October and December have been devastating for stocks. It wasn’t until Friday though that we officially reached the depths of a bear market.

For the last Riddler of the year I attempt to solve both the Express and Classic Riddlers!

Argumentation Mining The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people’s argumentation. …

The {tidyverse} collection of packages can do much more than simply data manipulation anddescriptive statisics. You can use the principles we have covered and the functions you now knowto do much more. For instance, you can use a few {tidyverse} functions to do Monte Carlo simulations,for example to estimate (\pi).

In the spirit of trying out new things in Hugo since my last post on modifying the RSS feed for this website, I attempted to implement the new citation feature from the new radix package by RStudio.

In some scenarios a data scientist may want to train a model for which there exists an abundance of observations, but only a small fraction of is labeled, making the sample size available to train the model rather small. Although there’s plenty of literature on the subject (e.g. “Active learning”, “Semi-supervised learning” etc) one may be tempted (maybe due to fast approaching deadlines) to train a model with the labelled data and use it to impute the missing labels.

The post Gold-Mining Week 16 (2018) appeared first on Fantasy Football Analytics.

Henge We present Henge, a system to support intent-based multi-tenancy in modern stream processing applications. Henge supports multi-tenancy as a first-class citizen: everyone inside an organization can now submit their stream processing jobs to a single, shared, consolidated cluster. Additionally, Henge allows each tenant (job) to specify its own intents (i.e., requirements) as a Service Level Objective (SLO) that captures latency and/or throughput. In a multi-tenant cluster, the Henge scheduler adapts continually to meet jobs’ SLOs in spite of limited cluster resources, and under dynamic input workloads. SLOs are soft and are based on utility functions. Henge continually tracks SLO satisfaction, and when jobs miss their SLOs, it wisely navigates the state space to perform resource allocations in real time, maximizing total system utility achieved by all jobs in the system. Henge is integrated in Apache Storm and we present experimental results using both production topologies and real datasets. …

Having absorbed an average of 181 new packages each month over the last 28 months, CRAN is still growing at a pretty amazing rate. The following plot shows the number of new packages since I started keeping track in August 2016.

Introduction