Classifying Supernova Explosions Using Artificial Intelligence
Image Credit: Shutterstock/NASA images
Machine learning and classification could help astronomers better understand and classify the Universe’s most explosive events.
Supernovae, massive cosmic explosions that represent the final death throes of stars, offer astronomers and cosmologists a vital tool for understanding the Universe. In particular, one type of these massive explosions Type Ia supernovae can be used to measure distances in the depths of space. Aside from this, learning more about supernovae can tell us how stars live and die and how elements are dispersed throughout galaxies.
Currently, supernovae are studied by using their observed spectra the set of colors into which light from these objects can be split which contains characteristic ‘gaps’ that tell astronomers what light is being emitted and absorbed, and thus, which elements are present in the explosion’s remains.
Researchers use machine intelligence to improve brain-mapping technique
Scientists in Japan s brain science project have used machine intelligence to improve the accuracy and reliability of a powerful brain-mapping technique, a new study reports.
Their development, published on December 18th in
Scientific Reports, gives researchers more confidence in using the technique to untangle the human brain s wiring and to better understand the changes in this wiring that accompany neurological or mental disorders such as Parkinson s or Alzheimer s disease. Working out how all the different brain regions are connected - what we call the connectome of the brain - is vital to fully understand the brain and all the complex processes it carries out, said Professor Kenji Doya, who leads the Neural Computation Unit at the Okinawa Institute of Science and Technology Graduate University (OIST).
Novel method reveals the synaptic basis for feature selectivity
A common analogy used to describe the brain is that it consists of tiny interconnected computers. Each one of these computers, or neurons, process and relay activity from thousands of other neurons, forming complex networks that allow us to perceive our surroundings, make decisions, and guide our actions. Communication between neurons occurs through tiny connections called synapses, and each neuron integrates the activity across these synapses to form a single output signal.
However, not all synapses are created equal. Synapses converging onto an individual neuron differ in size, and size is correlated with strength: larger synapses are stronger and have a greater influence on a neuron s output than smaller synapses. But why are some synapses stronger than others, and how does this impact individual neurons processing incoming signals?
Drug may increase vaccine protection in older adults
A drug that boosts the removal of cellular debris in immune cells may increase the protective effects of vaccines in older adults, a study published today in
eLife shows.
The results may lead to new approaches to protect older individuals from viruses such as the one causing the current COVID-19 pandemic and influenza.
Older adults are at high risk of being severely affected by infectious diseases, but unfortunately most vaccines in this age group are less efficient than in younger adults.
Ghada Alsaleh, Lead Author, Postdoctoral Researcher, Kennedy Institute of Rheumatology, University of Oxford, UK
Kate Saenko is an Assistant Professor at the Department of Computer Science at Boston University, and the director of the Computer Vision and Learning Group and member of the IVC Group. She also leads the new AI Research (AIR) initiative, which is housed at the Hariri Institute for Computing. She received her PhD from MIT. Previously, she was an Assistant Professor at the Department of Computer Science at UMass Lowell, a Postdoctoral Researcher at the International Computer Science Institute, a Visiting Scholar at UC Berkeley EECS and a Visiting Postdoctoral Fellow in the School of Engineering and Applied Science at Harvard University. Her research interests are in the broad area of Artificial Intelligence with a focus on Adaptive Machine Learning, Learning for Vision and Language Understanding, and Deep Learning.