arXiv subjects

Arushi Sharma

Publications and source records attributed to Arushi Sharma.

11 recordsLinked to original sources

arXiv2026

Astrophysical S-factor Calculation for p-p Fusion Reaction

The current S-factor calculations for weak pp interaction involve the determination of low-energy penetration probability using the bare Coulomb Gamow factor, which renders the nature and shape of actual interaction to be insignificant. In this work, the astrophysical S-factor is obtained utilizing the WKB action integral evaluated over the inverse scattering potential for the S-wave of pp-interaction, which does not involve bare Coulomb interaction in it. The np and pp inverse potentials are constructed using the phase function method by providing a reference potential consisting of three smoothly combined Morse functions, whose model parameters are optimized using a genetic algorithm to minimize the mean-squared error between the obtained and expected scattering phase shifts. The overlap integral between the bound-state deuteron and the scattering state of pp S-wave has been evaluated at different energies all the way up to 0.0001 MeV, and corresponding fusion cross-sections are determined. The WKB action integral has been computed at all energies without any approximations. Finally, the S-factor at various energies are calculated, and S(0) has been obtained using a supervised neural network. The value of S(0) obtained using our methodology involving complete evaluation of WKB action integral without approximations is $(0.1261\pm 0.0043)\times10^{-25}$, which is almost one order of magnitude lower than the currently accepted values using various methods. The inverse potentials constructed using the reference potential approach, which does not involve explicit consideration of nuclear $\&$ Coulomb interaction, resulting in a finite range of pp-interaction, have been successful in providing a new estimate for the astrophysical s-factor that depends on the nature and shape of the actual potentials.

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arXiv2026

Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study

Letters of recommendation (LoRs) can carry patterns of implicitly gendered language that can inadvertently influence downstream decisions, e.g. in hiring and admissions. In this work, we investigate the extent to which Transformer-based encoder models as well as Large Language Models (LLMs) can infer the gender of applicants in academic LoRs submitted to an U.S. medical-residency program after explicit identifiers like names and pronouns are de-gendered. While using three models (DistilBERT, RoBERTa, and Llama 2) to classify the gender of anonymized and de-gendered LoRs, significant gender leakage was observed as evident from up to 68% classification accuracy. Text interpretation methods, like TF-IDF and SHAP, demonstrate that certain linguistic patterns are strong proxies for gender, e.g. "emotional'' and "humanitarian'' are commonly associated with LoRs from female applicants. As an experiment in creating truly gender-neutral LoRs, these implicit gender cues were remove resulting in a drop of up to 5.5% accuracy and 2.7% macro $F_1$ score on re-training the classifiers. However, applicant gender prediction still remains better than chance. In this case study, our findings highlight that 1) LoRs contain gender-identifying cues that are hard to remove and may activate bias in decision-making and 2) while our technical framework may be a concrete step toward fairer academic and professional evaluations, future work is needed to interrogate the role that gender plays in LoR review. Taken together, our findings motivate upstream auditing of evaluative text in real-world academic letters of recommendation as a necessary complement to model-level fairness interventions.

cs.LG↗

arXiv2025

Analyzing Latent Concepts in Code Language Models

Interpreting the internal behavior of large language models trained on code remains a critical challenge, particularly for applications demanding trust, transparency, and semantic robustness. We propose Code Concept Analysis (CoCoA): a global post-hoc interpretability framework that uncovers emergent lexical, syntactic, and semantic structures in a code language model's representation space by clustering contextualized token embeddings into human-interpretable concept groups. We propose a hybrid annotation pipeline that combines static analysis tool-based syntactic alignment with prompt-engineered large language models (LLMs), enabling scalable labeling of latent concepts across abstraction levels. We analyse the distribution of concepts across layers and across three finetuning tasks. Emergent concept clusters can help identify unexpected latent interactions and be used to identify trends and biases within the model's learned representations. We further integrate LCA with local attribution methods to produce concept-grounded explanations, improving the coherence and interpretability of token-level saliency. Empirical evaluations across multiple models and tasks show that LCA discovers concepts that remain stable under semantic-preserving perturbations (average Cluster Sensitivity Index, CSI = 0.288) and evolve predictably with fine-tuning. In a user study on the programming-language classification task, concept-augmented explanations disambiguated token roles and improved human-centric explainability by 37 percentage points compared with token-level attributions using Integrated Gradients.

cs.SE↗

arXiv2025

Genetic Algorithm based Inverse Potentials for Resonant States of $\alpha-^{12}C$ Using Variable Phase Approach

Elastic scattering between $\alpha$-particles and $^{12}\mathrm{C}$ nuclei plays a crucial role in understanding resonance phenomena in light nuclear systems. In this work, we construct inverse potentials for resonant states in $\alpha$-$^{12}\mathrm{C}$ elastic scattering using the variable phase approach, in tandem with a genetic algorithm based optimization technique. The reference function for the potential in the phase equation is chosen as a combination of three smoothly joined Morse-type functions. The parameters of the reference function are genetically evolved to minimize the the mean squared error (MSE) between the numerically obtained scattering phase shifts and the expected values. The resulting inverse potentials accurately reproduce the resonance energies ($E_r$) and the resonance widths ($\Gamma_r$) for the $\ell^{\pi}$ states, $1^-$, $2^+$, $3^-$, and $4^+$, showing excellent agreement with experimental data. This computational approach to constructing inverse potentials serves as a complementary to conventional direct methods for investigating nuclear scattering phenomena.

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arXiv2025

Machine Learning Approach to Study of Low Energy Alpha-Deuteron Elastic Scattering using Phase Function Method

Central idea: To obtain the interaction potential using the inverse scattering method, we have employed the Physics-Informed Machine Learning (PIML) approach. In this framework, the machine learning algorithm is guided by the underlying physical laws, enabling the accurate extraction of the inverse scattering potential from the elastic scattering data. Methodology: As a reference potential, a combination of three smoothly joined Morse functions has been utilized, characterized by ten model parameters. These parameters are optimized in an iterative fashion using a Genetic Algorithm to ensure the best fit to the phase shifts extracted from the experimental scattering data. The process of optimization is guided by the computed scattering phase shifts by solving the phase equation using 5th order RK-method for the reference potential in each iteration Results: Our approach yields inverse potentials for both single and multi channel scattering. Using the Scattering Phase Shifts obtained from these inverse potentials, we calculate the partial cross-section to determine the resonance energies and decay width. The obtain values of resonance energies and decay width for 3D1, 3D2 and 3D3 states of alpha-deuteron are in correspondence with the experimental results. Conclusion: It can be concluded that our machine learning-based approach for constructing the inverse potential offers a novel and complementary technique to existing direct methods.

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arXiv2024

Ab-initio Approach for Constructing Inverse Potentials for Resonant States of {\alpha}-3H and {\alpha}-3He Scattering

In this paper, the inverse potentials for the resonant f states of {\alpha}-3H and {\alpha}-3He are constructed using the phase function method by utilizing an ab-initio approach. A combination of three Morse functions are joined smoothly to prepare the reference potential. While the regular Morse function captures the nuclear and Coulomb interactions at short and medium ranges, an inverse Morse function is chosen to obtain the Coulomb barrier that arises because of the long-range Coulomb interaction. This reference potential is representative of a large family of curves consisting of eight distinct model parameters and two intermediate points that define the boundaries that exist between the three regions. The phase equation is solved using the Runge-Kutta 5th order method for the input reference potential to obtain the scattering phase shifts at various center of mass energies. The model parameters are then adjusted using the genetic algorithm in an iterative fashion to minimize the mean square error between the simulated and expected phase shift values. Our approach successfully constructed the inverse potentials for the resonant f states of the {\alpha}-3H and {\alpha}-3He systems, achieving convergence with a minimized mean square error. The resonance energies and widths for the {\alpha}-3H system for the f-5/2 and f-7/2 states are determined to be [4.19 (4.14), 1.225 (0.918)] MeV and [2.20 (2.18), 0.099 (0.069)] MeV, respectively. For the f-5/2 and f-7/2 states of the {\alpha}-3He system, the resonance energies and widths are [5.03 (5.14), 1.6 (1.2)] MeV and [2.99 (2.98), 0.182(0.175)] MeV, respectively. Our ab-initio approach to solve the phase equation utilizing a combination of smoothly joined Morse functions effectively captures both short-range nuclear and long-range Coulomb interactions, providing an accurate model for nuclear scattering involving charged particles.

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arXiv2024

Variational Optimization for Constructing Inverse Potentials of Proton-Proton Scattering: A Phase Function Method Study

Background: The phase-shift analysis for proton-proton scattering has been studied by various research groups using the realistic potentials to be comprised of various internal interactions based on an exchange of pions and mesons, involving a large number of parameters. Purpose: The goal of the research is to construct inverse potentials for various l-channels of proton-proton (pp) elastic scattering using the 3-parameter Morse function in combination with atomic Hulthen by utilizing the phase function method and variational optimization technique. Methodology: The implementation of variational optimization begins with randomly assigning initial values to the Morse model parameters. Utilizing the Morse + Hulthen potential as input, the phase equations for various l-channels are numerically solved using the RK-5 method for obtaining the simulated Scattering Phase Shift (SPS). Mean Squared error between simulated and expected SPS has been chosen as the cost function. Variational optimization proceeds iteratively by adjusting potential parameters and re-evaluating the cost function until convergence is achieved. Results: All the obtained scattering phase shifts for various l-channels have been found to converge to a mean squared error <= 0.3. The computed cross-sections matched the experimental ones to less than 1% for energies up to 25 MeV. The scattering parameters are also found to closely match the experimental data. Conclusion: The inverse potentials constructed for various l-channels using Morse + atomic Hulthen are on par with the currently available high-precision realistic potentials.

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arXiv2024

Redundancy and Concept Analysis for Code-trained Language Models

Code-trained language models have proven to be highly effective for various code intelligence tasks. However, they can be challenging to train and deploy for many software engineering applications due to computational bottlenecks and memory constraints. Implementing effective strategies to address these issues requires a better understanding of these 'black box' models. In this paper, we perform the first neuron-level analysis for source code models to identify \textit{important} neurons within latent representations. We achieve this by eliminating neurons that are highly similar or irrelevant to the given task. This approach helps us understand which neurons and layers can be eliminated (redundancy analysis) and where important code properties are located within the network (concept analysis). Using redundancy analysis, we make observations relevant to knowledge transfer and model optimization applications. We find that over 95\% of the neurons are redundant with respect to our code intelligence tasks and can be eliminated without significant loss in accuracy. We also discover several subsets of neurons that can make predictions with baseline accuracy. Through concept analysis, we explore the traceability and distribution of human-recognizable concepts within latent code representations which could be used to influence model predictions. We trace individual and subsets of important neurons to specific code properties and identify 'number' neurons, 'string' neurons, and higher-level 'text' neurons for token-level tasks and higher-level concepts important for sentence-level downstream tasks. This also helps us understand how decomposable and transferable task-related features are and can help devise better techniques for transfer learning, model compression, and the decomposition of deep neural networks into modules.

cs.SE↗

arXiv2023

Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning

To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion. Our exploratory study attempts to evaluate the necessity of images for stance prediction in tweets and compare out-of-the-box text-based large-language models (LLM) in few-shot settings against fine-tuned unimodal and multimodal models. Our work suggests an ensemble of fine-tuned text-based language models (0.817 F1-score) outperforms both the multimodal (0.677 F1-score) and text-based few-shot prediction using a recent state-of-the-art LLM (0.550 F1-score). In addition to the differences in performance, our findings suggest that the multimodal models tend to perform better when image content is summarized as natural language over their native pixel structure and, using in-context examples improves few-shot performance of LLMs.

cs.CL↗

arXiv2023

Constructing Inverse Scattering Potentials for α-α System using Reference Potential Approach

Background: An accurate way to incorporate long range Coulomb interaction alongside short-range nuclear interaction has been a challenge for theoretical physicists. Purpose: In this paper, we propose a methodology based on the reference potential approach for constructing inverse potentials of alpha-alpha scattering. Methods: Two smoothly joined Morse potentials, regular for short-range nuclear interaction and inverted for long range Coulomb, are used in tandem as a reference potential in the phase function method to obtain the scattering phase shifts for the S, D and G states of alpha-alpha scattering. The model parameters are optimized by choosing to minimize the mean absolute percentage error between the obtained and experimental scattering phase shift values. Results: The constructed inverse potentials for S, D and G states have resulted in mean absolute percentage errors of 0.8, 0.5, and 0.4 respectively. The obtained resonances for D and G states closely match the experimental ones. Conclusion: The reference potential approach using a combination of smoothly joined Morse functions is successful in accurately accounting for the Coulomb interaction between charged particles in nuclear scattering studies.

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arXiv2021

Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language

Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the prevalence of regional languages in social media and the paucity of language flexible hate speech detectors. The proposed work focuses on analyzing hate speech in Hindi-English code-switched language. Our method explores transformation techniques to capture precise text representation. To contain the structure of data and yet use it with existing algorithms, we developed MoH or Map Only Hindi, which means "Love" in Hindi. MoH pipeline consists of language identification, Roman to Devanagari Hindi transliteration using a knowledge base of Roman Hindi words. Finally, it employs the fine-tuned Multilingual Bert and MuRIL language models. We conducted several quantitative experiment studies on three datasets and evaluated performance using Precision, Recall, and F1 metrics. The first experiment studies MoH mapped text's performance with classical machine learning models and shows an average increase of 13% in F1 scores. The second compares the proposed work's scores with those of the baseline models and offers a rise in performance by 6%. Finally, the third reaches the proposed MoH technique with various data simulations using the existing transliteration library. Here, MoH outperforms the rest by 15%. Our results demonstrate a significant improvement in the state-of-the-art scores on all three datasets.

cs.CL↗