Earthquake forecasting is a critical task for natural risk management, infrastructure resilience planning, and emergency response operations. For Central Asia, and the Tian Shan mountain system in particular, this problem carries heightened importance due to high tectonic activity, complex geodynamics, and pronounced spatiotemporal heterogeneity of seismic processes. In the applied setting, the goal is not a deterministic forecast of individual events, but a macroscopic forecast of seismicity intensity: estimating the expected number of earthquakes with magnitude M 3.0 on a spatial grid at a weekly horizon. Historically, count data forecasting in fixed spatiotemporal cells has been formulated within the Poisson framework. However, its key assumption--equality of the conditional mean and conditional variance--is systematically violated in real seismological data. Earthquakes exhibit pronounced clustering associated with swarm activity, foreshock-aftershock sequences, and episodes of anomalous activity, resulting in overdispersion in which the variance substantially exceeds the mean. Under these conditions, uncritical application of the Poisson distribution leads to biased uncertainty estimates and, consequently, to underestimation of the risk of extreme scenarios. Despite the widespread adoption of machine learning methods in seismological problems, a substantial portion of existing work remains methodologically vulnerable. On one hand, several approaches apply continuous regression loss functions and metrics (e.g., MSE), ignoring the

One line tucked into a federal highway bill would strip funds from cities and states unless they kill their automated plate tracking programs--effectively banning the tech for all but toll collection. US lawmakers plan to introduce an amendment Thursday at a House committee markup hearing that would prohibit any recipient of federal highway funding from using automated license plate readers for any purpose other than tolling--a sweeping restriction that, if adopted, would bring an immediate end to state and local ALPR programs across the United States. The amendment, obtained first by WIRED, is sponsored by Representative Scott Perry, a Pennsylvania Republican and Freedom Caucus member, and Representative Jesús "Chuy" García, an Illinois progressive whose state has become a flash point in the national fight over ALPR misuse. The House Transportation and Infrastructure Committee will mark up the underlying bill--a $580 billion, five-year reauthorization of federal surface transportation programs--at 10 am ET on Thursday. Neither Perry nor García's offices immediately responded to WIRED's request for comment. The amendment runs a single sentence: "A recipient of assistance under Title 23, United States Code, may not use automated license plate readers for any purpose other than tolling."

What do researchers of artificial intelligence, medicine and climate change have in common? They could all learn from the story of Rumpelstiltskin. As the fairy tale teaches us, knowing something's "true name", an ancient concept in folklore, gives us power over it. While this may not seem very scientific, psychologists have repeatedly found that your name changes how people perceive you . The same may be true for scientific terms. Take "artificial intelligence": while the technology is undeniably impressive, much of the drama around AI might have been avoided if we used the less grandiose name "machine learning".

Ukrainian mid-range strikes deal double blow to Russia's war effort KYIV/LONDON - From burning oil refineries to a stalling ground offensive, Russia is suffering problems in its war against Ukraine that partly stem from a growing Ukrainian military strength: the use of medium-range drone attacks. By targeting Russian air defenses and logistics dozens of kilometers behind front lines, Ukraine is disrupting Russia's battlefield advances and opening the way for long-range strikes on Russian oil and military facilities, said two Ukrainian commanders, two drone specialists and three military analysts. Ukrainian officials say more resources have in recent months been poured into "middle strikes," typically ranging between 30 kilometers and 180 km behind front lines. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

U.S. seeking transfer of intellectual property rights in drone deal, Kyiv says Through a combination of new technology and tactics, Kyiv's forces have been able to strike deep into Russian territory, to slow and in some cases even reverse battlefield gains by Moscow's bigger army and inflict significant damage on oil facilities that help finance the Kremlin's war machine. Kyiv has said that the U.S. is seeking the transfer of technology and access to intellectual property rights from Ukraine as part of a drone deal that is awaiting approval at the highest political level, a person familiar with the matter has said. The U.S. Department of Defense has asked to test a range of Ukrainian defense products, including drones and electronic warfare systems, as Washington is considering their potential purchase for military use, the official said. The agreement has not been finalized, the person added, speaking on condition of anonymity because the discussions are private. Growing interest from the U.S. shows how the world's largest military is looking to tap into the drone expertise Ukraine has acquired over four years fighting against the Russian invasion.

We propose non-parametric estimators for the average run length (ARL) and average detection delay (ADD) in quickest changepoint detection (QCD) under finite and irregular sequence lengths. Although ARL and ADD are widely used as optimality criteria in theoretical and simulation studies, their application to real-world datasets is hindered by limited and irregular sequence lengths. To address this issue, we propose non-parametric estimators for the ARL and ADD, termed KM-ARL and KM-ADD, by drawing an analogy between QCD and survival analysis to model detection probabilities under sequence truncation. We derive estimation bias bounds and prove that they are asymptotically unbiased unless extrapolation is required. Experiments on simulated and real-world datasets demonstrate their practical utility, enhancing robustness against limited and irregular sequence lengths, improving interpretability, and facilitating empirical, intuitive model selection. Our Python code is provided at https://github.com/TaikiMiyagawa/Kaplan-Meier-Average-Run-Length, offering ready-to-use implementations for practitioners.

Matrix-structured parameters frequently appear in many artificial intelligence models such as large language models. More recently, an efficient Muon optimizer is designed for matrix parameters of large-scale models, and shows markedly faster convergence than the vector-wise algorithms. Although some works have begun to study convergence properties (i.e., optimization error) of the Muon optimizer, its generalization properties (i.e., generalization error) is still not established. Thus, in this paper, we study generalization error of the Muon optimizer based on algorithmic stability and mathematical induction, and prove that the Muon has a generalization error of $O\big(\frac{1}{Nκ^{T}}\big)$, where $N$ is training sample size, and $T$ denotes iteration number, and $κ>0$ denotes minimum difference between singular values of gradient estimate. To enhance generalization of the Muon, we propose an effective mixed Muon (MiMuon) optimizer by cautiously using orthogonalization of gradient, which is a hybrid of Muon and momentum-based SGD optimizers. Then we prove that our MiMuon optimizer has a lower generalization error of $O\big(\frac{1}{N}\big)$ than $O\big(\frac{1}{Nκ^{T}}\big)$ of Muon optimizer, since $κ$ generally is very small. Meanwhile, we also studied the convergence properties of our MiMuon algorithm, and prove that our MiMuon algorithm has the same convergence rate of $O(\frac{1}{T^{1/4}})$ as the Muon algorithm. Some numerical experimental results on training large models including Qwen3-0.6B and YOLO26m demonstrate efficiency of the MiMuon optimizer.

Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models often require sequential solvers prone to drift. To bridge this gap, we present Latent Laplace Diffusion (LLapDiff), a generative framework that models the target as a low-dimensional latent trajectory, enabling horizon-wide generation without step-by-step integration over physical time. We guide the reverse process utilizing a stable modal parameterization motivated by stochastic port-Hamiltonian dynamics, and parameterize its mean evolution in the Laplace domain via learnable complex-conjugate poles, enabling direct evaluation over irregular timestamps. We also link continuous dynamics to irregular observations through renewal-averaging analysis, which maps sampling gaps to effective event-domain poles and motivates a gap-aware history summarizer. Extensive experiments show that LLapDiff improves over baselines in long-horizon forecasting, and its continuous-time generative nature supports missing-value imputation by querying the same model at historical timestamps. Code is available at https://github.com/pixelhero98/LLapDiffusion.

Israeli air strikes have killed at least 19 people in southern Lebanon, the country's health ministry has said. Ten of them, including three children and three women, were killed in a single attack that hit a house in the town of Deir Qanoun, the ministry said. Lebanon was drawn into the war on 2 March, when the Iran-backed armed Shia Islamist group Hezbollah fired rockets at Israel in retaliation for US-Israeli strikes that killed Iran's supreme leader. The latest deaths less than a week after the US said that Lebanon and Israel had agreed to extend a ceasefire by 45 days, with the two sides set to resume talks at the beginning of June. Despite the extension, both Israel and Hezbollah have continued to exchange fire, especially in southern Lebanon.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' More than 15,800 people killed in Russia's all-out war on Ukraine: UN The United Nations has said 15,850 people, including 791 children, have been killed in Ukraine since Russia's full-scale invasion of the neighbouring country in February 2022. The "actual figures are likely significantly higher", Kayoko Gotoh, Europe and Central Asia director of the UN's Department of Political and Peacebuilding Affairs (DPPA), told the UN Security Council on Tuesday. US President Donald Trump has attempted to mediate and announced the most recent three-day ceasefire earlier this month, but fighting has resumed. Tuesday's Russian attacks on Ukraine killed at least six people. A 15-year-old boy was among three people killed in a Russian ballistic missile attack on the city of Pryluky in north-central Ukraine's Chernihiv region on Tuesday morning, according to the State Emergency Service of Ukraine.