arXiv subjects

Raj Singh

Publications and source records attributed to Raj Singh.

6 recordsLinked to original sources

arXiv2026

Hexagonal Boron Nitride Spin Defects for Quantum Photonics: Annealing-Free Generation by Krypton Ion Implantation

Controlled, reproducible generation of luminescent defect centres in hBN remains a key challenge for scalable quantum-photonic technologies. Here, we report Kr$^{+}$ ion implantation as a tunable, annealing-free, and chemically inert route to room-temperature near-infrared luminescent spin defects in hBN, requiring no pre- or post-implantation annealing. SRIM Monte Carlo simulations were used to optimise the parameters for 40 keV Kr$^{+}$ irradiation of hBN flakes. The implanted samples exhibit a stable near-infrared photoluminescence (PL) band centred at $\sim$830 nm whose intensity increases with implantation fluence over $10^{11}$-$10^{15}$ions/cm$^{2}$. Temperature-dependent PL measurements (20-300 K) reveal a linewidth broadening well described by a $T^{3}$ dependence, consistent with acoustic-phonon-mediated dephasing. Raman spectra show the characteristic $E_{2g}$ mode of pristine hBN at $\sim$1366 cm$^{-1}$ alongside an implantation-induced defect feature at $\sim$1295 cm$^{-1}$, confirming irradiation-induced lattice disorder. Electron paramagnetic resonance (EPR) measurements reveal a paramagnetic centre with a $g$-factor of 2.003, and density functional theory (DFT) calculations indicate that a spatially separated $V_{\mathrm{N}}$-$C_{\mathrm{B}}$ donor-acceptor pair complex is a viable origin of the observed optical and magnetic signatures. Overall, Kr$^{+}$ implantation offers an effective, annealing-free, and scalable platform for generating stable room-temperature luminescent defects, providing a promising route toward quantum photonics.

physics.optics↗

arXiv2026

Presupposition and Reasoning in Conditionals: A Theory-Based Study of Humans and LLMs

Presupposition projection in conditionals is central to theories of meaning and pragmatics, yet it remains largely unevaluated in large language models. We address this gap through a parallel behavioral study comparing human judgments and LLM predictions on a normed dataset of conditional sentences that controls the relation between the antecedent and the projected presupposition. We collect likelihood ratings from 120 participants and four LLMs under matched contextual conditions. Results show that humans integrate probabilistic and pragmatic cues in their judgment, whereas LLMs show variable alignment with human patterns. Using a linguistically motivated checklist within an LLM-as-a-Judge framework, we further evaluate model reasoning. We observe models that best match human ratings often lack coherent pragmatic reasoning, while models with stronger reasoning produce less human-like judgments. These findings suggest that LLMs' performance on such tasks may result from surface pattern matching rather than pragmatic competence. Our findings highlight the importance of benchmarks grounded in linguistic theory for comparing humans and models.

cs.CL↗

arXiv2026

Do Language Models Know Theo Has a Wife? Investigating the Proviso Problem

We investigate how language models handle the proviso problem, an unresolved issue in pragmatics where presuppositions in conditional sentences diverge between theoretical and human interpretations. We reformulate this phenomenon as a Natural Language Inference task and introduce a diagnostic dataset designed to probe presupposition projection in conditionals. We evaluate RoBERTa, DeBERTa, LLaMA, and Gemma using explainability analyses. The results show that models broadly align with human judgments but rely on shallow pattern matching rather than semantic or pragmatic reasoning. Our work provides the first computational evaluation framework for the proviso problem and highlights the need for diagnostic, multi-method approaches to assess pragmatic competence and context-dependent meaning in language models.

cs.CL↗

arXiv2025

Let's CONFER: A Dataset for Evaluating Natural Language Inference Models on CONditional InFERence and Presupposition

Natural Language Inference (NLI) is the task of determining whether a sentence pair represents entailment, contradiction, or a neutral relationship. While NLI models perform well on many inference tasks, their ability to handle fine-grained pragmatic inferences, particularly presupposition in conditionals, remains underexplored. In this study, we introduce CONFER, a novel dataset designed to evaluate how NLI models process inference in conditional sentences. We assess the performance of four NLI models, including two pre-trained models, to examine their generalization to conditional reasoning. Additionally, we evaluate Large Language Models (LLMs), including GPT-4o, LLaMA, Gemma, and DeepSeek-R1, in zero-shot and few-shot prompting settings to analyze their ability to infer presuppositions with and without prior context. Our findings indicate that NLI models struggle with presuppositional reasoning in conditionals, and fine-tuning on existing NLI datasets does not necessarily improve their performance.

cs.CL↗

arXiv2024

Ensemble Classification-Based Spectrum Sensing Using Support Vector Machine for CRN

As the demand for internet of things (IoT) and device-to-device (D2D) applications in next generation communication systems increases, we are confronted with a challenge of spectrum scarcity. One promising solution to this problem is cognitive radio network (CRN), where the key element is the spectrum - a valuable and sharable natural resource that should not be wasted. To design efficient and sustainable networks for the future, it is crucial to ensure that spectrum sensing is not only accurate and rapid, but also energy-efficient. Spectrum sensing is a critical aspect of CRNs, and this study is mainly focused on it. In this research, we employ the supervised machine learning algorithm, support vector machine (SVM), to detect primary users (PU). We investigate different variants of SVM, including linear, polynomial, and Gaussian radial basic function (RBF), and employ an ensemble classification-based approach to improve the classifier's performance and productivity. The simulation results demonstrate that the ensemble classifier achieves the highest performance.

cs.IT↗

arXiv2020

Application of high frequency biasing and its effect in STOR-M tokamak

A pulsed oscillating power amplifier has been developed to apply high frequency biasing voltage to an electrode at the edge of the STOR-M tokamak plasma. The power amplifier can deliver a peak-to-peak oscillating voltage up to 120V and current 30A within the frequency range of 1kHz-50kHz. The electrode is located in the equatorial plane at radius $ρ= 0.88$. The frequency of the applied voltage has been varied between discharges. It is observed that the plasma density and soft x-ray intensity from the plasma core region usually increase at lower frequency regime 1kHz-5kHz as well as relatively higher frequency regime 20kHz-25kHz but seldom increase in between them. Increment of $τ_{p}$ \& $τ_{E}$ have been observed from the derivations of experimental data in both frequency regimes. Transport simulation has been carried out using the ASTRA simulation code for STOR-M tokamak parameters to understand the physical process behind experimental observations at higher frequency branch. The model is based on GAM excitement at resonance frequency associated with Ware-pinch due to oscillating electric field produced by biasing voltage which can suppress anomalous transport. Simulation results reproduce the experiment quite well in terms of the density, particle confinement as well as energy confinement time evolution. All those results indicate high frequency biasing is capable of improving confinement efficiently.

physics.plasm-ph↗