Concentrating light in a volume as small as the wavelength itself is a challenge that is crucial for numerous applications. Researchers from AMOLF, TU Delft, and Cornell University in the U.S. have demonstrated a new way to focus light on an extremely small scale. Their method utilizes special properties of a photonic crystal and works for a broader spectrum of wavelengths than alternative methods. The researchers published their findings in Science Advances on April 18.

Synaptic plasticity underlies learning by modifying specific synaptic inputs to reshape neural activity and behavior. However, the rules governing which synapses will undergo different forms of plasticity in vivo during learning and whether these rules…

This paper introduces an adaptive multi-agent framework to enhance collaborative reasoning in large language models (LLMs). The authors address the challenge of effectively scaling collaboration and reasoning in multi-agent systems (MAS), which is an open question despite recent advances in test-time scaling (TTS) for single-agent performance.

The core methodology revolves around three key contributions:

1. **Dataset Construction:** The authors create a high-quality dataset, M500, comprising 500 multi-agent collaborative reasoning traces. This dataset is generated automatically using an open-source MAS framework (AgentVerse) and a strong reasoning model (DeepSeek-R1). To ensure quality, questions are selected based on difficulty, diversity, and interdisciplinarity. The generation process involves multiple agents with different roles collaborating to solve challenging problems. Data filtering steps are applied to ensure consensus among agents, adherence to specified formats (e.g., using tags like “ and ‘boxed{}‘), and correctness of the final answer. The filtering criteria are based on Consensus Reached, Format Compliance, and Correctness. The data generation is described in Algorithm 1 in the Appendix.

Supported by the U.S. National Science Foundation, physicists have revealed the presence of a previously unobserved type of subatomic phenomenon called a fractional exciton. Their findings confirm theoretical predictions of a quasiparticle with unique quantum properties that behaves as though it is made of equal fractions of opposite electric charges bound together by mutual attraction.

The discovery was supported by NSF through multiple grants and laboratory work performed at the NSF National High Magnetic Field Laboratory in Tallahassee, Florida. The results are published in Nature and show potential for developing new ways to improve how information is stored and manipulated at the quantum level, which could lead to faster and more reliable quantum computers.

“Our findings point toward an entirely new class of quantum particles that carry no overall charge but follow unique quantum statistics,” says Jia Li, leader of the research team and associate professor of physics at Brown University. “The most exciting part is that this discovery unlocks a range of novel quantum phases of matter, presenting a new frontier for future research, deepening our understanding of fundamental physics and even opening up new possibilities in quantum computation.”

Modern brain–computer interfaces (BCI), utilizing electroencephalograms for bidirectional human–machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin–electrode impedance, and bulky electronics, diminishing the system’s continuous use and portability. Here, we introduce motion artifact–controlled micro–brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm⁻²) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject’s excessive motions, including standing, walking, and running. A demonstration captures this system’s capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI’s applications for interactive digital environments.

Quantum mechanics is, at least at first glance and at least in part, a mathematical machine for predicting the behaviors of microscopic particles — or, at least, of the measuring instruments we use to explore those behaviors — and in that capacity, it is spectacularly successful: in terms of power and precision, head and shoulders above any theory we have ever had. Mathematically, the theory is well understood; we know what its parts are, how they are put together, and why, in the mechanical sense (i.e., in a sense that can be answered by describing the internal grinding of gear against gear), the whole thing performs the way it does, how the information that gets fed in at one end is converted into what comes out the other. The question of what kind of a world it describes, however, is controversial; there is very little agreement, among physicists and among philosophers, about what the world is like according to quantum mechanics. Minimally interpreted, the theory describes a set of facts about the way the microscopic world impinges on the macroscopic one, how it affects our measuring instruments, described in everyday language or the language of classical mechanics. Disagreement centers on the question of what a microscopic world, which affects our apparatuses in the prescribed manner, is, or even could be, like intrinsically; or how those apparatuses could themselves be built out of microscopic parts of the sort the theory describes.^[1]

That is what an interpretation of the theory would provide: a proper account of what the world is like according to quantum mechanics, intrinsically and from the bottom up. The problems with giving an interpretation (not just a comforting, homey sort of interpretation, i.e., not just an interpretation according to which the world isn’t too different from the familiar world of common sense, but any interpretation at all) are dealt with in other sections of this encyclopedia. Here, we are concerned only with the mathematical heart of the theory, the theory in its capacity as a mathematical machine, and — whatever is true of the rest of it — this part of the theory makes exquisitely good sense.

(Spanish: [sanˈtjaɣo raˈmon i kaˈxal] ; 1 May 1852 – 17 October 1934) ^{[ 1 ] [ 2 ]} was a Spanish neuroscientist, pathologist, and histologist specializing in neuroanatomy and the central nervous system. He and Camillo Golgi received the Nobel Prize in Physiology or Medicine in 1906. ^{[ 3 ]} Ramón y Cajal was the first Spaniard to win a scientific Nobel Prize. His original investigations of the microscopic structure of the brain made him a pioneer of modern neuroscience.

GPT‑4.1, GPT‑4.1 mini, and GPT‑4.1 nano are available now to all developers.

Through efficiency improvements to our inference systems, we’ve been able to offer lower prices on the GPT‑4.1 series. GPT‑4.1 is 26% less expensive than GPT‑4o for median queries, and GPT‑4.1 nano is our cheapest and fastest model ever. For queries that repeatedly pass the same context, we are increasing the prompt caching discount to 75% (up from 50% previously) for these new models. Finally, we offer long context requests at no additional cost beyond the standard per-token costs.

The Euler formula, sometimes also called the Euler identity (e.g., Trott 2004, p. 174), states.

World Quantum Day is celebrated every April 14 – a date chosen to reflect “4.14,” the first digits of Planck’s constant (4.14×10⁻¹⁵ eV·s).