Located in China, Juno is a 17-country collaboration that will try to detect neutrinos and antineutrinos to learn more about their mass. Juno's sphere (bottom left) and photomultipliers (top right) for neutrino detection. Located 700 meters underground near the city of Jiangmen in southern China, a giant sphere--35 meters in diameter and filled with more than 20,000 tons of liquid--has just started a mission that will last for decades. This is Juno, the Jiangmen Underground Neutrino Observatory, a new, large-scale experiment studying some of the most mysterious and elusive particles known to science. Neutrinos are the most abundant particles in the universe with mass.

The internal structure of Jupiter is constrained by the precise gravity field measurements by NASA's Juno mission, atmospheric data from the Galileo entry probe, and Voyager radio occultations. Not only are these observations few compared to the possible interior setups and their multiple controlling parameters, but they remain challenging to reconcile. As a complex, multidimensional problem, characterizing typical structures can help simplify the modeling process. We used NeuralCMS, a deep learning model based on the accurate concentric Maclaurin spheroid (CMS) method, coupled with a fully consistent wind model to efficiently explore a wide range of interior models without prior assumptions. We then identified those consistent with the measurements and clustered the plausible combinations of parameters controlling the interior. We determine the plausible ranges of internal structures and the dynamical contributions to Jupiter's gravity field. Four typical interior structures are identified, characterized by their envelope and core properties. This reduces the dimensionality of Jupiter's interior to only two effective parameters. Within the reduced 2D phase space, we show that the most observationally constrained structures fall within one of the key structures, but they require a higher 1 bar temperature than the observed value. We provide a robust framework for characterizing giant planet interiors with consistent wind treatment, demonstrating that for Jupiter, wind constraints strongly impact the gravity harmonics while the interior parameter distribution remains largely unchanged. Importantly, we find that Jupiter's interior can be described by two effective parameters that clearly distinguish the four characteristic structures and conclude that atmospheric measurements may not fully represent the entire envelope.

Large-scale detectors consisting of a liquid scintillator target surrounded by an array of photo-multiplier tubes (PMTs) are widely used in the modern neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and the upcoming JUNO with its satellite detector TAO. Such apparatuses are able to measure neutrino energy which can be derived from the amount of light and its spatial and temporal distribution over PMT channels. However, achieving a fine energy resolution in large-scale detectors is challenging. In this work, we present machine learning methods for energy reconstruction in the JUNO detector, the most advanced of its type. We focus on positron events in the energy range of 0-10 MeV which corresponds to the main signal in JUNO -- neutrinos originated from nuclear reactor cores and detected via the inverse beta decay channel. We consider the following models: Boosted Decision Trees and Fully Connected Deep Neural Network, trained on aggregated features, calculated using the information collected by PMTs. We describe the details of our feature engineering procedure and show that machine learning models can provide the energy resolution $\sigma = 3\%$ at 1 MeV using subsets of engineered features. The dataset for model training and testing is generated by the Monte Carlo method with the official JUNO software.

From flying taxis to robotic pets, the 2020 Consumer Electronics Show has displayed more concepts and prototypes than ever before. Of all the hundreds of thousands of products on show for tech enthusiasts in Las Vegas, here are some of the highlights from the latest CES extravaganza. Uber's dream of mass-market flying taxis has taken another small step towards becoming reality. The South Korean auto-firm Hyundai unveiled its S-A1 concept, an electric-powered aircraft with four rotors to lift its vertical takeoffs and landings, four to drive it up to 180mph in the air and a parachute for emergencies. Hyundai said it will carry four people and their luggage a distance of up to 60 miles and at altitudes up to 2,000ft. Although the initial expectation is for a pilot to fly it, Hyundai ultimately expects it to operate autonomously.

One of the most difficult things about learning a new skill is finding time to study. Being able to complete assignments in between meetings or while traveling can make all the difference in the ability to make regular progress. Unfortunately, none of the online courses in programming, data science and machine learning I've taken over this past year have great mobile solutions. Much of the work still requires a laptop. After a great deal of searching, I finally found a solution in two applications that allow users to run Jupyter notebooks and python terminal commands, both of which are common tools for completing machine learning tasks.

A stunning new Nasa image shows raging storms on Jupiter with clouds that stretch for thousands of miles - and it looks just like an oil painting. Nasa's Juno spacecraft was a little more than one Earth diameter from Jupiter - or 8,292 miles (13,345 kilometres) - when it captured this mind-bending view of the planet's tumultuous atmosphere. The incredible colour-enhanced image was captured at a latitude of 48.9 degrees and depicts vasts swirling cloud formations that travel at about 129,000 mph (60 km/s) over the gas giant planet's surface. Jupiter fills the image, with only a hint of the terminator (where daylight fades to night) in the upper right corner, and no visible limb (the curved edge of the planet). Juno took this image of colorful, turbulent clouds in Jupiter's northern hemisphere on December 16, from 8,292 miles (13,345 kilometers) above the tops of Jupiter's clouds The incredible colour-enhanced image, showing swirling cloud formations over the gas giant planet's surface, was captured at a latitude of 48.9 degrees.

Nasa's Juno probe has sent back stunning images of the planet during its latest flyby. One of the most breathtaking photos shows a'string of pearls' – a series of eight massive rotating storms on Jupiter. The image was taken on October 24 when Juno was 20,577 miles (33,115 kilometres) above the tops of the clouds of the planet, which travel at about 129,000 mph (60 km/s). This image was taken by Juno on October 24 and was processed and colour enhanced. The swirling lines are cyclones on the planet, while the white ovals are'pearls' - massive rotating storms.

The LaGuardia Plaza Hotel is a four-minute drive from LaGuardia Airport, in Queens, and on a recent August afternoon nearly every car parked in the hotel's lot was black. One after the other, men in shirtsleeves pulled up in Chevy Suburbans and GMC Yukon XLs and gleaming Lexus RS 300s with leather-trimmed seats, got out, then made their way across the marble lobby and up a flight of stairs. A brightly smiling woman approached them as they congregated around a registration desk. She jotted the letters onto a yellow sticky note and worked her way down the line. "Do you have an appointment? The men were black-car drivers, currently working for the ride-summoning companies Uber or Lyft, or both, and they were there, in all likelihood, because another driver had told them that they could get more money, and better treatment, if they signed up to drive for a new rival, Juno. New York City--which has no shortage of ways to get around, from pedicabs to one of the largest public-transportation systems in the world--is just one stage upon which a handful of companies are fighting to dominate the future of personal transportation. Juno has decided that the most effective way to do that is by being extra-nice to the drivers. After the men registered, they were ushered into a waiting room, where draped café tables had been set up with brochures: "How to Be a 5 Star Juno Driver." The drivers were soon called by name--"Khaleed?" "Julio?"--and brought into another room, where a Juno manager, Lucas Smith, was waiting for them with a laptop and an overhead projector. "Drive Your Future," the slogan on the screen urged. A pink-skinned forty-year-old in jeans with a bushy red beard and an intense gaze, Smith joined Juno last January. Like several of his colleagues, he was recruited from Apple's retail division, where he conducted training sessions for Apple-store employees, based on Apple's carefully designed protocols. In fact, many details of the drivers' experience had been modelled on the interactions that customers have when they enter an Apple store, from the "concierges" who greeted them to the low driver-to-employee ratio. At first, Smith was put off by the whiff of exploitation that he detected around rising Silicon Valley enterprises such as Instacart, where people buy your groceries for you, and TaskRabbit, where freelancers can underbid one another to take on errands and other jobs. In middle school, Smith told me, he was assigned a book by Ayn Rand. "I remember finishing it, and realizing that I wanted to be the opposite of everything that was in that book," he said. "Everything about the celebration of selfishness was just anathema." So he was initially skeptical about Juno. "I'm a super-progressive, and I have incredibly mixed feelings about the'sharing economy' and companies like Uber," he said. "Rather than creating wealth, they felt extractive.