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Category: information science – Page 6

A new complexity in protein chemistry: Algorithm uncovers overlooked chemical linkages

Proteins are among the most studied molecules in biology, yet new research from the University of Göttingen shows they can still hold surprising secrets. Researchers have discovered previously undetected chemical bonds within archived protein structures, revealing an unexpected complexity in protein chemistry.

These newly identified nitrogen-oxygen-sulfur (NOS) linkages broaden our understanding of how proteins respond to , a condition where harmful oxygen-based molecules build up and can damage proteins, DNA, and other essential parts of the cell. The new findings are published in Communications Chemistry.

The research team systematically re-analyzed over 86,000 high-resolution protein structures from the Protein Data Bank, a global public repository of protein structures, using a new algorithm that they developed inhouse called SimplifiedBondfinder. This pipeline combines , quantum mechanical modeling, and structural refinement methods to reveal subtle that were missed by conventional analyses.

On the Qualia Problem of Perception and the Measurement Problem of Quantum Theory

The qualia problem of perception is simply pointing out that the way we perceive the world is in terms of subjective qualities rather than numerical quantities. For example, we perceive the color of light in the things we see rather than the frequency of light wave vibrations or wavelengths, just as we perceive the quality of the sounds we hear rather than the frequency of sound wave vibrations. Another example is emotional qualities, like the perception of pleasure and pain and the perception of other emotional qualities, like the emotional qualities that color the perception of the emotional body feelings we perceive with emotional expressions of fear and desire. There is no possible way to understand the perception of these emotional qualities, just as there is no way to understand the perception of the colors we see or the qualities of the sounds we hear, in terms of the neuronal firing rates of neurons in the brain or other nervous systems. The frequency of wave vibrations and the neuronal firing rates of neurons are both examples of quantities. The problem is we do not perceive things in terms of numerical quantities, but rather in terms of subjective qualities.

All our physical theories are formulated in terms of numerical quantities, not in terms of subjective qualities. For example, in ordinary quantum theory or in quantum field theory, we speak of the frequency of light wave vibrations or the wavelength of a light wave in terms of a quantum particle called the photon. A photon or light wave is characterized by the numerical quantities of frequency and wavelength. When we formulate the nature of a light wave or photon in quantum theory in terms of Maxwell’s equations for the electromagnetic field, we can only describe numerical quantities. In ordinary quantum theory and quantum field theory, the electromagnetic field is the quantum wave-function, ψ(x, t), that specifies the quantum probability that the point particle called the photon can be measured at a position x in space at a moment t in time. That quantum probability is specified in terms of the frequency and wavelength that characterizes the wave-function for the photon.

A comprehensive suite for extracting neuron signals across multiple sessions in one-photon calcium imaging

Neural data analysis algorithms capable of tracking neuronal signals from one-photon functional imaging data longitudinally and reliably are still lacking. Here authors developed CaliAli, a tool for extracting calcium signals across multiple days. Validated with optogenetic tagging, dual-color imaging, and place cell data, CaliAli demonstrated stable neuron tracking for up to 99 days.

«QUANTUM TELEPORTATION»: A New Technological Breakthrough

Physicists from Oxford have, for the first time, scaled quantum computing using distributed teleportation technology — and this could change everything. From «parallel universes» to Grover’s algorithm, from cryptography to molecular modeling — the world is entering an era where «impossible» problems

DeepMind’s AlphaEvolve AI: History In The Making!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers.

Guide for using DeepSeek on Lambda:
https://docs.lambdalabs.com/education/large-language-models/…dium=video.

📝 AlphaEvolve: https://deepmind.google/discover/blog/alphaevolve-a-gemini-p…lgorithms/
📝 My genetic algorithm for the Mona Lisa: https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/

📝 My paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD

Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Continue reading “DeepMind’s AlphaEvolve AI: History In The Making!” | >

Deepnight AI-Powered Night Vision: Revolutionizing Visibility in Complete Darkness

Deepnight’s Algorithm-intensified image enhancement for NIGHT VISION

Instead of using expensive image-intensification tubes, this startup is using ordinary low light sensors coupled with special computer algorithms to produce night vision. This will bring night vision to the general public. At present, even a generation 2 monocular costs around $2000, while a generation 3 device costs around $3500. The new system has the added advantage of being in color, instead of monochromatic. Hopefully, this will pan out, and change the situation for Astronomy enthusiasts worldwide.

Lucas Young, CEO of Deepnight, showcases how their AI technology transforms a standard camera into an affordable and effective night vision device in extremely dark environments.

AI Discovers Suspected Trigger of Alzheimer’s, And Maybe a Treatment

Artificial intelligence is a broad term encompassing many different subtypes, from apps that can write poetry to algorithms that are able to spot patterns that would otherwise get missed – and now AI modeling has just played a major role in an Alzheimer’s study.

Google DeepMind creates super-advanced AI that can invent new algorithms

The team turned AlphaEvolve loose on Google’s Borg cluster management system for its data centers. The AI suggested a change to the scheduling heuristics, which has been implemented to save Google 0.7 percent on its computing resources globally. For a company the size of Google, that’s a significant financial benefit.

AlphaEvolve may also be able to make generative AI more efficient, which is necessary if anyone is ever going to make money on the technology. The internal workings of generative systems are based on matrix multiplication operations. The most efficient way to multiply 4×4 complex-valued matrices was devised by mathematician Volker Strassen in 1969, and that held for decades, but DeepMind says AlphaEvolve has discovered a new algorithm that’s even more efficient. DeepMind has worked on this problem before with narrowly trained AI agents like AlphaTensor. Despite being a general AI, AlphaEvolve came up with a better solution than AlphaTensor.

Google’s next-generation Tensor processing hardware will also benefit from AlphaEvolve. DeepMind reports that the AI created a change to the chip’s Verilog hardware description language that dropped unnecessary bits to increase efficiency. Google is still working to verify the change but expects this to be part of the upcoming processor.

Superconductivity Inspires New Dark Matter Contender

As searches for the leading dark matter candidates—weakly interacting massive particles, axions, and primordial black holes—continue to deliver null results, the door opens on the exploration of more exotic alternatives. Guanming Liang and Robert Caldwell of Dartmouth College in New Hampshire have now proposed a dark matter candidate that is analogous with a superconducting state [1]. Their proposal involves interacting fermions that could exist in a condensate similar to that formed by Cooper pairs in the Bardeen-Cooper-Schrieffer theory of superconductivity.

The novel fermions considered by Liang and Caldwell emerge in the Nambu–Jona-Lasinio model, which can be regarded as a low-energy approximation of the quantum chromodynamics theory that describes the strong interaction. The duo considers a scenario where, in the early Universe, the fermions behave like radiation, reaching thermal equilibrium with standard photons. As the Universe expands and the temperature drops below a certain threshold, however, the fermions undergo a phase transition that leads them to pair up and form a massive condensate.

The proposed scenario has several appealing features, say Liang and Caldwell. The fermions’ behavior would be consistent with that of the cold dark matter considered by the current standard model of cosmology. Further, the scenario implies a slight imbalance between fermions with different chiralities (left-and right-handed). Such an imbalance might be related to the yet-to-be-explained matter–antimatter asymmetry seen in the Universe. What’s more, the model predicts that the fermions obey a time-dependent equation of state that would produce unique, potentially observable signatures in the cosmic microwave background (CMB) radiation. The researchers suggest that next-generation CMB measurements—by the Simons Observatory and by so-called stage 4 CMB telescopes—might reach sufficient precision to vet their idea.