Structural and optical properties of MOCVD AllnN epilayers

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Capital as Artificial Intelligence

This article examines science-fictional allegorizations of Soviet-style planned economies, financial markets, autonomous trading algorithms, and global capitalism writ large as nonhuman artificial intelligences, focussing primarily on American science fiction of the Cold War period. Key fictional texts discussed include Star Trek, Isaac Asimov\u27s Machine stories, Terminator, Kurt Vonnegut\u27s Player Piano (1952), Charles Stross\u27s Accelerando (2005), and the short stories of Philip K. Dick. The final section of the article discusses Kim Stanley Robinson\u27s novel 2312 (2012) within the contemporary political context of accelerationist anticapitalism, whose advocates propose working with “the machines” rather than against them

Rachael Schlosberg, violin and Jayoung Kim, piano, March 30, 2017

This is the concert program of the Rachael Schlosberg, violin and Jayoung Kim, piano performance on Thursday, March 30, 2017 at 6:00 p.m., at the Concert Hall, 855 Commonwealth Avenue. Works performed were Violin Concerto No. 5 in A major, K. 219 by Wolfgang Amadeus Mozart and Violin Concerto in D major, Op. 35 by Pyotr Ilyich Tchaikovsky. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund

Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution