Bookbinders -- Fiction.

Museums -- Collection management -- United States.

Prince Alfred College (Adelaide, S.A.) -- History.

English language -- Glossaries, vocabularies, etc.

Women -- South Australia -- Biography.

Library administration.

The Historical Dietary Guidance Digital Collection (HDGDC) combines more than 900 documents on diet and nutrition published by the U.S. Government , representing more than 100 years of history. Materials in this collection include historical nutrition education materials, such as posters, recipes, and radio transcripts, as well as current nutrition education materials reviewed by the Dietary Guidance Review Committee.

British Periodicals provides access to the searchable full text of hundreds of periodicals from 1681-1937. Topics covered include literature, music, art, drama, archaeology and architecture.

This is a subset of full text journal backfiles in the arts, humanities, and social sciences from the Periodicals Archive Online collection, dating from 1891-.

Indiana's efforts to give students more control over their academic transcripts may prove a boon for researchers and school reformers, too.

Research from REL Midwest examines the student characteristics associated with early college success in Indiana, with a focus on financial aid.

A state leader shares how research helped raise important considerations for increasing college success and completion in Indiana.

Edinburgh : Hodges and Smith, 1834-1840.

Stuttgart : E. Schweizerbart, 1867.

London : Macmillan, 1891.

London : Hurst, Chance, 1831.

Edinburgh : Young J. Pentland, 1889.

Edinburgh : printed by James Ballantyne & Co, [1807?]

Paris : G. Steinheil, 1892.

Paris ; Londres : Baillière ... : Legoubey, libraire ... ; J.B. Baillière ..., 1831.

Dublin : printed for Hodges and Smith, 1831.

London : Longmans, Green, 1902.

Philadelphia, Pa. : Lippincott, 1896.

London : Longmans, Green, 1868.

Genova : G. Bonaudo, 1812.

[Manchester] : [James Mudd], [between 1800 and 1899]

London : James Nisbet & Co., MDCCCLXXX [1880]

[London?], [1993?]

Rockville, Maryland : National Institute on Drug Abuse, 1986.

Rockville, Maryland : National Institute on Drug Abuse, 1980.

Rockville, Maryland : National Institute on Drug Abuse, 1978.

Rockville, Maryland : National Institute on Drug Abuse, 1977.

Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share their data, which increases the interest in collaborative privacy-preserving forecasting. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing Vector Autoregressive (VAR) models. The paper also provides mathematical proofs and numerical analysis to evaluate existing privacy-preserving methods, dividing them into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-off between privacy and forecasting accuracy, while the original data in iterative model fitting processes, in which intermediate results are shared, can be inferred after some iterations.

People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions. Therefore, it is crucial to design the learning objective which leads the model to converge and to perform predictions unbiasedly without explicit negative signals. In this paper, we propose a Collectively loss function to learn from only Positive and Unlabeled data (cPU). We theoretically elicit the loss function from the setting of PU learning. We perform intensive experiments on the benchmark and real-world datasets. The results show that cPU consistently outperforms the current state-of-the-art PU learning methods.

Fuhlbohm (Family)

Fuhlbohm (Family)