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Hello and welcome! My name is Anton and in this video, we will talk about another Fermi Paradox hypothesis: The Dark Forest.
Fermi Paradox playlist: https://www.youtube.com/playlist?list=PL9hNFus3sjE7lB0sJRnGLWng0uIPxUVqG
Images/Videos:
Henry Söderlund-CC BY 4.0 https://en.wikipedia.org/wiki/Liu_Cixin#/media/File: Cixin_Liu_at_Worldcon_75,_Helsinki,_before_the_Hugo_Awards.jpg.
Midjourney CC BY SA 4.0 https://midjourney.com/
Davidguam CC BY-SA 4.0 https://en.wikipedia.org/wiki/Hunt–Lenox_Globe#/media/File: Anfuorin.png.
#darkforest #fermiparadox #aliens.

Continue reading “But Where Is Everybody? Fermi Paradox Explored: The Dark Forest Hypothesis” »

Leading scientists in the field predict that lithium niobate chips, which are extremely thin, will surpass silicon chips in light-based technologies. These chips have a wide range of potential applications, from detecting ripe fruit from a distance on Earth to guiding navigation on the Moon.

According to the scientists, the artificial crystal of lithium niobate is the preferred platform for these technologies because of its superior performance and advancements in manufacturing techniques.

RMIT University’s Distinguished Professor Arnan Mitchell and University of Adelaide’s Dr. Andy Boes led this team of global experts to review lithium niobate’s capabilities and potential applications in the journal Science.

Summary: Researchers have identified the in-vivo dynamics of synapses that underlie fear memory formation and extinction in the living brain.

Source: Seoul National University.

Ensembles of synaptic networks are known to underlie cognitive functions, and the connections between engram neurons are enhanced during memory formation.

Link to everywhere from your bio link.

https://youtube.com/watch?v=JfQWe–kciM

This is the second novel in “Remembrance of Earth’s Past”, the near-future trilogy written by China’s multiple-award-winning science fiction author, Cixin Liu.

In The Dark Forest, Earth is reeling from the revelation of a coming alien invasion — four centuries in the future. The aliens’ human collaborators have been defeated but the presence of the sophons, the subatomic particles that allow Trisolaris instant access to all human information, means that Earth’s defense plans are exposed to the enemy. Only the human mind remains a secret.

Continue reading “The Dark Forest by Liu Cixin (Part 3) — Science Fiction Audiobook” »

Two neutron stars collided which caused a huge explosion but with an unusually flawless form, baffling scientists. Usually, a collision between neutron stars would lead to a flattened cloud but the recently observed explosion formed a perfectly spherical shape, SpaceAcademy.org reports.

It is still unclear how this is possible, but a new study may shed light on the fundamental physics involved and help scientists calculate the universe’s age. Astrophysicists from the Universe of Copenhagen discovered the kilonova and described it in full in their study, titled “Spherical Symmetry in the Kilonova At2017gfo/GW170817,” which was published in the journal Nature.

https://youtube.com/watch?v=0zOL6-u5GJo

This is the second novel in “Remembrance of Earth’s Past”, the near-future trilogy written by China’s multiple-award-winning science fiction author, Cixin Liu.

In The Dark Forest, Earth is reeling from the revelation of a coming alien invasion — four centuries in the future. The aliens’ human collaborators have been defeated but the presence of the sophons, the subatomic particles that allow Trisolaris instant access to all human information, means that Earth’s defense plans are exposed to the enemy. Only the human mind remains a secret.

Continue reading “The Dark Forest by Liu Cixin (Part 2) — Science Fiction Audiobook” »

https://youtube.com/watch?v=Zc7ysvsa6U0

This is the second novel in “Remembrance of Earth’s Past”, the near-future trilogy written by China’s multiple-award-winning science fiction author, Cixin Liu.

In The Dark Forest, Earth is reeling from the revelation of a coming alien invasion — four centuries in the future. The aliens’ human collaborators have been defeated but the presence of the sophons, the subatomic particles that allow Trisolaris instant access to all human information, means that Earth’s defense plans are exposed to the enemy. Only the human mind remains a secret.

Continue reading “The Dark Forest by Liu Cixin (Part 1) — Science Fiction Audiobook” »

Human beings are capable of processing several sound sources at once, both in terms of musical composition or synthesis and analysis, i.e., source separation. In other words, human brains can separate individual sound sources from a mixture and vice versa, i.e., synthesize several sound sources to form a coherent combination. When it comes to mathematically expressing this knowledge, researchers use the joint probability density of sources. For instance, musical mixtures have a context such that the joint probability density of sources does not factorize into the product of individual sources.

A deep learning model that can synthesize many sources into a coherent mixture and separate the individual sources from a mixture does not exist currently. When it comes to musical composition or generation tasks, models directly learn the distribution over the mixtures, offering accurate modeling of the mixture but losing all knowledge of the individual sources. Models for source separation, in contrast, learn a single model for each source distribution and condition on the mixture at inference time. Thus, all the crucial details regarding the interdependence of the sources are lost. It is difficult to generate mixtures in either scenario.

Taking a step towards building a deep learning model that is capable of performing both source separation and music generation, researchers from the GLADIA Research Lab, University of Rome, have developed Multi-Source Diffusion Model (MSDM). The model is trained using the joint probability density of sources sharing a context, referred to as the prior distribution. The generation task is carried out by sampling using the prior, whereas the separation task is carried out by conditioning the prior distribution on the mixture and then sampling from the resulting posterior distribution. This approach is a significant first step towards universal audio models because it is a first-of-its-kind model that is capable of performing both generation and separation tasks.