Summary: Transposable elements team up with evolutionary recent neurons to influence differentiation and physiological function of neurons in brain development.
Source: EPFL
The human genome contains over 4.5 million sequences of DNA called “transposable elements”, these virus-like entities that “jump” around and help regulate gene expression. They do this by binding transcription factors, which are proteins that regulate the rate of transcription of DNA to RNA, influencing gene expression in a broad range of biological events.
Solar flares emit sudden, strong bursts of electromagnetic radiation from the Sun’s surface and its atmosphere, and eject plasma and energetic particles into inter-planetary space. Since large solar flares can cause severe space weather disturbances affecting Earth, to mitigate their impact their occurrence needs to be predicted. However, as the onset mechanism of solar flares is unclear, most flare prediction methods so far have relied on empirical methods.
The research team led by Professor Kanya Kusano (Director of the Institute for Space-Earth Environmental Research, Nagoya University) recently succeeded in developing the first physics-based model that can accurately predict imminent large solar flares. The work was published in the journal Science on July 31, 2020.
The new method of flare prediction, called the kappa scheme, is based on the theory of “double-arc instability,” that is a magnetohydrodynamic (MHD) instability triggered by magnetic reconnection. The researchers assumed that a small-scale reconnection of magnetic field lines can form a double-arc (m-shape) magnetic field and trigger the onset of a solar flare (Figure 1). The kappa scheme can predict how a small magnetic reconnection triggers a large flare and how a large solar flare can occur.
When Gary Kasparov was dethroned by IBM’s Deep Blue chess algorithm, the algorithm did not use Machine Learning, or at least in the way that we define Machine Learning today.
Adji Bousso Dieng will be Princeton’s School of Engineering’s first Black female faculty.
Not only has Adji Bousso Dieng, an AI researcher from Senegal, contributed to the field of generative modeling and about to become one of the first black female faculty in Computer Science in the Ivy League, she is also helping Africans in STEM tell their own success stories.
Dieng, who is currently a researcher at Google and an incoming computer science faculty at Princeton, works in an area of Artificial Intelligence called generative modeling.
IAIFI will advance physics knowledge — from the smallest building blocks of nature to the largest structures in the universe — and galvanize AI research innovation.
The U.S. National Science Foundation (NSF) announced last week an investment of more than $100 million to establish five artificial intelligence (AI) institutes, each receiving roughly $20 million over five years. One of these, the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), will be led by MIT ’s Laboratory for Nuclear Science (LNS) and become the intellectual home of more than 25 physics and AI senior researchers at MIT and Harvard, Northeastern, and Tufts universities.
By merging research in physics and AI, the IAIFI seeks to tackle some of the most challenging problems in physics, including precision calculations of the structure of matter, gravitational-wave detection of merging black holes, and the extraction of new physical laws from noisy data.
Autonomous unmanned aerial vehicles (UAVs) have shown great potential for a wide range of applications, including automated package delivery and the monitoring of large geographical areas. To complete missions in real-world environments, however, UAVs need to be able to navigate efficiently and avoid obstacles in their surroundings.
Researchers at Luleå University of Technology in Sweden and California Institute of Technology have recently developed a nonlinear model predictive control (NMPC)-based computational technique that could provide UAVs with better navigation and obstacle avoidance capabilities. The NMPC approach they used, presented in a paper published in IEEE Robotics and Automation Letters, is based on the structure of OpEn (Optimization Engine), a parametric optimization software developed by Dr. Pantelis Sopasakis at Queen’s University Belfast.
“Our team has previously published several works on autonomous obstacle avoidance and navigation for UAVs,” Björn Lindqvist, one of the researchers who carried out the study, told TechXplore. “In our recent study, we set out to extend the notion of obstacle avoidance to include a direct consideration of moving or dynamic obstacles, using NMPC. Our objective was to offer a technical demonstration of how modern and intelligent control structures could allow UAVs to be used in, for example, urban environments where the surroundings are always moving and where collision avoidance is of great importance to ensure the safety of persons and other vehicles.”
