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Einstein Probe detects mysterious X-ray transient that doesn’t fit any known class

Astronomers have reported the discovery of an unusual X-ray transient detected by the Einstein Probe that does not fit any known class of cosmic explosions. The paper presenting its multiwavelength analysis was published in the journal Monthly Notices of the Royal Astronomical Society on June 13.

On March 5, 2024, a space telescope called the Einstein Probe—designed to scan the sky for sudden X-ray flashes—caught a brief, never-before-seen source called EP240305a. It produced two brief X-ray flares, one right after the other, separated by about 200 seconds of quiet.

Researchers quickly pointed several telescopes at this source to gather more data in X-rays, infrared, optical and radio wavelengths; the analysis of these multiwavelength data is presented in the new study.

Hotter Than a Hot Tub: The 45°C Breakthrough to Cool AI’s Biggest Machines

In favorable climates, NVIDIA’s 45-degree liquid-cooling architecture can enable chiller-less operation with dry coolers, reducing facility cooling water consumption from roughly 2.6 million gallons per megawatt per year for conventional cooling-tower-based systems to near zero — up to a 100% reduction in water use.

The reason: traditional air-cooled data centers depend on large volumes of cooled air to remove heat from IT equipment, often requiring energy-intensive cooling infrastructure during hot weather. With NVIDIA’s 45-degree liquid cooling, heat is captured directly at the chip and transported through liquid loops operating at much higher temperatures, allowing outdoor dry coolers to reject heat efficiently for much of the year while significantly reducing mechanical cooling requirements and facility water consumption.

The data center ambient temperature is flexible — warm summer air is fine — because nothing in the server depends on cool air. The liquid does all the work — and the same liquid can be recirculated in a closed loop so no new water is consumed to cool the chips.

NVIDIA’s latest AI servers can run on coolant warmer than a hot tub — and that counterintuitive choice is one of the biggest efficiency leaps in data center history.

SpaceX Is HIDING Something! | Starship Update

SpaceX continued preparing for Starship Flight 13 this week with an incredible series of Pad 2 deluge tests, ongoing work at the Gigabay, Launch Pad 1 refurbishment, LC-39A proof testing, SLC-37 construction, McGregor Raptor testing, and activity across Massey’s Test Site.

This week we take a closer look at the massive water deluge system that will support future high-cadence Starship operations, progress on Florida’s launch infrastructure, and the mysterious covered structure at McGregor that continues to spark speculation.

🚀 In this episode:

• Pad 2 conducts an unprecedented series of deluge tests • Gigabay construction reaches another milestone • Pad 1 launch mount refurbishment continues • LC-39A \.

Homing pigeon navigation relies on superparamagnetic macrophages under overcast conditions

Birds use a variety of navigational strategies, including the geomagnetic field, especially when other cues are not available, such as under overcast or nocturnal conditions. Magnetite particles in the beak, cryptochromes in the eye, cellular ion-channel alterations, and changes in the vestibular system have been proposed to explain magnetoreception, but the exact mechanisms remain debated. Here, we used physical, morphological, functional, and genomic assays to identify the presence of superparamagnetic macrophages in the liver. We found that after macrophage depletion, pigeons flying under overcast conditions lacked their usual orientation capabilities. Orientation was unimpaired in birds without macrophages when the sun was visible, suggesting that this was their primary cue.

Mouse moves unlock realistic AI video control with no extra computing cost

A technology developed at the Technion enables ordinary users to create realistic video clips intuitively, without the need for massive computing resources. Called Time-to-Move (TTM), it offers unprecedented control over the movement of objects and characters in AI-generated videos using nothing more than mouse movements, eliminating the need for complex and expensive infrastructure or training on millions of videos.

Dr. Or Litany of the Henry and Marilyn Taub Faculty of Computer Science, who led the research together with faculty colleague Prof. Ron Kimmel and students Asaf Singer, Noam Rotstein and Amir Mann, presented the work at the International Conference on Learning Representations (ICLR) 2026 conference, held in Brazil last month. ICLR is one of the world’s leading conferences in deep learning and AI.

“Our development,” Litany explains, “solves one of the main limitations of AI-based video generation: the difficulty of precisely controlling the movement of objects and characters over time. TTM does not require retraining and can be integrated as a plug-in into existing video models. Unlike previous approaches, which require model-specific adaptation and substantial computing resources, this technology operates with no additional computational cost. In doing so, it helps democratize AI video creation by expanding access beyond giant companies such as Google and Meta.”

Feeding data to AI to speed up drug discovery

Developing new medicines can require thousands of chemistry experiments to identify the right recipe for a safe, effective and ideally affordable drug.

The process is slow and labor-intensive, and many of the reactions depend on hard-to-source metals that act as essential catalysts.

While artificial intelligence is helping speed up the process of drug discovery, it can only learn from the data available, and when it comes to chemical reactions, the large, high-quality data sets needed to train powerful AI tools aren’t there.

Can String Theory Be Explained with No Strings Attached?

Using a “bootstrap” approach, researchers show that a small set of assumptions may naturally lead to a string-theory description of certain high-energy processes.

String theory has been a remarkably influential conceptual framework for modern theoretical physics. While its description of nature in terms of tiny strings captures the imagination, the string framework has had profound impact in a broad range of subfields, going well beyond its lead role as a viable theory of quantum gravity. For instance, it has led to deeper understanding of black holes and their relation to entanglement and quantum information [1], and it has provided theoretical benchmarks for explaining quark–gluon plasma observations in quantum chromodynamics [2]. As a complement to direct calculations, theoretical physicists would like to understand string theory as emerging from a set of fundamental principles that any theory of nature must respect. Consistency with these bedrock conditions, so goes the idea, could perhaps make string theory inevitable.

Broken time-reversal symmetry phase in kagome metals may establish conditions for superconductivity

Physicists have long suspected that a peculiar quantum state lurks inside a class of materials known as kagome metals, but proving its existence has been elusive. Now, a team led by Yeongkwan Kim at the Korea Advanced Institute of Science and Technology has performed experiments on a kagome metal that provide the strongest evidence yet for this exotic state.

Published in Nature Physics, the team’s results could shed new light on how these materials transition into superconductivity.

Experiment upends beliefs on how electrons actually behave in warm dense matter

Researchers at European XFEL, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Rostock University and other collaborating institutions have used high-precision experiments to demonstrate that the most widely used models for the behavior of electrons in warm dense matter are inaccurate. Warm dense matter is challenging to study, but also is of key importance for a plethora of research, including the investigation of planetary interiors, materials science and laser fusion experiments. The study is published in Physical Review Letters.

In warm dense matter, electron density oscillates. The collective oscillations are called plasmons. They carry important information and can be observed using X-rays, resulting in scattering spectra—abstract images captured by a detector. In many experiments, these spectra are interpreted using simplified uniform electron gas models. However, the new measurements show that for warm dense aluminum, these models consistently overestimate the plasmon energy by up to about 25% (about 8 electronvolts) and fail to reproduce the full measured shape of the signal.

“Our measurements are precise enough to clearly distinguish between competing models,” says Dr. Thomas Preston of European XFEL. “That is important because these models are widely used to diagnose extreme states of matter. If the model is incorrect, that leads to inaccurately inferred properties.”

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