What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Three people, including two crew members of a cargo vessel, were killed in overnight Ukrainian drone attacks on the Russian port of Rostov-on-Don and the town of Bataysk in the country's southern Rostov region, local governor Yury Slyusar said. Russian strikes near Ukraine's Black Sea port of Odesa killed a woman in her car and hit infrastructure.

Fox News contributors Katie Pavlich and Miranda Devine discuss how President Donald Trump could be the one to bring an end to the Russia-Ukraine war on'Hannity.' Rep. Marjorie Taylor Greene, R-Ga., late Thursday took shots at Ukrainian President Volodymyr Zelenskyy, accusing him of trying to sabotage Friday's highly anticipated peace talks between President Donald Trump and Russian President Vladimir Putin by launching drone strikes on Russia. Greene responded to a post on X from the account, "Open Source Intel," which reported that Ukraine had in recent hours launched "one of the largest" drone attacks on Russia. "On the eve of the historic peace talks between President Trump and President Putin, Zelensky does this," the Republican lawmaker wrote. "Zelensky doesn't want peace and obviously is trying to sabotage President Trump's heroic efforts to end the war in Ukraine. Fox News Digital has reached out to the Ukrainian embassy, seeking a response to Greene's post. Rep. Marjorie Taylor Greene, R-Ga., accused Ukrainian President Zelenskyy of trying to sabotage peace talks between President Trump and Russian President Putin by launching drone strikes on Russia. Ukraine launched multiple drone strikes into Russia overnight Thursday, damaging several apartment buildings in the southern city of Rostov-on-Don and injuring more than a dozen civilians, according to acting governor of the region, Yuri Slyusar. Two of those wounded were hospitalized in serious condition, he said. The Ukrainian strikes came after Russian strikes in Ukraine's Sumy region overnight Wednesday, resulting in multiple injuries, including a 7-year-old girl, per officials. Local officials also accused Ukraine of launching a drone strike in Belgorod that injured three people, and another that struck a car in the village of Pristen that killed at least one individual. Ukrainian President Volodymyr Zelenskyy will not attend the summit in Alaska on Friday between President Donald Trump and Russian President Vladimir Putin. Despite the violence, Trump and Putin are scheduled to meet in Anchorage, Alaska, on Friday for a high-stakes summit on the future of the Ukraine war. The meeting will mark Putin's first visit to the U.S. since 2015 and the first U.S.-Russia summit since June 2021. President Donald Trump will meet with Russian President Vladimir Putin in Alaska on Aug. 15, 2025. Putin praised the U.S. on Thursday for making "sincere efforts" to end the war between Russia and Ukraine, which has been raging since early 2022. Appearing on television, the Russian president said the U.S. was "making, in my opinion, quite energetic and sincere efforts to stop hostilities, stop the crisis and reach agreements that are of interest to all parties involved in this conflict." Zelenskyy accused Russia of not being sincere in its intention to wind down the war. "This war must be ended.

New video shows the moment a Ukrainian drone struck an apartment building in Rostov-on-Don, Russia. Officials say at least 13 people were injured in the attack.

We introduce the first open-source model for translation between the vulnerable Chechen language and Russian, and the dataset collected to train and evaluate it. We explore fine-tuning capabilities for including a new language into a large language model system for multilingual translation NLLB-200. The BLEU / ChrF++ scores for our model are 8.34 / 34.69 and 20.89 / 44.55 for translation from Russian to Chechen and reverse direction, respectively. The release of the translation models is accompanied by the distribution of parallel words, phrases and sentences corpora and multilingual sentence encoder adapted to the Chechen language.

Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, significant challenges remain, including biases and privacy concerns. To systematically address these issues in the context of geographic information recognition, we introduce a benchmark dataset consisting of 1,200 images paired with detailed geographic metadata. Evaluating four VLMs, we find that while these models demonstrate the ability to recognize geographic information from images, achieving up to $53.8\%$ accuracy in city prediction, they exhibit significant regional biases. Specifically, performance is substantially higher for economically developed and densely populated regions compared to less developed ($-12.5\%$) and sparsely populated ($-17.0\%$) areas. Moreover, the models exhibit regional biases, frequently overpredicting certain locations; for instance, they consistently predict Sydney for images taken in Australia. The strong performance of VLMs also raises privacy concerns, particularly for users who share images online without the intent of being identified. Our code and dataset are publicly available at https://github.com/uscnlp-lime/FairLocator.

This paper presents a Multi-Agent Norm Perception and Induction Learning Model aimed at facilitating the integration of autonomous agent systems into distributed healthcare environments through dynamic interaction processes. The nature of the medical norm system and its sharing channels necessitates distinct approaches for Multi-Agent Systems to learn two types of norms. Building on this foundation, the model enables agents to simultaneously learn descriptive norms, which capture collective tendencies, and prescriptive norms, which dictate ideal behaviors. Through parameterized mixed probability density models and practice-enhanced Markov games, the multi-agent system perceives descriptive norms in dynamic interactions and captures emergent prescriptive norms. We conducted experiments using a dataset from a neurological medical center spanning from 2016 to 2020.

When designing a public transportation network in a country, one may want to minimise the sum of travel duration of all inhabitants. This corresponds to a purely utilitarian view and does not involve any fairness consideration, as the resulting network will typically benefit the capital city and/or large central cities while leaving some peripheral cities behind. On the other hand, a more egalitarian view will allow some people to travel between peripheral cities without having to go through a central city. We define a model, propose algorithms for computing solution networks, and report on experiments based on real data.

Ukraine has launched one of its largest drone attacks on Moscow, as it presses on with a major incursion into Russia's Kursk region, Russian authorities said. Russia's Ministry of Defence said on Wednesday that air defence forces shot down 11 drones over Moscow and its surrounding region, with some reportedly downed over the city of Podolsk some 38km (24 miles) south of the Kremlin. "This is one of the largest ever attempts to attack Moscow with drones," Moscow Mayor Sergei Sobyanin said on the Telegram messaging app. No damage or casualties were reported, he said in an earlier post. Drone attacks on Moscow are rare.

Social media platforms hold valuable insights, yet extracting essential information can be challenging. Traditional top-down approaches often struggle to capture critical signals in rapidly changing events. As global events evolve swiftly, social media narratives, including instances of disinformation, become significant sources of insights. To address the need for an inductive strategy, we explore a niche social media platform GAB and an established messaging service Telegram, to develop methodologies applicable on a broader scale. This study investigates narrative evolution on these platforms using quantitative corpus-based discourse analysis techniques. Our approach is a novel mode to study multiple social media domains to distil key information which may be obscured otherwise, allowing for useful and actionable insights. The paper details the technical and methodological aspects of gathering and preprocessing GAB and Telegram data for a keyness (Log Ratio) metric analysis, identifying crucial nouns and verbs for deeper exploration. Empirically, this approach is applied to a case study of a well defined event that had global impact: the 2023 Wagner mutiny. The main findings are: (1) the time line can be deconstructed to provide useful data features allowing for improved interpretation; (2) a methodology is applied which provides a basis for generalization. The key contribution is an approach, that in some cases, provides the ability to capture the dynamic narrative shifts over time with elevated confidence. The approach can augment near-real-time assessment of key social movements, allowing for informed governance choices. This research is important because it lays out a useful methodology for time series relevant info-culling, which can enable proactive modes for positive social engagement.

Statistical mechanics has made significant contributions to the study of biological neural systems by modeling them as recurrent networks of interconnected units with adjustable interactions. Several algorithms have been proposed to optimize the neural connections to enable network tasks such as information storage (i.e. associative memory) and learning probability distributions from data (i.e. generative modeling). Among these methods, the Unlearning algorithm, aligned with emerging theories of synaptic plasticity, was introduced by John Hopfield and collaborators. The primary objective of this thesis is to understand the effectiveness of Unlearning in both associative memory models and generative models. Initially, we demonstrate that the Unlearning algorithm can be simplified to a linear perceptron model which learns from noisy examples featuring specific internal correlations. The selection of structured training data enables an associative memory model to retrieve concepts as attractors of a neural dynamics with considerable basins of attraction. Subsequently, a novel regularization technique for Boltzmann Machines is presented, proving to outperform previously developed methods in learning hidden probability distributions from data-sets. The Unlearning rule is derived from this new regularized algorithm and is showed to be comparable, in terms of inferential performance, to traditional Boltzmann-Machine learning.