Tootfinder

The Shape of AGN-Driven Winds in the Seyfert Galaxy NGC 3516
Jacob Tutterow, Nicholas Ferree, D. Michael Crenshaw, Julia Falcone, Maura Kathleen Shea, Travis C. Fischer, Beena Meena, Mitchell Revalski, Kesha Patel, Madeline Davis
https://arxiv.org/abs/2509.06476

The Shape of AGN-Driven Winds in the Seyfert Galaxy NGC 3516
Active galactic nuclei (AGN) are known to drive ionized gas into their host galaxies, which may affect the evolution of both the central supermassive black holes and their hosts. In the case of NGC 3516, a nearby Seyfert 1 galaxy, these AGN winds have historically proven difficult to disentangle from galactic rotation. Using long slit spectroscopy at multiple position angles from the Hubble Space Telescope Space Telescope Imaging Spectrograph and the Apache Point Observatory Kitt Peak Ohio Stat…

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Vortical interactions in turbulent thermoacoustic systems
Ankit Sahay, Muralikrishnan Gopalakrishnan Meena, R. I. Sujith

Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach
Adam Nelson-Archer, Aleia Sen, Meena Al Hasani, Sofia Davila, Jessica Le, Omar Abbouchi
https://arxiv.org/abs/2507.01979

Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach
We present a deep learning approach for forecasting short-term employment changes and assessing long-term industry health using labor market data from the U.S. Bureau of Labor Statistics. Our system leverages a Long- and Short-Term Time-series Network (LSTNet) to process multivariate time series data, including employment levels, wages, turnover rates, and job openings. The model outputs both 7-day employment forecasts and an interpretable Industry Employment Health Index (IEHI). Our approach o…

Multimodal Appearance based Gaze-Controlled Virtual Keyboard with Synchronous Asynchronous Interaction for Low-Resource Settings
Yogesh Kumar Meena, Manish Salvi
https://arxiv.org/abs/2508.16606

Multimodal Appearance based Gaze-Controlled Virtual Keyboard with Synchronous Asynchronous Interaction for Low-Resource Settings
Over the past decade, the demand for communication devices has increased among individuals with mobility and speech impairments. Eye-gaze tracking has emerged as a promising solution for hands-free communication; however, traditional appearance-based interfaces often face challenges such as accuracy issues, involuntary eye movements, and difficulties with extensive command sets. This work presents a multimodal appearance-based gaze-controlled virtual keyboard that utilises deep learning in conj…

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
Mayank Kumar Jain, Dinesh Gopalani, Yogesh Kumar Meena, Nishant Jain
https://arxiv.org/abs/2508.16223

Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
With the rapid evolution of technology and the Internet, the proliferation of fake news on social media has become a critical issue, leading to widespread misinformation that can cause societal harm. Traditional fact checking methods are often too slow to prevent the dissemination of false information. Therefore, the need for rapid, automated detection of fake news is paramount. We introduce DaCFake, a novel fake news detection model using a divide and conquer strategy that combines content and…

Transient star B/R ratio and star formation in $z\gtrsim 1$ lensed galaxies
Sung Kei Li, Jose M. Palencia, Jose M. Diego, Jeremy Lim, Patrick L. Kelly, Ashish K. Meena, James Nianias, Hayley Williams, Liliya L. R. Williams, Adi Zitrin
https://arxiv.org/abs/2506.17565

Transient star B/R ratio and star formation in $z\gtrsim 1$ lensed galaxies
The extreme magnification from galaxy clusters and microlenses therein allows the detection of individual, luminous stars in lensed galaxies as transient events, and hence provides a valuable window into the high mass stellar population in $z>1$ galaxies. As these bright stars can only be formed at specific ages, the relative abundance of transient events at blue (B) and red (R) optical wavelengths ($B/R$ ratio) can provide insights into the star formation history of lensed galaxies. Here, we f…

Geometry of Clairaut Riemannian warped product submersions
Arkadeepta Roy, Kiran Meena, Hemangi Madhusudan Shah
https://arxiv.org/abs/2508.11413 https://ar…

Geometry of Clairaut Riemannian warped product submersions
In this paper, we introduce and study the concept of \textit{Clairaut Riemannian warped product submersions} between Riemannian warped product manifolds. By generalizing the notion of Clairaut Riemannian submersions to the setting of Riemannian warped product submersions, we define such submersions via a warping function satisfying a Clairaut relation along geodesics. We establish necessary and sufficient conditions under which a Riemannian warped product submersion satisfies the Clairaut condi…

Gravitational lensing of fast radio bursts: prospects for probing microlens populations in lensing galaxies
Ashish Kumar Meena, Prasenjit Saha
https://arxiv.org/abs/2507.20305 h…

Gravitational lensing of fast radio bursts: prospects for probing microlens populations in lensing galaxies
Gravitational lensing by a stellar microlens of mass $M$ forms two images separated by micro-arcseconds on the sky and has a time delay of $2\times10^{-5}(M/{\rm M_\odot})$ seconds. Although we cannot resolve such micro-images in the sky, they could be resolved in time if the source is a fast radio burst (FRB). In this work, we study the magnification ($|μ|$) and time delay~($t_d$) distributions of micro-images led by different microlens populations. We find that, in microlensing of …

Unconventional superconductivity in a non-centrosymmetric ${\alpha}$-Mn alloy NbTaOs$_{2}$
R. K. Kushwaha, Arushi, S. Jangid, P. K. Meena, R. Stewart, A. D. Hillier, R. P. Singh
https://arxiv.org/abs/2506.12449

Unconventional superconductivity in a non-centrosymmetric $α$-Mn alloy NbTaOs$_{2}$
Non-centrosymmetric superconductors have emerged as a fascinating avenue for exploring unconventional superconductivity. Their broken inversion and time-reversal symmetries make them prime candidates for realizing the intrinsic superconducting diode effect (SDE). In this work, we synthesize the ternary non-centrosymmetric $α$-Mn alloy NbTaOs$_{2}$ and conduct a comprehensive investigation of its superconducting properties through resistivity, magnetization, specific heat and muon spin rotation…

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
Junqi Yin, Mijanur Palash, M. Paul Laiu, Muralikrishnan Gopalakrishnan Meena, John Gounley, Stephen M. de Bruyn Kops, Feiyi Wang, Ramanan Sankaran, Pei Zhang
https://arxiv.org/abs/2507.16697

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models b…

Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration
Parth G. Dangi, Yogesh Kumar Meena
https://arxiv.org/abs/2508.07677

Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration
Brain-machine interfaces (BMIs) have significantly advanced neuro-rehabilitation by enhancing motor control. However, accurately decoding continuous grasp force remains a challenge, limiting the effectiveness of BMI applications for fine motor tasks. Current models tend to prioritise algorithmic complexity rather than incorporating neurophysiological insights into force control, which is essential for developing effective neural engineering solutions. To address this, we propose EEGForceMap, an…

Deep learning for classifying dynamical states from time series via recurrence plots
Athul Mohan, G. Ambika, Chandrakala Meena
https://arxiv.org/abs/2506.17498

Deep learning for classifying dynamical states from time series via recurrence plots
Recurrence Quantification Analysis (RQA) is a widely used method for capturing the dynamical structure embedded in time series data, relying on the analysis of recurrence patterns in the reconstructed phase space via recurrence plots. Although RQA proves effective across a range of applications, it typically requires the computation of multiple quantitative measures, making it both computationally intensive and sensitive to parameter choices. In this study, we adopt an alternative approach that…