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Recognizing markets from natural language. For General Assembly lecture.

Emerging technologies continue to transform the ways we collect, synthesize, disseminate, and consume information. These advances present both hazards and opportunities for the future of scholarly publication and communication. During this book sprint—presented by the Center for Science and the Imagination at Arizona State University and the Society for Scholarly Publishing (SSP) and embedded in SSP’s 2016 annual meeting in Vancouver—we discussed issues of increasing scholarly impact and accessibility, wondered whether computers can make scholarly contributions that warrant co-authorship, speculated about what forms scholarly books may take in the future, and more. Tackling ambitious and often ambiguous questions like these requires a diverse group of thinkers and writers and an innovative approach to writing. The book sprint method provides this innovation. Throughout the annual meeting, we held six miniature book sprints. During each sprint, we convened a group of four to six writers to tackle one of six big questions. Each sprint began with a facilitated conversation, followed by time for our writers to reflect and compose a piece of writing inspired by the conversation. Each piece was composed on Overleaf using this template specially created for this undertaking. Conferences like the SSP annual meeting and scholarly publications themselves are often undergirded by spontaneous, inspiring, thought-provoking conversations among colleagues and collaborators, but those conversations are rarely captured and shared, and are often clouded in memory, even for the participants. The book sprint process hopefully absorbs some of the kismet and energy of those initial conversations, right at the start of a big idea, and makes it part of a more durable intellectual product—and a possible springboard for additional conversations in a broader range of times and places. The work would not have been possible without the contributions of our four core sprinters—Madeline Ashby, Annalee Newitz, Roopika Risam, and Ido Roll—who participated in every session, and the many SSP members who participated in the individual sprints and shared their expertise. All of our content is free to read at http://sprintbeyondthebook.com, and free to download and share under a Creative Commons license.

Introduction Future Work Introduction Our lab has been working on analyzing data on mast cell cancer in Guiding Eyes for the Blind dogs using R programming. This presentation will illustrate the work we have done so far and what we hope to accomplish in the upcoming weeks.

Abstract: Studying the impacts of climate change requires looking at a multitude of variables across a broad range of sectors [1,2]. Information on the variables involved is often unevenly available or offers different uncertainties [3,4], and a lack of uniform terminology and methods further complicates the process of analysis, resulting in communication gaps when research enterprises span different sectors. For example, models designed by experts in one given discipline might assume conventions in language or oversimplify cross-disciplinary links in a way that is unfamiliar for scientists in another discipline. Geospatial Semantic Array Programming (GeoSemAP) offers the potential to move toward overcoming these challenges by promoting a uniform approach to data collection and sharing [5]. The Joint Research Centre of the European Commission has been exploring the use of geospatial semantics through a module in the PESETA II project (Projection of economic impacts of climate change in sectors of the European Union based on bottom-up analysis).

Text Mining Library for Biodiversity Literature (Spanish)

My CV in english

recent years, there has been a population increase, and with it, an increased density in our environment. The massive building in residential areas, various technologies which have become more available for individual use (cars, varied music players, TV sets). In this article, we will try to introduce a number of functions that eliminate noise.

Università degli studi di Napoli Federico II Scuola Politecnica e delle Scienze di Base Corso di Laurea Magistrale in Ingegneria Informatica Tesi di Laurea Magistrale in Big Data Analytics and Business Intelligence Artificial Bee Colony Influence Maximization