Lifeboat Foundation

All memory storage devices, from your brain to the RAM in your computer, store information by changing their physical qualities. Over 130 years ago, pioneering neuroscientist Santiago Ramón y Cajal first suggested that the brain stores information by rearranging the connections, or synapses, between neurons.

Since then, neuroscientists have attempted to understand the physical changes associated with memory formation. But visualizing and mapping synapses is challenging to do. For one, synapses are very small and tightly packed together. They’re roughly 10 billion times smaller than the smallest object a standard clinical MRI can visualize. Furthermore, there are approximately 1 billion synapses in the mouse brains researchers often use to study brain function, and they’re all the same opaque to translucent color as the tissue surrounding them.

Continue reading “New brain imaging technique suggests memories are stored in the connections between your neurons” »

The NASA TESS mission hit a milestone of 5,000 exoplanet candidates or TOIs (TESS Object of Interest). The TESS catalog has been growing steadily since the start of the mission in 2018, and the batch of TOIs boosting the catalog to over 5,000 comes primarily from the Faint Star Search led by MIT postdoc Michelle Kunimoto. Now in its extended mission, TESS is observing the Northern Hemisphere and ecliptic plane, including regions of the sky previously observed by the Kepler and K2 missions, so we can expect more discoveries until 2025.

To discuss this achievement, SETI Institute Senior Astronomer Franck Marchis is joined by Dr. Kunimoto, TESS postdoctoral associate at MIT Kavli Institute. Dr. Kunimoto focuses her work on detecting transiting exoplanets and the statistical determination of exoplanet demographics. She will tell us how astronomers worldwide will study each of these TOIs to confirm whether they are bonafide planets and what we can expect from this complicated task.

Continue reading “SETI Live: 5,000 Exoplanet Candidates and Counting!” »

Dozens of Chinese jets entered Taiwan’s air defense zone, the island’s officials said. Taiwan responded by scrambling its own fighter jets and activating missile defense systems.

The algorithms spot and classify synthetic-material objects based on the distinctive manner in which they reflect polarized light. Polarized light reflected from human-made objects often differs from natural objects, such as vegetation, soil, and rocks.

The researchers tested such a camera, both on the ground and from a US Coast Guard helicopter, which was flying at the altitude at which the polarimetric-camera-equipped drones will fly.

Once fully operational, data collected by the drone-based machine learning system will be used to make maps that show where marine debris is concentrated along the coast to guide rapid response and removal efforts. The researchers will provide NOAA Marine Debris Program staff with training in the use of the new system, along with standard operating procedures manual.

Farmers struggling to adapt to rising temperatures in tropical regions can unleash the benefits of natural cooling, alongside a host of other wins, simply by dotting more trees across their pasturelands. For the first time, a study led by the University of Washington puts tangible numbers to the cooling effects of this practice.

Researchers at the UW and The Nature Conservancy, along with Duke University, the University of California San Diego and Stony Brook University Hospital, find that adding trees to pastureland, technically known as silvopasture, can cool local temperatures by up to 2.4 C (4.3 F) for every 10 metric tons of woody material added per hectare (about 4 tons per acre) depending on the density of trees, while also delivering a range of other benefits for humans and wildlife.

The paper was published Feb. 4 in Nature Communications.

Quantum computing and machine learning are two of the most exciting technologies that can transform businesses. We can only imagine how powerful it can be if we can combine the power of both of these technologies. When we can integrate quantum algorithms in programs based on machine learning, that is called quantum machine learning. This fascinating area has been a major area of tech firms, and they have brought out tools and platforms to deploy such algorithms effectively. Some of these include TensorFlow Quantum from Google, Quantum Machine Learning (QML) library from Microsoft, QC Ware Forge built on Amazon Braket, etc.

Students skilled in working with quantum machine learning algorithms can be in great demand due to the opportunities the field holds. Let us have a look at a few online courses one can use to learn quantum machine learning.

In this course, the students will start with quantum computing and quantum machine learning basics. The course will also cover topics on building Qnodes and Customised Templates. It also teaches students to calculate Autograd and Loss Function with quantum computing using Pennylane and to develop with the Pennylane.ai API. The students will also learn how to build their own Pennylane Plugin and turn Quantum Nodes into Tensorflow Keras Layers.

Could a laser send a spacecraft to Mars? That’s a proposed mission from a group at McGill University, designed to meet a solicitation from NASA. The laser, a 10-meter wide array on Earth, would heat hydrogen plasma in a chamber behind the spacecraft, producing thrust from hydrogen gas and sending it to Mars in only 45 days. There, it would aerobrake in Mars’ atmosphere, shuttling supplies to human colonists or, someday perhaps, even humans themselves.

Transistors based on semiconductor materials are widely used electronic components with many remarkable properties. For instance, they have a nonreciprocal electrical response, which means that they can isolate two parts of a circuit in such a way that one of the parts (the input section) can influence the other part (the output section), but not the other way around. In addition, transistors can amplify voltage signals, and thereby can supply energy to a system. Non-energy conserving interactions are usually referred to as “non-Hermitian.”

Researchers from Instituto de Telecomunicações at the University of Coimbra and University of Lisbon have recently introduced a new class of bulk materials that draws inspiration from the non-reciprocal and non-Hermitian responses of conventional semiconductor-based transistors. They presented these transistor-like three-dimensional (3D) bulk metamaterials in a paper published in Physical Review Letters.

Mário Silveirinha, one of the researchers who carried out the study, told Phys.org, “The ideas developed in our paper were mostly driven by the question: Would it be possible to somehow imitate the response of standard transistors in a bulk metamaterial? We were intrigued if it would be feasible to have a bulk material which, when suitably biased, could manipulate electromagnetic waves in the same way as a transistor manipulates a voltage signal.”

Can you crumple up two sheets of paper the exact same way? Probably not—the very flexibility that lets flexible structures from paper to biopolymers and membranes undergo many types of large deformations makes them notoriously difficult to control. Researchers from the Georgia Institute of Technology, Universiteit van Amsterdam, and Universiteit Leiden have shed new light on this fundamental challenge, demonstrating that new physical theories provide precise predictions of the deformations of certain structures, as recently published in Nature Communications.

In the paper, Michael Czajkowski and D. Zeb Rocklin from Georgia Tech, Corentin Coulais from Universiteit van Amsterdam, and Martin van Hecke of AMOLF and Universiteit Leiden approach a highly studied exotic elastic material, uncover an intuitive geometrical description of the pronounced—or nonlinear—soft deformations, and show how to activate any of these deformations on-demand with minimal inputs. This new theory reveals that a flexible mechanical structure is governed by some of the same math as electromagnetic waves, phase transitions, and even black holes.

“So many other systems struggle with how to be strong and solid in some ways but flexible and compliant in others, from the human body and micro-organisms to clothing and industrial robots,” said Rocklin. “These structures solve that problem in an incredibly elegant way that permits a single folding mechanism to generate a wide family of deformations. We’ve shown that a single folding mode can transform a structure into an infinite family of shapes.”