arXiv subjects

Nisarg Bhatt

Publications and source records attributed to Nisarg Bhatt.

3 recordsLinked to original sources

arXiv2025

Conservation laws and chaos propagation in a non-reciprocal classical magnet

We study a nonreciprocal generalization [EPL 60, 418 (2002)] of the classical Heisenberg spin chain, in which the exchange coupling is nonsymmetric, and show that it displays a ballistic spreading of chaos as measured by the decorrelator. We show that the interactions are reciprocal in terms of transformed variables, with conserved quantities that can be identified as magnetization and energy, with a Poisson-bracket algebra and Hamiltonian dynamics. For strictly antisymmetric couplings in the original model the conserved quantities diffuse, the decorrelator spreads symmetrically, and a simple hydrodynamic theory emerges. The general case in which the interaction has symmetric and antisymmetric parts presents complexities in the limit of large scales. Ballistic propagation of chaos survives the inclusion of interactions beyond nearest neighbours, but the conservation laws in general do not.

cond-mat.stat-mech↗

arXiv2023

Emergent hydrodynamics in a non-reciprocal classical isotropic magnet

The Hamiltonian nature of the precessional dynamics of the classical Heisenberg model leads to reciprocal interactions amongst the spins. Heisenberg spins are reciprocal in nature. In this work, we study the dynamics of a nonequilibrium classical spin chain in which the neighbours interact through a purely non-reciprocal exchange coupling [EPL 60, 418 (2002)] which preserves rotational symmetry. The resultant dynamics conserves neither magnetization nor energy. We uncover other local conservation laws in their place in the extreme case of a strictly antisymmetric coupling. We show numerically that the model undergoes an analogue of thermalization. We present results on the presence of conserved quantities, their diffusive spreading and a hydrodynamic picture, and the nature of the decorrelation front upon adding an initial perturbation to the system.

cond-mat.stat-mech↗

arXiv2021

Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation

Contrastive Learning (CL) is a recent representation learning approach, which encourages inter-class separability and intra-class compactness in learned image representations. Since medical images often contain multiple semantic classes in an image, using CL to learn representations of local features (as opposed to global) is important. In this work, we present a novel semi-supervised 2D medical segmentation solution that applies CL on image patches, instead of full images. These patches are meaningfully constructed using the semantic information of different classes obtained via pseudo labeling. We also propose a novel consistency regularization (CR) scheme, which works in synergy with CL. It addresses the problem of confirmation bias, and encourages better clustering in the feature space. We evaluate our method on four public medical segmentation datasets and a novel histopathology dataset that we introduce. Our method obtains consistent improvements over state-of-the-art semi-supervised segmentation approaches for all datasets.

cs.CV↗