Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.

In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this collection of datasets, the $k$-thNN (distance to the $k$-nearest neighbor) algorithm significantly outperforms the most other algorithms. Visualizing and then clustering the relative performance of the considered algorithms on all datasets, we identify two clear clusters: one with ``local'' datasets, and another with ``global'' datasets. ``Local'' anomalies occupy a region with low density when compared to nearby samples, while ``global'' occupy an overall low density region in the feature space. On the local datasets the $k$NN ($k$-nearest neighbor) algorithm comes out on top. On the global datasets, the EIF (extended isolation forest) algorithm performs the best. Also taking into consideration the algorithms' computational complexity, a toolbox with these three unsupervised anomaly detection algorithms suffices for finding anomalies in this representative collection of multivariate datasets. By providing access to code and datasets, our study can be easily reproduced and extended with more algorithms and/or datasets.

WASHINGTON - U.S. computer scientist Katie Bouman, who became a global sensation over her role in generating the world's first image of a black hole, has described the painstaking process as akin to listening to a piano with missing keys. Testifying before Congress on Thursday, the postdoctoral fellow at the Harvard Smithsonian Center for Astrophysics also suggested that the technology developed by the project could have practical applications in the fields of medical imaging, seismic prediction and self-driving cars. A photo released last month of the star-devouring monster in the heart of the Messier 87 (M87) galaxy revealed a dark core encircled by a flame-orange halo of white hot plasma. Because M87 is 55 million light-years away, "This ring appears incredibly small on the sky: roughly 40 microarcseconds in size, comparable to the size of an orange on the surface of the moon as viewed from our location on Earth," said Bouman. The laws of physics require a telescope the size of our entire planet to view it.

On Wednesday 10 April, the first image ever taken of a black hole was released. The picture, which shows a black hole surrounded by a hazy red and yellow circle, provides an unprecedented peek at one of the most mysterious entities in the universe. One of the scientists involved in the development of the picture is Dr Katie Bouman. We'll tell you what's true. You can form your own view.

Scientist Katherine Bouman has become one of the world's most popular people for helping create the first ever picture of a black hole. The researcher was one of a team made up of a huge number of experts who produced the image, which shows the blazing red and yellow of the event horizon that surrounds the first black hole ever to be seen. And one image in particular of Dr Bouman doing part of that work – using an algorithm she wrote to generate the image that made headlines around the world – has served as a reminder of the vast amount of expertise that has gone into creating such an achievement. We'll tell you what's true. You can form your own view.

A 29-year-old computer scientist has earned plaudits worldwide for helping develop the algorithm that created the first-ever image of a black hole. Katie Bouman led development of a computer program that made the breakthrough image possible. The remarkable photo, showing a halo of dust and gas 500 million trillion km from Earth, was released on Wednesday. For Dr Bouman, its creation was the realisation of an endeavour previously thought impossible. Excitedly bracing herself for the groundbreaking moment, Dr Bouman was pictured loading the image on her laptop.

Artificial intelligence that allows self-driving cars to detect people and objects hidden around blind corners has been developed by researchers at MIT. The imaging system--dubbed CornerCameras--was built by AI researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) for seeing around obstructions using standard camera technology. Using information about light reflections, MIT's artificial intelligence system is able to measure the speed and trajectory of hidden objects in real time using footage from smartphone cameras. "The technology has a range of applications, from firefighters finding people in burning buildings to self-driving cars detecting pedestrians in their blind spots," an MIT spokesperson tells Newsweek. "What's impressive is that this approach works using footage from a smartphone camera, such as an iPhone 8." The artificial intelligence system can be used on footage filmed with a smartphone.

The CSAIL team's imaging system, which can work with smartphone cameras, uses information about light reflections to detect objects or people in a hidden scene and measure their speed and trajectory -- all in real-time. Specifically, researchers shine cameras on specific points that are visible to both the observable and hidden scene, and then measure how long it takes for the light to return. But by observing the scene over several seconds and stitching together dozens of distinct images, the system can distinguish distinct objects in motion and determine their speed and trajectory. The team was surprised to find that CornerCameras worked in a range of challenging situations, including weather conditions like rain.

Magically seeing around corners to spot moving people or objects may not rank first in most people's superhero daydreams. But MIT researchers have shown how they could someday bestow that superpower upon anyone with a smartphone. Their secret to peeking around corners is detecting slight differences in light patterns reflected from moving objects or people. Those reflected light patterns form subtle variations in the shadowy area near the base of each corner. MIT's Computer Science and Artificial Intelligence Lab (CSAIL) created simple software that can detect fuzzy pattern variations in the pixels of a 2-D video--taken by a basic consumer camera or even a smartphone camera--and reconstruct the speed and trajectory of moving objects by stitching together multiple, distinct 1-D images.

Robots can pull off a lot of righteous tricks. Or teaching themselves to play children's games. Or even rolling through one of San Francisco's most chaotic neighborhoods to deliver you falafel. One thing they definitely can't do, though: see around corners. Because engineers at the MIT Computer Science and Artificial Intelligence Laboratory have developed a clever and surprisingly simple way to see around corners.