Smartphones with multiple cameras and blurry photos could become a thing of the past as engineers have created a flat lens that doesn't need focusing. The technology -- which uses a single lens covered in tiny patterns that is just one-thousandth of an inch thick -- was developed by experts from the University of Utah. Not only could the lens lead to better and more compact cameras, but it could also find applications in biomedical imaging and remote sensing. Smartphones with multiple cameras and blurry photos could become a thing of the past as engineers have created a flat lens that doesn't need focusing, pictured'Our flat lenses can drastically reduce the weight, complexity and cost of cameras and other imaging systems, while increasing their functionality,' said research team leader and engineer Rajesh Menon, of the University of Utah. 'Such optics could enable thinner smartphone cameras, improved and smaller cameras for biomedical imaging such as endoscopy, and more compact cameras for automobiles.'

We study the ability of Wasserstein Generative Adversarial Network (WGAN) to generate missing audio content which is, in context, (statistically similar) to the sound and the neighboring borders. We deal with the challenge of audio inpainting long range gaps (500 ms) using WGAN models. We improved the quality of the inpainting part using a new proposed WGAN architecture that uses a short-range and a long-range neighboring borders compared to the classical WGAN model. The performance was compared with two different audio instruments (piano and guitar) and on virtuoso pianists together with a string orchestra. The objective difference grading (ODG) was used to evaluate the performance of both architectures. The proposed model outperforms the classical WGAN model and improves the reconstruction of high-frequency content. Further, we got better results for instruments where the frequency spectrum is mainly in the lower range where small noises are less annoying for human ear and the inpainting part is more perceptible. Finally, we could show that better test results for audio dataset were reached where a particular instrument is accompanist by other instruments if we train the network only on this particular instrument neglecting the other instruments.

Artificial intelligence (AI) systems, powered by massive data and sophisticated algorithms -- including but not limited to -- deep neural networks and statistical machine learning (ML)(support vector machines, clustering, random forest, etc.), are having profound and transformative impact on our daily lives as they make their way into everything from finance to healthcare, from retail to transportation. Netflix movie recommender, Amazon's product prediction, Facebook's uncanny ability to show what you may like, Google's assistant, DeepMind's AlphaGo, Stanford's AI beating human doctors. Machine learning is eating software. However, one of the common features of these powerful algorithms is that they utilize sophisticated mathematics to do their job -- to classify and segment an image, to arrive at the key decisions, to make a product recommendation, to model a complex phenomenon, or to extract and visualize a hidden pattern from a deluge of data. All of these mathematical processes are, quite simply, beyond the scope of a single human (or a team) to perform manually (even on a computer) or inside their head.

Social media may offer a wealth of intelligence to journalists as they scout for stories, but harnessing its true potential becomes a challenge given how easy it is to misrepresent information online. With false facts and narratives intertwining these platforms, it becomes an arduous task for them to filter accurate information. And it is not just with texts. Fuelled by the meme culture, images that we find in our newsfeeds may often be fake. With the second-highest number of internet users after China, these issues are magnified in India.

My smart TV is old (Samsung, 2014) so I watch BBC iPlayer on my Now TV box (also old and discontinued). What is the best device for all the popular streaming services including the upcoming Disney? To my knowledge, it is not yet confirmed if it will be available on my Roku-powered Now TV box. You may be in luck, because Disney has just signed a deal with Sky. As a result, Disney will be available via Sky from its UK launch on 24 March, to be followed by Now TV in the coming months, says Sky. It's not clear how many months that means.

Sentiment Analysis is a branch of Affective Computing usually considered a binary classification task. In this line of reasoning, Sentiment Analysis can be applied in several contexts to classify the attitude expressed in text samples, for example, movie reviews, sarcasm, among others. A common approach to represent text samples is the use of the Vector Space Model to compute numerical feature vectors consisting of the weight of terms. The most popular term weighting scheme is TF-IDF (Term Frequency - Inverse Document Frequency). It is an Unsupervised Weighting Scheme (UWS) since it does not consider the class information in the weighting of terms. Apart from that, there are Supervised Weighting Schemes (SWS), which consider the class information on term weighting calculation. Several SWS have been recently proposed, demonstrating better results than TF-IDF. In this scenario, this work presents a comparative study on different term weighting schemes and proposes a novel supervised term weighting scheme, named as TF-IDFC-RF (Term Frequency - Inverse Document Frequency in Class - Relevance Frequency). The effectiveness of TF-IDFC-RF is validated with SVM (Support Vector Machine) and NB (Naive Bayes) classifiers on four commonly used Sentiment Analysis datasets. TF-IDFC-RF outperforms all other weighting schemes and achieves F1 results of more than 99.9% on all datasets with SVM classifier.

Computers and robots are now learning to make decisions! Of course, "deciding" is a big word for machines that have no consciousness and whose level of "reasoning" is not even as evolved as that of a frog. But the latest developments in artificial intelligence (AI) are enough to frighten some and to arouse the fantasies of others. Between myth and reality, where exactly does the current research stand in this technology that threatens to disrupt all others? In its Wide Angle section, the Courier attempts to untangle the various paths of inquiry and offers some terminological signposts to help uninitiated readers to find their way through the fascinating but scary world of AI.