Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science

Matter wave

Schrödinger applied Hamilton’s optico-mechanical analogy to develop his wave mechanics for subatomic particles. [ 67 ] : xi Consequently, wave solutions to the Schrödinger equation share many properties with results of light wave optics. In particular, Kirchhoff’s diffraction formula works well for electron optics [ 29 ] : 745 and for atomic optics. [ 68 ] The approximation works well as long as the electric fields change more slowly than the de Broglie wavelength. Macroscopic apparatus fulfill this condition; slow electrons moving in solids do not.

Cracking a long-standing weakness in a classic algorithm for programming reconfigurable chips

Researchers from EPFL, AMD, and the University of Novi Sad have uncovered a long-standing inefficiency in the algorithm that programs millions of reconfigurable chips used worldwide, a discovery that could reshape how future generations of these are designed and programmed.

Many industries, including telecoms, automotive, aerospace and rely on a special breed of chip called the Field-Programmable Gate Array (FPGA). Unlike traditional chips, FPGAs can be reconfigured almost endlessly, making them invaluable in fast-moving fields where designing a custom chip would take years and cost a fortune. But this flexibility comes with a catch: FPGA efficiency depends heavily on the software used to program them.

Since the late 1990s, an algorithm known as PathFinder has been the backbone of FPGA routing. Its job: connecting thousands of tiny circuit components without creating overlaps.

View a PDF of the paper titled Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play, by Qinsi Wang and 8 other authors

Although reinforcement learning (RL) can effectively enhance the reasoning capabilities of vision-language models (VLMs), current methods remain heavily dependent on labor-intensive datasets that require extensive manual construction and verification, leading to extremely high training costs and consequently constraining the practical deployment of VLMs. To address this challenge, we propose Vision-Zero, a domain-agnostic framework enabling VLM self-improvement through competitive visual games generated from arbitrary image pairs. Specifically, Vision-Zero encompasses three main attributes: Strategic Self-Play Framework: Vision-Zero trains VLMs in “Who Is the Spy”-style games, where the models engage in strategic reasoning and actions across multiple roles. Through interactive gameplay, models autonomously generate their training data without human annotation. Gameplay from Arbitrary Images: Unlike existing gamified frameworks, Vision-Zero can generate games from arbitrary images, thereby enhancing the model’s reasoning ability across diverse domains and showing strong generalization to different tasks. We demonstrate this versatility using three distinct types of image datasets: CLEVR-based synthetic scenes, charts, and real-world images. Sustainable Performance Gain: We introduce Iterative Self-Play Policy Optimization (Iterative-SPO), a novel training algorithm that alternates between Self-Play and reinforcement learning with verifiable rewards (RLVR), mitigating the performance plateau often seen in self-play-only training and achieving sustained long-term improvements. Despite using label-free data, Vision-Zero achieves state-of-the-art performance on reasoning, chart question answering, and vision-centric understanding tasks, surpassing other annotation-based methods. Models and code has been released at https://github.com/wangqinsi1/Vision-Zero.

AI techniques excel at solving complex equations in physics, especially inverse problems

Differential equations are fundamental tools in physics: they are used to describe phenomena ranging from fluid dynamics to general relativity. But when these equations become stiff (i.e. they involve very different scales or highly sensitive parameters), they become extremely difficult to solve. This is especially relevant in inverse problems, where scientists try to deduce unknown physical laws from observed data.

To tackle this challenge, the researchers have enhanced the capabilities of Physics-Informed Neural Networks (PINNs), a type of artificial intelligence that incorporates physical laws into its .

Their approach, reported in Communications Physics, combines two innovative techniques: Multi-Head (MH) training, which allows the neural network to learn a general space of solutions for a family of equations—rather than just one specific case—and Unimodular Regularization (UR), inspired by concepts from differential geometry and , which stabilizes the learning process and improves the network’s ability to generalize to new, more difficult problems.

AI tensor network-based computational framework cracks a 100-year-old physics challenge

Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.

The Tensors for High-dimensional Object Representation (THOR) AI framework employs tensor network algorithms to efficiently compress and evaluate the extremely large configurational integrals and central to determining the thermodynamic and mechanical properties of materials.

The framework was integrated with machine learning potentials, which encode interatomic interactions and dynamical behavior, enabling accurate and scalable modeling of materials across diverse physical conditions.

Compact camera uses 25 color channels for high-speed, high-definition hyperspectral video

A traditional digital camera splits an image into three channels—red, green and blue—mirroring how the human eye perceives color. But those are just three discrete points along a continuous spectrum of wavelengths. Specialized “spectral” cameras go further by sequentially capturing dozens, or even hundreds, of these divisions across the spectrum.

This process is slow, however, meaning that hyperspectral cameras can only take still images, or videos with very low frame rates, or frames per second (fps). But what if a high-fps video camera could capture dozens of wavelengths at once, revealing details invisible to the naked eye?

Now, researchers at the University of Utah’s John and Marcia Price College of Engineering have developed a new way of taking a high-definition snapshot that encodes spectral data into images, much like a traditional camera encodes color. Instead of a filter that divides light into three color channels, their specialized filter divides it into 25. Each pixel stores compressed spectral information along with its , which computer algorithms can later reconstruct into a “cube” of 25 separate images—each representing a distinct slice of the visible spectrum.

Physics-based algorithm enables nuclear microreactors to autonomously adjust power output

A new physics-based algorithm clears a path toward nuclear microreactors that can autonomously adjust power output based on need, according to a University of Michigan-led study published in Progress in Nuclear Energy.

Easily transportable and able to generate up to 20 megawatts of thermal energy for heat or electricity, nuclear microreactors could be useful in such as , disaster zones, or even cargo ships, in addition to other applications.

If integrated into an , nuclear microreactors could provide stable, carbon-free energy, but they must be able to adjust to match shifting demand—a capability known as load following. In large reactors, staff make these adjustments manually, which would be cost-prohibitive in remote areas, imposing a barrier to adoption.

Time crystals arise from quantum interactions once thought to prevent their formation

Nature has many rhythms: the seasons result from Earth’s movement around the sun, the ticking of a pendulum clock results from the oscillation of its pendulum. These phenomena can be understood with very simple equations. However, regular rhythms can also arise in a completely different way—by themselves, without an external clock, through the complex interaction of many particles. Instead of uniform disorder, a fixed rhythm emerges—this is referred to as a “time crystal.”

Calculations by TU Wien (Vienna) now show that such time crystals can also be generated in a completely different way than previously thought. The quantum physical correlations between the particles, which were previously thought to be harmful for the emergence of such phenomena, can actually stabilize time crystals. This is a surprising new insight into the quantum physics of many-particle systems.

The findings are published in the journal Physical Review Letters.

/* */