arXiv subjects

Sourav Dutta

Publications and source records attributed to Sourav Dutta.

At least 19 recordsLinked to original sources

arXiv2026

Multi-hour stable trapping and threshold steady state of non-laser-coolable ions with ultracold atoms in a hybrid trap

We report multi-hour stable trapping and steady state of non-laser-coolable Cs$^{+}$ ions in a linear Paul trap, achieved via resonant charge-exchange (RCE) cooling with a precisely centered ultracold Cs cloud. Without cold atoms, all ions are lost within 3 minutes. With centered cold atoms, RCE cooling establishes a stable population of ions for 6 hours with no measurable decay -- more than two orders of magnitude longer hold times compared to prior hybrid atom-ion systems. We observe a threshold behavior in the steady-state number of ions ($N_{s}$): initial ion loadings above $N_{s}$ ions converge downward to $N_{s}$ ions and remain constant thereafter; initial loadings below $N_{s}$ ions show no measurable decay. The dynamics is governed by a competition between ion-ion rf heating and ion-atom collisional cooling. The prolonged, simultaneous ion-atom trapping suggests an upper bound on the three-body recombination-induced Cs$_{2}^{+}$ formation rate constant: $k_{3} << 1.4 \times 10^{-25} cm^{6} s^{-1}$. This remarkable realization of long-lived steady state overcomes a critical limitation of prior hybrid atom-ion systems and enables extended studies of ultracold ion-neutral chemistry, rare inelastic collisions, and sympathetic cooling routes for complex molecular ions.

physics.atom-ph↗

arXiv2026

Adams type Dunkl Stein-Weiss inequality on Dunkl Morrey spaces on the real line

In this paper, we study the weighted boundedness of the Dunkl fractional integral operator (i.e., Dunkl Stein-Weiss inequality) associated with the Dunkl operator on $\mathbb{R}$. Indeed, we obtain the Adams-type Dunkl Stein-Weiss inequality on Dunkl-Morrey spaces. Our result extends the classical Adams type Stein-Weiss inequality on Morrey space result to the Dunkl setting. Furthermore, we establish the weighted boundedness of the Dunkl fractional maximal function on Dunkl Morrey spaces.

math.CA↗

arXiv2026

Operator Learning for Predicting Bulk Wave Parameters of Spectral Wave Models

The impact of wave-induced forcing on the mean water level and nearshore currents is typically modeled through excess momentum fluxes, also known as radiation stresses, and their spatial gradients. Accurate storm surge prediction requires coupled circulation and wave models, but the high computational cost of numerical wave models limits their temporal resolution. In this work, we explore a proof-of-concept application of Deep Operator Networks (DeepONets) as a surrogate for the Simulating WAves Nearshore (SWAN) numerical wave model. Unlike grid-dependent surrogate models, DeepONets learn the underlying continuous operator, and thus, can provide highly efficient prediction while enabling discretization-invariant inference. The proposed surrogate model is evaluated using two distinct 1-D and 2-D steady-state numerical examples with variable boundary wave conditions and wind fields. When applied to a realistic numerical example of steady-state wave simulation in Duck, NC, the DeepONet surrogate improves computational efficiency by four orders of magnitude. Furthermore, the model demonstrates consistently high accuracy in predicting the significant wave height and the x- and y- components of the radiation stress gradient, by achieving relative L_2 errors bounded by 1.91%, 10.98%, and 6.88%, respectively, across all unseen test scenarios.

physics.comp-ph↗

arXiv2026

Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints

Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics. Exact Riemann solvers faithfully solve the underlying equations, but can be computationally expensive due to the iterative root-finding procedures they often require. Consequently, most practical computations rely on classical approximate Riemann solvers, such as Rusanov and Roe, which trade accuracy for computational speed. Neural networks have recently shown promise as an alternative for approximating exact Riemann solvers, but most existing approaches are data-driven or impose weak constraints. This may result in problems with maintaining balanced states, symmetry breaking, and conservation errors when integrated into a Godunov-type scheme. To address these issues, we propose a hard-constrained neural Riemann solver (HCNRS) and enforce five constraints: positivity, consistency, mirror symmetry, Galilean invariance, and scaling invariance. Numerical experiments are carried out for the shallow water and ideal-gas Euler equations on standard benchmark problems. In the absence of hard constraints, violations of the well-balanced property, mass conservation, and symmetry are observed. Notably, in the Euler implosion problem, the exact Riemann solver with MUSCL-Hancock captures the jet structure well, whereas the Rusanov flux is too diffusive and smears it out. HCNRS accurately reproduces the solution obtained by the exact Riemann solver. In contrast, an unconstrained neural formulation lacks mirror symmetry, which makes the solution depend on the choice of flux normal direction. As a result, the jet is either shifted or lost, along with diagonal symmetry.

physics.comp-ph↗

arXiv2026

ColBERT-Att: Late-Interaction Meets Attention for Enhanced Retrieval

Vector embeddings from pre-trained language models form a core component in Neural Information Retrieval systems across a multitude of knowledge extraction tasks. The paradigm of late interaction, introduced in ColBERT, demonstrates high accuracy along with runtime efficiency. However, the current formulation fails to take into account the attention weights of query and document terms, which intuitively capture the "importance" of similarities between them, that might lead to a better understanding of relevance between the queries and documents. This work proposes ColBERT-Att, to explicitly integrate attention mechanism into the late interaction framework for enhanced retrieval performance. Empirical evaluation of ColBERT-Att depicts improvements in recall accuracy on MS-MARCO as well as on a wide range of BEIR and LoTTE benchmark datasets.

cs.IR↗

arXiv2026

Demonstrating Noise-adapted Quantum Error Correction With Break-Even Performance

The promise of quantum computing is closer to reality today than ever before, thanks to rapid progress in the development of quantum hardware. Even as qubit lifetimes and gate fidelities continue to improve, realizing robust, fault-tolerant quantum computers is contingent upon the successful implementation of quantum error correction (QEC). Conventional QEC schemes have rather high resource overheads and low threshold requirements, making them challenging to implement on present day hardware. Here, we use a recently developed noise-adapted 3-qubit QEC scheme to demonstrate break-even performance against native amplitude-damping (AD) noise on IBM quantum hardware. We use variational quantum circuits to construct hardware-efficient encoding and decoding circuits. This scheme is probabilistic due to the non-unitary nature of the recovery operators, which are implemented via the block-encoding technique. We demonstrate logical qubit lifetimes exceeding those of the physical qubits by performing multiple rounds of QEC. To further protect the qubits from dephasing due to crosstalk, we incorporate dynamical decoupling into our noise-adapted QEC scheme in a seamless fashion. To account for the post-selection overhead, we define a measure of gain, that allows for faithful performance benchmarking of the protocol. Our analysis suggests that the performance of our protocol is limited primarily by the measurement readout fidelity, and is bound to improve with successive generations of quantum processors.

quant-ph↗

arXiv2026

Scheduling for TWDM-EPON-Based Fronthaul Without a Dedicated Registration Wavelength

The adoption of Centralized Radio Access Network (C-RAN) architectures requires fronthaul systems capable of carrying large volumes of radio data while meeting stringent delay and jitter requirements. Ethernet Passive Optical Networks (EPONs) have emerged as a promising fronthaul solution due to their cost efficiency and compatibility with existing infrastructure. However, the traditional registration process for EPON systems halts the ongoing data transmissions during the registration period, thereby violating the enhanced Common Public Radio Interface (eCPRI) delay and jitter requirements. This limitation has been acknowledged by the ITU-T, which recommends the use of a dedicated wavelength channel for registration, leading to inefficient bandwidth utilization. In this paper, we propose a novel scheduling framework for a Time and Wavelength Division Multiplexed (TWDM) EPON-based fronthaul that enables periodic registration without wasting an additional wavelength channel. Performance evaluation demonstrates that the proposed method achieves up to a 71\% increase in the number of Radio Units (RUs) supported for a given number of wavelength channels, compared to a baseline scheme employing a dedicated registration wavelength.

cs.NI↗

arXiv2025

Similar Fermi-GBM sGRBs to GW/sGRB 170817A in MeV-GeV energies

The rate of observed gravitational waves (GWs) from neutron star-neutron star (NS-NS) mergers detected by the Laser Interferometer Gravitational-Wave Observatory (LIGO) indicates the existence of more than one short gamma-ray bursts (sGRBs) similar to GW/sGRB 170817A within the total gamma-ray bursts (GRBs) recorded by satellite detectors such as BATSE, Fermi-Gamma-ray Burst Monitor (Fermi-GBM), and Swift-Burst Alert Telescope (Swift-BAT). We investigated sGRBs in the Fermi-GBM dataset based on their MeV-GeV $\gamma$-ray emission features, to identify sGRBs similar to sGRB 170817A. Any addition of such events can impact the rate of NS-NS CBC events observed by LIGO. SGRB 170817A exhibits two distinct emission components: a non-thermal peak and a thermal component. We adopted a multifaceted approach to identify analogous sGRBs, which involved computing the hardness ratios $HR_{1}$ and $HR_{2}$ and then clustering them via the K-means algorithm. Our further studies reveal the presence of eight such events in Fermi-GBM data, which will enable us to calculate the rate of electromagnetic (EM) counterparts associated with LIGO GW events (GW+sGRB events) across all observing runs. Giving an estimation, by the end of the $O_4$ LIGO run, there could be nearly 5 GW+sGRB events. Deviation from this number may raise concerns about our understanding of the evolution of such events over distance.

astro-ph.HE↗

arXiv2025

DROID: Dual Representation for Out-of-Scope Intent Detection

Detecting out-of-scope (OOS) user utterances remains a key challenge in task-oriented dialogue systems and, more broadly, in open-set intent recognition. Existing approaches often depend on strong distributional assumptions or auxiliary calibration modules. We present DROID (Dual Representation for Out-of-Scope Intent Detection), a compact end-to-end framework that combines two complementary encoders -- the Universal Sentence Encoder (USE) for broad semantic generalization and a domain-adapted Transformer-based Denoising Autoencoder (TSDAE) for domain-specific contextual distinctions. Their fused representations are processed by a lightweight branched classifier with a single calibrated threshold that separates in-domain and OOS intents without post-hoc scoring. To enhance boundary learning under limited supervision, DROID incorporates both synthetic and open-domain outlier augmentation. Despite using only 1.5M trainable parameters, DROID consistently outperforms recent state-of-the-art baselines across multiple intent benchmarks, achieving macro-F1 improvements of 6--15% for known and 8--20% for OOS intents, with the most significant gains in low-resource settings. These results demonstrate that dual-encoder representations with simple calibration can yield robust, scalable, and reliable OOS detection for neural dialogue systems.

cs.CL↗

arXiv2025

FeNOMS: Enhancing Open Modification Spectral Library Search with In-Storage Processing on Ferroelectric NAND (FeNAND) Flash

The rapid expansion of mass spectrometry (MS) data, now exceeding hundreds of terabytes, poses significant challenges for efficient, large-scale library search - a critical component for drug discovery. Traditional processors struggle to handle this data volume efficiently, making in-storage computing (ISP) a promising alternative. This work introduces an ISP architecture leveraging a 3D Ferroelectric NAND (FeNAND) structure, providing significantly higher density, faster speeds, and lower voltage requirements compared to traditional NAND flash. Despite its superior density, the NAND structure has not been widely utilized in ISP applications due to limited throughput associated with row-by-row reads from serially connected cells. To overcome these limitations, we integrate hyperdimensional computing (HDC), a brain-inspired paradigm that enables highly parallel processing with simple operations and strong error tolerance. By combining HDC with the proposed dual-bound approximate matching (D-BAM) distance metric, tailored to the FeNAND structure, we parallelize vector computations to enable efficient MS spectral library search, achieving 43x speedup and 21x higher energy efficiency over state-of-the-art 3D NAND methods, while maintaining comparable accuracy.

cs.AR↗

arXiv2025

Classical and Quantum cosmology for two scalar field Brans-Dicke type theory: A Noether Symmetry approach

The paper deals with a cosmological model containing two scalar fields which can be considered as an extension of the Brans-Dicke scalar field model. Due to highly coupled non linear field equations, Noether Symmetry analysis has been imposed and as a result the Lagrangian as well as the field equations become much simple in form to have the classical solutions. The relevant cosmological parameters are analyzed graphically. Finally, quantum cosmology has been studied by constructing the Wheeler-DeWitt equation and the solution of this second order partial differential equation has been done using this symmetry analysis.

gr-qc↗

arXiv2025

Noise-adapted qudit codes for amplitude-damping noise

Quantum error correction (QEC) plays a critical role in preventing information loss in quantum systems and provides a framework for reliable quantum computation. Identifying quantum codes with nice code parameters for physically motivated noise models remains an interesting challenge. While past work has primarily focused on qubit codes, here we identify a $[4,1]$ qudit error correcting code tailored to protect against amplitude-damping noise. We show that this four-qudit code satisfies the error correction conditions for all single-qudit and a few two-qudit damping errors up to the leading order in the damping parameter $γ$. We devise a protocol to extract syndromes that unambiguously identify this set of errors, leading to a noise-adapted recovery scheme that achieves a fidelity loss of $\mathcal{O}(γ^{2})$. For the $d=2$ case, our QEC scheme is identical to the known example of the $4$-qubit code and the associated syndrome-based recovery. We also assess the performance of this code using the Petz recovery map and note some interesting deviations from the qubit case. Finally, we generalize this construction to a family of $[2M+2, M]$ qudit codes that can approximately correct all the single-qudit and a few two-qudit amplitude-damping errors.

quant-ph↗

arXiv2025

A Bio-inspired Asymmetric Double-Gate Ferroelectric FET for Emulating Astrocyte and Dendrite Dynamics in Neuromorphic Systems

Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms. However, these systems have generally remained limited to emulation of simple neurons and synapses; and ignored higher order functionalities enabled by other components of the brain like astrocytes and dendrites. In this work, drawing inspiration from biology, we introduce a compact Double-Gate Ferroelectric Field Effect Transistor (DG-FeFET) cell that can emulate the dynamics of both astrocytes and dendrites within neuromorphic architectures. We demonstrate that with a ferroelectric top gate for synaptic weight programming as in conventional synapses and a non-ferroelectric back gate, the DG-FeFET realizes a synapse with a dynamic gain modulation mechanism. This can be leveraged as an analog for a compact astrocyte-tripartite synapse, as well as enabling dendrite-like gain modulation operations. By employing a fully-depleted silicon-on-insulator (FDSOI) FeFET as our double-gate device, we validate the linear control of the synaptic weight via the back gate terminal (i.e., the gate underneath the buried oxide (BOX) layer) through comprehensive theoretical and experimental studies. We showcase the promise such a tripartite synaptic device holds for numerous important neuromorphic applications, including autonomous self-repair of faulty neuromorphic hardware mediated by astrocytic functionality. Coordinate transformations based on dragonfly prey-interception circuitry models are also demonstrated based on dendritic function emulation by the device. This work paves the way forward for developing truly "brain-like" neuromorphic hardware that go beyond the current dogma focusing only on neurons and synapses.

cs.ET↗

arXiv2025

An In-Situ Spatial-Temporal Sequence Detector for Neuromorphic Vision Sensor Empowered by High Density Vertical NAND Storage

Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel in-situ spatiotemporal sequence detector that leverages vertical NAND storage to achieve massively parallel pattern detection. By encoding each cell with two single-transistor-based multi-level cell (MLC) memory elements, such as ferroelectric field-effect transistors (FeFETs), and mapping a pixel's temporal sequence onto consecutive word lines (WLs), we enable direct temporal pattern detection within NAND strings. Each NAND string serves as a dedicated reference for a single pixel, while different blocks store patterns for distinct pixels, allowing large-scale spatial-temporal pattern recognition via simple direct bit-line (BL) sensing, a well-established operation in vertical NAND storage. We experimentally validate our approach at both the cell and array levels, demonstrating that vertical NAND-based detector achieves more than six orders of magnitude improvement in energy efficiency and more than three orders of magnitude reduction in latency compared to conventional CPU-based methods. These findings establish vertical NAND storage as a scalable and energy-efficient solution for next-generation neuromorphic vision processing.

cs.ET↗

arXiv2025

A Neural Operator Emulator for Coastal and Riverine Shallow Water Dynamics

Coastal regions and river floodplains are particularly vulnerable to the impacts of extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas is essential for infrastructure planning and climate adaptation. Yet high-fidelity numerical models are often too computationally expensive for real-time use, and lower-cost approaches, such as traditional model order reduction algorithms or conventional neural networks, typically struggle to generalize to out-of-distribution conditions. In this study, we present the Multiple-Input Temporal Operator Network (MITONet), a novel autoregressive neural emulator that employs latent-space operator learning to efficiently approximate high-dimensional numerical solvers for complex, nonlinear problems that are governed by time-dependent, parameterized partial differential equations. We showcase MITONet's predictive capabilities by forecasting regional tide-driven dynamics in the Shinnecock Inlet in New York and riverine flow in a section of the Red River in Louisiana, both described by the two-dimensional shallow-water equations (2D SWE), while incorporating initial conditions, time-varying boundary conditions, and domain parameters such as the bottom friction coefficient. Despite the distinct flow regimes, the complex geometries and meshes, and the wide range of bottom friction coefficients studied, MITONet displays consistently high predictive skill, with anomaly correlation coefficients above 0.9, a maximum normalized root mean square error of 0.011, and computational speedups between 100x-1,250x, even for 175 days of autoregressive rollout forecast from random initial conditions and with unseen parameter values.

cs.CE↗

arXiv2024

RSVP: Beyond Weisfeiler Lehman Graph Isomorphism Test

Graph isomorphism, a classical algorithmic problem, determines whether two input graphs are structurally identical or not. Interestingly, it is one of the few problems that is not yet known to belong to either the P or NP-complete complexity classes. As such, intelligent search-space pruning based strategies were proposed for developing isomorphism testing solvers like nauty and bliss, which are still, unfortunately, exponential in the worst-case scenario. Thus, the polynomial-time Weisfeiler-Lehman (WL) isomorphism testing heuristic, based on colour refinement, has been widely adopted in the literature. However, WL fails for multiple classes of non-isomorphic graph instances such as strongly regular graphs, block structures, and switched edges, among others. In this paper, we propose a novel polynomial-time graph isomorphism testing heuristic, RSVP, and depict its enhanced discriminative power compared to the Weisfeiler-Lehman approach for several challenging classes of graphs. Bounded by a run-time complexity of O(m^2+mn^2+n^3) (where n and m are the number of vertices and edges respectively), we show that RSVP can identify non-isomorphism in several 'hard' graph instance classes including Miyazaki, Paulus, cubic hypohamiltonian, strongly regular, Latin series and Steiner triple system graphs, where the 3-WL test fails. Similar to the WL test, our proposed algorithm is prone to only one-sided errors, where isomorphic graphs will never be determined to be non-isomorphic, although the reverse can happen.

cs.DS↗

arXiv2024

Smallest quantum codes for amplitude damping noise

We describe the smallest quantum error correcting (QEC) code to correct for amplitude-damping (AD) noise, namely, a 3-qubit code that corrects all the single-qubit damping errors. We generalize this construction to a family of codes that correct AD noise up to any fixed order of the damping strength. We underpin the fundamental connection between the structure of our codes and the noise structure, via a relaxed form of the Knill-Laflamme conditions, different from existing formulations of approximate QEC conditions. Although the recovery procedure for this code is non-deterministic, our codes are optimal with respect to overheads and outperform existing codes to tackle AD noise in terms of entanglement fidelity. This formulation of probabilistic QEC further leads us to new family of quantum codes tailored to AD noise and also gives rise to a noise-adapted quantum Hamming bound for AD noise. Finally, we construct a set of universal logical gates for the 3-qubit code, thus providing a potential pathway to fault tolerance via this class of codes.

quant-ph↗

arXiv2024

A description of classical and quantum cosmology for a single scalar field torsion gravity

In the background of homogeneous and isotropic flat FLRW space-time, both classical and quantum cosmology has been studied for teleparallel dark energy (DE) model. Using Noether symmetry analysis, not only the symmetry vector but also the coupling function in the Lagrangian and the potential of the scalar field has been determined. Also symmetry analysis identifies a cyclic variable in the Lagrangian along the symmetry vector and as a result the Lagrangian simplifies to a great extend so that classical solution is obtained. Subsequently, in quantum cosmology Wheeler-DeWitt(WD) equation has been constructed and the quantum version of the conserved momenta corresponding to Noether symmetry identifies the periodic part of the wave function of the universe and as a result the Wheeler-DeWitt equation becomes solvable. Finally, quantum description shows finite non-zero probability at the classical big-bang singularity.

gr-qc↗