#YonhapInfomax #Trump #Ukraine #PeaceAgreement #Negotiations #Breakthrough #Economics #FinancialMarkets #Banking #Securities #Bonds #StockMarket
https://en.infomaxai.com/news/articleView.html?idxno=92431
Breakthrough in antimatter production
https://home.cern/news/news/experiments/breakthrough-antimatter-production
#HackerNews #antimatter #breakthrough #CERN #physics #science #news
In a paper published today in Nature Communications, researchers at the ALPHA experiment at CERN’s Antimatter Factory report a new technique that allows them to produce over 15 000 antihydrogen atoms – the simplest form of atomic antimatter – in a matter of hours. “These numbers would have been considered science fiction 10 years ago,” said Jeffrey Hangst, spokesperson for the ALPHA experiment. “With larger numbers of antihydrogen atoms now more readily available, we can investigate atomic antimatter in greater detail and at a faster pace than before.” To create atomic antihydrogen (a positron orbiting an antiproton), the ALPHA collaboration must produce and trap clouds of antiprotons and positrons separately, then cool them down and merge them so that antihydrogen atoms can form. This process has been refined and steadily improved over many years. But now, using a pioneering technique to cool the positrons, the ALPHA team has increased the rate of production of antihydrogen atoms eightfold. This spectacular advance in the production rate is all down to how the positrons are prepared. First, the positrons are collected from a radioactive form of sodium and contained in what is known as a Penning trap, where fine-tuned electromagnetic fields hold the antiparticles in place. However, they do not remain still. Like a tiger in a zoo, the positrons circle their cage, causing them to lose energy. This cools the cloud of positrons, but not enough for them to efficiently merge with the antiprotons to form antihydrogen atoms. So, the ALPHA team recently tried a new approach, which was to add a cloud of laser-cooled beryllium ions to the trap so that the positrons would lose energy in a process called sympathetic cooling. This got the positron cloud down to a temperature of around -266 °C, making it much more likely to form antihydrogen atoms when mixed with the antiprotons. This approach allowed over 15 000 antihydrogen atoms to be accumulated in under seven hours. To put this into perspective, it took a previous experiment 10 weeks to accumulate the 16 000 antihydrogen atoms required to measure the spectral structure of antihydrogen with unprecedented precision. “The new technique is a real game-changer when it comes to investigating systematic uncertainties in our measurements. We can now accumulate antihydrogen overnight and measure a spectral line the following day”, said Niels Madsen, deputy spokesperson for ALPHA and leader of the positron-cooling project. Using this approach for cooling positrons, the ALPHA experiment produced over 2 million antihydrogen atoms over the course of the experimental runs of 2023–24. And this year, the researchers are making use of the unprecedented numbers of antihydrogen atoms to study the effect of gravity on antimatter as part of the ALPHA-g experiment. This technique will allow even more precise measurements to be made and make it possible to probe deeper into the properties and behaviour of atomic antimatter.
🤖🌌 Researchers in #Japan developed the first #galaxy simulation tracking over 100 billion individual #stars by combining #AI with traditional #physics models, completing in 115 days what would normally take 36 years.
👉 https://www.sciencedaily.com/releases/2025/11/251116105515.htm

Researchers combined deep learning with high-resolution physics to create the first Milky Way model that tracks over 100 billion stars individually. Their AI learned how gas behaves after supernovae, removing one of the biggest computational bottlenecks in galactic modeling. The result is a simulation hundreds of times faster than current methods.

Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of practical guidance and theory has led to ad-hoc R&D. We present a comprehensive theory of JEPAs and instantiate it in {\bf LeJEPA}, a lean, scalable, and theoretically grounded training objective. First, we identify the isotropic Gaussian as the optimal distribution that JEPAs' embeddings should follow to minimize downstream prediction risk. Second, we introduce a novel objective--{\bf Sketched Isotropic Gaussian Regularization} (SIGReg)--to constrain embeddings to reach that ideal distribution. Combining the JEPA predictive loss with SIGReg yields LeJEPA with numerous theoretical and practical benefits: (i) single trade-off hyperparameter, (ii) linear time and memory complexity, (iii) stability across hyper-parameters, architectures (ResNets, ViTs, ConvNets) and domains, (iv) heuristics-free, e.g., no stop-gradient, no teacher-student, no hyper-parameter schedulers, and (v) distributed training-friendly implementation requiring only $\approx$50 lines of code. Our empirical validation covers 10+ datasets, 60+ architectures, all with varying scales and domains. As an example, using imagenet-1k for pretraining and linear evaluation with frozen backbone, LeJEPA reaches 79\% with a ViT-H/14. We hope that the simplicity and theory-friendly ecosystem offered by LeJEPA will reestablish self-supervised pre-training as a core pillar of AI research (\href{git@github.com:rbalestr-lab/lejepa.git}{GitHub repo}).
Breakthrough chemotherapy treatment eliminates side effects and is 20,000 times stronger
https://fed.brid.gy/r/https://www.upworthy.com/chemotherapy-treatment-eliminates-side-effects
We've all experienced those magical instances when complex problems become simple. The breakthrough that seemed impossible becomes obvious in retrospective analysis.
Share this with someone struggling through a difficult project phase. The solution is closer than they think.
Describe your most satisfying problem-solving moment. When did impossible suddenly become inevitable?
#motorcycle #breakthrough #solution #clarity #success
🎥 👉 @white_fortyeight_48
Bộ Chính trị vừa phê duyệt cơ chế đặc biệt dành cho cán bộ tài năng, chuyên gia giỏi thông qua Kết luận số 205. Quyết định này nhằm tạo đột phá trong công tác tuyển dụng, bố trí, sử dụng và đãi ngộ cán bộ, hướng tới nâng cao chất lượng nguồn nhân lực quản lý.
#chínhtri #cánbộ #tuyểndụng #độtphá #vietnamnet #politics #talentmanagement #breakthrough #việtnam
