Tootfinder

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
Ryan Punamiya, Dhruv Patel, Patcharapong Aphiwetsa, Pranav Kuppili, Lawrence Y. Zhu, Simar Kareer, Judy Hoffman, Danfei Xu
https://arxiv.org/abs/2509.19626

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
Egocentric human experience data presents a vast resource for scaling up end-to-end imitation learning for robotic manipulation. However, significant domain gaps in visual appearance, sensor modalities, and kinematics between human and robot impede knowledge transfer. This paper presents EgoBridge, a unified co-training framework that explicitly aligns the policy latent spaces between human and robot data using domain adaptation. Through a measure of discrepancy on the joint policy latent featu…

LeakageDetector 2.0: Analyzing Data Leakage in Jupyter-Driven Machine Learning Pipelines
Owen Truong, Terrence Zhang, Arnav Marchareddy, Ryan Lee, Jeffery Busold, Michael Socas, Eman Abdullah AlOmar
https://arxiv.org/abs/2509.15971

LeakageDetector 2.0: Analyzing Data Leakage in Jupyter-Driven Machine Learning Pipelines
In software development environments, code quality is crucial. This study aims to assist Machine Learning (ML) engineers in enhancing their code by identifying and correcting Data Leakage issues within their models. Data Leakage occurs when information from the test dataset is inadvertently included in the training data when preparing a data science model, resulting in misleading performance evaluations. ML developers must carefully separate their data into training, evaluation, and test sets t…

Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Akshay Govind Srinivasan, Ryan Jacob George, Jayden Koshy Joe, Hrushikesh Kant, Harshith M R, Sachin Sundar, Sudharshan Suresh, Rahul Vimalkanth, Vijayavallabh
https://arxiv.org/abs/2509.16369

Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Accurate and reliable knowledge retrieval is vital for financial question-answering, where continually updated data sources and complex, high-stakes contexts demand precision. Traditional retrieval systems rely on a single database and retriever, but financial applications require more sophisticated approaches to handle intricate regulatory filings, market analyses, and extensive multi-year reports. We introduce a framework for financial Retrieval Augmented Generation (RAG) that leverages agent…

Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
Ryan Burn
https://arxiv.org/abs/2508.14368 https://arxiv.org/pdf/2508.14368

Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation either globally or locally, and its practicality is demonstrated on real-world data sets.

An Open-Source HW-SW Co-Development Framework Enabling Efficient Multi-Accelerator Systems
Ryan Albert Antonio, Joren Dumoulin, Xiaoling Yi, Josse Van Delm, Yunhao Deng, Guilherme Paim, Marian Verhelst
https://arxiv.org/abs/2508.14582

An Open-Source HW-SW Co-Development Framework Enabling Efficient Multi-Accelerator Systems
Heterogeneous accelerator-centric compute clusters are emerging as efficient solutions for diverse AI workloads. However, current integration strategies often compromise data movement efficiency and encounter compatibility issues in hardware and software. This prevents a unified approach that balances performance and ease of use. To this end, we present SNAX, an open-source integrated HW-SW framework enabling efficient multi-accelerator platforms through a novel hybrid-coupling scheme, consisti…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Conformal prediction for uncertainties in nucleon-nucleon scattering
Habib Yousefi Dezdarani, Ryan Curry, Alexandros Gezerlis

Surya: Foundation Model for Heliophysics
Sujit Roy, Johannes Schmude, Rohit Lal, Vishal Gaur, Marcus Freitag, Julian Kuehnert, Theodore van Kessel, Dinesha V. Hegde, Andr\'es Mu\~noz-Jaramillo, Johannes Jakubik, Etienne Vos, Kshitiz Mandal, Ata Akbari Asanjan, Joao Lucas de Sousa Almeida, Amy Lin, Talwinder Singh, Kang Yang, Chetraj Pandey, Jinsu Hong, Berkay Aydin, Thorsten Kurth, Ryan McGranaghan, Spiridon Kasapis, Vishal Upendran, Shah Bahauddin, Daniel da Silva, Nikolai V. Pogo…

Surya: Foundation Model for Heliophysics
Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, includin…

The Bayesian SIAC filter
Jan Glaubitz, Tongtong Li, Jennifer Ryan, Roman Stuhlmacher
https://arxiv.org/abs/2509.14771 https://arxiv.org/pdf/2509.14771

The Bayesian SIAC filter
We propose the Bayesian smoothness-increasing accuracy-conserving (SIAC) filter -- a hierarchical Bayesian extension of the existing deterministic SIAC filter. The SIAC filter is a powerful numerical tool for removing high-frequency noise from data or numerical solutions without degrading accuracy. However, current SIAC methodology is limited to (i) nodal data (direct, typically noisy function values) and (ii) deterministic point estimates that do not account for uncertainty propagation from in…

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan, Yi Wang, Ryan Farell, Minh Nguyen, Chandrajit Bajaj
https://arxiv.org/abs/2509.07280

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesian inference. Although conservative, dissipative, and port-Hamiltonian systems might share the same initial total energy of a closed system, it is challenging for a single Hamiltonian network model to capture the distinctive and varying motion dynamics and physics of a phase space, from sampled observational phase spac…

Searching for continuous gravitational waves in the Parkes Pulsar Timing Array DR3
Shi-Yi Zhao, Zu-Cheng Chen, Jacob Cardinal Tremblay, Boris Goncharov, Xing-Jiang Zhu, N. D. Ramesh Bhat, Ma{\l}gorzata Cury{\l}o, Shi Dai, Valentina Di Marco, Hao Ding, George Hobbs, Agastya Kapur, Wenhua Ling, Tao Liu, Rami Mandow, Saurav Mishra, Daniel J. Reardon, Christopher J Russell, Ryan M. Shannon, Shuangqiang Wang, Lei Zhang, Andrew Zic

Searching for continuous gravitational waves in the Parkes Pulsar Timing Array DR3
We present results from an all-sky search for continuous gravitational waves from individual supermassive binary black holes using the third data release (DR3) of the Parkes Pulsar Timing Array (PPTA). Even though we recover a common-spectrum stochastic process, potentially induced by a nanohertz gravitational wave background, we find no evidence of continuous waves. Therefore, we place upper limits on the gravitational-wave strain amplitude: in the most sensitive frequency range around 10 nHz,…

Demonstration and Non-volatile Trimming of a Highly-Parallel, High-Capacity Silicon Microdisk Transmitter
Chao Luan, Alex Sludds, Chao Li, Ian Christen, Ryan Hamerly, Dirk Englund
https://arxiv.org/abs/2508.11146

Demonstration and Non-volatile Trimming of a Highly-Parallel, High-Capacity Silicon Microdisk Transmitter
Optical interconnects are the most promising solution to address the data-movement bottleneck in data centers. Silicon microdisks, benefiting from their compact footprint, low energy consumption, and wavelength division multiplexing (WDM) capability, have emerged as an attractive and scalable platform for optical modulation. However, microdisk resonators inherently exhibit low fabrication error tolerance, limiting their practical deployment. Here, utilizing a CMOS photonics platform, we demonst…

In-domain SSL pre-training and streaming ASR
Jarod Duret, Salima Mdhaffar, Ga\"elle Laperri\`ere, Ryan Whetten, Audrey Galametz, Catherine Kobus, Marion-C\'ecile Martin, Jo Oleiwan, Yannick Est\`eve
https://arxiv.org/abs/2509.12101

In-domain SSL pre-training and streaming ASR
In this study, we investigate the benefits of domain-specific self-supervised pre-training for both offline and streaming ASR in Air Traffic Control (ATC) environments. We train BEST-RQ models on 4.5k hours of unlabeled ATC data, then fine-tune on a smaller supervised ATC set. To enable real-time processing, we propose using chunked attention and dynamic convolutions, ensuring low-latency inference. We compare these in-domain SSL models against state-of-the-art, general-purpose speech encoders …

National Running Club Database: Assessing Collegiate Club Athletes' Cross Country Race Results
Jonathan A. Karr Jr, Ben Darden, Nicholas Pell, Ryan M. Fryer, Kayla Ambrose, Evan Hall, Ramzi K. Bualuan, Nitesh V. Chawla
https://arxiv.org/abs/2509.10600

National Running Club Database: Assessing Collegiate Club Athletes' Cross Country Race Results
The National Running Club Database (NRCD) aggregates 15,397 race results of 5,585 athletes from the 2023 and 2024 cross country seasons. This paper introduces the NRCD dataset, which provides insights into individual athlete progressions, enabling data-driven decision-making. Analysis reveals that runners' improvement per calendar day for women, racing 6,000m, and men, racing 8,000m, is more pronounced in athletes with slower initial race times and those who race more frequently. Additionally, …

Non-Decaying Solutions to the 2D Dissipative Quasi-Geostrophic Equations
David M. Ambrose, Ryan Aschoff, Elaine Cozzi, James P. Kelliher
https://arxiv.org/abs/2508.10254 https:/…

Non-Decaying Solutions to the 2D Dissipative Quasi-Geostrophic Equations
We consider the surface quasi-geostrophic equation in two spatial dimensions, with subcritical diffusion (i.e. with fractional diffusion of order $2α$ for $α>\frac{1}{2}$.) We establish existence of solutions without assuming either decay at spatial infinity or spatial periodicity. One obstacle is that for $L^{\infty}$ data, the constitutive law may not be applicable, as Riesz transforms are unbounded. However, for $L^{\infty}$ initial data for which the constitutive law does converge, we dem…

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
Sujit Roy, Dinesha V. Hegde, Johannes Schmude, Amy Lin, Vishal Gaur, Rohit Lal, Kshitiz Mandal, Talwinder Singh, Andr\'es Mu\~noz-Jaramillo, Kang Yang, Chetraj Pandey, Jinsu Hong, Berkay Aydin, Ryan McGranaghan, Spiridon Kasapis, Vishal Upendran, Shah Bahauddin, Daniel da Silva, Marcus Freitag, Iksha Gurung, Nikolai Pogorelov, Campbell Watson, Manil Maskey, Juan Bernabe-Moreno,…

SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed…

EMMA: Scaling Mobile Manipulation via Egocentric Human Data
Lawrence Y. Zhu, Pranav Kuppili, Ryan Punamiya, Patcharapong Aphiwetsa, Dhruv Patel, Simar Kareer, Sehoon Ha, Danfei Xu
https://arxiv.org/abs/2509.04443

EMMA: Scaling Mobile Manipulation via Egocentric Human Data
Scaling mobile manipulation imitation learning is bottlenecked by expensive mobile robot teleoperation. We present Egocentric Mobile MAnipulation (EMMA), an end-to-end framework training mobile manipulation policies from human mobile manipulation data with static robot data, sidestepping mobile teleoperation. To accomplish this, we co-train human full-body motion data with static robot data. In our experiments across three real-world tasks, EMMA demonstrates comparable performance to baselines …

GW231109_235456: A Sub-threshold Binary Neutron Star Merger in the LIGO-Virgo-KAGRA O4a Observing Run?
Wanting Niu, Chad Hanna, Carl-Johan Haster, Shomik Adhicary, Pratyusava Baral, Amanda Baylor, Bryce Cousins, Jolien D. E. Creighton, Heather Fong, Yun-Jing Huang, Rachael Huxford, Prathamesh Joshi, James Kennington, Alvin K. Y. Li, Ryan Magee, Duncan Meacher, Cody Messick, Soichiro Morisaki, Cort Posnansky, Surabhi Sachdev, Shio Sakon, Urja Shah, Divya Singh, Ron Tapia, Leo Tsukada, A…

GW231109_235456: A Sub-threshold Binary Neutron Star Merger in the LIGO-Virgo-KAGRA O4a Observing Run?
We present a sub-threshold search for gravitational-wave inspirals from binary neutron stars using data from the first part of the fourth observing run of the LIGO-Virgo-KAGRA Collaboration. To enhance sensitivity to this targeted population, we incorporate a redshift-corrected population model based on radio observations of Galactic double neutron star systems. The search identifies a significant trigger with a false-alarm rate of about one per 50 years and a network signal-to-noise ratio of 9…

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Jack Jackman, David Ryan, Arun Narayanan, Pedro Nardelli, Indrakshi Dey
https://arxiv.org/abs/2510.11286

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fo…

Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
Lukas Schach, Christian Rack, Ryan P. McMahan, Marc Erich Latoschik
https://arxiv.org/abs/2509.08539

Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the per…

Reanalysis of the eclipses of LHS 1140 c: No evidence of an atmosphere and implications for the internal structure of the planet
Alexandra Rochon, \'Etienne Artigau, Drew Weisserman, Lisa Dang, Ren\'e Doyon, Charles Cadieux, Ryan Cloutier
https://arxiv.org/abs/2510.11397

Reanalysis of the eclipses of LHS 1140 c: No evidence of an atmosphere and implications for the internal structure of the planet
We present the reanalysis of three 15 micron JWST/MIRI secondary eclipses of LHS 1140 c, a warm super-Earth (R$_{\rm{p}}$ = 1.272 R$_{\oplus}$) in a 3.78-day orbit around an M4.5 dwarf. We present a novel method for data reduction that leverages spatial derivatives of the point-spread function and compare it to widely used aperture photometry. Both methods yield eclipse depth consistent within 1 sigma of the values reported in the literature. We measure an eclipse depth of 271$^{+31}_{-30}$ ppm…

Foundation Models for Astrobiology: Paper I -- Workshop and Overview
Ryan Felton, Caleb Scharf, Stuart Bartlett, Nathalie A. Cabrol, Victoria Da Poian, Diana Gentry, Jian Gong, Adrienne Hoarfrost, Manil Maskey, Floyd Nichols, Conor A. Nixon, Tejas Panambur, Joseph Pasterski, Anton S. Petrov, Anirudh Prabhu, Brenda Thomson, Hamed Valizadegan, Kimberley Warren-Rhodes, David Wettergreen, Michael L. Wong, Anastasia Yanchilina

Foundation Models for Astrobiology: Paper I -- Workshop and Overview
Advances in machine learning over the past decade have resulted in a proliferation of algorithmic applications for encoding, characterizing, and acting on complex data that may contain many high dimensional features. Recently, the emergence of deep-learning models trained across very large datasets has created a new paradigm for machine learning in the form of Foundation Models. Foundation Models are programs trained on very large and broad datasets with an extensive number of parameters. Once …

A path towards AI-scale, interoperable biological data
Brian Aevermann, Andrea Califano, Chi-Li Chiu, Nathan Clack, William M. Clemons Jr., Jonah Cool Florence D. D'Orazi, Joseph L. DeRisi, Joshua E. Elias, Elizabeth Fahsbender, Scott E. Fraser, Carlos G. Gonzalez, Matthias Haury, Theofanis Karaletsos, Shana O. Kelley, Aly A. Khan, Alan R. Lowe, Emma Lundberg, Ryan A. McClure, Stephani Otte, Evan O. Paull, Lo\"ic A. Royer, Dana Sadgat, Sandra L. Schmid, Samantha Scovanner, Cat…

A path towards AI-scale, interoperable biological data
Biology is at the precipice of a new era where AI accelerates and amplifies the ability to study how cells operate, organize, and work as systems, revealing why disease happens and how to correct it. Organizations globally are prioritizing AI to accelerate basic research, drug discovery, personalized medicine, and synthetic biology. However, despite these opportunities, scientific data have proven a bottleneck, and progress has been slow and fragmented. Unless the scientific community takes a t…

PDRs4All XVII: Formation and excitation of HD in photodissociation regions. Application to the Orion Bar
Marion Zannese, Jacques Le Bourlot, Evelyne Roueff, Emeric Bron, Franck Le Petit, Dries Van De Putte, Maryvonne Gerin, Naslim Neelamkodan, Javier R. Goicoechea, John Black, Ryan Chown, Ameek Sidhu, Emilie Habart, Els Peeters, Olivier Bern\'e
https://

PDRs4All XVII: Formation and excitation of HD in photodissociation regions. Application to the Orion Bar
The James Webb Space Telescope enabled the first detection of several rovibrational emission lines of HD in the Orion Bar, a prototypical photodissociation region. This provides an incentive to examine the physics of HD in dense and strong PDRs. Using the latest data available on HD excitation by collisional, radiative and chemical processes, our goal is to unveil HD formation and excitation processes in PDRs by comparing our state-of-the-art PDR model with observations made in the Orion Bar an…

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Ryan DeWolfe
https://arxiv.org/abs/2509.02334 https://arxiv.org/p…

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). For vector data, Hierarchical Density Based Spatial Clustering for Applications with Noise (HDBSCAN) has emerged as a leading clustering algorithm that allows for outlier points that do not belong to any cluster. The first step in HDBSCAN is to redefine the distance between vectors in such a way that single-…

Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL
Hamza Ezzaoui Rahali, Abhilasha Dave, Larry Ruckman, Mohammad Mehdi Rahimifar, Audrey C. Therrien, James J. Russel, Ryan T. Herbst
https://arxiv.org/abs/2508.21739

Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL
The LCLS-II Free Electron Laser (FEL) will generate X-ray pulses for beamline experiments at rates of up to 1~MHz, with detectors producing data throughputs exceeding 1 TB/s. Managing such massive data streams presents significant challenges, as transmission and storage infrastructures become prohibitively expensive. Machine learning (ML) offers a promising solution for real-time data reduction, but conventional implementations introduce excessive latency, making them unsuitable for high-speed …

A nonparametric approach to practical identifiability of nonlinear mixed effects models
Tyler Cassidy, Stuart T. Johnston, Michael Plank, Imke Botha, Jennifer A. Flegg, Ryan J. Murphy, Sara Hamis
https://arxiv.org/abs/2507.20288

A nonparametric approach to practical identifiability of nonlinear mixed effects models
Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data from individual trial participants. Despite the widespread adoption of this individual-based fitting, it is becoming increasingly common to take a hierarchical approach to parameter estimation, where modellers characterize the population parameter distributions, rather than considering each individual independently. T…

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei, Ryan Wang, James Flemings, Krishna Gummadi, Willie Neiswanger, Robin Jia
https://arxiv.org/abs/2507.19419

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often inaccessible to researchers. We present TokenSmith, an open-source library for interactive editing, inspection, and analysis of datasets used in Megatron-style pretraining frameworks such as GPT-NeoX, Megatron, and NVIDIA NeMo. TokenSmith supports a wide range of operations including searching, viewing, ingesting, expor…

The LCLStream Ecosystem for Multi-Institutional Dataset Exploration
David Rogers, Valerio Mariani, Cong Wang, Ryan Coffee, Wilko Kroeger, Murali Shankar, Hans Thorsten Schwander, Tom Beck, Fr\'ed\'eric Poitevin, Jana Thayer
https://arxiv.org/abs/2510.04012

The LCLStream Ecosystem for Multi-Institutional Dataset Exploration
We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications -- AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design choices merging cloud microservices with traditional HPC batch execution models for security and flexibility. This project makes a unique con…

Link Statistics of Dislocation Network during Strain Hardening
Sh. Akhondzadeh, Hanfeng Zhai, Wurong Jian, Ryan B. Sills, Nicolas Bertin, Wei Cai
https://arxiv.org/abs/2509.03743

Link Statistics of Dislocation Network during Strain Hardening
Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring nodes within this network, contains crucial information about the evolving dislocation microstructure. By analyzing data from Discrete Dislocation Dynamics (DDD) simulations in face-centered cubic (fcc) Cu, we characterize the statistical distribution of link lengths of dislocation networks during strain hardening on in…

NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference
Edresson Casanova, Paarth Neekhara, Ryan Langman, Shehzeen Hussain, Subhankar Ghosh, Xuesong Yang, Ante Juki\'c, Jason Li, Boris Ginsburg
https://arxiv.org/abs/2508.05835

NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference
Large Language Models (LLMs) have significantly advanced audio processing by leveraging audio codecs to discretize audio into tokens, enabling the application of language modeling techniques to speech data. However, existing audio codecs often operate at high frame rates, leading to slow training and inference, particularly for autoregressive models. To address this, there is growing interest in low frame-rate audio codecs, which reduce the number of autoregressive steps required to generate on…

Distinguishability of causal structures under latent confounding and selection
Ryan Carey, Marina Maciel Ansanelli, Elie Wolfe, Robin J. Evans
https://arxiv.org/abs/2509.20433 h…

Distinguishability of causal structures under latent confounding and selection
Statistical relationships in observed data can arise for several different reasons: the observed variables may be causally related, they may share a latent common cause, or there may be selection bias. Each of these scenarios can be modelled using different causal graphs. Not all such causal graphs, however, can be distinguished by experimental data. In this paper, we formulate the equivalence class of causal graphs as a novel graphical structure, the selected-marginalized directed graph (smDG)…

Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting
Jingyi Yin, Jingke Zhang, Lijie Huang, U-Wai Lok, Ryan M DeRuiter, Kaipeng Ji, Yanzhe Zhao, Kate M. Knoll, Kendra E. Petersen, Tao Wu, Xiang-yang Zhu, James D Krier, Kathryn A. Robinson, Lilach O Lerman, Andrew J. Bentall, Shigao Chen, Chengwu Huang
https://

Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting
Microvascular imaging has advanced significantly with ultrafast data acquisition and improved clutter filtering, enhancing the sensitivity of power Doppler imaging to small vessels. However, the image quality remains limited by spatial resolution and elevated background noise, both of which impede visualization and accurate quantification. To address these limitations, this study proposes a high-resolution cross-correlation Power Doppler (HR-XPD) method that integrates spatial radiality weighti…

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Ryan Warnick
https://arxiv.org/abs/2508.01988 https://

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Aleatoric and Epistemic uncertainty have achieved recent attention in the literature as different sources from which uncertainty can emerge in stochastic modeling. Epistemic being intrinsic or model based notions of uncertainty, and aleatoric being the uncertainty inherent in the data. We propose a novel decision theoretic framework for outlier detection in the context of aleatoric uncertainty; in the context of Bayesian modeling. The model incorporates bayesian false discovery rate control for…

Masses, Star-Formation Efficiencies, and Dynamical Evolution of 18,000 HII Regions
Debosmita Pathak, Adam K. Leroy, Ashley. T. Barnes, Todd A. Thompson, Laura A. Lopez, Karin M. Sandstrom, Jiayi Sun, Simon C. O. Glover, Ralf S. Klessen, Eric W. Koch, Kirsten L. Larson, Janice Lee, Sharon Meidt, Patricia Sanchez-Blazquez, Eva Schinnerer, Zein Bazzi, Francesco Belfiore, M\'ed\'eric Boquien, Ryan Chown, Dario Colombo, Enrico Congiu, Oleg V. Egorov, Cosima Eibensteiner, Sushma Kura…

Masses, Star-Formation Efficiencies, and Dynamical Evolution of 18,000 HII Regions
We present measurements of the masses associated with $\sim18,000$ HII regions across 19 nearby star-forming galaxies by combining data from JWST, HST, MUSE, ALMA, VLA, and MeerKAT from the multi-wavelength PHANGS survey. We report 10 pc-scale measurements of the mass of young stars, ionized gas, and older disk stars coincident with each HII region, as well as the initial and current mass of molecular gas, atomic gas, and swept-up shell material, estimated from lower resolution data. We find th…

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, Joseph J. LaViola Jr
https://arxiv.org/abs/2508.17124

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
We explore natural user interactions using a virtual reality simulation of a robot arm for assembly tasks. Using a Wizard-of-Oz study, participants completed collaborative LEGO and instructive PCB assembly tasks, with the robot responding under experimenter control. We collected voice, hand tracking, and gaze data from users. Statistical analyses revealed that instructive and collaborative scenarios elicit distinct behaviors and adopted strategies, particularly as tasks progress. Users tended t…

Replaced article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- Starkiller: subtracting stars and other sources from IFU spectroscopic data through forward modeling
Ryan Ridden-Harper, Michele T. Bannister, Sophie E. Deam, Thomas Nordlander

GRB 250702B: Discovery of a Gamma-Ray Burst from a Black Hole Falling into a Star
Eliza Neights, Eric Burns, Chris L. Fryer, Dmitry Svinkin, Suman Bala, Rachel Hamburg, Ramandeep Gill, Michela Negro, Megan Masterson, James DeLaunay, David J. Lawrence, Sophie E. D. Abrahams, Yuta Kawakubo, Paz Beniamini, Christian Aa. Diget, Dmitry Frederiks, John Goldsten, Adam Goldstein, Alexander D. Hall-Smith, Erin Kara, Alison M. Laird, Gavin P. Lamb, Oliver J. Roberts, Ryan Seeb, V. Ashley Villar,…

GRB 250702B: Discovery of a Gamma-Ray Burst from a Black Hole Falling into a Star
Gamma-ray bursts are the most luminous electromagnetic events in the universe. Their prompt gamma-ray emission has typical durations between a fraction of a second and several minutes. A rare subset of these events have durations in excess of a thousand seconds, referred to as ultra-long gamma-ray bursts. Here, we report the discovery of the longest gamma-ray burst ever seen with a ~25,000 s gamma-ray duration, GRB 250702B, and characterize this event using data from four instruments in the Int…

Enriched volatiles and refractories but deficient titanium on the dayside atmosphere of WASP-121b revealed by JWST/NIRISS
Stefan Pelletier, Louis-Philippe Coulombe, Jared Splinter, Bj\"orn Benneke, Ryan J. MacDonald, David Lafreni\`ere, Nicolas B. Cowan, Romain Allart, Emily Rauscher, Robert C. Frazier, Michael R. Meyer, Lo\"ic Albert, Lisa Dang, Ren\'e Doyon, David Ehrenreich, Laura Flagg, Doug Johnstone, Adam B. Langeveld, Olivia Lim, Caroline Piaulet-Ghorayeb, Michael …

Enriched volatiles and refractories but deficient titanium on the dayside atmosphere of WASP-121b revealed by JWST/NIRISS
With dayside temperatures elevated enough for all atmospheric constituents to be present in gas form, ultra-hot Jupiters offer a unique opportunity to probe the composition of giant planets. We aim to infer the composition and thermal structure of the dayside atmosphere of the ultra-hot Jupiter WASP-121b from two NIRISS$/$SOSS secondary eclipses observed as part of a full phase curve. We extract the eclipse spectrum of WASP-121b with two independent data reduction pipelines and analyse it using…

The clustering of C IV and Si IV at the end of reionisation: A perspective from the E-XQR-30 survey
Louise Welsh, Valentina D'Odorico, Fabio Fontanot, Rebecca Davies, Sarah E. I. Bosman, Guido Cupani, George Becker, Laura Keating, Emma Ryan-Weber, Manuela Bischetti, Martin Haehnelt, Huanqing Chen, Yongda Zhu, Samuel Lai, Michaela Hirschmann, Lizhi Xie, Yuxiang Qin

The clustering of C IV and Si IV at the end of reionisation: A perspective from the E-XQR-30 survey
We aim to study the clustering of metal absorption lines and the structures that they arise in as a function of cosmic time. We focus on C IV and Si IV absorption features that are identified along a given quasar sightline. We exploit the two-point correlation function (2PCF) to investigate the clustering of these structures as a function of their separation. We utilise the E-XQR-30 data to perform a novel analysis at z>5. We also draw on literature surveys (including XQ-100) of lower redshift …

A Multi-Messenger Search for the Supermassive Black Hole Binary in 3C 66B with the Parkes Pulsar Timing Array
Jacob Cardinal Tremblay, Boris Goncharov, Rutger van Haasteren, N. D. Ramesh Bhat, Zu-Cheng Chen, Valentina Di Marco, Satoru Iguchi, Agastya Kapur, Wenhua Ling, Rami Mandow, Saurav Mishra, Daniel J. Reardon, Ryan M. Shannon, Hiroshi Sudou, Jingbo Wang, Shi-Yi Zhao, Xing-Jiang Zhu, Andrew Zic

A Multi-Messenger Search for the Supermassive Black Hole Binary in 3C 66B with the Parkes Pulsar Timing Array
A sub-parsec supermassive black hole binary at the center of the galaxy 3C 66B is a promising candidate for continuous gravitational wave searches with Pulsar Timing Arrays. In this work, we search for such a signal in the third data release of the Parkes Pulsar Timing Array. Matching our priors to estimates of binary parameters from electromagnetic observations, we find log Bayes factor of 0.22, highlighting that the source can be neither confirmed nor ruled out. We place upper limits at 95% c…

SDSS-C4 3028: The Nearest Blue Galaxy Cluster Devoid of an Intracluster Medium
Shweta Jain, Yuanyuan Su, Andra Stroe, Paul Nulsen, Hyejeon Cho, Kim HyeongHan, M. James Jee, Ralph P. Kraft, Scott Randall, Jimmy A. Irwin, Ryan L. Sanders, Christine Jones
https://arxiv.org/abs/2509.22523

SDSS-C4 3028: The Nearest Blue Galaxy Cluster Devoid of an Intracluster Medium
SDSS-C4 3028 is a galaxy cluster at $z=0.061$, notable for its unusually high fraction of star-forming galaxies with 19 star-forming and 11 quiescent spectroscopically-confirmed member galaxies. From Subaru/HSC imaging, we derived a weak lensing mass of $M_{200} = (1.3 \pm 0.9) \times 10^{14} \rm M_\odot$, indicating a low-mass cluster. This is in excellent agreement with its dynamical mass of $M_{200} = (1.0\pm0.4)\times10^{14} \rm M_\odot$, derived from SDSS spectroscopic data. XMM-Newton obs…