arXiv subjects

Aparup Ghosh

Publications and source records attributed to Aparup Ghosh.

3 recordsLinked to original sources

arXiv2026

Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection

Remote sensing change detection requires both global reasoning across bitemporal images and precise localization of changed regions. However, dense attention is computationally expensive for high-resolution imagery, while conventional feature fusion and coarse decoding may inadequately separate genuine changes from appearance variations or preserve object boundaries. We present Bitemporal Mamba-Diffusion for Change Detection (BMD-CD), which combines temporally structured state-space modeling with logit-space diffusion refinement. BMD-CD converts deep bitemporal features into region tokens and arranges them in explicit temporal partitions before bidirectional state-space propagation. Its Bitemporal Ordered Mamba Operator enables long-range cross-temporal interaction with linear sequence complexity, while Orthogonal Feature Disentanglement forms a change-oriented output and a complementary rotated output using learned pairwise rotations and unchanged-region consistency. Multiscale decoding then produces coarse change logits, which are refined through a five-step Conditional Diffusion Decoder operating directly in logit space. Experiments on LEVIR-CD, WHU-CD, DSIFN-CD, CDD, and S2Looking demonstrate strong performance across diverse change-detection settings. BMD-CD achieves F1 scores of 93.7%, 96.0%, 97.8%, and 99.0% on the four standard benchmarks and improves 3-pixel Boundary-F1 to 87.7% and 91.4% on LEVIR-CD and WHU-CD, respectively. The full model requires 32.09 GFLOPs and 47 ms per 256 x 256 image pair, while also showing zero-shot transfer to ValaisCD and B-FLAIR-test. Our code is available at https://github.com/Aparup2139/Public_WACV/

cs.CV↗

arXiv2024

Characterizing the solar cycle variability using nonlinear time series analysis at different amounts of dynamo supercriticality: Solar dynamo is not highly supercritical

The solar dynamo is essentially a cyclic process in which the toroidal component of the magnetic field is converted into the poloidal one and vice versa. This cyclic loop is disturbed by some nonlinear and stochastic processes mainly operating in the toroidal to poloidal part. Hence, the memory of the polar field decreases in every cycle. On the other hand, the dynamo efficiency and, thus, the supercriticality of the dynamo decreases with the Sun's age. Previous studies have shown that the memory of the polar magnetic field decreases with the increase of supercriticality of the dynamo. In this study, we employ popular techniques of time series analysis, namely, compute Higuchi's fractal dimension, Hurst exponent, and Multi-Fractal Detrended Fluctuation Analysis, to the amplitude of the solar magnetic cycle obtained from dynamo models operating at near-critical and supercritical regimes. We show that the magnetic field in the near-critical regime is governed by strong memory, less stochasticity, intermittency, and breakdown of self-similarity. On the contrary, the magnetic field in the supercritical region has less memory, strong stochasticity, and shows a good amount of self-similarity. Finally, applying the same time series analysis techniques in the reconstructed sunspot data of 85 cycles and comparing their results with that from models, we conclude that the solar dynamo is possibly operating near the critical regime and not too much supercritical regime. Thus Sun may not be too far from the critical dynamo transition.

astro-ph.SR↗

arXiv2021

Is the Hemispheric Asymmetry of Monthly Sunspot Area an Irregular Process with Long-Term Memory?

The hemispheric asymmetry of the sunspot cycle is a real feature of the Sun. However, its origin is still not well understood. Here we perform nonlinear time series analysis of the sunspot area (and number) asymmetry to explore its dynamics. By measuring the correlation dimension of the sunspot area asymmetry, we conclude that there is no strange attractor in the data. Further computing Higuchi's dimension, we conclude that the hemispheric asymmetry is largely governed by stochastic noise. However, the behaviour of Hurst exponent reveals that the time series is not completely determined by a memory-less stochastic noise, rather there is a long-term persistence, which can go beyond two solar cycles. Therefore, our study suggests that the hemispheric asymmetry of the sunspot cycle is predominantly originated due to some irregular process in the solar dynamo. The long-term persistence in the solar cycle asymmetry suggests that the solar magnetic field has some memory in the convection zone.

astro-ph.SR↗