Tootfinder

Sample Size Calculations for the Development of Risk Prediction Models that Account for Performance Variability
Menelaos Pavlou, Rumana Z. Omar, Gareth Ambler
https://arxiv.org/abs/2509.14028

Sample Size Calculations for the Development of Risk Prediction Models that Account for Performance Variability
Existing approaches to sample size calculations for developing clinical prediction models have focused on ensuring that the expected value of a chosen performance measure meets a pre-specified target. For example, to limit model-overfitting, the sample size is commonly chosen such that the expected calibration slope (CS) is 0.9, close to 1 for a perfectly calibrated model. In practice, due to sampling variability, model performance can vary considerably across different development samples of t…

Tomorrow we have a Symposium at the Advanced Medical BIOimaging core facility #AMBIO of the @… , to celebrate our 10 year anniversary. There will be lots of great talks about microscopy stuff (single-molecule spatial omics, multi scale microscopy, light sheet,…

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…

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

I have just received a malware via e-mail, and I'm like... what year is it??? A .js file inside a .rar file, creating an ActiveXObject????
The original (obfuscated) sample is here:
https://www.virustotal.com/gui/file/d6b9f8e88bc3…

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…

Graph-Regularized Learning of Gaussian Mixture Models
Shamsiiat Abdurakhmanova, Alex Jung
https://arxiv.org/abs/2509.13855 https://arxiv.org/pdf/2509.13855…

Graph-Regularized Learning of Gaussian Mixture Models
We present a graph-regularized learning of Gaussian Mixture Models (GMMs) in distributed settings with heterogeneous and limited local data. The method exploits a provided similarity graph to guide parameter sharing among nodes, avoiding the transfer of raw data. The resulting model allows for flexible aggregation of neighbors' parameters and outperforms both centralized and locally trained GMMs in heterogeneous, low-sample regimes.

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

Coherent Driving of a Quantum System with Modulated Free-Space Electrons
Matthias Kolb, Thomas Spielauer, Thomas Weigner, Giovanni Boero, Dennis R\"atzel, Philipp Haslinger
https://arxiv.org/abs/2509.13904

Coherent Driving of a Quantum System with Modulated Free-Space Electrons
Control of quantum systems typically relies on the interaction with electromagnetic radiation. In this study, we experimentally show that the electromagnetic near-field of a spatially modulated freespace electron beam can be used to drive spin systems, demonstrating free-electron-bound-electron resonant interaction. By periodically deflecting the electron beam of a scanning electron microscope in close proximity to a spin-active solid-state sample, and sweeping the deflection frequency across t…

SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
Jannick Strangh\"oner, Philipp Hartmann, Marco Braun, Sebastian Wrede, Klaus Neumann
https://arxiv.org/abs/2509.13949

SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
High-mix low-volume (HMLV) industrial assembly, common in small and medium-sized enterprises (SMEs), requires the same precision, safety, and reliability as high-volume automation while remaining flexible to product variation and environmental uncertainty. Current robotic systems struggle to meet these demands. Manual programming is brittle and costly to adapt, while learning-based methods suffer from poor sample efficiency and unsafe exploration in contact-rich tasks. To address this, we prese…

On the Rate of Gaussian Approximation for Linear Regression Problems
Marat Khusainov, Marina Sheshukova, Alain Durmus, Sergey Samsonov
https://arxiv.org/abs/2509.14039 https://

On the Rate of Gaussian Approximation for Linear Regression Problems
In this paper, we consider the problem of Gaussian approximation for the online linear regression task. We derive the corresponding rates for the setting of a constant learning rate and study the explicit dependence of the convergence rate upon the problem dimension $d$ and quantities related to the design matrix. When the number of iterations $n$ is known in advance, our results yield the rate of normal approximation of order $\sqrt{\log{n}/n}$, provided that the sample size $n$ is large enoug…

The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Jamie Cummins
https://arxiv.org/abs/2509.13397 https://

The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. However, there are many analytic choices which must be made to produce these samples. Though many of these choices are defensible, their impact on sample quality is poorly understood. I map out these analytic choices and demonstrate how a very small number of decisions can dramatically change the corr…

Classification Filtering
Ilker Bayram
https://arxiv.org/abs/2509.13975 https://arxiv.org/pdf/2509.13975

Classification Filtering
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for…

On Tackling High-Dimensional Nonconvex Stochastic Optimization via Stochastic First-Order Methods with Non-smooth Proximal Terms and Variance Reduction
Yue Xie, Jiawen Bi, Hongcheng Liu
https://arxiv.org/abs/2509.13992

On Tackling High-Dimensional Nonconvex Stochastic Optimization via Stochastic First-Order Methods with Non-smooth Proximal Terms and Variance Reduction
When the nonconvex problem is complicated by stochasticity, the sample complexity of stochastic first-order methods may depend linearly on the problem dimension, which is undesirable for large-scale problems. To alleviate this linear dependence, we adopt non-Euclidean settings and propose two choices of nonsmooth proximal terms when taking the stochastic gradient steps. This approach leads to stronger convergence metric, incremental computational overhead, and potentially dimension-insensitive …

DESI DR1 Ly$\alpha$ 1D power spectrum: Validation of estimators
N. G. Kara\c{c}ayl{\i}, C. Ravoux, P. Martini, J. M. Le Goff, E. Armengaud, M. Abdul-Karim, J. Aguilar, S. Ahlen, A. Anand, S. BenZvi, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, A. de la Macorra, Biprateep Dey, P. Doel, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, H. K. Herrera-Alcantar, K. Honscheid, M. Ishak, J. Jimenez, R. Joyce, D. Kirkby, T. Kisner, A. Kremin…

DESI DR1 Ly$α$ 1D power spectrum: Validation of estimators
The Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) is the largest sample to date for small-scale Ly$α$ forest cosmology, accessed through its one-dimensional power spectrum ($P_{\mathrm{1D}}$). The Ly$α$ forest $P_{\mathrm{1D}}$ is extracted from quasar spectra that are highly inhomogeneous (both in wavelength and between quasars) in noise properties due to intrinsic properties of the quasar, atmospheric and astrophysical contamination, and also sensitive to low-level…

Visual morphological classification of the full MaNGA DR17 sample: a general characterization
J. A. V\'azquez-Mata, H. M. Hern\'andez-Toledo, V. Avila-Reese, A. Rodr\'iguez-Puebla, L. A. Mart\'inez-V\'azquez, M. Herrera-Endoqui, I. Lacerna, L. C. Mascherpa, D. F. Morell
https://arxiv.org/abs/2510.12792

Visual morphological classification of the full MaNGA DR17 sample: a general characterization
We present the MaNGA Visual Morphology (MVM) catalogue, featuring a visual morphological classification of 10,059 galaxies in the final MaNGA sample. By combining SDSS and DESI Legacy Survey (DLS) images, we classified galaxies into 13 Hubble types, detected tidal features, categorized bars into different families, and estimated concentration, asymmetry, and clumpiness. The depth of the DLS images allowed us to identify structural details that were not evident in the SDSS images, resulting in a…

Distributionally Robust Equilibria over the Wasserstein Distance for Generalized Nash Game
Yixun Wen, Yulong Gao, Boli Chen
https://arxiv.org/abs/2509.13985 https://

Distributionally Robust Equilibria over the Wasserstein Distance for Generalized Nash Game
Generalized Nash equilibrium problem (GNEP) is fundamental for practical applications where multiple self-interested agents work together to make optimal decisions. In this work, we study GNEP with shared distributionally robust chance constraints (DRCCs) for incorporating inevitable uncertainties. The DRCCs are defined over the Wasserstein ball, which can be explicitly characterized even with limited sample data. To determine the equilibrium of the GNEP, we propose an exact approach to transfo…

Extensive Analysis of gamma-Ray Periodicity in Jetted AGN from the 4FGL Catalog Using Fermi-LAT Observations
P. Pe\~nil, A. Dom\'inguez, S. Buson, M. Ajello, S. Adhikari, A. Rico
https://arxiv.org/abs/2509.14013

Extensive Analysis of gamma-Ray Periodicity in Jetted AGN from the 4FGL Catalog Using Fermi-LAT Observations
The quest to uncover periodic patterns within the $γ$-ray emissions of jetted active galactic nuclei (AGN) has recently emerged as a focal point in astrophysics. One of the primary challenges has been the necessity for prolonged exposures in the $γ$-ray energy band. In our investigation, we leverage 12 years' worth of observations from the \textit{Fermi}-LAT to systematically explore periodicity across 1492 jetted AGN cataloged in 4FGL, representing the largest sample analyzed to date. Our an…

Shape optimization for trailing-edge noise reduction using large-eddy simulation and ensemble-based method
Qingyong Luo, Xin-Lei Zhang, Guowei He
https://arxiv.org/abs/2509.13752

Shape optimization for trailing-edge noise reduction using large-eddy simulation and ensemble-based method
In this work, the trailing-edge shape of an airfoil is optimized to reduce the acoustic noise based on large-eddy simulation (LES). It is achieved by the ensemble Kalman method, which can enhance the optimization efficiency by using the gradient of cost function approximated with sample covariances. Moreover, the update scheme is reformulated to impose smoothness regularization and enable simultaneous reduction in the trailing edge noise and the drag-to-lift ratio. The trailing edge is optimize…

Automatic Music Sample Identification with Multi-Track Contrastive Learning
Alain Riou, Joan Serr\`a, Yuki Mitsufuji
https://arxiv.org/abs/2510.11507 https://

Automatic Music Sample Identification with Multi-Track Contrastive Learning
Sampling, the technique of reusing pieces of existing audio tracks to create new music content, is a very common practice in modern music production. In this paper, we tackle the challenging task of automatic sample identification, that is, detecting such sampled content and retrieving the material from which it originates. To do so, we adopt a self-supervised learning approach that leverages a multi-track dataset to create positive pairs of artificial mixes, and design a novel contrastive lear…

Anyone know what the format of this "dotnet.config" file is?
It's mentioned in the #dotnet release notes, but it's the first time I see mention of it and my googling seems to be failing me.

Comment on `High-resolution Measurements of Thermal Conductivity Matrix and Search for Thermal Hall Effect in La$_2$CuO$_4$'
Shan Jiang, Qiaochao Xiang, Beno\^it Fauqu\'e, Xiaokang Li, Zengwei Zhu, Kamran Behnia
https://arxiv.org/abs/2509.14105

Comment on `High-resolution Measurements of Thermal Conductivity Matrix and Search for Thermal Hall Effect in La$_2$CuO$_4$'
Recently, Jiayi Hu and co-workers reported that they did not resolve any thermal Hall signal in La$_2$CuO$_4$ by `high resolution' measurements, setting an upper bound of $|κ_{xy}| <2\times 10^{-3}~$Wm$^{-1}$K$^{-1}$ at 20 K. Two points have apparently escaped their attention. First, thermal Hall signals with an amplitude well below this resolution bound have been detected in disordered perovskites. Second, the longitudinal thermal conductivity of their sample is significantly lower …

Selective and marginal selective inference for exceptional groups
Peter Hoff, Surya Tokdar
https://arxiv.org/abs/2509.13538 https://arxiv.org/pdf/2509.1353…

Selective and marginal selective inference for exceptional groups
Statistical analyses of multipopulation studies often use the data to select a particular population as the target of inference. For example, a confidence interval may be constructed for a population only in the event that its sample mean is larger than that of the other populations. We show that for the normal means model, confidence interval procedures that maintain strict coverage control conditional on such a selection event will have infinite expected width. For applications where such sel…

🇺🇦 #NowPlaying on #BBC6Music's #CraigCharles
Gang Starr:
🎵 Love Sick
#GangStarr
https://miketempo.bandcamp.com/track/gang-starr-love-sick-mike-tempo-sample-edit
https://open.spotify.com/track/6biAvuLvtDrIZI9kKKkhgC

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…

Sample-Efficient Omniprediction for Proper Losses
Isaac Gibbs, Ryan J. Tibshirani
https://arxiv.org/abs/2510.12769 https://arxiv.org/pdf/2510.12769

Sample-Efficient Omniprediction for Proper Losses
We consider the problem of constructing probabilistic predictions that lead to accurate decisions when employed by downstream users to inform actions. For a single decision maker, designing an optimal predictor is equivalent to minimizing a proper loss function corresponding to the negative utility of that individual. For multiple decision makers, our problem can be viewed as a variant of omniprediction in which the goal is to design a single predictor that simultaneously minimizes multiple los…

Precise boron abundance in a sample of metal-poor stars from far-ultraviolet lines
Monique Spite, Beatriz Barbuy, Kefeng Tan
https://arxiv.org/abs/2510.11594 https://

Precise boron abundance in a sample of metal-poor stars from far-ultraviolet lines
The light elements beryllium (Be; $Z=4$) and boron (B; $Z=5$) are mainly produced by spallation reactions between cosmic rays and carbon (C; $Z=6$), nitrogen (N; $Z=7$), and oxygen (O; $Z=8$) nuclei. Only traces of Be or B would have been produced in the Big Bang, but there could be a contribution from the $ν$-process in type II supernovae. Their abundances at very low metallicities have been debated in the literature, with the aim of understanding their origin. Our aim is to derive the bo…

A Martingale Kernel Two-Sample Test
Anirban Chatterjee, Aaditya Ramdas
https://arxiv.org/abs/2510.11853 https://arxiv.org/pdf/2510.11853

A Martingale Kernel Two-Sample Test
The Maximum Mean Discrepancy (MMD) is a widely used multivariate distance metric for two-sample testing. The standard MMD test statistic has an intractable null distribution typically requiring costly resampling or permutation approaches for calibration. In this work we leverage a martingale interpretation of the estimated squared MMD to propose martingale MMD (mMMD), a quadratic-time statistic which has a limiting standard Gaussian distribution under the null. Moreover we show that the test is…

Towards Inference-time Scaling for Continuous Space Reasoning
Minghan Wang, Thuy-Trang Vu, Ehsan Shareghi, Gholamreza Haffari
https://arxiv.org/abs/2510.12167 https://

Towards Inference-time Scaling for Continuous Space Reasoning
Inference-time scaling through multiple sample generation in combination with Process- or Outcome-Reward Model (PRM or ORM) re-ranking has proven effective for text-based reasoning in large language models. This paper investigates whether such established techniques can be successfully adapted to reasoning in the continuous space, using COCONUT (Hao et al. 2024) continuous space reasoning LM as the backbone. We demonstrate the feasibility of generating diverse reasoning paths through dropout-ba…

CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving
Xiaoji Zheng, Ziyuan Yang, Yanhao Chen, Yuhang Peng, Yuanrong Tang, Gengyuan Liu, Bokui Chen, Jiangtao Gong
https://arxiv.org/abs/2510.12560

CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving
End-to-end autonomous driving models trained solely with imitation learning (IL) often suffer from poor generalization. In contrast, reinforcement learning (RL) promotes exploration through reward maximization but faces challenges such as sample inefficiency and unstable convergence. A natural solution is to combine IL and RL. Moving beyond the conventional two-stage paradigm (IL pretraining followed by RL fine-tuning), we propose CoIRL-AD, a competitive dual-policy framework that enables IL an…

Formal Models and Convergence Analysis for Context-Aware Security Verification
Ayush Chaudhary
https://arxiv.org/abs/2510.12440 https://arxiv.org/pdf/2510.…

Formal Models and Convergence Analysis for Context-Aware Security Verification
We present a formal framework for context-aware security verification that establishes provable guarantees for ML-enhanced adaptive systems. We introduce context-completeness - a new security property - and prove: (1) sample complexity bounds showing when adaptive verification succeeds, (2) information-theoretic limits relating context richness to detection capability, (3) convergence guarantees for ML-based payload generators, and (4) compositional soundness bounds. We further provide a formal…

Deep Extragalactic VIsible Legacy Survey (DEVILS): Galaxy group catalogue for the D10-COSMOS field with 90% spectroscopic redshift completeness
Mat\'ias Bravo, Luke J. M. Davies, Aaron S. G. Robotham, Claudia del P. Lagos, Sabine Bellstedt, Joss Bland-Hawthorn, Malgorzata Siudek, Trystan S. Lambert, Chris Power
https://arxiv.org/abs/25…

Deep Extragalactic VIsible Legacy Survey (DEVILS): Galaxy group catalogue for the D10-COSMOS field with 90% spectroscopic redshift completeness
Large-scale galaxy redshift surveys conducted over the last couple of decades have proven crucial in deepening our understanding of structure growth in the Universe and galaxy evolution. While there have been several such surveys, until now those that achieve the high completeness and precision necessary to probe the low-mass end of galaxy groups have been limited to relatively low redshifts ($z\lesssim0.3$), with surveys exploring the more distant Universe being constrained by small sample siz…

🇺🇦 #NowPlaying on #BBC6Music's #CraigCharles
Gang Starr:
🎵 Love Sick
#GangStarr
https://miketempo.bandcamp.com/track/gang-starr-love-sick-mike-tempo-sample-edit
https://open.spotify.com/track/6biAvuLvtDrIZI9kKKkhgC

Correcting sample selection bias with categorical outcomes
Onil Boussim
https://arxiv.org/abs/2510.05551 https://arxiv.org/pdf/2510.05551

Correcting sample selection bias with categorical outcomes
In this paper, we propose a method for correcting sample selection bias when the outcome of interest is categorical, such as occupational choice, health status, or field of study. Classical approaches to sample selection rely on strong parametric distributional assumptions, which may be restrictive in practice. While the recent framework of Chernozhukov et al. (2023) offers a nonparametric identification using a local Gaussian representation (LGR) that holds for any bivariate joint distribution…

When you say "we don't need user research, I already know what users want" this is what you look like
#ux #UXDesign #userresearch

Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Puneet Velidi, Zhengxiao Wei, Shreena Nisha Kalaria, Yimeng Liu, C\'eline M. Laumont, Brad H. Nelson, Farouk S. Nathoo
https://arxiv.org/abs/2510.10358

Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Concerns about the misuse and misinterpretation of p-values and statistical significance have motivated alternatives for quantifying evidence. We define a generalized form of Jeffreys's approximate objective Bayes factor (eJAB), a one-line calculation that is a function of the p-value, sample size, and parameter dimension. We establish conditions under which eJAB is model-selection consistent and verify them for ten statistical tests. We assess finite-sample accuracy by comparing eJAB with Mark…

Lossless Derandomization for Undirected Single-Source Shortest Paths and Approximate Distance Oracles
Shuyi Yan
https://arxiv.org/abs/2510.12598 https://ar…

Lossless Derandomization for Undirected Single-Source Shortest Paths and Approximate Distance Oracles
A common step in algorithms related to shortest paths in undirected graphs is that, we select a subset of vertices as centers, then grow a ball around each vertex until a center is reached. We want the balls to be as small as possible. A randomized algorithm can uniformly sample $r$ centers to achieve the optimal (expected) ball size of $Θ(n/r)$. A folklore derandomization is to use the $O(\log n)$ approximation for the set cover problem in the hitting set version where we want to hit all the …

OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching
Jingxuan Wu, Zhenglin Wan, Xingrui Yu, Yuzhe Yang, Bo An, Ivor Tsang
https://arxiv.org/abs/2510.09060

OSCAR: Orthogonal Stochastic Control for Alignment-Respecting Diversity in Flow Matching
Flow-based text-to-image models follow deterministic trajectories, forcing users to repeatedly sample to discover diverse modes, which is a costly and inefficient process. We present a training-free, inference-time control mechanism that makes the flow itself diversity-aware. Our method simultaneously encourages lateral spread among trajectories via a feature-space objective and reintroduces uncertainty through a time-scheduled stochastic perturbation. Crucially, this perturbation is projected …

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
Abdurakhmon Sadiev, Peter Richt\'arik, Ilyas Fatkhullin
https://arxiv.org/abs/2510.10690

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
Heavy-tailed noise is pervasive in modern machine learning applications, arising from data heterogeneity, outliers, and non-stationary stochastic environments. While second-order methods can significantly accelerate convergence in light-tailed or bounded-noise settings, such algorithms are often brittle and lack guarantees under heavy-tailed noise -- precisely the regimes where robustness is most critical. In this work, we take a first step toward a theoretical understanding of second-order opt…

Construction of Basis Functions for the Geometry Conforming Immersed Finite Element Method
Slimane Adjerid, Tao Lin, Haroun Meghaichi
https://arxiv.org/abs/2510.12018 https://…

Construction of Basis Functions for the Geometry Conforming Immersed Finite Element Method
The Frenet apparatus is a new framework for constructing high order geometry-conforming immersed finite element functions for interface problems. In this report, we present a procedure for constructing the local IFE bases in some detail as well as a new approach for constructing orthonormal bases using the singular value decomposition of the local generalized Vandermonde matrix. A sample implementation in MATLAB is provided to showcase the simplicity and extensionability of the framework.

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…

Which one do you use the most, while not at work, but in your free personal time?
Please consider boosting for a larger sample size. Thank you.
#poll #computing #fediverse

Back to the HMCAD1520 FA.
This is sample 6A1 after initial partial decap. It's still covered in the north and easy because I guessed how big the die would be and apparently underestimated its size.
The good news is that it's gold ball bonded, so more time in the acid shouldn't risk damaging the bond wires at all.
Time to re-mount it with a bigger mask and try again.

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…

Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, B\'alint M\'at\'e, Artur P. Toshev, Manasa Kaniselvan, Rafael G\'omez-Bombarelli, Ricky T. Q. Chen, Brandon Wood, Guan-Horng Liu, Benjamin Kurt Miller
https://arxiv.org/abs/2510.11923

Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, information-rich, low-dimensional projections of atomic coordinates known as collective variables (CVs)…

🗳️🇺🇸 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…

This guy made a Synthetic Aperture Radar (i.e. a radar that gives you camera-like pictures) mounted on a drone. Scroll to the bottom for actual sample pictures and a movie. Lots of tech detail and open-source designs and software.
https://hforsten.com/synthetic-apertur

> Cambridge’s preservationists don’t just mount disks and hope for the best; they sample the raw magnetic signal itself. Specialized hardware, such as the KryoFlux and open-hardware Greaseweazle interfaces, captures the flux transitions — the tiny changes in polarity that encode data — and reconstructs the file structure later in software.

Cambridge University launches project to rescue data trapped on old floppy disks
Cambridge’s 'Future Nostalgia' project is racing to save decades of digital history from vanishing floppy disks.

I swear the finale of one of #kpopdemonhunters songs is a Dead Can Dance/Lisa Gerrard sample.

Spectral Thresholds for Identifiability and Stability:Finite-Sample Phase Transitions in High-Dimensional Learning
William Hao-Cheng Huang
https://arxiv.org/abs/2510.03809 https…

Spectral Thresholds for Identifiability and Stability:Finite-Sample Phase Transitions in High-Dimensional Learning
In high-dimensional learning, models remain stable until they collapse abruptly once the sample size falls below a critical level. This instability is not algorithm-specific but a geometric mechanism: when the weakest Fisher eigendirection falls beneath sample-level fluctuations, identifiability fails. Our Fisher Threshold Theorem formalizes this by proving that stability requires the minimal Fisher eigenvalue to exceed an explicit $O(\sqrt{d/n})$ bound. Unlike prior asymptotic or model-specifi…

TOI-3288 b and TOI-4666 b: two gas giants transiting low-mass stars characterised by NIRPS
Yolanda G. C. Frensch, Fran\c{c}ois Bouchy, Gaspare Lo Curto, Alexandrine L'Heureux, Roseane de Lima Gomes, Jo\~ao Faria, Xavier Dumusque, Lison Malo, Marion Cointepas, Avidaan Srivastava, Xavier Bonfils, Elisa Delgado-Mena, Nicola Nari, Khaled Al Moulla, Romain Allart, Jose M. Almenara, \'Etienne Artigau, Khalid Barkaoui, Fr\'ed\'erique Baron, Susana C. C. Barros, Bj\"orn Be…

TOI-3288 b and TOI-4666 b: two gas giants transiting low-mass stars characterised by NIRPS
Gas giant planets orbiting low-mass stars are uncommon outcomes of planet formation. Increasing the sample of well-characterised giants around early M dwarfs will enable population-level studies of their properties, offering valuable insights into their formation and evolutionary histories. We aim to characterise giant exoplanets transiting M dwarfs identified by TESS. High-resolution spectroscopic data are obtained in the optical and nIR, combining HARPS and NIRPS. We derive RVs via the cross-…

Superradiance and Superabsorption Engine of $N$ Two-Level Systems: $N^{2}$-Power Scaling at Near-Unity Efficiency
L. F. Alves da Silva, H. Sanchez, M. A. Ponte, M. H. Y. Moussa, Norton G. de Almeida
https://arxiv.org/abs/2510.12017

Superradiance and Superabsorption Engine of $N$ Two-Level Systems: $N^{2}$-Power Scaling at Near-Unity Efficiency
We present a thermal engine that exploits the \emph{cooperative superradiance} and \emph{superabsorption} of a sample of \(N\) two-level atoms. This engine operates using a single cold reservoir via cycles of collective pumping followed by decay. Using an effective mean-field Hamiltonian to describe the many-body dynamics, we design optimized drive pulses that preserve adiabaticity and achieve an average power output scaling quadratically with the system size, \(P \propto N^2\). An experimental…

The Blue Jay Survey: Deep JWST Spectroscopy for a Representative Sample of Galaxies at Cosmic Noon
Sirio Belli, Letizia Bugiani, Minjung Park, J. Trevor Mendel, Rebecca L. Davies, Amir H. Khoram, Benjamin D. Johnson, Joel Leja, Sandro Tacchella, Vanessa Brown, Charlie Conroy, Razieh Emami, Yijia Li, Caterina Liboni, Gabriel Maheson, Elijah P. Mathews, Rohan P. Naidu, Erica J. Nelson, Bryan A. Terrazas, Rainer Weinberger

The Blue Jay Survey: Deep JWST Spectroscopy for a Representative Sample of Galaxies at Cosmic Noon
We present the Blue Jay survey, a Cycle-1 JWST program aimed at studying the stellar and gas content of galaxies at Cosmic Noon. The survey consists of deep spectroscopy for 153 targets observed over two pointings in the COSMOS field using the NIRSpec micro-shutter assembly (MSA). We employ the three medium-resolution gratings G140M, G235M, and G395M, with exposure times of 13 hours, 3.2 hours, and 1.6 hours, respectively. We thus obtain full coverage of the 1-5 micron range, corresponding to t…

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…

An optimal two-step estimation approach for two-phase studies
Qingning Zhou, Kin Yau Wong
https://arxiv.org/abs/2510.11587 https://arxiv.org/pdf/2510.11587…

An optimal two-step estimation approach for two-phase studies
Two-phase sampling is commonly adopted for reducing cost and improving estimation efficiency. In many two-phase studies, the outcome and some cheap covariates are observed for a large sample in Phase I, and expensive covariates are obtained for a selected subset of the sample in Phase II. As a result, the analysis of the association between the outcome and covariates faces a missing data problem. Complete-case analysis, which relies solely on the Phase II sample, is generally inefficient. In th…

Magnetic Decay Index Profile and Coronal Mass Ejection Speed
Bernhard Kliem, Georgios Chintzoglou, Tibor T\"or\"ok, Jie Zhang
https://arxiv.org/abs/2510.11889 https://…

Magnetic Decay Index Profile and Coronal Mass Ejection Speed
We study the relationship between the speed of coronal mass ejections (CMEs) and the height profile of the ambient magnetic field, quantified by its decay index, n(h). Our sample is composed of 15 very fast CMEs (Vcme > 1500 km/s; all halo CMEs) and 22 halo CMEs below this speed limit from Solar Cycle 23. The very fast CMEs yield a high correlation of 0.81 between Vcme and the slope of n(h) in a height range above the onset height of the torus instability if one extremely fast outlier, which cl…

Eruptive mass loss less than a year before the explosion of superluminous supernovae. II. A systematic search for pre-explosion eruptions with VLT/X-shooter
A. Gkini, C. Fransson, R. Lunnan, S. Schulze, J. Sollerman K. Tsalapatas, N. Sarin, M. Nicholl, C. Angus, U. Burgaz, S. J. Brennan, T. -W. Chen, A. Gal-Yam, A. Gangopadhyay, Y. Hu, M. M. Kasliwal, R. R. Laher, P. J. Pessi, B. Rusholme, E. Russeil, A. Singh, C. Skoglund, R. Smith, B. van Baal, S. L. West, L. Yan

Eruptive mass loss less than a year before the explosion of superluminous supernovae. II. A systematic search for pre-explosion eruptions with VLT/X-shooter
We present X-shooter observations of a sample of 21 hydrogen-poor superluminous supernovae (SLSNe-I), spanning a redshift range of z=0.13-0.95, aimed at searching for shells of circumstellar material (CSM). Specifically, we focus on identifying broad Mg II absorption features that are blueshifted by several thousand kilometers per second and have previously been interpreted as arising from resonance line scattering of the SLSN continuum by rapidly expanding CSM ejected shortly before explosion.…

The joys of doing semiconductor lab work while getting over the tail end of some kind of respiratory bug: trying not to cough and yeet the sample you've been working on for two weeks across the lab

Moment-based Posterior Sampling for Multi-reference Alignment
Axel Janson, Joakim And\'en
https://arxiv.org/abs/2510.12651 https://arxiv.org/pdf/2510.1…

Moment-based Posterior Sampling for Multi-reference Alignment
We propose a Bayesian approach to the problem of multi-reference alignment -- the recovery of signals from noisy, randomly shifted observations. While existing frequentist methods accurately recover the signal at arbitrarily low signal-to-noise ratios, they require a large number of samples to do so. In contrast, our proposed method leverages diffusion models as data-driven plug-and-play priors, conditioning these on the sample power spectrum (a shift-invariant statistic) enabling both accurate…

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…

Generalized Taylor's Law for Dependent and Heterogeneous Heavy-Tailed Data
Pok Him Cheng, Joel E. Cohen, Hok Kan Ling, Sheung Chi Phillip Yam
https://arxiv.org/abs/2510.09562

Generalized Taylor's Law for Dependent and Heterogeneous Heavy-Tailed Data
Taylor's law, also known as fluctuation scaling in physics and the power-law variance function in statistics, is an empirical pattern widely observed across fields including ecology, physics, finance, and epidemiology. It states that the variance of a sample scales as a power function of the mean of the sample. We study generalizations of Taylor's law in the context of heavy-tailed distributions with infinite mean and variance. We establish the probabilistic limit and analyze the associated con…

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

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

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 …

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Learning to sample fibers for goodness-of-fit testing
Ivan Gvozdanovi\'c, Sonja Petrovi\'c

Group Diffusion: Enhancing Image Generation by Unlocking Cross-Sample Collaboration
Sicheng Mo, Thao Nguyen, Richard Zhang, Nick Kolkin, Siddharth Srinivasan Iyer, Eli Shechtman, Krishna Kumar Singh, Yong Jae Lee, Bolei Zhou, Yuheng Li
https://arxiv.org/abs/2512.10954

Out of the following operating systems, which one do you use the most, not at work, but in your free personal time?
Please consider boosting for a larger sample size. Thank you.
#poll #os #computing

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Markus Walker, Daniel Frisch, Uwe D. Hanebeck
https://arxiv.org/abs/2510.05706

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance on random sampling. This conventional approach can lead to inefficient exploration of the solution space and non-smooth control inputs, requiring a large number of samples to achieve satisfactory results. To address these limitations, we propose deterministic sampling CEM (dsCEM), a novel framework that replaces the ra…

MANGOS II: Five new giant planets orbiting low-mass stars
G. Dransfield, M. Timmermans, D. Sebastian, B. V. Rackham, A. Burgasser, K. Barkaoui, A. H. M. J. Triaud, M. Gillon, J. M. Almenara, S. L. Casewell, K. A. Collins, A. Fukui, C. Jano-Munoz, S. Kanodia, N. Narita, E. Palle, M. G. Scott, A. Soubkiou, A. Stokholm, J. Audenaert, G. \'A. Bakos, Y. Beletsky, Z. L. de Beurs, Z. Benkhaldoun, A. Burdanov, R. P. Butler, D. Caldwell, J. D. Crane, Y. T. Davis, B. O. Demory, E. Ducrot, Y.…

MANGOS II: Five new giant planets orbiting low-mass stars
Giant planets orbiting low-mass stars on short orbits present a conundrum, as in the most extreme cases their existence cannot be reconciled with current models of core accretion. Therefore, surveys dedicated to finding these rare planets have a key role to play by growing the sample to overcome small number statistics. In this work we present MANGOS, a programme dedicated to the search for giant objects (planets, brown dwarfs, and low-mass stars) orbiting M dwarfs. We report on the discovery o…

Another few minutes in HF on the PIC12F683 sample and it looks like parts of M2 are starting to delaminate, while other parts still had oxide over them??
Gonna clean and see how this goes. This is the sort of thing that makes wet-etch deprocessing challenging though.

Ego-Vision World Model for Humanoid Contact Planning
Hang Liu, Yuman Gao, Sangli Teng, Yufeng Chi, Yakun Sophia Shao, Zhongyu Li, Maani Ghaffari, Koushil Sreenath
https://arxiv.org/abs/2510.11682

Ego-Vision World Model for Humanoid Contact Planning
Enabling humanoid robots to exploit physical contact, rather than simply avoid collisions, is crucial for autonomy in unstructured environments. Traditional optimization-based planners struggle with contact complexity, while on-policy reinforcement learning (RL) is sample-inefficient and has limited multi-task ability. We propose a framework combining a learned world model with sampling-based Model Predictive Control (MPC), trained on a demonstration-free offline dataset to predict future outco…

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
Shai Zucker, Xiong Wang, Fei Lu, Inbar Seroussi
https://arxiv.org/abs/2510.11789 https:…

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
We study the convergence rate of learning pairwise interactions in single-layer attention-style models, where tokens interact through a weight matrix and a non-linear activation function. We prove that the minimax rate is $M^{-\frac{2β}{2β+1}}$ with $M$ being the sample size, depending only on the smoothness $β$ of the activation, and crucially independent of token count, ambient dimension, or rank of the weight matrix. These results highlight a fundamental dimension-free statistical efficie…

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…

Sample complexity for entropic optimal transport with radial cost
Ruiyu Han. Johannes Wiesel
https://arxiv.org/abs/2510.05685 https://arxiv.org/pdf/2510.05…

Sample complexity for entropic optimal transport with radial cost
We prove a new sample complexity result for entropy regularized optimal transport. Our bound holds for probability measures on $\mathbb R^d$ with exponential tail decay and for radial cost functions that satisfy a local Lipschitz condition. It is sharp up to logarithmic factors, and captures the intrinsic dimension of the marginal distributions through a generalized covering number of their supports. Examples that fit into our framework include subexponential and subgaussian distributions and r…

Out of the following mobile operating systems, which one do you use the most, not at work, but in your free personal time?
Please consider boosting for a larger sample size. Thank you.
#poll #os #mobile

The ZZ Ceti Instability Strip for The Most Massive White Dwarf Pulsators
Gracyn Jewett, Mukremin Kilic, Adam Moss, Alejandro H. C\'orsico, Matthew J. Green, Murat Uzundag, Pierre Bergeron, Warren R. Brown, Francisco C. De Ger\'onimo, Alberto Rebassa-Mansergas, Alex J. Brown, Vikram S. Dhillon, Stuart Littlefair
https://arxiv.org/ab…

The ZZ Ceti Instability Strip for The Most Massive White Dwarf Pulsators
We present time-series photometry of 31 massive DA white dwarfs with $M\gtrsim 0.9~M_\odot$ within the ZZ Ceti instability strip from the Montreal White Dwarf Database 100 pc sample. The majority of the targets had no previous time-series photometry available, though several were classified as non-variable or potential pulsators in the literature. Out of the 31 candidates, we confirm 16 as pulsating. Our observations at three observatories have led us to discover the most massive pulsating whit…

LATIS: Galaxy-Environment Relations at Cosmic Noon and the Role of Sample Selection
Nima Chartab, Andrew B. Newman, Gwen C. Rudie, Guillermo Blanc, Daniel D. Kelson, Mahdi Qezlou, Simeon Bird, Brian C. Lemaux, Olga Cucciati
https://arxiv.org/abs/2510.07445

LATIS: Galaxy-Environment Relations at Cosmic Noon and the Role of Sample Selection
We investigate the environmental dependence of galaxy properties at $z\sim2.5$ using the Ly$α$ Tomography IMACS Survey (LATIS), which provides high-resolution three-dimensional maps of intergalactic medium (IGM) overdensity via Ly$α$ forest tomography. Our analysis focuses on a UV-selected spectroscopic sample of 2185 galaxies from LATIS and a complementary set of 1157 galaxies from heterogeneous spectroscopic surveys in the COSMOS field. We compare these datasets to forward-modeled mock cata…

High-Probability Bounds For Heterogeneous Local Differential Privacy
Maryam Aliakbarpour, Alireza Fallah, Swaha Roy, Ria Stevens
https://arxiv.org/abs/2510.11895 https://…

High-Probability Bounds For Heterogeneous Local Differential Privacy
We study statistical estimation under local differential privacy (LDP) when users may hold heterogeneous privacy levels and accuracy must be guaranteed with high probability. Departing from the common in-expectation analyses, and for one-dimensional and multi-dimensional mean estimation problems, we develop finite sample upper bounds in $\ell_2$-norm that hold with probability at least $1-β$. We complement these results with matching minimax lower bounds, establishing the optimality (up to con…

Quasi-Periodic Oscillations in Gamma-Ray Bursts' Prompt Light Curves
Mariusz Tarnopolski
https://arxiv.org/abs/2510.11136 https://arxiv.org/pdf/2510.11…

Quasi-Periodic Oscillations in Gamma-Ray Bursts' Prompt Light Curves
I report on the discovery of 34 new quasi-periodic oscillations (QPOs) in the prompt light curves of long gamma-ray bursts (GRBs) from the Swift/BAT catalog: with one or more constant leading periods, as well as several chirping signals. This is the largest homogenously identified sample or GRB QPOs to date. The presence of QPOs suggests the existence of characteristic time scales that at least in some GRBs might be related to the dynamical properties of plasma trajectories in the accretion dis…

Samples are here and Andrew's Back Room IC FA Lab has a new project: a large lot of HMCAD1520s from the ThunderScope project.
My lab assistant helped me catalog them and we got 30 in inventory before having to pause for dinner and bath time, which should be more than enough to get started. She had lots of fun picking up the parts, putting them in labeled tubes, and putting the tubes into the correct location on the sample tray.
For somebody who loves stickers as much as most …

Sample Smart, Not Hard: Correctness-First Decoding for Better Reasoning in LLMs
Xueyan Li, Guinan Su, Mrinmaya Sachan, Jonas Geiping
https://arxiv.org/abs/2510.05987 https://

Sample Smart, Not Hard: Correctness-First Decoding for Better Reasoning in LLMs
Large Language Models (LLMs) are increasingly applied to complex tasks that require extended reasoning. In such settings, models often benefit from diverse chains-of-thought to arrive at multiple candidate solutions. This requires two competing objectives: to inject enough stochasticity to explore multiple reasoning chains, and to ensure sufficient accuracy and quality in each path. Existing works pursue the first objective by increasing exploration at highly uncertain steps with higher tempera…

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…

A new composite Mann-Whitney test for two-sample survival comparisons with right-censored data
Abid Hussain, Touqeer Ahmad
https://arxiv.org/abs/2510.05353 https://

A new composite Mann-Whitney test for two-sample survival comparisons with right-censored data
A fundamental challenge in comparing two survival distributions with right censored data is the selection of an appropriate nonparametric test, as the power of standard tests like the Log rank and Wilcoxon is highly dependent on the often unknown nature of the alternative hypothesis. This paper introduces a new, distribution free two sample test designed to overcome this limitation. The proposed method is based on a strategic decomposition of the data into uncensored and censored subsets, from …

A Sample of HeII {\lambda}4686 "Changing-Look" Quasars
Wentao Lu, Jun-Xian Wang
https://arxiv.org/abs/2510.06753 https://arxiv.org/pdf/2510.06753…

A Sample of HeII λ4686 "Changing-Look" Quasars
We present the first systematic search for "changing-look" ("CL") behavior in the broad He ii λ4686 emission line in quasars, utilizing repeated spectroscopy from the Sloan Digital Sky Survey (SDSS). The He ii line, originating from high-ionization gas and powered by extreme ultraviolet photons, serves as a sensitive tracer of changes in the ionizing continuum. After applying strict spectral selection criteria and visual inspection to a parent sample of over 9,000 quasars with multi-epoch spec…

Insights into Planet Formation from the Ages, Masses, and Elemental Abundances of Host Stars
Xunzhou Chen, Tiancheng Sun, Lifei Ye
https://arxiv.org/abs/2510.10092 https://

Insights into Planet Formation from the Ages, Masses, and Elemental Abundances of Host Stars
How planetary systems form and evolve is a key question in astronomy. Revealing how host star properties, such as elemental abundances, age, and mass, differ from those of non-host stars, and how they correlate with planetary characteristics such as radius, provides new insights into the formation and evolutionary pathways of planetary systems. We determine precise ages for 18890 dwarfs and subgiants from the LAMOST-Kepler-Gaia sample with a mean age uncertainty of about 15 percent (median abou…

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…

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 …

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…

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…

A census of quiescent galaxies across $0.5 < z < 8$ with JWST/MIRI: Mass-dependent number density evolution of quiescent galaxies in the early Universe
Tiancheng Yang, Tao Wang, Ke Xu, Hanwen Sun, Luwenjia Zhou, Lizhi Xie, Gabriella De Lucia, Claudia del P. Lagos, Kai Wang, Fabio Fontanot, Yuxuan Wu, Shiying Lu, Longyue Chen, Michaela Hirschmann
https://

A census of quiescent galaxies across $0.5 < z < 8$ with JWST/MIRI: Mass-dependent number density evolution of quiescent galaxies in the early Universe
JWST observations reveal numerous quiescent galaxies (QGs) at high redshift ($z \sim 4-8$), challenging models of early galaxy formation and quenching. Accurate number density estimates are crucial for comparison with theory but remain uncertain. We systematically study QGs at $0.5 < z < 8$ using a mass-complete sample from the JWST/PRIMER survey with deep NIRCam and MIRI imaging. The MIRI data, probing rest-frame near-infrared at $z \sim 3-8$, are vital for robust stellar mass measurement and …

Locally Optimal Private Sampling: Beyond the Global Minimax
Hrad Ghoukasian, Bonwoo Lee, Shahab Asoodeh
https://arxiv.org/abs/2510.09485 https://arxiv.org/…

Locally Optimal Private Sampling: Beyond the Global Minimax
We study the problem of sampling from a distribution under local differential privacy (LDP). Given a private distribution $P \in \mathcal{P}$, the goal is to generate a single sample from a distribution that remains close to $P$ in $f$-divergence while satisfying the constraints of LDP. This task captures the fundamental challenge of producing realistic-looking data under strong privacy guarantees. While prior work by Park et al. (NeurIPS'24) focuses on global minimax-optimality across a class …

Back to delayering the PIC12F683. I have a ton of imagery and could do some narrated analysis already but my throat is still sore from being sick so I don't want to be recording any audio.
This is after another three minutes in room temperature Whink. I got a fresh squirt of acid in case the old stuff was getting reacted away but I don't think there is enough glass in this sample to meaningfully dilute it.

A Black-Box Debiasing Framework for Conditional Sampling
Han Cui, Jingbo Liu
https://arxiv.org/abs/2510.11071 https://arxiv.org/pdf/2510.11071

A Black-Box Debiasing Framework for Conditional Sampling
Conditional sampling is a fundamental task in Bayesian statistics and generative modeling. Consider the problem of sampling from the posterior distribution $P_{X|Y=y^*}$ for some observation $y^*$, where the likelihood $P_{Y|X}$ is known, and we are given $n$ i.i.d. samples $D=\{X_i\}_{i=1}^n$ drawn from an unknown prior distribution $π_X$. Suppose that $f(\hatπ_{X^n})$ is the distribution of a posterior sample generated by an algorithm (e.g. a conditional generative model or the Ba…

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…

Rate optimal learning of equilibria from data
Till Freihaut, Luca Viano, Emanuele Nevali, Volkan Cevher, Matthieu Geist, Giorgia Ramponi
https://arxiv.org/abs/2510.09325 https:/…

Rate optimal learning of equilibria from data
We close open theoretical gaps in Multi-Agent Imitation Learning (MAIL) by characterizing the limits of non-interactive MAIL and presenting the first interactive algorithm with near-optimal sample complexity. In the non-interactive setting, we prove a statistical lower bound that identifies the all-policy deviation concentrability coefficient as the fundamental complexity measure, and we show that Behavior Cloning (BC) is rate-optimal. For the interactive setting, we introduce a framework that …

The AURORA Survey: Ionizing Photon Production Efficiency with Minimal Nebular Dust Attenuation Systematics
Anthony J. Pahl, Alice Shapley, Naveen A. Reddy, Ryan Sanders, Michael W. Topping, Danielle A. Berg, Callum T. Donnan, James S. Dunlop, Richard S. Ellis, N. M. F\"orster Schreiber, K. Glazebrook, Derek J. McLeod, Max Pettini, Daniel Schaerer
https://

The AURORA Survey: Ionizing Photon Production Efficiency with Minimal Nebular Dust Attenuation Systematics
We present ionizing photon production efficiencies ($ξ_{\rm ion}$) for 63 z=1.5-6.9 star-forming galaxies using precise nebular dust attenuation corrections from the JWST/AURORA survey. A subset of objects within AURORA have individually-determined nebular dust attenuation curves, which vary significantly in shape and normalization, resulting in reduced systematic uncertainty when constraining the total attenuation of H$α$ luminosity, and thus the intrinsic ionizing output within our sample. …

Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Janaina Mourao-Miranda, Vanessa G\'omez-Verdejo
https://arxiv.org/abs/2510.09513

Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Real-world clinical problems are often characterized by multimodal data, usually associated with incomplete views and limited sample sizes in their cohorts, posing significant limitations for machine learning algorithms. In this work, we propose a Bayesian approach designed to efficiently handle these challenges while providing interpretable solutions. Our approach integrates (1) a generative formulation to capture cross-view relationships with a semi-supervised strategy, and (2) a discriminati…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Ankush Pratap Singh, Houwei Cao, Yong Liu
https://arxiv.org/abs/2510.09382 https://arxiv.org…

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Curriculum learning (CL) structures training from simple to complex samples, facilitating progressive learning. However, existing CL approaches for emotion recognition often rely on heuristic, data-driven, or model-based definitions of sample difficulty, neglecting the difficulty for human perception, a critical factor in subjective tasks like emotion recognition. We propose CHUCKLE (Crowdsourced Human Understanding Curriculum for Knowledge Led Emotion Recognition), a perception-driven CL frame…

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…

Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning
Shangzhe Li, Dongruo Zhou, Weitong Zhang
https://arxiv.org/abs/2510.09487 https://

Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning
We study online adversarial imitation learning (AIL), where an agent learns from offline expert demonstrations and interacts with the environment online without access to rewards. Despite strong empirical results, the benefits of online interaction and the impact of stochasticity remain poorly understood. We address these gaps by introducing a model-based AIL algorithm (MB-AIL) and establish its horizon-free, second-order sample-complexity guarantees under general function approximations for bo…

JWST Catches a Strongly Gravitationally Lensed AGN In Transition from Type II to Type I
Michael K Florian, Michael D Gladders, Gourav Khullar, Keren Sharon, Aidan P Cloonan, Eirk Solhaug, Brian Welch, Matthew Bayliss, Hakon Dahle, Taylor A Hutchison, Jane R Rigby
https://arxiv.org/abs/2510.10376

JWST Catches a Strongly Gravitationally Lensed AGN In Transition from Type II to Type I
JWST has enabled the discovery of a statistical sample of obscured (type II) active galactic nuclei (AGN) at cosmic noon. Studies comparing those type II AGN with type I AGN at that epoch have reinforced the long-standing idea of an evolutionary link between those classes of objects. Mergers, the idea goes, disturb the morphologies and angular momentum of galaxies. The disruption of angular momentum allows material to be funneled toward galactic cores, sparking AGN activity and potentially also…