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Category: information science – Page 55

Tokamak fusion plasmas benefit from high pressures but are then susceptible to modes of instability. These magnetohydrodynamic (MHD) modes are macroscopic distortions of the plasma, but certain collective motions of individual particles can provide stabilizing effects opposing them. The presence of a resistive wall slows the mode growth, converting a kink to a resistive wall mode (RWM). A kinetic MHD model includes Maxwell’s equations, ideal MHD constraints, and kinetic effects included through the pressure tensor, calculated with the perturbed drift-kinetic distribution function of the particles. The kinetic stabilizing effects on the RWM arise through resonances between the plasma rotation and particle drift motions: precession, bounce, and transit. A match between particle motions and the mode allows efficient transfer of energy that would otherwise drive the growth of the mode, thus damping the growth. The first approach to calculating RWM stability is to write a set of equations for the complex mode frequency in terms of known quantities and then to solve the system. The “energy principle” approach, which has the advantage of clarity in distinguishing the various stabilizing and destabilizing effects, is to change the force balance equation into an equation in terms of changes of kinetic and potential energies, and then to write a dispersion relation for the mode frequency in terms of those quantities. These methods have been used in various benchmarked codes to calculate kinetic effects on RWM stability. The theory has illuminated the important roles of plasma rotation, energetic particles, and collisions in RWM stability.

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Research conducted by a team of scientists from Kaunas universities, Lithuania, revealed that low-frequency ultrasound influences blood parameters. The findings suggest that ultrasound’s effect on haemoglobin can improve oxygen’s transfer from the lungs to bodily tissues.

The research was undertaken on 300 blood samples collected from 42 pulmonary patients. The samples were exposed to six different low-frequency ultrasound modes at the Institute of Mechatronics of Kaunas University of Technology (KTU).

The changes in 20 blood parameters were registered using the blood analysing equipment at the Lithuanian University of Health Sciences (LSMU) laboratories. For the prediction of ultrasound exposure, artificial intelligence, i.e. analysis of variance (ANOVA), non-parametric Kruskal-Wallis method and machine learning algorithms were applied. The calculations were made at the KTU Artificial Intelligence Centre.

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Mastercard has announced that it has developed an in-house generative AI to help combat fraud on its payment processing network.

Instead of relying on textual inputs, Mastercard’s algorithm uses a cardholder’s merchant visit history as a prompt to determine whether a transaction involves a business that the customer would likely visit. The algorithm generates pathways through Mastercard’s network, akin to heat-sensing radar, to provide a score as an answer.

A lower score indicates a behavior that deviates from the cardholder’s usual pattern, while a higher score reflects typical behavior. Mastercard claims that this entire process takes only 50 milliseconds. And, it turns out, the AI appears to be very good at its job.

Continue reading “Mastercard’s new anti-fraud AI has a success rate of up to 300%” | >

There has been significant progress in the field of quantum computing. Big global players, such as Google and IBM, are already offering cloud-based quantum computing services. However, quantum computers cannot yet help with problems that occur when standard computers reach the limits of their capacities because the availability of qubits or quantum bits, i.e., the basic units of quantum information, is still insufficient.

One of the reasons for this is that bare qubits are not of immediate use for running a quantum algorithm. While the binary bits of customary computers store information in the form of fixed values of either 0 or 1, qubits can represent 0 and 1 at one and the same time, bringing probability as to their value into play. This is known as quantum superposition.

This makes them very susceptible to external influences, which means that the information they store can readily be lost. In order to ensure that quantum computers supply reliable results, it is necessary to generate a genuine entanglement to join together several physical qubits to form a logical . Should one of these physical qubits fail, the other qubits will retain the information. However, one of the main difficulties preventing the development of functional quantum computers is the large number of physical qubits required.

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Window to the soul? Maybe, but the eyes are also a flashing neon sign for a new artificial intelligence-based system that can read them to predict what you’ll do next.

A University of Maryland researcher and two colleagues have used and a new deep-learning AI to predict study participants’ choices while they viewed a comparison website with rows and columns of products and their features.

The algorithm, known as RETINA (Raw Eye Tracking and Image Ncoder Architecture), could accurately zero in on selections before people had even made their decisions.

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Researchers mapped over 10,000 mouse hippocampal #neurons, creating the world’s most comprehensive database of single-neuron #connectivity #patterns.

Summary: Researchers unveiled the most extensive single-neuron projectome database to date, featuring over 10,000 mouse hippocampal neurons.

The study provides an unprecedented view of the spatial connectivity patterns at the mesoscopic level, crucial for understanding learning, memory, and emotional processing in the hippocampus. By employing machine learning algorithms for categorizing axonal trajectories and integrating spatial transcriptome data, researchers identified 43 distinct projectome cell types, revealing intricate projection patterns and soma locations’ correspondence to projection targets.

This work, accessible via the Digital Brain CEBSIT portal, lays the structural foundation for advancing our knowledge of hippocampal functions and their molecular underpinnings.

Continue reading “Mapping the Brain: The Largest Neuron Projectome Unveiled” | >

A consensus has arisen in the astronomical community that familiar matter made of atoms is not the dominant form of matter in the Universe. Instead, an invisible form of matter, called dark matter, is thought to be far more prevalent. However, a small group of researchers deny the existence of dark matter, instead saying our understanding of how objects move is incomplete. A recent paper in the Monthly Notices of the Royal Astronomical Society seems to have ruled this out definitively.

Stars, planets, and galaxies move under the direction of the force of gravity, and Isaac Newton worked out the laws that govern that motion, which we now call Newtonian dynamics. However, despite the enormous success of Newtonian dynamics, this success is not universal. Indeed, when Newton’s equations are applied to certain astronomical phenomena, they do not make the correct predictions. One such example is the speed at which galaxies rotate. When astronomers measure the speed of stars in the periphery of a galaxy, they move faster than can be explained by accepted theory. Instead, the galaxies should fly apart.

The solution to this mystery favored by most scientists is that beyond the familiar stars and clouds of gas, our galaxy also hosts a large amount of invisible matter, called dark matter. This dark matter adds to the gravitational force holding the galaxy together. Thus, the evidence for dark matter is indirect. It has never been observed in the laboratory; yet its ability to explain the motion of galaxies is strong circumstantial evidence that it exists.

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In the world of quantum computing, the spotlight often lands on the hardware: qubits, superconducting circuits, and the like. But it’s time to shift our focus to the unsung hero of this tale – the quantum software, the silent maestro orchestrating the symphony of qubits. From turning abstract quantum algorithms into executable code to optimizing circuit designs, quantum software plays a pivotal role.

Here, we’ll explore the foundations of quantum programming, draw comparisons to classical computing, delve into the role of quantum languages, and forecast the transformational impact of this nascent technology. Welcome to a beginner’s guide to quantum software – a journey to the heart of quantum computing.

Quantum vs. Classical Programming: The Core Differences.

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