What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia strikes Ukraine's Odesa port, kills railway worker in Zaporizhia Russian drones have attacked Ukraine's main Black Sea port in the southern city of Odesa and a railway in the region of Zaporizhia, killing a train driver, according to Ukraine's Deputy Prime Minister Oleksii Kuleba. The overnight attacks damaged the infrastructure of the Odesa port, including berths, warehouses, railway infrastructure and port operators' facilities, Kuleba said in a statement on X on Wednesday. Kuleba said this is "another proof of terrorism, Russia is at war against peaceful people, against those who were simply doing their job and keeping the country moving". Russia also launched several drones and missiles on a flight path near the disused Chornobyl nuclear plant, elevating the risk of a significant accident, according to Ukraine's top state prosecutor. This comes as Ukraine prepares to mark the 40th anniversary of the 1986 Chornobyl disaster on Sunday.

We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multiple single-antenna user equipments (UEs) with a unique beam per UE and discrete data transmission rates. BSs learn about transmission success based on ACK/NACK feedback. To encode service goals, we introduce a satisficing throughput threshold $τ_r$ and cast joint beam and rate adaptation as a combinatorial semi-bandit over beam-rate tuples. Within this framework, we propose SAT-CTS, a lightweight, threshold-aware policy that blends conservative confidence estimates with posterior sampling, steering learning toward meeting $τ_r$ rather than merely maximizing. Our main theoretical contribution provides the first finite-time regret bounds for combinatorial semi-bandits with satisficing objective: when $τ_r$ is realizable, we upper bound the cumulative satisficing regret to the target with a time-independent constant, and when $τ_r$ is non-realizable, we show that SAT-CTS incurs only a finite expected transient outside committed CTS rounds, after which its regret is governed by the sum of the regret contributions of restarted CTS rounds, yielding an $O((\log T)^2)$ standard regret bound. On the practical side, we evaluate the performance via cumulative satisficing regret to $τ_r$ alongside standard regret and fairness. Experiments with time-varying sparse multipath channels show that SAT-CTS consistently reduces satisficing regret and maintains competitive standard regret, while achieving favorable average throughput and fairness across users, indicating that feedback-efficient learning can equitably allocate beams and rates to meet QoS targets without channel state knowledge.

Preprocessing leakage arises when scaling, imputation, or other data-dependent transformations are estimated before resampling, inflating apparent performance while remaining hard to detect. We present fastml, an R package that provides a single-call interface for leakage-aware machine learning through guarded resampling, where preprocessing is re-estimated inside each resample and applied to the corresponding assessment data. The package supports grouped and time-ordered resampling, blocks high-risk configurations, audits recipes for external dependencies, and includes sandboxed execution and integrated model explanation. We evaluate fastml with a Monte Carlo simulation contrasting global and fold-local normalization, a usability comparison with tidymodels under matched specifications, and survival benchmarks across datasets of different sizes. The simulation demonstrates that global preprocessing substantially inflates apparent performance relative to guarded resampling. fastml matched held-out performance obtained with tidymodels while reducing workflow orchestration, and it supported consistent benchmarking of multiple survival model classes through a unified interface.

Our contributions are: (1) We develop a generalized framework to optimize non-differentiable ranking-based functions byextending theerror-drivenoptimization ofAPLoss.(2)Weprovethat

Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

In contrast to existing approaches, we propose a novel framework built on PanoHead, which excels in synthesizing images from a 360-degree perspective.

Doctors look at an analysis of cellular data as part of their research into using artificial intelligence to repurpose existing drugs to fight rare diseases, in Philadelphia, Pennsylvania, in February 2025. There is concern some apps that claim to offer medical guidance may not have an adequate data set to accurately asses information their users submit. Artificial intelligence is shaking up industries from software and law to entertainment and education. And as physicians like Dr. Cem Aksoy are learning, it's posing special challenges in medicine as patients tap the technology for advice. Aksoy, a medical resident at a hospital in Ankara, Turkey, says an 18-year-old patient and his family recently panicked after the young man was diagnosed with a cancerous tumor on his left leg.