arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 37 records · Page 2Linked to original sources

arXiv2026

KI-Adventskalender: An Informal Learning Intervention for Data & AI Literacy

Secondary school students increasingly encounter AI systems whose outputs depend on data quality, evaluation choices and modeling assumptions. To provide accessible entry points to these interconnected concepts, we developed KI-Adventskalender, a free web-based extracurricular initiative with 24 didactically curated, short, guided micro-challenges released daily in December, targeting data-centric competencies and socio-technical themes that shape how data are interpreted in practice. Drawing on two annual iterations, we report aggregate platform traces characterizing participation and task-level engagement. Participation increased substantially in 2025, but early attrition persists. Progression stabilized after midpoint: among users reaching Day 12 in 2025, more than 75% completed the calendar. Competence cluster performance shifted across years; higher revision rates co-occurred with strong pass rates, suggesting sustained engagement. We use these observations to motivate a next-step measurement agenda: tighter task instrumentation, embedded micro-assessments and mixed-method evaluation designs that can distinguish persistence from conceptual uptake, knowledge progression and durable learning outcomes.

cs.HC↗

arXiv2026

Thickness-Dependent Spintronic Terahertz Emission in MBE-Grown PtTe$_2$: From Semiconductor to Type-II Dirac Semimetal

Spintronic terahertz (THz) emitters have established themselves as among the most practical broadband THz sources available, yet their performance remains fundamentally limited by the spin Hall conductivity of the nonmagnetic conversion layer - a quantity that is fixed once the material is chosen. Here, we demonstrate that in PtTe$_2$, a type-II Dirac semimetal within the transition metal dichalcogenide family, this limitation can be circumvented by exploiting the dramatic thickness-driven electronic phase evolution of the material itself. Using molecular beam epitaxy to grow PtTe$_2$ films with single-monolayer precision from 1 to 20 ML, we show that the spintronic THz emission tracks the underlying electronic phase diagram directly: it is absent in the single-layer semiconducting phase, turns on sharply at the semimetal transition near 2 ML, and reaches a peak amplitude six times that of an equivalent Pt reference at 10 ML, before declining at larger thicknesses due to THz reabsorption in the increasingly metallic film. This non-monotonic behavior is inconsistent with a bulk inverse spin Hall mechanism and instead reflects a multi-channel spin-to-charge conversion process in which spin-momentum-locked topological surface states and a thickness-dependent interfacial Rashba splitting both contribute and strengthen as the type-II Dirac band structure develops. First-principles calculations of the interfacial spin accumulation reproduce the experimental trend quantitatively, confirming this physical picture. These findings introduce thickness engineering of van der Waals semimetals as a new and accessible route to optimizing spintronic THz emitters and spin-orbit torques in magnetic memories (SOT-MRAMs), with direct implications for the broader class of dimensionally tunable topological materials.

cond-mat.mtrl-sci↗

arXiv2026

Leveraging LLMs for Grammar Adaptation: A Study on Metamodel-Grammar Co-Evolution

In model-driven engineering, metamodel evolution leads to the need to adapt corresponding grammars to maintain consistency, which typically requires tedious manual work. Existing rule-based methods can achieve partial automation but have limitations when handling complex grammar scenarios. This paper proposes a Large Language Model-based approach that automatically applies adaptations to new grammars after evolution by learning grammar adaptations from previous versions. We evaluated this approach on six real-world Xtext domain-specific languages, using four DSLs as a training set to develop prompting strategies, two DSLs as a test set for validation, and conducting a longitudinal case study on QVTo. The evaluation used three Large Language Models (Claude Sonnet 4.5, ChatGPT 5.1, Gemini 3) and measured grammar adaptation quality from three dimensions: grammar rule-level adaptation consistency, output similarity, and metamodel conformance. Results show that on the test set, all three LLMs achieved 100% adaptation consistency and output similarity, while the rule-based approach achieved only 84.21% on DOT and 62.50% on Xcore. In the QVTo longitudinal study, the LLM-based approach successfully reused learned adaptations across all three evolution steps without manual grammar editing, while the rule-based approach required manual adjustments in two of three transitions. However, on large-scale grammars (EAST-ADL, 297 rules), LLMs' adaptation consistency was far below 90%. This study demonstrates the advantages of LLM-based approaches in handling complex grammar scenarios, while revealing their limitations in large-scale grammar adaptation.

cs.CL↗

arXiv2026

Accretion geometry and spectral evolution in 1A 1118-61: a comparison of the 2009 and 2026 outbursts

We present a detailed spectro-temporal study of the Be X-ray binary pulsar $1A$ $1118-61$ during its brightest recorded outburst in 2026, using \textit{Swift} and \textit{NuSTAR} observations, and compare its properties with the 2009 outburst. Coherent pulsations at $\sim400$ s are detected throughout the outburst, with pulse profiles evolving strongly with energy and luminosity, indicating changes in emission geometry. A transient quasi-periodic oscillation (QPO) at $\sim$0.11 Hz is observed during the rising phase. The luminosity dependence of the QPO frequency during the current and previous outbursts suggests an origin associated with instabilities near the magnetospheric radius. The broadband spectra are well described by thermal Comptonization and show clear spectral hardening at higher luminosities. A cyclotron line is detected in the two \textit{NuSTAR} observations, with its energy remaining nearly constant despite a factor of $\sim25$ change in luminosity. Long-term monitoring reveals that the 2026 outburst is systematically harder and brighter, suggesting significant difference in the accretion structure and emission regions between the two outbursts.

astro-ph.HE↗

arXiv2026

Rashba engineering at van der Waals interfaces

Two-dimensional transition metal dichalcogenide (TMD) interfaces offer a versatile platform for studying emergent quantum phenomena and enabling novel device functionalities. When distinct TMD monolayers are stacked vertically or laterally stitched, their interfaces can exhibit unique electronic band alignments, giving rise to long-lived interlayer excitons, charge transfer effects, and moiré superlattices with correlated states. Here, we demonstrate that the interface between a large variety of two different epitaxially grown TMD monolayers controls the intensity and sign of the Rashba spin splitting, which is probed using THz spintronic emission. Optimized TMD heterobilayers, such as HfSe$_2$/PtSe$_2$, show enhanced THz emission that surpass the spin-to-charge conversion efficiency of bulk TMDs, confirming the presence of Rashba states with large spin splitting at the interface. By combining spin- and angle-resolved photoemission spectroscopy with density functional theory, we reveal that the electronic hybridization between the two different TMD monolayers gives rise to extended in-gap states with strong Rashba spin-orbit coupling. The choice of TMD layers enables to engineer the sign and strength of spin-to-charge conversion in van der Waals heterobilayers opening up perspectives to build efficient and tunable THz spintronic emitters.

cond-mat.mes-hall↗

arXiv2026

Towards Leveraging LLMs to Generate Abstract Penetration Test Cases from Software Architecture

Software architecture models capture early design decisions that strongly influence system quality attributes, including security. However, architecture-level security assessment and feedback are often absent in practice, allowing security weaknesses to propagate into later phases of the software development lifecycle and, in some cases, to remain undiscovered, ultimately leading to vulnerable systems. In this paper, we bridge this gap by proposing the generation of Abstract Penetration Test Cases (APTCs) from software architecture models as an input to support architecture-level security assessment. We first introduce a metamodel that defines the APTC concept, and then investigate the use of large language models with different prompting strategies to generate meaningful APTCs from architecture models. To design the APTC metamodel, we analyze relevant standards and state of the art using two criteria: (i) derivability from software architecture, and (ii) usability for both architecture security assessment and subsequent penetration testing. Building on this metamodel, we then proceed to generate APTCs from software architecture models. Our evaluation shows promising results, achieving up to 93\% usefulness and 86\% correctness, indicating that the generated APTCs can substantially support both architects (by highlighting security-critical design decisions) and penetration testers (by providing actionable testing guidance).

cs.SE↗

arXiv2026

Broadband Timing and Spectral Study of Accreting Millisecond X-ray Pulsar SAX J1808.4$-$3658 during Its 2022 Outburst

We report on our investigation of the NuSTAR and AstroSat observations along with simultaneous NICER observations of the accreting millisecond X-ray pulsar SAX J1808.4$-$3658, obtained during its tenth outburst from 2022. The NuSTAR observation captured the source near the outburst peak, while AstroSat observed it during the decay phase. Coherent pulsations at $\sim$401 Hz were detected throughout the outburst, with the fundamental amplitude in the 3--30 keV range increasing from $\sim$4% near the peak to $\sim$6% during the decay. The pulsations display strong energy dependence and negative time lags of $\sim$0.2--0.3 ms, with harder photons leading softer ones. The broadband spectra in both epochs are well described by a soft thermal component and Comptonized continuum, together with a prominent relativistic reflection component. As the outburst evolved, the continuum softened ($Γ$ increasing from $\sim$1.88 to $\sim$1.99) and the coronal electron temperature decreased ($kT_{\rm e}$ from $\sim$31 to $\sim$18 keV), consistent with enhanced Compton cooling at lower accretion rates. The ionization parameter declined ($\log ξ$ from $\sim$3.4 to $\sim$1.8) while the reflection fraction increased, suggesting a changing accretion geometry with a more compact corona and a larger disk covering fraction during the decay phase. The X-ray luminosity decreased by a factor of $\sim$3 between the two epochs. Our results suggest the coupled evolution of the corona, disk, and magnetosphere as the mass accretion rate declines.

astro-ph.HE↗

arXiv2025

Editing with AI: How Doctors Refine LLM-Generated Answers to Patient Queries

Patients frequently seek information during their medical journeys, but the rising volume of digital patient messages has strained healthcare systems. Large language models (LLMs) offer promise in generating draft responses for clinicians, yet how physicians refine these drafts remains underexplored. We present a mixed-methods study with nine ophthalmologists answering 144 cataract surgery questions across three conditions: writing from scratch, directly editing LLM drafts, and instruction-based indirect editing. Our quantitative and qualitative analyses reveal that while LLM outputs were generally accurate, occasional errors and automation bias revealed the need for human oversight. Contextualization--adapting generic answers to local practices and patient expectations--emerged as a dominant form of editing. Editing workflows revealed trade-offs: indirect editing reduced effort but introduced errors, while direct editing ensured precision but with higher workload. We conclude with design and policy implications for building safe, scalable LLM-assisted clinical communication systems.

cs.HC↗

arXiv2025

Probing accretion dynamics and spin evolution in the X-ray pulsar RX J0520.5-6932 during its 2024 Outburst

After nearly a decade of quiescence, the transient Be/X-ray binary pulsar RX J0520.5-6932 underwent an outburst in 2024. We performed X-ray monitoring of the source with NICER and AstroSat near the peak of the event. Our primary objective is to investigate the energy and luminosity dependence of the pulsed emission, characterize the spin evolution, and study the broadband X-ray spectral properties of RX J0520.5-6932 during the outburst. The AstroSat/LAXPC and NICER light curves reveal pronounced short-duration flaring activity lasting ~400-700 s, with enhancements by a factor of ~2. The pulse profile exhibits a strong dependence on both energy and intensity, evolving from a simple single-peaked structure at low energies to complex multi-peaked shapes at intermediate energies, and reverting to simpler morphologies at higher energies. Pulse profiles during the flares differ significantly from those in the persistent state, indicating changes in the pulsed beam pattern with a change in the intensity on a short timescale. Broadband spectral analysis reveals a soft excess and an emission feature at ~1 keV, likely arising from reprocessed emission in the accretion disc and fluorescence from Ne K and Fe L ions. Continuous NICER monitoring over nearly one orbital cycle enabled us to track spin evolution with accretion-driven spin-up and spectral variability in the soft X-ray band. Additionally, a declining spin-up rate is observed during the outburst, likely due to a gradual reduction in mass accretion rate. Our results provide a comprehensive view of the complex accretion dynamics in RX J0520.5-6932 during its 2024 outburst. The strong variability in pulse shape and spin behaviour highlights rapid changes in the accretion geometry and torque as a function of accretion rate. [Abridged]

astro-ph.HE↗

arXiv2025

Integrated physics-informed learning and resonance process signature for the prediction of fatigue crack growth for laser-fused alloys

Fatigue behaviors of metal components by laser fusion suffer from scattering due to random geometrical defects (e.g., porosity, lack of fusion). Monitoring fatigue crack initiation and growth is critical, especially for laser-fused components with significant inherent fatigue scattering. Conventional statistics-based curve-fitting fatigue models have difficulty incorporating significant scattering in their fatigue life due to the random geometrical defects. A scattering-informed predictive method is needed for laser-fused materials' crack size and growth. Current data-driven machine learning could circumvent the issue of deterministic modeling, but results in a black-box function that lacks interpretability. To address these challenges, this study explores a novel nondimensionalized physics-informed machine learning (PIML) model to predict fatigue crack growth of laser-fused SS-316L by integrating fatigue laws and constraints with small data to ensure a realistic and interpretable prediction. Resonance process signature data were leveraged with Paris's law to train the PIML model without experimental crack growth data. The results show that Paris's law constants can be learned with good similarity to comparable data from the literature, and the crack growth rate can be predicted to compute crack sizes.

cs.CE↗

arXiv2025

Crystal Growth, Band Structure, Magnetism and Electrochemical Properties of Hexavalent Strontium Ruthenium Oxyhydroxide

Ruthenates comprise an interesting class of materials with a wide range of extremely exciting properties, and thus the discovery of new stable ruthenates remains an active area of investigation. We report the crystal growth and comprehensive studies including crystal and electronic structure, magnetic and electrochemical properties of a hexavalent ruthenium oxyhydroxide Sr3Ru2O9H2 prepared through a low-temperature hydrothermal method. Single crystals and powder samples of this phase are isolated by optimising the Sr(OH)2 to KRuO4 ratio while maintaining a high base concentration. The new structure consists of a rare five-coordinated RuVI featuring isolated trigonal prisms and crystallising in a non-centrosymmetric tetragonal system. Isolated Ru polyhedra leading to a large spatial distance ~ 50 pm between the Ru metal centres render the compound paramagnetic despite strong antiferromagnetic correlation. Band structure calculation suggests a metal-like electronic ground state with mostly Ru d and O p orbitals contributing to the Fermi surface. The electrochemical performance of Sr3Ru2O9H2, though not as impressive as RuO2, remains relevant and is on par with other reported OER catalysts.

cond-mat.mtrl-sci↗

arXiv2025

Enterprise AI Must Enforce Participant-Aware Access Control

Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. While fine-tuning enhances performance by internalizing domain knowledge, it also introduces a critical security risk: leakage of confidential training data to unauthorized users. These risks are exacerbated when LLMs are combined with Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch contextual documents at inference time. We demonstrate data exfiltration attacks on AI assistants where adversaries can exploit current fine-tuning and RAG architectures to leak sensitive information by leveraging the lack of access control enforcement. We show that existing defenses, including prompt sanitization, output filtering, system isolation, and training-level privacy mechanisms, are fundamentally probabilistic and fail to offer robust protection against such attacks. We take the position that only a deterministic and rigorous enforcement of fine-grained access control during both fine-tuning and RAG-based inference can reliably prevent the leakage of sensitive data to unauthorized recipients. We introduce a framework centered on the principle that any content used in training, retrieval, or generation by an LLM is explicitly authorized for \emph{all users involved in the interaction}. Our approach offers a simple yet powerful paradigm shift for building secure multi-user LLM systems that are grounded in classical access control but adapted to the unique challenges of modern AI workflows. Our solution has been deployed in Microsoft Copilot Tuning, a product offering that enables organizations to fine-tune models using their own enterprise-specific data.

cs.CR↗

arXiv2025

Thermonuclear X-ray bursts across the eclipse transitions in the LMXBs EXO 0748-676 and XTE J1710-281

The primary radiation from thermonuclear X-ray bursts observed in the neutron star low-mass X-ray binary (LMXB) systems can interact with various parts of the binary system. This interaction gives rise to secondary radiation in different wavelength ranges, known as reprocessed emission. In eclipsing LMXBs, the reprocessed emission from the bursts can be examined during eclipses, as the primary emission is blocked and only the reprocessed emission is visible. We searched for bursts during eclipses in the archival RXTE data of the eclipsing LMXBs and found them in EXO 0748-676 and XTE J1710-281. In EXO 0748-676, seven bursts were found to occur near eclipse egress, with their tails extending beyond the eclipse, and one such burst was found for XTE J1710-281. We estimate the reprocessing fraction at orbital phases near eclipse egress by modeling the peculiar eclipse bursts detected in both systems, which have tails extending beyond the eclipses. We observe an increasing trend in reprocessing fraction as these eclipse bursts occur closer to the eclipse egress. We discuss the possibilities of reprocessing in the ablated wind from the companion star, the accretion disc, and the disc wind in EXO 0748-676 and XTE J1710-281. Additionally, we observe two decay components in the bursts in EXO 0748-676, which could suggest a complex composition of the accreting fuel. From the burst rise timescales, we place an upper limit on the size of the reprocessing regions in both EXO 0748-676 and XTE J1710-281, finding it comparable to the size of the respective X-ray binaries.

astro-ph.HE↗

arXiv2025

Spectral analysis of ultraluminous X-ray pulsars with models of X-ray pulsars

A fraction of the Ultra Luminous X-ray (ULX) sources are known to be accreting neutron stars as they show coherent X-ray pulsations with pulse periods ranging from ~1-30 seconds. While initially thought to host intermediate-mass black holes, ULXs have since been recognized as a diverse class of objects, including ULX pulsars. These pulsars require models specifically tailored to account for their unique accretion physics, distinct from those used for Galactic black hole binaries. The X-ray spectra of all Galactic accreting X-ray pulsars (including sources in the Magellanic Clouds) are dominated by a high energy cut-off power-law and some of the sources show a soft excess, some emission lines, cyclotron absorption features, etc. In this work, we undertake a comprehensive analysis of the broadband X-ray spectra of five ULX pulsars using simultaneous XMM-Newton and NuSTAR observations and show that their X-ray spectra can be effectively described by spectral models, similar to those used for the local accretion-powered X-ray pulsars. A soft excess is detected in all the sources which is also consistent with the local X-ray pulsars that have low absorption column density. We have marginal detection or low upper limit on the presence of the iron K-alpha emission line from these sources, which is a key difference of the ULX pulsars with the local accreting X-ray pulsars. We discuss the implication of this on the nature of the binary companion and the accretion mechanism in the ULX pulsars.

astro-ph.HE↗

arXiv2025

Solar Alfvenic Pulses and Mesoscale Solar Wind

Large-scale solar ejections are well understood, but the extent to which small-scale solar features directly influence the solar wind remains an open question, primarily due to the challenges of tracing these small-scale ejections and their impact. Here, we measure the fine-scale motions of network bright points along a coronal hole boundary in high-resolution H-alpha images from the 1.6m Goode Solar Telescope at Big Bear Solar Observatory to quantify the agitation of open flux tubes into generating Alfvenic pulses. We combine the motion, magnetic flux, and activity duration of the flux tubes to estimate the energy content carried by individual Alfvenic pulses, which is ~10+25 erg, adequately higher than the energies ~10+23 erg estimated for the magnetic switchbacks observed by the Parker Solar Probe (PSP). This implies the possibility that the surface-generated Alfvenic pulses could reach the solar wind with sufficient energy to generate switchbacks, even though some of then are expected to be reflected back in the stratified solar atmosphere. Alfvenic pulses further reproduce for the first time other properties of switchbacks, including the filling factor above ~8% at granular and supergranular scales, which correspond best to the lower end of the mesoscale structure. This quantitative result for solar energy output in the form of Alfvenic pulses through magnetic funnels provides a crucial clue to the ongoing debate about the dynamic cycle of energy exchange between the Sun and the mesoscale solar wind that has been raised, but has not been adequately addressed, by PSP near-Sun observations.

astro-ph.SR↗

arXiv2025

X Hacking: The Threat of Misguided AutoML

Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusions. This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as SHAP values. We show how easily an automated machine learning pipeline can be adapted to exploit model multiplicity at scale: searching a Rashomon set of 'defensible' models with similar predictive performance to find a desired explanation. We formulate the trade-off between explanation and accuracy as a multi-objective optimisation problem, and illustrate empirically on familiar real-world datasets that, on average, Bayesian optimisation accelerates X-hacking 3-fold for features susceptible to it, versus random sampling. We show the vulnerability of a dataset to X-hacking can be determined by information redundancy among features. Finally, we suggest possible methods for detection and prevention, and discuss ethical implications for the credibility and reproducibility of XAI.

cs.LG↗

arXiv2025

RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation

Recent Large Language Models (LLMs) have reported high accuracy on reasoning benchmarks. However, it is still unclear whether the observed results arise from true reasoning or from statistical recall of the training set. Inspired by the ladder of causation (Pearl, 2009) and its three levels (associations, interventions and counterfactuals), this paper introduces RE-IMAGINE, a framework to characterize a hierarchy of reasoning ability in LLMs, alongside an automated pipeline to generate problem variations at different levels of the hierarchy. By altering problems in an intermediate symbolic representation, RE-IMAGINE generates arbitrarily many problems that are not solvable using memorization alone. Moreover, the framework is general and can work across reasoning domains, including math, code, and logic. We demonstrate our framework on four widely-used benchmarks to evaluate several families of LLMs, and observe reductions in performance when the models are queried with problem variations. These assessments indicate a degree of reliance on statistical recall for past performance, and open the door to further research targeting skills across the reasoning hierarchy.

cs.CL↗

arXiv2025

Investigating the effects of acceptor removal mechanism and impact ionization on proton irradiated 300 $μ$m thick LGAD

Low-Gain Avalanche Detectors (LGADs) are the leading 4D sensing technology selected for use in the High Luminosity Large Hadron Collider (HL-LHC). However, their proximity to the interaction point makes them highly susceptible to radiation-induced damage. Such degradation effects can be effectively studied through TCAD simulations. In this work, we extend the validation of a previously developed proton damage model for transitional sensors. The enhanced model for LGAD also incorporates an acceptor removal mechanism and modifications in impact ionization behavior, resulting in a more comprehensive and reliable tool for fabrication and performance analysis.

physics.ins-det↗