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Category: information science – Page 91

A small team of chemists at the Russian Academy of Sciences, has found that metal atoms, not nanoparticles, play the key role in catalysts used in fine organic synthesis. In the study, reported in the Journal of the American Chemical Society, the group used multiple types of electron microscopy to track a region of a catalyst during a reaction to learn more about how it was proceeding.

Prior research has shown that there are two main methods for studying a reaction. The first is the most basic: As ingredients are added, the reaction is simply observed and/or measured. This can be facilitated through use of high-speed cameras. This approach will not work with nanoscale reactions, of course. In such cases, chemists use a second method: They attempt to capture the state of all the components before and after the reaction and then compare them to learn more about what happened.

This second approach leaves much to be desired, however, as there is no way to prove that the objects under study correspond with one another. In recent years, have been working on a new approach: Following the action of a single particle during the reaction. This new method has proven to have merit but it has limitations as well—it also cannot be used for reactions that occur in the nanoworld. In this new effort, the researchers used multiple types of electron microscopy coupled with .

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Researchers from the University of Aberdeen develop an AI algorithm to detect planetary craters with high accuracy, efficiency, and flexibility.

A team of scientists from the University of Aberdeen has developed a new algorithm that could revolutionize planetary studies. The new technology enables scientists to detect planetary craters and accurately map their surfaces using different data types, according to a release.

The team used a new universal crater detection algorithm (CDA) developed using the Segment Anything Model (SAM), an artificial intelligence (AI) model that can automatically identify and cut out any object in any image.

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Researchers have taught an AI to make artificial genomes — possibly overcoming the problem of how to protect people’s genetic information while also amassing enough DNA for research.

Generative adversarial networks (GANs) pit two neural networks against each other to produce new, synthetic data that is so good it can pass for real data. Examples have been popping up all over the web — generating pictures and videos (a la “this city does not exist”). AIs can even generate convincing news articles, food blogs, or human faces (take a look here for a complete list of all the oddities created by GANs).

Now, researchers from Estonia are going more in-depth with deepfakes of human DNA. They created an algorithm that repeatedly generates the genetic code of people that don’t exist.

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Advances in quantum computation for electronic structure, and particularly heuristic quantum algorithms, create an ongoing need to characterize the performance and limitations of these methods. Here we discuss some potential pitfalls connected with the use of hardware-efficient Ansätze in variational quantum simulations of electronic structure. We illustrate that hardware-efficient Ansätze may break Hamiltonian symmetries and yield nondifferentiable potential energy curves, in addition to the well-known difficulty of optimizing variational parameters. We discuss the interplay between these limitations by carrying out a comparative analysis of hardware-efficient Ansätze versus unitary coupled cluster and full configuration interaction, and of second-and first-quantization strategies to encode Fermionic degrees of freedom to qubits.

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People’s Liberation Army (PLA) researchers claim they have created algorithm-based technology to defeat sophisticated hypersonic missile interception systems.

Engineers led by Zhang Xuesong from China’s Strategic Support Force Information Engineering University developed the algorithm that analyzes the trajectory of hypersonic missiles in order to avoid detection by missile defense systems, South China Morning Post (SCMP) reported on Saturday.

The algorithm “can analyze the trajectory of these hypersonic weapons to help them avoid missile defense systems, including advanced systems under development” in the US, claimed the engineers in a paper published in the Chinese journal Common Control and Simulation last month.

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“The new capability of low thermal budget growth on an 8-inch scale enables the integration of this material with silicon CMOS technology and paves the way for its future electronics application.”

With our pockets and houses filling with electronic gadgets and AI and Big Data fueling the rise of data centers, there is a need for more computer chips— more powerful, potent, and denser than ever.

These chips are traditionally made with boxy 3D materials bulky in nature, making stacking these into layers difficult.

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Within a year, Karl Schwarzschild, who was “a lieutenant in the German army, by conscription, but a theoretical astronomer by profession,” as Mann puts it, heard of Einstein’s theory. He was the first person to work out a solution to Einstein’s equations, which showed that a singularity could form–and nothing, once it got too close, could move fast enough to escape a singularity’s pull.

Then, in 1939, physicists Rober Oppenheimer (of Manhattan Project fame, or infamy) and Hartland Snyder tried to find out whether a star could create Schwarzschild’s impossible-sounding object. They reasoned that given a big enough sphere of dust, gravity would cause the mass to collapse and form a singularity, which they showed with their calculations. But once World War II broke out, progress in this field stalled until the late 1950s, when people started trying to test Einstein’s theories again.

Physicist John Wheeler, thinking about the implications of a black hole, asked one of his grad students, Jacob Bekenstein, a question that stumped scientists in the late 1950s. As Mann paraphrased it: “What happens if you pour hot tea into a black hole?”

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