Lifeboat Foundation

A research team led by the late Professor Liang Haojun from the Hefei National Laboratory for Physical Sciences at the Microscale of University of Science and Technology of China (USTC) has developed a facile enthalpy-mediated strategy to precisely control the replication and catalytic assembly of DNA-functionalized colloids in a time-dependent manner, facilitating the creation of large-scale ordered nanomaterials. The study was published in Angewandte Chemie International Edition.

The replication of information is a fundamental characteristic of nature, with nucleic acids playing a crucial role in biological systems. However, creating synthetic systems that can produce large-scale, three-dimensionally ordered nanomaterials using self-replicating nanostructures has remained a formidable challenge.

Existing artificial self-replicating systems often fall short in programmable assembly into sophisticated nanostructures, limiting their potential functions and applications.

What is the philosophy of information.

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The elegant equations of classical electromagnetism written by James Clark Maxwell in 1861 display a remarkable symmetry between electric and magnetic fields except for their sources. We know about electric charges but we have not found magnetic charges. Bar magnets are dipoles with two poles, north and south, for the magnetic field, resembling the configuration of an electric field sourced by a pair of positive and negative electric charges. However, we had never seen experimental evidence for a magnetic monopole, namely a magnetic charge with only one magnetic pole, a net north or south, from where magnetic field lines emanate, just like the electric field sourced by an electric charge. In a symmetric theory of electromagnetism, magnetic monopoles should exist.

The existence of monopoles with a net magnetic charge was proposed by Paul Dirac in 1931 to explain the quantized (discrete) values of electric charges. Dirac found that magnetic charges should be an integer multiple of a fundamental unit, g_D, equal to the electron charge, e, divided by twice the fine-structure constant, or about 68.5e.

In classical physics, the existence of magnetic monopoles restores symmetry to Maxwell’s equations. But in the broader context of quantum mechanics, Gerard ‘t Hooft and Alexander Polyakov showed in 1974 that magnetic monopoles are required in Grand Unified Theories of the strong, weak and electromagnetic interactions. Since the electric charge is quantized, magnetic charges are unavoidable in these theories. Magnetic charges with the lowest mass must be stable because magnetic charge is conserved and they cannot decay into lower-mass particles.

Why we should reposition how we think of the mind.

Recently I saw a post on twitter claiming that AI could be powered with quantum vacuum energy. The post was accompanied by a figure from a paper published in Nature. Unfortunately for the poster, but fortunately for science, the paper had nothing to do with extracting energy from the vacuum. Rather, it was a description of an experimental realization of a transistor that uses the Casimir effect to mediate and amplify energy transfer across a new kind of transistor.

The report features six studies that thoroughly examined MIT’s superconducting magnets design used in its breakthrough fusion test.

An archaeologists has been able to find forgotten structures hidden in Ireland that were probably used as pathways for the dead.

Using Olympics for pushing food agenda, I wonder how that food will influence sport results?

With 60% of the food served to the public being meat-free and 80% sourced locally, the Paris Olympic Games are setting a new standard for environmental sustainability.

1/ Researchers have found that AI models can solve complex tasks like “3SUM” by using simple dots like “…” instead of sentences.

Researchers have found that specifically trained LLMs can solve complex problems just as well using dots like “…” instead of full sentences. This could make it harder to control what’s happening in these models.

The researchers trained Llama language models to solve a difficult math problem called “3SUM”, where the model has to find three numbers that add up to zero.

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