Latvia parliament approves new gov't after drone dispute toppled coalition Latvia's parliament has approved a new coalition government that will lead the European Union and NATO member country in the coming months after its predecessor collapsed following an argument over its handling of stray drones suspected to be from Ukraine. By a margin of 66 deputies in the 100-seat assembly, lawmakers on Thursday confirmed 47-year-old centrist Andris Kulbergs as prime minister, who will lead the Baltic nation of more than 1.8 million people until parliamentary elections on October 3. She quit after Defence Minister Andris Spruds, a member of the Progressives Party, was forced to resign over the government's handling of multiple incidents involving stray drones suspected to be from Ukraine crossing into Latvian territory. Silina accused the minister of not deploying anti-drone defences fast enough to parry two wayward Ukraine attack drones, which are thought to have been knocked off course by Russian jamming. At the time, she said Spruds had lost her trust and that of the public.

Recent weeks have seen a significant escalation of military tensions in and around the Baltics. Lithuania, Latvia and Estonia, which are all NATO members, now experience regular incursions into their airspace by Ukrainian drones. According to both Kyiv and the Baltic capitals, those drones, en route to hit targets in western Russia, get diverted by Russian electronic jamming and end up entering these countries' territories. In early May, several stray unmanned aircraft crashed in Latvia, one of them damaging an oil storage facility. Those developments triggered a political crisis in Latvia and led to the collapse of its government.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Latvia's president asks opposition leader to form new government Latvian President Edgars Rinkevics has backed opposition lawmaker Andris Kulbergs to replace Evika Silina for the top job after the prime minister resigned over an incident involving Ukrainian drones. Kulbergs, leader of the United List of smaller parties, which forms the largest opposition bloc in parliament, will take office if lawmakers approve him and his cabinet. "Considering recent events, I think the new prime minister should come from opposition parties," President Rinkevics told a news conference on Saturday. Last weekend, the former Prime Minister Silina fired her defence minister, Andris Spruds, after two Ukrainian drones strayed into Latvia from Russia and exploded at an oil storage facility. The incident is only the latest in a series of such events in NATO members Latvia, Estonia, and Lithuania.

Online health communities (OHCs) are vital for fostering peer support and improving health outcomes. Support groups within these platforms can provide more personalized and cohesive peer support, yet traditional support group formation methods face challenges related to scalability, static categorization, and insufficient personalization. To overcome these limitations, we propose two novel machine learning models for automated support group formation: the Group specific Dirichlet Multinomial Regression (gDMR) and the Group specific Structured Topic Model (gSTM). These models integrate user generated textual content, demographic profiles, and interaction data represented through node embeddings derived from user networks to systematically automate personalized, semantically coherent support group formation. We evaluate the models on a large scale dataset from MedHelp, comprising over 2 million user posts. Both models substantially outperform baseline methods including LDA, DMR, and STM in predictive accuracy (held out log likelihood), semantic coherence (UMass metric), and internal group consistency. The gDMR model yields group covariates that facilitate practical implementation by leveraging relational patterns from network structures and demographic data. In contrast, gSTM emphasizes sparsity constraints to generate more distinct and thematically specific groups. Qualitative analysis further validates the alignment between model generated groups and manually coded themes, showing the practical relevance of the models in informing groups that address diverse health concerns such as chronic illness management, diagnostic uncertainty, and mental health. By reducing reliance on manual curation, these frameworks provide scalable solutions that enhance peer interactions within OHCs, with implications for patient engagement, community resilience, and health outcomes.

Your Out-of-Distribution Detection Method is Not Robust!

Neural networks are often trained on multiple tasks, either simultaneously (multi-task learning, MTL) or sequentially (pretraining and subsequent finetuning, PT+FT). In particular, it is common practice to pretrain neural networks on a large auxiliary task before finetuning on a downstream task with fewer samples. Despite the prevalence of this approach, the inductive biases that arise from learning multiple tasks are poorly characterized. In this work, we address this gap.