Gunbir Singh Baveja

Gunbir Singh Baveja, Jaspreet Singh

Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as input for an Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$ decades from the Assam-Guwahati region were used to create this model for magnitude prediction. The Testing Accuracy and Testing Speed were computed taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the mode. As confirmed by the results, ELM shows better scalability with much faster training and testing speed (up to a thousand times faster) than traditional Support Vector Machines. The testing RMSE came out to be around $0.097$. To further test the model's robustness -- magnitude-time data from California was used to calculate the seismic indicators which were then fed into an ELM and then tested on the Assam-Guwahati region. The model proves to be robust and can be implemented in early warning systems as it continues to be a major part of Disaster Response and management.

Gunbir Singh Baveja, Aaryavir Verma

The stock market has become an increasingly popular investment option among new generations, with individuals exploring more complex assets. This rise in retail investors' participation necessitates a deeper understanding of the driving factors behind this trend and the role of financial literacy in enhancing investment decisions. This study aims to investigate how financial literacy influences financial decision-making and stock market participation. By identifying key barriers and motivators, the findings can provide valuable insights for individuals and policymakers to promote informed investing practices. Our research is qualitative in nature, utilizing data collected from social media platforms to analyze real-time investor behavior and attitudes. This approach allows us to capture the nuanced ways in which financial literacy impacts investment choices and participation in the stock market. The findings indicate that financial literacy plays a critical role in stock market participation and financial decision-making. Key barriers to participation include low financial literacy, while increased financial knowledge enhances investment confidence and decision-making. Additionally, behavioral finance factors and susceptibility to financial scams are significantly influenced by levels of financial literacy. These results underscore the importance of targeted financial education programs to improve financial literacy and empower individuals to participate effectively in the stock market.

Gunbir Singh Baveja, Mark Schmidt

Loss of trainability (LoT) in continual learning occurs when gradient steps no longer yield improvement as tasks evolve, so accuracy stalls or degrades despite adequate capacity and supervision. We analyze LoT incurred with Adam through an optimization lens and find that single indicators such as Hessian rank, sharpness level, weight or gradient norms, gradient-to-parameter ratios, and unit-sign entropy are not reliable predictors. Instead we introduce two complementary criteria: a batch-size-aware gradient-noise bound and a curvature volatility-controlled bound that combine into a per-layer predictive threshold that anticipates trainability behavior. Using this threshold, we build a simple per-layer scheduler that keeps each layers effective step below a safe limit, stabilizing training and improving accuracy across concatenated ReLU (CReLU), Wasserstein regularization, and L2 weight decay, with learned learning-rate trajectories that mirror canonical decay.

Gunbir Singh Baveja

This paper investigates the application of Diffusion Policy in non-stationary, vision-based RL settings, specifically targeting environments where task dynamics and objectives evolve over time. Our work is grounded in practical challenges encountered in dynamic real-world scenarios such as robotics assembly lines and autonomous navigation, where agents must adapt control strategies from high-dimensional visual inputs. We apply Diffusion Policy -- which leverages iterative stochastic denoising to refine latent action representations-to benchmark environments including Procgen and PointMaze. Our experiments demonstrate that, despite increased computational demands, Diffusion Policy consistently outperforms standard RL methods such as PPO and DQN, achieving higher mean and maximum rewards with reduced variability. These findings underscore the approach's capability to generate coherent, contextually relevant action sequences in continuously shifting conditions, while also highlighting areas for further improvement in handling extreme non-stationarity.