arXiv subjects

Shubham Sharma

Publications and source records attributed to Shubham Sharma.

At least 37 records · Page 2Linked to original sources

arXiv2025

aims-PAX: Parallel Active eXploration for the automated construction of Machine Learning Force Fields

Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and material systems. Here we introduce aims-PAX, an expedited, multi-trajectory active learning framework that streamlines the development of stable and accurate MLFFs. Designed for a wide range of researchers, aims-PAX offers a modular, high-performance workflow that couples diversified sampling with scalable training across CPU and GPU architectures. Integrated with the widely used ab initio code FHI-aims, the framework supports state-of-the-art ML models and dataset generation using general-purpose (or "foundational") force-fields for rapid deployment in diverse systems. We demonstrate the capabilities of aims-PAX in various challenging tasks: creating datasets and models for highly flexible peptides, multiple organic molecules at once, explicitly solvated molecules, and for efficiently handling computationally demanding systems such as the CsPbI$_3$ perovskite. We show that aims-PAX achieves a reduction of up to three orders of magnitude in the number of required reference calculations, automatically selects challenging systems within a given chemical space, facilitates simulation of solvated molecules with more than thousand atoms, while enabling a ten-fold speedup in active-learning time through optimized resource utilization. This positions aims-PAX as a powerful and versatile platform for next-generation atomistic simulations in both academic and industrial settings.

physics.chem-ph↗

arXiv2025

Analyticup E-commerce Product Search Competition Technical Report from Team Tredence_AICOE

This study presents the multilingual e-commerce search system developed by the Tredence_AICOE team. The competition features two multilingual relevance tasks: Query-Category (QC) Relevance, which evaluates how well a user's search query aligns with a product category, and Query-Item (QI) Relevance, which measures the match between a multilingual search query and an individual product listing. To ensure full language coverage, we performed data augmentation by translating existing datasets into languages missing from the development set, enabling training across all target languages. We fine-tuned Gemma-3 12B and Qwen-2.5 14B model for both tasks using multiple strategies. The Gemma-3 12B (4-bit) model achieved the best QC performance using original and translated data, and the best QI performance using original, translated, and minority class data creation. These approaches secured 4th place on the final leaderboard, with an average F1-score of 0.8857 on the private test set.

cs.IR↗

arXiv2025

When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning

Learning effective reinforcement learning (RL) policies to solve real-world complex tasks can be quite challenging without a high-fidelity simulation environment. In most cases, we are only given imperfect simulators with simplified dynamics, which inevitably lead to severe sim-to-real gaps in RL policy learning. The recently emerged field of offline RL provides another possibility to learn policies directly from pre-collected historical data. However, to achieve reasonable performance, existing offline RL algorithms need impractically large offline data with sufficient state-action space coverage for training. This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches? In this study, we propose the Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning (H2O) framework to provide an affirmative answer to this question. H2O introduces a dynamics-aware policy evaluation scheme, which adaptively penalizes the Q function learning on simulated state-action pairs with large dynamics gaps, while also simultaneously allowing learning from a fixed real-world dataset. Through extensive simulation and real-world tasks, as well as theoretical analysis, we demonstrate the superior performance of H2O against other cross-domain online and offline RL algorithms. H2O provides a brand new hybrid offline-and-online RL paradigm, which can potentially shed light on future RL algorithm design for solving practical real-world tasks.

cs.LG↗

arXiv2025

EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos

As digital platforms redefine educational paradigms, ensuring interactivity remains vital for effective learning. This paper explores using Multimodal Large Language Models (MLLMs) to automatically respond to student questions from online lectures - a novel question answering task of real world significance. We introduce the EduVidQA Dataset with 5252 question-answer pairs (both synthetic and real-world) from 296 computer science videos covering diverse topics and difficulty levels. To understand the needs of the dataset and task evaluation, we empirically study the qualitative preferences of students, which we provide as an important contribution to this line of work. Our benchmarking experiments consist of 6 state-of-the-art MLLMs, through which we study the effectiveness of our synthetic data for finetuning, as well as showing the challenging nature of the task. We evaluate the models using both text-based and qualitative metrics, thus showing a nuanced perspective of the models' performance, which is paramount to future work. This work not only sets a benchmark for this important problem, but also opens exciting avenues for future research in the field of Natural Language Processing for Education.

cs.CL↗

arXiv2025

Geometric Mixture Classifier (GMC): A Discriminative Per-Class Mixture of Hyperplanes

Many real world categories are multimodal, with single classes occupying disjoint regions in feature space. Classical linear models (logistic regression, linear SVM) use a single global hyperplane and perform poorly on such data, while high-capacity methods (kernel SVMs, deep nets) fit multimodal structure but at the expense of interpretability, heavier tuning, and higher computational cost. We propose the Geometric Mixture Classifier (GMC), a discriminative model that represents each class as a mixture of hyperplanes. Within each class, GMC combines plane scores via a temperature-controlled soft-OR (log-sum-exp), smoothly approximating the max; across classes, standard softmax yields probabilistic posteriors. GMC optionally uses Random Fourier Features (RFF) for nonlinear mappings while keeping inference linear in the number of planes and features. Our practical training recipe: geometry-aware k-means initialization, silhouette-based plane budgeting, alpha annealing, usage-aware L2 regularization, label smoothing, and early stopping, makes GMC plug-and-play. Across synthetic multimodal datasets (moons, circles, blobs, spirals) and tabular/image benchmarks (iris, wine, WDBC, digits), GMC consistently outperforms linear baselines and k-NN, is competitive with RBF-SVM, Random Forests, and small MLPs, and provides geometric introspection via per-plane and class responsibility visualizations. Inference scales linearly in planes and features, making GMC CPU-friendly, with single-digit microsecond latency per example, often faster than RBF-SVM and compact MLPs. Post-hoc temperature scaling reduces ECE from about 0.06 to 0.02. GMC thus strikes a favorable balance of accuracy, interpretability, and efficiency: it is more expressive than linear models and lighter, more transparent, and faster than kernel or deep models.

cs.LG↗

arXiv2025

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models

Large Language Models (LLMs) are transforming AI across industries, but their development and deployment remain complex. This survey reviews 16 key challenges in building and using LLMs and examines how these challenges are addressed by two state-of-the-art models with unique approaches: OpenAI's closed source GPT-4o (May 2024 update) and DeepSeek-V3-0324 (March 2025), a large open source Mixture-of-Experts model. Through this comparison, we showcase the trade-offs between closed source models (robust safety, fine-tuned reliability) and open source models (efficiency, adaptability). We also explore LLM applications across different domains (from chatbots and coding tools to healthcare and education), highlighting which model attributes are best suited for each use case. This article aims to guide AI researchers, developers, and decision-makers in understanding current LLM capabilities, limitations, and best practices.

cs.CL↗

arXiv2025

The Effect of Data Poisoning on Counterfactual Explanations

Counterfactual explanations are a widely used approach for examining the predictions of black-box systems. They can offer the opportunity for computational recourse by suggesting actionable changes on how to alter the input to obtain a different (i.e., more favorable) system output. However, recent studies have pointed out their susceptibility to various forms of manipulation. This work studies the vulnerability of counterfactual explanations to data poisoning. We formally introduce and investigate data poisoning in the context of counterfactual explanations for increasing the cost of recourse on three different levels: locally for a single instance, a sub-group of instances, or globally for all instances. In this context, we formally introduce and characterize data poisonings, from which we derive and investigate a general data poisoning mechanism. We demonstrate the impact of such data poisoning in the critical real-world application of explaining event detections in water distribution networks. Additionally, we conduct an extensive empirical evaluation, demonstrating that state-of-the-art counterfactual generation methods and toolboxes are vulnerable to such data poisoning. Furthermore, we find that existing defense methods fail to detect those poisonous samples.

cs.LG↗

arXiv2025

Impacts of flow velocity and microbubbles on water flushing in a horizontal pipeline

Water flushing to remove particle sediment is essential for safe and continuous transport of many industrial slurries through pipelines. An efficient flushing strategy may reduce water consumption and the cost associated with water usage, and help water conservation for sustainability. In this study, a computational fluid dynamics (CFD) model coupled with the kinetic theory of granular flow for the flushing process is presented. The CFD models were validated against field data collected from a coal slurry pipeline of 128 $km$ in length, 0.575~$m$ in diameter, achieving an average error of less than 15\% for outlet solid concentration over time. A parametric study evaluated the effects of water velocity (1.88-5.88~$m/s$), bubble size (50~$μm$, 150~$μm$, and 1000~$μm$) and bubble volume fraction (0.05-0.2) on flushing performance including pipeline cleanness, cleanness efficiency, and water consumption. The obtained outcomes indicate that higher water velocity is preferred and an increase in water velocity from $1.88~m/s$ to $5.88~m/s$ reduces the water consumption by $28\%$. Large bubbles may hinder the flushing process and increase the water consumption by $23\%$. Remarkably, small bubbles facilitates the flushing process and lead to $35\%$ reduction in water consumption. These effects are attributed to the turbulent characteristics in the pipelines in presence of microbubbles.

physics.flu-dyn↗

arXiv2025

Numerical investigation on solids settling in a non-Newtonian slurry inside a horizontal flume

Slurry transportation is always crucial for many industrial processes. This study numerically investigates the settling behavior of multisize solid particles in a non-newtonian slurry inside a semicircular open channel (flume). The non-newtonian slurry is modelled using a three-dimensional (3D) unsteady Eulerian-Eulerian (E-E) model coupled with the Hershel-Bulkley (HB) rheological model. A detailed sensitivity analysis of drag models is performed to establish the solid-fluid interaction in the slurry flow. The numerical model is validated with the experimental data from the literature and shows a fair agreement. The validated model is used to simulate the settling behavior of the slurry in the flume. The mean particle diameter of the solid particles in the slurry is in the range of 75-296 $μ$m with a median diameter of 188 $μ$m. The effect of particle size distributions (PSDs), flume inclination, bubble size and bubble volume fraction on the particle settling inside the flume is investigated in the parametric study. The analysis of our results revealed that the settling of solids is significantly affected by PSDs in the open channel system. In particular, the increase in flume inclination progresses the settling and dissipation of fine and coarse particles, respectively. Additional simulations showed that the inception of bubbles influences the settling velocity of solids, which changes the settling behavior of multisize solids inside the flume. The presented study can be used as a valuable guideline for the optimisation of intermediate exclusion of water from thickened slurry in order to ensure the stability of tailing storage facility.

physics.flu-dyn↗

arXiv2025

Interpretable LLM-based Table Question Answering

Interpretability in Table Question Answering (Table QA) is critical, especially in high-stakes domains like finance and healthcare. While recent Table QA approaches based on Large Language Models (LLMs) achieve high accuracy, they often produce ambiguous explanations of how answers are derived. We propose Plan-of-SQLs (POS), a new Table QA method that makes the model's decision-making process interpretable. POS decomposes a question into a sequence of atomic steps, each directly translated into an executable SQL command on the table, thereby ensuring that every intermediate result is transparent. Through extensive experiments, we show that: First, POS generates the highest-quality explanations among compared methods, which markedly improves the users' ability to simulate and verify the model's decisions. Second, when evaluated on standard Table QA benchmarks (TabFact, WikiTQ, and FeTaQA), POS achieves QA accuracy that is competitive to existing methods, while also offering greater efficiency-requiring significantly fewer LLM calls and table database queries (up to 25x fewer)-and more robust performance on large-sized tables. Finally, we observe high agreement (up to 90.59% in forward simulation) between LLMs and human users when making decisions based on the same explanations, suggesting that LLMs could serve as an effective proxy for humans in evaluating Table QA explanations.

cs.CL↗

arXiv2025

From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper

The introduction of transformer architecture was a turning point in Natural Language Processing (NLP). Models based on the transformer architecture such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) have gained widespread popularity in various applications such as software development and education. The availability of Large Language Models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analysed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted.

cs.AI↗

arXiv2025

Understanding the Valence Quark Structure of the Pion through GTMDs

We investigate the internal structure of the pion using generalized transverse momentum-dependent parton distributions (GTMDs) within the light-cone quark model. By solving the quark-quark correlator, we derive the twist-$2$, $3$, and $4$ quark GTMDs in terms of light-front wave functions (LFWFs). Out of the $16$ possible GTMDs, $12$ are found to be nonzero. Furthermore, we extract the valence quark transverse momentum-dependent parton distributions (TMDs) and generalized parton distributions (GPDs) from their corresponding GTMDs. Additionally, we compute the valence quark electromagnetic form factors (FFs) and parton distribution functions (PDFs) up to twist-$4$. The elastic charge radius of the pion is determined to be $0.558$ fm. Our results exhibit a qualitative agreement with predictions from other theoretical model like Nambu-Jona-Lasinio model, Light-front holographic model, and spectator model at the leading twist. This study provides a comprehensive insight into the internal structure of the pion.

hep-ph↗

arXiv2025

Physics for the environment and sustainable development

A reliable understanding of the Earth system is essential for the life quality of modern society. Natural hazards are the cause of most life and resource losses. The ability to define the conditions for a sustainable development of humankind, to keep the Earth system within the boundaries of habitable states, or to predict critical transitions and events in the dynamics of the Earth system are crucial to mitigate and adapt to Earth system related events and changes (e.g., volcanic eruptions, earthquakes, climate change) and to avert the disastrous consequences of natural hazards. In this chapter, we discuss key concepts from nonlinear physics and show that they enable us to treat challenging problems of Earth sciences which cannot be solved by classic methods. In particular, the concepts of multi-scaling, recurrence, synchronization, and complex networks have become crucial in the very last decades for a substantially more profound understanding of the dynamics of earthquakes, landslides, or (palaeo-)climate. They can even provide a significantly improved prediction of several high-impact extreme events. Additionally, crucial open challenges in the realm of methodological nature and applications to Earth sciences are given.

nlin.CD↗

arXiv2025

Study of the subleading twist GTMD $E_{21}$ for proton in light-front quark-diquark model

In this work, we study the generalized transverse momentum dependent distribution (GTMD) $E_{21}$ for proton using light-front quark-diquark model. We construct the expression of $E_{21}$ GTMD using the overlap equation in light-front wave functions obtained from the GTMD correlator with Dirac matrix structure $Γ=1$, in both situations of scalar and vector diquark. The $3$-dimensional plots of GTMD $E_{21}$ have been analyzed with respect to its variables by taking two variables at a time while holding others constant.

hep-ph↗

arXiv2025

Exploring the Impact of Generative Artificial Intelligence in Education: A Thematic Analysis

The recent advancements in Generative Artificial intelligence (GenAI) technology have been transformative for the field of education. Large Language Models (LLMs) such as ChatGPT and Bard can be leveraged to automate boilerplate tasks, create content for personalised teaching, and handle repetitive tasks to allow more time for creative thinking. However, it is important to develop guidelines, policies, and assessment methods in the education sector to ensure the responsible integration of these tools. In this article, thematic analysis has been performed on seven essays obtained from professionals in the education sector to understand the advantages and pitfalls of using GenAI models such as ChatGPT and Bard in education. Exploratory Data Analysis (EDA) has been performed on the essays to extract further insights from the text. The study found several themes which highlight benefits and drawbacks of GenAI tools, as well as suggestions to overcome these limitations and ensure that students are using these tools in a responsible and ethical manner.

cs.AI↗

arXiv2025

The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

cs.DC↗

arXiv2024

Statistical mechanics of an active wheel rolling in circles

Vibrated granular matter constitutes a useful system for studying the physics of active matter. Usually, self-propulsion is induced in grains through suitable asymmetry in the particle design. In this paper, we show that a symmetrical mini wheel placed on a vibrating plate self-propels along circular trajectories, showing chiral active dynamics. The chiral activity emerges through a sequence of spontaneous symmetry breaking in the particle's kinetics. The fact that isotropy, fore-aft, and chiral symmetries are broken spontaneously leads to distinct statistics, which include a temporal evolution involving stochastic resetting, a non-Gaussian velocity distribution with multiple peaks, broad power-law curvature distribution, and a bounded chirality probability, along with a phase transition from passive achiral to active chiral state as a function of vibration amplitude. Our study establishes the vibrated wheel as a three-state chiral active system that can serve as a model experimental system to study the non-equilibrium statistical mechanics and stochastic thermodynamics of chiral active systems and can inspire novel locomotion strategies in robotics.

cond-mat.soft↗

arXiv2024

Frontier orbitals control dynamical disorder in molecular semiconductors

Charge transport in organic semiconductors is limited by dynamical disorder. Design rules for new high-mobility materials have therefore focused on limiting its two foundations: structural fluctuations and the transfer integral gradient. However, it has remained unclear how these goals should be translated into molecular structures. Here we show that a specific shape of the frontier orbital, with a lack of nodes along the long molecular axis, reduces the transfer integral gradient and therefore the dynamical disorder. We investigated single crystals of the prototypical molecular semiconductors pentacene and picene by angle-resolved photoemission spectroscopy and dynamical disorder calculations. We found that picene exhibits a remarkably low dynamical disorder. By separating in- and out-of-plane components of dynamical disorder, we identify the reason as a reduced out-of-plane disorder from a small transfer integral derivative. Our results demonstrate that molecules with an armchair $π$-electron topology and same-phase frontier orbitals like picene are promising molecular building blocks for the next generation of organic semiconductors.

cond-mat.mtrl-sci↗