Lifeboat Foundation

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 qubit. 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.

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 eye-tracking technology and a new deep-learning AI algorithm 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.

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.

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

Why is Adam the most popular optimizer in Deep Learning? Let’s understand it by diving into its math, and recreating the algorithm.

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.

Continue reading “Scientists rule out a popular alternative theory to dark matter” »

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.

Scientists identified 18 new Tidal Disruption Events (TDEs), instances where a nearby black hole violently tears apart a neighboring star.

The powerful gravitational force of the black holes rips apart the star in its vicinity, resulting in a substantial release of energy across the entire electromagnetic spectrum.

The new catalog of TDEs was found by combing through the archival data of the satellite telescope NEOWISE. The team identified infrared patterns associated with these intense, transient bursts using a novel algorithm.

A new approach to solving arrays of two-dimensional differential equations may allow researchers to go beyond the one-dimensional oscillator paradigm.

A frictionless pendulum and a pendulum clock behave alike, but they belong to different worlds: Hamiltonian systems and dissipative systems, respectively. In the Hamiltonian world, completely integrable—that is, solvable—systems serve as a mathematical basis for dealing with more general cases that aren’t integrable. An analogous strategy doesn’t work for nonlinear non-Hamiltonian dissipative systems, however. In that case, the best researchers can achieve is partial integrability. Until recently, it was thought that an array of globally coupled oscillators could be partially integrable only if each oscillator has only one degree of freedom. Now Rok Cestnik and Erik Martens, both at Lund University in Sweden, report on a quasi-integrable system consisting of N two-dimensional oscillators described by ordinary differential equations (ODEs) [1].

Performance Factors Include Spike Geometry

This technology is perfectly suited to the spike plates in bobsleigh, which, until now, was essentially off-the-shelf. 3D printing opens up entirely new possibilities. Performance factors such as geometry – where exactly the spikes placed, the number of struts and teeth, and the weight can be efficiently varied. The spike plates can be printed quickly and inexpensively, tested by athletes until the optimal result is achieved. There is no longer a standard; the efficiency of the process allows for the production of individual plates for each athlete. The ongoing optimisations are expected to be completed by the 2026 Winter Olympics. The experts are also targeting the stiffness of the plates and, consequently, the shoes because not every athlete performs best with the same shoe stiffness.

Another milestone in this journey was reached this year. Various materials for 3D printing are now available for the spikes, tested by athletes. The use of special construction software is also new. It is utilised to optimise components for vehicles as well as equipment for BMW Group production systems in terms of weight and stiffness. This software also aids engineers at the BMW Group in designing the spike plates. It allows for the rapid, automated, and, above all, individually tailored creation of the respective 3D print data. The preferred parameters of each athlete – such as geometry, stiffness, number, and shape of spikes – are automatically incorporated into the design and adapted to the individual plates, based on 3D scans of the athletes’ shoes. This algorithmic design process results in significant time savings and maximum variability.