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An Accordion Lattice Playing a Soliton Tune

Decades after their experimental realization, wave patterns known as discrete solitons continue to fascinate.

Localized wave patterns in a lattice or other periodic media have been observed using arrays of coupled torsion pendula, chains of Josephson junctions, and arrays of optical waveguides. Joining this diverse repertoire is a recent experiment by Robbie Cruickshank of the University of Strathclyde in the UK and his collaborators [1]. Starting from a Bose-Einstein condensate (BEC) of cesium atoms, the researchers used an ingenious combination of experimental methods to realize, visualize, and theoretically explore coherent wave structures known as discrete solitons. These nonlinear waveforms have long been theorized to exist, and their implications have been extensively studied. In my view, Cruickshank and company’s experiment constitutes the clearest manifestation of discrete solitons so far achieved in ultracold atomic systems, paving the way for a variety of future explorations.

Solitons are localized wave packets that emerge from the interplay of dispersion and nonlinearity. Dispersion tends to make wave packets spread, and nonlinearity tends to localize them. The interplay can be robust and balanced, resulting in long-lived structures. The presence of a lattice introduces a new dimensional unit, the lattice constant, to the interplay, enabling a potential competition between the lattice constant and the scale of the soliton. When the latter is much larger than the former, the soliton is effectively insensitive to the lattice, which it experiences as a continuum. But as the two scales approach one another, lattice effects become more pronounced, and the associated waveforms become discrete solitons. In nonlinear variants of the Schrödinger equation, discreteness typically favors standing waves rather than traveling ones. That’s because the lattice-induced energy barrier known as the Peierls-Nabarro barrier makes discrete solitons less mobile.

Ray Dalio: AI Is Accelerating the Collapse — Most People Aren’t Ready for What’s Next

With rapid advancements in AI and automation, individuals must prepare for a potentially unstable future by building financial strength, adapting to change, and rethinking traditional economic policies to avoid societal collapse ## ## Questions to inspire discussion.

Financial Preparation.

💰 Q: How should I structure my finances to build wealth? A: Focus on the fundamental equation: earn minus spend equals save, then invest that saved amount wisely to determine your financial success, as this simple formula is the foundation of building financial strength.

🏃 Q: When should I consider relocating geographically? A: Evaluate your location during major financial shifts and changing world orders, as the ability to move to better places and away from bad places has been historically important for protecting wealth and opportunity.

Career Strategy.

🎯 Q: How do I choose a career that maximizes financial success? A: Select careers that align with your passions while understanding their financial implications, since the work you do will directly impact your financial success during economic transitions.

Deformable lens enables real-time correction of image aberrations in single-pixel microscopy

Researchers from the Optics Group at the Universitat Jaume I in Castellón have managed to correct in real time problems related to image aberrations in single-pixel microscopy using a recent technology: programmable deformable lenses. The new method was described by the research team in an open-access article recently published in Nature Communications and is part of the development of the European CONcISE project.

The solution proposed by this team combines an adaptive lens (which “shapes” the light wavefront in real time) with a sensorless method that evaluates image sharpness directly from the data, without complex algorithms. This approach corrects distortions caused both by the system and by the sample itself, producing sharper images, close to the physical resolution limit, without adding complexity to the microscope.

This adaptive lens is known as a “multi-actuator adaptive lens” (M-AL), which can be easily integrated into the system without significantly modifying the traditional configuration of a single-pixel microscope based on structured illumination. These types of lenses consist of an optically transparent and deformable membrane (similar to a thin sheet of glass or polymer) that can change shape via actuators distributed around or behind it.

Atom-thin, content-addressable memory enables edge AI applications

Recent advances in the field of artificial intelligence (AI) have opened new exciting possibilities for the rapid analysis of data, the sourcing of information and the generation of use-specific content. To run AI models, current hardware needs to continuously move data from internal memory components to processors, which is energy-intensive and can increase the time required to tackle specific tasks.

Over the past few years, engineers have been trying to develop new systems that could overcome this limitation, running AI algorithms more reliably and efficiently. One proposed solution is the development of in-memory computing systems.

Content-addressable memory (CAM) is one of the earliest in-memory computing hardware systems, where memory components search for stored data faster, comparing each stored entry simultaneously based on its content, but faces challenges for AI applications because of the fundamental limitation of silicon transistors.

The 6 Steps to Reach the Singularity. Ep #114

The 6 steps to reach the singularity.

## The technological singularity, a point where AI surpasses human intelligence, is predicted to occur by 2045 and will profoundly transform humanity, requiring proactive adaptation and integration of AI into daily life ## ## Questions to inspire discussion.

Advancing AI and Machine Learning.

🧠 Q: How can we progress towards autonomous machine learning? A: Shift from supervised to unsupervised learning, enabling AI to identify patterns and make predictions without labeled data, thus advancing towards independent learning and improvement.

🤖 Q: What is the significance of achieving Artificial General Intelligence (AGI)? A: AGI represents the pinnacle of AI development, capable of matching or surpassing human-level intelligence across various domains, potentially leading to an unprecedented technological growth boom.

🧬 Q: What are initial steps towards neural augmentation? A: Develop brain-interfacing technologies to enhance specific aspects of human cognition, such as implants or non-invasive devices for improving memory, processing speed, or sensory perception.

Discrimination of normal from slow-aging mice by plasma metabolomic and proteomic features

Tests that can predict whether a drug is likely to extend mouse lifespan could speed up the search for anti-aging drugs. We have applied a machine learning algorithm, XGBoost regression, to seek sets of plasma metabolites (n = 12,000) and peptides (n = 17,000) that can discriminate control mice from mice treated with one of five anti-aging interventions (n = 278 mice). When the model is trained on any four of these five interventions, it predicts significantly higher lifespan extension in mice exposed to the intervention which was not included in the training set. Plasma peptide data sets also succeed at this task. Models trained on drug-treated normal mice also discriminate long-lived mutant mice from their respective controls, and models trained on males can discriminate drug-treated from control females.

One image is all robots need to find their way

While the capabilities of robots have improved significantly over the past decades, they are not always able to reliably and safely move in unknown, dynamic and complex environments. To move in their surroundings, robots rely on algorithms that process data collected by sensors or cameras and plan future actions accordingly.

Researchers at Skolkovo Institute of Science and Technology (Skoltech) have developed SwarmDiffusion, a new lightweight Generative AI model that can predict where a robot should go and how it should move relying on a single image. SwarmDiffusion, introduced in a paper pre-published on the server arXiv, relies on a diffusion model, a technique that gradually adds noise to input data and then removes it to attain desired outputs.

“Navigation is more than ‘seeing,” a robot also needs to decide how to move, and this is where current systems still feel outdated,” Dzmitry Tsetserukou, senior author of the paper, told Tech Xplore.

Algorithm matches drugs to glioblastoma’s diverse cell types, offering hope for individualized therapies

Researchers have developed a new computational approach that uncovers possible drugs for specific cellular targets for treating glioblastoma, a lethal brain tumor. This approach enabled them to predict more effective treatment combinations to fight the disease on an individualized basis.

This laboratory and computational research effort was led by scientists at Georgetown’s Lombardi Comprehensive Cancer Center.

“The cellular targets we identified could be key to effectively fighting a disease that has seen only one new targeted drug approved in the last two decades,” says Nagi G. Ayad, Ph.D., senior author, associate director for translational research, and professor of oncology at Georgetown Lombardi.

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