In this article I’m going to talk about how to efficiently create a ranking system that orders lists based on the concept of popularity and recency. |In this article I’m going to talk about how to efficiently create a ranking system that orders lists based on the concept of popularity and recency.I’m sure you’ve encountered these ranking systems many times on the Internet; they have many excellent applications. They are popular on news sites to rank stories, or on bulletin board services to filter ‘hot’ posts. They are used on games sites to bubble up popular titles, to help surface trending hashtags, to advertise top selling items, and, of course, to order our social media feeds …|| ||The concept is pretty simple; we want to order the list based on popularity of some score (which could be a sales, ranking, rating, up/down votes, players, or some other feedback such as if the article has been read or interacted with).However, we also want to encourage new and fresh content to appear high up the list, and arrange for the older, staler, content to be slowly moved down to make room for this. Sure, an evergreen article should able to stay high up on the list if it continues to receive a regular cadence of votes or comments, but an article that “supernovas” (burns bright, then fades away) should slide down the list if it does garner any more votes.|

In my previous post, I showed how to use cdata package along with ggplot2‘s faceting facility to compactly plot two related graphs from the same data. This got me thinking: can I use cdata to produce a ggplot2 version of a scatterplot matrix, or pairs plot?

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks. Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

Mean reversion is the assumption that things tend to revert back to their means in the long run. This is especially true for valuations and certain macroeconomic variables, but not so much for stock prices themselves. In this post we’ll look at the mean reversion of different valuation measures by forming equal sized baskets from each valuation decile and letting the valuations change as time goes on.

Tentacular AI We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as ‘tentacular.’ Tentacular AI is distinguished by six attributes, which among other things entail a capacity for reasoning and planning based in highly expressive calculi (logics), and which enlists subsidiary agents across distances circumscribed only by the reach of one or more given networks. …

R is an interpreted programming language with vectorized data structures. This means a single R command can ask for very many arithmetic operations to be performed. This also means R computation can be fast. We will show an example of this using Conway’s Game of Life.

Full many a gem of purest ray serene,The dark unfathomed caves of ocean bear;Full many a flower is born to blush unseen,And waste its sweetness on the desert air.

A Differential Expression Analysis Toolkit for Visual Analytics and Data Aggregation (DEVis)Differential expression analysis tools for data aggregation, visualization, exploratory analysis, and project organization.

Expertise Critical for Every Data Scientist

R is an interpreted programming language with vectorized data structures. This means a single R command can ask for very many arithmetic operations to be performed. This also means R computation can be fast. We will show an example of this using Conway’s Game of Life.