Three aid workers killed, 4 wounded in RSF drone attack in Sudan's Kordofan At least three aid workers have been killed and four others wounded in a drone attack by the paramilitary Rapid Support Forces (RSF) on an aid convoy in Sudan's South Kordofan state, according to the Sudan Doctors Network, in the latest carnage against civilians caught up in the nation's brutal civil war. The convoy of trucks carrying food and humanitarian supplies was targeted by the RSF, and its ally, the Sudan People's Liberation Movement-North, while travelling through the Kartala area on its way to the cities of Kadugli and Dilling on Thursday. The network said that this attack marked the "second such incident in less than a month, following the shelling of a United Nations aid convoy in the town of Al-Rahad," adding: "this dangerous escalation threatens the safety of humanitarian operations and further exacerbates civilian suffering". The Sudan Doctors Network reiterated its call to the "international community, the United Nations, and human rights organisations to exert urgent and effective pressure on the leadership of the Rapid Support Forces to ensure the protection of aid convoys and their workers, to open safe and sustainable humanitarian corridors, and to hold those responsible for targeting aid accountable". Al Jazeera could not independently verify the latest RSF attack, which came a month after the government-aligned Sudanese Armed Forces (SAF) announced that it had broken a nearly two-year-long RSF siege on Dilling.

Aid reaches Sudan's Kordofan as over 30 countries alarmed by drone attacks United Nations aid trucks have reached an area in Sudan that has been cut off from relief efforts amid warnings that deadly drone strikes are making the hunger crisis in that part of the country worse. The UN said on Wednesday that its aid trucks had reached Dilling and Kadugli, in central Sudan's Kordofan region, with "life-saving" humanitarian supplies. "Dilling and the nearby city of Kadugli have been largely cut off from aid for more than two years, leaving communities with severe shortages of humanitarian assistance," the UN said. The aid delivery came as more than 30 countries issued a statement on Wednesday expressing "grave concern" at a recent "severe escalation" in drone attacks amid "heavy fighting" in the Darfur and Kordofan regions. The 28 European Union countries, plus Canada, New Zealand and the United Kingdom, warned that "intentional attacks" against aid workers and "wilfully impeding" relief supplies, "may amount to war crimes".

Dozens killed in RSF drone attack in war-torn Sudan's South Kordofan Dozens of people have been killed in a drone attack by the paramilitary Rapid Support Forces (RSF) on a key town in war-torn Sudan's South Kordofan state, according to local media reports. Multiple areas of Dilling, including the headquarters of the Sudanese army's 54th Brigade and the central market, were struck by suicide drones during Wednesday's attack, the Sudan Tribune reported, citing local sources and medical groups. Dilling lies halfway between Kadugli - the besieged state capital - and el-Obeid, the capital of neighbouring North Kordofan province, which the RSF has sought to encircle. The RSF and the SAF have been waging a brutal civil war for control of Sudan since April 2023, which has killed thousands of people and displaced millions. Since the siege was lifted, Dilling has endured a wave of drone attacks that have destroyed service facilities and caused several casualties.

Major cities across Sudan, including the capital, Khartoum, and coastal city Port Sudan, have been plunged into darkness after drone strikes hit a key power plant in the country's east. Flames and smoke rose from the facility in Atbara, River Nile state on Thursday, which is controlled by the government-aligned Sudanese Armed Forces (SAF) and under attack by the paramilitary Rapid Support Forces (RSF) in the ongoing civil war that has ripped the nation apart. Sudan's RSF trying to hide atrocities: Report Two civil defence members were killed, power plant officials said, while trying to extinguish the fire that erupted after the first strike, adding that rescue workers were injured when a second drone hit as they battled the flames. Al Jazeera correspondent Mohamed Vall in Port Sudan reported that residents initially thought a routine power cut had occurred, only to learn it was linked to incidents in Atbara, roughly 320km (about 230 miles) north of Khartoum. He added that such strikes have become a frequent occurrence in Sudan's war.

Drone attacks kill over 100 civilians across war-torn Sudan's Kordofan At least 104 civilians have been killed in drone attacks across Sudan's Kordofan region as fighting between rival military factions reached deadly new heights in the brutal civil war deep into its third year. The attacks have battered the central region since early December, right up to Friday, following the capture of a significant army base by the paramilitary Rapid Support Forces (RSF) in Babnusa after a week of intense fighting. Sudan's RSF trying to hide atrocities: Report The deadliest attack was reported from a kindergarten and a hospital in Kalogi, South Kordofan, where 89 people were killed, including 43 children and eight women. United Nations human rights chief Volker Turk said he was "alarmed by the further intensification in hostilities" and warned that targeting medical facilities violates international humanitarian law. Six Bangladeshi peacekeepers serving with the UN mission were killed when drones hit their base in Kadugli, South Kordofan's capital, on December 13.

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.

This thesis is a corpus-based, quantitative, and typological analysis of the functions of Early Slavic participle constructions and their finite competitors ($jegda$-'when'-clauses). The first part leverages detailed linguistic annotation on Early Slavic corpora at the morphosyntactic, dependency, information-structural, and lexical levels to obtain indirect evidence for different potential functions of participle clauses and their main finite competitor and understand the roles of compositionality and default discourse reasoning as explanations for the distribution of participle constructions and $jegda$-clauses in the corpus. The second part uses massively parallel data to analyze typological variation in how languages express the semantic space of English $when$, whose scope encompasses that of Early Slavic participle constructions and $jegda$-clauses. Probabilistic semantic maps are generated and statistical methods (including Kriging, Gaussian Mixture Modelling, precision and recall analysis) are used to induce cross-linguistically salient dimensions from the parallel corpus and to study conceptual variation within the semantic space of the hypothetical concept WHEN.

Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. To date, only ~31 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks, achieving 82.27 average F_1. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research.\footnote{\href{https://github.com/UBC-NLP/serengeti}{https://github.com/UBC-NLP/serengeti}}

Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world's 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for $517$ African languages and varieties. AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems. When evaluated on our blind Test set, AfroLID achieves 95.89 F_1-score. We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages. We further show the utility of AfroLID in the wild by testing it on the acutely under-served Twitter domain. Finally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both showcase AfroLID's powerful capabilities and limitations.