Monu | Searcharxiv

arXiv2026

Interband response in spin-orbit coupled nodal line semimetals

This study investigates the interband conductivity for nodal line semimetals (NLSMs) in the presence of spin-orbit coupling (SOC), where the disorder reshapes the transport properties. The SOC breaks spin degeneracy, thus fundamentally altering the band dispersion and enabling multiple interband transport channels. Using a quantum kinetic framework, we analyze the interband conductivity originating from disorder-driven (extrinsic) and field-driven (intrinsic) mechanisms. We find that the interband response shows an anisotropic nature due to disorder driven counterparts. Additionally, our predictions show a tunable prominent transition peak arising from non-Pauli-blocked states that can be controlled via band parameters as well as external stimuli. To have an experimental relevance, we provide a numerical estimation for the interband response of TaAs using density functional theory estimated parameters. These results suggest the investigation of disorder-enabled signatures in spin systems.

cond-mat.mes-hall↗

arXiv2024

Herd Mentality in Augmentation -- Not a Good Idea! A Robust Multi-stage Approach towards Deepfake Detection

The rapid increase in deepfake technology has raised significant concerns about digital media integrity. Detecting deepfakes is crucial for safeguarding digital media. However, most standard image classifiers fail to distinguish between fake and real faces. Our analysis reveals that this failure is due to the model's inability to explicitly focus on the artefacts typically in deepfakes. We propose an enhanced architecture based on the GenConViT model, which incorporates weighted loss and update augmentation techniques and includes masked eye pretraining. This proposed model improves the F1 score by 1.71% and the accuracy by 4.34% on the Celeb-DF v2 dataset. The source code for our model is available at https://github.com/Monu-Khicher-1/multi-stage-learning

cs.CV↗