arXiv subjects

Amrita Singh

Publications and source records attributed to Amrita Singh.

At least 19 recordsLinked to original sources

arXiv2026

SDSS-V Local Volume Mapper (LVM): The Integrated Light and Internal Rotation of Omega Centauri

The SDSS-V Local Volume Mapper (LVM) is a wide-field integral field spectroscopic survey of the Southern Milky Way plane, the Magellanic Clouds, and nearby Local Group galaxies. We use Early Science observations of the whole body of the nearest nuclear cluster, Omega Centauri, to extend the LVM beyond its primary interstellar-medium science case. The wide LVM field allows us to precisely map $\omega$ Cen's line-of-sight rotation out to $\sim 3r_{HL}$ or $15^\prime$, reaching a maximum value of $(8.4 \pm 0.8)$ km s$^{-1}$ at $r \approx 4.7^\prime$. Within the central region, comparisons with existing VLT MUSE oMEGACat data show explicitly that the unresolved-light signal is dominated by a small number of bright stars, with an effective sample size of only $\sim$12 per resolution element. Using Gaia DR3 as an external reference, we verify that the SDSS-V's LVM reduction pipeline recovers integrated stellar fluxes to 1-4 % across six magnitudes of surface brightness. Our resulting total spectrum of $\omega$ Cen is one of the highest S/N integrated spectrum for any globular or nuclear star cluster. We use it to test four widely-used SSP template libraries against resolved age-metallicity ground truth from oMEGACat. All templates recover an old, metal-poor population. But, even at S/N $\sim$1300, the inferred mean ages and mean [Fe/H] vary by $\sim$7 Gyr and $\sim$0.4 dex, respectively, across libraries and wavelength ranges, reflecting a systematic floor for integrated-light studies of old multi-population systems.

astro-ph.GA↗

arXiv2026

There is no single density: star-forming regions and galaxies hold more dense ionized gas than long assumed

Ionized gas fills star-forming regions and galaxies, and nearly everything we know about its temperature, pressure, mass, and composition is inferred from its emission lines [1-3]. The electron density is needed for all of these, yet a longstanding puzzle has resisted explanation: different density-sensitive lines, applied to the same gas, return values that disagree by up to two orders of magnitude. This is usually attributed either to each line tracing a physically distinct ionization zone or to imperfect atomic data [4-7]. Here we show that the disagreement is neither a flaw in the atomic data nor an ionization-stratification effect, but something more fundamental. Each diagnostic is tuned to a particular density, and when a nebula contains gas across a wide range of densities, as real nebulae do, each line reports the part of that range it is most sensitive to. The diagnostics do not measure a representative average density; they respond to different parts of a broad density distribution. This resolves the discrepancy with a simple relation between the density each line returns and the density it is most sensitive to, a relation that holds from individual H II regions to whole galaxies, near and far, and reveals that ionized nebulae contain far more dense gas than any one diagnostic implies. A nebula has no single electron density to measure, but a broad density distribution, and the masses, pressures, abundances and energetics built on the single-density assumption must be reconsidered, from nearby star-forming regions to galaxies across cosmic time.

astro-ph.GA↗

arXiv2026

CADRE: Stable, Parameter Efficient Adaptation of Medical Vision Language Models with Bounded Forgetting and Prior Drift

Medical vision-language models (VLMs) such as BiomedCLIP generalize broadly, but adapting them to a clinical service is as much a safety problem as an accuracy one. Updating a deployed model for a new imaging modality can fail silently in two ways that harm patients: it can forget modalities it already handled (catastrophic forgetting), and it can drift from its trustworthy pretrained prior toward modality-specific shortcuts. We study parameter-efficient continual adaptation through these two properties rather than leaderboard accuracy, presenting CADRE: a frozen-backbone framework combining low-rank adaptation (LoRA) with an online, self-scaling, similarity-aware elastic weight consolidation term that bounds retained-competence loss, and an anchor-to-prior penalty bounding embedding drift from the frozen prior. Two short guarantees, a bound on total consolidation mass and a scale-invariance property, remove the scale-related sources of vanilla EWC's order fragility. Using breast cancer across three maximally dissimilar modalities (histopathology, ultrasound, chest radiography) as a controlled cross-modality stress test, under a multi-seed, multi-order protocol with paired significance testing and training approximately 0.23% of parameters, CADRE attains the highest accuracy, SPQ, and backward transfer and the lowest forgetting among adapting methods, reducing forgetting roughly sevenfold versus the strongest regularized baseline (0.075 to 0.011; paired p=0.023) and achieving positive backward transfer where every baseline is negative. We frame these as stability properties aligned with clinical-safety desiderata, not a deployment guarantee; robustness to distribution shift and adversarial inputs is out of scope.

cs.AI↗

arXiv2026

LAUKIN: A Multi-jurisdictional Common Law Contract Dataset

Multinational companies increasingly require cross-jurisdictional contract review, yet existing legal NLP datasets are largely restricted to a single jurisdiction. We introduce LAUKIN (Legal equivalence dataset of Australia, UK, and INdia), a dataset of clause pairs (AU-UK, UK-IN, IN-AU) labelled for boolean legal equivalence. We develop a novel multi-stage retrieval and reranking pipeline to construct the initial clause pair mapping, with a subset of clause pairs subsequently annotated by legal experts as Equivalent or Not Equivalent. The dataset comprises 14,727 clause pairs from 204 contracts across 8 agreement types, of which 3,000 are manually labelled: 900 train, 600 dev, and 1,500 test. We evaluate 12 models across 4 techniques, achieving a best macro-F1 of 65.11%, establishing LAUKIN as a challenging benchmark. Results reveal that, despite shared legal heritage, drafting conventions diverge significantly across jurisdictions, making cross-jurisdictional equivalence classification non-trivial. LAUKIN also includes 11,727 unlabelled training pairs to support future semi-supervised learning research in legal NLP.

cs.CL↗

arXiv2026

Detection of Virus and Small Cell Patches in Foci Images Using Switchable Convolution and Feature Pyramid Networks

Accurate detection and counting of virus patches in focus-forming unit (FFU) images, also known as foci images, are important for quantifying viral infection and analyzing cellular structures. This task is challenging because biomedical targets often vary substantially in size, density, contrast, and shape. In this paper, we propose an enhanced YOLOv2-based detector that integrates a Feature Pyramid Network (FPN) to improve multi-scale feature representation. We also incorporate a switchable atrous convolution mechanism to adapt the receptive field for fine-grained targets in dense microscopy images. The proposed method is evaluated on biomedical foci image datasets for virus patch and small cell patch detection. For small cell patch detection, the model achieves a mean average precision (mAP) of 40.5% at a 25% Intersection over Union (IoU) threshold. For FFU virus patch detection, the model achieves an mAP of 68%. These results indicate that combining FPN-based feature fusion with switchable convolution improves the suitability of YOLOv2 for specialized biomedical object detection tasks

cs.CV↗

arXiv2026

SDSS-V LVM: A spatially resolved study of the physical conditions and the chemical abundance discrepancy in the Lagoon Nebula (M 8)

The abundance discrepancy problem refers to the systematic differences observed between chemical abundances derived from collisionally excited lines (CELs) and recombination lines (RLs) of heavy ions. It remains a major unsolved problem in the study of ionized nebulae and is quantified by the abundance discrepancy factor (ADF). In this work, we present a deep integral field spectroscopic dataset covering the entire Lagoon Nebula (M 8), obtained by the SDSS-V Local Volume Mapper project, at a spatial resolution of 0.21 pc per spaxel. This unique dataset allows us, for the first time, to investigate spatially resolved maps of oxygen RL intensities (O II V1), together with maps of H I RLs, heavy-ion CELs, and dust attenuation across a whole H II region. We map the electron temperature using CELs and RLs of $O^{2+}$, CELs of $N^{+}$, and the electron density using CELs of $S^{+}$. We derive CEL-based ionic and elemental oxygen abundances and, for the first time, a spatially resolved map of the RL-based $O^{2+}$ abundance in an H II region. These measurements enable the construction of the first spatially resolved ADF($O^{2+}$) map of an H II region and yield a global mean ADF of ~0.47 +/- 0.02 dex. Focusing on the central region of M 8, where ionization is dominated by the O-type star Her 36, we find radial variations in the ADF ranging between ~0.35-0.50 dex. Our findings provide novel constraints on the spatial behavior and origin of the abundance discrepancy in H II regions.

astro-ph.GA↗

arXiv2025

SDSS-V LVM: Resolving Physical Conditions in the Trifid Nebula

The chemical abundance of the interstellar medium sets the initial conditions for star formation and provides a probe of chemical galaxy evolution models. However, unresolved inhomogeneities in the electron temperature can lead to a systematic underestimation of the abundances. We aim to directly test this effect. We use the SDSS-V Local Volume Mapper to spatially map the physical conditions of the Trifid Nebula (M 20), a Galactic H II region ionized by a single mid-type O star, at 0.24 pc resolution. We exploit various emission lines (e.g., Hydrogen recombination lines and collisionally excited lines, including also faint auroral lines) and compute spatially resolved maps of [O II] and [S II] electron densities; [N II], [O II], [S II], [S III] electron temperatures; and the ionic oxygen abundances. We find internal variations of electron density that result from the ionization front, along with a negative radial gradient. However, we do not find strong gradients or structures in the electron temperature and the total oxygen abundance, making the Trifid Nebula a relatively homogeneous H II region at the observed spatial scale. We compare these spatially resolved properties with equivalent integrated measurements of the Trifid Nebula and find no significant variations between integrated and spatially resolved conditions. This isolated H II region, ionized by a single O-star, represents a test case of an ideal Str\"omgren sphere. The physical conditions in the Trifid Nebula behave as expected, with no significant differences between integrated and resolved measurements.

astro-ph.GA↗

arXiv2025

Unified Spectrospatial Forward Models: Spatially Continuous Maps of Weak Emission Lines in the Rosette Nebula with SDSS-V LVM

Analyses of IFU data are typically performed on a per-spaxel basis, with each spectrum modelled independently. For low signal-to-noise (S/N) features such as weak emission lines, estimating properties is difficult and imprecise. Arbitrary binning schemes boost S/N at the cost of resolution, and risk introducing biases. We present a general forward-modelling approach that assumes spectra close on the sky are more similar than distant ones, and so can be modelled jointly. These "spectrospatial" models exploit spatial correlation to provide robust inferences, while simultaneously providing continuous predictions of line properties like strength and kinematics across the sky. Instrumental and calibration systematics are straightforward to include and infer. The model provides a natural trade-off between spatial resolution and S/N in a data-driven way. We apply this to Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) data of the Rosette Nebula, producing continuous maps of fluxes and kinematics for Balmer, nebular, and auroral lines, as well as weak C II and N II recombination lines, demonstrating the approach across three orders of magnitude in S/N, including in the very low-S/N regime. The method recovers identical morphologies across different lines tracing similar ionisation volumes, at varying resolutions set by the S/N. We additionally provide a general framework for building and fitting such models in JAX, suitable for many applications. The implementation is fast and memory efficient, scales to large data volumes as in LVM, and can be deployed on hardware accelerators.

astro-ph.GA↗

arXiv2025

Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification

Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as `legal-specific' models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They also help reduce misclassification of rare classes in imbalanced datasets. Legal-BERT and Contracts-BERT establish new SOTAs on two of the three tasks, despite having 69% fewer parameters than the best-performing generalist models. We also identify CaseLaw-BERT and LexLM as strong additional baselines for contract classification. Our results highlight the shortcomings of generalist models, emphasizing the need for domain-specific customization, particularly in the context of legal applications.

cs.CL↗

arXiv2025

A Survey of Classification Tasks and Approaches for Legal Contracts

Given the large size and volumes of contracts and their underlying inherent complexity, manual reviews become inefficient and prone to errors, creating a clear need for automation. Automatic Legal Contract Classification (LCC) revolutionizes the way legal contracts are analyzed, offering substantial improvements in speed, accuracy, and accessibility. This survey delves into the challenges of automatic LCC and a detailed examination of key tasks, datasets, and methodologies. We identify seven classification tasks within LCC, and review fourteen datasets related to English-language contracts, including public, proprietary, and non-public sources. We also introduce a methodology taxonomy for LCC, categorized into Traditional Machine Learning, Deep Learning, and Transformer-based approaches. Additionally, the survey discusses evaluation techniques and highlights the best-performing results from the reviewed studies. By providing a thorough overview of current methods and their limitations, this survey suggests future research directions to improve the efficiency, accuracy, and scalability of LCC. As the first comprehensive survey on LCC, it aims to support legal NLP researchers and practitioners in improving legal processes, making legal information more accessible, and promoting a more informed and equitable society.

cs.CL↗

arXiv2024

Analyzing the Abundance Discrepancy Problem in HII Regions with Photoionization Modeling

Understanding the complex ionization structure and chemical composition of \hii\ regions poses a significant challenge in astrophysics. The abundance discrepancy problem, characterized by inconsistencies between abundances derived from recombination lines (RLs) and collisionally excited lines (CELs), has long been a puzzle in the field. In this theoretical study, we present novel photoionization models that incorporate temperature, density, and chemical inhomogeneities within a single cloud to comprehensively address this discrepancy. By accounting for the intricate interplay between ionization, excitation, and chemistry, our models successfully reproduce both observed RLs and CELs with with an average difference between our models and the observations of 25% -- within uncertainties inherent in Galactic archival long-slit and new SDSS-V Local Volume Mapper observations. Through comparisons between generic inhomogeneous model predictions and observations, demonstrating the ability of our theoretical framework to analyze the abundance discrepancy problem within \hii\ regions. Our results highlight the importance of incorporating spatially resolved temperature, density, and chemical structures when interpreting the physical processes governing emission line spectra in these astrophysical environments.

astro-ph.GA↗

arXiv2024

Scale-Invariant Object Detection by Adaptive Convolution with Unified Global-Local Context

Dense features are important for detecting minute objects in images. Unfortunately, despite the remarkable efficacy of the CNN models in multi-scale object detection, CNN models often fail to detect smaller objects in images due to the loss of dense features during the pooling process. Atrous convolution addresses this issue by applying sparse kernels. However, sparse kernels often can lose the multi-scale detection efficacy of the CNN model. In this paper, we propose an object detection model using a Switchable (adaptive) Atrous Convolutional Network (SAC-Net) based on the efficientDet model. A fixed atrous rate limits the performance of the CNN models in the convolutional layers. To overcome this limitation, we introduce a switchable mechanism that allows for dynamically adjusting the atrous rate during the forward pass. The proposed SAC-Net encapsulates the benefits of both low-level and high-level features to achieve improved performance on multi-scale object detection tasks, without losing the dense features. Further, we apply a depth-wise switchable atrous rate to the proposed network, to improve the scale-invariant features. Finally, we apply global context on the proposed model. Our extensive experiments on benchmark datasets demonstrate that the proposed SAC-Net outperforms the state-of-the-art models by a significant margin in terms of accuracy.

cs.CV↗

arXiv2024

The SDSS-V Local Volume Mapper (LVM): Scientific Motivation and Project Overview

We present the Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM). The LVM is an integral-field spectroscopic survey of the Milky Way, Magellanic Clouds, and of a sample of local volume galaxies, connecting resolved pc-scale individual sources of feedback to kpc-scale ionized interstellar medium (ISM) properties. The 4-year survey covers the southern Milky Way disk at spatial resolutions of 0.05 to 1 pc, the Magellanic Clouds at 10 pc resolution, and nearby large galaxies at larger scales totaling $>4300$ square degrees of sky, and more than 55M spectra. It utilizes a new facility of alt-alt mounted siderostats feeding 16 cm refractive telescopes, lenslet-coupled fiber-optics, and spectrographs covering 3600-9800A at R ~ 4000. The ultra-wide field IFU has a diameter of 0.5 degrees with 1801 hexagonally packed fibers of 35.3 arcsec apertures. The siderostats allow for a completely stationary fiber system, avoiding instability of the line spread function seen in traditional fiber feeds. Scientifically, LVM resolves the regions where energy, momentum, and chemical elements are injected into the ISM at the scale of gas clouds, while simultaneously charting where energy is being dissipated (via cooling, shocks, turbulence, bulk flows, etc.) to global scales. This combined local and global view enables us to constrain physical processes regulating how stellar feedback operates and couples to galactic kinematics and disk-scale structures, such as the bar and spiral arms, as well as gas in- and out-flows.

astro-ph.GA↗

arXiv2023

Deep Learning based Systems for Crater Detection: A Review

Craters are one of the most prominent features on planetary surfaces, used in applications such as age estimation, hazard detection, and spacecraft navigation. Crater detection is a challenging problem due to various aspects, including complex crater characteristics such as varying sizes and shapes, data resolution, and planetary data types. Similar to other computer vision tasks, deep learning-based approaches have significantly impacted research on crater detection in recent years. This survey aims to assist researchers in this field by examining the development of deep learning-based crater detection algorithms (CDAs). The review includes over 140 research works covering diverse crater detection approaches, including planetary data, craters database, and evaluation metrics. To be specific, we discuss the challenges in crater detection due to the complex properties of the craters and survey the DL-based CDAs by categorizing them into three parts: (a) semantic segmentation-based, (b) object detection-based, and (c) classification-based. Additionally, we have conducted training and testing of all the semantic segmentation-based CDAs on a common dataset to evaluate the effectiveness of each architecture for crater detection and its potential applications. Finally, we have provided recommendations for potential future works.

cs.CV↗

arXiv2022

InAs-Al Hybrid Devices Passing the Topological Gap Protocol

We present measurements and simulations of semiconductor-superconductor heterostructure devices that are consistent with the observation of topological superconductivity and Majorana zero modes. The devices are fabricated from high-mobility two-dimensional electron gases in which quasi-one-dimensional wires are defined by electrostatic gates. These devices enable measurements of local and non-local transport properties and have been optimized via extensive simulations to ensure robustness against non-uniformity and disorder. Our main result is that several devices, fabricated according to the design's engineering specifications, have passed the topological gap protocol defined in Pikulin et al. [arXiv:2103.12217]. This protocol is a stringent test composed of a sequence of three-terminal local and non-local transport measurements performed while varying the magnetic field, semiconductor electron density, and junction transparencies. Passing the protocol indicates a high probability of detection of a topological phase hosting Majorana zero modes as determined by large-scale disorder simulations. Our experimental results are consistent with a quantum phase transition into a topological superconducting phase that extends over several hundred millitesla in magnetic field and several millivolts in gate voltage, corresponding to approximately one hundred micro-electron-volts in Zeeman energy and chemical potential in the semiconducting wire. These regions feature a closing and re-opening of the bulk gap, with simultaneous zero-bias conductance peaks at both ends of the devices that withstand changes in the junction transparencies. The extracted maximum topological gaps in our devices are 20-60 $\mu$eV. This demonstration is a prerequisite for experiments involving fusion and braiding of Majorana zero modes.

cond-mat.mes-hall↗

arXiv2020

Underpotential electroless deposition of metals on polyaniline

A novel technique to deposit metals on highly conjugated polyaniline films has been developed. In general, electrodeposition of metals, having low reduction potential, from aqueous solution, is difficult due to disruptive effect of hydrogen which evolves during the process. This difficulty is avoided using conducting polymers films with high surface mass density. The polymer chains of these films possess a high degree of conjugation. Such a polymer produces highly stable polarons and therefore has the ability to perform underpotential deposition. Our method involves reduction of polyaniline film with formic acid followed by dipping the coated electrode in the metal salt solution. Deposition of the metal is monitored by rise in the open circuit potential of the electrode. Deposition of metals with high surface mass density has been achieved. The metal is most likely present in the polymer as a coordination complex with amine nitrogen. Such form of metal is expected to have higher catalytic activity than the zero-valent metal. We have been able to deposit metals such as Mn and Cu. Among these, Mn cannot be deposited on polymer by any other method.

physics.app-ph↗

arXiv2018

Rapid detection of coherent tunneling in an InAs nanowire quantum dot through dispersive gate sensing

Dispersive sensing is a powerful technique that enables scalable and high-fidelity readout of solid-state quantum bits. In particular, gate-based dispersive sensing has been proposed as the readout mechanism for future topological qubits, which can be measured by single electrons tunneling through zero-energy modes. The development of such a readout requires resolving the coherent charge tunneling amplitude from a quantum dot in a Majorana-zero-mode host system faithfully on short time scales. Here, we demonstrate rapid single-shot detection of a coherent single-electron tunneling amplitude between InAs nanowire quantum dots. We have realized a sensitive dispersive detection circuit by connecting a sub-GHz, lumped element microwave resonator to a high-lever arm gate on one of dots. The resulting large dot-resonator coupling leads to an observed dispersive shift that is of the order of the resonator linewidth at charge degeneracy. This shift enables us to differentiate between Coulomb blockade and resonance, corresponding to the scenarios expected for qubit state readout, with a signal to noise ratio exceeding 2 for an integration time of 1 microsecond. Our result paves the way for single shot measurements of fermion parity on microsecond timescales in topological qubits.

cond-mat.mes-hall↗

arXiv2018

Role of a polymeric component in the phase separation of ternary fluid mixtures: A dissipative particle dynamics study

We present the results from dissipative particle dynamics (DPD) simulations of phase separation dynamics in ternary (ABC) fluids mixture in $d=3$ where components A and B represent the simple fluids and component C represents a polymeric fluid. Here, we study the role of polymeric fluid (C) on domain morphology by varying composition ratio, polymer chain length, and polymer stiffness. We observe that the system under consideration lies in the same dynamical universality class as a simple ternary fluids mixture. However, the scaling functions depend upon the parameters mentioned above as they change the time scale of the evolution morphologies. In all cases, the characteristic domain size follows: $l(t) \sim t^ϕ $ with dynamic growth exponent $ϕ$, showing a crossover from the viscous hydrodynamic regime $(ϕ=1)$ to the inertial hydrodynamic regime $(ϕ=2/3)$ in the system at late times.

cond-mat.soft↗