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.

Amazon's new Echo Show 10 (third-generation), the first Alexa device with a motorized swiveling display, is now available for preorder for delivery starting on February 25. The new model, which was announced in September 2020, retails for $249.99 and is available in two colors: charcoal and glacier white. With the Echo Show 10, you can stream your favorite shows, follow along with recipes, call your friends and family, and more. The main draw for the Echo Show 10 is its smart motion. The touch-enabled display and embedded camera rotates atop a round base, which allows the built-in smart technology to keep the camera and screen in your line of sight automatically.

A renowned photographer has captured the highest resolution shots of snowflakes ever using a homemade prototype described as one part microscope and one part camera. Nathan Myhrvold, an American scientist, inventor, photographer and ex-chief technology officer of Microsoft, took 18 months to build the 100 megapixel camera capable of capturing a snowflake's microscopic detail. Using the camera, which he describes as the'highest resolution snowflake camera in the world', he took 100 frames of each snowflake in quick succession then stacked them for the whole image to be in focus. The results show the lush variety of snowflakes measuring only a few tens of millimetres in diameter, captured when Myhrvold was in Alaska and Canada. Pictured, stellar dendrite captured in Yellowknife, Canada.

Google's latest Chromecast streaming media dongle is a bit different. With a full interface and a remote, the new Chromecast with Google TV costs £59.99 and sits above the basic £30 Chromecast. You can still Google Cast to the new device, but the new flat plastic dongle is more than just a simple receiver, running the full Android TV software similar to the Nvidia Shield or smart TVs from Sony and others. Once plugged in, the new Chromecast is set up using the Google Home app on an Android, iPhone or iPad in about five minutes. Scan the QR code on your TV, log in with the required Google account, and choose some apps to install.