A Stanford geophysicist and lawyer team up to use big data for water quality monitoring and governance.
The intricate relationship between quantum mechanics and classical physics has long puzzled scientists. Quantum mechanics operates in a bizarre world where particles can exist in multiple states simultaneously, a concept known as superposition. However, this principle appears to break down in the macroscopic realm.
Planets, stars, and even the universe itself don’t exhibit such superpositions, creating a significant challenge in understanding how the universe transitions from quantum to classical behavior.
At the heart of this enigma is the question: how does the universe, if fundamentally quantum, adhere to classical laws like general relativity? This puzzle has led to groundbreaking work by researchers such as Matteo Carlesso and his colleagues at the University of Trieste.
Dive into the mesmerizing world of quantum mechanics and uncover the secrets of the quantum vacuum—a concept that challenges everything we thought we knew about empty space. This video explores the dynamic, energy-filled realm of the quantum vacuum, where virtual particles pop in and out of existence and Zero Point Energy offers tantalizing possibilities for clean, limitless power.
Learn about the Casimir Effect, a fascinating phenomenon where quantum fluctuations create forces between metal plates, and discover how these principles could revolutionize fields like nanotechnology, energy production, and even space exploration. From the Heisenberg Uncertainty Principle to the Reverse Casimir Effect, this journey into quantum mechanics highlights the incredible potential of harnessing Zero Point Energy for a sustainable future.
Continue reading “Hidden Energy in Empty Space: Casimir Effect and Zero Point Energy” »
AI algorithms analyzes the OAM spectrum and intensity patterns of twisted light as it propagates through a turbulent medium, enabling the detection of the key features of the medium.
A breakthrough in artificial intelligence.
Artificial Intelligence (AI) is a branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. These tasks include understanding natural language, recognizing patterns, solving problems, and learning from experience. AI technologies use algorithms and massive amounts of data to train models that can make decisions, automate processes, and improve over time through machine learning. The applications of AI are diverse, impacting fields such as healthcare, finance, automotive, and entertainment, fundamentally changing the way we interact with technology.
Simulations of neutron stars provide new bounds on their properties, such as their internal pressure and their maximum mass.
Studying neutron stars is tricky. The nearest one is about 400 light-years away, so sending a probe would likely take half a million years with current space-faring technology. Telescopes don’t reveal much detail from our vantage point, since neutron stars are only the size of a small city and thus appear as mere points in the sky. And no laboratory on Earth can reproduce the inside of neutron stars, because their density is too great, being several times that of atomic nuclei. That high density also poses a problem for theory, as the equations for neutron-star matter cannot be solved with standard computational techniques. But these difficulties have not stopped efforts to understand these mysterious objects. Using a combination of theory-based methods and computer simulations, Ryan Abbott from MIT and colleagues have obtained new, rigorous constraints for the properties of the interior of neutron stars [1].
Quantum physics is a very diverse field: it describes particle collisions shortly after the Big Bang as well as electrons in solid materials or atoms far out in space. But not all quantum objects are equally easy to study. For some—such as the early universe—direct experiments are not possible at all.
However, in many cases, quantum simulators can be used instead: one quantum system (for example, a cloud of ultracold atoms) is studied in order to learn something about another system that looks physically very different, but still follows the same laws, i.e. adheres to the same mathematical equations.
It is often difficult to find out which equations determine a particular quantum system. Normally, one first has to make theoretical assumptions and then conduct experiments to check whether these assumptions prove correct.
Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.
Scientists have developed an advanced swarm navigation algorithm for cyborg insects that prevents them from becoming stuck while navigating challenging terrain.
Published in Nature Communications, the new algorithm represents a significant advance in swarm robotics. It could pave the way for applications in disaster relief, search-and-rescue missions, and infrastructure inspection.
Cyborg insects are real insects equipped with tiny electronic devices on their backs—consisting of various sensors like optical and infrared cameras, a battery, and an antenna for communication—that allow their movements to be remotely controlled for specific tasks.
