A Feasibility Study of Task-Based fMRI at 0.55 T

0.55T MRI offers advantages compared to conventional field strengths, including reduced susceptibility artifacts and better compatibility with simultaneous EEG recordings. However, reliable task-based fMRI at 0.55T has not been significantly demonstrated. In this study, we establish a robust task-based fMRI protocol and analysis pipeline at 0.55T that achieves full brain coverage and results comparable to what is expected for activation extent and location. We attempted fMRI at 0.55T by combining EPI acquisition with custom analysis techniques. Finger-tapping and visual tasks were used, comparing 5- and 10-minute runs to enhance activation detection. The results show significant activations, demonstrating that high-quality task-based fMRI is achievable at 0.55T in single subjects. This study demonstrates that reliable task-based fMRI is feasible on 0.55T scanners, potentially broadening functional neuroimaging access in clinical and research settings where high-field MRI is unavailable or impractical, supporting broader diagnostic and research applications.

VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis

Stock price prediction remains a complex and high-stakes task in financial analysis, traditionally addressed using statistical models or, more recently, language models. In this work, we introduce VISTA (Vision-Language Inference for Stock Time-series Analysis), a novel, training-free framework that leverages Vision-Language Models (VLMs) for multi-modal stock forecasting. VISTA prompts a VLM with both textual representations of historical stock prices and their corresponding line charts to predict future price values. By combining numerical and visual modalities in a zero-shot setting and using carefully designed chain-of-thought prompts, VISTA captures complementary patterns that unimodal approaches often miss. We benchmark VISTA against standard baselines, including ARIMA and text-only LLM-based prompting methods. Experimental results show that VISTA outperforms these baselines by up to 89.83%, demonstrating the effectiveness of multi-modal inference for stock time-series analysis and highlighting the potential of VLMs in financial forecasting tasks without requiring task-specific training.

Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques

The COVID-19 pandemic has underscored the necessity for advanced diagnostic tools in global health systems. Infrared Thermography (IRT) has proven to be a crucial non-contact method for measuring body temperature, vital for identifying febrile conditions associated with infectious diseases like COVID-19. Traditional non-contact infrared thermometers (NCITs) often exhibit significant variability in readings. To address this, we integrated machine learning algorithms with IRT to enhance the accuracy and reliability of temperature measurements. Our study systematically evaluated various regression models using heuristic feature engineering techniques, focusing on features' physiological relevance and statistical significance. The Convolutional Neural Network (CNN) model, utilizing these techniques, achieved the lowest RMSE of 0.2223, demonstrating superior performance compared to results reported in previous literature. Among non-neural network models, the Binning method achieved the best performance with an RMSE of 0.2296. Our findings highlight the potential of combining advanced feature engineering with machine learning to improve diagnostic tools' effectiveness, with implications extending to other non-contact or remote sensing biomedical applications. This paper offers a comprehensive analysis of these methodologies, providing a foundation for future research in the field of non-invasive medical diagnostics.