Zelenskyy rejected the China-Brazil six-point plan to end Russia's war and questioned'true' intent. Ukrainian President Volodymyr Zelenskyy took to the podium at the 79th United Nations General Assembly (UNGA) for the third time since Russia's deadly invasion began more than two and half years ago, though this time he took direct aim at nations aiding Moscow: China, North Korea and Iran. Zelenskyy – who has long toed the line when it comes to maintaining murky geopolitical relations amid the war – for the first time called out not only the nations supplying direct arms to Moscow, but those who have remained complacent in their refusal to back Ukraine's demands that Russian President Vladimir Putin withdraw his troops. "We need to make it clear the war is over. This is the peace formula – what part of this could be unacceptable to anyone who upholds the U.N. Charter?" he questioned.

Michael Skelly hasn't learned to take no for an answer. For much of the last 15 years, the energy entrepreneur has worked to develop long-haul transmission lines to carry wind power across the Great Plains, Midwest, and Southwest. But so far, he has little to show for the effort. Skelly has long argued that building such lines and linking together the nation's grids would accelerate the shift from coal- and natural-gas-fueled power plants to the renewables needed to cut the pollution driving climate change. But his previous business shut down in 2019, after halting two of its projects and selling off interests in three more.

As world leaders gather in New York City for the United Nations General Assembly, there's a lot to get done, with just six years left to achieve the bold ambitions laid out for the world's 2030 agenda. When world governments agreed to the 2030 plan back in 2015, a decade and a half seemed like plenty of time to achieve the 17 Sustainable Development Goals (SDGs) designed to create a more prosperous, safe and fair global society. While amazing progress has been made, we are in danger of falling short. I believe the U.N.'s goals can be attained through a collaborative commitment to make digital networks available to everybody in the world. Mobility, broadband and the cloud are the infrastructure of 21st century life and everybody should have that opportunity.

A preliminary report on the July 13 assassination attempt on former President Trump from the Senate Committee on Homeland Security and Governmental Affairs ripped into newly revealed missteps that went into the Secret Service's planning and execution of security at the event during which a spectator was killed, two others were seriously wounded and the GOP candidate was struck on the ear. Among the key failures, an agent inexperienced with drone equipment called a toll-free tech support hotline for help after a request ahead of time for additional unmanned assets was denied, according to a preliminary summary of findings made public Wednesday. According to the committee, he had just an hour of informal training with the device. "Multiple foreseeable and preventable planning and operational failures by USSS contributed to [Thomas] Crooks' ability to carry out the assassination attempt of former President Trump on July 13," the preliminary report read. "These included unclear roles and responsibilities, insufficient coordination with state and local law enforcement, the lack of effective communications, and inoperable C-UAS systems, among many others."

Border police in China's northeast have been given quotas to identify and expel undocumented migrants -- one key aspect of broader surveillance that is making it harder for North Korean defectors to evade capture, according to previously undisclosed official documents and a dozen people familiar with the matter. China has implemented new deportation centers, hundreds of smart facial-recognition cameras and extra boat patrols along its 1,400-kilometer frontier with North Korea, according to a review of more than 100 publicly available government documents that outline spending on border surveillance and infrastructure. In addition, Chinese police have begun to closely monitor the social media accounts of North Koreans in China, and collect their fingerprints, voice and facial data, four defectors and two missionaries have said.

In this study, we propose a novel asset pricing approach, LLM Agent-based Asset Pricing Models (AAPM), which fuses qualitative discretionary investment analysis from LLM agents and quantitative manual financial economic factors to predict excess asset returns. The experimental results show that our approach outperforms machine learning-based asset pricing baselines in portfolio optimization and asset pricing errors. Specifically, the Sharpe ratio and average $|\alpha|$ for anomaly portfolios improved significantly by 9.6\% and 10.8\% respectively. In addition, we conducted extensive ablation studies on our model and analysis of the data to reveal further insights into the proposed method.

We present initial results of a forthcoming benchmark for evaluating LLM agents on white-collar tasks of economic value. We evaluate agents on real-world "messy" open-web research tasks of the type that are routine in finance and consulting. In doing so, we lay the groundwork for an LLM agent evaluation suite where good performance directly corresponds to a large economic and societal impact. We built and tested several agent architectures with o1-preview, GPT-4o, Claude-3.5 Sonnet, Llama 3.1 (405b), and GPT-4o-mini. On average, LLM agents powered by Claude-3.5 Sonnet and o1-preview substantially outperformed agents using GPT-4o, with agents based on Llama 3.1 (405b) and GPT-4o-mini lagging noticeably behind. Across LLMs, a ReAct architecture with the ability to delegate subtasks to subagents performed best. In addition to quantitative evaluations, we qualitatively assessed the performance of the LLM agents by inspecting their traces and reflecting on their observations. Our evaluation represents the first in-depth assessment of agents' abilities to conduct challenging, economically valuable analyst-style research on the real open web.

Cyber-attacks are becoming increasingly sophisticated and frequent, highlighting the importance of network intrusion detection systems. This paper explores the potential and challenges of using deep reinforcement learning (DRL) in network intrusion detection. It begins by introducing key DRL concepts and frameworks, such as deep Q-networks and actor-critic algorithms, and reviews recent research utilizing DRL for intrusion detection. The study evaluates challenges related to model training efficiency, detection of minority and unknown class attacks, feature selection, and handling unbalanced datasets. The performance of DRL models is comprehensively analyzed, showing that while DRL holds promise, many recent technologies remain underexplored. Some DRL models achieve state-of-the-art results on public datasets, occasionally outperforming traditional deep learning methods. The paper concludes with recommendations for enhancing DRL deployment and testing in real-world network scenarios, with a focus on Internet of Things intrusion detection. It discusses recent DRL architectures and suggests future policy functions for DRL-based intrusion detection. Finally, the paper proposes integrating DRL with generative methods to further improve performance, addressing current gaps and supporting more robust and adaptive network intrusion detection systems.

This paper investigates the emerging challenges posed by the integration of Artificial Intelligence (AI) in the military domain, particularly within the context of United Nations Security Council Resolution 1540 (UNSCR 1540), which seeks to prevent the proliferation of weapons of mass destruction (WMDs). While the resolution initially focused on nuclear, chemical, and biological threats, the rapid advancement of AI introduces new complexities that were previously unanticipated. We critically analyze how AI can both exacerbate existing risks associated with WMDs (e.g., thorough the deployment of kamikaze drones and killer robots) and introduce novel threats (e.g., by exploiting Generative AI potentialities), thereby compromising international peace and security. The paper calls for an expansion of UNSCR 1540 to address the growing influence of AI technologies in the development, dissemination, and potential misuse of WMDs, urging the creation of a governance framework to mitigate these emerging risks.

Fraud is a prevalent offence that extends beyond financial loss, causing psychological and physical harm to victims. The advancements in online communication technologies alowed for online fraud to thrive in this vast network, with fraudsters increasingly using these channels for deception. With the progression of technologies like AI, there is a growing concern that fraud will scale up, using sophisticated methods, like deep-fakes in phishing campaigns, all generated by language generation models like ChatGPT. However, the application of AI in detecting and analyzing online fraud remains understudied. We conduct a Systematic Literature Review on AI and NLP techniques for online fraud detection. The review adhered the PRISMA-ScR protocol, with eligibility criteria including relevance to online fraud, use of text data, and AI methodologies. We screened 2,457 academic records, 350 met our eligibility criteria, and included 223. We report the state-of-the-art NLP techniques for analysing various online fraud categories; the training data sources; the NLP algorithms and models built; and the performance metrics employed for model evaluation. We find that current research on online fraud is divided into various scam activitiesand identify 16 different frauds that researchers focus on. This SLR enhances the academic understanding of AI-based detection methods for online fraud and offers insights for policymakers, law enforcement, and businesses on safeguarding against such activities. We conclude that focusing on specific scams lacks generalization, as multiple models are required for different fraud types. The evolving nature of scams limits the effectiveness of models trained on outdated data. We also identify issues in data limitations, training bias reporting, and selective presentation of metrics in model performance reporting, which can lead to potential biases in model evaluation.