Tootfinder

Spezi Data Pipeline: Streamlining FHIR-based Interoperable Digital Health Data Workflows
Vasiliki Bikia, Paul Schmiedmayer, Aydin Zahedivash, Lauren Aalami, Adrit Rao, Vishnu Ravi, Matthew Turk, Scott R. Ceresnak, Oliver Aalami
https://arxiv.org/abs/2509.14296

Spezi Data Pipeline: Streamlining FHIR-based Interoperable Digital Health Data Workflows
The increasing adoption of digital health technologies has amplified the need for robust, interoperable solutions to manage complex healthcare data. We present the Spezi Data Pipeline, an open-source Python toolkit designed to streamline the analysis of digital health data, from secure access and retrieval to processing, visualization, and export. The Pipeline is integrated into the larger Stanford Spezi open-source ecosystem for developing research and translational digital health software sys…

Near-Discovery Observations of Interstellar Comet 3I/ATLAS with the NASA Infrared Telescope Facility
Theodore Kareta (Villanova U.), Chansey Champagne (NAU), Lucas McClure (NAU), Joshua Emery (NAU), Benjamin N. L. Sharkey (UMD), James M. Bauer (UMD), Michael Connelly (UH/IRTF), John Rayner (UH/IRTF), Cristina Thomas (NAU), Vishnu Reddy (UAz), Megan Firgard (UCF)

Near-Discovery Observations of Interstellar Comet 3I/ATLAS with the NASA Infrared Telescope Facility
Interstellar Objects are comets and asteroids that formed around other stars but were ejected before they could accrete into exoplanets. They therefore represent a rare opportunity to compare the building blocks of planets in the Solar System to those in other stellar systems. The third Interstellar Object, 3I/ATLAS, is the newest, brightest, potentially largest, and fastest member of this population. We report observations of 3I/ATLAS taken on 2025 July 3 and 4 with the NASA Infrared Telescope…

Gravitational Wave Signature and the Nature of Neutrino Masses: Majorana, Dirac, or Pseudo-Dirac?
Sudip Jana, Sudip Manna, Vishnu P. K
https://arxiv.org/abs/2509.10456 https://

Gravitational Wave Signature and the Nature of Neutrino Masses: Majorana, Dirac, or Pseudo-Dirac?
The fermionic nature of neutrinos and the origin of their tiny masses remain unresolved issues in particle physics, intrinsically connected to lepton number symmetry-conserved for Dirac, violated for Majorana, and effectively pseudo-Dirac when global symmetries invoked for conservation are broken by quantum gravity. We investigate whether distinctive gravitational-wave (GW) signatures can illuminate the nature of neutrino masses and their underlying symmetries, particularly in scenarios where Y…

Suppression of measurement-induced state transitions in cos{\phi}-coupling transmon readout
Cyril Mori, Francesca D Esposito, Alexandru Petrescu, Lucas Ruela, Shelender Kumar, Vishnu Narayanan Suresh, Wael Ardati, Dorian Nicolas, Giulio Cappelli, Arpit Ranadive, Gwenael Le Gal, Martina Esposito, Quentin Ficheux, Nicolas Roch, Olivier Buisson
https://

Suppression of measurement-induced state transitions in cosϕ-coupling transmon readout
Drive-induced unwanted state transitions (DUST) are limiting both for microwave readout and parametric operations of superconducting qubits. Among them, measurement-induced state transitions (MIST) are due to intrinsic resonances described by the readout Hamiltonian. They were previously studied with a qubit linearly coupled to its readout mode, which constitutes the usual readout Hamiltonian. Since MIST can appear even at moderate powers, they limit the readout SNR and the QND readout fidelity…

Control Closure Certificates
Vishnu Murali, Mohammed Adib Oumer, Majid Zamani
https://arxiv.org/abs/2508.03947 https://arxiv.org/pdf/2508.03947

Control Closure Certificates
This paper introduces the notion of control closure certificates to synthesize controllers for discrete-time control systems against $ω$-regular specifications. Typical functional approaches to synthesize controllers against $ω$-regular specifications rely on combining inductive invariants (for example, via barrier certificates) with proofs of well-foundedness (for example, via ranking functions). Transition invariants, provide an alternative where instead of standard well-foundedness argumen…

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts
Vishnu Menon, Andy Cherney, Elizabeth B. Cloude, Li Zhang, Tiffany D. Do
https://arxiv.org/abs/2508.04787

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts
This study examined whether embedding LLM-guided reflection prompts in an interactive AI-generated podcast improved learning and user experience compared to a version without prompts. Thirty-six undergraduates participated, and while learning outcomes were similar across conditions, reflection prompts reduced perceived attractiveness, highlighting a call for more research on reflective interactivity design.

Transient segregation of bi-disperse granular mixtures in a periodic chute flow
Soniya Kumawat, Vishnu Kumar Sahu, Anurag Tripathi
https://arxiv.org/abs/2507.04025

Transient segregation of bi-disperse granular mixtures in a periodic chute flow
Transient size segregation of a bi-disperse granular mixture flowing over a periodic chute is studied using the Discrete Element Method and continuum simulations. A recently developed particle force-based size segregation model is used to predict the time-dependent flow properties of binary mixtures starting from rest. A two-way coupled continuum model that solves the momentum balance and convection-diffusion equations by incorporating the mixture segregation model along with the generalized in…

The fate of rotating massive stars across cosmic times
R. Hirschi, K. Goodman, G. Meynet, A. Maeder, S. Ekstr\"om, P. Eggenberger, C. Georgy, Y. Sibony, N. Yusof, S. Martinet, Vishnu Varma, K. Nomoto
https://arxiv.org/abs/2508.21233

The fate of rotating massive stars across cosmic times
The initial mass and metallicity of stars both have a strong impact on their fate. Stellar axial rotation also has a strong impact on the structure and evolution of massive stars. In this study, we exploit the large grid of GENEC models, covering initial masses from 9 to 500 $M_{\odot}$ and metallicities ranging from $Z=10^{-5}$ (nearly zero) to 0.02 (supersolar), to determine the impact of rotation on their fate across cosmic times. Using the carbon-oxygen core mass and envelope composition as…

ODKE : Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar, Yisi Sang, Yash Govind, Hyun Jang, Ali Kasgari, Alexis McClimans, Mohamed Soliman, Vishnu Konda, Ahmed Fakhry, Xiaoguang Qi
https://arxiv.org/abs/2509.04696

ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting documents, (3) hybrid Knowledge Extractors apply …

Approximate Message Passing for Multi-Preamble Detection in OTFS Random Access
Alessandro Mirri, Vishnu Teja Kunde, Enrico Paolini, Jean-Francois Chamberland
https://arxiv.org/abs/2509.03980

Approximate Message Passing for Multi-Preamble Detection in OTFS Random Access
This article addresses the problem of multiple preamble detection in random access systems based on orthogonal time frequency space (OTFS) signaling. This challenge is formulated as a structured sparse recovery problem in the complex domain. To tackle it, the authors propose a new approximate message passing (AMP) algorithm that enforces double sparsity: the sparse selection of preambles and the inherent sparsity of OTFS signals in the delay-Doppler domain. From an algorithmic standpoint, the n…

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Ayush Roy, Samin Enam, Jun Xia, Vishnu Suresh Lokhande, Won Hwa Kim
https://arxiv.org/abs/2507.19575

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (adding more data from a new dataset) have been shown to enhance model performance, they are not without complications. Specifically, increasing the size of the training dataset through pooling or addition can induce distributional shifts, negatively affecting downstream model performance, a phenomenon known as the "Data Ad…

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan, Ze Yang Ding, Hao Wang, David L. Dowe
https://arxiv.org/abs/2507.15307

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Electric Vehicles (EVs) are becoming increasingly prevalent nowadays, with studies highlighting their potential as mobile energy storage systems to provide grid support. Realising this potential requires effective charging coordination, which are often formulated as mixed-integer programming (MIP) problems. However, MIP problems are NP-hard and often intractable when applied to time-sensitive tasks. To address this limitation, we propose a deep learning assisted approach for optimising a day-ah…

Perfect adaptation in eukaryotic gradient sensing using cooperative allosteric binding
Vishnu Srinivasan, Wei Wang, Brian A. Camley
https://arxiv.org/abs/2509.00219 https://

Perfect adaptation in eukaryotic gradient sensing using cooperative allosteric binding
Eukaryotic cells generally sense chemical gradients using the binding of chemical ligands to membrane receptors. In order to perform chemotaxis effectively in different environments, cells need to adapt to different concentrations. We present a model of gradient sensing where the affinity of receptor-ligand binding is increased when a protein binds to the receptor's cytosolic side. This interior protein (allosteric factor) alters the sensitivity of the cell, allowing the cell to adapt to differ…

From Chiral Topological Dynamics to Chiral Topological Amplification: Real vs Imaginary Parameters in a Hermitian Bosonic Chain
Kiran Babasaheb Estake, T. R. Vishnu, Dibyendu Roy
https://arxiv.org/abs/2508.14560

From Chiral Topological Dynamics to Chiral Topological Amplification: Real vs Imaginary Parameters in a Hermitian Bosonic Chain
We propose a Hermitian quadratic bosonic model (QBH) whose dynamical matrix exhibits distinct topological and dynamical phenomena depending on whether the hopping and pairing amplitudes are real or purely imaginary. In the real-parameter regime, the dynamical matrix is unitarily equivalent to four decoupled copies of the sublattice-symmetric non-Hermitian Su-Schrieffer-Heeger (nSSH2) model, thereby inheriting its topological phases and energy spectrum-including the Möbius phase, a gapless topo…

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers
Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan, Ze Yang Ding, Hao Wang, David L. Dowe
https://arxiv.org/abs/2507.15385

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers
The growing integration of renewable energy sources in modern power systems has introduced significant operational challenges due to their intermittent and uncertain outputs. In recent years, mobile energy storage systems (ESSs) have emerged as a popular flexible resource for mitigating these challenges. Compared to stationary ESSs, mobile ESSs offer additional spatial flexibility, enabling cost-effective energy delivery through the transportation network. However, the widespread deployment of …