The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion. Automatic Rumor Detection on Microblogs: A Survey

At: DataCamp Location: RemoteWeb: www.datacamp.comPosition: Part-time Contract Instructors

I wasn’t at the Use of R in Official Statistics (uRos2018) conference in the Netherlands last month, but I’m thankful to Jeroen Ooms for sharing the slides from his keynote presentation. In addition to being a postdoc staffer at ROpenSci, Jeroen maintains the official repository for the daily R builds on Windows — you might recognize his name from the verification certificate that pops up when installing R on Windows. His uRos2018 talk provides a fascinating glimpse into the complex systems, dependencies, and processes that come together to make installing R as easy as as double-click.

Tensor Network (TN) The harnessing of modern computational abilities for many-body wave-function representations is naturally placed as a prominent avenue in contemporary condensed matter physics. Specifically, highly expressive computational schemes that are able to efficiently represent the entanglement properties of many-particle systems are of interest. In the seemingly unrelated field of machine learning, deep network architectures have exhibited an unprecedented ability to tractably encompass the dependencies characterizing hard learning tasks such as image classification. However, key questions regarding deep learning architecture design still have no adequate theoretical answers. In this paper, we establish a Tensor Network (TN) based common language between the two disciplines, which allows us to offer bidirectional contributions. By showing that many-body wave-functions are structurally equivalent to mappings of ConvACs and RACs, we construct their TN equivalents, and suggest quantum entanglement measures as natural quantifiers of dependencies in such networks. Accordingly, we propose a novel entanglement based deep learning design scheme. In the other direction, we identify that an inherent re-use of information in state-of-the-art deep learning architectures is a key trait that distinguishes them from TNs. We suggest a new TN manifestation of information re-use, which enables TN constructs of powerful architectures such as deep recurrent networks and overlapping convolutional networks. This allows us to theoretically demonstrate that the entanglement scaling supported by these architectures can surpass that of commonly used TNs in 1D, and can support volume law entanglement in 2D polynomially more efficiently than RBMs. We thus provide theoretical motivation to shift trending neural-network based wave-function representations closer to state-of-the-art deep learning architectures. …

I wasn’t at the Use of R in Official Statistics (uRos2018) conference in the Netherlands last month, but I’m thankful to Jeroen Ooms for sharing the slides from his keynote presentation. In addition to being a postdoc staffer at ROpenSci, Jeroen maintains the official repository for the daily R builds on Windows — you might recognize his name from the verification certificate that pops up when installing R on Windows. His uRos2018 talk provides a fascinating glimpse into the complex systems, dependencies, and processes that come together to make installing R as easy as as double-click.

Stop jumping between applications. Mode Studio connects a SQL editor, Python and R notebooks, and a visualization builder in one platform.

We at Locke Data maintain a few R packages that we’ve submitted to CRAN to help increase their userbase. After running devtools::release(), clicking in a confirmation email… what remains is waiting. Inspired by our experience, we’ve created a dashboard to help other package maintainers follow their package’s journey to CRANterbury. Read more about its making in this post!

*A special guest post by cricket legend and founder of Spektacom Technologies, Anil Kumble. This post was co-authored by Tara Shankar Jana, Senior Technical Product Marketing Manager at Microsoft. *

Go from “I think” to “I know how much.”