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Ayan Banerjee

Publications and source records attributed to Ayan Banerjee.

At least 19 recordsLinked to original sources

Traversable wormholes in $f(T,\tau)$ gravity: a complete classification of the non-exotic sector

We study static and spherically symmetric traversable wormholes in $f(T,\tau)$ gravity, where the torsion scalar $T$ is coupled to the trace $\tau$ of the matter energy--momentum tensor. We consider the linear model $f(T,\tau)=T+\beta\tau$ with an anisotropic fluid and the mean-pressure matter Lagrangian $\Lm=\Pmean=(p_r+2p_t)/3$. The field equations are obtained for the Morris--Thorne geometry without fixing the redshift or shape function at the outset. For a constant redshift function, the energy-condition problem takes a simple form. On the branch $\beta>8\pi$ and for $b(r)>0$, the energy density together with the null, weak, and strong energy conditions is satisfied throughout the spacetime if and only if $r b(r)$ is non-increasing. The same condition also implies asymptotic flatness, $b(r)<r$ outside the throat, and $b'(r_0)\leq -1$. The allowed geometries can therefore be written as $b(r)=r_0^2 h(r)/r$, where $h(r_0)=1$ and $h(r)$ is positive and non-increasing. For the representative family $b(r)=r_0(r_0/r)^n$, the null, weak, and strong energy conditions hold for $n\geq1$, while the dominant energy condition requires $n\geq3(\beta-2\pi)/(\beta-6\pi)$. We also separate the physical matter from the effective source and show how the trace coupling allows the physical matter to remain non-exotic although the effective source violates the null energy condition. Finally, we examine the marginal case $b(r)=r_0^2/r$ with a non-constant redshift function. A decreasing redshift function can improve the tangential null energy condition at the throat, but this improvement cannot be maintained throughout an asymptotically flat exterior. These results show that the matter--torsion coupling can support a broad class of traversable wormholes without requiring exotic physical matter.

gr-qc

Gravitational Wave Standard Sirens as Probes of Lorentz Violation in Bumblebee Gravity

Gravitational-wave standard sirens provide a direct measurement of luminosity distance and therefore offer a new way to test gravity over cosmological scales. We use this idea to forecast the sensitivity of the Einstein Telescope (ET) to Lorentz violation in Bumblebee gravity, and we examine how the forecast changes when Type~Ia supernova information is added. A timelike Bumblebee vacuum expectation value can affect the cosmic expansion and, when it evolves with redshift, the propagation amplitude of gravitational waves. We study a constant-field case and an evolving-field case using mock ET catalogues with $10^3$ events together with a Pantheon+-like supernova sample. The supernova data substantially improve the background parameters: in the constant-field case the uncertainties in $H_0$ and $\Omega_m$ decrease by a factor of about $4.4$, while in the evolving-field case they decrease by factors of about $1.6$ and $6.2$, respectively. By contrast, the Lorentz-violating parameter $\ell_0$ remains prior dominated, and the evolution index $\beta$ is constrained only by the gravitational-wave sector. The best forecast precision, $\Delta\ell_0\simeq0.028$, is about $4.7\times10^{12}$ times weaker than the bound implied by GW170817. The principal result is therefore a quantified sensitivity gap rather than a forecast detection. We also express the prediction in the phenomenological $(\Xi,n)$ description of modified gravitational-wave propagation, allowing direct comparison with standard-siren studies of other gravity models.

gr-qc

Many-Body Mobility Edge and Non-Hermitian Skin Effect in an Interacting Quasi-Periodic Spin Chain

Non-Hermitian many-body physics reveals a rich interplay between topology, localization, and boundary effects, yet their collective behavior in interacting disordered systems remains largely unexplored. In this work, we study an interacting non-Hermitian spin chain subject to a quasi-periodic longitudinal field, providing a unified and controlled setting, where non-Hermitian dynamics, interactions, and localization mechanisms intertwine. Remarkably, we discover a "D-shaped" many-body mobility edge that separates extended and localized eigenstates, while simultaneously delineating regimes of many-body localization and the many-body skin effect (where many-body eigenstates acquire an anomalous drift towards a boundary under open boundaries) emerging from the combined action of interactions, non-Hermiticity, and driving amplitude. We demonstrate that the skin effect induces multifractal scaling in the non-Hermitian eigenstates, providing a clear signature of the many-body skin effect. Employing diagnostics such as the fractal dimension, complex eigenvalue fractions, and many-body inverse participation ratios, we map out a unified phase diagram in which all measures consistently identify the "D-shaped" mobility edge. Finally, we probe this interplay using both complex level-spacing statistics and dynamical observables such as density imbalance, entanglement growth, and wave-packet evolution, culminating in a rich many-body mobility phase diagram that captures both the many-body skin effect and localization transitions. Our results identify a clear, defining signature of the "D-shaped" many-body mobility edge, and underscore its pivotal role in shaping the physics of open quantum many-body systems.

cond-mat.dis-nn

Quark Stars in $f(T,\mathcal{T}) $ Gravity: Structure, Stability, and Observational Constraints

Quark stars-hypothetical compact stars made entirely of deconfined quark matter-offer a clean testing ground for gravity beyond general relativity. We study their structure in $f(T,\mathcal{T})$ gravity, a teleparallel theory in which torsion is coupled directly to the trace of the energy-momentum tensor through a single constant coupling. Using the standard MIT bag description of quark matter, we solve the modified stellar structure equations and follow how the mass, radius, compactness, and surface redshift respond as the coupling is varied across its full admissible range. The maximum mass turns out to depend on the coupling in a non-monotonic way: it rises above the general relativity value, peaks near 2.02 solar masses at a moderate positive coupling, and then falls steeply as the coupling approaches a critical value at which the structure equations become singular. The two-solar-mass pulsar constraint is satisfied within a finite window of positive couplings. All configurations on the candidate stable branch satisfy causality and remain below the standard general-relativistic compactness and surface-redshift benchmarks.

gr-qc

Tidal Deformability of Neutron Stars in Bumblebee Gravity: Probing Lorentz Symmetry Breaking with Gravitational Waves

We investigate the tidal properties of static neutron stars in the Neves--Gardim branch of bumblebee gravity, a vector--tensor theory in which a nonzero vacuum expectation value of the bumblebee field spontaneously breaks local Lorentz symmetry. The stellar background is determined from the full modified Tolman--Oppenheimer--Volkoff system, including the term containing $m''(r)$. For a differentiable barotropic equation of state, this system is recast into an algebraically equivalent first-order form without discarding any bumblebee-dependent contribution. Because a complete coupled derivation of static even-parity metric and bumblebee-field perturbations is not presently available for this stellar branch, the tidal sector is treated through an explicit effective metric-deformation prescription: the Hinderer fluid perturbation equation is evaluated on the modified bumblebee background, and the standard surface matching relation is retained as part of the same prescription. We compute the quadrupolar tidal Love number $k_2$ and the dimensionless tidal deformability $\Lambda=(2/3)k_2\mathcal{C}^{-5}$ for the BSk20, BSk21, DD2, and MS1 equations of state over $\ell\in[-0.4,+1.0]$. The general-relativistic limit is recovered at $\ell=0$ to within $0.2\%$ in the benchmark calculations. Within the adopted effective prescription, comparison with the GW170817 binary tidal-deformability bound $\widetilde{\Lambda}\leq720$ yields the EOS-dependent limiting values $\ell_{\max}=+0.25$ (BSk20), $+0.06$ (BSk21), $-0.09$ (DD2), and $-0.25$ (MS1). These tidal limits are conditional on the stated perturbative prescription and should be reassessed when the complete linearised vector--tensor problem becomes available.

gr-qc

Spectral-topology-induced criticality in non-Hermitian fermionic metals

Quantum matter emerges from the interplay of fluctuations, topology, and entanglement, which - in equilibrium - governs quantized transport, universal criticality, and topological classification. Non-Hermitian systems, widely explored in platforms ranging from electric circuits to photonics, are intrinsically out-of-equilibrium, and display fundamentally new phenomena, including complex spectra, spectral winding, exceptional topology, and non-unitary dynamics. A central challenge is understanding how the complex single-particle spectrum governs universal many-body behavior. We introduce a symmetry-protected dynamical topological index derived directly from the complex spectrum. Through the lens of algebraic topology, more specifically Morse theory, we identify critical points in the spectrum with topological defects, whose curvature and stability are protected under continuous deformations. This links spectral geometry to many-body observables, unifying non-Hermitian band topology, entanglement, and transport. We demonstrate that non-Hermitian quantum criticality in non-interacting systems is controlled by gain-and-loss-selected non-equilibrium steady states, which dynamically generate an emergent imaginary Fermi surface whose Fermi points host scale-invariant gapless modes with logarithmic entanglement scaling and algebraic correlations. Our work establishes a unified framework for non-Hermitian quantum matter, connecting spectral topology to Morse theory, revealing a topological foundation of non-equilibrium quantum criticality.

cond-mat.mes-hall

Probing the Broken Spatial Symmetry of a Stratified Medium with Structured Light

We study near-symmetric resonant stratified media to show how a tiny broken spatial symmetry can effectively be probed by structured light with or without orbital angular momentum. This is achieved by examining both the in-plane and out of plane Goos-H\"anchen and Imbert Fedorov shifts, respectively, in the reflected light, magnified by resonant enhancement and weak value amplification. We show that non-reciprocity in reflection for illumination from opposite ends can result in different shifts, even to the extent of shifts with opposite signs for tiny imbalance resulting from the broken symmetry. We believe that our results can lead to new type of extra-sensitive sensors for any agent (eg. refractive index, displacement, etc.) that can break the symmetry.

physics.optics

EMMA: Extracting Multiple physical parameters from Multimodal Data

We introduce EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-series observations. Unlike prior video-only approaches that struggle with occluded states, hidden actuation inputs, or assumptions about known initial conditions and coordinate frames, EMMA performs joint inference of explicit parameters, implicit dynamical components, and calibration invariants within a unified continuous-time model. EMMA leverages a Liquid Time-Constant (LTC) network to learn latent dynamics from heterogeneous modalities while a physics-constrained loss enforces consistency with the governing differential equations. A unified feature pipeline enables consistent alignment across video trajectories, acoustic signatures, and chart-derived measurements, allowing EMMA to estimate parameters under forced, implicit, and multivariate dynamics without requiring segmentation masks, differentiable rendering, or specialized sensors. Across 100+ scenarios including five standard dynamical benchmarks (75 Delfys videos), real-world rover and quadrotor systems with hidden inputs, and simulation-chart case studies spanning biological and chaotic systems, EMMA delivers robust multi-parameter recovery and significantly outperforms existing single-modality and equation-discovery baselines. Our results establish EMMA as a general, scalable solution for physics-consistent model extraction from opportunistic multimodal data. Code and data are available at: https://github.com/ImpactLabASU/EMMA-CVPR2026

cs.CV

Tunable Optical Torque by Asymmetry-Induced Spin-Hall Effect in Tightly Focused Spinless Gaussian Beams

A linearly polarized Gaussian beam, carrying zero net spin angular momentum, is conventionally not expected to exert optical torque or induce rotational motion in birefringent microparticles. When such a beam is tightly focused, the constituent left- and right-circular polarization components separate spatially due to spin-orbit interaction, commonly known as the spin Hall effect of light. However, this separation is at wavelength scales and is also axially symmetric, resulting in zero net spin angular momentum, and concomitantly no optical torque near the focal plane. Here, we demonstrate that this limitation can be overcome using several commonly encountered asymmetric illumination modalities that break the axial symmetry of the focusing system, thereby disrupting the symmetric separation of the spin components for the same linearly polarized Gaussian beam. As a consequence, trapped microparticles experience a tunable optical torque and exhibit rotational motion with distinct rotational frequencies at the same input power. The particles also undergo controlled reversal of the rotation direction simply by rotating the incident plane of polarization using a half-wave plate. Despite their apparent diversity, all these methods share the same physical origin rooted in asymmetric illumination. These results establish an experimentally accessible and minimal strategy for realizing controllable optical rotation devices exploiting spin-orbit optomechanics without requiring intrinsic angular momentum in the light.

physics.optics

DocRevive: A Unified Pipeline for Document Text Restoration

In Document Understanding, the challenge of reconstructing damaged, occluded, or incomplete text remains a critical yet unexplored problem. Subsequent document understanding tasks can benefit from a document reconstruction process. In response, this paper presents a novel unified pipeline combining state-of-the-art Optical Character Recognition (OCR), advanced image analysis, masked language modeling, and diffusion-based models to restore and reconstruct text while preserving visual integrity. We create a synthetic dataset of 30{,}078 degraded document images that simulates diverse document degradation scenarios, setting a benchmark for restoration tasks. Our pipeline detects and recognizes text, identifies degradation with an occlusion detector, and uses an inpainting model for semantically coherent reconstruction. A diffusion-based module seamlessly reintegrates text, matching font, size, and alignment. To evaluate restoration quality, we propose a Unified Context Similarity Metric (UCSM), incorporating edit, semantic, and length similarities with a contextual predictability measure that penalizes deviations when the correct text is contextually obvious. Our work advances document restoration, benefiting archival research and digital preservation while setting a new standard for text reconstruction. The OPRB dataset and code are available at \href{https://huggingface.co/datasets/kpurkayastha/OPRB}{Hugging Face} and \href{https://github.com/kunalpurkayastha/DocRevive}{Github} respectively.

cs.CV

Human Knowledge Integrated Multi-modal Learning for Single Source Domain Generalization

Generalizing image classification across domains remains challenging in critical tasks such as fundus image-based diabetic retinopathy (DR) grading and resting-state fMRI seizure onset zone (SOZ) detection. When domains differ in unknown causal factors, achieving cross-domain generalization is difficult, and there is no established methodology to objectively assess such differences without direct metadata or protocol-level information from data collectors, which is typically inaccessible. We first introduce domain conformal bounds (DCB), a theoretical framework to evaluate whether domains diverge in unknown causal factors. Building on this, we propose GenEval, a multimodal Vision Language Models (VLM) approach that combines foundational models (e.g., MedGemma-4B) with human knowledge via Low-Rank Adaptation (LoRA) to bridge causal gaps and enhance single-source domain generalization (SDG). Across eight DR and two SOZ datasets, GenEval achieves superior SDG performance, with average accuracy of 69.2% (DR) and 81% (SOZ), outperforming the strongest baselines by 9.4% and 1.8%, respectively.

cs.CV

Experience with Single Domain Generalization in Real World Medical Imaging Deployments

A desirable property of any deployed artificial intelligence is generalization across domains, i.e. data generation distribution under a specific acquisition condition. In medical imagining applications the most coveted property for effective deployment is Single Domain Generalization (SDG), which addresses the challenge of training a model on a single domain to ensure it generalizes well to unseen target domains. In multi-center studies, differences in scanners and imaging protocols introduce domain shifts that exacerbate variability in rare class characteristics. This paper presents our experience on SDG in real life deployment for two exemplary medical imaging case studies on seizure onset zone detection using fMRI data, and stress electrocardiogram based coronary artery detection. Utilizing the commonly used application of diabetic retinopathy, we first demonstrate that state-of-the-art SDG techniques fail to achieve generalized performance across data domains. We then develop a generic expert knowledge integrated deep learning technique DL+EKE and instantiate it for the DR application and show that DL+EKE outperforms SOTA SDG methods on DR. We then deploy instances of DL+EKE technique on the two real world examples of stress ECG and resting state (rs)-fMRI and discuss issues faced with SDG techniques.

eess.IV

Personalized Model-Based Design of Human Centric AI enabled CPS for Long term usage

Human centric critical systems are increasingly involving artificial intelligence to enable knowledge extraction from sensor collected data. Examples include medical monitoring and control systems, gesture based human computer interaction systems, and autonomous cars. Such systems are intended to operate for a long term potentially for a lifetime in many scenarios such as closed loop blood glucose control for Type 1 diabetics, self-driving cars, and monitoting systems for stroke diagnosis, and rehabilitation. Long term operation of such AI enabled human centric applications can expose them to corner cases for which their operation is may be uncertain. This can be due to many reasons such as inherent flaws in the design, limited resources for testing, inherent computational limitations of the testing methodology, or unknown use cases resulting from human interaction with the system. Such untested corner cases or cases for which the system performance is uncertain can lead to violations in the safety, sustainability, and security requirements of the system. In this paper, we analyze the existing techniques for safety, sustainability, and security analysis of an AI enabled human centric control system and discuss their limitations for testing the system for long term use in practice. We then propose personalized model based solutions for potentially eliminating such limitations.

cs.AI

Detection of Deployment Operational Deviations for Safety and Security of AI-Enabled Human-Centric Cyber Physical Systems

In recent years, Human-centric cyber-physical systems have increasingly involved artificial intelligence to enable knowledge extraction from sensor-collected data. Examples include medical monitoring and control systems, as well as autonomous cars. Such systems are intended to operate according to the protocols and guidelines for regular system operations. However, in many scenarios, such as closed-loop blood glucose control for Type 1 diabetics, self-driving cars, and monitoring systems for stroke diagnosis. The operations of such AI-enabled human-centric applications can expose them to cases for which their operational mode may be uncertain, for instance, resulting from the interactions with a human with the system. Such cases, in which the system is in uncertain conditions, can violate the system's safety and security requirements. This paper will discuss operational deviations that can lead these systems to operate in unknown conditions. We will then create a framework to evaluate different strategies for ensuring the safety and security of AI-enabled human-centric cyber-physical systems in operation deployment. Then, as an example, we show a personalized image-based novel technique for detecting the non-announcement of meals in closed-loop blood glucose control for Type 1 diabetics.

cs.CV

XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging

Explainability domain generalization and rare class reliability are critical challenges in medical AI where deep models often fail under real world distribution shifts and exhibit bias against infrequent clinical conditions This paper introduces XAIMeD an explainable medical AI framework that integrates clinically accurate expert knowledge into deep learning through a unified neuro symbolic architecture XAIMeD is designed to improve robustness under distribution shift enhance rare class sensitivity and deliver transparent clinically aligned interpretations The framework encodes clinical expertise as logical connectives over atomic medical propositions transforming them into machine checkable class specific rules Their diagnostic utility is quantified through weighted feature satisfaction scores enabling a symbolic reasoning branch that complements neural predictions A confidence weighted fusion integrates symbolic and deep outputs while a Hunt inspired adaptive routing mechanism guided by Entropy Imbalance Gain EIG and Rare Class Gini mitigates class imbalance high intra class variability and uncertainty We evaluate XAIMeD across diverse modalities on four challenging tasks i Seizure Onset Zone SOZ localization from rs fMRI ii Diabetic Retinopathy grading across 6 multicenter datasets demonstrate substantial performance improvements including 6 percent gains in cross domain generalization and a 10 percent improved rare class F1 score far outperforming state of the art deep learning baselines Ablation studies confirm that the clinically grounded symbolic components act as effective regularizers ensuring robustness to distribution shifts XAIMeD thus provides a principled clinically faithful and interpretable approach to multimodal medical AI.

cs.AI

Hardware Acceleration for Neural Networks: A Comprehensive Survey

Neural networks have become dominant computational workloads across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bottlenecks increasingly dominated by memory movement, communication, and irregular operators rather than peak arithmetic throughput. This survey reviews the current technology landscape for hardware acceleration of deep learning, spanning GPUs and tensor-core architectures, domain-specific accelerators (TPUs, NPUs), FPGA-based designs, ASIC inference engines, and emerging LLM-serving accelerators such as LPUs, alongside in-/near-memory computing and neuromorphic/analog approaches. We organize the survey using a unified taxonomy across (i) workloads (CNNs, RNNs, GNNs, Transformers/LLMs), (ii) execution settings (training vs.\ inference; datacenter vs.\ edge), and (iii) optimization levers (reduced precision, sparsity and pruning, operator fusion, compilation and scheduling, memory-system/interconnect design). We synthesize key architectural ideas such as systolic arrays, vector and SIMD engines, specialized attention and softmax kernels, quantization-aware datapaths, and high-bandwidth memory, and discuss how software stacks and compilers bridge model semantics to hardware. Finally, we highlight open challenges -- including efficient long-context LLM inference (KV-cache management), robust support for dynamic and sparse workloads, energy- and security-aware deployment, and fair benchmarking -- pointing to promising directions for the next generation of neural acceleration.

eess.SY

Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics

Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recovery (MR), which identifies governing equations from sensor data, is a key primitive for safe and explainable monitoring in mission-critical autonomous systems operating under strict latency, compute, and power constraints. However, state-of-the-art MR methods (e.g., EMILY and PINN+SR) rely on Neural ODE formulations that require iterative solvers and are difficult to accelerate efficiently on edge hardware. We present \textbf{MERINDA} (Model Recovery in Reconfigurable Dynamic Architecture), an FPGA-accelerated MR framework designed to make physical AI practical on resource-constrained devices. MERINDA replaces expensive Neural ODE components with a hardware-friendly formulation that combines (i) GRU-based discretized dynamics, (ii) dense inverse-ODE layers, (iii) sparsity-driven dropout, and (iv) lightweight ODE solvers. The resulting computation is structured for streaming parallelism, enabling critical kernels to be fully parallelized on the FPGA. Across four benchmark nonlinear dynamical systems, MERINDA delivers substantial gains over GPU implementations: \textbf{114$\times$ lower energy} (434~J vs.\ 49{,}375~J), \textbf{28$\times$ smaller memory footprint} (214~MB vs.\ 6{,}118~MB), and \textbf{1.68$\times$ faster training}, while matching state-of-the-art model-recovery accuracy. These results demonstrate that MERINDA can bring accurate, explainable MR to the edge for real-time monitoring of autonomous systems.

cs.LG

NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI

Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-stakes clinical decision-making. Most existing vision models rely on purely data-driven learning and produce black-box predictions with limited interpretability and poor cross-domain generalization, hindering their real-world clinical adoption. We present NEURO-GUARD, a novel knowledge-guided vision framework that integrates Vision Transformers (ViTs) with language-driven reasoning to improve performance, transparency, and domain robustness. NEURO-GUARD employs a retrieval-augmented generation (RAG) mechanism for self-verification, in which a large language model (LLM) iteratively generates, evaluates, and refines feature-extraction code for medical images. By grounding this process in clinical guidelines and expert knowledge, the framework progressively enhances feature detection and classification beyond purely data-driven baselines. Extensive experiments on diabetic retinopathy classification across four benchmark datasets APTOS, EyePACS, Messidor-1, and Messidor-2 demonstrate that NEURO-GUARD improves accuracy by 6.2% over a ViT-only baseline (84.69% vs. 78.4%) and achieves a 5% gain in domain generalization. Additional evaluations on MRI-based seizure detection further confirm its cross-domain robustness, consistently outperforming existing methods. Overall, NEURO-GUARD bridges symbolic medical reasoning with subsymbolic visual learning, enabling interpretable, knowledge-aware, and generalizable medical image diagnosis while achieving state-of-the-art performance across multiple datasets.

cs.AI