Surface water contaminated by fecal bacteria can cause diarrheal illness, threatening human's health (especially among children). In recent years, supervised machine learning (ML) has been used to predict fecal indicator bacteria (FIB) levels. However, training ML models is challenging and, in some cases, even impractical due to sparsity of labeled data in all locations (e.g., in rural areas or low-income countries). In this paper, we introduce the largest water quality dataset available collected from beaches in Chicago and San Diego, USA. We utilized various models to predict historical FIB levels on this dataset establishing strong baseline models for supervised learning and transfer learning. Our models include Random Forest (RF), extreme gradient boosting (XGBoost), and attentionbased tabular deep learning (TabNet) models. Additionally, given the widespread use of large language models (LLMs), we have fine-tuned the LLaMA3-8B model for regression in a tabular-to-text setting. Our results show that supervised and unsupervised domain adaptation methods can enhance transfer learning performance. Specifically, the supervised methods, especially RF, represent a promising solution for FIB level prediction, while domain adaptation could be successfully employed to predict FIB levels in locations where they are rarely measured. Our code and dataset are available on: https://github.com/aliielahi/ONR-WQ.

Predicting financial markets and stock price movements requires analyzing a company’s performance, historic price movements, industry-specific events alongside the influence of human factors such as social media and press coverage. We assume that financial reports (such as income statements, balance sheets, and cash flow statements), historical price data, and recent news articles can collectively represent aforementioned factors.We combine financial data in tabular format with textual news articles and employ pre-trained Large Language Models (LLMs) to predict market movements. Recent research in LLMs has demonstrated that they are able to perform both tabular and text classification tasks, making them our primary model to classify the multi-modal data. We utilize retrieval augmentation techniques to retrieve and attach relevant chunks of news articles to financial metrics related to a company and prompt the LLMs in zero, two, and four-shot settings. Our dataset contains news articles collected from different sources, historic stock price, and financial report data for 20 companies with the highest trading volume across different industries in the stock market. We utilized recently released language models for our LLM-based classifier, including GPT- 3 and 4, and LLaMA- 2 and 3 models.We introduce an LLM-based classifier capable of performing classification tasks using combination of tabular (structured) and textual (unstructured) data. By using this model, we predicted the movement of a given stock’s price in our dataset with a weighted F1-score of 58.5% and 59.1% and Matthews Correlation Coefficient of 0.175 for both 3-month and 6-month periods.The dataset and codes for this paper can be found on Github. https://github.com/aliielahi/FinedFMP1