'No-one feels safe now': Residents of Romanian city hit by drone share fears In some parts of Europe, Russia's full-scale invasion of Ukraine can feel like a distant threat. But in Romania, that war is right next door and increasingly dangerous. In Galati, there is an apartment block with a hole in the roof that proves it. Residents have just begun returning to check on their homes, after an attack drone slammed into the building early on Friday as dozens of people slept. It sparked a fire and panic.

Romanian President Nicusor Dan says that the Russian consul in the southeastern city of Constanta will be expelled and the consulate shut down after a drone intended for Ukraine crashed into an apartment complex in the border town of Galati. Vance says US, Iran have made "a lot of progress" towards deal

Romania and its NATO allies have reacted angrily after a Russian drone crashed into an apartment building in eastern Romania, injuring two people. The Ministry of Foreign Affairs in Bucharest on Friday labelled the crash of the drone, part of an overnight attack aimed at Ukraine, a serious violation of international law. The incident is just the latest incursion along the alliance's eastern flank, raising concern that the risk of an open confrontation between Russia and NATO states is rising. Romania said the overnight drone was tracked by radar in its airspace before crashing onto the roof of a residential building in the city of Galati. Two F-16 fighter jets and a helicopter were scrambled, as authorities issued emergency alerts to residents.

Romania says a drone struck an apartment building in Galați, injuring a woman and child, marking the first time a Russian drone hit a populated area in the NATO member state.

A Russian drone hit an apartment building in Romania, the country's defence ministry said early on Friday, causing a fire and injuring two people. The drone crashed in the eastern city of Galati as Russia carried out attacks in Ukraine near the border, the ministry said in a statement. The Romanian General Inspectorate for Emergency Situations said the drone's entire explosive payload detonated, causing a fire on the 10th floor of the residential building. Russian drones have strayed across the border of the Nato member country a number of times during the four-year war with Ukraine, but this was the first time citizens from Romania had been hurt. Russia has yet to comment on the incident. This incident represents a serious and irresponsible escalation on the part of the Russian Federation, Romania's foreign ministry said, adding Bucharest had informed the Nato secretary general and requested measures to accelerate the transfer of anti-drone capabilities to Romania.

Inference-time LLM alignment methods, particularly activation steering, offer an alternative to fine-tuning by directly modifying activations during generation. Existing methods, however, often rely on non-anticipative interventions that ignore how perturbations propagate through transformer layers and lack online error feedback, resulting in suboptimal, open-loop control. To address this, we show empirically that, despite the nonlinear structure of transformer blocks, layer-wise dynamics across multiple LLM architectures and scales are well-approximated by locally-linear models. Exploiting this property, we model LLM inference as a linear time-varying dynamical system and adapt the classical linear quadratic regulator to compute feedback controllers using layer-wise Jacobians, steering activations toward desired semantic setpoints in closed-loop with minimal computational overhead and no offline training. We also derive theoretical bounds on setpoint tracking error, enabling formal guarantees on steering performance. Using a novel adaptive semantic feature setpoint signal, our method yields robust, fine-grained behavior control across models, scales, and tasks, including state-of-the-art modulation of toxicity, truthfulness, refusal, and arbitrary concepts, surpassing baseline steering methods. Our code is available at: https://github.com/trustworthyrobotics/lqr-activation-steering

MathNet contains 30,000 free math problems collected over half a century. 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. The International Mathematical Olympiad was first held in Romania in 1959. Breakthroughs, discoveries, and DIY tips sent six days a week. In 1959, countries around the world sent their most talented students to Romania to compete in the first-ever International Mathematical Olympiad (IMO).

For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations and symbolic regression has become a popular tool to discover these missing physics. Universal differential equations employ neural networks to represent missing parts of the model structure, and symbolic regression aims to make these neural networks interpretable. These machine learning techniques require high-quality data to successfully recover the true model structure. To gather such informative data, a sequential experimental design technique is developed which is based on optimally discriminating between the plausible model structures suggested by symbolic regression. This technique is then applied to discovering the missing physics of a bioreactor.

Test-time adaptation (TTA) enables neural forecasters to adapt to distribution shifts in streaming time series, but existing methods apply the same adaptation intensity regardless of the nature of the shift. We propose Regime-Guided Test-Time Adaptation (RG-TTA), a meta-controller that continuously modulates adaptation intensity based on distributional similarity to previously-seen regimes. Using an ensemble of Kolmogorov-Smirnov, Wasserstein-1, feature-distance, and variance-ratio metrics, RG-TTA computes a similarity score for each incoming batch and uses it to (i) smoothly scale the learning rate -- more aggressive for novel distributions, conservative for familiar ones -- and (ii) control gradient effort via loss-driven early stopping rather than fixed budgets, allowing the system to allocate exactly the effort each batch requires. As a supplementary mechanism, RG-TTA gates checkpoint reuse from a regime memory, loading stored specialist models only when they demonstrably outperform the current model (loss improvement >= 30%). RG-TTA is model-agnostic and strategy-composable: it wraps any forecaster exposing train/predict/save/load interfaces and enhances any gradient-based TTA method. We demonstrate three compositions -- RG-TTA, RG-EWC, and RG-DynaTTA -- and evaluate 6 update policies (3 baselines + 3 regime-guided variants) across 4 compact architectures (GRU, iTransformer, PatchTST, DLinear), 14 datasets (6 real-world multivariate benchmarks + 8 synthetic regime scenarios), and 4 forecast horizons (96, 192, 336, 720) under a streaming evaluation protocol with 3 random seeds (672 experiments total). Regime-guided policies achieve the lowest MSE in 156 of 224 seed-averaged experiments (69.6%), with RG-EWC winning 30.4% and RG-TTA winning 29.0%. Overall, RG-TTA reduces MSE by 5.7% vs TTA while running 5.5% faster; RG-EWC reduces MSE by 14.1% vs standalone EWC.

Model-based design of experiments (MBDOE) is essential for efficient parameter estimation in nonlinear dynamical systems. However, conventional adaptive MBDOE requires costly posterior inference and design optimization between each experimental step, precluding real-time applications. We address this by combining Deep Adaptive Design (DAD), which amortizes sequential design into a neural network policy trained offline, with differentiable mechanistic models. For dynamical systems with known governing equations but uncertain parameters, we extend sequential contrastive training objectives to handle nuisance parameters and propose a transformer-based policy architecture that respects the temporal structure of dynamical systems. We demonstrate the approach on four systems of increasing complexity: a fed-batch bioreactor with Monod kinetics, a Haldane bioreactor with uncertain substrate inhibition, a two-compartment pharmacokinetic model with nuisance clearance parameters, and a DC motor for real-time deployment.