Predicting influencers' views and public sentiment on social media is crucial for anticipating societal trends and guiding strategic responses. This study introduces a novel computational framework to predict opinion leaders' perspectives and the emotive reactions of the populace, addressing the inherent challenges posed by the unstructured, context-sensitive, and heterogeneous nature of online communication. Our research introduces an innovative module that starts with the automatic 5W1H (Where, Who, When, What, Why, and How) questions formulation engine, tailored to emerging news stories and trending topics. We then build a total of 60 anonymous opinion leader agents in six domains and realize the views generation based on an enhanced large language model (LLM) coupled with retrieval-augmented generation (RAG). Subsequently, we synthesize the potential views of opinion leaders and predicted the emotional responses to different events. The efficacy of our automated 5W1H module is corroborated by an average GPT-4 score of 8.83/10, indicative of high fidelity. The influencer agents exhibit a consistent performance, achieving an average GPT-4 rating of 6.85/10 across evaluative metrics. Utilizing the 'Russia-Ukraine War' as a case study, our methodology accurately foresees key influencers' perspectives and aligns emotional predictions with real-world sentiment trends in various domains.

The growing demand for air travel requires technological advancements in air traffic management as well as mechanisms for monitoring and ensuring safe and efficient operations. In terminal airspaces, predictive models of future movements and traffic flows can help with proactive planning and efficient coordination; however, varying airport topologies, and interactions with other agents, among other factors, make accurate predictions challenging. Data-driven predictive models have shown promise for handling numerous variables to enable various downstream tasks, including collision risk assessment, taxi-out time prediction, departure metering, and emission estimations. While data-driven methods have shown improvements in these tasks, prior works lack large-scale curated surface movement datasets within the public domain and the development of generalizable trajectory forecasting models. In response to this, we propose two contributions: (1) Amelia-48, a large surface movement dataset collected using the System Wide Information Management (SWIM) Surface Movement Event Service (SMES). With data collection beginning in Dec 2022, the dataset provides more than a year's worth of SMES data (~30TB) and covers 48 airports within the US National Airspace System. In addition to releasing this data in the public domain, we also provide post-processing scripts and associated airport maps to enable research in the forecasting domain and beyond. (2) Amelia-TF model, a transformer-based next-token-prediction large multi-agent multi-airport trajectory forecasting model trained on 292 days or 9.4 billion tokens of position data encompassing 10 different airports with varying topology. The open-sourced model is validated on unseen airports with experiments showcasing the different prediction horizon lengths, ego-agent selection strategies, and training recipes to demonstrate the generalization capabilities.

In this paper, we introduce a system based on transfer learning for detecting segment misalignment in multimirror satellites, such as future CubeSat designs and the James Webb Space Telescope (JWST), using image-based methods. When a mirror segment becomes misaligned due to various environmental factors, such as space debris, the images can become distorted with a shifted copy of itself called a "ghost image". To detect whether segments are misaligned, we use pre-trained, large-scale image models trained on the Fast Fourier Transform (FFT) of patches of satellite images in grayscale. Multi-mirror designs can use any arbitrary number of mirrors. For our purposes, the tests were performed on simulated CubeSats with 4, 6, and 8 segments. For system design, we took this into account when we want to know when a satellite has a misaligned segment and how many segments are misaligned. The intensity of the ghost image is directly proportional to the number of segments misaligned. Models trained for intensity classification attempted to classify N-1 segments. Across eight classes, binary models were able to achieve a classification accuracy of 98.75%, and models for intensity classification were able to achieve an accuracy of 98.05%.

Automated soft moderation systems are unable to ascertain if a post supports or refutes a false claim, resulting in a large number of contextual false positives. This limits their effectiveness, for example undermining trust in health experts by adding warnings to their posts or resorting to vague warnings instead of granular fact-checks, which result in desensitizing users. In this paper, we propose to incorporate stance detection into existing automated soft-moderation pipelines, with the goal of ruling out contextual false positives and providing more precise recommendations for social media content that should receive warnings. We develop a textual deviation task called Contrastive Textual Deviation (CTD) and show that it outperforms existing stance detection approaches when applied to soft moderation.We then integrate CTD into the stateof-the-art system for automated soft moderation Lambretta, showing that our approach can reduce contextual false positives from 20% to 2.1%, providing another important building block towards deploying reliable automated soft moderation tools on social media.

U.S. intelligence officials believe that Iran is trying to sabotage former President Trump's presidential campaign through online influence operations, according to a press briefing on Monday. Speaking to reporters, an official with the Office of the Director of National Intelligence (ODNI) said U.S. spy agencies "observed Tehran working to influence the presidential election," likely because Iranian leaders want to avoid increased tensions with the U.S. The official didn't directly say that Iran was trying to undermine Trump, but that American spies "haven't observed a shift in Iran's preferences" since 2020, meaning that Iran was still targeting Trump. During the briefing, an intelligence official also said Iran is utilizing "vast webs of online personas and propaganda mills to spread disinformation," in addition to different online campaigns. U.S. intelligence officials believe Iran is meddling in the 2024 election. Earlier in July, Tehran was accused of a separate plot to kill Trump after a gunman shot the former president at a rally in Butler, Pennsylvania, on July 13.

'The Big Weekend Show' co-hosts discuss Vice President Kamala Harris' positions on key issues. Billionaire Elon Musk suggested that Google's omission of search functions for the assassination attempt against former President Trump may be improper. Musk took to social media to highlight that Google Search's autocomplete feature omitted results relating to the July 13 shooting. Google has denied taking any action to limit the results. "Wow, Google has a search ban on President Donald Trump." "They're getting themselves into a lot of trouble if they interfere with the election," he wrote in a follow-up post.

JERUSALEM – The looming Israeli response against the Iran-backed Hezbollah terrorist movement in Lebanon is said to be imminent in response to the group's rocket attack on a children's soccer field on Saturday, resulting in the murders of 12 young people. Early Monday, Israel Defense Forces (IDF) reportedly executed a drone strike in southern Lebanon, resulting in the deaths of two Hezbollah terrorists. The IDF has not commented on the strike. The IDF drone attacks came after Prime Minister Benjamin Netanyahu held a three-hour cabinet meeting on Sunday, during which ministers authorized the prime minister and his minister of defense to determine the "manner and timing" of a military response to the lethal Hezbollah attack. Danny Danon, Israel's new ambassador to the United Nations, told "Fox and Friends" host Steve Doocy on Monday that, Israel's "response will be swift, harsh and painful, and we are now picking the targets and I believe in the next few days, and I'm sure Hezbollah will learn their lesson."

Tech companies like to make two grand pronouncements about the future of artificial intelligence. First, the technology is going to usher in a revolution akin to the advent of fire, nuclear weapons, and the internet. And second, it is going to cost almost unfathomable sums of money. Silicon Valley has already triggered tens or even hundreds of billions of dollars of spending on AI, and companies only want to spend more. Their reasoning is straightforward: These companies have decided that the best way to make generative AI better is to build bigger AI models.

Kamala Harris's election campaign has accused Elon Musk of spreading "manipulated lies" after the Tesla chief executive posted a doctored video featuring the vice-president on his X account. Musk reposted a manipulated Harris campaign video on Friday evening in which a fake Harris voiceover says "I was selected because I am the ultimate diversity hire" and that anyone who criticises her is "both sexist and racist". The video has been viewed 128m times on Musk's account after the world's richest man posted it with the words "this is amazing" followed by a laughing emoji. Musk owns X, which he rebranded from Twitter last year. Amy Klobuchar, a Democrat senator, accused Musk of violating the platform's guidelines.

Israeli drone attacks have reportedly killed two people in southern Lebanon as conflict spirals between the bordering states. The Israeli attack was the first lethal action following a rocket attack on Saturday that Israel says killed 12 children and youths in the Israeli-occupied Golan Heights. The strike has increased concern that the war in Gaza threatens to escalate into a regional conflict. Lebanese state media said one strike hit a motorcycle close to the border, killing two riders and injuring a child. Two others were injured in a separate strike in southern Lebanon.