Literature abounds on the use of ultrasound. The technology is applied to everything from peering at a fetus inside a body to helping to diagnose shock. However, it demands a fair amount of expertise and concentration to obtain correct measurements from ultrasound images and to interpret those images. Today, artificial intelligence (AI) adds to ultrasound a powerful layer that lets users gain critically needed information – especially when time is of the essence. When a patient arrives in the ER with symptoms characteristic of shock, the attending physician needs to act quickly.

WASHINGTON – Hey, did my congressman really say that? Is that really President Donald Trump on that video, or am I being duped? New technology on the internet lets anyone make videos of real people appearing to say things they've never said. Republicans and Democrats predict this high-tech way of putting words in someone's mouth will become the latest weapon in disinformation wars against the United States and other Western democracies. This technology uses facial mapping and artificial intelligence to produce videos that appear so genuine it's hard to spot the phonies.

Hey, did my congressman really say that? Is that really President Donald Trump on that video, or am I being duped? New technology on the internet lets anyone make videos of real people appearing to say things they've never said. Republicans and Democrats predict this high-tech way of putting words in someone's mouth will become the latest weapon in disinformation wars against the United States and other Western democracies. This technology uses facial mapping and artificial intelligence to produce videos that appear so genuine it's hard to spot the phonies.

Hey, did my congressman really say that? Is that really President Donald Trump on that video, or am I being duped? New technology on the internet lets anyone make videos of real people appearing to say things they've never said. Republicans and Democrats predict this high-tech way of putting words in someone's mouth will become the latest weapon in disinformation wars against the United States and other Western democracies. This technology uses facial mapping and artificial intelligence to produce videos that appear so genuine it's hard to spot the phonies.

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.

Most of metric learning approaches are dedicated to be applied on data described by feature vectors, with some notable exceptions such as times series, trees or graphs. The objective of this paper is to propose a metric learning algorithm that specifically considers relational data. The proposed approach can take benefit from both the topological structure of the data and supervised labels. For selecting relative constraints representing the relational information, we introduce a link-strength function that measures the strength of relationship links between entities by the side-information of their common parents. We show the performance of the proposed method with two different classical metric learning algorithms, which are ITML (Information Theoretic Metric Learning) and LSML (Least Squares Metric Learning), and test on several real-world datasets. Experimental results show that using relational information improves the quality of the learned metric.

Sonos might not be primarily known for TV sound: it is famous for its internet-connected speakers, which can stream music from just about anywhere in lush audio. But that works just as well when stuck under your television, bringing great sound and all the same smarts to your films and shows. It came first with the Playbar, a long rectangle of speakers that was the first Sonos speaker to plug into your TV; that was followed by the Playbase, which sat under the television rather than in front of it. Now Sonos has brought the Beam, which takes the best of everything it has done so far and shrinks it down. Until now, bringing Sonos into your living room meant some big – literally – compromises. The Playbar and Playbase are both very large, meaning that small TVs or living rooms can make them look out of place.

Well they are, but not quite as remorselessly or as swiftly as the movies might have conditioned us to imagine. And when the robot age does arrive, the impact on New Zealand -- in jobs and economic disruption -- may not be as apocalyptic as some future scenarios imagine. At least that is the position of two leading robotics researchers, Armin Werner of Lincoln Agritech, and Bruce MacDonald, an Auckland University computer engineering specialist with over 30 years' skin in the robot game. Werner, whose background is in precision agriculture that will have its most advanced developments in robotics, believes the broad use of automation will create more jobs, at least at the skilled end of the labour market. His view is that the embrace of technology will lead to different forms of work, rather than making work more difficult.

After yesterday's insane live rocket launch/reality shattering event, we have arrived at the end of season 4 of Fortnite, and season 5 is just over the horizon. That means one final week of challenges, and as ever, the week 10 challenges have leaked online courtesy of Fortnite Tracker. Here's what you'll be asked to do in this final week, though these are subject to change before the list actually goes live: So while there are no obviously incomplete challenges here like there have been in previous weeks, I still am a little skeptical that this is the final list. Search between movie titles was listed last week when Fortnite's challenges leaked, but it was apparently pushed to this week, and I would expect it to actually land this time given that this is now the final week. I do not believe that "Skydive through floating rings" is going to be an actual challenge.

Maika Monroe stars in this sci-fi thriller as a woman taken captive by a tech entrepreneur and Tau, his cutting-edge, artificial intelligence system.