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Ashley Madison, the website for cheaters that was hit by a massive hack that exposed millions, is rebooting under new leadership as an “open-minded dating” service. And its Canadian-based parent company said it will no longer use female “bots” or automated programs that respond to members pretending to be women on the hunt for men. A statement Monday from Avid Life Media, the parent firm of Ashley Madison, announced “a new direction and total repositioning” of the service.
It said that newly appointed chief executive Rob Segal and president James Millership are three months into their new roles and were making “transformative changes” for the company and its flagship brand Ashley Madison. The statement said the company’s forensic accounting investigation confirmed that “bots” were no longer in use, stopped in North America in and internationally in Some media reports after last year’s hack claimed that many of the female members on Ashley Madison were in fact bots that gave the impression there were more women on the site.
Last year, a hacker group released what was said to be personal data on more than 30 million members of Ashley Madison.
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The series premiered on October 10, , on The Hub which was renamed as Discovery Family in late , and ended on October 12, Hasbro selected animator Lauren Faust as the creative director and executive producer for the show. Faust sought to challenge the established nature of the existing My Little Pony line, creating more in-depth characters and adventurous settings; she left the series during season 2, to be replaced by Meghan McCarthy as showrunner for the remainder of the series.
The show follows a studious anthropomorphic unicorn later an alicorn pony named Twilight Sparkle as her mentor, Princess Celestia , guides her to learn about friendship in the town of Ponyville. Each of the ponies represent a different facet of friendship, and Twilight discovers herself to be a key part of the magical artifacts known as the “Elements of Harmony”. The ponies travel on adventures and help others around Equestria while working out problems that arise in their own friendships.
The series has become a major commercial success, becoming the highest rated original production in Hub Network’s broadcast history and leading to new merchandising opportunities for Hasbro, including books, clothing, collectible trading cards, and comics. Despite the target demographic of young girls, Friendship Is Magic has also gained a large following of older viewers, mainly young and middle-aged men, who call themselves ” bronies “. Portions of the show have become part of the remix culture , and have also formed the basis for a variety of internet memes.
The first two films, Equestria Girls and Rainbow Rocks , were shown in limited theatrical screenings prior to television broadcast and home media release. Hasbro, Inc. Animator and writer Lauren Faust approached Hasbro, seeking to develop her girls’ toys property “Galaxy Girls” into an animated series. Licht considered that Faust’s style was well suited to that line, and asked her to consider “some ideas where to take a new version of the franchise”.
Faust was initially hired by Hasbro to create a pitch bible for the show, allowing her to get additional help with conceptualization.
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and conference papers and presentations had been acquired dating back to Citation metrics: metrics were obtainable through the Scholars Hub itself.
Most people take for granted the ability to view an object from several different angles, but still recognize that it’s the same object— a dog viewed from the front is still a dog when viewed from the side. While people do this naturally, computer scientists need to explicitly enable machines to learn representations that are view-invariant , with the goal of seeking robust data representations that retain information that is useful to downstream tasks.
Of course, in order to learn these representations, manually annotated training data can be used. Currently, a popular paradigm for training with such data is contrastive multiview learning , where two views of the same scene for example, different image channels , augmentations of the same image , and video and text pairs will tend to converge in representation space while two views of different scenes diverge.
To verify this hypothesis, we devise unsupervised and semi-supervised frameworks that learn effective views by aiming to reduce their mutual information. We also consider data augmentation as a way to reduce mutual information, and show that increasing data augmentation indeed leads to decreasing mutual information while improving downstream classification accuracy.
To encourage further research in this space, we have open-sourced the code and pre-trained models. The InfoMin Hypothesis The goal of contrastive multiview learning is to learn a parametric encoder, whose output representations can be used to discriminate between pairs of views with the same identities, and pairs with different identities.
The amount and type of information shared between the views determines how well the resulting model performs on downstream tasks. We hypothesize that the views that yield the best results should discard as much information in the input as possible except for the task relevant information e. The training objective is to identify that the two views belong to the same image.