Tootfinder

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 …

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…

Calibration-sample free distortion correction of electron diffraction patterns using deep learning
Matthew R. C. Fitzpatrick, Arthur M. Blackburn, Cristina Cordoba
https://arxiv.org/abs/2509.01075

Calibration-sample free distortion correction of electron diffraction patterns using deep learning
The accuracy of the information that can be extracted from electron diffraction patterns is often limited by the presence of optical distortions. Existing distortion characterization techniques typically require knowledge of the reciprocal lattice of either the sample of interest or a separate calibration sample, the latter of which would need to be swapped in, thus adding time and inconvenience to an experiment. To overcome this limitation, we develop a deep learning (DL) framework for measuri…

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…

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…

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).…

An Analysis of the Radius Gap in a Sample of Kepler, K2 and TESS exoplanets orbiting M Dwarf Stars
F\'abio Wanderley, Katia Cunha, Verne V. Smith, Diogo Souto, Ilaria Pascucci, Aida Behmard, Carlos Allende Prieto, Rachael L. Beaton, Dmitry Bizyaev, Simone Daflon, Sten Hasselquist, Steve Howell, Steven R. Majewski, Marc Pinsonneault
https://

An Analysis of the Radius Gap in a Sample of Kepler, K2 and TESS exoplanets orbiting M Dwarf Stars
Planetary radii are derived for 218 exoplanets orbiting 161 M dwarf stars. Stellar radii are based on an analysis of APOGEE high-resolution near-IR spectra for a subsample of the M-dwarfs; these results are used to define a stellar radius-M$_{\rm K_{\rm s}}$ calibration that is applied to the sample of M-dwarf planet hosts. The planetary radius distribution displays a gap over R$_{\rm p}$$\sim$1.6-2.0 R$_{\oplus}$, bordered by two peaks at R$_{\rm p}$$\sim$1.2-1.6 R$_{\oplus}$ (super-Earths) an…

Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
Bo Li, Wei Wang, Peng Ye
https://arxiv.org/abs/2510.01291 https://arxiv.o…

Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
The realizable-to-agnostic transformation (Beimel et al., 2015; Alon et al., 2020) provides a general mechanism to convert a private learner in the realizable setting (where the examples are labeled by some function in the concept class) to a private learner in the agnostic setting (where no assumptions are imposed on the data). Specifically, for any concept class $\mathcal{C}$ and error parameter $α$, a private realizable learner for $\mathcal{C}$ can be transformed into a private agnostic le…

🇺🇦 Auf radioeins läuft...
Foxwarren:
🎵 Listen2me
#NowPlaying #Foxwarren
https://foxwarren.bandcamp.com/track/listen2me-stem20-sample-listen2me
https://open.spotify.com/track/29WWHCuzU0OCWTfiBKbOm1

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 …

Assortment Optimization and the Sample Average Approximation
Hassaan Khalid, Bradley Sturt
https://arxiv.org/abs/2510.00850 https://arxiv.org/pdf/2510.0085…

Assortment Optimization and the Sample Average Approximation
We consider a simple approach to solving assortment optimization under the random utility maximization model. The approach uses Monte-Carlo simulation to construct a ranking-based choice model that serves as a proxy for the true choice model, followed by finding an assortment that is optimal with respect to that proxy. In this paper, we make that approach more viable by developing faster algorithms for finding assortments that are optimal under ranking-based choice models. Our algorithms are ba…

A Large Sample of JWST/NIRSpec Brown Dwarfs: New Distant Discoveries
Zhijun Tu, Shu Wang, Xiaodian Chen, Jifeng Liu
https://arxiv.org/abs/2510.02026 https://

A Large Sample of JWST/NIRSpec Brown Dwarfs: New Distant Discoveries
Brown dwarfs are essential probes of stellar and planetary formation, yet their low luminosities pose challenges for detection at large Galactic distances. The James Webb Space Telescope (JWST), with its unprecedented near-infrared sensitivity, enables the discovery and characterization of distant substellar objects, including those in the Milky Way's thick disk and halo. We conducted a systematic search using over 40,000 publicly available JWST/NIRSpec PRISM/CLEAR spectra and identified 68 bro…

Covariate balance under Bayesian decision theory
Andr\'e A. F. Fumis, Victor Fossaluza, Rafael B. Stern
https://arxiv.org/abs/2509.01828 https://arxiv.…

Covariate balance under Bayesian decision theory
We study optimal sample allocation between treatment and control groups under Bayesian linear models. We derive an analytic expression for the Bayes risk, which depends jointly on sample size and covariate mean balance across groups. Under a flat conditional prior, the covariate mean balance term simplifies to the Mahalanobis distance. Our results reveal that the optimal allocation does not always correspond to equal sample sizes, and we provide sufficient conditions under which equal allocatio…

"Many ships found breaching pollution limits despite tighter controls"
#Ships #Pollution

Reviewing my sample ballot for Concord's municipal elections Tuesday; my recommendations:
- Mayor: I like both Byron and Kate West. Vote your conscience.
- Councilor-at-large: Fisto is my first choice, then either Amanda Grady Sexton or Fred Keach. Definitely not Robert Washburn, though.
- Ward 6 Councilor: Vote Aislinn Kalob!
- School Board: Vote Cara Meeker!
- Sale of Eastman: Kinda wish we could know who it'd be sold to first, but sure, why not
- Social districts: Sure, although I'll want to follow this up by attending the city council meetings where they work out the details, and make sure they limit them to individual fully-pedestrianized city blocks (e.g. Bicentennial Square, Eagle Square)
- Keno: It is currently prohibited. It is necessary to vote YES on this question to continue to prohibit it (because otherwise new state law would require it to be allowed by-right), so I say yes, do that, vote to prohibit it.

Characterization of a sample of $\gamma$-ray active galactic nuclei
Alberto Ulgiati, Paolo Padovani, Paolo Giommi, Simona Paiano, Ciro Pinto
https://arxiv.org/abs/2509.02092 htt…

Characterization of a sample of $γ$-ray active galactic nuclei
We analyse 77 \textit{Fermi} sources and their potential low-energy counterparts previously proposed in the literature. These sources were classified as active galactic nuclei, mainly blazars, based on optical spectroscopy. The main goals of this work are to examine these associations, classify the blazars based on their multi-wavelength spectral energy distributions (SEDs), and identify potential masquerading BL Lac objects. Through SED analysis, we assess whether the multi-wavelength emission…

Minimum Sample Size Calculation for Multivariable Regression of Continuous Outcomes in Chemometrics for Astrobiology and Planetary Science
M. Konstantinidis, E. A. Lalla, S. J. Gonzalez, J. Manrique, G. Lopez-Reyes, A. Barlow, E. Sawyers, B. Barrios, M. G. Daly
https://arxiv.org/abs/2510.01210

Minimum Sample Size Calculation for Multivariable Regression of Continuous Outcomes in Chemometrics for Astrobiology and Planetary Science
Over the last few decades, prediction models have become a fundamental tool in statistics, chemometrics, and related fields. However, to ensure that such models have high value, the inferences that they generate must be reliable. In this regard, the internal validity of a prediction model might be threatened if it is not calibrated with a sufficiently large sample size, as problems such as overfitting may occur. Such situations would be highly problematic in many fields, including space science…

in the following days i'm gonna taste 8 gyokuros ordered from different shops. turned out, the shop i'm currently ordering from is a far cry in taste from a random sample bought in a b&m store. but also only half the price. this will be a (taste-bud-)exciting competition! #tea #gyokuro

Sample-based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds
Saurabh Shivpuje, Tanvi P. Gujarati, Richard Van, Frank C. Pickard IV, Triet Friedhoff, Ieva Liepuoniute, Wade Davis, Gavin O. Jones, Alexey Galda
https://arxiv.org/abs/2510.00484

Sample-based Quantum Diagonalization Methods for Modeling the Photochemistry of Diazirine and Diazo Compounds
Diazirines and diazo compounds are widely employed as photoreactive precursors for generating carbenes, key intermediates in chemical biology and materials science. However, computationally modeling their reaction pathways remains challenging due to a need for large active spaces and the requirement to accurately capture excited-state surfaces along with transition states and conical intersections. In this work, we utilize a hybrid quantum-classical workflow for investigating carbene formation …

Most English poems
I read on the Internet
Are just sentences
That don’t rhyme. How come?
(Not to deride the very cute xmas poem here but serious question. Do I have a bad sample size? Is this rhyming just for song lyrics and rap now? Or was non-rhyming poetry always bigger in English than in other languages?) https://

Rapid Scaling of Compositional Uncertainty from Sample to Population Levels
Yiran Wang, Martin Lysy, Audrey B\'eliveau
https://arxiv.org/abs/2510.00980 https://

Rapid Scaling of Compositional Uncertainty from Sample to Population Levels
Understanding population composition is essential across ecological, evolutionary, conservation, and resource management contexts. Modern methods such as genetic stock identification (GSI) estimate the proportion of individuals from each subpopulation using genetic data. Ideally, these estimates are obtained through mixture analysis, which captures both sampling and genetic uncertainty. However, historical datasets often rely on individual assignment methods that only account for sample-level 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…

Higher moment theory and learnability of bosonic states
Joseph T. Iosue, Yu-Xin Wang, Ishaun Datta, Soumik Ghosh, Changhun Oh, Bill Fefferman, Alexey V. Gorshkov
https://arxiv.org/abs/2510.01610

Higher moment theory and learnability of bosonic states
We present a sample- and time-efficient algorithm to learn any bosonic Fock state acted upon by an arbitrary Gaussian unitary. As a special case, this algorithm efficiently learns states produced in Fock state BosonSampling, thus resolving an open question put forth by Aaronson and Grewal (Aaronson, Grewal 2023). We further study a hierarchy of classes of states beyond Gaussian states that are specified by a finite number of their higher moments. Using the higher moments, we find a full spectru…

Linking Path-Dependent and Stochastic Volatility Models
Samuel N. Cohen, Cephas Svosve
https://arxiv.org/abs/2510.02024 https://arxiv.org/pdf/2510.02024

Linking Path-Dependent and Stochastic Volatility Models
We explore a link between stochastic volatility (SV) and path-dependent volatility (PDV) models. Using assumed density filtering, we map a given SV model into a corresponding PDV representation. The resulting specification is lightweight, improves in-sample fit, and delivers robust out-of-sample forecasts. We also introduce a calibration procedure for both SV and PDV models that produces standard errors for parameter estimates and supports joint calibration of SPX/VIX smile.

UniSage: A Unified and Post-Analysis-Aware Sampling for Microservices
Zhouruixing Zhu, Zhihan Jiang, Tianyi Yang, Pinjia He
https://arxiv.org/abs/2509.26336 https://

UniSage: A Unified and Post-Analysis-Aware Sampling for Microservices
Traces and logs are essential for observability and fault diagnosis in modern distributed systems. However, their ever-growing volume introduces substantial storage overhead and complicates troubleshooting. Existing approaches typically adopt a sample-before-analysis paradigm: even when guided by data heuristics, they inevitably discard failure-related information and hinder transparency in diagnosing system behavior. To address this, we introduce UniSage, the first unified framework to sample …

Carbon and nitrogen as indicators of stellar evolution and age. A homogeneous sample of 44 open clusters from the Gaia-ESO Survey
G. Tautvai\v{s}ien\.e, A. Drazdauskas, \v{S}. Mikolaitis, R. Minkevi\v{c}i\=ut\.e, E. Stonkut\.e, S. Randich, A. Bragaglia, L. Magrini, R. Smiljanic, M. Ambrosch, V. Bagdonas, G. Casali, Y. Chorniy, C. Viscasillas V\'azquez
https…

Carbon and nitrogen as indicators of stellar evolution and age. A homogeneous sample of 44 open clusters from the Gaia-ESO Survey
Context. Low- and intermediate-mass giants undergo a complex chemical evolution that has yet to be observationally probed. The influence of core helium flash on the chemical composition of stellar atmospheres has been an open question since its theoretical prediction 60 years ago. Aims. Based on high-resolution spectral observations of 44 open star clusters in the Gaia-ESO survey, our aim is to perform the first large-scale homogeneous investigation into the carbon and nitrogen photospheric con…

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…

A substitution lemma for multiple context-free languages
Andrew Duncan, Murray Elder, Lisa Frenkel, Mengfan Lyu
https://arxiv.org/abs/2509.02117 https://ar…

A substitution lemma for multiple context-free languages
We present a new criterion for proving that a language is not multiple context-free, which we call a Substitution Lemma. We apply it to show a sample selection of languages are not multiple context-free, including the word problem of $F_2\times F_2$. Our result is in contrast to Kanazawa et al. [2014, Theory Comput. Syst.] who proved that it was not possible to generalise the standard pumping lemma for context-free languages to multiple context-free languages, and Kanazawa [2019, Inform. and …

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…

Optimizing Paths for Adaptive Fly-Scan Microscopy: An Extended Version
Yu Lu, Thomas F. Lynn, Ming Du, Zichao Di, Sven Leyffer
https://arxiv.org/abs/2509.01869 https://

Optimizing Paths for Adaptive Fly-Scan Microscopy: An Extended Version
In x-ray microscopy, traditional raster-scanning techniques are used to acquire a microscopic image in a series of step-scans. Alternatively, scanning the x-ray probe along a continuous path, called a fly-scan, reduces scan time and increases scan efficiency. However, not all regions of an image are equally important. Currently used fly-scan methods do not adapt to the characteristics of the sample during the scan, often wasting time in uniform, uninteresting regions. One approach to avoid unne…

Reusing Samples in Variance Reduction
Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang
https://arxiv.org/abs/2509.02526 https://arxiv.org/pdf/2509.02…

Reusing Samples in Variance Reduction
We provide a general framework to improve trade-offs between the number of full batch and sample queries used to solve structured optimization problems. Our results apply to a broad class of randomized optimization algorithms that iteratively solve sub-problems to high accuracy. We show that such algorithms can be modified to reuse the randomness used to query the input across sub-problems. Consequently, we improve the trade-off between the number of gradient (full batch) and individual functio…

Spectral Flow Learning Theory: Finite-Sample Guarantees for Vector-Field Identification
Chi Ho Leung, Philip E. Par\'e
https://arxiv.org/abs/2509.25000 https://

Spectral Flow Learning Theory: Finite-Sample Guarantees for Vector-Field Identification
We study the identification of continuous-time vector fields from irregularly sampled trajectories. We introduce Spectral Flow Learning (SFL), which learns in a windowed flow space using a lag-linear label operator that aggregates lagged Koopman actions. We provide finite-sample high-probability (FS-HP) guarantees for the class of variable-step linear multistep methods (vLLM). The FS-HP rates are constructed using spectral regularization with qualification-controlled filters for flow predictors…

Progressively Sampled Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
https://arxiv.org/abs/2510.00417 https://arxiv.org…

Progressively Sampled Equality-Constrained Optimization
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main idea of the algorithm is to solve a sequence of equality-constrained problems, each involving a finite sample of constraint-function terms, over which the sample set grows progressively. Under assumptions about the constraint functions and their first- and secon…

Mediation Analysis in the Presence of Sample Selection Bias with an Application to Disparities in Liver Transplantation Listing
Zain Khan, Lynnette Sequeira, Alexandra T. Strauss, Vedant Jain, Juliette Dixon, Eric Moughames, Tyrus Vong, Daniel Malinsky
https://arxiv.org/abs/2509.01969

Mediation Analysis in the Presence of Sample Selection Bias with an Application to Disparities in Liver Transplantation Listing
The study of disparities in the liver transplantation process may focus on quantifying causal effects, particularly the average, direct, or indirect effects of various social determinants of health on being listed as a candidate for transplant. Selection bias arises when the data sample does not represent the target population, defined here as all individuals referred to the transplant clinic. Listing decisions are made for the subset of patients who complete the evaluation process, who may dif…

Identifying tidal disruption events among radio transient galaxies
M. Kunert-Bajraszewska, D. Kozie{\l}-Wierzbowska, D. Stern, A. Krauze, N. Zafar, T. Connor, M. J. Graham
https://arxiv.org/abs/2510.01773

Identifying tidal disruption events among radio transient galaxies
We present the optical and infrared properties of a sample of 24 radio transient sources discovered in the Very Large Array Sky Survey (VLASS). Previous studies of their radio emission showed that these sources resemble young gigahertz-peaked spectrum (GPS) radio sources, but are less powerful and characterized by low-power jets. The bursts of radio activity in most cases are likely due to intrinsic changes in the accretion processes. However, for a few sources in this sample, we cannot rule ou…

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…

JWST-discovered AGN: evidence for heavy obscuration in the type-2 sample from the first stacked X-ray detection
Andrea Comastri, Giorgio Lanzuisi, Fabio Vito, Stefano Marchesi, Marcella Brusa, Roberto Gilli, Ignas Juodzbalis, Roberto Maiolino, Giovanni Mazzolari, Guido Risaliti, Jan Scholtz, Cristian Vignali
https://arxiv.org/abs/2510.0011…

JWST-discovered AGN: evidence for heavy obscuration in the type-2 sample from the first stacked X-ray detection
One of the most puzzling properties of the high-redshift AGN population recently discovered by JWST, including both broad-line and narrow-line sources, is their X-ray weakness. With very few exceptions, and regardless of the optical classification, they are undetected at the limits of the deepest Chandra fields, even when stacking signals from tens of sources in standard observed-frame energy intervals (soft, hard, and full bands). It has been proposed that their elusive nature in the X-ray ban…

Hamiltonicity of Step-graphons
Xudong Chen
https://arxiv.org/abs/2510.02074 https://arxiv.org/pdf/2510.02074

Hamiltonicity of Step-graphons
In this paper, we sample directed random graphs from (asymmetric) step-graphons and investigate the probability that the random graph has at least a Hamiltonian cycle (or a node-wise Hamiltonian decomposition). We show that for almost all step-graphons, the probability converges to either zero or one as the order of the random graph goes to infinity--we term it the zero-one law. We identify the key objects of the step-graphon that matter for the zero-one law, and establish a set of conditions t…

Cosmological constraints from the Planck cluster catalogue with DES shear profiles and Chandra observations
G. Aymerich, S. Grandis, M. Douspis, G. W. Pratt, L. Salvati, F. Andrade-Santos, S. Bocquet, M. Costanzi, W. R. Forman, C. Jones, M. Aguena, F. Andrade-Oliveira, D. Bacon, D. Brooks, D. L. Burke, J. Carretero, L. N. da Costa, M. E. da Silva Pereira, T. M. Davis, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, B. Flaugher, J. Frieman, E. Gaztanaga, D. Gruen, G. Gutierre…

Cosmological constraints from the Planck cluster catalogue with DES shear profiles and Chandra observations
We present cosmological constraints from the Planck PSZ2 cosmological cluster sample, using weak-lensing shear profiles from Dark Energy Survey (DES) data and X-ray observations from the Chandra telescope for the mass calibration. We compute hydrostatic mass estimates for all clusters in the PSZ2 sample with a scaling relation between their Sunyaev-Zeldovich signal and X-ray derived hydrostatic mass, calibrated with the Chandra data. We introduce a method to correct these masses with a hydrosta…

Prebiotic organic compounds in samples of asteroid #Bennu indicate heterogeneous aqueous alteration: https://www.pnas.org/doi/10.1073/pnas.2512461122 -> Scientists find evidence that an asteroid contains tryptophan: https://edition.cnn.com/2025/11/27/science/tryptophan-asteroid-bennu-nasa-sample

Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space
Aditya Sengar, Ali Hariri, Pierre Vandergheynst, Patrick Barth
https://arxiv.org/abs/2509.02196 …

Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space
Simulating the long-timescale dynamics of biomolecules is a central challenge in computational science. While enhanced sampling methods can accelerate these simulations, they rely on pre-defined collective variables that are often difficult to identify. A recent generative model, LD-FPG, demonstrated that this problem could be bypassed by learning to sample the static equilibrium ensemble as all-atom deformations from a reference structure, establishing a powerful method for all-atom ensemble g…

Correlation function metrology for warm dense matter: Recent developments and practical guidelines
Maximilian Peter B\"ohme, Willow Martin, Hannah Bellenbaum, Magaret Berrens, Jan Vorberger, Sebastian Schwalbe, Zhandos Moldabekov, Thomas Gawne, Sebastien Hamel, Brianna Aguilar-Solis, Abhiraj Sharma, Frank Graziani, Tilo D\"oppner, Siegfried Glenzer, Tobias Dornheim, David Bishel

Correlation function metrology for warm dense matter: Recent developments and practical guidelines
X-ray Thomson scattering (XRTS) has emerged as a valuable diagnostic for matter under extreme conditions, as it captures the intricate many-body physics of the probed sample. Recent advances, such as the model-free temperature diagnostic of Dornheim et al. [Nat.Commun. 13, 7911 (2022)], have demonstrated how much information can be extracted directly within the imaginary-time formalism. However, since the imaginary-time formalism is a concept often difficult to grasp, we provide here a systemat…

Time-Frequency Analysis of Non-Uniformly Sampled Signals via Sample Density Adaptation
Ashwini Kulkarni, Santosh Nannuru
https://arxiv.org/abs/2509.22891 https://

Time-Frequency Analysis of Non-Uniformly Sampled Signals via Sample Density Adaptation
The analysis of non-stationary signals in non-uniformly sampled data is a challenging task. Time-integrated methods, such as the generalised Lomb-Scargle (GLS) periodogram, provide a robust statistical assessment of persistent periodicities but are insensitive to transient events. Conversely, existing time-frequency methods often rely on fixed-duration windows or interpolation, which can be suboptimal for non-uniform data. We introduce the non-uniform Stockwell-transform (NUST), a time-frequenc…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[10/17]:
- Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
Mudit Gaur, Prashant Trivedi, Sasidhar Kunapuli, Amrit Singh Bedi, Vaneet Aggarwal

Just back from Ireland and found colleagues alcohol consumption not dissimilar from that in UK (OK opportunity sample).
Will new warning labels in Ireland turn people away from alcohol? - BBC News
https://www.bbc.co.uk/news/articles/c2465lndrnno

Will new warning labels in Ireland turn people away from alcohol?
The labels, which warn of cancer risks, are among the strongest warnings on alcohol anywhere in the world.

Eigenvector overlaps of sample covariance matrices with intersecting time periods
Volodymyr Riabov, Konstantin Tikhonov, Jean-Philippe Bouchaud
https://arxiv.org/abs/2509.25076 …

Eigenvector overlaps of sample covariance matrices with intersecting time periods
We compute exactly the overlap between the eigenvectors of two large empirical covariance ma- trices computed over intersecting time intervals, generalizing the results obtained previously for non-intersecting intervals. Our method relies on a particular form of Girko linearisation and ex- tended local laws. We check our results numerically and apply them to financial data.

Quality of Helicity-Dependent Magnetization Switching by Phonons
F. G. N. Fennema, C. S. Davies, A. Tsukamoto, A. Kirilyuk
https://arxiv.org/abs/2509.01480 https://

Quality of Helicity-Dependent Magnetization Switching by Phonons
Optical control of magnetization has emerged as a promising approach to achieve ultrafast and energy-efficient magnetization reversal. Here, we investigate helicity-dependent switching of magne- tization driven by the resonant excitation of circularly-polarized transverse-optical phonons, using a polarization-modulated transient grating. Our results show that the polarized phonons within the sample substrate induce robust, helicity-defined magnetization reversal in the magnetic overlayer. Moreo…

Radio-Frequency Method for Detecting Superconductivity Under High Pressure
Dmitrii V. Semenok, Di Zhou, Jianbo Zhang, Toni Helm, Yang Ding, Ho-kwang Mao, Viktor V. Struzhkin
https://arxiv.org/abs/2509.00563

Radio-Frequency Method for Detecting Superconductivity Under High Pressure
We introduce a contactless technique for probing superconductivity, metal-insulator transitions, and magnetic ordering in micron-sized samples under extreme pressure. Utilizing a multistage Lenz lens system, directly sputtered onto diamond anvils, we realize a radio-frequency (RF, 50 kHz - 200 MHz) transformer with a sample, of 50-100 microns in diameter, as its core. This configuration enables efficient transfer and focusing of an electromagnetic field within the diamond anvil cell's chamber. …

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…

On Estimating the Quantum Tsallis Relative Entropy
Jinge Bao, Minbo Gao, Qisheng Wang
https://arxiv.org/abs/2510.00752 https://arxiv.org/pdf/2510.00752

On Estimating the Quantum Tsallis Relative Entropy
The relative entropy between quantum states quantifies their distinguishability. The estimation of certain relative entropies has been investigated in the literature, e.g., the von Neumann relative entropy and sandwiched Rényi relative entropy. In this paper, we present a comprehensive study of the estimation of the quantum Tsallis relative entropy. We show that for any constant $α\in (0, 1)$, the $α$-Tsallis relative entropy between two quantum states of rank $r$ can be estimated with sampl…

xkcd: Geologic Core Sample
https://xkcd.com/3171/

JADES Data Release 4 -- Paper II: Data reduction, analysis and emission-line fluxes of the complete spectroscopic sample
J. Scholtz, S. Carniani, E. Parlanti, F. D'Eugenio, E. Curtis-Lake, P. Jakobsen, A. J. Bunker, A. J. Cameron, S. Arribas, W. M. Baker, S. Charlot, J. Chevellard, C. Circosta, M. Curti, Q. Duan, D. J. Eisenstein, K. Hainline, Z. Ji, B. D. Johnson, G. C. Jones, N. Kumari, R. Maiolino, M. V. Maseda, M. Perna, P. G. P\'erez-Gonz\'alez, T. Rawle, M. Rieke, P. …

JADES Data Release 4 - Paper II: Data reduction, analysis and emission-line fluxes of the complete spectroscopic sample
We present the fourth data release of JADES, the JWST Advanced Deep Extragalactic Survey, providing deep spectroscopic observations in the two GOODS fields. A companion paper presents the target selection, spectroscopic redshifts and success rates, and in this paper, we discuss the data reduction and present emission line flux measurements. The spectroscopy in this work consists of medium-depth, deep and ultradeep NIRSpec/MSA spectra of 5,190 targets, covering the spectral range $0.6\text{--}5.…

Removal of Redundant Candidate Points for the Exact D-Optimal Design Problem
Radoslav Harman, Samuel Rosa
https://arxiv.org/abs/2509.00719 https://arxiv.or…

Removal of Redundant Candidate Points for the Exact D-Optimal Design Problem
One of the most common problems in statistical experimentation is computing D-optimal designs on large finite candidate sets. While optimal approximate (i.e., infinite-sample) designs can be efficiently computed using convex methods, constructing optimal exact (i.e., finite-sample) designs is a substantially more difficult integer-optimization problem. In this paper, we propose necessary conditions, based on approximate designs, that must be satisfied by any support point of a D-optimal exact d…

User Prompting Strategies and ChatGPT Contextual Adaptation Shape Conversational Information-Seeking Experiences
Haoning Xue, Yoo Jung Oh, Xinyi Zhou, Xinyu Zhang, Berit Oxley
https://arxiv.org/abs/2509.25513

User Prompting Strategies and ChatGPT Contextual Adaptation Shape Conversational Information-Seeking Experiences
Conversational AI, such as ChatGPT, is increasingly used for information seeking. However, little is known about how ordinary users actually prompt and how ChatGPT adapts its responses in real-world conversational information seeking (CIS). In this study, a nationally representative sample of 937 U.S. adults engaged in multi-turn CIS with ChatGPT on both controversial and non-controversial topics across science, health, and policy contexts. We analyzed both user prompting strategies and the com…

The typical algebraic shifting of a surface
Denys Bulavka, Eran Nevo, Yuval Peled
https://arxiv.org/abs/2509.26525 https://arxiv.org/pdf/2509.26525

The typical algebraic shifting of a surface
We initiate a statistical study of Kalai's exterior algebraic shifting, focusing on concentration phenomena for random triangulations of a fixed space. First, for a uniform $n$-vertex refinement of any given graph $G$, we show that asymptotically almost-surely (a.a.s.) its exterior algebraic shifting is an explicit shifted graph depending only on $n$ and the Betti numbers of $G$. Next, for any given compact connected Riemannian surface $S$, sample $n$ points independently at random according to…

I FOUND IT! I FOUND THE DiC "DICK!" SAMPLE!
The original DiC:
https://youtu.be/aDOuFnZnPMs
The DiC in question: https://youtu.be/NHO84rOp8FQ

Bias-Reduced Estimation of Structural Equation Models
Haziq Jamil, Yves Rosseel, Oliver Kemp, Ioannis Kosmidis
https://arxiv.org/abs/2509.25419 https://arx…

Bias-Reduced Estimation of Structural Equation Models
Finite-sample bias is a pervasive challenge in the estimation of structural equation models (SEMs), especially when sample sizes are small or measurement reliability is low. A range of methods have been proposed to improve finite-sample bias in the SEM literature, ranging from analytic bias corrections to resampling-based techniques, with each carrying trade-offs in scope, computational burden, and statistical performance. We apply the reduced-bias M-estimation framework (RBM, Kosmidis & Lunard…

🗳️🇺🇸 Hey, Maine! 2 weeks from tomorrow (Nov 4) you’ve got a referendum that could shape elections for years. It may be an off-year but the outcome will sway every election from now on.
Bipartisan info at https://ballotpedia.org/wiki/index.php?title=Sample_Ballot…

NFL overreaction Monday: Fishy betting lines and 4 early picks for Week 5 https://www.nytimes.com/athletic/6673734/2025/09/29/nfl-overreaction-monday-week-5/

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
Zhixiao Wu, Yao Lu, Jie Wen, Hao Sun, Qi Zhou, Guangming Lu
https://arxiv.org/abs/2509.19947

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
Poison-only Clean-label Backdoor Attacks aim to covertly inject attacker-desired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple \textbf{triggers} are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting ``hard'' samples instead of random samples to poison. Current methods 1) usually han…

Incentives in Federated Learning with Heterogeneous Agents
Ariel D. Procaccia, Han Shao, Itai Shapira
https://arxiv.org/abs/2509.21612 https://arxiv.org/pd…

Incentives in Federated Learning with Heterogeneous Agents
Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the contributor but boosts every participant. We introduce a game-theoretic framework that captures heterogeneous data: an agent's utility depends on who supplies each sample, not just how many. Agents aim to meet a PAC-style accuracy threshold at minimal personal cost. We show that uncoordinated play yields pathologies: pure…

The Accretion Disk Size Problem in AGN Disk Reverberation Mapping is an Obscuration Effect: A Uniform AGN Sample Study with Swift
Collin Lewin, Erin Kara, Christos Panagiotou, Edward M. Cackett, Jonathan Gelbord, Juan V. Hern\'andez Santisteban, Keith Horne, Gerard A. Kriss
https://arxiv.org/abs/2509.25315

The Accretion Disk Size Problem in AGN Disk Reverberation Mapping is an Obscuration Effect: A Uniform AGN Sample Study with Swift
In the past decade, Swift has performed several AGN high-cadence reverberation mapping campaigns, and generally found that the UV/optical interband lags are $\sim$3 times longer than predicted for a standard thin disk, thus coined "the accretion disk size problem". Here we present a systematic sample of Swift-monitored AGN. In this analysis, we confirm the accretion disk size problem, but find that the lag excess occurs only in the subset of obscured AGN, which show a significantly elevated mea…

🇺🇦 Auf radioeins läuft...
Foxwarren:
🎵 Listen2me
#NowPlaying #Foxwarren
https://foxwarren.bandcamp.com/track/listen2me-stem20-sample-listen2me
https://open.spotify.com/track/29WWHCuzU0OCWTfiBKbOm1

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es
https://arxiv.org/abs/2510.01475

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
Advanced control strategies like Model Predictive Control (MPC) offer significant energy savings for HVAC systems but often require substantial engineering effort, limiting scalability. Reinforcement Learning (RL) promises greater automation and adaptability, yet its practical application in real-world residential settings remains largely undemonstrated, facing challenges related to safety, interpretability, and sample efficiency. To investigate these practical issues, we performed a direct com…

Isotopic composition of cometary water and the origin of Earth's oceans
Dariusz C. Lis, Martin Cordiner, Nicolas Biver, Dominique Bockelee-Morvan, Paul F. Goldsmith, Arielle Moullete, Paul von Allmen
https://arxiv.org/abs/2509.01834

Isotopic composition of cometary water and the origin of Earth's oceans
Studies of the water content and isotopic composition of water-rich asteroids and comets are of key interest for understanding the late accretion stage of the Solar System cometary and chondritic materials. The PRobe far-infrared Mission for Astrophysics (PRIMA) can make an important contribution to solving this long-standing problem by carrying out direct measurements of the D/H ratio in a significant sample of Oort cloud and Kuiper belt comets, sampling the isotopic composition of the present…

On the Adversarial Robustness of Learning-based Conformal Novelty Detection
Daofu Zhang, Mehrdad Pournaderi, Hanne M. Clifford, Yu Xiang, Pramod K. Varshney
https://arxiv.org/abs/2510.00463

On the Adversarial Robustness of Learning-based Conformal Novelty Detection
This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on AdaDetect, a powerful learning-based framework for novelty detection with finite-sample false discovery rate (FDR) control. While AdaDetect provides rigorous statistical guarantees under benign conditions, its behavior under adversarial perturbations remains unexplored. We first formulate an oracle attack setting that quantifies the worst-case degradation of FDR, deriving an upper bound that…

A comprehensive radio study of narrow-line Seyfert 1 galaxies
I. Varglund, E. J\"arvel\"a, M. J. Hardcastle, S. Varglund, A. L\"ahteenm\"aki
https://arxiv.org/abs/2510.01307

A comprehensive radio study of narrow-line Seyfert 1 galaxies
Narrow-line Seyfert 1 (NLS1) galaxies are a type of active galactic nuclei (AGN) that were originally classified as sources with little to no radio emission. Although the class is rather unified from an optical perspective, their radio characteristics are diverse. One of the biggest curiosities found in these sources is their ability to form and maintain powerful relativistic jets. We studied the radio properties of a sample of 3998 NLS1 galaxies which is the largest clean sample available, thu…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani
https://arxiv.org/abs/2510.01184 htt…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the training distribution. We build on the observation that these models leverage (learned) score functions of noisy data distributions for sampling and show that rescaling these allows one to effectively control a `local' sampling temperature. Notably, this approach does not require any finetuning or alterations to training…

Doubling the field of view and eliminating the replica overlap problem in common-path shearing quantitative phase imaging
Miko{\l}aj Rogalski, Piotr Zda\'nkowski, Matyas Heto, Jolanta Mierzejewska, Ma{\l}gorzata Lenarcik, Zhuoshi Li, Jiasong Sun, Chao Zuo, Maciej Trusiak
https://arxiv.org/abs/2510.01762…

Doubling the field of view and eliminating the replica overlap problem in common-path shearing quantitative phase imaging
Quantitative phase imaging (QPI) enables label-free, high-contrast visualization of transparent specimens, but its common implementation in off-axis digital holographic microscopy (DHM) requires a separate reference beam, which increases system complexity and sensitivity to noise and vibrations. Common-path shearing DHMs overcome these drawbacks by eliminating the reference arm, yet they suffer from sheared object beam (replica) overlap, as both interfering sheared beams traverse the sample and…

Systematic Alias Sampling: an efficient and low-variance way to sample from a discrete distribution
Ilari Vallivaara, Katja Poikselk\"a, Pauli Rikula, Juha R\"oning
https://arxiv.org/abs/2509.24089

Systematic Alias Sampling: an efficient and low-variance way to sample from a discrete distribution
In this paper we combine the Alias method with the concept of systematic sampling, a method commonly used in particle filters for efficient low-variance resampling. The proposed method allows very fast sampling from a discrete distribution: drawing k samples is up to an order of magnitude faster than binary search from the cumulative distribution function (cdf) or inversion methods used in many libraries. The produced empirical distribution function is evaluated using a modified Cramér-Von Mis…

Diversity-Incentivized Exploration for Versatile Reasoning
Zican Hu, Shilin Zhang, Yafu Li, Jianhao Yan, Xuyang Hu, Leyang Cui, Xiaoye Qu, Chunlin Chen, Yu Cheng, Zhi Wang
https://arxiv.org/abs/2509.26209

Diversity-Incentivized Exploration for Versatile Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a crucial paradigm for incentivizing reasoning capabilities in Large Language Models (LLMs). Due to vast state-action spaces and reward sparsity in reasoning tasks, existing methods often struggle with deficient exploration and poor sample efficiency. In the paper, we propose \textbf{DIVER} (\textbf{D}iversity-\textbf{I}ncentivized Exploration for \textbf{V}ersatil\textbf{E} \textbf{R}easoning), an innovative framework that hi…

On a closed-loop identification challenge in feedback optimization
Kristian Lindb\"ack L{\o}vland, Lars Struen Imsland, Bjarne Grimstad
https://arxiv.org/abs/2509.01188 htt…

On a closed-loop identification challenge in feedback optimization
Feedback optimization has emerged as an effective strategy for steady-state optimization of dynamical systems. By exploiting models of the steady-state input-output sensitivity, methods of this type are often sample efficient, and their use of feedback ensures that they are robust against model error. Still, this robustness has its limitations, and the dependence on a model may hinder convergence in settings with high model error. We investigate here the effect of a particular type of model err…

JADES: An Abundance of Ultra-Distant T- and Y-Dwarfs in Deep Extragalactic Data
Kevin N. Hainline, Jakob M. Helton, Brittany E. Miles, Jarron Leisenring, Mark S. Marley, Sagnick Mukherjee, Nicholas F. Wogan, Andrew J. Bunker, Benjamin D. Johnson, Roberto Maiolino, Marcia Rieke, Pierluigi Rinaldi, Brant Robertson, Fengwu Sun, Sandro Tacchella, Christina C. Williams, Christopher N. A. Willmer

JADES: An Abundance of Ultra-Distant T- and Y-Dwarfs in Deep Extragalactic Data
Ultra-cool T- (T$_{\mathrm{eff}} \approx$ 500 - 1200 K) and Y-dwarfs (T$_{\mathrm{eff}}$ $\lessapprox 500$ K) have historically been found only a few hundred parsecs from the Sun. The sensitivity and wavelength coverage of the NIRCam instrument on board the James Webb Space Telescope offer a unique method for finding low-temperature brown dwarfs in deep extragalactic datasets out to multiple kiloparsecs. Here we report on the selection of a sample of 41 brown dwarf and brown dwarf candidates ac…

DNA shotgun sequencing evidence: sample-specific and unknown genotyping error probabilities
Mikkel Meyer Andersen
https://arxiv.org/abs/2509.26112 https://…

DNA shotgun sequencing evidence: sample-specific and unknown genotyping error probabilities
DNA shotgun sequencing evidence is starting to gain a lot of attraction in forensic genetics. Methods to correctly interpret such evidence, including properly accounting for sequencing errors, are needed. This paper extends the wgsLR model by Andersen et. al. (2025) from only allowing for the same, known genotyping error probability for the two samples (trace sample from unknown donor and reference sample from person of interest), to allowing for different genotyping error probabilities (e.g., …

DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao, Weipu Zhang, Jian Sun, Gao Huang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.24804 …

DyMoDreamer: World Modeling with Dynamic Modulation
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-based reinforcement learning (MBRL) mitigates this by building world models that simulate environmental dynamics and generate synthetic experience, improving sample efficiency. However, conventional world models process observations holistically, failing to decouple dynamic objects and temporal features from static backg…

The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements
Youssef Chaabouni, David Gamarnik
https://arxiv.org/abs/2509.01809 http…

The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements
We consider the problem of recovering the support of a sparse signal using noisy projections. While extensive work has been done on the dense measurement matrix setting, the sparse setting remains less explored. In this work, we establish sufficient conditions on the sample size for successful sparse recovery using sparse measurement matrices. Bringing together our result with previously known necessary conditions, we discover that, in the regime where $ds/p \rightarrow +\infty$, sparse recover…

Impact of projection-induced optical selection bias on the weak lensing mass calibration of galaxy clusters
Titus Nyarko Nde, Hao-Yi Wu, Shulei Cao, Gladys Muthoni Kamau, Andrius Tamosiunas, Chun-Hao To, Conghao Zhou
https://arxiv.org/abs/2510.00753

Impact of projection-induced optical selection bias on the weak lensing mass calibration of galaxy clusters
Weak gravitational lensing signals of optically identified clusters are impacted by a selection bias -- halo triaxiality and large-scale structure along the line of sight simultaneously boost the lensing signal and richness (the inferred number of galaxies associated with a cluster). As a result, a cluster sample selected by richness has a mean lensing signal higher than expected from its mean mass, and the inferred mass will be biased high. This selection bias is currently limiting the accurac…

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…

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
Heng Ge, Qing Lu
https://arxiv.org/abs/2510.01418 https://arxiv.org/pdf/2510.0…

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
We introduce DiffKnock, a diffusion-based knockoff framework for high-dimensional feature selection with finite-sample false discovery rate (FDR) control. DiffKnock addresses two key limitations of existing knockoff methods: preserving complex feature dependencies and detecting non-linear associations. Our approach trains diffusion models to generate valid knockoffs and uses neural network--based gradient and filter statistics to construct antisymmetric feature importance measures. Through simu…

Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control
Hongyi Zhou, Jingwei Li, Jingzhao Zhang
https://arxiv.org/abs/2509.22741 http…

Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control
Real world evolves in continuous time but computations are done from finite samples. Therefore, we study algorithms using finite observations in continuous-time linear dynamical systems. We first study the system identification problem, and propose a first non-asymptotic error analysis with finite observations. Our algorithm identifies system parameters without needing integrated observations over certain time intervals, making it more practical for real-world applications. Further we propose a…

Estimating Dimensionality of Neural Representations from Finite Samples
Chanwoo Chun, Abdulkadir Canatar, SueYeon Chung, Daniel Lee
https://arxiv.org/abs/2509.26560 https://

Estimating Dimensionality of Neural Representations from Finite Samples
The global dimensionality of a neural representation manifold provides rich insight into the computational process underlying both artificial and biological neural networks. However, all existing measures of global dimensionality are sensitive to the number of samples, i.e., the number of rows and columns of the sample matrix. We show that, in particular, the participation ratio of eigenvalues, a popular measure of global dimensionality, is highly biased with small sample sizes, and propose a b…

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
Yudong Wei, Liang Zhang, Bingcong Li, Niao He
https://arxiv.org/abs/2510.01175 https://

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
While normalization techniques are widely used in deep learning, their theoretical understanding remains relatively limited. In this work, we establish the benefits of (generalized) weight normalization (WN) applied to the overparameterized matrix sensing problem. We prove that WN with Riemannian optimization achieves linear convergence, yielding an exponential speedup over standard methods that do not use WN. Our analysis further demonstrates that both iteration and sample complexity improve p…

Crosslisted article(s) found for astro-ph.HE. https://arxiv.org/list/astro-ph.HE/new
[1/1]:
- JWST-discovered AGN: evidence for heavy obscuration in the type-2 sample from the first stacked X...
Andrea Comastri, et al.

Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Nayari Marie Lessa, Melya Boukheddimi, Frank Kirchner
https://arxiv.org/abs/2509.24539 https://

Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Learning-based methods have enabled robots to acquire bio-inspired movements with increasing levels of naturalness and adaptability. Among these, Imitation Learning (IL) has proven effective in transferring complex motion patterns from animals to robotic systems. However, current state-of-the-art frameworks predominantly rely on Proximal Policy Optimization (PPO), an on-policy algorithm that prioritizes stability over sample efficiency and policy generalization. This paper proposes a novel IL f…

Identifying All {\epsilon}-Best Arms in (Misspecified) Linear Bandits
Zhekai Li, Tianyi Ma, Cheng Hua, Ruihao Zhu
https://arxiv.org/abs/2510.00073 https://…

Identifying All ε-Best Arms in (Misspecified) Linear Bandits
Motivated by the need to efficiently identify multiple candidates in high trial-and-error cost tasks such as drug discovery, we propose a near-optimal algorithm to identify all ε-best arms (i.e., those at most ε worse than the optimum). Specifically, we introduce LinFACT, an algorithm designed to optimize the identification of all ε-best arms in linear bandits. We establish a novel information-theoretic lower bound on the sample complexity of this problem and demonstrate that LinFACT achieve…

LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design
Matteo Boschini, Davide Gerosa, Alessandro Crespi, Matteo Falcone
https://arxiv.org/abs/2509.00159

LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design
Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a priori knowledge of the targeted sample size. In this work, we present LHS in LHS, a new expansion algorithm for LHS that enables the addition of new samples to an existing LHS-dist…

Maternal Characteristics and Newborn Birth Weight: A Comprehensive Statistical Analysis
Prithwiraj Chatterjee, Abhinav Tanwar, Devadharshini Udayakumar
https://arxiv.org/abs/2509.00617

Maternal Characteristics and Newborn Birth Weight: A Comprehensive Statistical Analysis
This report presents a statistical analysis of the impact of key maternal characteristics, including age, smoking status, parity, height, weight, and gestation period, on newborn birth weight. A realworld dataset comprising 1,236 observations was utilized for this investigation. The methodology involved comprehensive data cleaning, exploratory data analysis (EDA), and a series of parametric statistical tests, specifically the One-Sample t-test, Two-Sample t-test, Chi-Square tests, and Analysis …

JADES: The Star Formation and Dust Attenuation Properties of Galaxies at 3<z<7
Charity Woodrum, Irene Shivaei, Joris Witstok, Aayush Saxena, Charlotte Simmonds, Jan Scholtz, Rachana Bhatawdekar, Andrew J. Bunker, St\'efano Carniani, Stephane Charlot, Mirko Curti, Emma Curtis-Lake, Jacopo Chevallard, Francesco D'Eugenio, Kevin Hainline, Jakob M. Helton, Roberto Maiolino, Michele Perna, Pierluigi Rinaldi, Brant Robertson, Amber Straughn, Yang Sun, Sandro Tacchella, Christin…

JADES: The Star Formation and Dust Attenuation Properties of Galaxies at 3
We present the star formation and dust attenuation properties for a sample of 602 galaxies at redshifts $\rm{3