arXiv subjects

Mayank Singh

Publications and source records attributed to Mayank Singh.

At least 55 records · Page 3Linked to original sources

arXiv2025

Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance

Functional Electrical Stimulation (FES) can be an effective tool to augment paretic muscle function and restore normal ankle function. Our approach incorporates a real-time, data-driven Model Predictive Control (MPC) scheme, built upon a Koopman operator theory (KOT) framework. This framework adeptly captures the complex nonlinear dynamics of ankle motion in a linearized form, enabling application of linear control approaches for highly nonlinear FES-actuated dynamics. Utilizing inertial measurement units (IMUs), our method accurately predicts the FES-induced ankle movements, while accounting for nonlinear muscle actuation dynamics, including the muscle activation for both plantarflexors, and dorsiflexors (Tibialis Anterior (TA)). The linear prediction model derived through KOT allowed us to formulate the MPC problem with linear state space dynamics, enhancing the real-time feasibility, precision and adaptability of the FES driven control. The effectiveness and applicability of our approach have been demonstrated through comprehensive simulations and experimental trials, including three participants with no disability and a participant with Multiple Sclerosis. Our findings highlight the potential of a KOT-based MPC approach for FES based gait assistance that offers effective and personalized assistance for individuals with gait impairment conditions.

eess.SY↗

arXiv2024

Covariant formulation of spinodal decomposition in rapidly expanding quark gluon plasma

Quantum Chromodynamics (QCD) is expected to have a first order phase transition between the confined hadron gas and the deconfined quark gluon plasma at high baryon densities. This will result in phase boundary effects in the metastable and unstable regions. It is important to include these effects in phenomenological models of heavy ion collisions to identify experimental signatures of a phase transition. This requires building intuition on phase separation in rapidly expanding fluids. In this work we present the covariant equations of relativistic hydrodynamics with a phase boundary, provide prescriptions to extend the equation of state to metastable and unstable regions, and show the effects of spinodal separation in a Bjorken flow.

hep-ph↗

arXiv2024

Analytical insights into the interplay of momentum, multiplicity and the speed of sound in heavy-ion collisions

We introduce a minimal model of ultracentral heavy-ion collisions to study the relation between the speed of sound of the produced plasma and the final particles' energy and multiplicity. We discuss how the particles' multiplicity $N_{\textrm{tot}}$ and average energy $E_{\textrm{tot}}/N_{\textrm{tot}}$ is related to the speed of sound $c_s$ by $c_s^2=d \ln (E_{\textrm{tot}}/N_{\textrm{tot}})/d\ln N_{\textrm{tot}}$ if the fluid is inviscid, its speed of sound is constant and all final particles can be measured. We show that finite rapidity cuts on the particles' multiplicity $N$ and energy $E$ introduce corrections between $c_s^2$ and $d \ln (E/N)/d\ln N$ that depend on the system's lifetime. We study analytically these deviations with the Gubser hydrodynamic solution, finding that, for ultrarelativistic bosons, they scale as the ratio of the freezeout temperature $T_{\mathrm{FO}}$ over the maximum initial temperature of the fluid $T_{0}$; the non-thermodynamic aspect of these corrections is highlighted through their dependence on the system's initial conditions.

hep-ph↗

arXiv2024

Disoriented isospin condensates in heavy-ion collisions

Anomalous neutral to charged kaon correlations measured by the ALICE collaboration have defied usual explanations. We propose that the large fluctuations could arise because of a disoriented isospin condensate where there is an imbalance between up and down condensates at the time kaons hadronize. This could happen in heavy-ion collisions when the quark condensate re-forms as the system cools and the approximate chiral symmetry of QCD is broken. Within the linear sigma model, we show that the energy cost of forming a disoriented isospin condensate is small making it very plausible.

hep-ph↗

arXiv2024

Spinodal decomposition in Bjorken flow

The QCD first-order phase transition at large baryon densities is expected to proceed by spinodal decomposition. This spinodal phase is likely to leave its signatures on the experimental observables measured in heavy-ion collision experiments. Identifying these signatures requires phenomenological models integrating surface effects resulting from the phase transition into the hydrodynamical description of the expanding quark gluon plasma. We write the equations of relativistic hydrodynamics with spinodal decomposition and solve it in on a background of Bjorken flow relevant for heavy-ion collisions.

hep-ph↗

arXiv2024

Simulating Charm Quarks in IP-Glasma Initial Stage and Quark-Gluon Plasma: A Hybrid Approach for charm quark phenomenology

We present phenomenological findings on charm quark transport while including its energy loss in both pre-equilibrium and hydrodynamic stages of the evolution. We employed the MARTINI event generator for the production and evolution of heavy quarks in the relativistic heavy-ion collisions. The sensitivity of the heavy meson nuclear modification factor and flow coefficient to the early stage of heavy-ion collisions and bulk medium evolution is analyzed for Pb+Pb collisions at 5.02 TeV. Our study provides insights into the interaction strength of charm quarks during the early phase and within the quark-gluon plasma.

hep-ph↗

arXiv2024

COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework

As the NLP community increasingly addresses challenges associated with multilingualism, robust annotation tools are essential to handle multilingual datasets efficiently. In this paper, we introduce a code-mixed multilingual text annotation framework, COMMENTATOR, specifically designed for annotating code-mixed text. The tool demonstrates its effectiveness in token-level and sentence-level language annotation tasks for Hinglish text. We perform robust qualitative human-based evaluations to showcase COMMENTATOR led to 5x faster annotations than the best baseline. Our code is publicly available at \url{https://github.com/lingo-iitgn/commentator}. The demonstration video is available at \url{https://bit.ly/commentator_video}.

cs.CL↗

arXiv2024

Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning

Global sustainable fund universe encompasses open-end funds and exchange-traded funds (ETF) that, by prospectus or other regulatory filings, claim to focus on Environment, Social and Governance (ESG). Challengingly, the claims can only be confirmed by examining the textual disclosures to check if there is presence of intentionality and ESG focus on its investment strategy. Currently, there is no regulation to enforce sustainability in ESG products space. This paper proposes a unique method and system to classify and score the fund prospectuses in the sustainable universe regarding specificity and transparency of language. We aim to employ few-shot learners to identify specific, ambiguous, and generic sustainable investment-related language. Additionally, we construct a ratio metric to determine language score and rating to rank products and quantify sustainability claims for US sustainable universe. As a by-product, we publish manually annotated quality training dataset on Hugging Face (ESG-Prospectus-Clarity-Category under cc-by-nc-sa-4.0) of more than 1K ESG textual statements. The performance of the few-shot finetuning approach is compared with zero-shot models e.g., Llama-13B, GPT 3.5 Turbo etc. We found that prompting large language models are not accurate for domain specific tasks due to misalignment issues. The few-shot finetuning techniques outperform zero-shot models by large margins of more than absolute ~30% in precision, recall and F1 metrics on completely unseen ESG languages (test set). Overall, the paper attempts to establish a systematic and scalable approach to measure and rate sustainability intention quantitatively for sustainable funds using texts in prospectus. Regulatory bodies, investors, and advisors may utilize the findings of this research to reduce cognitive load in investigating or screening of ESG funds which accurately reflects the ESG intention.

cs.CL↗

arXiv2024

Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models

Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and reason about temporal information remains limited, hindering their application in real-world scenarios where understanding the sequential nature of events is crucial. Our study experiments with 12 state-of-the-art models (ranging from 2B to 70B+ parameters) on a novel numerical-temporal dataset, \textbf{TempUN}, spanning from 10,000 BCE to 2100 CE, to uncover significant temporal retention and comprehension limitations. We propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. Our findings reveal that open-source models exhibit knowledge gaps more frequently, suggesting a trade-off between limited knowledge and incorrect responses. Additionally, various fine-tuning approaches significantly improved performance, reducing incorrect outputs and impacting the identification of 'information not available' in the generations. The associated dataset and code are available at (https://github.com/lingoiitgn/TempUN).

cs.CL↗

arXiv2024

PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs

Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two popular benchmarks for Python code generation, analyzing their diversity and difficulty. Our findings unveil a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. Furthermore, we uncover a worrying prevalence of easy tasks, potentially inflating model performance estimations. To address these limitations, we propose a novel benchmark, PythonSaga, featuring 185 hand-crafted prompts on a balanced representation of 38 programming concepts across diverse difficulty levels. The robustness of our benchmark is demonstrated by the poor performance of existing Code-LLMs.

cs.CL↗

arXiv2024

How Robust are the Tabular QA Models for Scientific Tables? A Study using Customized Dataset

Question-answering (QA) on hybrid scientific tabular and textual data deals with scientific information, and relies on complex numerical reasoning. In recent years, while tabular QA has seen rapid progress, understanding their robustness on scientific information is lacking due to absence of any benchmark dataset. To investigate the robustness of the existing state-of-the-art QA models on scientific hybrid tabular data, we propose a new dataset, "SciTabQA", consisting of 822 question-answer pairs from scientific tables and their descriptions. With the help of this dataset, we assess the state-of-the-art Tabular QA models based on their ability (i) to use heterogeneous information requiring both structured data (table) and unstructured data (text) and (ii) to perform complex scientific reasoning tasks. In essence, we check the capability of the models to interpret scientific tables and text. Our experiments show that "SciTabQA" is an innovative dataset to study question-answering over scientific heterogeneous data. We benchmark three state-of-the-art Tabular QA models, and find that the best F1 score is only 0.462.

cs.CL↗

arXiv2024

Anomalous kaon correlations measured in Pb-Pb collisions at the LHC as evidence for the melting and refreezing of the QCD vacuum

Measurements of the dynamical correlations between neutral and charged kaons in central Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV by the ALICE Collaboration display anomalous behavior relative to conventional heavy-ion collision simulators. We consider other conventional statistical models, none of which can reproduce the magnitude and centrality dependence of the correlations. The data can be reproduced by coherent emission from domains which grow in number and volume with increasing centrality. We study the dynamical evolution of the strange quark condensate and show that the energy released during the expansion and cooling of the system may be sufficient to explain the anomaly.

hep-ph↗

arXiv2024

Disoriented isospin condensates may be the source of anomalous kaon correlations measured in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV

The magnitude of fluctuations between charged and neutral kaons measured by the ALICE Collaboration in heavy-ion collisions at the LHC exceeds conventional explanation. Here it is shown that if the scalar condensate, which is typically associated with chiral symmetry, is accompanied by an isospin=1 field, then the combination can produce large fluctuations where $\langle \bar{u}u\rangle \ne \langle \bar{d}d\rangle$. Hadronizing strange and anti-strange quarks might then strongly fluctuate between charged ($u\bar{s}$ or $s\bar{u}$) and neutral ($d\bar{s}$ or $s\bar{d}$) kaons.

hep-ph↗

arXiv2024

LEGOBench: Scientific Leaderboard Generation Benchmark

The ever-increasing volume of paper submissions makes it difficult to stay informed about the latest state-of-the-art research. To address this challenge, we introduce LEGOBench, a benchmark for evaluating systems that generate scientific leaderboards. LEGOBench is curated from 22 years of preprint submission data on arXiv and more than 11k machine learning leaderboards on the PapersWithCode portal. We present four graph-based and two language model-based leaderboard generation task configurations. We evaluate popular encoder-only scientific language models as well as decoder-only large language models across these task configurations. State-of-the-art models showcase significant performance gaps in automatic leaderboard generation on LEGOBench. The code is available on GitHub ( https://github.com/lingo-iitgn/LEGOBench ) and the dataset is hosted on OSF ( https://osf.io/9v2py/?view_only=6f91b0b510df498ba01595f8f278f94c ).

cs.CL↗

arXiv2024

Cross-lingual Editing in Multilingual Language Models

The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (METs) have emerged to efficiently update model outputs without retraining, their effectiveness in multilingual LLMs, where knowledge is stored in diverse languages, remains an underexplored research area. This research paper introduces the cross-lingual model editing (\textbf{XME}) paradigm, wherein a fact is edited in one language, and the subsequent update propagation is observed across other languages. To investigate the XME paradigm, we conducted experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts: \textit{Latin} (English, French, and Spanish) and \textit{Indic} (Hindi, Gujarati, and Bengali). The results reveal notable performance limitations of state-of-the-art METs under the XME setting, mainly when the languages involved belong to two distinct script families. These findings highlight the need for further research and development of XME techniques to address these challenges. For more comprehensive information, the dataset used in this research and the associated code are publicly available at the following URL\url{https://github.com/lingo-iitgn/XME}.

cs.CL↗

arXiv2023

Heavy flavor transport and observables in heavy-ion collisions within the MARTINI+MUSIC framework

We study the transport dynamics of charm quarks within an expanding quark-gluon plasma for Pb+Pb collisions at 2.76 TeV. The analysis incorporates the hydrodynamical approach-MUSIC with fluctuating IP-Glasma initial state and Bayesian-quantified viscous coefficients. We study the interaction strength of charm quarks in the medium, including elastic collisional processes with medium constituents, gluon emission processes, and the impact of non-perturbative interactions on heavy quark transport. Further, we analyze the dynamics of heavy flavors using a hybrid framework that incorporates the MARTINI event generator, with PYTHIA8.1 for the initial production of heavy quarks, and Langevin dynamics to describe the evolution of heavy quarks.

hep-ph↗

arXiv2023

Simultaneous minijets and QGP evolution

Minijets traversing through the QGP formed in heavy-ion collisions deposit significant amount of energy in the bulk medium. They also create gradients in temperatures which alter the flow profile and enhance entropy production. We study the effects of minijets in a simultaneous hydro + jet framework and find that inclusion of minijets requires recalibration of transport properties extracted from the model-to-data comparisons.

hep-ph↗

arXiv2023

Open charm phenomenology with a multi-stage approach to relativistic heavy-ion collisions

We study open charm flavor observables in Pb+Pb collision at $\sqrt{s_{NN}}= 2.76$ TeV within the MARTINI framework. The space-time expansion of the quark-gluon plasma is described using the hydrodynamical approach-MUSIC with IP-Glasma initial conditions. The model parameters, including the viscous coefficients, were obtained from a recent Bayesian model-to-data comparison. We evolve heavy quarks in this background using Langevin dynamics while incorporating their collisional and radiative processes in the medium. The sensitivity of charm observables to the IP-Glasma initial state, bulk evolution, and centrality of the collision is studied. We find that the elliptic flow of open charm flavor has a strong dependence on the fluctuating initial conditions in addition to the strength of the interaction of heavy quarks with the medium constituents. Within this framework, the nuclear suppression factor and elliptic flow of D-mesons act as efficient probes to study the initial stages of heavy-ion collisions, transport coefficients associated with QGP medium as well as heavy quark interactions.

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