Neuroscript (United States)

On August 27, 1992, the General Motors (GM) auto plant in Van Nuys closed after a half-century serving the Northeast San Fernando Valley. Its closure undercut the livelihoods of auto workers like Raymond Álvarez and his father Ramón. Today, the father and son duo look at “The Plant,” an outdoor shopping mall, and wonder whether the In-N-Out fast-food restaurant or T-Mobile store marks where they once stood on the assembly line. The departure of the GM plant and other long-standing manufacturing firms propelled the area into economic distress as Los Angeles was reeling from another crisis, the 1992 Uprising. In the wake of these events, elected officials clamored to revitalize the city. Six years later, “revitalization” came in the form of the shopping center, The Plant. By tracing the historical trajectory of one shuttered auto plant, from factory to shopping mall, this article demonstrates how neoliberal ideology gained legitimacy over the last several decades.

Under the influence of modern geopolitical processes the media are increasingly resorting to manipulative techniques and techniques. The article examines the information activities of the Euronews channel and the its role in shaping the political agenda. The relevance of the study is determined by the growing media confrontation between European countries and Russia in light of the crisis in Ukraine in 2022 – 2023. The purpose of the study is to identify methods for generating content and managing information flow. The study us based on the content analysis of the materials retrieved from the official Euronews website (in English) from the point of view of the phenomenon of linear communication “source – communication channel – recipient”. The author shows the general approaches to the design of the news agenda of the Euronews and the types of manipulative influence on the audience used in the materials. The study showed that (a) the content is formed on the principle of filtering the information flow and broadcasting selective information; (b) the most commonly used key messages include exploitation of intimidating themes, shock content and asymmetrical argumentation.

These paper discusses the following: Jeremy Bentham and the Computer Age: Reflections on Crowdsourcing the Transcription of Handwritten Documents; Handwriting: New Times, New Needs.

Check amount recognition is one of the most promising commercial applications of handwriting recognition. This paper is devoted to the description of the check reading system developed to recognize amounts on American personal checks. Special attention is paid to a reliable procedure developed to reject doubtful answers. For this purpose the legal (worded) amount on a personal check is recognized along with the courtesy (digit) amount. For both courtesy and legal amount fields, a brief description of all recognition stages beginning with field extraction and ending with the recognition itself are presented. We also present the explanation of problems existing at each stage and their possible solutions. The numeral recognizer used to read the amounts written in figures is described. This recognizer is based on the procedure of matching input subgraphs to graphs of symbol prototypes. Main principles of the handwriting recognizer used to read amounts written in words are explained. The recognizer is based on the idea of describing the handwriting with the most stable handwriting elements. The concept of the optimal confidence level of the recognition answer is introduced. It is shown that the conditional probability of the answer correctness is an optimal confidence level function. The algorithms of the optimal confidence level estimation for some special cases are described. The sophisticated algorithm of cross validation between legal and courtesy amount recognition results based on the optimal confidence level approach is proposed. Experimental results on real checks are presented. The recognition rate at 1% error rate is 67%. The recognition rate without reject is 85%. Significant improvement is achieved due to legal amount processing in spite of a relatively low recognition rate for this field.