131,321 research outputs found
Sustaining relationships: working with positive feelings:III. Love and Positive Feelings
No full textActing Governor of Washington Territory, L. Jay S. Turney, letter to Reverend Daniel Bagley, John Webster, and Edward Carr, regarding the sale and acquisition of lands for the University of Washington Territory, August 29, 1861
No full textActing governor of Washington Territory and territorial secretary, L. Jay S. Turney, writes to the commissioners in charge of selecting a site for the territorial university, Reverend Daniel Bagley, John Webster and Edmund Carr. Turney asks for information regarding how many acres have been selected, how many acres have been sold, descriptions and prices of land tracts, a list of expenditures and any other information the commission can offer. Turney also asks for the specific law that gives them the legal authority to manage the school land.In 1854, territorial governor, Isaac Ingalls Stevens, suggested that a university for Washington Territory be established and Congress passed an act reserving the land for the school. The territorial legislature settled on Seattle as the setting for the school in 1858 but it was not until 1861 when appointed commissioners, Methodist minister Reverend Daniel Bagley, John Webster and Edmund Carr looked for a 10-acre site. Arthur A. Denny, Edward Lander and Charlie Terry eventually donated land for the school in downtown Seattle. As president of the commission and major manager of university land sales, Bagley faced several challenges from different individuals, as seen through this letter from L. Jay S. Turney (d. 1881) questioning the commissioners' authority in their actions. Common practice in the territories reserved lands for schools but did not sell the lands until the territory became a state. In this case, Bagley was authorized by the territorial legislature to sell the land work with land office officials as well. When Bagley received this letter from Turney, construction of school buildings had already begun. Situated between 4th Avenue, 6th Avenue, Union Street and Seneca Street, the Territorial University of Washington officially opened on November 4, 1861 with 30 students. In 1862, the Washington territorial legislature incorporated the school and appointed a Board of Regents. Throughout the university's early years, the university consisted not only of college curricula but preparatory school curricula as well. The school also faced constant changes in administration, enrollment and financial support. In 1863, the school had to close due to lack of students. In 1867 and 1876, the school closed again, this time due to lack of funds. Throughout the late 1870s and 1880s, strong leadership from school presidents helped the university form a stable base of students and a strong reputation. The school started to draw pupils from various parts of the territory and even Oregon and California. In 1889, the number of students approximated 300. About this time, discussion arose regarding a new site for the growing school. A graduate of the school and later professor, Edmond Meany, served as head of a committee to choose a new site off of Union Bay, further north and east of its current site. In 1895, the school formally moved to this new campus. Daniel Bagley was born in Pennsylvania in 1818. Bagley was a Methodist minister in the 1840s. In 1852, he was chosen to act as a missionary in Oregon Territory. With his wife, Susannah Rogers Whipple, he headed along the Oregon Trail to Oregon Territory. In his wagon train he met a number of men who were to become prominent citizens in Washington Territory including Dexter Horton, Thomas Mercer, Aaron Mercer, William Shoudy and John Pike. Following a number of years serving throughout the Williamette Valley, Bagley and his family settled in Seattle in 1860. In 1861, Bagley was appointed president of the commission on the new territorial university and became the manager of acquiring and selling university lands. This letter to Captain Blinn indicates his power in deciding who had claim to university lands. Bagley was also a major member of the Republican Party and was heavily involved in Seattle's real estate. Bagley died in 1905
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
No full textThis paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus
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Deferred prosecution agreements and directors' liability /
No full text"This book provides in-depth analysis of deferred prosecution agreements (DPAs), a tool first introduced in the United States and since implemented in the United Kingdom and other jurisdictions. The central focus of the book is the impact of DPAs on company directors: DPAs were first introduced in the US for individuals, but are now used predominantly for corporate defendants. In the UK, DPAs have only ever been available for companies. The consideration of individuals in the introductory stage in the UK is explored in depth, as well as the consideration and targeting of individuals in cases that have followed. Company directors are exposed to liability because of this negotiated deal between the company and prosecutors, and this book addresses the key areas of exposure, and how various parties should address these risk areas in accordance with the law. The book is an increasingly necessary contribution to the topical discussion of the fallout of unsuccessful prosecutions of individuals implicated in the wrongdoing constituting the basis of DPAs, calling into question not only treatment of those individuals but also the integrity of the DPA tool itself. It also considers the impact of DPAs and arising exposures on directors' and officers' (D&O) liability insurance, therefore covering potential risk areas and the ability of directors to access a defence in protecting themselves from liability. The book covers the impact on all areas of a D&O policy, considering D&O policy wording and insurance law in doing so, providing a rounded account of issues arising in relation to company directors and how interested parties can act in the best interests of all whilst in accordance with law and policy. The primary audience for this book will be lawyers and practitioners in the corporate crime and/or insurance law space, including general counsels, solicitors, barristers, consultants, prosecuting authorities, legal academics, and so forth. It will also be of interest to company directors, and to students of financial crime, corporate criminal crime and insurance law, and will have great international appeal. Organisations likely to use the book will include prosecuting authorities, law firms working on corporate criminal liability or D&O insurance cases, and companies looking to protect themselves where there is alleged wrongdoing"-
Learning algorithms for keyphrase extraction
No full textMany academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a wide variety of tasks for which keyphrases are useful, as we discuss in this paper. We approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. We evaluate the performance of nine different configurations of C4.5. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for automatically extracting keyphrases from text. The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Subjective human evaluation of the keyphrases generated by GenEx suggests that about 80% of the keyphrases are acceptable to human readers. This level of performance should be satisfactory for a wide variety of applications
MeSH term explosion and author rank improve expert recommendations
Get PDFInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
Get PDFBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Similarity of Semantic Relations
No full textThere are at least two kinds of similarity. Relational similarity is
correspondence between relations, in contrast with attributional similarity,
which is correspondence between attributes. When two words have a high
degree of attributional similarity, we call them synonyms. When two pairs
of words have a high degree of relational similarity, we say that their
relations are analogous. For example, the word pair mason:stone is analogous
to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA),
a method for measuring relational similarity. LRA has potential applications in many
areas, including information extraction, word sense disambiguation,
and information retrieval. Recently the Vector Space Model (VSM) of information
retrieval has been adapted to measuring relational similarity,
achieving a score of 47% on a collection of 374 college-level multiple-choice
word analogy questions. In the VSM approach, the relation between a pair of words is
characterized by a vector of frequencies of predefined patterns in a large corpus.
LRA extends the VSM approach in three ways: (1) the patterns are derived automatically
from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency
data, and (3) automatically generated synonyms are used to explore variations of the
word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the
average human score of 57%. On the related problem of classifying semantic relations, LRA
achieves similar gains over the VSM
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