Could this be the reason why we haven’t spotted them yet?
Believers in the Drake Equation may have found just the right explanation for why alien civilizations haven’t been spotted by humanity yet. A new study published by U.S.-based researchers states that alien civilizations are likely looking for particular types of stars when trying to establish an intra-galactic base, and our Sun simply does not meet their criterion, Universe Today.
SETI does not make sense
Years later, Hart published a detailed paper further analyzing the Paradox wherein he stated that civilizations could rapidly expand through a galaxy by sending out ships to the nearest 100 stars who would then repeat the process, enabling galaxy-wide expansion in a short period of time.
According to hart’s calculations, our galaxy could be traversed in just 650,000 years, and an advanced civilization would have made contact with humanity by now. Since there haven’t been any, Hart concluded there are no alien civilizations out there, and therefore, missions like Search for Extra-Terrestrial Intelligence (SETI) do not make sense.
Researchers at the University of Texas at Austin have developed a decoder that uses information from fMRI scans to reconstruct human thoughts. Jerry Tang, Amanda LeBel, Shailee Jain and Alexander Huth have published a paper describing their work on the preprint server bioRxiv.
Prior efforts to create technology that can monitor brain waves and decode them to reconstruct a person’s thoughts have all consisted of probes placed in the brains of willing patients. And while such technology has proven useful for research efforts, it is not practical for use in other applications such as helping people who have lost the ability to speak. In this new effort, the researchers have expanded on work from prior studies by applying findings about reading and interpreting brain waves to data obtained from fMRI scans.
Recognizing that attempting to reconstruct brainwaves into individual words using fMRI was impractical, the researchers designed a decoding device that sought to gain an overall understanding of what was going on in the mind rather than a word-for-word decoding. The decoder they built was a computer algorithm that accepted fMRI data and returned paragraphs describing general thoughts. To train their algorithm, the researchers asked two men and one woman to lie in an fMRI machine while they listened to podcasts and recordings of people telling stories.
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Particle physicists have taught algorithms to solve previously unsolvable issues.
Hey everyone! I upgraded a previous redstone build to support 3D Wireframe Rendering! Thanks everyone who suggested this, it was a lot of fun! bigsmile
!!! WATCH PART 1 HERE!!!
https://youtu.be/vfPGuUDuwmo.
0:00 Introduction.
1:00 Defining a Wireframe.
1:36 Building UI and Vertex memory.
3:31 Deriving the Rendering Equations.
8:15 Python Simulator.
9:09 Building the Renderer.
13:32 First successful render!
14:34 Python Schematic Generator.
16:02 Building the Frame Buffer.
17:25 Rotation time!
21:21 Vertex Rotator.
23:06 Final Assembly.
23:49 Showcase.
Big thank you to @Sloimay for miscellaneous help, and of course for writing MCSchematic.
MCSchematic Python Package — https://pypi.org/project/mcschematic/
3Blue1Brown’s Linear Algebra Series — https://www.youtube.com/playlist?list=PL0-GT3co4r2y2YErbmuJw2L5tW4Ew2O5B
If you’ve ever played the claw game at an arcade, you know how hard it is to grab and hold onto objects using robotics grippers. Imagine how much more nerve-wracking that game would be if, instead of plush stuffed animals, you were trying to grab a fragile piece of endangered coral or a priceless artifact from a sunken ship.
Most of today’s robotic grippers rely on embedded sensors, complex feedback loops, or advanced machine learning algorithms, combined with the skill of the operator, to grasp fragile or irregularly shaped objects. But researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have demonstrated an easier way.
Taking inspiration from nature, they designed a new type of soft, robotic gripper that uses a collection of thin tentacles to entangle and ensnare objects, similar to how jellyfish collect stunned prey. Alone, individual tentacles, or filaments, are weak. But together, the collection of filaments can grasp and securely hold heavy and oddly shaped objects. The gripper relies on simple inflation to wrap around objects and doesn’t require sensing, planning, or feedback control.
Researchers can now predict exactly how soap molecules spread across a body of water, an everyday but surprisingly complex process.
When a tiny drop of soapy water falls onto a pool of liquid, its contents spread out over the pool’s surface. The dynamics of this spreading depend on the local concentration of soap—which varies in time and is difficult to predict—at each point across the entire pool’s surface. Now Thomas Bickel of the University of Bordeaux in Talence, France, and Francois Detcheverry of the University of Lyon, France, have derived an exact time-dependent solution for these distributions [1]. The solution reveals surprisingly rich behaviors in this everyday phenomenon.
The duo considered a surfactant-laden drop spreading over the surface of a deep pool of fluid. Researchers have previously shown that the equations governing the transport of the surfactant particles can be mapped to a partial differential equation known as the Burgers’ equation, which was initially developed to describe flows in turbulent fluids.
A Brazilian study published in Scientific Reports shows that artificial intelligence (AI) can be used to create efficient models for genomic selection of sugarcane and forage grass varieties and predict their performance in the field on the basis of their DNA.
In terms of accuracy compared with traditional breeding techniques, the proposed methodology improved predictive power by more than 50%. This is the first time a highly efficient genomic selection method based on machine learning has been proposed for polyploid plants (in which cells have more than two complete sets of chromosomes), including the grasses studied.
Machine learning is a branch of AI and computer science involving statistics and optimization, with countless applications. Its main goal is to create algorithms that automatically extract patterns from datasets. It can be used to predict the performance of a plant, including whether it will be resistant to or tolerant of biotic stresses such as pests and diseases caused by insects, nematodes, fungi or bacteria, and or abiotic stresses such as cold, drought, salinity or insufficient soil nutrients.
In quantum physics, Fermi’s golden rule, also known as the golden rule of time-dependent perturbation theory, is a formula that can be used to calculate the rate at which an initial quantum state transitions into a final state, which is composed of a continuum of states (a so-called “bath”). This valuable equation has been applied to numerous physics problems, particularly those for which it is important to consider how systems respond to imposed perturbations and settle into stationary states over time.
Fermi’s golden rule specifically applies to instances in which an initial quantum state is weakly coupled to a continuum of other final states, which overlap its energy. Researchers at the Centro Brasileiro de Pesquisas Físicas, Princeton University, and Universität zu Köln have recently set out to investigate what happens when a quantum state is instead coupled to a set of discrete final states with a nonzero mean level spacing, as observed in recent many-body physics studies.
“The decay of a quantum state into some continuum of final states (i.e., a ‘bath’) is commonly associated with incoherent decay processes, as described by Fermi’s golden rule,” Tobias Micklitz, one of the researchers who carried out the study, told Phys.org. “A standard example for this is an excited atom emitting a photon into an infinite vacuum. Current date experimentations, on the other hand, routinely realize composite systems involving quantum states coupled to effectively finite size reservoirs that are composed of discrete sets of final states, rather than a continuum.”