Lifeboat Foundation

Reliably measuring the polarization state of light is crucial for various technological applications, ranging from optical communication to biomedical imaging. Yet conventional polarimeters are made of bulky components, which makes them difficult to reduce in size and limits their widespread adoption.

Researchers at the Shanghai Institute of Technical Physics (SITP) of the Chinese Academy of Sciences and other institutes recently developed an on-chip full-Stokes polarimeter that could be easier to deploy on a large scale. Their device, presented in a paper in Nature Electronics, is based on optoelectronic polarization eigenvectors, mathematical equations that represent the linear relationship between the incident Stokes vector and a detector’s photocurrent.

“This work was driven by the growing demand for compact, high-performance polarization analysis devices in optoelectronics,” Jing Zhou, corresponding author of the paper, told Phys.org. “Traditional polarimeters, which rely on discrete bulky optical components, present significant challenges to miniaturization and limit their broader applicability. Our main goal is to develop an on-chip solution capable of direct electrical readout to reconstruct full-Stokes polarization states.”

Quantum walks are a powerful theoretical model using quantum effects such as superposition, interference and entanglement to achieve computing power beyond classical methods.

A research team at the National Innovation Institute of Defense Technology from the Academy of Military Sciences (China) recently published a review article that thoroughly summarizes the theories and characteristics, physical implementations, applications and challenges of quantum walks and quantum walk computing. The review was published Nov. 13 in Intelligent Computing in an article titled “Quantum Walk Computing: Theory, Implementation, and Application.”

As quantum mechanical equivalents of classical random walks, quantum walks use quantum phenomena to design advanced algorithms for applications such as database search, network analysis and navigation, and quantum simulations. Different types of quantum walks include discrete-time quantum walks, continuous-time quantum walks, discontinuous quantum walks, and nonunitary quantum walks. Each model presents unique features and computational advantages.

Algorithm finds the optimal transducer location to focus ultrasound through the skull onto a target in the brain.

Researchers at UC San Diego have developed SMART, a software package capable of realistically simulating cell-signaling networks.

This tool, tested across various biological systems, enhances the understanding of cellular responses and aids in advancing research in fields like systems biology and pharmacology.

Continue reading “Scientists Have Finally Cracked the Code of Cellular Communication” »

Researchers at University of North Carolina at Chapel Hill and University of Maryland recently developed MyTimeMachine (MyTM), a new AI-powered method for personalized age transformation that can make human faces in images or videos appear younger or older, accounting for subjective factors influencing aging.

This algorithm, introduced in a paper posted to the arXiv preprint server, could be used to broaden or enhance the features of consumer-facing picture-editing platforms, but could also be a valuable tool for the film, TV and entertainment industries.

Continue reading “AI-powered algorithm enables personalized age transformation for human faces” »

Researchers at University of California San Diego have developed and tested a new software package, called Spatial Modeling Algorithms for Reactions and Transport (SMART), that can realistically simulate cell-signaling networks—the complex systems of molecular interactions that allow cells to respond to diverse cues from their environment.

Cell-signaling networks involve many distinct steps and are also greatly influenced by the complex, three-dimensional shapes of cells and subcellular components, making them difficult to simulate with existing tools. SMART offers a solution to this problem, which could help accelerate research in fields across the life sciences, such as systems biology, pharmacology and biomedical engineering.

The researchers successfully tested the new software in biological systems at several different scales, from cell signaling in response to adhesive cues, to calcium release events in subcellular regions of neurons and cardiac muscle cells, to the production of ATP (the energy currency in cells) within a detailed representation of a single mitochondrion.

At night, charged particles from the sun caught by Earth’s magnetosphere rain down into the atmosphere. The impacting particles rip electrons from atoms in the atmosphere, creating both beauty and chaos. These high-energy interactions cause the northern and southern lights, but they also scatter radio signals, wreaking havoc on ground-based and satellite communications.

Scientists would like to track electrical activity in the ionosphere by measuring the distribution of plasma, the form matter takes when positive ions are separated from their electrons, to help better predict how communications will be affected by electromagnetic energy.

But analyzing plasma in the ionosphere is a challenge because its distribution changes quickly and its movements are often unpredictable. In addition, collisional physics makes detecting true motion in the lower ionosphere exceedingly difficult.

A new visual recognition approach improved a machine learning technique’s ability to both identify an object and how it is oriented in space, according to a study presented in October at the European Conference on Computer Vision in Milan, Italy.

Self-supervised learning is a machine learning approach that trains on unlabeled data, extending generalizability to real-world data. While it excels at identifying objects, a task called semantic classification, it may struggle to recognize objects in new poses.

This weakness quickly becomes a problem in situations like autonomous vehicle navigation, where an algorithm must assess whether an approaching car is a head-on collision threat or side-oriented and just passing by.

While classical physics presents a deterministic universe where cause must precede effect, quantum mechanics and relativity theory paint a more nuanced picture. There are already well-known examples from relativity theory like wormholes, which are valid solutions of Einstein’s Field Equations, and similarly in quantum mechanics the non-classical state of quantum entanglement—the “spooky action at a distance” that troubled Einstein—which demonstrates that quantum systems can maintain instantaneous correlations across space and, potentially, time.

Perhaps most intriguingly, the protocol suggests that quantum entanglement can be used to effectively send information about optimal measurement settings “back in time”—information that would normally only be available after an experiment is complete. This capability, while probabilistic in nature, could revolutionize quantum computing and measurement techniques. Recent advances in multipartite hybrid entanglement even suggest these effects might be achievable in real-world conditions, despite environmental noise and interference. The realization of such a retrocausal quantum computational network would, effectively, be the construction of a time machine, defined in general as a system in which some phenomenon characteristic only of chronology violation can reliably be observed.

This article explores the theoretical foundations, experimental proposals, significant improvements, and potential applications of the retrocausal teleportation protocol. From its origins in quantum mechanics and relativity theory to its implications for our understanding of causality and the nature of time itself, we examine how this cutting-edge research challenges our classical intuitions while opening new possibilities for quantum technology. As we delve into these concepts, we’ll see how the seemingly fantastic notion of time travel finds a subtle but profound expression in the quantum realm, potentially revolutionizing our approach to quantum computation and measurement while deepening our understanding of the universe’s temporal fabric.