Losses in Internal Combustion Engines

The drop in wall heat losses with increasing engine size is shown not to be due to the decreasing surface-volume ratio, because this influence is compensated by the engine speed, which also drops. Instead, the heat-transfer coefficient decreases as the cylinder bore becomes larger. The dependence of the heat-transfer coefficient on engine size is explained using Reynolds analogy. This dependence can be used to determine approximately changes in wall heat losses resulting from differences in engine size and the subsequent effects on indicated efficiency. It is found that only a minor part of the differences in efficiency between small and large engines is due to size-dependent changes in wall heat losses. © 1991, Institution of Mechanical Engineers. All rights reserved

Factors affecting gestation duration in the bitch

A retrospective analysis was performed to determine the effects of age, breed, parity, and litter size on the duration of gestation in the bitch. Bitches at two locations were monitored from breeding to whelping. A total of 764 litters whelped from 308 bitches (36 large hounds, 34 Golden Retrievers, 23 German Shepherd Dogs (GSD), and 215 Labrador Retrievers). By breed, the number of whelpings was 152, 72, 58, and 482 for the hounds, Golden Retrievers, German Shepherd Dogs, and Labrador Retrievers, respectively. Whelping was predicted to be 57 d from the first day of cytologic diestrus in the hounds or 65 d from the initial progesterone rise in the other breeds. The average gestation duration (calculated as 8 d prior to Day 1 of cytologic diestrus in hounds or measured from the initial progesterone rise in other breeds) by breed (days +/- S.D.) was 66.0 +/- 2.8, 64.7 +/- 1.5, 63.6 +/- 2.1, and 62.9 +/- 1.3 for the hounds, Golden Retrievers, German Shepherd Dogs, and Labrador Retrievers, respectively. The relationship of age, breed, parity, and litter size with the difference in gestation duration was evaluated using log linear modeling. Age or parity had no effect on gestation duration. Compared to Labrador Retrievers, the German Shepherd Dogs, Golden Retrievers and hounds were more likely to have a longer gestation duration; three, four and nearly eight times as likely, respectively. Bitches whelping four or fewer pups were significantly more likely to have a longer gestation duration than those whelping five or more pups; the prolongation averaging 1 d