arXiv subjects

Fatemeh Bagheri

Publications and source records attributed to Fatemeh Bagheri.

16 recordsLinked to original sources

arXiv2026

Multi-Phonation Graph Learning with Self-Supervised Speech Embeddings for ALS Detection and Progression Prediction

Amyotrophic lateral sclerosis (ALS) progressively impairs speech motor control, making acoustic analysis a promising biomarker for severity and progression estimation. We propose a subject-level graph framework that aggregates multiple phonation recordings into a unique k-nearest-neighbor graph built from pretrained SSL embeddings of 2s segments. We compare four SSL front-ends (wav2vec 2.0, HuBERT, data2vec-audio, and UniSpeech-SAT) and five graph neural networks (GCN, residual GCN, GAT, GraphSAGE, and GIN) on the SAND dataset tasks (339 participants: 205 ALS, 134 control): 5-class dysarthria severity and 4-class ALSFRS-R progression prediction. On the official validation set, the best configuration (HuBERT+GIN) achieves macro-F$_1$ of 0.73 for Task 1 and 0.69 for Task 2, outperforming SAND validation baselines (0.61 and 0.58). These results highlight the potential of combining GNNs with pretrained cross-lingual speech representations for low-resource ALS detection and progression monitoring.

eess.AS↗

arXiv2026

Self-Supervised Audio Representation Learning for Pediatric Asthma Detection in Emergency Care Using Digital Stethoscope Recordings

Accurate diagnosis of pediatric asthma in emergency departments remains challenging due to overlapping respiratory symptoms, time constraints, and the limited feasibility of pulmonary function testing in young children. This study investigates the feasibility of pediatric asthma detection in the emergency department using breath sound recordings and machine learning. Thirty-second breath sounds were collected from six chest locations in 31 pediatric patients (10 asthmatic, 21 non-asthmatic) and analyzed using pretrained self-supervised speech representation models (HuBERT, WavLM, and Wav2Vec 2.0) for feature extraction, with patient age and sex incorporated into the feature representations. Conventional machine learning classifiers were trained and evaluated using patient-level stratified group 5-fold cross-validation and leave-one-patient-out validation to ensure the generalizability of the findings. Among the evaluated approaches, Wav2Vec 2.0 combined with histogram-based gradient boosting achieved the strongest and most consistent performance, yielding an accuracy of 0.84, sensitivity of 0.80, specificity of 0.86, and F1-score of 0.76 under both evaluation protocols. The consistency of performance across validation strategies suggests promising generalization to unseen patients. These findings suggest that pretrained self-supervised audio representations offer a promising, non-invasive approach for pediatric asthma detection in real-world emergency department settings, where objective respiratory assessment is often limited.

eess.AS↗

arXiv2025

Predictions of the Nancy Grace Roman Space Telescope Galactic Exoplanet Survey. IV. Lens Mass and Distance Measurements

As part of the Galactic Bulge Time Domain Survey (GBTDS), the Nancy Grace Roman Galactic Exoplanet Survey (RGES) will use microlensing to discover cold outer planets and free-floating planets unbound to stars. NASA has established several science requirements for the GBTDS to ensure RGES success. A key advantage of RGES is Roman's high angular resolution, which will allow detection of flux from many host stars. One requirement specifies that Roman must measure the masses and distances of 40% of detected planet hosts with 20% precision or better. To test this, we simulated microlensing events toward the GBTDS fields and used Fisher matrix analysis to estimate light curve parameter uncertainties. Combining these with Roman imaging observables (lens flux, relative lens-source proper motion), we estimated the achievable precision of lens mass and distance measurements. Using pyLIMASS, a publicly available code for estimating lens properties, we applied this analysis to 3,000 simulated events. Assuming the Cassan et al. (2012) exoplanet mass function, we find that >40% of host stars meet the required 20% precision threshold, confirming that the GBTDS can satisfy the mission requirement. We validated our approach by comparing our inferred lens masses and distances to empirical measurements from detailed image-constrained light curve modeling of historical microlensing events with Hubble and Keck follow-up imaging. Our results agree within roughly 1 sigma, demonstrating that both approaches yield consistent and reliable mass and distance estimates, and confirming the robustness of our simulations for Roman-era microlensing science.

astro-ph.EP↗

arXiv2025

Impacts of Tidal Locking on Magnetospheric Energy Input to Exoplanet Atmospheres

We investigate the effect of planetary corotation on energy dissipation within the magnetosphere-ionosphere system of exoplanets. Using MHD simulations, we find that tidally locked exoplanets have a higher cross-polar cap potential (CPCP) compared to fast-rotating planets with the same magnetic field strength, confirming previous studies. Our simulations show that for a given interplanetary magnetic field, an increase in corotation period leads to a higher CPCP. Notably, this difference in CPCP between tidally locked and rotating planets persists across a range of solar wind conditions, including extreme environments such as those experienced by hot Jupiters. Furthermore, we observe that variations in corotation have little impact on CPCP for Earth-sized planets. These results underscore the significance of both corotation dynamics and planetary size in understanding how exoplanets interact with their stellar environments.

astro-ph.EP↗

arXiv2024

Infrared-Radio-follow-up Observations for Detection of the Magnetic Radio Emission of Extra-Solar Planets: A New Window to Detect Exoplanets

There are several methods for indirectly detecting exoplanets, such as transit, radial velocity, astrometry, and the conventional gravitational microlensing approach. These methods rely on observing the effects of exoplanets on the emission or motion of observed stars. All these techniques have focused on the optical or infrared domains. However, an alternative method for exoplanet detection via microlensing events involves planets orbiting the source star, creating a binary source system. In this study, we explore a novel approach to detecting and studying exoplanets exclusively through their radio emissions resulting from magnetospheric processes. We propose utilizing the Roman telescope as a survey observer to detect microlensing events. Subsequently, we investigate the potential for detecting planetary radio signals through follow-up observations of these microlensing events in the radio band using the SKA telescope. This method is viable due to the comparable radio emission levels of exoplanets and their parent stars, unlike optical and infrared emissions. We conduct a Monte Carlo simulation to replicate the observations by the Nancy Roman Telescope, followed by a follow-up observation in radio frequencies using the SKA telescope. We determine that approximately 1317 exoplanets exhibit detectable signals by the SKA telescope during the 7-season observations by the Nancy Roman Telescope. This result indicates that such a method cannot only facilitate the direct detection of exoplanets but also enable the measurement of their magnetic field strength through analysis of their radio emissions.

astro-ph.EP↗

arXiv2024

An alternative statistical interpretation for the apparent plateaus in the duration distributions of GRBs

The existence of a plateau in the short-duration tail of the observed distribution of cosmological Long-soft Gamma Ray Bursts (LGRBs) has been argued as the first direct evidence of Collapsars. A similar plateau in the short-duration tail of the observed duration distribution of Short-hard Gamma Ray Bursts (SGRBs) has been suggested as evidence of compact binary mergers. We present an equally plausible alternative interpretation for this evidence, which is purely statistical. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs can similarly result from a combination of sample incompleteness and inhomogeneous binning of data. The observed plateau in the short-duration tail of the observed distributions of SGRBs can similarly result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of Gamma-Ray Bursts (GRBs) through extensive numerical Monte Carlo simulations. Our presented analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs without invoking the physics of Collapsars and jet-propagation through the stellar envelope.

astro-ph.HE↗

arXiv2024

Exploring Radio Emissions from Confirmed Exoplanets Using SKA

Currently, our understanding of magnetic fields in exoplanets remains limited compared to those within our solar system. Planets with magnetic fields emit radio signals primarily due to the Electron Cyclotron Maser Instability mechanism. In this study, we explore the feasibility of detecting radio emissions from exoplanets using the Square Kilometre Array (SKA) radio telescope. Utilizing data from the NASA Exoplanet Archive, we compile information on confirmed exoplanets and estimate their radio emissions using the RBL model. Our analysis reveals that three exoplanets- Qatar-4 b, TOI-1278 b, and WASP-173 A b- exhibit detectable radio signals suitable for observation with the SKA telescope.

astro-ph.EP↗

arXiv2024

A Fresh Look into the Interaction of Exoplanets Magnetosphere with Stellar Winds using MHD Simulations

Numerous numerical studies have been carried out in recent years that simulate different aspects of exoplanets' magnetosphere and stellar winds. These studies have focused primarily on hot Jupiters with sun-like stars. This study addresses the challenges inherent in utilizing existing MHD codes to model hot Jupiter-star systems. Due to the scaling of the system and the assumption of a uniformly flowing stellar wind at the outer boundary of the simulation, MHD codes necessitate a minimum distance of greater than 0.4 au for a Jupiter-like planet orbiting a sun-like star to avoid substantial violations of the code's assumptions. Additionally, employing the GAMERA (Grid Agnostic MHD for Extended Research Applications) MHD code, we simulate star-planet interactions considering various stellar types (Sun-like and M Dwarf stars) with both Jupiter-like and Earth-like planets positioned at varying orbital distances. Furthermore, we explore the impact of tidal locking on the total power within the magnetosphere-ionosphere systems.

astro-ph.EP↗

arXiv2024

Comparison of Empirical Models of Ionospheric Heating to Global Simulations

Intense currents produced during geomagnetic storms dissipate energy in the ionosphere through Joule heating. This dissipation has significant space weather effects, and thus it is important to determine the ability of physics-based simulations to replicate real events quantitatively. Several empirical models estimate Joule heating based on ionospheric currents using the AE index. In this study, we select 11 magnetic storm simulations from the CCMC database and compare the integrated Joule heating in the simulations with the results of empirical models. We also use the SWMF global magnetohydrodynamic simulations for 12 storms to reproduce the correlation between the simulated AE index and simulated Joule heating. We find that the scale factors in the empirical models are half what is predicted by the SWMF simulations.

astro-ph.EP↗

arXiv2023

EOG Artifact Removal from Single and Multi-channel EEG Recordings through the combination of Long Short-Term Memory Networks and Independent Component Analysis

Introduction: Electroencephalogram (EEG) signals have gained significant popularity in various applications due to their rich information content. However, these signals are prone to contamination from various sources of artifacts, notably the electrooculogram (EOG) artifacts caused by eye movements. The most effective approach to mitigate EOG artifacts involves recording EOG signals simultaneously with EEG and employing blind source separation techniques, such as independent component analysis (ICA). Nevertheless, the availability of EOG recordings is not always feasible, particularly in pre-recorded datasets. Objective: In this paper, we present a novel methodology that combines a long short-term memory (LSTM)-based neural network with ICA to address the challenge of EOG artifact removal from contaminated EEG signals. Approach: Our approach aims to accomplish two primary objectives: 1) estimate the horizontal and vertical EOG signals from the contaminated EEG data, and 2) employ ICA to eliminate the estimated EOG signals from the EEG, thereby producing an artifact-free EEG signal. Main results: To evaluate the performance of our proposed method, we conducted experiments on a publicly available dataset comprising recordings from 27 participants. We employed well-established metrics such as mean squared error, mean absolute error, and mean error to assess the quality of our artifact removal technique. Significance: Furthermore, we compared the performance of our approach with two state-of-the-art deep learning-based methods reported in the literature, demonstrating the superior performance of our proposed methodology.

eess.SP↗

arXiv2020

Are there radio-loud and radio-quiet Gamma-Ray Bursts?

The potential existence of two separate classes of Long-duration Gamma-Ray Bursts (LGRBs) with and without radio afterglow emission, corresponding to radio-bright/loud and radio-dark/quiet populations, has been recently argued and favored in the GRB literature. The radio-quiet LGRBs have been found to have, on average, lower total isotropic gamma-ray emissions (Eiso) and shorter intrinsic prompt gamma-ray duration (e.g., T90z). In addition, a redshift-T90z anti-correlation has been discovered among the radio-loud LGRBs, which is reportedly missing in the radio-quiet class. Here we discuss the significance of the differences between the energetics and temporal properties of the two proposed classes of radio-loud and radio-quiet LGRBs. We show that much of the proposed evidence in support of the two distinct radio populations of LGRBs can be explained away in terms of selection effects and sample incompleteness. Our arguments are based on the recent discovery of the relatively-strong highly-significant positive correlation between the total isotropic emission (Eiso) and the intrinsic prompt duration (T90z) that is present in both populations of short-hard and long-soft GRBs, predicted, quantified, and reported for the first time by Shahmoradi (2013) and Shahmoradi and Nemiroff (2015).

astro-ph.HE↗

arXiv2020

Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library

ParaMonte::Python (standing for Parallel Monte Carlo in Python) is a serial and MPI-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for sampling mathematical objective functions, in particular, the posterior distributions of parameters in Bayesian modeling and analysis in data science, Machine Learning, and scientific inference in general. In addition to providing access to fast high-performance serial/parallel Monte Carlo and MCMC sampling routines, the ParaMonte::Python library provides extensive post-processing and visualization tools that aim to automate and streamline the process of model calibration and uncertainty quantification in Bayesian data analysis. Furthermore, the automatically-enabled restart functionality of ParaMonte::Python samplers ensure seamless fully-deterministic into-the-future restart of Monte Carlo simulations, should any interruptions happen. The ParaMonte::Python library is MIT-licensed and is permanently maintained on GitHub at https://github.com/cdslaborg/paramonte/tree/master/src/interface/Python.

cs.MS↗

arXiv2020

ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran

ParaMonte (standing for Parallel Monte Carlo) is a serial and MPI/Coarray-parallelized library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference. The ParaMonte library has been developed with the design goal of unifying the **automation**, **accessibility**, **high-performance**, **scalability**, and **reproducibility** of Monte Carlo simulations. The current implementation of the library includes **ParaDRAM**, a **Para**llel **D**elyaed-**R**ejection **A**daptive **M**etropolis Markov Chain Monte Carlo sampler, accessible from a wide range of programming languages including C, C++, Fortran, with a unified Application Programming Interface and simulation environment across all supported programming languages. The ParaMonte library is MIT-licensed and is permanently located and maintained at [https://github.com/cdslaborg/paramonte](https://github.com/cdslaborg/paramonte).

cs.MS↗

arXiv2020

ParaDRAM: A Cross-Language Toolbox for Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo Simulations

We present ParaDRAM, a high-performance Parallel Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo software for optimization, sampling, and integration of mathematical objective functions encountered in scientific inference. ParaDRAM is currently accessible from several popular programming languages including C/C++, Fortran, MATLAB, Python and is part of the ParaMonte open-source project with the following principal design goals: 1. full automation of Monte Carlo simulations, 2. interoperability of the core library with as many programming languages as possible, thus, providing a unified Application Programming Interface and Monte Carlo simulation environment across all programming languages, 3. high-performance 4. parallelizability and scalability of simulations from personal laptops to supercomputers, 5. virtually zero-dependence on external libraries, 6. fully-deterministic reproducibility of simulations, 7. automatic comprehensive reporting and post-processing of the simulation results. We present and discuss several novel techniques implemented in ParaDRAM to automatically and dynamically ensure the good-mixing and the diminishing-adaptation of the resulting pseudo-Markov chains from ParaDRAM. We also discuss the implementation of an efficient data storage method used in ParaDRAM that reduces the average memory and storage requirements of the algorithm by, a factor of 4 for simple simulation problems, to an order of magnitude and more for sampling complex high-dimensional mathematical objective functions. Finally, we discuss how the design goals of ParaDRAM can help users readily and efficiently solve a variety of machine learning and scientific inference problems on a wide range of computing platforms.

cs.CE↗

arXiv2019

Detection of Exoplanet as a Binary Source of Microlensing Events in WFIRST Survey

We investigate the possibility of exoplanet detection orbiting source stars in microlensing events through WFIRST observations. We perform a Monto Carlo simulation on the detection rate of exoplanets via microlensing, assuming that each source star has at least one exoplanet. The exoplanet can reflect part of the light from the parent star or emit internal thermal radiation. In this new detection channel, we use microlensing as an amplifier to magnify the reflection light from the planet. In the literature, this mode of detecting exoplanets has been investigated much less than the usual mode in which the exoplanets are considered as one companion in binary lens events. Assuming $72$ days of observation per season with the cadence of $15$ minutes, we find the probability of rocky planet detection with this method to be virtually zero. However, there is non-zero probability, for the detection of Jovian planets. We estimate the detection rates of the exoplanets by this method, using WFIRST observation to be $0.012\%$ in single lens events and $0.9\%$ in the binary lens events.

astro-ph.EP↗

arXiv2019

The symmetry of the energy momentum tensor does not necessarily reflect the space-time symmetry: a viscous axially symmetric cosmological solution

Applying the method of conformal metric to a given static axially symmetric vacuum solution of the Einstein equations, we have shown that there is no solution representing a cosmic ideal fluid which is asymtotically FLRW. Letting the cosmic fluid to be imperfect there are axially symmetric solutions tending to FLRW at space infinity. The solution we have found represents an axially symmetric spacetime leading to a spherically symmetric Einstein tensor. Therefore, we have found a solution of Einstein equations representing a spherically symmetric matter distribution corresponding to a spacetime which does not reflect the same symmetry. We have also found another solution of Einstein equation corresponding to the same energy tensor with spherical symmetry.

gr-qc↗