Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. The common research practice typically examines the biased structure of online user communities for a given topic or qualitatively measuring the impacts of polarized topics on social media. However, there is limited work focusing on analyzing polarization at the ground-level, specifically in the social media posts themselves. Such existing analysis heavily relies on annotated data, which often requires laborious human labeling, offers labels only to specific problems, and lacks the ability to determine the near-future bias state of a social media conversations. Understanding the degree of political orientation conveyed in social media posts is crucial for quantifying the bias of online user communities and investigating the spread of polarized content. In this work, we first introduce two heuristic methods that leverage on news media bias and post content to label social media posts. Next, we compare the efficacy and quality of heuristically labeled dataset with a randomly sampled human-annotated dataset. Additionally, we demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts, employing both traditional supervised learning and few-shot learning setups. We conduct experiments using the proposed heuristic methods and machine learning approaches to predict the political orientation of posts collected from two social media forums with diverse political ideologies: Gab and Twitter.

AGI, while powerful, could have negative consequences, warned Diveplane CEO Mike Capps and Liberty Blockchain CCO Christopher Alexander. Legislation moving through the House would provide millions of dollars for research on how to incorporate artificial intelligence into drone technology in an effort to keep the U.S. ahead of China in this increasingly important component of national security. The House Committee on Science, Space, and Technology last week approved legislation from committee Chairman Frank Lucas, R-Okla., that he says needs to pass before China becomes locked in as the world's major supplier of drones. His bill, the National Drone and Advanced Air Mobility Research and Development Act, would fund about $1.6 billion in research over the next five years to give a boost to U.S.-based drone manufacturers. "To say China has cornered this market is an understatement," Lucas said last week. "One single company with extensive ties to the Chinese Communist Party and the People's Liberation Army produces 80% of the drones used recreationally in the U.S." A staff member works on an unmanned aerial vehicle at Guizhou University in Guiyang, China, on May 23, 2023.

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.

When the sun disappears behind the moon on Monday, scientists will be ready. The astrophysics of the eclipse are known, so for space watchers it will be a time to relax and partake in the strange beauty of day gone suddenly dark. For the atmospheric scientist however, the eclipse provides a shining opportunity to directly study how the sun influences weather patterns by heating the atmosphere. To that end, a team of researchers from Oklahoma State University and the University of Nebraska is going to spend Monday tracking changes in the atmosphere in the path of the eclipse. And to get just how the eclipse changes the weather in the low sky, the team will fly drones during the totality.

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The job market for individuals with analytical skills is hot, and it's only getting hotter. A recent study by the McKinsey Global Institute puts the situation in perspective, citing a shortfall of nearly 200,000 professionals with strong analytical skills by the year 2018. Businesses are looking to colleges and universities to help fill that gap, asking them to provide their students with the analytical skills they need to fill many of these currently vacant analytical roles. How students can prepare for a career in analytics, and the role universities play in preparing them, is the focus of Dursun Delen's recent article, Mandate for STEM Educators, found in the August issue of INFORMS magazine. Given the shortage, Delen, a professor of business analytics within the Department of Management Science and Information Systems at the Spears School of Business at Oklahoma State University, says educators haven't had a "mandate this clear since the space race of the 1960s."