Tootfinder

Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models
Raha Askari, Sina Zarrie{\ss}, \"Ozge Alacam, Judith Sieker
https://arxiv.org/abs/2510.04764

Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models
Implicit meanings are integral to human communication, making it essential for language models to be capable of identifying and interpreting them. Grice (1975) proposed a set of conversational maxims that guide cooperative dialogue, noting that speakers may deliberately violate these principles to express meanings beyond literal words, and that listeners, in turn, recognize such violations to draw pragmatic inferences. Building on Surian et al. (1996)'s study of children's sensitivity to viol…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/13]:
- Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
Mudit Gaur, Prashant Trivedi, Sasidhar Kunapuli, Amrit Singh Bedi, Vaneet Aggarwal

Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin
Lauren Anderson, Lucas Chatelain, Nicolas Tremblay, Kathryn Grandfield, David Rousseau, Aur\'elien Gourrier
https://arxiv.org/abs/2510.08407

Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin
The mechanosensory system of teeth is currently believed to partly rely on Odontoblast cells stimulation by fluid flow through a porosity network extending through dentin. Visualizing the smallest sub-microscopic porosity vessels therefore requires the highest achievable resolution from confocal fluorescence microscopy, the current gold standard. This considerably limits the extent of the field of view to very small sample regions. To overcome this limitation, we tested different deep learning …

First Measurement of the $D_s^ \rightarrow K^0\mu^ \nu_{\mu}$ Decay
BESIII Collaboration, et al.
https://arxiv.org/abs/2510.05904 https://arxiv.org/pdf/251…

First Measurement of the $D_s^+\rightarrow K^0μ^+ν_μ$ Decay
We report the first measurement of the semileptonic decay $D^+_s \rightarrow K^0μ^+ν_μ$, using a sample of $e^+e^-$ annihilation data corresponding to an integrated luminosity of $7.33~\mathrm{fb}^{-1}$ collected at center-of-mass energies between 4.128 to 4.226~GeV with the BESIII detector at the BEPCII collider. The branching fraction of the decay is measured to be $\mathcal{B}(D^+_s\rightarrow K^0μ^+ν_μ) = (2.89 \pm 0.27_{\rm stat} \pm 0.12_{\rm syst})\times 10^{-3}$, where the first u…

Researching DIY solutions for preparing digital negatives for my salt/kallitype printing experiments. Working on a little curve fitting tool/library (of course using https://thi.ng/umbrella) for computing tone mapping curves derived from sample swatches (here 5% steps of gray/density). The screenshot shows 2nd - 5th d…

Replaced article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
[1/1]:
- Exploring stellar activity in a sample of active M dwarfs
A. S. Rajpurohit, V. Kumar, K. Srivastava Mudit, L. Labadie, K. Rajpurohit, J. G. Fernandez-Trincado

how to get a bunch of hosts on the internet to generate some sample TCP SYNs for you:
http://rage4.infra.grebedoc.dev/
mastodons gooo

Quantum Lattice Boltzmann Method for Multiple Time Steps Without Reinitialization for Linear Advection-Diffusion Problems
Aaron Nagel, Johannes L\"owe
https://arxiv.org/abs/2510.05965

Quantum Lattice Boltzmann Method for Multiple Time Steps Without Reinitialization for Linear Advection-Diffusion Problems
To simulate highly-resolved flow fields, we extend the Quantum Lattice Boltzmann Method (QLBM) to be able to simulate multiple time steps without state extraction or reinitialization. We adjust and extend given QLBM approaches from the literature to completely remove the need to measure or reinitialize the flow field in between the simulation time steps. Therefore, our algorithm does not require to sample the entire flow field at any time. We solve the linear advection-diffusion problem and der…

When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
Wenbin Zhou, Shixiang Zhu
https://arxiv.org/abs/2510.07750 https://

When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches using conformal prediction construct data-driven uncertainty sets with finite-sample coverage guarantees, but they still fix coverage targets a priori and offer little guidance for selecting rob…

A Langevin Model-Based Magneto-Optic Response SuperParamagnetic Nanoparticles Recorded with a Michelson Interferometer Setup
Syed Azer Reza, Maarij Syed
https://arxiv.org/abs/2510.07209

A Langevin Model-Based Magneto-Optic Response SuperParamagnetic Nanoparticles Recorded with a Michelson Interferometer Setup
In this paper, we provide expressions for the detected optical irradiance for magneto-optic characterization of superparamagnetic nanoparticles (SPNs) in solution imparting Faraday rotation (FR) to an optical beam passing through the sample solution. For our analysis, we assume a Langevin model for SPN samples and show the presence of odd and even harmonics for SPN samples characterized with a Michelson interferometer. This optical geometry is potentially useful in understanding nonlinear SPN F…

Data-driven Practical Stabilization of Nonlinear Systems via Chain Policies: Sample Complexity and Incremental Learning
Roy Siegelmann, Enrique Mallada
https://arxiv.org/abs/2510.03982

Data-driven Practical Stabilization of Nonlinear Systems via Chain Policies: Sample Complexity and Incremental Learning
We propose a method for data-driven practical stabilization of nonlinear systems with provable guarantees, based on the concept of Nonparametric Chain Policies (NCPs). The approach employs a normalized nearest-neighbor rule to assign, at each state, a finite-duration control signal derived from stored data, after which the process repeats. Unlike recent works that model the system as linear, polynomial, or polynomial fraction, we only assume the system to be locally Lipschitz. Our analysis buil…

twitter: Twitter followers (2010)
A directed network of following relationships from Twitter, from a snowball sample crawl across "quality" users in 2009. A directed edge (i, j) indicates that user i follows user j.
This network has 465017 nodes and 834797 edges.
Tags: Social, Online, Unweighted
https://network…

MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation
Jan Ole von Hartz, Lukas Schweizer, Joschka Boedecker, Abhinav Valada
https://arxiv.org/abs/2509.24956

MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation
Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency, it does not resolve this limitation. In this work, we propose Multi-Stream Generative Policy (MSG), an inference-time composition framework that trains multiple object-centric policies and combines them at inference to improve generalization and sample efficiency. MSG is model-agnostic and inference-only, hence widely…

Updated Radio Sigma-D Relation and Distances to the Shell-Like Galactic Supernova Remnants -- IV
D. Uro\v{s}evi\'c, B. Vukoti\'c, M. An{\dj}eli\'c, N. Mladenovi\'c
https://arxiv.org/abs/2510.03410

Updated Radio Sigma-D Relation and Distances to the Shell-Like Galactic Supernova Remnants -- IV
We present a new selected sample of 69 Galactic supernova remnants (SNRs) for calibration of radio $Σ-D$ relation at 1 GHz. Calibrators with the most reliable distances were selected through an extensive literature search. The calibration is performed using kernel smoothing of the selected sample of calibrators in $Σ-D$ plane and an orthogonal offsets fitting procedure. We use the obtained calibration to derive the distances to 164 Galactic SNRs and 27 new detected SNRs/SNR candidates with no…

Disk fraction among free-floating planetary-mass objects in Upper Scorpius
Tommy Rodrigues, Herv\'e Bouy, Sean N. Raymond, Eduardo L. Mart\'in, Emmanuel Bertin, Javier Olivares, David Barrado, N\'uria Hu\'elamo, Motohide Tamura, N\'uria Miret Roig, Phillip A. B. Galli, Jean-Charles Cuillandre
https://arxiv.org/abs/2510.…

Disk fraction among free-floating planetary-mass objects in Upper Scorpius
Free-floating planetary-mass objects (FFPs) have been detected through direct imaging within several young, nearby star-forming regions. The properties of circumstellar disks around these objects may provide a valuable probe into their origin, but is currently limited by the small sizes of the sample explored. We aim to perform a statistical study of the occurrence of circumstellar disks down to the planetary-mass regime. We performed a systematic survey of disks among the population identified…

Structured covariance estimation via tensor-train decomposition
Artsiom Patarusau, Nikita Puchkin, Maxim Rakhuba, Fedor Noskov
https://arxiv.org/abs/2510.08174 https://

Structured covariance estimation via tensor-train decomposition
We consider a problem of covariance estimation from a sample of i.i.d. high-dimensional random vectors. To avoid the curse of dimensionality we impose an additional assumption on the structure of the covariance matrix $Σ$. To be more precise we study the case when $Σ$ can be approximated by a sum of double Kronecker products of smaller matrices in a tensor train (TT) format. Our setup naturally extends widely known Kronecker sum and CANDECOMP/PARAFAC models but admits richer interaction acros…

Can language models boost the power of randomized experiments without statistical bias?
Xinrui Ruan, Xinwei Ma, Yingfei Wang, Waverly Wei, Jingshen Wang
https://arxiv.org/abs/2510.05545

Can language models boost the power of randomized experiments without statistical bias?
Randomized experiments or randomized controlled trials (RCTs) are gold standards for causal inference, yet cost and sample-size constraints limit power. Meanwhile, modern RCTs routinely collect rich, unstructured data that are highly prognostic of outcomes but rarely used in causal analyses. We introduce CALM (Causal Analysis leveraging Language Models), a statistical framework that integrates large language models (LLMs) predictions with established causal estimators to increase precision whil…

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Ethan Baron, Boris Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova
https://arxiv.org/abs/2510.02224

Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step lookahead predictions to improve the quality and efficiency of multi-step forecasts. However, these models only predict independent marginal distributions for each time step, rather than a full joint predictive distribution. To generate forecast sample paths with realistic correlation structures, one typically resorts to …

A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia, Changseok Kim, Xiaozhi Lin
https://arxiv.org/abs/2510.05687

A Comparison of Star Formation Rates by Different Tracers in Nearby Galaxies
We utilize a large sample of $\sim$113,000 galaxies ($z < 0.3$) from the Sloan Digital Sky Survey with high-quality data to compare star formation rates (SFRs) across multiple diagnostic methods and examine their connection to Active Galactic Nuclei (AGNs) strength, indicated by Eddington ratio. Our sample encompassed star-forming (SF), composite, Seyfert, and LINER galaxies. Our analysis utilizes various SFRs indicators, including observed infrared flux ($\rm SFR_{FIR}$) from AKARI/Herschel ($…

Angular BAO Measurements with the DESI DR1 BGS Sample
Paula S. Ferreira, Ulisses Ribeiro, Pedro da Silveira Ferreira, Cl\'ecio R. Bom, Armando Bernui
https://arxiv.org/abs/2510.02144

Angular BAO Measurements with the DESI DR1 BGS Sample
We employ a model-independent approach in both the correlation function estimation and the angular BAO feature estimation by computing the angular two-point correlation function. First, we conducted a series of tests to the available DESI tracers to check their representativeness to angular clustering; the result was that, considering the completeness of the first data release across the footprint, we could only make use of the BGS sample for the effective redshifts 0.21 (BGS1) and 0.25 (BGS2).…

The First Dedicated Survey of Atmospheric Escape from Planets Orbiting F Stars
Morgan Saidel, Shreyas Vissapragada, Heather Knutson, Ethan Schreyer, Mike Greklek-McKeon, Jonathan Gomez Barrientos, W. Garrett Levine, Carlos Gasc\'on, Morgan MacLeod, Haedam Im, Nick Tusay
https://arxiv.org/abs/2510.05240

The First Dedicated Survey of Atmospheric Escape from Planets Orbiting F Stars
Hydrodynamic escape can strip the envelopes of close-in exoplanets, but most observations of atmospheric mass loss to date have been confined to planets orbiting K and M dwarfs. A growing body of detections of atmospheric escape from planets orbiting early-type stars indicates that they may have significantly stronger and more extended outflows than planets orbiting cooler stars. However, it is unclear whether this limited sample of planets is representative of all gas giants orbiting early-typ…

Understanding an origin of palladium in metal-poor stars based on the non-LTE analysis of Pd~I lines
L. Mashonkina, A. Smogorzhevskii
https://arxiv.org/abs/2510.06968 https://…

Understanding an origin of palladium in metal-poor stars based on the non-LTE analysis of Pd~I lines
Palladium is one of poorly observed neutron-capture elements. Abundance determinations for stellar samples covering a broad metallicity range are needed for better understanding the mechanisms of Pd synthesis during the Galaxy evolution. We aim to obtain accurate abundances of Pd for the Sun and the sample of metal-poor stars based on the non-local thermodynamic equilibrium (non-LTE) line formation for Pd~I. We present a new, comprehensive model atom of Pd~I. Abundances of Pd, Sr, Ba, and Eu we…

Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies
Yuhang Zhang, Jiaping Xiao, Chao Yan, Mir Feroskhan
https://arxiv.org/abs/2510.05692 https://

Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies
A prevailing approach for learning visuomotor policies is to employ reinforcement learning to map high-dimensional visual observations directly to action commands. However, the combination of high-dimensional visual inputs and agile maneuver outputs leads to long-standing challenges, including low sample efficiency and significant sim-to-real gaps. To address these issues, we propose Oracle-Guided Masked Contrastive Reinforcement Learning (OMC-RL), a novel framework designed to improve the samp…

Optimality and computational barriers in variable selection under dependence
Ming Gao, Bryon Aragam
https://arxiv.org/abs/2510.03990 https://arxiv.org/pdf/…

Optimality and computational barriers in variable selection under dependence
We study the optimal sample complexity of variable selection in linear regression under general design covariance, and show that subset selection is optimal while under standard complexity assumptions, efficient algorithms for this problem do not exist. Specifically, we analyze the variable selection problem and provide the optimal sample complexity with exact dependence on the problem parameters for both known and unknown sparsity settings. Moreover, we establish a sample complexity lower boun…

twitter: Twitter followers (2010)
A directed network of following relationships from Twitter, from a snowball sample crawl across "quality" users in 2009. A directed edge (i, j) indicates that user i follows user j.
This network has 465017 nodes and 834797 edges.
Tags: Social, Online, Unweighted
https://network…

CHILES X: Molecular and atomic gas at intermediate redshift
Kelley M. Hess, John Hibbard, Jennifer Donovan Meyer, Hansung B. Gim, Nicholas M. Luber, Min S. Yun, Julia Blue Bird, Richard Dodson, Aeree Chung, Danielle Lucero, Emmanuel Momijian, D. J. Pisano, J. H. van Gorkom
https://arxiv.org/abs/2510.07966

CHILES X: Molecular and atomic gas at intermediate redshift
We present ALMA CO observations of 14 HI-detected galaxies from the CHILES survey found in a cosmic over-density at z~0.12. This is the largest collection of spatially resolved CO + HI observations beyond the local Universe (z>0.05) to date. While the HI-detected parent sample spans a range of stellar masses, star formation rates (SFR), and environments, we only directly detect CO in the highest stellar mass galaxies, log(M_*/M_Sun)>10.0, with SFRs greater than ~2 M_Sun/yr. The detected CO has …

A chemodynamical study of $r$-process-enhanced stars
Pallavi Saraf (PRL), Thirupathi Sivarani, Carlos Allende Prieto, Shashikiran Ganesh, Drisya Karinkuzhi
https://arxiv.org/abs/2510.06630

A chemodynamical study of $r$-process-enhanced stars
The $r$-process enrichment in the Galaxy still remains elusive with regard to its nucleosynthesis conditions and the astrophysical sites where it occurs. As part of ongoing efforts to pinpoint the origin of chemically peculiar $r$-process-enhanced (RPE) stars, we concentrate in this study on the kinematics of RPE stars to investigate possible variations in the $r$-process enrichment among the Galactic components. We calculate the orbital parameters of a sample of 472 metal-poor RPE stars and as…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz
https://arxiv.org/abs/2510.05566 http…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Conf…

Estimation of intrinsic fast radio burst width and scattering distributions from CRAFT data
C. W. James, J. Hoffmann, J. X. Prochaska, M. Glowacki
https://arxiv.org/abs/2510.05654

Estimation of intrinsic fast radio burst width and scattering distributions from CRAFT data
The intrinsic width and scattering distributions of fast radio bursts (FRBs) inform on their emission mechanism and local environment, and act as a source of detection bias and, hence, an obfuscating factor when performing FRB population and cosmological studies. Here, we utilise a sample of 29 FRBs with measured high-time-resolution properties and known redshift, which were detected using the Australian Square Kilometre Array Pathfinder (ASKAP) by the Commensal Real-time ASKAP Fast Transients …

Active Semantic Perception
Huayi Tang, Pratik Chaudhari
https://arxiv.org/abs/2510.05430 https://arxiv.org/pdf/2510.05430

Active Semantic Perception
We develop an approach for active semantic perception which refers to using the semantics of the scene for tasks such as exploration. We build a compact, hierarchical multi-layer scene graph that can represent large, complex indoor environments at various levels of abstraction, e.g., nodes corresponding to rooms, objects, walls, windows etc. as well as fine-grained details of their geometry. We develop a procedure based on large language models (LLMs) to sample plausible scene graphs of unobser…

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Li Zeqiao, Wang Yijing, Wang Haoyu, Li Zheng, Li Peng, Liu Wenfei, Zuo Zhiqiang
https://arxiv.org/abs/2510.06038

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient, and robust learning. Incorporating human expertise into the learning process can help overcome these challenges by reducing risky exploration and improving sample efficiency. In this work, we propose a reward-free, active human-in-the-loop learning method called Human-Guided Distributional Soft Actor-Critic (H-DSAC). …

Quantum Subgradient Estimation for Conditional Value-at-Risk Optimization
Vasilis Skarlatos, Nikos Konofaos
https://arxiv.org/abs/2510.04736 https://arxiv.…

Quantum Subgradient Estimation for Conditional Value-at-Risk Optimization
Conditional Value-at-Risk (CVaR) is a leading tail-risk measure in finance, central to both regulatory and portfolio optimization frameworks. Classical estimation of CVaR and its gradients relies on Monte Carlo simulation, incurring $O(1/ε^2)$ sample complexity to achieve $ε$-accuracy. In this work, we design and analyze a quantum subgradient oracle for CVaR minimization based on amplitude estimation. Via a tripartite proposition, we show that CVaR subgradients can be estimated with $O(1/ε)$…

Empirical partially Bayes two sample testing
Wanyi Ling, Wufang Hong, Nikolaos Ignatiadis
https://arxiv.org/abs/2510.00432 https://arxiv.org/pdf/2510.00432…

Empirical partially Bayes two sample testing
A common task in high-throughput biology is to test for differences in means between two samples across thousands of features (e.g., genes or proteins), often with only a handful of replicates per sample. Moderated t-tests handle this problem by assuming normality and equal variances, and by applying the empirical partially Bayes principle: a prior is posited and estimated for the nuisance parameters (variances) but not for the primary parameters (means). This approach has been highly successfu…

The Star-forming Main Sequence and Bursty Star-formation Histories at $z>1.4$ in JADES and AURORA
Leonardo Clarke, Alice E. Shapley, Natalie Lam, Michael W. Topping, Gabriel B. Brammer, Ryan L. Sanders, Naveen A. Reddy, Shreya Karthikeyan
https://arxiv.org/abs/2510.06681

The Star-forming Main Sequence and Bursty Star-formation Histories at $z>1.4$ in JADES and AURORA
We analyze JWST spectroscopic and HST+JWST photometric observations of 659 star-forming galaxies at $1.4 < z < 9$ from DR3 of the JADES survey and the AURORA Cycle 1 program. We measure the star-forming main sequence (SFMS) for galaxies above $10^{8.5}\rm\ M_\odot$ where the sample is largely representative, estimating star-formation rates (SFRs) using the H$α$ line flux and rest-frame far UV (1600Å) continuum measurements, each independently corrected for dust attenuation. We find that the i…

Gaia DR3 Open Cluster Cepheids: A Unified Catalog with Calibrated Period-Age and Period-Wesenheit Relations
Shunhong Deng, Zhihong He, Anbing Ren, Qian Cui, Xiaoyue Zhou, Liming Peng, Chenxin Wang, Ziang Chen, Yangping Luo, Kun Wang
https://arxiv.org/abs/2510.08390

Gaia DR3 Open Cluster Cepheids: A Unified Catalog with Calibrated Period-Age and Period-Wesenheit Relations
Classical Cepheids (CCs) in Galactic open clusters (OCs) provide essential observational constraints for calibrating the period-age relation (PAR) and the period-Wesenheit relation (PWR) of CCs. However, distant and long-period OC Cepheids remain limited, while the confirmed samples still require more precise determinations of their physical properties, such as ages and extinctions. In this work, we present a comprehensive census of OC Cepheids based on an extensive sample of distant OCs from G…

The evolution of the bar fraction and bar lengths in the last 12 billion years
Zoe A. Le Conte, Dimitri A. Gadotti, Leonardo Ferreira, Christopher J. Conselice, Camila de S\'a-Freitas, Taehyun Kim, Justus Neumann, Francesca Fragkoudi, E. Athanassoula, Nathan J. Adams
https://arxiv.org/abs/2510.07407

The evolution of the bar fraction and bar lengths in the last 12 billion years
We investigate the evolution of the bar fraction and length using an extended JWST NIRCam imaging dataset of galaxies in the $1 \leq z \leq 4$ redshift range. We assess the wavelength dependence of the bar fraction in disc galaxies and bar length evolution by selecting a nearly mass-complete CEERS disc sample and performing independent visual classifications on the short (F200W) and long (F356W+F444W) wavelength channels. A similar bar fraction is observed for both samples, and combined we find…

twitter: Twitter followers (2010)
A directed network of following relationships from Twitter, from a snowball sample crawl across "quality" users in 2009. A directed edge (i, j) indicates that user i follows user j.
This network has 465017 nodes and 834797 edges.
Tags: Social, Online, Unweighted
https://network…

Characterizing the variability of a sample of massive stars in eclipsing binaries
C. I. Eze (Nicolaus Copernicus Astronomical Center, Warsaw, Poland, Department of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria), G. Handler (Nicolaus Copernicus Astronomical Center, Warsaw, Poland), F. Kahraman Ali\c{c}avu\c{s} (\c{C}anakkale Onsekiz Mart University, Faculty of Sciences, Physics Department, \c{C}anakkale, T\"urkiye), T. Pawar (Nicolaus Copernicus Astronomical Center,…

Characterizing the variability of a sample of massive stars in eclipsing binaries
Massive stars exhibit a perplexing mismatch between their inferred masses from different observational techniques, posing a significant challenge to our understanding of stellar evolution and structure. This discrepancy is believed to be caused by the underestimation of the convective core masses. The efficiency of such measurement is usually impaired by a lot of processes at work in the interior of the stars such as convective core overshooting and interior rotation. By integrating the precisi…

Double Descent as a Lens for Sample Efficiency in Autoregressive vs. Discrete Diffusion Models
Ahmad Fraij, Sam Dauncey
https://arxiv.org/abs/2509.24974 https://

Double Descent as a Lens for Sample Efficiency in Autoregressive vs. Discrete Diffusion Models
Data scarcity drives the need for more sample-efficient large language models. In this work, we use the double descent phenomenon to holistically compare the sample efficiency of discrete diffusion and autoregressive models. We show that discrete diffusion models require larger capacity and more training epochs to escape their underparameterized regime and reach the interpolation threshold. In the strongly overparameterized regime, both models exhibit similar behavior, with neither exhibiting a…

On Cryptography and Distribution Verification, with Applications to Quantum Advantage
Bruno Cavalar, Eli Goldin, Matthew Gray, Taiga Hiroka, Tomoyuki Morimae
https://arxiv.org/abs/2510.05028

On Cryptography and Distribution Verification, with Applications to Quantum Advantage
One of the most fundamental problems in the field of hypothesis testing is the identity testing problem: whether samples from some unknown distribution $\mathcal{G}$ are actually from some explicit distribution $\mathcal{D}$. It is known that when the distribution $\mathcal{D}$ has support $[N]$, the optimal sample complexity for the identity testing problem is roughly $O(\sqrt{N})$. However, many distributions of interest, including those which can be sampled efficiently, have exponential supp…

A JWST MIRI LRS Survey of 37 Massive Star-Forming Galaxies and AGN at Cosmic Noon - Overview and First Results
Jed McKinney, Miriam Eleazer, Alexandra Pope, Anna Sajina, Stacey Alberts, Meredith Stone, Leonid Sajkov, Virginia Vanicek, Allison Kirkpatrick, Thomas Lai, Caitlin M. Casey, Lee Armus, Tanio Diaz-Santos, Andrew Korkus, Olivia Cooper, Lindsay R. House, Hollis Akins, Erini Lambrides, Arianna Long, Lin Yan

A JWST MIRI LRS Survey of 37 Massive Star-Forming Galaxies and AGN at Cosmic Noon - Overview and First Results
We present a large spectroscopic survey with \textit{JWST}'s Mid-Infrared Instrument (MIRI) Low Resolution Spectrometer (LRS) targeting $37$ infrared-bright galaxies between $z=0.65-2.46$ with infrared luminosities $\log L_{\rm IR}/L_\odot>11.5$ and $\log M_*/M_\odot=10-11.5$. Targets were taken from a \textit{Spitzer} $24\,μ$m-selected sample with archival spectroscopy from the Infrared Spectrograph (IRS) and include a mix of star-forming galaxies and dust-obscured AGN. By combining IRS with …

Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexity
Ivan Lau, Jonathan Scarlett
https://arxiv.org/abs/2509.21940 https://arxiv.org/pdf/250…

Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexity
In this paper, we study the problem of distributed mean estimation with 1-bit communication constraints. We propose a mean estimator that is based on (randomized and sequentially-chosen) interval queries, whose 1-bit outcome indicates whether the given sample lies in the specified interval. Our estimator is $(ε, δ)$-PAC for all distributions with bounded mean ($-λ\le \mathbb{E}(X) \le λ$) and variance ($\mathrm{Var}(X) \le σ^2$) for some known parameters $λ$ and $σ$. We derive a sample c…

Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising
Ali Dadsetan, Frank Rudzicz
https://arxiv.org/abs/2510.01137 https://arxi…

Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising
We address the challenge of sample efficiency in differentially private fine-tuning of large language models (LLMs) using DP-SGD. While DP-SGD provides strong privacy guarantees, the added noise significantly increases the entropy of gradient matrices, disrupting their low-rank structure and slowing optimization. We propose a post-processing algorithm that leverages random matrix theory to denoise gradients, restore low-rank structure, and improve alignment with the original signal. Applied to …

Beyond the stars: Linking H$\alpha$ sizes, kinematics, and star formation in galaxies at $z\approx 4-6$ with JWST grism surveys and $\texttt{geko}$
A. Lola Danhaive, Sandro Tacchella, William McClymont, Brant Robertson, Stefano Carniani, Courtney Carreira, Eiichi Egami, Andrew J. Bunker, Emma Curtis-Lake, Daniel J. Eisenstein, Zhiyuan Ji, Benjamin D. Johnson, Marcia Rieke, Natalia C. Villanueva, Christopher N. A. Willmer, Chris Willot, Zihao Wu, Yongda Zhu

Beyond the stars: Linking H$α$ sizes, kinematics, and star formation in galaxies at $z\approx 4-6$ with JWST grism surveys and $\texttt{geko}$
Understanding how galaxies assemble their mass during the first billion years of cosmic time is a central goal of extragalactic astrophysics, yet joint constraints on their sizes and kinematics remain scarce. We present one of the first statistical studies of the $\mathrm{H}α$ size-mass relation at high redshift with a sample of 213 galaxies at spectroscopic redshifts of $z\approx 4-6$ from the FRESCO and CONGRESS NIRCam grism surveys. We measure the $\mathrm{H}α$ morphology and kinematics of…

General Divergence Regularized Optimal Transport: Sample Complexity and Central Limit Theorems
Jiaping Yang, Yunxin Zhang
https://arxiv.org/abs/2510.02489 https://

General Divergence Regularized Optimal Transport: Sample Complexity and Central Limit Theorems
Optimal transport has emerged as a fundamental methodology with applications spanning multiple research areas in recent years. However, the convergence rate of the empirical estimator to its population counterpart suffers from the curse of dimensionality, which prevents its application in high-dimensional spaces. While entropic regularization has been proven to effectively mitigate the curse of dimensionality and achieve a parametric convergence rate under mild conditions, these statistical gua…

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini, Alexander Langmann, Johannes Betz
https://arxiv.org/abs/2509.24313

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uniform or heuristic sampling often produces many infeasible or irrelevant trajectories. We address this limitation with a hybrid framework that learns where to sample while keeping trajectory generation and evaluation fully analytical and verifiable. A reinforcement learning (RL) agent guides the sampling process toward…

Beyond diagnostic-diagrams: A critical exploration on the classification of ionization processes
S. F. S\'anchez, C. Mu\~noz-Tu\~n\'on, J. S\'anchez Almeida, O. Gonz\'alez-Mart\'in, E. P\'erez
https://arxiv.org/abs/2510.07256

Beyond diagnostic-diagrams: A critical exploration on the classification of ionization processes
Optical emission-line diagnostic diagrams, such as the classical BPT, are widely used to identify ionisation mechanisms in galaxies but often suffer from degeneracies, especially when multiple sources coexist. We critically evaluate the effectiveness of these diagnostics in distinguishing star-forming galaxies, retired galaxies (RGs), and active galactic nuclei (AGNs), and propose refined methods to reduce misclassifications. Using a large sample of nearby galaxies from the NASA-Sloan Atlas cro…

Relative Information Gain and Gaussian Process Regression
Hamish Flynn
https://arxiv.org/abs/2510.04277 https://arxiv.org/pdf/2510.04277

Relative Information Gain and Gaussian Process Regression
The sample complexity of estimating or maximising an unknown function in a reproducing kernel Hilbert space is known to be linked to both the effective dimension and the information gain associated with the kernel. While the information gain has an attractive information-theoretic interpretation, the effective dimension typically results in better rates. We introduce a new quantity called the relative information gain, which measures the sensitivity of the information gain with respect to the o…

Exploring Stellar Activity in a Sample of Active M Dwarfs
A. S. Rajpurohit, V. Kumar, K. Srivastava Mudit, L. Labadie, K. Rajpurohit, J. G. Fernandez-Trincado
https://arxiv.org/abs/2510.02693

Exploring Stellar Activity in a Sample of Active M Dwarfs
Most M dwarfs show higher chromospheric activity, often exceeding solar levels. Characterizing stellar activity is essential, particularly since these stars are prime targets in the search for habitable exoplanets. We investigate the stellar activity of active M dwarfs using TESS photometry combined with spectroscopic observations. We explore relations between flare occurrence rate (FOR), flare energies, rotation period, starspot filling factor, and chromospheric indicators. We also examine cor…

Warm absorber outflows in radio-loud active galactic nucleus 3C~59
Yijun Wang, Tao Wang, Junjie Mao, Yerong Xu, Zhicheng He, Zheng Zhou, Chen Li, Yongquan Xue, Jiayi Chen, Fangzheng Shi, Missagh Mehdipour
https://arxiv.org/abs/2510.06752

Warm absorber outflows in radio-loud active galactic nucleus 3C~59
Both jets and ionized outflows in active galactic nuclei (AGNs) are thought to play important roles in affecting the star formation and evolution of host galaxies, but their relationship is still unclear. As a pilot study, we performed a detailed spectral analysis for a radio-loud (RL) AGN 3C~59 ($z=0.1096$) by systematically considering various factors that may affect the fitting results, and thereby establishing a general spectral fitting strategy for subsequent research with larger sample. 3…

Modular and Adaptive Conformal Prediction for Sequential Models via Residual Decomposition
William Zhang, Saurabh Amin, Georgia Perakis
https://arxiv.org/abs/2510.04406 https://…

Modular and Adaptive Conformal Prediction for Sequential Models via Residual Decomposition
Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opportunities to exploit modular structure. We introduce a conformal prediction framework for two-stage sequential models, where an upstream predictor generates intermediate representations for a downstream model. By decomposing the overall prediction residual into stage-specific components, our method enables practition…

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu
https://arxiv.org/abs/2510.05023 https:…

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Most existing approximate Thompson Sampling (TS) algorithms for multi-armed bandits use Stochastic Gradient Langevin Dynamics (SGLD) or its variants in each round to sample from the posterior, relaxing the need for conjugacy assumptions between priors and reward distributions in vanilla TS. However, they often require approximating a different posterior distribution in different round of the bandit problem. This requires tricky, round-specific tuning of hyperparameters such as dynamic learning …

Dormant BH candidates from Gaia DR3 summary diagnostics
Johanna M\"uller-Horn, Hans-Walter Rix, Kareem El-Badry, Ben Pennell, Matthew Green, Jiadong Li, Rhys Seeburger
https://arxiv.org/abs/2510.05982

Dormant BH candidates from Gaia DR3 summary diagnostics
We present a rigorous identification of candidates for dormant black holes (BHs) and neutron stars (NSs) in binaries using summary statistics from Gaia DR3, rather than full orbital solutions. Although Gaia astrometric orbits have already revealed a small sample of compact object binaries, many systems remain undetected due to stringent quality cuts imposed on the published orbits. Using a forward-modelling framework that simulates Gaia observables, in particular the renormalised unit weight er…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi
https://arxiv.org/abs/2510.05060 https…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive retraining when the underlying data distribution changes. To overcome these limitations, we propose Reservoir Conformal Prediction (ResCP), a…

H I Properties of Field Galaxies at $\boldsymbol{z\approx 0.2}$-0.6: Insights into Declining Cosmic Star Formation
David DePalma, Neeraj Gupta, Hsiao-Wen Chen, Robert A. Simcoe, Sergei Balashev, Erin Boettcher, Sebastiano Cantalupo, Mandy C. Chen, Fran\c{c}oise Combes, Claude-Andr\'e Faucher-Gigu\`ere, Sean D. Johnson, Hans-Rainer Kl\"ockner, Jens-Kristian Krogager, Jennifer I-Hsiu Li, Sebasti\'an L\'opez, Pasquier Noterdaeme, Patrick Petitjean, Zhijie Qu, Gwen C. Rudi…

H I Properties of Field Galaxies at $\boldsymbol{z\approx 0.2}$-0.6: Insights into Declining Cosmic Star Formation
We report statistically significant detection of H I 21-cm emission from intermediate-redshift ($z\approx0.2$-0.6) galaxies. By leveraging multi-sightline galaxy survey data from the Cosmic Ultraviolet Baryon Survey (CUBS) and deep radio observations from the MeerKAT Absorption Line Survey (MALS), we have established a sample of $\approx6000$ spectroscopically identified galaxies in 11 distinct fields to constrain the neutral gas content at intermediate redshifts. The galaxies sample a broad ra…

JADES Data Release 4 Paper I: Sample Selection, Observing Strategy and Redshifts of the complete spectroscopic sample
Emma Curtis-Lake, Alex J. Cameron, Andrew J. Bunker, Jan Scholtz, Stefano Carniani, Eleonora Parlanti, Francesco D'Eugenio, Peter Jakobsen, Christopher N. A. Willmer, Santiago Arribas, William M. Baker, St\'ephane Charlot, Jacopo Chevallard, Chiara Circosta, Mirko Curti, Daniel J. Eisenstein, Kevin Hainline, Zhiyuan Ji, Benjamin D. Johnson, Gareth C. Jones, Robe…

JADES Data Release 4 Paper I: Sample Selection, Observing Strategy and Redshifts of the complete spectroscopic sample
This paper accompanies Data Release 4 of the JWST Deep Extragalactic Survey (JADES), which presents the full NIRSpec spectroscopy of the survey. We provide spectra of 5190 targets across GOODS-North and GOODS-South (including the Hubble Ultra Deep Field), observed with the low-dispersion (R $\sim$ 30-300) prism and three medium-resolution (R $\sim$ 1000) gratings spanning 0.8 $< λ<$ 5.5 microns; 2654 were also observed with the higher-resolution (R $\sim$ 2700) G395H grating. The tiered survey…

WALLABY Pilot Survey: Characterizing Low Rotation Kinematically Modelled Galaxies
N. Deg, K. Spekkens, N. Arora, R. Dudley, H. White, A. Helias, J. English, T. O'Beirne, V. Kilborn, G. Ferrand, M. L. A. Richardson, B. Catinella, L. Cortese, H. D\'enes, A. Elagali, B. -Q. For, K. Lee-Waddell, J. Rhee, L. Shao, A. X. Shen, L. Staveley-Smith, T. Westmeier, O. I. Wong

WALLABY Pilot Survey: Characterizing Low Rotation Kinematically Modelled Galaxies
Many of the tensions in cosmological models of the Universe lie in the low mass, low velocity regime. Probing this regime requires a statistically significant sample of galaxies with well measured kinematics and robustly measured uncertainties. WALLABY, as a wide area, untargetted HI survey is well positioned to construct this sample. As a first step towards this goal we develop a framework for testing kinematic modelling codes in the low resolution, low $S/N$, low rotation velocity regime. We …