Technology Engineering The world's first nuclear clocks are ticking Nuclear clocks may surpass their atomic forebears and help solve the universe's most difficult mysteries. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Timekeeping has officially entered its nuclear age. Two independent teams, one in Vienna, Austria and another in Beijing, China have built working thorium-229 nuclear clock prototypes . Rather than track changes in an atom's electrons the way that current atomic clocks do, these nuclear versions use a laser that's tuned to the frequency where a thorium nucleus flips between two energy states for potentially even more accurate time keeping.

The US Army's laser system is part of a new generation of directed-energy weapons capable of detecting, tracking, and destroying drones with a concentrated beam of light. The Locust system, designed to be mounted onto military vehicles, targets drones with high-power lasers. This week, the US Army used a high-energy multipurpose laser to shoot down three drones near the US-Mexico border, which official reports claimed were "posing a physical threat to US military personnel and CBP partners," referring to Customs and Border Patrol. The operation took place between the night of August 25 and the early morning of August 26 in the vicinity of the Rio Grande Valley in Texas and was led by the Joint Task Force Southern Border (JTF-SB), which operates under US Northern Command. Authorities confirmed that, during the operation, agents used technology derived from the Army's High-Energy Multipurpose Laser (AMP-HEL) system, a military program focused on implementing high-energy lasers for new defense capabilities.

Reward Models (RMs) are crucial to aligning large language models (LLMs), but the degree to which an RM specialized to one task (e.g.

Reward Models (RMs) are crucial to aligning large language models (LLMs), but the degree to which an RM specialized to one task (e.g.

Recursive architectures such as Tiny Recursive Models (TRMs) perform implicit reasoning through iterative latent computation, yet the geometric structure of these reasoning trajectories remains poorly understood. We investigate the activation manifold of TRMs during recursive unrolling and find that activations occupy an effectively linear, low-dimensional subspace whose principal directions can be tracked dynamically with cheap power iterations. This suggests that weight-sharing concentrates iterative computation along a small number of dominant eigendirections, and we find that this concentration varies sharply across computational sites. We exploit this structure through LASER (Low-Rank Activation SVD for Efficient Recursion), a dynamic compression framework that maintains an evolving low-rank basis via matrix-free subspace tracking with a fidelity-triggered reset mechanism, achieving ${\sim}60\%$ activation memory savings with no statistically significant accuracy degradation. Our analysis raises questions about how recursive architectures allocate representational capacity during implicit reasoning, and whether this concentration can be exploited to improve the efficiency and stability of latent computation.

Fears over a drug cartel drone over Texas sparked a recent airspace shutdown in El Paso and New Mexico, highlighting just how tricky it can be to deploy anti-drone weapons near cities. A shocking but ultimately brief airspace closure over El Paso, Texas, and parts of New Mexico last week is stoking unease among pilots and the broader public about the status of United States anti-drone defenses. As low-cost UAV equipment proliferates around the world, analysts have repeatedly warned that destructive attacks perpetrated using drones are inevitable . It is challenging to develop nimble and safe countermeasures, though, given that things like jamming or attempting to shoot down a drone are difficult--or even impossible--to carry out safely in populated areas, much less densely populated cities. In the case of the El Paso incident, the Federal Aviation Administration originally set the airspace closure to last 10 days, but ultimately lifted it after eight hours.

Are lasers the future of anti-drone warfare? A drone appears on the grainy, gray-scaled image of the thermal camera. This is the type of drone used by groups such as Hezbollah, Hamas and the Yemeni Houthis. Seconds later, the wing of the drone snaps off, sending it tumbling down, exploding when it hits the ground. This is a video shared by the Israeli Ministry of Defence and arms producer Rafael, a hint towards the future of anti-drone warfare.

From a particle smasher encircling the moon to an "impossible" laser, five scientists reveal the experiments they would run in a world powered purely by imagination In physics, breakthroughs are rare. Experiments are slow, expensive and often end up refining, rather than rewriting, our understanding of the universe. But what if the only constraint on scientific ambition were imagination? We asked five physicists to describe the kind of experiment they would do if they didn't have to worry about budgets, engineering limitations or political realities. Not because we expect any of it to happen soon - though in a few cases, momentum is building - but because it is revealing to see where their minds go when the usual boundaries are stripped away. One researcher wants to launch radio telescopes deep into space to probe dark matter with cosmic energy flashes.

Laser cutting is a widely adopted technology in material processing across various industries, but it generates a significant amount of dust, smoke, and aerosols during operation, posing a risk to both the environment and workers' health. Speckle sensing has emerged as a promising method to monitor the cutting process and identify material types in real-time. This paper proposes a material classification technique using a speckle pattern of the material's surface based on deep learning to monitor and control the laser cutting process. The proposed method involves training a convolutional neural network (CNN) on a dataset of laser speckle patterns to recognize distinct material types for safe and efficient cutting. Previous methods for material classification using speckle sensing may face issues when the color of the laser used to produce the speckle pattern is changed. Experiments conducted in this study demonstrate that the proposed method achieves high accuracy in material classification, even when the laser color is changed. The model achieved an accuracy of 98.30 % on the training set and 96.88% on the validation set. Furthermore, the model was evaluated on a set of 3000 new images for 30 different materials, achieving an F1-score of 0.9643. The proposed method provides a robust and accurate solution for material-aware laser cutting using speckle sensing.

This research addresses the significant challenges of energy consumption and environmental impact in laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive control and the open-loop nature of CO2 laser suction pumps, this study utilizes closed-loop configurations that dynamically adjust pump power based on both the material being cut and the smoke level generated. To implement this adaptive system, diverse material classification methods are introduced, including techniques leveraging lens-less speckle sensing with a customized Convolutional Neural Network (CNN) and an approach using a USB camera with transfer learning via the pre-trained VGG16 CNN model. Furthermore, a separate DL model for smoke level detection is employed to simultaneously refine the pump's power output. This integration prompts the exhaust suction pump to automatically halt during inactive times and dynamically adjust power during operation, leading to experimentally proven and remarkable energy savings, with results showing a 20% to 50% reduction in the smoke suction pump's energy consumption, thereby contributing substantially to sustainable development in the manufacturing sector.