Researchers from the Department of Energy’s Oak Ridge National Laboratory have taken a major step forward in using quantum mechanics to enhance sensing devices, a new advancement that could be used in a wide range of areas, including materials characterization, improved imaging and biological and medical applications.
The second ATLAS study, presented recently at the 17th International Workshop on Top Quark Physics, broke new ground by providing the first dedicated ATLAS measurement of how often top-quark pairs are produced along with jets originating from charm quarks (c-jets).
ATLAS physicists analyzed events with one or two leptons (electrons and muons), using a custom flavor-tagging algorithm developed specifically for this study to distinguish c-jets from b-jets and other jets. This algorithm was essential because c-jets are even more challenging to identify than b-jets, as they have shorter lifetimes and produce less distinct signatures in the ATLAS detector.
The study found that most theoretical models provided reasonable agreement with the data, though they generally underpredicted the production rates of c-jets. These results, which for the first time separately determined the cross-sections for single and multiple charm-quark production in top-quark-pair events, highlight the need for refined simulations of these processes to improve future measurements.
Neutrons are among the basic building blocks of matter. As long as they are part of a stable atomic nucleus, they can stay there for arbitrary periods of time. However, the situation is different for free neutrons: They decay—after about 15 minutes, on average.
Thin-film lithium niobate is an emerging nonlinear integrated photonics platform ideally suited for quantum applications. Through spontaneous parametric down-conversion (SPDC), it can generate correlated photon pairs for quantum key distribution, teleportation, and computing.
While infrared cameras are valuable tools for enhanced vision, food and plant quality control, security, etc, today’s cameras often face significant drawbacks. For instance, they are bulky and power-hungry, requiring cooling systems that limit their functionalities.
More importantly, current semiconductor-based technology used in the cameras only captures a narrow band of the infrared spectrum based on the absorption band of the semiconductor detector. This means that every application would need a separate camera.
“Due to the complications of today’s bulky, power-hungry and expensive infrared imaging technology, we are unlikely to have an infrared camera at home. However, nonlinear frequency conversion, a process that manipulates and translates electromagnetic signals across various frequency regimes, holds a massive potential to revolutionize infrared detection technology,” said Prof Mohsen Ramhami, the leader of Advanced Optics and Photonics Lab, and a UK Research and Innovation Future Leaders Fellow.
Researchers from the Institute of Industrial Science, The University of Tokyo, have developed a method to control the direction of heat flow in crystals. This miniature device could eventually be used to create advanced thermal-management systems in electronic devices to prevent overheating.
7 linux distributions that feel just like windows.
I often think of Windows 10 as “Windows 8.1 done right”, and Windows 11 as a natural evolution of that refinement, with plenty of improvements under the hood.
However, considering that Windows is still a closed, commercial platform, many users with concerns about privacy or dissatisfaction with Windows 11 may continue to seek alternative operating systems that offer more control while providing a similar experience to the Windows GUI.
Continue reading “7 Windows-Like Linux Distros You Should Try Out” »
In recent years, artificial intelligence (AI) and deep learning models have advanced rapidly, becoming easily accessible. This has enabled people, even those without specialized expertise, to perform various tasks with AI. Among these models, generative adversarial networks (GANs) stand out for their outstanding performance in generating new data instances with the same characteristics as the training data, making them particularly effective for generating images, music, and text.
GANs consist of two neural networks, namely, a generator that creates new data distributions starting from random noise, and a discriminator which checks whether the generated data distribution is “real” (matching the training data) or “fake.” As training progresses, the generator improves at generating realistic distributions, and the discriminator at identifying the generated data as fake.
GANs use a loss function to measure differences between the fake and real distributions. However, this approach can cause issues like gradient vanishing and unstable learning, directly impacting stability and efficiency. Despite considerable progress in improving GANs, including structural modifications and loss function adjustments, challenges such as gradient vanishing and mode collapse, where the generator produces a limited variety, continue to limit their applicability.
