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Category: information science – Page 2

Physicists still divided about quantum world, 100 years on

The theory of quantum mechanics has transformed daily life since being proposed a century ago, yet how it works remains a mystery—and physicists are deeply divided about what is actually going on, a survey in the journal Nature said Wednesday.

“Shut up and calculate!” is a famous quote in that illustrates the frustration of scientists struggling to unravel one of the world’s great paradoxes.

For the last century, equations based on have consistently and accurately described the behavior of extremely small objects.

AI-powered headgear promises sharper focus from the comfort of home

A personalized brain stimulation system powered by artificial intelligence (AI) that can safely enhance concentration from home has been developed by researchers from the University of Surrey, the University of Oxford and Cognitive Neurotechnology. Designed to adapt to individual characteristics, the system could help people improve focus during study, work, or other mentally demanding tasks.

Published in npj Digital Medicine, the study is based on a patented approach that uses non-invasive brain alongside adaptive AI to maximize its impact.

The technology uses transcranial random noise stimulation (tRNS)—a gentle and painless form of electrical brain stimulation—and an AI algorithm that learns to personalize stimulation based on individual features, including level and head size.

Synthetic aperture waveguide holography for compact mixed-reality displays with large étendue

An ultra-thin mixed-reality (MR) display design that is based on a unique combination of waveguide holography and artificial intelligence-driven holography algorithms is demonstrated, creating visually comfortable and perceptually realistic 3D VR experiences in a compact wearable device.

Hybrid Crystal-Glass Materials from Meteorites Transform Heat Control

Crystals and glasses have opposite heat-conduction properties, which play a pivotal role in a variety of technologies. These range from the miniaturization and efficiency of electronic devices to waste-heat recovery systems, as well as the lifespan of thermal shields for aerospace applications.

The problem of optimizing the performance and durability of materials used in these different applications essentially boils down to fundamentally understanding how their chemical composition and atomic structure (e.g., crystalline, glassy, nanostructured) determine their capability to conduct heat. Michele Simoncelli, assistant professor of applied physics and applied mathematics at Columbia Engineering, tackles this problem from first principles — i.e., in Aristotle’s words, in terms of “the first basis from which a thing is known” — starting from the fundamental equations of quantum mechanics and leveraging machine-learning techniques to solve them with quantitative accuracy.

In research published on July 11 in the Proceedings of the National Academy of Sciences, Simoncelli and his collaborators Nicola Marzari from the Swiss Federal Technology Institute of Lausanne and Francesco Mauri from Sapienza University of Rome predicted the existence of a material with hybrid crystal-glass thermal properties, and a team of experimentalists led by Etienne Balan, Daniele Fournier, and Massimiliano Marangolo from the Sorbonne University in Paris confirmed it with measurements.

Technology Landscape Review of In-Sensor Photonic Intelligence: From Optical Sensors to Smart Devices

Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications.

This New Treatment Can Adjust to Parkinson’s Symptoms in Real Time

Starting today, people with Parkinson’s disease will have a new treatment option, thanks to U.S. Food and Drug Administration approval of groundbreaking new technology.

The therapy, known as adaptive deep brain stimulation, or aDBS, uses an implanted device that continuously monitors the brain for signs that Parkinson’s symptoms are developing. When it detects specific patterns of brain activity, it delivers precisely calibrated electric pulses to keep symptoms at bay.

The FDA approval covers two treatment algorithms that run on a device made by Medtronic, a medical device company. Both work by monitoring the same part of the brain, called the subthalamic nucleus. But they respond in different ways.

The FDA has approved an adaptive deep brain stimulation (aDBS) treatment for people with with Parkinson’s disease, making this groundbreaking technology available to people nationwide.

The dawn of quantum advantage

Quantum computing is about to enter an important stage — the era of quantum advantage. The first claims of quantum advantage are emerging, and over the next few years, we expect researchers and developers to continue presenting compelling hypotheses for quantum advantages. In turn, the broader community will either disprove these hypotheses with cutting-edge techniques — or the advantage holds.

Put simply, quantum advantage means that a quantum computer can run a computation more accurately, cheaply, or efficiently than a classical computer. Between now and the end of 2026, we predict that the quantum community will have uncovered the first quantum advantages. But there’s more to it than that.

We have arrived already at a place where quantum computing is a useful scientific tool capable of performing computations that even the best exact classical algorithms can’t. We and our partners are already conducting a range of experiments on quantum computers that are competitive with the leading classical approximation methods. At the same time, computing researchers are testing advantage claims with innovative new classical approaches.

New approach allows drone swarms to autonomously navigate complex environments at high speed

Unmanned aerial vehicles (UAVs), commonly known as drones, are now widely used worldwide to tackle various real-world tasks, including filming videos for various purposes, monitoring crops or other environments from above, assessing disaster zones, and conducting military operations. Despite their widespread use, most existing drones either need to be fully or partly operated by human agents.

In addition, many drones are unable to navigate cluttered, crowded or unknown environments without colliding with nearby objects. Those that can navigate these environments typically rely on expensive or bulky components, such as advanced sensors, graphics processing units (GPUs) or .

Researchers at Shanghai Jiao Tong University have recently introduced a new insect-inspired approach that could enable teams of multiple drones to autonomously navigate complex environments while moving at high speed. Their proposed approach, introduced in a paper published in Nature Machine Intelligence, relies on both a deep learning algorithm and core physics principles.

New tool predicts cardiovascular disease risk more accurately

A new risk prediction tool developed by the American Heart Association (AHA) estimated cardiovascular disease (CVD) risk in a diverse patient cohort more accurately than current models, according to a recent study published in Nature Medicine.

The tool, called the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations which was developed in 2023, could help health care providers more accurately identify patients who have higher CVD risk and enhance preventive care efforts, according to Sadiya Khan, the Magerstadt Professor of Cardiovascular Epidemiology and co-first author of the study.

“Evaluating the new PREVENT equations in a diverse sample of patients is critical to provide primary care providers and cardiologists with further assurance that they can utilize these equations to accurately predict patients’ CVD risk, particularly in vulnerable populations,” said Khan, who is also an associate professor of Medical Social Sciences in the Division of Determinants of Health and of Preventive Medicine in the Division of Epidemiology.

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