President Donald Trump's envoy to Russia and Ukraine says he doesn't believe Russian President Vladimir Putin wants to invade Europe. Envoy Steve Witkoff made the statement during a Sunday morning appearance on "Fox News Sunday," commenting on Putin's motives on a "larger scale." "Now I've been asked my opinion about what President Putin's motives are on a larger scale. And I simply have said that I just don't see that he wants to take all of Europe," Witkoff said. "This is a much different situation than it was in World War II. There was no NATO," he added.

Former U.S. senior diplomat Gregory Slayton joined'Fox & Friends First' to discuss President Donald Trump's phone call with Russian President Putin and the latest on the war in Gaza after the ceasefire between Israel and Hamas ended. Russia launched a massive drone attack targeting Kyiv and other major cities in Ukraine overnight on Sunday, killing at least seven people. The attack comes just days before Ukrainian and Russian delegations are set to meet for indirect peace talks with the U.S. in Saudi Arabia. The U.S. will mediate the talks and meet with representatives separately. Ukraine's air force says Sunday's attack from Russia involved 147 drones, 97 of which were shot down and another 25 failed to reach their targets.

At least seven people have been killed in overnight Russian drone attacks on the Ukrainian capital, as President Volodymyr Zelenskyy urged his Western allies to put more pressure on Moscow to cease its attacks on the country in advance of peace talks in Saudi Arabia. Three people, including a five-year-old, were killed and 10 were injured in a drone attack on Kyiv, the city's military administration said on Sunday. Elsewhere, four people were killed in Russian attacks in Donetsk region, regional Governor Vadym Filashkin said, including three who died in an attack on the front-line Ukrainian town of Dobropillya. Kyiv Mayor Vitali Klitschko wrote on Telegram that emergency services were dispatched to several city districts following fires and damage. Earlier, the country's air force said Russia launched 147 drones overnight on several Ukrainian regions.

In this paper, we evaluate the ability of Large Language Models (LLMs) to assess the veracity of claims in ''news reports'' generated by themselves or other LLMs. Our goal is to determine whether LLMs can effectively fact-check their own content, using methods similar to those used to verify claims made by humans. Our findings indicate that LLMs are more effective at assessing claims in national or international news stories than in local news stories, better at evaluating static information than dynamic information, and better at verifying true claims compared to false ones. We hypothesize that this disparity arises because the former types of claims are better represented in the training data. Additionally, we find that incorporating retrieved results from a search engine in a Retrieval-Augmented Generation (RAG) setting significantly reduces the number of claims an LLM cannot assess. However, this approach also increases the occurrence of incorrect assessments, partly due to irrelevant or low-quality search results. This diagnostic study highlights the need for future research on fact-checking machine-generated reports to prioritize improving the precision and relevance of retrieved information to better support fact-checking efforts. Furthermore, claims about dynamic events and local news may require human-in-the-loop fact-checking systems to ensure accuracy and reliability.

Taking advantage of their versatility and autonomous operation, unmanned aerial vehicles (UAVs) can be used for aerial load transportation, with many applications such as vertical replenishment of seaborne vessels [11], deployment of supplies in search-and-rescue missions [1], package delivery, and landmine detection [2]. Aerial load transportation using UA Vs is a challenging task in terms of modeling and control. The load may be connected to the UAV either rigidly or by means of a rope, which changes its dynamics considerably. In addition, the load physical parameters are often unknown in practice, and their knowledge is usually necessary to effectively accomplish the task. A model-free control approach based on trajectory generation by reinforcement learning has been proposed in [7] for path tracking of the load using a quadrotor UAV (QUAV). This work was in part supported by the project INCT (National Institute of Science and Technology) for Cooperative Autonomous Systems Applied to Security and Environment under the grants CNPq 465755/2014-3 and F APESP 2014/50851-0, and by the Brazilian agencies CAPES under the grant numbers 88887.136349/2017-00

This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance with the EU AI Act and addressing technical, ethical, and regulatory challenges. The paper highlights AIs transformative nature, driven by autonomy, data, and goal-oriented design, and how the EU AI Act promotes transparency, accountability, and safety. A key challenge is defining and assessing "high-risk" AI systems across industries, complicated by the lack of universally accepted standards and AIs rapid evolution. To address these challenges, the paper explores the relevance of ISO/IEC and IEEE standards, focusing on risk management, data quality, bias mitigation, and governance. It proposes a Fundamental Rights Impact Assessment (FRIA) methodology, a gate-based framework designed to isolate and assess risks through phases including an AI system overview, a human rights checklist, an impact assessment, and a final output phase. A filtering mechanism tailors the assessment to the system's characteristics, targeting areas like accountability, AI literacy, data governance, and transparency. The paper illustrates the FRIA methodology through a fictional case study of an automated healthcare triage service. The structured approach enables systematic filtering, comprehensive risk assessment, and mitigation planning, effectively prioritizing critical risks and providing clear remediation strategies. This promotes better alignment with human rights principles and enhances regulatory compliance.

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL, and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.

The modern enterprise is facing an unprecedented surge in digital identities, with machine identities now significantly outnumbering human identities. This paper examines the cybersecurity risks emerging from what we define as the "human-machine identity blur" - the point at which human and machine identities intersect, delegate authority, and create new attack surfaces. Drawing from industry data, expert insights, and real-world incident analysis, we identify key governance gaps in current identity management models that treat human and machine entities as separate domains. To address these challenges, we propose a Unified Identity Governance Framework based on four core principles: treating identity as a continuum rather than a binary distinction, applying consistent risk evaluation across all identity types, implementing continuous verification guided by zero trust principles, and maintaining governance throughout the entire identity lifecycle. Our research shows that organizations adopting this unified approach experience a 47 percent reduction in identity-related security incidents and a 62 percent improvement in incident response time. We conclude by offering a practical implementation roadmap and outlining future research directions as AI-driven systems become increasingly autonomous.

Counterfactual explanations have emerged as a prominent method in Explainable Artificial Intelligence (XAI), providing intuitive and actionable insights into Machine Learning model decisions. In contrast to other traditional feature attribution methods that assess the importance of input variables, counterfactual explanations focus on identifying the minimal changes required to alter a model's prediction, offering a ``what-if'' analysis that is close to human reasoning. In the context of XAI, counterfactuals enhance transparency, trustworthiness and fairness, offering explanations that are not just interpretable but directly applicable in the decision-making processes. In this paper, we present a novel framework that integrates perturbation theory and statistical mechanics to generate minimal counterfactual explanations in explainable AI. We employ a local Taylor expansion of a Machine Learning model's predictive function and reformulate the counterfactual search as an energy minimization problem over a complex landscape. In sequence, we model the probability of candidate perturbations leveraging the Boltzmann distribution and use simulated annealing for iterative refinement. Our approach systematically identifies the smallest modifications required to change a model's prediction while maintaining plausibility. Experimental results on benchmark datasets for cybersecurity in Internet of Things environments, demonstrate that our method provides actionable, interpretable counterfactuals and offers deeper insights into model sensitivity and decision boundaries in high-dimensional spaces.

Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniques namely in memory caching, distributed caching, and proxy caching and their functions in enhancing performance in decentralized settings. Through literature review we examine the utilization of AI techniques for alleviating risks related to market volatility, liquidity challenges, operational failures, regulatory compliance, and security threats. This comparison research evaluates various case studies from prominent DeFi technologies, emphasizing critical performance metrics like latency reduction, load balancing, and system resilience. Additionally, we examine the problems and trade offs associated with these technologies, emphasizing their effects on consistency, scalability, and fault tolerance. By meticulously analyzing real world applications, specifically centering on the Aave platform as our principal case study, we illustrate how the purposeful amalgamation of AI with contemporary caching methodologies has revolutionized risk management in distributed arbitrage systems.