This story is six years in the making, and it involves Zelda, Star Fox, another fox, College Humor, Netflix, Nintendo and Adam Conover. In February 2015, the Wall Street Journal reported Nintendo was putting together a live-action adaptation of the Legend of Zelda series for Netflix, described as "Game of Thrones for a family audience." The information came from an anonymous source close to the project. Other outlets covered the report, too -- but a Zelda Netflix show never materialized. Over the years, video game fans chalked it up to a crack in the rumor mill and moved on.

Apps have taken over dating. Gone is the stigma of using a service like Match.com or OKCupid to find a partner -- nowadays, finding someone via Tinder, Bumble or Hinge is the norm. Swiping mindlessly through potential lovers is so common we now do it whether we're alone or hanging out with friends or even during another date. If you've ever sat down with a friend and asked to go through people on a dating app with them, Searchers is a film for you. If you're one of the lucky people who have never had to use a dating app and are curious about the experience, Searchers is for you.

Australians trying to stay up to date with the news by searching online may be better off ditching Google and using its competitors, research by Monash University has shown. On Australia Day "Grace Tame" was the most popular search term used on Google – reflecting the fact that she had just been made Australian of the Year. The top 50 results delivered by Google included only 70% of professional news websites, compared with 94% for the same search term on Bing and 82% on Ecosia. Last Sunday Australians rushing to find out more about the suddenly announced coronavirus lockdown in Perth made "perth lockdown" the most popular search term. Google delivered only 80% of news websites in the top 50, compared with 90% from Bing and 86% from Ecosia.

Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as {\em plate tuning}) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits their applications in real scenarios. In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We focus on two issues of existing super-resolution algorithms: lack of realistic training data and insufficient utilization of visual information obtained from cameras. To address the first issue, we propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images. In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction. Our model is able to generalize to different cameras without deliberately training on images from specific camera types. Extensive experiments demonstrate that the proposed algorithm can recover fine details and clear structures, and achieve high-quality results for single image super-resolution in real scenes.

Our mission to make business better is fueled by readers like you. To enjoy unlimited access to our journalism, subscribe today. Alex Spinelli, chief technologist for business software maker LivePerson, says the recent U.S. Capitol riot shows the potential dangers of a technology not usually associated with pro-Trump mobs: artificial intelligence. The same machine-learning tech that helps companies target people with online ads on Facebook and Twitter also helps bad actors distribute propaganda and misinformation. In 2016, for instance, people shared fake news articles on Facebook, whose A.I. systems then funneled them to users.

The evolution of social media platforms have empowered everyone to access information easily. Social media users can easily share information with the rest of the world. This may sometimes encourage spread of fake news, which can result in undesirable consequences. In this work, we train models which can identify health news related to COVID-19 pandemic as real or fake. Our models achieve a high F1-score of 98.64%. Our models achieve second place on the leaderboard, tailing the first position with a very narrow margin 0.05% points.

So I am working on an image based deep learning project where the data is stored on an Amazon server and all the training is also being done there itself. However, I need to look at the training images to get better feel of the data. I think this must be a common situation in professional settings. How do you guys got about it? Is there a better method than having to download the data to my system?

Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode. In this paper, we introduce a novel method called CyclingNet for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road-users, the built and the natural environments.

Entity Linking is one of the essential tasks of information extraction and natural language understanding. Entity linking mainly consists of two tasks: recognition and disambiguation of named entities. Most studies address these two tasks separately or focus only on one of them. Moreover, most of the state-of-the -art entity linking algorithms are either supervised, which have poor performance in the absence of annotated corpora or language-dependent, which are not appropriate for multi-lingual applications. In this paper, we introduce an Unsupervised Language-Independent Entity Disambiguation (ULIED), which utilizes a novel approach to disambiguate and link named entities. Evaluation of ULIED on different English entity linking datasets as well as the only available Persian dataset illustrates that ULIED in most of the cases outperforms the state-of-the-art unsupervised multi-lingual approaches.