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Category: information science – Page 19

A quantum gas that refuses to heat—physicists observe many-body dynamical localization

In everyday life, continuously doing work on a system is found to heat it up. Rubbing your hands together warms them. Hammering a piece of metal makes it hot. Even without knowing the equations, we learn from experience: driving any system, whether by stirring, pressing, or striking, leads to a rise in the system’s temperature.

The same expectation holds for microscopic quantum systems: when we continuously excite a many-particle system, especially one with strong particle-particle interactions, we expect it to absorb energy and to heat up. But is this always the case, in particular at the ?

No, says an experiment carried out by a team from Hanns-Christoph Nägerl’s group at the Department of Experimental Physics of the University of Innsbruck. The research has been published in Science.

AI breakthrough designs peptide drugs to target previously untreatable proteins

A study published in Nature Biotechnology reveals a powerful new use for artificial intelligence: designing small, drug-like molecules that can stick to and break down harmful proteins in the body — even when scientists don’t know what those proteins look like. The breakthrough could lead to new treatments for diseases that have long resisted traditional drug development, including certain cancers, brain disorders, and viral infections.

The study was published on August 13, 2025 by a multi-institutional team of researchers from McMaster University, Duke University, and Cornell University. The AI tool, called PepMLM, is based on an algorithm originally built to understand human language and used in chatbots, but was trained to understand the “language” of proteins.

In 2024, the Nobel Prize in Chemistry was awarded to researchers at Google DeepMind for developing AlphaFold, an AI system that predicts the 3D structure of proteins – a major advance in drug discovery. But many disease-related proteins, including those involved in cancer and neurodegeneration, don’t have stable structures. That’s where PepMLM takes a different approach – instead of relying on structure, the tool uses only the protein’s sequence to design peptide drugs. This makes it possible to target a much broader range of disease proteins, including those that were previously considered “undruggable.”

Brain cells learn faster than machine learning, research reveals

Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as “DishBrain” and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.

The study, “Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning,” published in Cyborg and Bionic Systems, is the first known of its kind.

The research was led by Cortical Labs, the Melbourne-based startup which created the world’s first commercial biological computer, the CL1. The CL1, through which the research was conducted, fuses lab-cultivated neurons from human stem cells with hard silicon to create a more advanced and sustainable form of AI, known as SBI.

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate predictions by analyzing large amounts of data. These networks are structured in layers, each of which transforms input data into ‘features’ that guide the analysis of the next layer.

The process through which DNNs learn features has been the topic of numerous research studies and is ultimately the key to these models’ good performance on a variety of tasks. Recently, some computer scientists have started exploring the possibility of modeling feature learning in DNNs using frameworks and approaches rooted in physics.

Researchers at the University of Basel and the University of Science and Technology of China discovered a , a graph resembling those used in thermodynamics to delineate liquid, gaseous and solid phases of water, that represents how DNNs learn features under various conditions. Their paper, published in Physical Review Letters, models a DNN as a spring-block chain, a simple mechanical system that is often used to study interactions between linear (spring) and nonlinear (friction) forces.

Robotic drummer gradually acquires human-like behaviors

Humanoid robots, robots with a human-like body structure, have so far been primarily tested on manual tasks that entail supporting humans in their daily activities, such as carrying objects, collecting samples in hazardous environments, supporting older adults or acting as physical therapy assistants. In contrast, their potential for completing expressive physical tasks rooted in creative disciplines, such as playing an instrument or participating in performance arts, remains largely unexplored.

Researchers at SUPSI, IDSIA and Politecnico di Milano recently introduced Robot Drummer, a new humanoid robot that can play the drums both accurately and expressively, supported by a reinforcement learning algorithm. This robot, presented in a paper published on the arXiv preprint server, was found to gradually acquire human-like behaviors, including movements that are often performed by drummers.

“The idea for Robot Drummer actually emerged from a spontaneous conversation over coffee with my co-author, Loris Roveda,” Asad Ali Shahid, first author of the paper, told Tech Xplore. “We were discussing how humanoid robots have become increasingly capable at a wide range of tasks, but rarely engage in creative and expressive domains. That raised a fascinating question: what if a humanoid robot could take on a creative role, like performing music? Drumming seemed like a perfect frontier, as it’s rhythmic, physical, and requires rapid coordination across limbs.”

Anti-radar based on metasurface

In advanced multi-static radar (MSR), multidimensional information from target echo signals is collected by different receivers to enable precise localization using various algorithms. Owing to its efficient target localization and tracking capability, MSR has found wide applications in sensing, military operations, aviation, and aerospace. Multi-static nature of MSR also makes it difficult to counter. Here, we propose an anti-radar methodology based on space-time-coding metasurface (STCM) to counter MSR. By designing the physical characteristics of STCM and developing adaptive and robust electronic countermeasure (ECM) control strategies, we realize a cost-effective, miniaturized and low-complexity ECM system with the flexible controlling capabilities. Under non-cooperative and dynamic ECM scenarios, the proposed method shows exceptional concealment and deception performance. To validate the methodology, we develop a prototype of the STCM-based anti-MSR system and successfully demonstrate its ability to neutralize various MSR technologies. The proposed method is expected to find practical applications in the anti-MSR scenarios.

This study proposes an anti-radar methodology based on space-time-coding metasurface to counter multi-static radar, which enables a cost-effective, miniaturized, and low complexity electronic countermeasure system.

Computers reconstruct 3D environments from 2D photos in a fraction of the time

Imagine trying to make an accurate three-dimensional model of a building using only pictures taken from different angles—but you’re not sure where or how far away all the cameras were. Our big human brains can fill in a lot of those details, but computers have a much harder time doing so.

This scenario is a well-known problem in and robot navigation systems. Robots, for instance, must take in lots of 2D information and make 3D —collections of data points in 3D space—in order to interpret a scene. But the mathematics involved in this process is challenging and error-prone, with many ways for the computer to incorrectly estimate distances. It’s also slow, because it forces the computer to create its 3D point cloud bit by bit.

Computer scientists at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) think they have a better method: A breakthrough algorithm that lets computers reconstruct high-quality 3D scenes from 2D images much more quickly than existing methods.

Vibration energy harvesting by ferrofluids in external magnetic fields

The development of wearable electronics and the current era of big data requires the sustainable power supply of numerous distributed sensors. In this paper, we designed and experimentally studied an energy harvester based on ferrofluid sloshing. The harvester contains a horizontally positioned cylindrical vial, half-filled with a ferrofluid exposed to a magnetic field. The vial is excited by a laboratory shaker and the induced voltage in a nearby coil is measured under increasing and decreasing shaking rates. Five ferrofluid samples are involved in the study, yielding the dependence of the electromotive force on the ferrofluid magnetization of saturation. The energy harvesting by ferrofluid sloshing is investigated in various magnetic field configurations. It is found that the most effective magnetic field configuration for the energy harvesting is characterized by the field intensity perpendicular to the axis of the vial motion and gravity. The harvested electric power linearly increases with the ferrofluid magnetization of saturation. The electromotive force generated by each ferrofluid is found identical for measurements in acceleration and deceleration mode. A significant reduction in the induced voltage is observed in a stronger magnetic field. The magneto-viscous effect and partial immobilization of the ferrofluid in the stronger magnetic field is considered. The magneto-viscous effect is documented by a supplementing experiment. The results extend knowledge on energy harvesting by ferrofluid sloshing and may pave the way to applications of ferrofluid energy harvesters for mechanical excitations with changing directions in regard to the magnetic field induction.

Rajnak, M., Kurimsky, J., Paulovicova, K. et al. Vibration energy harvesting by ferrofluids in external magnetic fields. Sci Rep 15, 26,701 (2025). https://doi.org/10.1038/s41598-025-12490-w.

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