A brief analysis on the performance of the aviation industry through indicators of cancellation rate, on-time rate, and average delay minutes, fitted by and to forecast with reasonable ARIMA models.

Projet refait entièrement dans la v2 web

Epidemiological consequences of TB prophylactic intervention

Reports stuff that has happened on a particular day, based on the logs.

Image Uploading via laravel 5.7 framework with help of intervention and dropzonejs

Student database made with Laravel

What is the causal impact of an intervention?

Predicting outcomes of educational interventions before investing in large-scale implementation efforts in school settings is essential for educational policy-making. However, due to time and resource limitations, conducting longitudinal, large-scale experiments testing outcomes of interventions in authentic settings is difficult. Here, we intro…

Implementation for the NeurIPS 2025 paper: An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation

Describing and visualising baseline results of the Let's Move It intervention

Save images as static in real-time in different formats using Intervention Image.

This is the Github repository for the preprint https://arxiv.org/abs/2505.19612

This project explores methods to detect and mitigate jailbreak behaviors in Large Language Models (LLMs). By analyzing activation patterns—particularly in deeper layers—we identify distinct differences between compliant and non-compliant responses to uncover a jailbreak "direction." Using this insight, we develop intervention strategies that modify

Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

Second assignment for Artificial Intelligence course @USI19/20.

Tu Ayuda en Tiempos de Crisis

This code is part of the Work: Hosted Player - Revisiting Olia Lialina's web performance for a swimmer "Hosted".

Heart Disease Prediction Using Machine Learning is a logistic regression model that predicts heart disease based on medical data. It analyzes features like age and cholesterol, achieving 85.24% training accuracy and 80.49% testing accuracy, facilitating early detection for timely intervention.

Causal inference of post-transcriptional regulation timelines from long-read sequencing in Arabidopsis thaliana