arXiv subjects

Ananya Sharma

Publications and source records attributed to Ananya Sharma.

3 recordsLinked to original sources

arXiv2026

Formation of Quark-Gluon Plasma droplets under Ultra-Relativistic Heavy Ion Collisions in the presence of magnetic fields with a modified hadronic medium

We build upon the previous studies conducted in this domain using the density of states of various components of the quark-gluon plasma(QGP) modelled after the Thomas-Fermi and Bethe models. This study provides an analysis of the stability of the quark-gluon plasma droplet by the inclusion and variation of chemical potential and magnetic field. An effective mass term is included to account for both the rest mass and thermodynamic mass terms of the quarks and gluon. We also expand on the inclusion of a different meson, kaon, into the medium of the droplet. The analysis on these quantities is done by calculating the free energy of the system and examining it as a function of droplet radius, chemical potential and magnetic field to find conditions for stable droplet formation. We discover that a kaonic medium, along with a pre-existing pionic medium, provides greater stability to the system. At the same time, it is realised that the parameters of chemical potential and magnetic field, due to their opposing effect, help in containing the droplet. Further calculations on entropy and heat capacity yield information about the nature of the phase transition of the system. Our analysis and results are in accordance with lattice QCD simulations.

hep-ph↗

arXiv2026

Mantis: A Foundation Model for Mechanistic Disease Forecasting

Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address these challenges, we developed Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 78 forecasting models across sixteen diseases with diverse modes of transmission, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error than all models in the CDC's COVID-19 Forecast Hub when backtested on early pandemic forecasts which it had not previously seen. Across all other diseases tested, Mantis consistently ranked in the top two models across evaluation metrics. Mantis further generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it can capture fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities illustrate that purely simulation-based foundation models such as Mantis can provide a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models struggle.

cs.AI↗

arXiv2025

Utilizing API Response for Test Refinement

Most of the web services are offered in the form of RESTful APIs. This has led to an active research interest in API testing to ensure the reliability of these services. While most of the testing techniques proposed in the past rely on the API specification to generate the test cases, a major limitation of such an approach is that in the case of an incomplete or inconsistent specification, the test cases may not be realistic in nature and would result in a lot of 4xx response due to invalid input. This is indicative of poor test quality. Learning-based approaches may learn about valid inputs but often require a large number of request-response pairs to learn the constraints, making it infeasible to be readily used in the industry. To address this limitation, this paper proposes a dynamic test refinement approach that leverages the response message. The response is used to infer the point in the API testing flow where a test scenario fix is required. Using an intelligent agent, the approach adds constraints to the API specification that are further used to generate a test scenario accounting for the learned constraint from the response. Following a greedy approach, the iterative learning and refinement of test scenarios are obtained from the API testing system. The proposed approach led to a decrease in the number of 4xx responses, taking a step closer to generating more realistic test cases with high coverage that would aid in functional testing. A high coverage was obtained from a lesser number of API requests, as compared with the state-of-the-art search-based API Testing tools.

cs.SE↗