Cyberbullying on social media is inherently multilingual and multi-faceted, where abusive behaviors often overlap across multiple categories. Existing methods are commonly limited by monolingual assumptions or single-task formulations, which restrict their effectiveness in realistic multilingual and multi-label scenarios. In this paper, we propose HMS-BERT, a hybrid multi-task self-training framework for multilingual and multi-label cyberbullying detection. Built upon a pretrained multilingual BERT backbone, HMS-BERT integrates contextual representations with handcrafted linguistic features and jointly optimizes a fine-grained multi-label abuse classification task and a three-class main classification task. To address labeled data scarcity in low-resource languages, an iterative self-training strategy with confidence-based pseudo-labeling is introduced to facilitate cross-lingual knowledge transfer. Experiments on four public datasets demonstrate that HMS-BERT achieves strong performance, attaining a macro F1-score of up to 0.9847 on the multi-label task and an accuracy of 0.6775 on the main classification task. Ablation studies further verify the effectiveness of the proposed components.

Practitioners monitoring deployed probabilistic models face a fundamental trap: any fixed-sample test applied repeatedly over an unbounded stream will eventually raise a false alarm, even when the model remains perfectly stable. Existing methods typically lack formal error guarantees, conflate alarm time with changepoint location, and monitor indirect signals that do not fully characterize calibration. We present PITMonitor, an anytime-valid calibration-specific monitor that detects distributional shifts in probability integral transforms via a mixture e-process, providing Type I error control over an unbounded monitoring horizon as well as Bayesian changepoint estimation. On river's FriedmanDrift benchmark, PITMonitor achieves detection rates competitive with the strongest baselines across all three scenarios, although detection delay is substantially longer under local drift.

The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules remain poorly understood. We provide a theoretical analysis by modeling embedding evolution under Langevin dynamics on a compact Riemannian manifold. Under mild smoothness and energy-barrier assumptions, we show that classical simulated annealing guarantees extend to this setting: slow logarithmic inverse-temperature schedules ensure convergence in probability to a set of globally optimal representations, while faster schedules risk becoming trapped in suboptimal minima. Our results establish a link between contrastive learning and simulated annealing, providing a principled basis for understanding and tuning temperature schedules.

In this paper, we consider the problem of black-box optimization with noisy feedback revealed in batches, where the unknown function to optimize has a bounded norm in some Reproducing Kernel Hilbert Space (RKHS). We refer to this as the Batched Kernelized Bandits problem, and refine and extend existing results on regret bounds. For algorithmic upper bounds, (Li and Scarlett, 2022) shows that $B=O(\log\log T)$ batches suffice to attain near-optimal regret, where $T$ is the time horizon and $B$ is the number of batches. We further refine this by (i) finding the optimal number of batches including constant factors (to within $1+o(1)$), and (ii) removing a factor of $B$ in the regret bound. For algorithm-independent lower bounds, noticing that existing results only apply when the batch sizes are fixed in advance, we present novel lower bounds when the batch sizes are chosen adaptively, and show that adaptive batches have essentially same minimax regret scaling as fixed batches. Furthermore, we consider a robust setting where the goal is to choose points for which the function value remains high even after an adversarial perturbation. We present the robust-BPE algorithm, and show that a suitably-defined cumulative regret notion incurs the same bound as the non-robust setting, and derive a simple regret bound significantly below that of previous work.

See the full list of nominees and winners from the 98th Academy Awards, Hollywood's biggest night.

When you purchase through links in our articles, we may earn a small commission. Try Adobe Acrobat Studio for free today! Communication is a central part of any business or creative endeavour. Whether its sharing information between colleagues or highlighting the advantages of new products and services to customers, getting the messaging right is an essential part of success. Traditionally, this could involve hours of painstaking work, preparing documents and then replicating their data into slides for presentations.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

Pope Leo XIV called Sunday for an immediate ceasefire in the war involving Iran, marking the pontiff's strongest remarks yet on conflict.

Neural Information Processing Systems http://nips.cc/

Could Iran be using China's BeiDou system? Ukraine wants money and technology as payback after sending specialists to the Middle East to help down Iranian drones during the ongoing Israel-United States war with Iran . President Volodymyr Zelenskyy told reporters on Sunday that three teams were sent to the region to undertake expert assessments and demonstrate how drone defences work as countries in the Middle East continue to be targeted by Iran over hosting US military bases. We are not at war with Iran," Zelenskyy said. Earlier this week, Ukraine's leader announced military teams were sent to Qatar, the United Arab Emirates, Saudi Arabia, and a US military base in Jordan. But he explained that more long-term drone deals could be negotiated with Gulf countries, and what Kyiv gets in return for its assistance still needs to be established. "For us today, both the technology and the funding are important," Zelenskyy said. Throughout the four-year Russia-Ukraine war, Moscow has widely used Iranian Shahed-136 "suicide" drones, giving Kyiv expertise in knowing how to down the unmanned aerial vehicles through cheap drone interceptors, electronic jamming tools, and anti-aircraft weaponry. However, US President Donald Trump has said he does not need Ukraine's help in taking down Iranian drones attacking American targets. Zelenskyy said he doesn't know why Washington hasn't signed a drone agreement with Kyiv, which it has pushed for months. "I wanted to sign a deal worth about $35bn-50bn," he said. Still, as the Russia-Ukraine conflict continues with no end in sight, Zelenskyy raised concerns that the ongoing war in the Middle East will impact Kyiv's supplies of air defence missiles. "We would very much not like the United States to step away from the issue of Ukraine because of the Middle East," he told reporters. But as interest has grown for Ukrainian drone interceptors in light of the war, Zelenskyy said Kyiv's rules to buy the drones must be tightened, with foreign countries and firms being unable to bypass the government and talk directly to manufacturers. "Unfortunately, representatives of certain governments or companies want to bypass the Ukrainian state to purchase specific equipment," Zelensky told reporters. "Even in some free countries, we do not initially receive contracts from the private sector.