An artificial neural network built into a computer memory chip reconstructs the human cortex with high accuracy in real time
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Researchers Discovered Your Brain Really Can Sync Up With Someone Else’s. Here’s How It Works
If you’ve ever been riding a wave of creativity that feels like your brain and someone else’s have been Bluetooth-synced and are now finishing each other’s sentences, both instinctively knowing where the song/screenplay/woodworking project or whatever you’re building should go, then you’ve experienced what scientists call brain synchrony.
As described by a team of researchers publishing their findings as a press release on Eureka Alert, originally published in Trends in Cognitive Sciences, it’s a real phenomenon that’s been observed in laboratories and real-world settings. Now, researchers say it isn’t just measurable, but it can actually be strengthened.
Researchers reviewed a decade of studies involving thousands of people, from regular everyday students to professional artists. Using portable EEG headsets, researchers found that when people are genuinely engaged with one another, their brainwave activity begins to align. Even more interesting, when participants received real-time feedback showing how synchronized they were, that alignment often became even stronger.
Hybrid AI model cuts financial forecasting error across stocks and crypto
A hybrid artificial intelligence model that combines two well-established deep learning techniques has improved the accuracy of financial market forecasts across major stock indices and so-called cryptocurrency, according to work in the International Journal of Reasoning-based Intelligent Systems.
The researchers designed the model, CLSTM-HN, to address a long-standing problem in financial forecasting: balancing the detection of short-term market movements with the recognition of longer-term trends. The researchers tested the system on publicly available data and achieved a forecasting error 15% to 20% lower than that of conventional long-short-term memory (LSTM) models. They also saw an improvement in the accuracy of predicting whether prices would rise or fall by 10% to 14%.
Financial markets are difficult to predict because prices are volatile, noisy and subject to sudden structural shifts. Traditional statistical approaches often rely on assumptions about market behavior that break down during periods of instability.
New, improved method to find and isolate the strongest cancer-fighting immune cells
A new platform developed by researchers at the University of Texas MD Anderson Cancer Center quickly finds and isolates rare, tumor-reactive immune cells that are especially good at recognizing and attacking cancer cells, even without knowing which tumor targets are recognized by the immune cells. This approach addresses a major bottleneck in immunotherapy development and could accelerate the creation of personalized treatments.
The platform, called ATTACH (Assessment of T cells Tethered to Antigen Class I Histocompatibility), identifies the strongest interactions between T cells and cancer-specific proteins, isolating only the most effective, tumor-reactive T cells for further study and therapeutic use.
The study, published today in the Journal for ImmunoTherapy of Cancer, was led by Alexandre Reuben, Ph.D., assistant professor of Thoracic/Head and Neck Medical Oncology, and Amanda Montoya, senior research assistant in the Reuben lab.
Alcohol is one of the most dangerous drugs, yet its presence is ubiquitous in social settings and celebrations
Despite growing evidence of alcohol’s harms, it remains deeply embedded in social norms and cultural rituals, both in the US and abroad.
A Widening Anomaly Strains the Standard Model
Does a new measurement of a rare decay of the neutral B meson portend new physics?
In particle physics, ten years is a long time to sit with a puzzle. Since 2013, measurements of a rare decay—a neutral B meson (B0) transforming into an excited kaon (K*0) and a muon–antimuon pair (µ+µ –)—have stubbornly refused to match the predictions of the standard model, the theory that describes all known particles and forces [1]. Small enough to be dismissed at first as a statistical fluctuation, the pattern of discrepancies has grown with each new dataset into one of the most tantalizing hints of new physics in experimental particle physics. Now the LHCb Collaboration at CERN in Switzerland has published its most comprehensive analysis of the decay to date [2]. The result is clear: The anomaly persists. Encouragingly, the theoretical and experimental tools to understand it have never been sharper.
Within the mathematical framework of the standard model, the decay in question, B0 
→ K*0µ+µ–, can occur only through so-called higher-order electroweak loop diagrams in which a bottom, or b, quark transforms into a strange, or s, quark [3]. As a result, the decay is extraordinarily rare. In every million B-meson decays of all kinds, you can expect to find only one. That rarity makes the decay valuable: It could bear measurable imprints of particles beyond the standard model that contribute to the same loop processes but have so far escaped detection because they are too heavy.
Wavelength-multiplexed diffractive optical storage enables massively parallel image retrieval
The explosive growth of data generated by artificial intelligence, cloud computing and modern digital infrastructure is placing increasing pressure on existing information storage technologies. Although magnetic storage systems such as hard disk drives remain the dominant platform for digital storage, they face challenges including rising costs, limited lifespan and relatively slow information retrieval.
To address these challenges, researchers at the University of California, Los Angeles (UCLA) have developed a new optical information storage platform that uses engineered diffractive structures to store and rapidly retrieve thousands of images.
The UCLA team introduced a wavelength-multiplexed diffractive optical storage system composed of multiple passive dielectric layers that are spatially engineered using deep learning. The research is published in the journal Advanced Photonics.
Long-theorized electron-on-helium qubit achieves strong coupling to a single microwave photon
Quantum computers, devices that store and process information leveraging the principles of quantum mechanics, have been found to be promising for tackling some problems that cannot be solved by classical computers. Quantum computers store data in the form of qubits (i.e., quantum bits), units of information that can exist in combinations of different states, instead of being limited to a binary value (i.e., 0 or 1), like classical bits.
For decades, various theoretical physicists have been exploring the possibility of building a quantum computing system using electrons trapped above the surface of superfluid helium, a form of liquid helium cooled to extremely low temperatures. These trapped electrons could ultimately be more isolated from sources of noise (i.e., environmental disturbances) that can disrupt quantum states and lead to computational errors.
Researchers at EeroQ Corporation, a quantum computing company based in Chicago, recently introduced a strategy to enable strong interactions between a single electron floating above superfluid helium and a microwave photon.