While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability. We introduce a Wikipedia-derived benchmark, complemented by a rich set of crowd-sourced annotations, that supports $8$ interrelated tasks: (i) extractive summarization; (ii) abstractive summarization; (iii) topic-based summarization; (iv) compressing selected sentences into a one-line summary; (v) surfacing evidence for a summary sentence; (vi) predicting the factual accuracy of a summary sentence; (vii) identifying unsubstantiated spans in a summary sentence; (viii) correcting factual errors in summaries. We compare various methods on this benchmark and discover that on multiple tasks, moderately-sized fine-tuned models consistently outperform much larger few-shot prompted language models. For factuality-related tasks, we also evaluate existing heuristics to create training data and find that training on them results in worse performance than training on $20\times$ less human-labeled data. Our articles draw from $6$ domains, facilitating cross-domain analysis. On some tasks, the amount of training data matters more than the domain where it comes from, while for other tasks training specifically on data from the target domain, even if limited, is more beneficial.

A new robust and accurate approach for the detection and localization of flying objects with the purpose of highly dynamic aerial interception and agile multi-robot interaction is presented in this paper. The approach is proposed for use onboard an autonomous aerial vehicle equipped with a 3D LiDAR sensor providing input data for the algorithm. It relies on a novel 3D occupancy voxel mapping method for the target detection and a cluster-based multiple hypothesis tracker to compensate uncertainty of the sensory data. When compared to state-of-the-art methods of onboard detection of other flying objects, the presented approach provides superior localization accuracy and robustness to different environments and appearance changes of the target, as well as a greater detection range. Furthermore, in combination with the proposed multi-target tracker, sporadic false positives are suppressed, state estimation of the target is provided and the detection latency is negligible. This makes the detector suitable for tasks of agile multi-robot interaction, such as autonomous aerial interception or formation control where precise, robust, and fast relative localization of other robots is crucial. We demonstrate the practical usability and performance of the system in simulated and real-world experiments.

We introduce the notion of the Lie derivative in the context of dual quaternions that represent rigid motions and twists. First we define the wrench in terms of dual quaternions. Then we show how the Lie derivative helps understand how actuators affect an end effector in parallel robots, and make it explicit in the two cases case of Stewart Platforms, and cable-driven parallel robots. We also show how to use Lie derivatives with the Newton-Raphson Method to solve the forward kinematic problem for over constrained parallel actuators. Finally, we derive the equations of motion of the end effector in dual quaternion form, which include the effect of inertia from the actuators.

Content moderation at scale faces the challenge of considering local cultural distinctions when assessing content. While global policies aim to maintain decision-making consistency and prevent arbitrary rule enforcement, they often overlook regional variations in interpreting natural language as expressed in content. In this study, we are looking into how moderation systems can tackle this issue by adapting to local comprehension nuances. We train large language models on extensive datasets of media news and articles to create culturally attuned models. The latter aim to capture the nuances of communication across geographies with the goal of recognizing cultural and societal variations in what is considered offensive content. We further explore the capability of these models to generate explanations for instances of content violation, aiming to shed light on how policy guidelines are perceived when cultural and societal contexts change. We find that training on extensive media datasets successfully induced cultural awareness and resulted in improvements in handling content violations on a regional basis. Additionally, these advancements include the ability to provide explanations that align with the specific local norms and nuances as evidenced by the annotators' preference in our conducted study. This multifaceted success reinforces the critical role of an adaptable content moderation approach in keeping pace with the ever-evolving nature of the content it oversees.

While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks. In this work, we present Tree of Attacks with Pruning (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM. TAP utilizes an LLM to iteratively refine candidate (attack) prompts using tree-of-thoughts reasoning until one of the generated prompts jailbreaks the target. Crucially, before sending prompts to the target, TAP assesses them and prunes the ones unlikely to result in jailbreaks. Using tree-of-thought reasoning allows TAP to navigate a large search space of prompts and pruning reduces the total number of queries sent to the target. In empirical evaluations, we observe that TAP generates prompts that jailbreak state-of-the-art LLMs (including GPT4 and GPT4-Turbo) for more than 80% of the prompts using only a small number of queries. This significantly improves upon the previous state-of-the-art black-box method for generating jailbreaks.

Yemen's Houthi movement says it has targeted two Israeli ships with an armed drone and a naval missile, reports a spokesperson for the group's military. The spokesperson said the two ships, Unity Explorer and Number Nine, were targeted after they rejected warnings from the group's navy, the Reuters news agency reported on Sunday. British maritime security company Ambrey said a bulk carrier ship had been hit by at least two drones while sailing in the Red Sea. Another container ship reportedly suffered damage from a drone attack about 101km (63 miles) northwest of the northern Yemeni port of Hodeida, it added. The Pentagon also said a US warship and multiple commercial ships came under attack in the Red Sea, potentially marking a major escalation in a series of maritime attacks since the Israel-Hamas war began on October 7. "We are aware of reports regarding attacks on the USS Carney and commercial vessels in the Red Sea and will provide information as it becomes available," the Pentagon said.

NSC Communications Coordinator John Kirby responds to progressive pushback against U.S. aid to Israel on'FOX News Sunday.' The Pentagon said Sunday a U.S. warship and multiple commercial vessels are under attack in the Red Sea. The development signifies a serious escalation in a series of maritime attacks in the Middle East linked to the Israel-Hamas war. "We're aware of reports regarding attacks on the USS Carney and commercial vessels in the Red Sea and will provide information as it becomes available, later," Pentagon spokesman told Fox News, confirming reports of an attack on a U.S. warship in the Red Sea. The Pentagon initially told the Associated Press, "We're aware of reports regarding attacks on the USS Carney and commercial vessels in the Red Sea and will provide information as it becomes available." USS Carney is a Arleigh Burke-class guided-missile destroyer that has been shooting down drones and cruise missiles in recent weeks launched by Iran-backed Houthi rebels, who claimed credit for Sunday's attack.

NSC Communications Coordinator John Kirby responds to progressive pushback against U.S. aid to Israel on'FOX News Sunday.' Three commercial vessels were attacked in the Red Sea on Sunday, prompting a U.S. warship to shoot down multiple unmanned aerial vehicles (UAV) headed toward them. The development could signify a serious escalation in a series of maritime attacks in the Middle East linked to the Israel-Hamas war. "Today, there were four attacks against three separate commercial vessels operating in international waters in the southern Red Sea," a statement by U.S. Central Command (CENTCOM) explained. "These three vessels are connected to 14 separate nations." The USS Carney was in the southern Red Sea, just north of the Bab al-Mandab Strait, when it shot down three Houthi drones heading in its direction, a U.S. official told Fox News, adding that the action was taken in self-defense. The drones were launched from Houthi-controlled areas of Yemen, the official claimed.

Fox News' Martha MacCallum has the latest on her new Fox Nation documentary on'The Story.' My father-in-law passed away last month, days away from his 99th birthday. He lived with us for 13 years. He was a great man, a World War II veteran who loved his wife and raised three children. As his vascular dementia worsened – unlike Alzheimer's, his long-term memory remained intact almost until the end – my wife would set him up with a familiar film. "The Godfather" played most frequently, followed by "Patton."

The Mars rover Perseverance captured a dust devil moving across the rim of a crater. A robot chemist powered by artificial intelligence could solve the puzzle of providing oxygen to humans on Mars, according to the results of a new study. The study, published in Nature Synthesis, found that an AI robot could quickly figure out how to cook up vital oxygen for survival compared to humans, who would take a lifetime to complete such a task. The reason, according to the paper, is there are more than a million potential oxygen evolution reaction (OER) catalysts on Mars, which would give humans too many possibilities to work with when trying to create oxygen. Adding to the problem would be communication with Earth to solve the problems, with transmissions taking as long as 20 minutes to travel between the home planet and Mars.