Multivariate Hawkes processes are a widely used class of self-exciting point processes, but maximum likelihood estimation naively scales as $O(N^2)$ in the number of events. The canonical linear exponential Hawkes process admits a faster $O(N)$ recurrence, but prior work evaluates this recurrence sequentially, without exploiting parallelization on modern GPUs. We show that the Hawkes process intensity can be expressed as a product of sparse transition matrices admitting a linear-time associative multiply, enabling computation via a parallel prefix scan. This yields a simple yet massively parallelizable algorithm for maximum likelihood estimation of linear exponential Hawkes processes. Our method reduces the computational complexity to approximately $O(N/P)$ with $P$ parallel processors, and naturally yields a batching scheme to maintain constant memory usage, avoiding GPU memory constraints. Importantly, it computes the exact likelihood without any additional assumptions or approximations, preserving the simplicity and interpretability of the model. We demonstrate orders-of-magnitude speedups on simulated and real datasets, scaling to thousands of nodes and tens of millions of events, substantially beyond scales reported in prior work. We provide an open-source PyTorch library implementing our optimizations.

Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in the treatment of different languages arises at the tokenization stage, well before a model is even invoked. The same text translated into different languages can have drastically different tok-enization lengths, with differences up to 15 times in some cases. These disparities persist even for tokenizers that are intentionally trained for multilingual support.

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Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants.

During Sudan's civil war, which erupted in April 2023, both sides have increasingly relied on drones, and civilians have borne the brunt of the carnage. The conflict between the Sudanese armed forces (SAF) and the Rapid Support Forces (RSF) paramilitary group is an example of war transformed by commercially available, easily concealable unmanned aerial vehicles (UAVs), or drones. Modular, well-adapted to sanctions evasions and devastatingly effective, drones have killed scores of civilians, crippled infrastructure and plunged Sudanese cities into darkness. In this visual investigation, Al Jazeera examines the history of drone warfare in Sudan, the types of drones used by the warring sides, how they are sourced, where the attacks have occurred and the human toll. The RSF traces its origins to what at the time was a government-linked militia known as the Janjaweed.

UN Secretary General Antonio Guterres says six peacekeepers, all Bangladesh nationals, were killed when a drone strike hit a UN logistics base in Kadugli, in Sudan's central region of Kordofan. Sudan's army says the attack came from the paramilitary Rapid Support Forces.

The Longest Solar Eclipse for 100 Years Is Coming. NASA has announced when the longest total solar eclipse of the century will occur--and you won't have to wait long. Here's what you should know. The duration of a total solar eclipse always varies. In April 2024, the eclipse that crossed North America lasted 4 minutes and 28 seconds.

This study investigates logistic regression, linear support vector machine, multinomial Naive Bayes, and Bernoulli Naive Bayes for classifying Libyan dialect utterances gathered from Twitter. The dataset used is the QADI corpus, which consists of 540,000 sentences across 18 Arabic dialects. Preprocessing challenges include handling inconsistent orthographic variations and non-standard spellings typical of the Libyan dialect. The chi-square analysis revealed that certain features, such as email mentions and emotion indicators, were not significantly associated with dialect classification and were thus excluded from further analysis. Two main experiments were conducted: (1) evaluating the significance of meta-features extracted from the corpus using the chi-square test and (2) assessing classifier performance using different word and character n-gram representations. The classification experiments showed that Multinomial Naive Bayes (MNB) achieved the highest accuracy of 85.89% and an F1-score of 0.85741 when using a (1,2) word n-gram and (1,5) character n-gram representation. In contrast, Logistic Regression and Linear SVM exhibited slightly lower performance, with maximum accuracies of 84.41% and 84.73%, respectively. Additional evaluation metrics, including log loss, Cohen kappa, and Matthew correlation coefficient, further supported the effectiveness of MNB in this task. The results indicate that carefully selected n-gram representations and classification models play a crucial role in improving the accuracy of Libyan dialect identification. This study provides empirical benchmarks and insights for future research in Arabic dialect NLP applications.