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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.

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.

New CRISPR method makes it possible to control protein production in cells

The speed at which a cell produces proteins is a decisive factor in determining whether it divides, specializes or retains its stem cell properties. A team of researchers led by Professor Stefan H. Stricker, professor of epigenetic engineering at LMU’s Biomedical Center and research group leader at Helmholtz Munich, has worked with international partners to demonstrate directly for the first time that the amount of ribosomal RNA (rRNA) directly regulates these processes. Their results were published in the journal Science.

It has been established for some time that the amount of ribosomal RNA differs among different types of cells and is altered in a number of diseases. But it remained unclear whether these specific characteristics are the cause or merely the result of biological processes.

With the newly developed CRISPR-based method TAPIR (Targeted Activation of Protein Translation), researchers now have access to a tool that can boost the activity of ribosomal genes and, as a result, influence a cell’s protein production. “Our new study shows that targeted activation of rRNA production significantly increases protein synthesis,” explains Stricker, lead author of the publication.

Light-powered chip harvests energy, computes and senses chemicals in one stack

Most contemporary portable electronics, including laptops, smartphones and smart watches, are powered by batteries that need to be recharged daily or every few days. Over the past decade, however, some engineers have been exploring the possibility of developing battery-free electronic devices that autonomously derive electricity from renewable sources, such as sunlight, indoor lighting or heat.

A research team at Penn State University recently developed a compact integrated circuit (IC) that harvests energy solely from ambient light, using this energy to run computations and sense chemicals in its surroundings. This new chip, introduced in a paper published in Nature Electronics, could enable the development of devices that never require charging and thus continue working uninterrupted even in environments where replacement batteries and electrical sockets are not available.

“This work grew out of a broader question we have been asking in my group: Can we build electronic systems that do not simply sense information, but also process that information locally and power themselves from their environment?” Saptarshi Das, senior author of the paper, told Tech Xplore. “Many future Internet of Things (IoT) and edge-computing systems will need to operate in remote or hard-to-access locations, where replacing batteries is impractical. We wanted to demonstrate a compact, fully integrated chip that combines energy harvesting, sensing and computation in a single monolithic three-dimensional architecture.”

Optimizing RNA design with AI and an Ising machine: Encoding matters

RNA has emerged as one of the most promising molecules in modern medicine, enabling advances from mRNA vaccines and gene therapies to genome editing and synthetic biology. However, designing RNA molecules that reliably fold into a desired secondary structure remains a major challenge. Even for relatively short sequences, the number of possible nucleotide combinations grows exponentially, making it difficult to identify optimal candidates. As a result, conventional computational methods often require extensive candidate evaluations, creating a significant bottleneck when experimental validation is both time-consuming and costly.

To address this challenge, researchers from Keio University, led by Project Lecturer Shuta Kikuchi of the Graduate School of Science and Technology and Professor Shu Tanaka of the Department of Applied Physics and Physico-Informatics, developed a novel RNA inverse folding framework based on factorization machine with quadratic optimization annealing (FMQA). This machine learning– and Ising machine–driven black-box optimization approach is designed to identify high-quality RNA sequence candidates with relatively few evaluations.

“We investigated a new application of FMQA in biomolecular design, where its potential remains relatively unexplored. Since RNA, DNA and protein sequences are inherently categorical in nature, it is unclear how converting them into binary representations affects optimization performance. In this study, we examined RNA inverse folding and the influence of different encoding and assignment choices within FMQA,” says Dr. Kikuchi. The findings are published in Scientific Reports.

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