Tootfinder

Thank you #rhel

After sssd upgrade, useradd and usermod fail to flush the sssd cache with "DB version too old for domain implicit_files!". - Red Hat Customer Portal
After sssd upgrade, useradd and usermod fail to flush the sssd cache with "DB version too old for domain implicit_files!". Sample reproducer: # useradd testuser0 # systemctl is-enabled sssd.service # systemctl disable sssd.service # systemctl stop sssd.service # yum update sssd Sample outputs of a sssd upgrade system from sssd-2.3.0-9.el8(RHEL8.3) to sssd-2.5.2-2.el8(RHEL8.5): # useradd testuser1 [sss_cache] [sysdb_domain_cache_connect] (0x0010): DB version too old [0.22], expected [0.23] for d…

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…

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…

Strong progenitor age bias in supernova #cosmology – I. Robust and ubiquitous evidence from a larger sample of host galaxies in a broader redshift range / II. Alignment with DESI BAO and signs of a non-accelerating universe: https://academic.oup.com/mnras/article/538/4/3340/8098234 / https://academic.oup.com/mnras/article/544/1/975/8281988 -> Universe's expansion 'is now slowing, not speeding up': https://ras.ac.uk/news-and-press/research-highlights/universes-expansion-now-slowing-not-speeding (this has been claimed by the group since at least 2023 - http://vietnam.in2p3.fr/2023/windows/transparencies/02_tuesday/Astro_5/01_Son.pdf - and is rejected by the SN cosmology community as https://bsky.app/profile/astromarc.bsky.social/post/3m4xmikwfds26 explains).

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 …

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…

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 …

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

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…

Topic Modeling as Long-Form Generation: Can Long-Context LLMs revolutionize NTM via Zero-Shot Prompting?
Xuan Xu, Haolun Li, Zhongliang Yang, Beilin Chu, Jia Song, Moxuan Xu, Linna Zhou
https://arxiv.org/abs/2510.03174

Topic Modeling as Long-Form Generation: Can Long-Context LLMs revolutionize NTM via Zero-Shot Prompting?
Traditional topic models such as neural topic models rely on inference and generation networks to learn latent topic distributions. This paper explores a new paradigm for topic modeling in the era of large language models, framing TM as a long-form generation task whose definition is updated in this paradigm. We propose a simple but practical approach to implement LLM-based topic model tasks out of the box (sample a data subset, generate topics and representative text with our prompt, text assi…

Gearing up for tonight's decap run.
In the queue: 5x PIC12F683 for delayering, XC6SLX25, XC7S25, ATSAMA5D27, 25AA1024
I'm not bothering to label the sample beakers because I know what most of these chips will look like from having seen other parts in the family. The ATSAM is the main unknown.
Anybody know what foundry it was made at? What node? We'll find out soon I guess.

Neural Jump ODEs as Generative Models
Robert A. Crowell, Florian Krach, Josef Teichmann
https://arxiv.org/abs/2510.02757 https://arxiv.org/pdf/2510.02757…

Neural Jump ODEs as Generative Models
In this work, we explore how Neural Jump ODEs (NJODEs) can be used as generative models for Itô processes. Given (discrete observations of) samples of a fixed underlying Itô process, the NJODE framework can be used to approximate the drift and diffusion coefficients of the process. Under standard regularity assumptions on the Itô processes, we prove that, in the limit, we recover the true parameters with our approximation. Hence, using these learned coefficients to sample from the correspond…

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 https://adrianroselli.com/2025/10/openai-aria-and-seo-making-the-web-worse.html to link to a post from Stephanie Stimac that I missed, Simon Willison on prompt injection attacks, a sample attack from Brave, and Chris Coyier’s surprisingly long lis…

OpenAI, ARIA, and SEO: Making the Web Worse
OpenAI has announced it’s launched a new browser, Atlas, with ChatGPT built in. For those familiar with ARIA, OpenAI outlines what to expect (I left the code as I found it, other than removing the target): We’ll continue to make Atlas better, and our roadmap includes multi-profile support, improved developer…

Oracle-based Uniform Sampling from Convex Bodies
Thanh Dang, Jiaming Liang
https://arxiv.org/abs/2510.02983 https://arxiv.org/pdf/2510.02983

Oracle-based Uniform Sampling from Convex Bodies
We propose new Markov chain Monte Carlo algorithms to sample a uniform distribution on a convex body $K$. Our algorithms are based on the Alternating Sampling Framework/proximal sampler, which uses Gibbs sampling on an augmented distribution and assumes access to the so-called restricted Gaussian oracle (RGO). The key contribution of this work is the efficient implementation of RGO for uniform sampling on $K$ via rejection sampling and access to either a projection oracle or a separation oracle…

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).…

A supermassive black hole under the radar: repeating X-ray variability in a Seyfert galaxy
Matteo Imbrogno, Andrea Sacchi, Giovanni Miniutti, Francesco Tombesi, Gian Luca Israel, Enrico Piconcelli, Roberta Amato
https://arxiv.org/abs/2510.02832

A supermassive black hole under the radar: repeating X-ray variability in a Seyfert galaxy
In the last few years, a few supermassive black holes (SMBHs) have shown short-term (of the order of hours) X-ray variability. Given the limited size of the sample, every new addition to this class of SMBHs can bring invaluable information. Within the context of an automated search for X-ray sources showing flux variability in the \textit{Chandra} archive, we identified peculiar variability patterns in 2MASX J12571076+2724177 (J1257), a SMBH in the Coma cluster, during observations performed in…

Series C, Episode 07 - Children of Auron
DERAL: Is that all it takes, then? A sample of blood?
FRANTON: It's the genetic print we need. All body cells with a nucleus carry that. The white cells are here. As long as they're capable of differentiation, they're capable of growth, into another you.
https://blake.torpid…

Percolation and criticality of systems with competing interactions on Bethe lattices: limitations and potential strengths of cluster schemes
Greivin Alfaro Miranda, Mingyuan Zheng, Patrick Charbonneau, Antonio Coniglio, Leticia F. Cugliandolo, Marco Tarzia
https://arxiv.org/abs/2510.02961

Percolation and criticality of systems with competing interactions on Bethe lattices: limitations and potential strengths of cluster schemes
The random clusters introduced by Fortuin and Kasteleyn (FK) and analyzed by Coniglio and Klein (CK) for Ising and related models have led first Swendsen and Wang and then Wolff to formulate remarkably efficient Markov chain Monte Carlo sampling schemes that weaken the critical slowing down. In frustrated models, however, no standard way to produce a comparable gain at small frustration -- let alone efficiently sample the large frustration regime -- has yet been identified. In order to understa…

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…

ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui, Veronika Thost, Zhang-Wei Hong, Johannes D\"urholt, Jie Chen, Wojciech Matusik, Mina Konakovi\'c Lukovi\'c
https://arxiv.org/abs/2510.03051

ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its performance hinges on surrogate and acquisition function hyper-parameters that are often hand-tuned and fail to generalize across problem landscapes. We present ZeroShotOpt, a general-purpose, pretrained model for continuous black-box optimization tasks ranging from 2D to 20D. Our approach leverages offline reinforcement …

Weak localization and antilocalization corrections to nonlinear transport: a semiclassical Boltzmann treatment
Dmitry V. Chichinadze
https://arxiv.org/abs/2510.02684 https://

Weak localization and antilocalization corrections to nonlinear transport: a semiclassical Boltzmann treatment
The nonlinear transport regime is manifested in the nonlinear current-voltage characteristic of the system. An example of such a nonlinear regime is a setup in which current is injected into the sample and the measured voltage drop is quadratic in the injected current. Such a quadratic nonlinear regime requires inversion symmetry to be broken. This is the same symmetry condition as one needs to observe weak antilocalization, which can be prominent in two-dimensional systems. Here, we study the …

🇺🇦 #NowPlaying on #BBC6Music's #GillesPeterson
Ingram:
🎵 Mi Sabrina Tequana
#Ingram
https://soulchemistsamples.bandcamp.com/track/sample-07-ingram-mi-sabrina-tequana
https://open.spotify.com/track/1BStvkPecxt0aRa4re7ARw

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…

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

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…

Using #PHP arrays as pseudo-objects is almost never the right answer. They're less self-documenting, slower, worse on memory, and more bug prone.
https://peakd.com/php/@crell/php-use-a

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…

NFL overreaction Monday: Fishy betting lines and early picks for Week 10 https://www.nytimes.com/athletic/6774230/2025/11/03/nfl-overreaction-monday-week-10/

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…

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 …

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…

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…

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…

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…

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

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

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.

Optimising the MeerKAT Pulsar Timing Array and towards precision pulsar timing with SKA-mid
Pratyasha Gitika, Ryan M. Shannon, Matthew Bailes, Daniel J. Reardon, Matthew T. Miles, David J. Champion, Kathrin Grunthal
https://arxiv.org/abs/2510.03139

Optimising the MeerKAT Pulsar Timing Array and towards precision pulsar timing with SKA-mid
Pulsar timing arrays (PTAs) are Galactic-scale nanohertz-frequency gravitational wave (GW) detectors. Recently, several PTAs have found evidence for the presence of GWs in their datasets, but none of them have achieved a community-defined definitive (> 5$σ$) detection. Here, we identify limiting noise sources for PTAs and quantify their impact on sensitivity to GWs under different observing and noise modelling strategies. First, we search for intrinsic pulse jitter in a sample of 89 MSPs obser…

Finally here! Two years after #NASA 's #OSIRIS-REx mission returned samples of #asteroid #Bennu

Gravitational lensing by a spiral galaxy I: the influence from bar's structure to the flux ratio anomaly
Xikai Shan, Yunpeng Jin, Shude Mao
https://arxiv.org/abs/2510.02805 …

Gravitational lensing by a spiral galaxy I: the influence from bar's structure to the flux ratio anomaly
Gravitational lens flux ratio anomalies are a powerful probe of small-scale mass structures within lens galaxies. These anomalies are often attributed to dark matter subhalos, but the baryonic components of the lens can also play a significant role. This study investigates the impact of galactic bars, a common feature in spiral galaxies, on flux ratio anomalies. We conduct a systematic analysis using a sample of 21 barred galaxies from the high-resolution Auriga cosmological simulations. First,…

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 …

🗳️🇺🇸 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…

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 …

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

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.

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…

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…

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…

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…

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.

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…

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://

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Donna Summer:
🎵 Bad Girls
#DonnaSummer
https://funkhutrecords.bandcamp.com/track/donna-summer-bad-girls-funkhut-sample

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…

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

Probing the Low Radio Frequency Emission in PG Quasars with the uGMRT - II
Sanna Gulati (NCRA-TIFR), Silpa Sasikumar (Universidad de Concepci\'on), Preeti Kharb (NCRA-TIFR), Luis C. Ho (Peking University), Salmoli Ghosh (NCRA-TIFR), Janhavi Baghel (NCRA-TIFR)
https://arxiv.org/abs/2510.02736

Probing the Low Radio Frequency Emission in PG Quasars with the uGMRT - II
We present results from uGMRT 685 MHz observations of 87 QSOs belonging to the Palomar Green (PG) quasar sample with $z<0.5$. Radio emission is detected in all sources except for 3 radio-quiet (RQ) sources, viz., PG 0043+039, PG 1121+422, and PG 1552+085. The radio-loud (RL) $-$ RQ dichotomy persists at 685 MHz with only 1 source, PG 1216+069, changing its classification from RQ to RL. Approximately 1/3 of the detected RQ quasars display AGN-dominated radio emission while the rest may show addi…

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…

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…

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…

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

PIC12F683 sample 2H6, after 14 HF etch cycles and a clean.
This sample has now been imaged 30 times, ranging from fresh after decap to the current state, after each etch, after cleaning passes to remove loose debris, and sometimes at both 20x and 100x in the same state.
This sample is now mostly at via1, but there's still upper layer visible in the seal ring and in the analog IPs.
It's now imaging at 100x. Things go slow because a full focus-stacked 100x image is 10…

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…

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…

Analysis of the plane of satellites around Milky Way-like galaxies in $\Lambda$CDM cosmology
Xinghai Zhao, Guobao Tang, Paola Gonzalez, Grant J. Mathews, Lara Arielle Phillips
https://arxiv.org/abs/2510.02518

Analysis of the plane of satellites around Milky Way-like galaxies in $Λ$CDM cosmology
It has been suggested that the Plane of Satellites (PoS) phenomenon may imply a tension with current $Λ$CDM cosmology since a Milky-Way (MW)-like PoS is very rare in simulations. In this study, we analyze a large sample of satellite systems of MW-like galaxies in the IllustrisTNG simulations. We analyze their spatial aspect ratio, orbital pole dispersion, Gini coefficient, radial distribution, and bulk satellite velocity relative to the host galaxy. These are compared to the observed Milky~Way…

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

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…

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…

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…

🇺🇦 #NowPlaying on #BBC6Music's #BLOCKPARTYWithHueyMorgan
Hiroshi Suzuki:
🎵 Romance
#HiroshiSuzuki
https://dubztabeats.bandcamp.com/track/hiroshi-suzuki-romance-dubzta-remix-sample-dat-submission-free-dl
https://open.spotify.com/track/1eoL2MmJ4u61ktIs8wawiQ

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.

On the origin of counterrotating stellar disks in TNG50. I
M. C. Bugue\~no, Facundo A. G\'omez, Arianna Dolfi, Patricia B. Tissera
https://arxiv.org/abs/2510.03106 https://

On the origin of counterrotating stellar disks in TNG50. I
Understanding galaxy evolution is key to explaining the structures we observe in the present-day Universe. Counterrotating stellar disks (CRDs), i.e. co-spatial stellar disks rotating with opposite angular momentum, have been proposed as signatures of past accretion events. Therefore, they constitute potentially valuable tracers of galactic assembly. We aim to investigate the properties, formation, and significance of CRDs in a sample of Milky Way mass galaxies using the IllustrisTNG cosmologic…

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…

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…

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…

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…

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…

Clapping: Removing Per-sample Storage for Pipeline Parallel Distributed Optimization with Communication Compression
Boao Kong, Xu Huang, Yuqi Xu, Yixuan Liang, Bin Wang, Kun Yuan
https://arxiv.org/abs/2509.19029

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…

Sample-optimal single-copy quantum state tomography via shallow depth measurements
Gyungmin Cho, Dohun Kim
https://arxiv.org/abs/2509.12703 https://arxiv.o…

Sample-optimal single-copy quantum state tomography via shallow depth measurements
Quantum state tomography (QST) is one of the fundamental problems in quantum information. Among various metrics, sample complexity is widely used to evaluate QST algorithms. While multi-copy measurements are known to achieve optimal sample complexity, they are challenging to implement on near-term quantum devices. In practice, single-copy measurements with shallow-depth circuits are more feasible. Although a near-optimal QST algorithm under single-qubit measurements has recently been proposed, …

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…

Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
Amardeep Legha, Joie Ensor, Rebecca Whittle, Lucinda Archer, Ben Van Calster, Evangelia Christodoulou, Kym I. E. Snell, Mohsen Sadatsafavi, Gary S. Collins, Richard D. Riley
https://arxiv.org/abs/2509.1513…

Sequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals
When prospectively developing a new clinical prediction model (CPM), fixed sample size calculations are typically conducted before data collection based on sensible assumptions. But if the assumptions are inaccurate the actual sample size required to develop a reliable model may be very different. To safeguard against this, adaptive sample size approaches have been proposed, based on sequential evaluation of a models predictive performance. Aim: illustrate and extend sequential sample size calc…

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…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Baron, Oreshkin, Ma, Zhang, Torkkola, Mahoney, Wilson, Konstantinova

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…

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…

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…

Optimal and Efficient Sample Size Re-estimation: A Dynamic Cost Framework
Rui Jin, Cai Wu, Qiqi Deng
https://arxiv.org/abs/2509.16636 https://arxiv.org/pdf…

Optimal and Efficient Sample Size Re-estimation: A Dynamic Cost Framework
Adaptive sample size re-estimation (SSR) is a well-established strategy for improving the efficiency and flexibility of clinical trials. Its central challenge is determining whether, and by how much, to increase the sample size at an interim analysis. This decision requires a rational framework for balancing the potential gain in statistical power against the risk and cost of further investment. Prevailing optimization approaches, such as the Jennison and Turnbull (JT) method, address this by m…

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.…

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…

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…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- A fast and effective kernel two-sample test for large-scale data
Hoseung Song, Hao Chen

Sample completion, structured correlation, and Netflix problems
Leonardo N. Coregliano, Maryanthe Malliaris
https://arxiv.org/abs/2509.20404 https://arxiv.…

Sample completion, structured correlation, and Netflix problems
We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah's classification theory). This model suggests a theoretical explanation for the success of certain algorithms in the 2006~Netflix Prize competition.

Scale two-sample testing with arbitrarily missing data
Yijin Zeng, Niall M. Adams, Dean A. Bodenham
https://arxiv.org/abs/2509.20332 https://arxiv.org/pdf/…

Scale two-sample testing with arbitrarily missing data
This work proposes a novel rank-based scale two-sample testing method for univariate, distinct data when a subset of the data may be missing. Our approach is based on mathematically tight bounds of the Ansari-Bradley test statistic in the presence of missing data, and rejects the null hypothesis if the test statistic is significant regardless of the missing values. This proposed scale testing method is then combined with the location testing method proposed by Zeng et al. (2024) using the Holm-…

The Loss Kernel: A Geometric Probe for Deep Learning Interpretability
Maxwell Adam, Zach Furman, Jesse Hoogland
https://arxiv.org/abs/2509.26537 https://ar…

The Loss Kernel: A Geometric Probe for Deep Learning Interpretability
We introduce the loss kernel, an interpretability method for measuring similarity between data points according to a trained neural network. The kernel is the covariance matrix of per-sample losses computed under a distribution of low-loss-preserving parameter perturbations. We first validate our method on a synthetic multitask problem, showing it separates inputs by task as predicted by theory. We then apply this kernel to Inception-v1 to visualize the structure of ImageNet, and we show that t…

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…

A New Sample of Dwarf Galaxies with X-ray-Selected AGN Candidates from the eROSITA All-Sky Survey
John-Michael Eberhard, Amy E. Reines
https://arxiv.org/abs/2509.22783 https://

A New Sample of Dwarf Galaxies with X-ray-Selected AGN Candidates from the eROSITA All-Sky Survey
To investigate the population of massive black holes in dwarf galaxies, we conduct a systematic search for active galactic nuclei (AGNs) using data from the first data release of the eROSITA All-Sky Survey (eRASS1). We crossmatch dwarf galaxy positions in the NASA-Sloan Atlas with X-ray sources from eRASS and apply rigorous criteria to remove contaminants such as background sources, galaxies with dubious stellar masses, and X-ray binaries. Potential contamination from ultraluminous X-ray source…

A more efficient method for large-sample model-free feature screening via multi-armed bandits
Xiaxue Ouyang, Xinlai Kang, Mengyu Li, Zhenxing Dou, Jun Yu, Cheng Meng
https://arxiv.org/abs/2509.16085

A more efficient method for large-sample model-free feature screening via multi-armed bandits
We consider the model-free feature screening in large-scale ultrahigh-dimensional data analysis. Existing feature screening methods often face substantial computational challenges when dealing with large sample sizes. To alleviate the computational burden, we propose a rank-based model-free sure independence screening method (CR-SIS) and its efficient variant, BanditCR-SIS. The CR-SIS method, based on Chatterjee's rank correlation, is as straightforward to implement as the sure independence scr…