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Associate Professor of the Department of Information Technologies and Computer Sciences at MISIS University, Ph.D., mathematician and doctor Alexandra Bernadotte has developed algorithms that significantly increase the accuracy of recognition of mental commands by robotic devices. The result is achieved by optimizing the selection of a dictionary. Algorithms implemented in robotic devices can be used to transmit information through noisy communication channels. The results have been published in the peer-reviewed international scientific journal Mathematics.

The task of improving the object (audio, video or electromagnetic signals) classification accuracy, when compiling so-called “dictionaries” of devices is faced by developers of different systems aimed to improve the quality of human life.

The simplest example is a voice assistant. Audio or video transmission devices for remote control of an object in the line-of-sight zone use a limited set of commands. At the same time, it is important that the commands classifier based on the accurately understands and does not confuse the commands included in the device dictionary. It also means that the recognition accuracy should not fall below a certain value in the presence of extraneous noise.

An artificial intelligence system from Google’s sibling company DeepMind stumbled on a new way to solve a foundational math problem at the heart of modern computing, a new study finds. A modification of the company’s game engine AlphaZero (famously used to defeat chess grandmasters and legends in the game of Go) outperformed an algorithm that had not been improved on for more than 50 years, researchers say.

The new research focused on multiplying grids of numbers known as matrices. Matrix multiplication is an operation key to many computational tasks, such as processing images, recognizing speech commands, training neural networks, running simulations to predict the weather, and compressing data for sharing on the Internet.

When you cut yourself, a mass migration begins inside your body: Skin cells flood by the thousands toward the site of the wound, where they will soon lay down fresh layers of protective tissue.

In a new study, researchers from the University of Colorado Boulder have taken an important step toward unraveling the drivers behind this collective behavior. The team has developed an equation learning technique that might one day help scientists grasp how the body rebuilds skin, and could potentially inspire new therapies to accelerate wound healing.

“Learning the rules for how respond to the proximity and relative motion of other is critical to understanding why cells migrate into a wound,” said David Bortz, professor of applied mathematics at CU Boulder and senior author of the new study.

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 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 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.

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