Accurate prediction of trips between zones is critical for transportation planning, as it supports resource allocation and infrastructure development across various modes of transport. Although the gravity model has been widely used due to its simplicity, it often inadequately represents the complex factors influencing modern travel behavior. This study introduces a data-driven approach to enhance the gravity model by integrating geographical, economic, social, and travel data from the counties in Tennessee and New York state. Using machine learning techniques, we extend the capabilities of the traditional model to handle more complex interactions between variables. Our experiments demonstrate that machine learning-enhanced models significantly outperform the traditional model. Our results show a 51.48% improvement in R-squared, indicating a substantial enhancement in the model's explanatory power. Also, a 63.59% reduction in Mean Absolute Error (MAE) reflects a significant increase in prediction accuracy. Furthermore, a 44.32% increase in Common Part of Commuters (CPC) demonstrates improved prediction reliability. These findings highlight the substantial benefits of integrating diverse datasets and advanced algorithms into transportation models. They provide urban planners and policymakers with more reliable forecasting and decision-making tools.

We present Latent Diffeomorphic Dynamic Mode Decomposition (LDDMD), a new data reduction approach for the analysis of non-linear systems that combines the interpretability of Dynamic Mode Decomposition (DMD) with the predictive power of Recurrent Neural Networks (RNNs). Notably, LDDMD maintains simplicity, which enhances interpretability, while effectively modeling and learning complex non-linear systems with memory, enabling accurate predictions. This is exemplified by its successful application in streamflow prediction.

Counterspeech is a key strategy against harmful online content, but scaling expert-driven efforts is challenging. Large Language Models (LLMs) present a potential solution, though their use in countering conspiracy theories is under-researched. Unlike for hate speech, no datasets exist that pair conspiracy theory comments with expert-crafted counterspeech. We address this gap by evaluating the ability of GPT-4o, Llama 3, and Mistral to effectively apply counterspeech strategies derived from psychological research provided through structured prompts. Our results show that the models often generate generic, repetitive, or superficial results. Additionally, they over-acknowledge fear and frequently hallucinate facts, sources, or figures, making their prompt-based use in practical applications problematic.

--Modern cyber attacks unfold through multiple stages, requiring defenders to dynamically prioritize mitigations under uncertainty. While game-theoretic models capture attacker-defender interactions, existing approaches often rely on static assumptions and lack integration with real-time threat intelligence, limiting their adaptability. This paper presents Cy-GATE, a game-theoretic framework modeling attacker-defender interactions, using large language models (LLMs) with retrieval-augmented generation (RAG) to enhance tactic selection and patch prioritization. Applied to a two-agent scenario, CyGATE frames cyber conflicts as a partially observable stochastic game (POSG) across Cyber Kill Chain stages. Both agents use belief states to navigate uncertainty, with the attacker adapting tactics and the defender re-prioritizing patches based on evolving risks and observed adversary behavior . The framework's flexible architecture enables extension to multi-agent scenarios involving coordinated attackers, collaborative defenders, or complex enterprise environments with multiple stakeholders. The evolving cybersecurity landscape presents increasingly sophisticated threats that necessitate adaptive, proactive defense strategies. Patch management, a cornerstone of cyber defense, requires intelligent prioritization of vulnerabilities under resource constraints such as maintenance windows and operational cost [1] [2] . However, traditional scoring systems like common vulnerability scoring system (CVSS) [3] fail to capture the evolving nature of cyber threats, where attackers adapt their strategies based on defender actions. Game theory provides a structured framework for modeling attacker-defender interactions [4], with chained or multistage games particularly suited to representing complex attack progressions along the Cyber Kill Chain (CKC) [5][6][7]. These models allow defenders to reason about long-term risks and preempt cascading compromises. Despite these advancements, existing models remain constrained by fixed strategies, static payoff structures, and minimal integration of threat intelligence, failing to dynamically prioritize vulnerabilities based on evolving exploitation trends [8]. Traditional game-theoretical approaches typically use predefined rules to analyze strategies, hence are limited in dynamic cyber environments where adversaries continuously adapt, operate under uncertainty, and employ unpredictable tactics [9].

Long-form factuality evaluation assesses the ability of models to generate accurate, comprehensive responses to short prompts. Existing benchmarks often lack human verification, leading to potential quality issues. To address this limitation, we introduce FACTORY, a large-scale, human-verified prompt set. Developed using a model-in-the-loop approach and refined by humans, FACTORY includes challenging prompts that are fact-seeking, answerable, and unambiguous. We conduct human evaluations on 6 state-of-the-art language models using FACTORY and existing datasets. Our results show that FACTORY is a challenging benchmark: approximately 40% of the claims made in the responses of SOTA models are not factual, compared to only 10% for other datasets. Our analysis identifies the strengths of FACTORY over prior benchmarks, emphasizing its reliability and the necessity for models to reason across long-tailed facts.

A Russian attack killed three people in Ukraine's southeastern Zaporizhia region on Sunday, Governor Ivan Fedorov wrote on Telegram. A Ukrainian drone attack sparked a major fire at an oil depot in Sochi in southern Russia, the governor of Russia's Krasnodar region, Veniamin Kondratiev, said on Sunday. The fire was extinguished hours later after 120 firefighters were deployed, officials said. Russia's civil aviation authority, Rosaviatsia, briefly halted flights at Sochi's airport during the fire. Ukraine's military says it used drones to target several sites inside Russia, including refineries, an airfield and an electronics plant.

EXCLUSIVE: North Carolina's state auditor said he is looking forward to making a positive impact on taxpayers by implementing a state version of Department of Government Efficiency (DOGE). In an exclusive interview with Fox News Digital, North Carolina state auditor Dave Boliek said his office would look into how the state government can be more efficient and utilize the resources it has in the "best possible way" for taxpayers. He plans on doing that through House Bill 125, a state-level DOGE initiative named after him that recently passed the legislature. "It helps to give our office and the state auditor's office more resources to take a look at efficiencies and ways to really drill down on determining a good return on investment of taxpayer dollars across North Carolina," Boliek said. "I really support the effort," he said, in part.

I come from Trinidad and Tobago. As a country that was once colonized by the British, I am wary of the ways that inequalities between the global north and global south risk being perpetuated in the digital age. When we consider the lack of inclusion of the global south in discussions about artificial intelligence (AI), I think about how this translates to an eventual lack of economic leverage and geopolitical engagement in this technology that has captivated academics within the industrialised country I reside, the United States. As a scientist, I experienced an early rite of passage into the world of Silicon Valley, the land of techno-utopianism, and the promise of AI as a net positive for all. But, as an academic attending my first academic AI conference in 2019, I began to notice inconsistencies in the audience to whom the promise of AI was directed.

Russia fired more than 6,000 drones on Ukraine in July, more than any other month since it launched its full-scale invasion in 2022, the AFP news agency and the Kyiv Independent reported. The drone attacks killed dozens of people and injured many more. They also damaged civilian targets, including many homes, a kindergarten and an ambulance. According to the AFP news agency, data published by Ukraine's air force showed that Russia fired 6,297 long-range drones into Ukraine last month, up by nearly 16 percent compared with June. The Kyiv Independent reported that Russia launched a record 6,129 Shahed-type drones in July, 14 times more than in the same month last year, when Russia launched just 423 drones.

Zelensky's government faced an extensive backlash after introducing a bill that would strip the National Anti-Corruption Bureau and Specialised Anti-Corruption Prosecutor's Office, known as Nabu and Sap respectively, of their independence. The president claimed the agencies needed to be "cleared of Russian influence", and sought to give the general prosecutor the authority to decide who should be prosecuted in high-level corruption cases. Many saw the move as a step backwards for corruption in Ukraine, resulting in the largest anti-government demonstrations since Russia launched its full-scale invasion of the country in 2022. Zelensky acknowledged public anger and submitted a new bill restoring the agencies' former independence, which was voted through by parliament just nine days after the original bill had been passed. The head of the Ukrainian Defence Ministry's Main Intelligence Directorate (HUR), Kyrylo Budanov, thanked Zelensky for "hearing the public's call" regarding the powers of anti-corruption agencies and "not making a mistake".