Computers and Math from Science Daily Feed

Researchers have developed a new optical computing material from photon avalanching nanoparticles.

Physicists bring unprecedented levels of predictability to the ancient practice of knitting by developing a mathematical model that could be used to create a new class of lightweight, ultra-strong materials.

In a breakthrough that could transform bioelectronic sensing, an interdisciplinary team of researchers has developed a new method to dramatically enhance the sensitivity of enzymatic and microbial fuel cells using organic electrochemical transistors (OECTs).

In a significant advancement for boosting renewable energy generation development, engineers have examined nanoscale properties of perovskite solar cells (PSCs). This initiative has resulted in the development of more efficient and durable cells, poised to substantially diminish costs and broaden applications, thereby connecting scientific research with the needs of the business community.

Identifying and delineating cell structures in microscopy images is crucial for understanding the complex processes of life. This task is called 'segmentation' and it enables a range of applications, such as analyzing the reaction of cells to drug treatments, or comparing cell structures in different genotypes. It was already possible to carry out automatic segmentation of those biological structures but the dedicated methods only worked in specific conditions and adapting them to new conditions was costly. An international research team has now developed a method by retraining the existing AI-based software Segment Anything on over 17,000 microscopy images with over 2 million structures annotated by hand.

Traditional machine learning models for automatic information classification require retraining data for each task. Researchers have demonstrated that art data can be automatically classified with sufficient accuracy by using a large language model (LLM), without requiring additional training data.

Labor market policies shape firms' innovation dynamics. A new study shows for the first time that higher minimum wages for low-skill jobs drive firms to develop automation technologies. Rising wages for high-skill labor, in contrast, can hamper this effect.

Silicon is the best-known semiconductor material. However, controlled nanostructuring drastically alters the material's properties. Using a specially developed etching apparatus, a team has now produced mesoporous silicon layers with countless tiny pores and investigated their electrical and thermal conductivity. For the first time, the researchers elucidated the electronic transport mechanism in this mesoporous silicon. The material has great potential for applications and could also be used to thermally insulate qubits for quantum computers.

Researchers have invented an entirely new field of microscopy -- nuclear spin microscopy. The team can visualize magnetic signals of nuclear magnetic resonance with a microscope. Quantum sensors convert the signals into light, enabling extremely high-resolution optical imaging.

How does a tennis player like Carlos Alcaraz decide where to run to return Novak Djokovic's ball by just looking at the ball's initial position? These behaviours, so common in elite athletes, are difficult to explain with current computational models, which assume that the players must continuously follow the ball with their eyes. Now, researchers have developed a model that, by combining optical variables with environmental factors such as gravity, accurately predicts how a person will move to catch a moving object just from an initial glance. These results could have potential applications in fields such as robotics, sports training or even space exploration.

Heteroepitaxial growth technology has made it possible to create larger diamond substrates, opening new opportunities for industrial-scale production of diamond quantum sensors. A research team has successfully fabricated large-area (111)-orientated diamond crystal substrates on heterogeneous (non-diamond) substrates, demonstrating the potential for industrialization of precise, noise-resistant current measurements for electric vehicle battery monitoring.

Researchers detail advances in the measurement of quantum devices that will be needed to realize a topological quantum computer. In an announcement, the team describes the operation of a device that is a necessary building block for a topological quantum computer. The published results are an important milestone along the path to construction of quantum computers that are potentially more robust and powerful than existing technologies.

Technology could lead to faster, more precise and more cost-effective alternatives to traditional diagnostic methods.

Most AI diagnostic tools are black boxes, but the approach allows doctors and patients to understand how the computer reached a diagnosis.

A new artificial intelligence model measures how fast a patient's brain is aging and could be a powerful new tool for understanding, preventing and treating cognitive decline and dementia.

Scientists have developed an AI-powered tool that detects 64% of brain abnormalities linked to epilepsy that human radiologists miss.

Researchers are blurring the lines between robotics and materials, with a proof-of-concept material-like collective of robots with behaviors inspired by biology.

Artificial Intelligence (AI), particularly large language models like GPT-4, has shown impressive performance on reasoning tasks. But does AI truly understand abstract concepts, or is it just mimicking patterns? A new study reveals that while GPT models perform well on some analogy tasks, they fall short when the problems are altered, highlighting key weaknesses in AI's reasoning capabilities.

Rubidium could be the next key player in oxide-ion conductors. Researchers have discovered a rare rubidium (Rb)-containing oxide-ion conductor with exceptionally high conductivity. Identified through computational screening and experiments, its superior performance stems from low activation energy and structural features like large free volume and tetrahedral motion. Its stability under various conditions offers a promising direction for solid oxide fuel cells and clean energy technologies.

Groundbreaking study shows machine learning can decode emotions in seven ungulate species. A game-changer for animal welfare? Can artificial intelligence help us understand what animals feel? A pioneering study suggests the answer is yes. Researchers have successfully trained a machine-learning model to distinguish between positive and negative emotions in seven different ungulate species, including cows, pigs, and wild boars. By analyzing the acoustic patterns of their vocalizations, the model achieved an impressive accuracy of 89.49%, marking the first cross-species study to detect emotional valence using AI.