“I need some love like I’ve never needed love before” – Gerri, Mel C, Mel B, Victoria, Emma (noted Arianists) 

Real-time Anomaly Detection on Streaming Data

FedMark: A Marketplace for Federated Data on the Web

Andrew Han at Retraction Watch reports on a paper, “Structural stigma and all-cause mortality in sexual minority populations,” published in 2014 by Mark Hatzenbuehler, Anna Bellatorre, Yeonjin Lee, Brian Finch, Peter Muennig, and Kevin Fiscella, that claimed:

“I need some love like I’ve never needed love before” – Geri, Mel C, Mel B, Victoria, Emma (noted Arianists) 

Full Reference Image Quality Assessment (FR-IQA) While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on the ImageNet classification task has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called ‘perceptual losses’? What elements are critical for their success? To answer these questions, we introduce a new Full Reference Image Quality Assessment (FR-IQA) dataset of perceptual human judgments, orders of magnitude larger than previous datasets. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by huge margins. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations. …

Study of Set-Membership Adaptive Kernel Algorithms

Recommendation engines are being used to give your favorite websites the ability to predict what information you would most like to see. While this is an incredible tool for driving traffic to websites (potentially increasing revenues by billions), it presents a huge challenge when it comes to the dreaded problem of fake news.Many do not realize that Google search results for identical terms can vary significantly based on the person doing the search and that Facebook news feeds are not at all a chronological. In theory, this is how these sites are able to give you the results that you are looking for and quickly as possible. However, an unintended consequence of this has surfaced in the form of filter bubbles.

Vectorised Nested if-else Statements Similar to CASE WHEN in ‘SQL’ (lest)Functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like ‘CASE WHEN’ in ‘ …

Syntree2Vec – An algorithm to augment syntactic hierarchy into word embeddings