I am fascinated by technology and its application to data analysis in finance, especially investing. Below is a compiled list of freely available academic papers published in 2017 on deep learning…
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We introduce a new lambda calculus with futures, Lambda(fut), that models the operational semantics of concurrent statically typed functional programming languages with mixed eager and lazy threads such as Alice ML, a concurrent extension of Standard ML. Lambda(fut) is a minimalist extension of the call-by-value lambda-calculus that is sufficiently expressive to define and combine a variety of standard concurrency abstractions, such as channels, semaphores, and ports. Despite its minimality, the basic machinery of Lambda(fut) is sufficiently powerful to support explicit recursion and call-by-need evaluation. We present a static type system for Lambda(fut) and distinguish a fragment of Lambda(fut) that we prove to be uniformly confluent. This result confirms our intuition that reference cells are the sole source of indeterminism.
This article will discuss the uproar over privacy issues in social networks by describing a privacy paradox; private versus public space; and, social networking privacy issues.