Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Trump spurns Kremlin's Putin residence attack claim, Russia kills 2 in Kyiv United States President Donald Trump has dismissed claims that Russian President Vladimir Putin's residence had been attacked by Ukraine as the war grinds on, saying he did not "believe that strike happened", after having initially accepted the Kremlin's version of events at face value. On Sunday night, Trump, on board Air Force One, told reporters that "nobody knew at that moment" whether a report about the alleged incident was accurate.

Russia releases video allegedly showing Ukrainian drone wreckage from a failed attack on President Vladimir Putin's residence, but Ukraine dismisses claims as fabrication.

EU's top diplomat rejects Russian claims of Ukrainian attack on government sites The EU's top diplomat has called Moscow's claims that Ukraine targeted Russian government sites a deliberate distraction and an attempt to derail the peace process. Kaja Kallas' comments on social media appear to be a reference to the Kremlin's allegation that Ukraine attempted a drone strike on one of Vladimir Putin's residences. No one should accept unfounded claims from the aggressor who has indiscriminately targeted Ukraine's infrastructure and civilians, Kallas wrote on social media. Earlier this week Moscow accused Ukraine of targeting Putin's private home on Lake Valdai in north-west Russia. Russia would review its position in the ongoing peace negotiations as a result, the Kremlin said.

Vladimir Putin drone attack claims dismissed by military expert Cameron Chell, who calls the alleged Ukrainian strike on the Russian president's residence 'hard to fathom.'

Ukraine denies drone attack on Putin's residence President Volodymyr Zelensky has denied allegations by Russia that Ukraine launched a drone attack on one of President Vladimir Putin's residences. Russia's Foreign Minister Sergei Lavrov claimed Kyiv had launched an attack overnight using 91 long-range unmanned aerial vehicles (UAVs) on Putin's state residence in Russia's northwestern Novgorod region. Russia said it would now review its position in peace negotiations. Zelensky dismissed the claim as typical Russian lies, intended to give the Kremlin an excuse to continue attacks on Ukraine. He said that Russia had previously targeted government buildings in Kyiv.

Scientists have identified more than fifty ways that houses can ignite. It's possible to defend against all of them--but it's arduous, and homeowners can't do it alone. In June, 2012, hundreds of homes in Mountain Shadows, Colorado, a subdivision in the foothills of the Rockies, were reduced to ash during the wind-whipped Waldo Canyon Fire. On a cul-de-sac called Hot Springs Court, however, four dwellings somehow remained standing. The mystery of their survival nagged at Alex Maranghides, a fire-protection engineer at the National Institute of Standards and Technology (), who worked with several colleagues on a meticulous reconstruction of the fire. How did the homes make it through? Was there something special about them--a fireproof roof, say, or a fancy sprinkler system? The team collected weather reports, topographic data, G.P.S. records from fire engines, photos, videos, and property-damage reports.

Continual Relation Extraction (CRE) aims to continually learn new emerging relations while avoiding catastrophic forgetting. Existing CRE methods mainly use memory replay and contrastive learning to mitigate catastrophic forgetting. However, these methods do not attach importance to the error cases that can reveal the model's cognitive biases more effectively. To address this issue, we propose an instruction-based continual contrastive tuning approach for Large Language Models (LLMs) in CRE. Different from existing CRE methods that typically handle the training and memory data in a unified manner, this approach splits the training and memory data of each task into two parts respectively based on the correctness of the initial responses and treats them differently through dual-task fine-tuning. In addition, leveraging the advantages of LLM's instruction-following ability, we propose a novel instruction-based contrastive tuning strategy for LLM to continuously correct current cognitive biases with the guidance of previous data in an instruction-tuning manner, which mitigates the gap between old and new relations in a more suitable way for LLMs. We experimentally evaluate our model on TACRED and FewRel, and the results show that our model achieves new state-of-the-art CRE performance with significant improvements, demonstrating the importance of specializing in exploiting error cases.

Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.

Consumer energy forecasting is essential for managing energy consumption and planning, directly influencing operational efficiency, cost reduction, personalized energy management, and sustainability efforts. In recent years, deep learning techniques, especially LSTMs and transformers, have been greatly successful in the field of energy consumption forecasting. Nevertheless, these techniques have difficulties in capturing complex and sudden variations, and, moreover, they are commonly examined only on a specific type of consumer (e.g., only offices, only schools). Consequently, this paper proposes HyperEnergy, a consumer energy forecasting strategy that leverages hypernetworks for improved modeling of complex patterns applicable across a diversity of consumers. Hypernetwork is responsible for predicting the parameters of the primary prediction network, in our case LSTM. A learnable adaptable kernel, comprised of polynomial and radial basis function kernels, is incorporated to enhance performance. The proposed HyperEnergy was evaluated on diverse consumers including, student residences, detached homes, a home with electric vehicle charging, and a townhouse. Across all consumer types, HyperEnergy consistently outperformed 10 other techniques, including state-of-the-art models such as LSTM, AttentionLSTM, and transformer.

This study uses the Palestinian Oral History Archive (POHA) to investigate how Palestinian refugee groups in Lebanon sustain a cohesive collective memory of the Nakba through shared narratives. Grounded in Halbwachs' theory of group memory, we employ statistical analysis of pairwise similarity of narratives, focusing on the influence of shared gender and location. We use textual representation and semantic embeddings of narratives to represent the interviews themselves. Our analysis demonstrates that shared origin is a powerful determinant of narrative similarity across thematic keywords, landmarks, and significant figures, as well as in semantic embeddings of the narratives. Meanwhile, shared residence fosters cohesion, with its impact significantly amplified when paired with shared origin. Additionally, women's narratives exhibit heightened thematic cohesion, particularly in recounting experiences of the British occupation, underscoring the gendered dimensions of memory formation. This research deepens the understanding of collective memory in diasporic settings, emphasizing the critical role of oral histories in safeguarding Palestinian identity and resisting erasure.