As a former military intelligence officer, serving in the Defense Intelligence Agency (DIA), I tracked foreign threats to the U.S. homeland, identifying adversaries' plans, intentions and capabilities that could harm Americans. I predicted Russia's invasion of Ukraine more than a year before it took place. In March, in my Fox News Digital article titled "Ignore FBI director's urgent warning about terrorist threats at our own peril," I predicted terrorist attacks striking inside the U.S. homeland, the kind that took place on New Year's Day in New Orleans and in Las Vegas. Here are the top three reasons why we will likely face more terrorism in America this year. This time, it will be something we haven't seen before.

Style is an integral component of text that expresses a diverse set of information, including interpersonal dynamics (e.g. formality) and the author's emotions or attitudes (e.g. disgust). Humans often employ multiple styles simultaneously. An open question is how large language models can be explicitly controlled so that they weave together target styles when generating text: for example, to produce text that is both negative and non-toxic. Previous work investigates the controlled generation of a single style, or else controlled generation of a style and other attributes. In this paper, we expand this into controlling multiple styles simultaneously. Specifically, we investigate various formulations of multiple style rewards for a reinforcement learning (RL) approach to controlled multi-style generation. These reward formulations include calibrated outputs from discriminators and dynamic weighting by discriminator gradient magnitudes. We find that dynamic weighting generally outperforms static weighting approaches, and we explore its effectiveness in 2- and 3-style control, even compared to strong baselines like plug-and-play model. All code and data for RL pipelines with multiple style attributes will be publicly available.

This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. Focusing on tasks like Human Trafficking Risk Prediction (HTRP) and Organized Activity Detection (OAD), we employ cutting-edge Transformer models for analysis. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement. This work not only fills a critical gap in the literature but also offers a scalable, machine learning-driven approach to combat human exploitation online. It serves as a foundation for future research and practical applications, emphasizing the role of machine learning in addressing complex social issues.

Thomas Villarreal of the Austin Police Association discusses the police department's decision to implement artificial intelligence software in an effort to alleviate their officer shortage on "Fox & Friends Weekend." The family of a man killed in one of Austin, Texas' most infamous shootings blasted the local district attorney for putting the case on the "back burner" because it didn't fit his progressive agenda. Travis County District Attorney Jose Garza, funded by left-wing billionaire George Soros, is letting the nearly two-year case languish and is instead prioritizing cases that fit a political agenda, said Nick Kantor, whose brother, Doug, was killed in gang crossfire on June 12, 2021, that left more than a dozen innocent bystanders wounded. Doug Kantor, then 25 and working for Ford Motor Co., was visiting Austin from Michigan to celebrate earning his master's degree with friends when two rival gangs of teenagers from Killeen, Texas, opened fire on each other in the city's packed Sixth Street entertainment and nightlife hub. Doug Kantor, a New York native who had just bought a new home and was set to marry his high school sweetheart, was killed in the shooting and 13 other innocent bystanders were injured in the hail of bullets from both gangs that became the largest mass casualty incident in Austin in about a decade.

Rep. Rob Wittman, R-Va., joins'Fox News Live' to react to the United States Air Force's unveiling of its new B-21 raider stealth bomber, named'The Raider' for Jimmy Doolittle's famous bombing raid on Japan in WW2. With the launch of the Air Force's hypersonic missile off the coast of California earlier this month, the Navy's development of water-based drones over the summer and the recent unveiling of the B-21 Raiders, the U.S. military has made major technological advancements over the past year. The military unveiled the U.S. Air Force B-21 Raider in Palmdale, California. The B-21 Raider is the first new American bomber aircraft in more than three decades. In an email to Fox News Digital, a spokesperson confirmed the Air Force would transition its three-bomber fleet to a two-bomber fleet of B-21s and modernized B-52s.

We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer ``fill-in-the-blank'' questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pre-trained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zero-shot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets. Our code and datasets are available at https://github.com/cgraywang/IELM

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Army test-fired Javelin anti-tank missiles at a recent exhibition in Fort Hood, Texas to demonstrate technological advancement in its fighting capabilities. During a series of weapons drills and exercises, soldiers fired Javelins and .50-caliber A Javelin missile fired by soldiers with the 2nd Stryker Brigade Combat Team, separate from the exhibition in Texas.

Current modeling approaches for hydrological modeling often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to rigid structure resulting in unrealistic parameter values in certain instances, ML algorithms establish the input-output relationship while ignoring the constraints imposed by well-known physical processes. While there is a notion that the physics model enables better process understanding and ML algorithms exhibit better predictive skills, scientific knowledge that does not add to predictive ability may be deceptive. Hence, there is a need for a hybrid modeling approach to couple ML algorithms and physics-based models in a synergistic manner. Here we develop a Physics Informed Machine Learning (PIML) model that combines the process understanding of conceptual hydrological model with predictive abilities of state-of-the-art ML models. We apply the proposed model to predict the monthly time series of the target (streamflow) and intermediate variables (actual evapotranspiration) in the Narmada river basin in India. Our results show the capability of the PIML model to outperform a purely conceptual model ($abcd$ model) and ML algorithms while ensuring the physical consistency in outputs validated through water balance analysis. The systematic approach for combining conceptual model structure with ML algorithms could be used to improve the predictive accuracy of crucial hydrological processes important for flood risk assessment.

The Army's plans for robotic wingmen in vehicle formations, a drone on every soldier and robotic mules carrying gear all aim to take the load off the fighter. But how will the two communicate, robot and human? Voice commands like automated assistants on smartphones are great, but not when the threat of incoming fire means the robot battle buddy needs to decipher a range of priorities that humans might take for granted. The next test will come in late 2021 and involve a company-sized maneuver at Fort Hood, Texas. Think more C3PO or R2D2 in the "Star Wars" movies than Hal in "2001: A Space Odyssey" --or better yet, a friendly cyborg from "Terminator" might be the best way to see your robot combatant squad mate of the distant future.

This paper shows how to construct knowledge graphs (KGs) from pre-trained language models (e.g., BERT, GPT-2/3), without human supervision. Popular KGs (e.g, Wikidata, NELL) are built in either a supervised or semi-supervised manner, requiring humans to create knowledge. Recent deep language models automatically acquire knowledge from large-scale corpora via pre-training. The stored knowledge has enabled the language models to improve downstream NLP tasks, e.g., answering questions, and writing code and articles. In this paper, we propose an unsupervised method to cast the knowledge contained within language models into KGs. We show that KGs are constructed with a single forward pass of the pre-trained language models (without fine-tuning) over the corpora. We demonstrate the quality of the constructed KGs by comparing to two KGs (Wikidata, TAC KBP) created by humans. Our KGs also provide open factual knowledge that is new in the existing KGs. Our code and KGs will be made publicly available.