Tootfinder

RAD@home citizen science discovery of two spiral galaxies where the 30-220 kpc radio lobes are possibly shaped by ram pressure stripping
Prakash Apoorva, Ananda Hota, Pratik Dabhade, P. K. Navaneeth, Dhruv Nayak, Arundhati Purohit
https://arxiv.org/abs/2508.09518

RAD@home citizen science discovery of two spiral galaxies where the 30-220 kpc radio lobes are possibly shaped by ram pressure stripping
We report the RAD@home citizen science discovery of two rare spiral-host radio galaxies (NGC 3898 and WISEA J221656.57-132042434.1 or RAD-"Thumbs up" galaxy), both exhibiting asymmetric radio lobes extending over 30 to 220 kiloparsec scales. We present a multi-wavelength image analysis of these two sources using radio, optical, and ultraviolet data. Both host galaxies are young, star-forming systems with asymmetric or distorted stellar disks. These disks show similarities to those in galaxies u…

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
Ananda Prakash Verma
https://arxiv.org/abs/2508.07224 https://arxiv.org/pdf/2508.07…

EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning
We present EDGE, a general-purpose, misconception-aware adaptive learning framework composed of four stages: Evaluate (ability and state estimation), Diagnose (posterior infer-ence of misconceptions), Generate (counterfactual item synthesis), and Exercise (index-based retrieval scheduling). EDGE unifies psychometrics (IRT/Bayesian state space models), cog-nitive diagnostics (misconception discovery from distractor patterns and response latencies), contrastive item generation (minimal perturbati…

A double selection entanglement distillation-based state estimator
Joshua Carlo A. Casapao, Ananda G. Maity, Naphan Benchasattabuse, Michal Hajdu\v{s}ek, Akihito Soeda, Rodney Van Meter, David Elkouss
https://arxiv.org/abs/2505.24280

A double selection entanglement distillation-based state estimator
With the advent of practical quantum communication networks drawing closer, there is a growing need for reliable estimation protocols that can efficiently characterize quantum resources with minimum resource overhead requirement. A novel approach to this problem is to integrate an estimator into an existing network task, thereby removing the need for an additional characterization protocol. In this work, we show that the measurement statistics of a double selection distillation protocol alone c…

The NASA Exoplanet Archive and Exoplanet Follow-up Observing Program: Data, Tools, and Usage
Jessie L. Christiansen, Douglas L. McElroy, Marcy Harbut, David R. Ciardi, Megan Crane, John Good, Kevin K. Hardegree-Ullman, Aurora Y. Kesseli, Michael B. Lund, Meca Lynn, Ananda Muthiar, Ricky Nilsson, Toba Oluyide, Michael Papin, Amalia Rivera, Melanie Swain, Nicholas D. Susemiehl, Raymond Tam, Julian van Eyken, Charles Beichman

The NASA Exoplanet Archive and Exoplanet Follow-up Observing Program: Data, Tools, and Usage
The NASA Exoplanet Archive and the Exoplanet Follow-up Observing Program service are two widely used resources for the exoplanet community. The NASA Exoplanet Archive provides a complete and accurate accounting of exoplanetary systems published by NASA missions and by the community in the refereed literature. In anticipation of continued exponential growth in the number of exoplanetary systems, and the increasing complexity in our characterization of these systems, the NASA Exoplanet Archive ha…

Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Girish, Swarup Ranjan Behera, Ananda Chandra Nayak, Sanjib Kumar Nayak, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.02258

Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
In this work, we focus on non-verbal vocal sounds emotion recognition (NVER). We investigate mamba-based audio foundation models (MAFMs) for the first time for NVER and hypothesize that MAFMs will outperform attention-based audio foundation models (AAFMs) for NVER by leveraging its state-space modeling to capture intrinsic emotional structures more effectively. Unlike AAFMs, which may amplify irrelevant patterns due to their attention mechanisms, MAFMs will extract more stable and context-aware…