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A London-based artist named Matt DesLauriers has developed a tool to generate color palettes from any text prompt, allowing someone to type in “beautiful sunset” and get a series of colors that matches a typical sunset scene, for example. Or you could get more abstract, finding colors that match “a sad and rainy Tuesday.”

DesLauriers has posted his code on GitHub; it requires a local Stable Diffusion installation and Node. JS. It’s a rough prototype at the moment that requires some technical skill to set up, but it’s also a noteworthy example of the unexpected graphical innovations that can come from open source releases of powerful image synthesis models. Stable Diffusion, which went open source on August 22, generates images from a neural network that has been trained on tens of millions of images pulled from the Internet.

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Related: The feds are coming for the metaverse, from Axie Infinity to Bored Apes

That isn’t to say we’ve yet reached a decentralized utopia. Though decentralized systems are also ostensibly “trustless” systems, it is ironically trust that still must be built up in these systems for both developers and users. Whatever the disadvantages of relying on companies like Amazon, Google, Microsoft and Apple, they have banked decades’ worth of that trust, credibility and familiarity that makes it difficult for both developers and users to switch to an entirely new way of doing things.

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Self-organizing lumps of human brain tissue grown in the laboratory have been successfully transplanted into the nervous systems of newborn rats in a step towards finding new ways to treat neuropsychiatric disorders.

The 3D organoids, developed from stem cells to resemble a simplified model of the human cortex, connected and integrated with the surrounding tissue in each rat’s cortex to form a functional part of the rodent’s own brain, displaying activity related to sensory perception.

This, according to a team of researchers led by neuroscientist Sergiu Pașca of Stanford University, overcomes the limitations of dish-grown organoids, and gives us a new platform for modeling human brain development and disease in a living system.

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The best examples are simple. This is especially true in quantum computing, where complexity can get out of hand pretty fast. A team of researchers at D-Wave, with collaborators from USC, Tokyo Tech, and Saitama Medical University, recently explored a quantum phase transition — a complex subject by anyone’s standards — in a very simple 1D chain of magnetic spins. Our work, published today in Nature Physics, studies quantum critical dynamics in a coherently annealed Ising chain. Here are a few things we learned along the way.

Programmable quantum phase transitions, as ordered

Phase transitions, such as water to ice, are commonly attributed to changes in temperature. But there is another type of phase transition —-a quantum phase transition (QPT) —-where quantum effects determine the properties of a physical system, in the absence of thermal effects. In a 1D chain, spins at the end of the simulation are either “up” or “down”, and we get “kinks” separating blocks of up spins and down spins (during the simulation, spins can be in a superposition of up and down). The density and spacing of kinks depend on, among other things, the speed and “quantumness” of the experiment. In this work we guided the programmable system of spins through a QPT and investigated the effect of varying parameters such as speed, system size, and temperature.

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Cancer has the terrifying ability to spread from any part of the body to another – and it’s part of what has always made these debilitating diseases so deadly. This process, known as metastasis, has always baffled scientists. Now, though, a new study may have pointed researchers in the right direction to help them understand how cancer spreads, which could also lead to new treatment options in the future.

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In The Analysis of Matter (1927) Bertrand Russell defended a couple of theses that amounted to a novel approach to the mind-body problem. Similar claims were defended by Eddington in his Gifford lectures of the same year. This approach was forgotten about in the latter half of the twentieth century, perhaps because it didn’t fit with the physicalist predilections of the period. However, it has recently been rediscovered, leading to a view – or better a school of views – known as ‘Russellian monism.’ Russellian monism is increasingly seen as a promising middle way between dualism and physicalism, avoiding the problems associated with either of these extremes. In this lecture, I explain the basic idea.

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