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Can machine learning be used to advance exoplanet science, and can this be done by non-scientists, as well? This is what Ariel Data Challenge 2024 hopes to address as participants from around the world will compete to develop machine learning algorithms designed to analyze data from space telescopes with the goal of gaining greater insight into exoplanet atmospheres. This competition will be featured at the NeurIPS 2024 machine learning conference and holds the potential to not only advance the field of exoplanets but also enable non-scientists to conduct pioneering research, as well.
“By supporting this challenge, we aim to find new ways of using AI and machine learning to develop our understanding of the universe,” said Dr. Caroline Harper, who is the Head of Space Science at the UK Space Agency. “Exoplanets are likely to be more numerous in our galaxy than the stars themselves and the techniques developed through this prestigious competition could help open new windows for us to learn about the composition of their atmospheres, and even their weather.”
Along with the UK Space Agency, other institutions supporting this challenge include the STFC DiRAC HPC Facility, European Space Agency (ESA), and STFC RAL Space. The competition is named after the ESA’s Ariel Space Mission, which is currently scheduled for launch in 2029 with the goal of using the transit method for identifying more than 1,000 exoplanets.
Pain management is an important component of caring for adults with cerebral palsy. However, it’s the least understood comorbidity in the adult cerebral palsy population.
A study led by Mark Peterson, Ph.D., M.S., FACSM, a professor of physical medicine and rehabilitation at University of Michigan Health, found that adults living with cerebral palsy had a very high occurrence of pain, with 90% having a pain history and 74% having multiple diagnoses of pain coming from different origins such as the lower back, irritable bowels, joint arthritis and chronic headaches.
The research is published in the journal JAMA Neurology.
Heat and pressure can deteriorate the properties of piezoelectric materials that make state-of-the-art ultrasound and sonar technologies possible – and fixing that damage has historically required disassembling devices and exposing the materials to even higher temperatures. Now researchers have developed a technique to restore those properties at room temperature, making it easier to repair these devices – and paving the way for new ultrasound technologies.
Piezoelectric materials have many applications, including sonar technologies and devices that generate and sense ultrasound waves. But for these devices to efficiently generate sonar or ultrasound waves, the material needs to be “poled.”
That’s because the piezoelectric materials used for sonar and ultrasound applications are mostly ferroelectric. And like all ferroelectric materials, they exhibit a phenomenon called spontaneous polarization. That means they contain pairs of positively and negatively charged ions called dipoles. When a ferroelectric material is poled, that means all of its dipoles have been pulled into alignment with an external electric field. In other words, the dipoles are all oriented in the same direction, which makes their piezoelectric properties more pronounced.
Researchers at Penn State are working on advanced electronics using something called kink states, which are special pathways for electrons in materials. These paths could help create networks for quantum information, which is essential for the next generation of electronics. Credit: SciTechDaily.com.
Researchers at Penn State are developing advanced quantum electronics using kink states, which are unique electron pathways in semiconducting materials.
These states could potentially form the backbone of a quantum interconnect network, crucial for transmitting quantum information efficiently. The team has made significant advancements in controlling these states through innovative material combinations and device designs, enhancing the potential for scalable quantum electronics.
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We are constantly surrounded by electromagnetic waves such as Wi-Fi and Bluetooth signals. What if we could turn the unused excess into usable energy? Researchers at Tohoku University, the National University of Singapore, and the University of Messina developed a novel technology to efficiently harvest ambient low-power radiofrequency (RF) signals into direct-current (DC) power. This ‘rectifier’ technology can be easily integrated into energy harvesting modules to power electronic devices and sensors, enabling battery-free operation.
The results were published in Nature Electronics (“Nanoscale spin rectifiers for harvesting ambient radiofrequency energy”).
Schematic illustration of a wireless network with energy-harvesting modules. RF signals that are unused by electronic gadgets and would otherwise go to waste are used to generate usable DC power to drive sensors and devices. (Image: Shunsuke Fukami & Hyunsoo Yang)
Our future energy may depend on high-temperature superconducting (HTS) wires. This technology’s ability to carry electricity without resistance at temperatures higher than those required by traditional superconductors could revolutionize the electric grid and even enable commercial nuclear fusion.
Astronomers have created the most detailed weather report so far for two distant worlds beyond our own solar system.
The international study — the first of its kind — reveals the extreme atmospheric conditions on the celestial objects, which are swathed in swirling clouds of hot sand amid temperatures of 950C.
Using NASA’s powerful James Webb Space Telescope (JWST), researchers set out to capture the weather on a pair of brown dwarfs — cosmic bodies that are bigger than planets but smaller than stars.
Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics, demonstrating a method much simpler than previous approaches.
Machine learning and artificial intelligence are becoming increasingly widespread with applications ranging from computer vision to text generation, as demonstrated by ChatGPT. However, these complex tasks require increasingly complex neural networks; some with many billion parameters. This rapid growth of neural network size has put the technologies on an unsustainable path due to their exponentially growing energy consumption and training times. For instance, it is estimated that training GPT-3 consumed more than 1,000 MWh of energy, which amounts to the daily electrical energy consumption of a small town. This trend has created a need for faster, more energy-and cost-efficient alternatives, sparking the rapidly developing field of neuromorphic computing. The aim of this field is to replace the neural networks on our digital computers with physical neural networks.
