America's fastest-growing state is selling the perfect lifestyle... and everyone's falling for it I was using my vape 160 times a day, it was costing me a fortune and its toll on my face was truly shocking. Then I discovered a miracle one-day cure... and stopped overnight: MARY KILLEN Lost tomb of the mysterious'cloud people' unearthed after 1,400 years in'discovery of the decade' Devastating truth about Blind Side actor Quinton Aaron: More to this'than everyone is letting on', friends reveal... as co-star Sandra Bullock'monitors' situation Harper Beckham, 14, puts on a stylish display in a fluffy coat and vintage Chanel bag as she heads out in Paris with her family... after Nicola's Peltz's heartbreaking comments about sister-in-law America's earthquake hotspot is more dangerous than feared as scientists make surprising discovery Terrifying animation shows pilot's-eye view of DC mid-air collision between airliner and helicopter that killed 67 Explosive twist in'diva' inmate Bryan Kohberger's life in prison revealed in the FREE The Crime Desk newsletter Marco Rubio'cocoons like a mummy' in bizarre strategy to hide naps from Trump Frozen woman who was'stiff as a rock' is found outside Texas convenience store Inside the Super Bowl hotels home to Seattle Seahawks and New England Patriots... where guests complained of cockroaches, loud noise and'being bitten' Lost tomb of the mysterious'cloud people' unearthed after 1,400 years in'discovery of the decade' It has been hailed as'the most significant archaeological discovery in a decade.' Archaeologists in Mexico have uncovered a 1,400-year-old tomb in the Central Valleys of Oaxaca that had been lost to history. The stone structure, built by the Zapotec culture, known as Be'ena'a, or'The Cloud People', is adorned with sculptures, murals and carved symbols that suggest ritual significance. The Zapotec believed their ancestors descended from the clouds and that, in death, their souls returned to the heavens as spirits.

Interaction between learner and feedback-giver has come into focus recently for post-training of Large Language Models (LLMs), through the use of reward models that judge the appropriateness of a model's response. In this paper, we investigate whether Dialogue Games -- goal-directed and rule-governed activities driven predominantly by verbal actions -- can also serve as a source of feedback signals for learning. We introduce Playpen, an environment for off- and online learning through Dialogue Game self-play, and investigate a representative set of post-training methods: supervised fine-tuning; direct alignment (DPO); and reinforcement learning with GRPO. We experiment with post-training a small LLM (Llama-3.1-8B-Instruct), evaluating performance on unseen instances of training games as well as unseen games, and on standard benchmarks. We find that imitation learning through SFT improves performance on unseen instances, but negatively impacts other skills, while interactive learning with GRPO shows balanced improvements without loss of skills. We release the framework and the baseline training setups to foster research in the promising new direction of learning in (synthetic) interaction.

A "massive" Russian drone and missile attack has killed at least six people in Ukraine's capital and the surrounding region, according to Ukrainian officials. Officials said the strikes on Monday morning targeted residential areas in numerous districts across Kyiv. The assault on the city, the second huge overnight blitz in a week, suggests Russia is eager to raise the pressure as global attention is dominated by the United States's decision to join Israel's escalating air campaign against Iran. Possibly, several waves of enemy drones," Tymur Tkachenko, head of Kyiv's military administration, said in a statement. "The Russians' style is unchanged – to hit where there may be people," Tkachenko said on Telegram. "Residential buildings, exits from shelters – this is the Russian style.

Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored. We introduce a benchmark to evaluate whether video-large language models (Video-LLMs) can naturally process continuous first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. We have manually control drones to collect 3D embodied motion video data from real-world cities and simulated environments, resulting in 1.5k video clips. Then we design a pipeline to generate 5.2k multiple-choice questions. Evaluations of 17 widely-used Video-LLMs reveal current limitations in urban embodied cognition. Correlation analysis provides insight into the relationships between different tasks, showing that causal reasoning has a strong correlation with recall, perception, and navigation, while the abilities for counterfactual and associative reasoning exhibit lower correlation with other tasks. We also validate the potential for Sim-to-Real transfer in urban embodiment through fine-tuning.

The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Routing Problem (VRP) where the scale of cities influences routing outcomes, necessitating consideration of their entrances. This paper addresses EDVRP in agriculture, focusing on multi-parameter vehicle planning for irregularly shaped fields. To address the limitations of traditional methods, such as heuristic approaches, which often overlook field geometry and entrance constraints, we propose a Joint Probability Distribution Sampling Neural Network (JPDS-NN) to effectively solve the EDVRP. The network uses an encoder-decoder architecture with graph transformers and attention mechanisms to model routing as a Markov Decision Process, and is trained via reinforcement learning for efficient and rapid end-to-end planning. Experimental results indicate that JPDS-NN reduces travel distances by 48.4-65.4%, lowers fuel consumption by 14.0-17.6%, and computes two orders of magnitude faster than baseline methods, while demonstrating 15-25% superior performance in dynamic arrangement scenarios. Ablation studies validate the necessity of cross-attention and pre-training. The framework enables scalable, intelligent routing for large-scale farming under dynamic constraints.

Vehicle Routing Problems (VRP) are widely studied issues that play important roles in many production scenarios. We have noticed that in some practical scenarios of VRP, the size of cities and their entrances can significantly influence the optimization process. To address this, we have constructed the Entrance Dependent VRP (EDVRP) to describe such problems. We provide a mathematical formulation for the EDVRP in farms and propose an Ordered Genetic Algorithm (OGA) to solve it. The effectiveness of OGA is demonstrated through our experiments, which involve a multitude of randomly generated cases. The results indicate that OGA offers certain advantages compared to a random strategy baseline and a genetic algorithm without ordering. Furthermore, the novel operators introduced in this paper have been validated through ablation experiments, proving their effectiveness in enhancing the performance of the algorithm.

The Israeli military "repeatedly" fired at UNIFIL headquarters and positions in southern Lebanon, injuring two members of the peacekeeping force, the United Nations says, as Israel presses on with its assault on Hezbollah. UNIFIL – the UN Interim Force in Lebanon – said on Thursday that two of its peacekeepers were injured after an Israeli tank "fired its weapon" at a guard tower at the group's headquarters, located in the border area town of Naqoura. The attack on the tower had caused the two peacekeepers to fall. "The injuries are fortunately, this time, not serious, but they remain in hospital," said UNIFIL in a statement. The Israeli soldiers also fired on a UN position – named "1-31"- in the village of Labbouneh, "hitting the entrance to the bunker where peacekeepers were sheltering, and damaging vehicles and a communications system", it said. The peacekeeping force reported that it had observed an Israeli military drone flying inside the UN position up to the bunker entrance.

Thousands of people lined up outside Citi Field in Queens, New York on Wednesday to watch the Mets face off with the Orioles. But outside the ticketing booth, a handful of protesters handed out flyers. They were there to protest a recent Major League Baseball program, and one that's increasingly common in professional sports: using facial recognition on fans. Facial recognition companies and their customers argue that these systems save time, and therefore money, by shortening lines at stadium entrances. However, skeptics argue that the surveillance tools are never totally secure, make it easier for police to get information about fans, and fuel "mission creep" where surveillance technology becomes more common, or even required.

The global surge in AI applications is transforming industries, leading to displacement and complementation of existing jobs, while also giving rise to new employment opportunities. Human oversight of AI is an emerging task in which human workers interact with an AI model to improve its performance, safety, and compliance with normative principles. Data annotation, encompassing the labelling of images or annotating of texts, serves as a critical human oversight process, as the quality of a dataset directly influences the quality of AI models trained on it. Therefore, the efficiency of human oversight work stands as an important competitive advantage for AI developers. This paper delves into the foundational economics of human oversight, with a specific focus on the impact of norm design and monetary incentives on data quality and costs. An experimental study involving 307 data annotators examines six groups with varying task instructions (norms) and monetary incentives. Results reveal that annotators provided with clear rules exhibit higher accuracy rates, outperforming those with vague standards by 14%. Similarly, annotators receiving an additional monetary incentive perform significantly better, with the highest accuracy rate recorded in the group working with both clear rules and incentives (87.5% accuracy). However, both groups require more time to complete tasks, with a 31% increase in average task completion time compared to those working with standards and no incentives. These empirical findings underscore the trade-off between data quality and efficiency in data curation, shedding light on the nuanced impact of norm design and incentives on the economics of AI development. The paper contributes experimental insights to discussions on the economical, ethical, and legal considerations of AI technologies.

Imagine the Colossus of Rhodes, the Statue of Zeus and the other ancient Seven Wonders of the World standing as they did thousands of years ago when first built. Artificial intelligence has done just that by recreating each historic structure in modern society with bustling tourists snapping photos with smartphones. Only one of the original seven survives today, with the others lost over time due to war, crumbling civilizations and natural disasters. But using the imagine generator Midjourney, AI has brought them back from the dead, allowing the world to take another look. Ancient artwork depicting the Colossus of Rhodes shows the statue straddling the harbor entrance, but researchers have determined such a feat would be impossible.