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.

Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a $5\times5$ grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfall