This is the first time NASA has encountered a situation like this, and it took nearly a week to resolve. The Curiosity rover landed on Mars in August 2012. While it has enabled many exciting discoveries, the Curiosity Rover has also encountered its share of setbacks. The latest left NASA engineers speechless. On April 25, Curiosity drilled into a rock nicknamed "Atacama" to collect a sample.

The Defense Department has released a new trove of declassified documents about government UFO sightings. The Pentagon released a batch of much-anticipated files about unidentified anomalous phenomena (UAP) on Friday, including newly declassified documents that have never been seen by the public before. The release of roughly 160 documents was rolled out on a new website . Among the trove is video footage and images of tantalizing UAP sightings captured around the world. The files also contain scanned historical material about government UAP and unidentified flying object (UFO) programs dating back to the 1940s and the Apollo program.

The launch of "Xoli" adds to the technological efforts promoted by the federal government to turn the 2026 World Cup into an engine of development for the entire country. Xoli, the new chatbot, is named after the axolotl, a salamander with external gills. The Government of Mexico City has launched Xoli, a chatbot that will provide information on services, tourism, and cultural offerings. The platform was designed to meet the demand of the millions of visitors expected to arrive during the 2026 FIFA World Cup . However, the authorities assure that the tool will remain active once the sporting event is over, with the aim of promoting economic activities and facilitating access to public services in the capital.

Amazon Prime Day is live. See the best deals HERE. Clues are left behind in the ruins of the Mesoamerican megacity Teotihuacan. Breakthroughs, discoveries, and DIY tips sent every weekday. At the height of its power, the ancient Mesoamerican city of Teotihuacan near present-day Mexico City was home to over 125,000 inhabitants.

William Phelps was at work when he got the call from the FBI that he had to return home at once. It was December 2023 and his wife, Aurora Phelps, was in big trouble, something to do with a fraud scheme. About a dozen agents turned his apartment upside down looking for evidence in their case, and William Phelps wouldn't see his wife again. That is, until this week, when William came to learn the scope of the allegations against his wife. According to federal prosecutors, Aurora was the perpetrator of a deadly romance scam, connecting with older men on the internet, then drugging them and stealing from their bank accounts.

In this paper we develop a pseudo Markov-chain model to understand time-elapsed flows, over multiple intervals, from time and space aggregated collective inter-location trip data, given as a time-series. Building on the model, we develop measures of mobility that parallel those known for individual mobility data, such as the radius of gyration. We apply these measures to the NetMob 2024 Data Challenge data, and obtain interesting results that are consistent with published statistics and commuting patterns in cities. Besides building a new framework, we foresee applications of this approach to an improved understanding of human mobility in the context of environmental changes and sustainable development.

Precursor concentration and solar radiation intensity determine the dynamic equilibrium between ozone creation and destruction. Tropospheric ozone is a dangerous pollutant that can lead to a number of health problems as well as environmental difficulties. In contrast, stratospheric ozone creates a protective ozone layer. Exposure to high levels of tropospheric ozone can cause a range of respiratory problems, including coughing, throat irritation, and worsening of asthma symptoms. Long-term exposure can lead to more severe health issues such as chronic respiratory diseases, reduced lung function, and increased mortality rates. Children, the elderly, and individuals with pre-existing health conditions are particularly vulnerable to the adverse effects of ozone. Ground-level ozone can also damage flora, which can result in decreased agricultural production, damage to forests, and a decline in biodiversity. It prevents plants from photosynthesizing, which slows down their growth and increases their vulnerability to pests, illnesses, and harsh weather condition.

The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions. In addition to emotion recognition in conversations, the task of identifying the potential causes behind an individual's emotional state in conversations, is of great importance in many application scenarios. We organize SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, which aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE). The shared task has attracted 143 registrations and 216 successful submissions. In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.

We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs.

While vertical thinking relies on logical and commonsense reasoning, lateral thinking requires systems to defy commonsense associations and overwrite them through unconventional thinking. Lateral thinking has been shown to be challenging for current models but has received little attention. A recent benchmark, BRAINTEASER, aims to evaluate current models' lateral thinking ability in a zero-shot setting. In this paper, we split the original benchmark to also support fine-tuning setting and present SemEval Task 9: BRAIN-TEASER(S), the first task at this competition designed to test the system's reasoning and lateral thinking ability. As a popular task, BRAINTEASER(S)'s two subtasks receive 483 team submissions from 182 participants during the competition. This paper provides a fine-grained system analysis of the competition results, together with a reflection on what this means for the ability of the systems to reason laterally. We hope that the BRAINTEASER(S) subtasks and findings in this paper can stimulate future work on lateral thinking and robust reasoning by computational models.