Tootfinder

TrajLens: Visual Analysis for Constructing Cell Developmental Trajectories in Cross-Sample Exploration
Qipeng Wang, Shaolun Ruan, Rui Sheng, Yong Wang, Min Zhu, Huamin Qu
https://arxiv.org/abs/2507.15620

TrajLens: Visual Analysis for Constructing Cell Developmental Trajectories in Cross-Sample Exploration
Constructing cell developmental trajectories is a critical task in single-cell RNA sequencing (scRNA-seq) analysis, enabling the inference of potential cellular progression paths. However, current automated methods are limited to establishing cell developmental trajectories within individual samples, necessitating biologists to manually link cells across samples to construct complete cross-sample evolutionary trajectories that consider cellular spatial dynamics. This process demands substantial…

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

Two dynamical scenarios for the binned Master sample interpretation
Giovanni Montani, Elisa Fazzari, Nakia Carlevaro, Maria Giovanna Dainotti
https://arxiv.org/abs/2507.14048

Two dynamical scenarios for the binned Master sample interpretation
We analyze two different scenarios for the late Universe dynamics, resulting into Hubble parameters deviating from the $Λ$CDM, mainly for the presence of an additional free parameter, which is the dark energy parameter. The first model consists of a pure evolutionary dark energy paradigm, as result of its creation by the gravitational field of the expanding Universe. The second model also considers an interaction of the evolutionary dark energy with the matter component, postulated via the con…

The Alma catalogue of OB stars. III. A cross-match with Gaia DR3 and an extension based on new spectral classifications
M. Pantaleoni Gonz\'alez, J. Ma\'iz Apell\'aniz, R. H. Barb\'a, B. Cameron Reed, S. R. Berlanas, A. Parras Rico, A. Bodaghee
https://arxiv.org/abs/2508.14875

The Alma catalogue of OB stars. III. A cross-match with Gaia DR3 and an extension based on new spectral classifications
We present the third installment of the Alma Luminous Star (ALS) catalogue, aimed at creating the most comprehensive and clean sample of Galactic massive stars. This update extends the sample by adding approximately 2000 OB stars, incorporating astrometric and photometric data from the Gaia Data Release 3 (DR3) alongside spectroscopic information from the Galactic O-Star Catalog based on recent ground-based spectroscopic surveys. Rigorous astrometric corrections are applied to Gaia DR3 parallax…

16WW Electricity Usage Manual Snapshot (2022-08) - A data sample for a research project into how to make best use of shared PV generation. - https://www.earth.org.uk/electricity-usage-snapshot-202208.html

16WW Electricity Usage Manual Snapshot (2022-08)
A data sample for a research project into how to make best use of shared PV generation.

Generalized cluster algorithms for Potts lattice gauge theory
Anthony E. Pizzimenti, Paul Duncan, Benjamin Schweinhart
https://arxiv.org/abs/2507.13503 htt…

Generalized cluster algorithms for Potts lattice gauge theory
Monte Carlo algorithms, like the Swendsen-Wang and invaded-cluster, sample the Ising and Potts models asymptotically faster than single-spin Glauber dynamics do. Here, we generalize both algorithms to sample Potts lattice gauge theory by way of a $2$-dimensional cellular representation called the plaquette random-cluster model. The invaded-cluster algorithm targets Potts lattice gauge theory at criticality by implementing a stopping condition defined in terms of homological percolation, the eme…

Adaptive-basis sample-based neural diagonalization for quantum many-body systems
Simone Cantori, Luca Brodoloni, Edoardo Recchi, Emanuele Costa, Bruno Juli\'a-D\'iaz, Sebastiano Pilati
https://arxiv.org/abs/2508.12724

Adaptive-basis sample-based neural diagonalization for quantum many-body systems
Accurately estimating ground-state energies of quantum many-body systems is still a challenging computational task because of the exponential growth of the Hilbert space with the system size. Sample-based diagonalization (SBD) methods address this problem by projecting the Hamiltonian onto a subspace spanned by a selected set of basis configurations. In this article, we introduce two neural network-enhanced approaches for SBD: sample-based neural diagonalization (SND) and adaptive-basis SND (AB…

Neuer Klingelton! 💩AfD

Scaling Group Inference for Diverse and High-Quality Generation
Gaurav Parmar, Or Patashnik, Daniil Ostashev, Kuan-Chieh Wang, Kfir Aberman, Srinivasa Narasimhan, Jun-Yan Zhu
https://arxiv.org/abs/2508.15773

Scaling Group Inference for Diverse and High-Quality Generation
Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, in real-world applications, users are often presented with a set of multiple images (e.g., 4-8) for each prompt, where independent sampling tends to lead to redundant results, limiting user choices and hindering idea exploration. In this work, we introduce a scalable group inference method that improves both the divers…

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Thomas Carta, Cl\'ement Romac, Loris Gaven, Pierre-Yves Oudeyer, Olivier Sigaud, Sylvain Lamprier
https://arxiv.org/abs/2508.14751

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
Open-ended AI agents need to be able to learn efficiently goals of increasing complexity, abstraction and heterogeneity over their lifetime. Beyond sampling efficiently their own goals, autotelic agents specifically need to be able to keep the growing complexity of goals under control, limiting the associated growth in sample and computational complexity. To adress this challenge, recent approaches have leveraged hierarchical reinforcement learning (HRL) and language, capitalizing on its compos…

The C-index Multiverse
Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Catalina A. Vallejos
https://arxiv.org/abs/2508.14821 https://arxiv.org/pdf/2508.14…

The C-index Multiverse
Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and pyth…

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein, Todd Millstein, Benjamin Pierce, Guy Van den Broeck
https://arxiv.org/abs/2508.14394

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Property-based testing validates software against an executable specification by evaluating it on randomly generated inputs. The standard way that PBT users generate test inputs is via generators that describe how to sample test inputs through random choices. To achieve a good distribution over test inputs, users must tune their generators, i.e., decide on the weights of these individual random choices. Unfortunately, it is very difficult to understand how to choose individual generator weights…

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Cassandra Tong Ye, Shamus Li, Tyler King, Kristina Monakhova
https://arxiv.org/abs/2507.14760

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Deep learning models often hallucinate, producing realistic artifacts that are not truly present in the sample. This can have dire consequences for scientific and medical inverse problems, such as MRI and microscopy denoising, where accuracy is more important than perceptual quality. Uncertainty quantification techniques, such as conformal prediction, can pinpoint outliers and provide guarantees for image regression tasks, improving reliability. However, existing methods utilize a linear consta…

The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints
Hadi Kazemi, Ankit Pensia, Varun Jog
https://arxiv.org/abs/2506.13686

The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints
This paper resolves two open problems from a recent paper, arXiv:2403.16981, concerning the sample complexity of distributed simple binary hypothesis testing under information constraints. The first open problem asks whether interaction reduces the sample complexity of distributed simple binary hypothesis testing. In this paper, we show that sequential interaction does not help. The second problem suggests tightening existing sample complexity bounds for communication-constrained simple binary …

Two-Sample Testing with Missing Data via Energy Distance: Weighting and Imputation Approaches
Danijel G. Aleksi\'c, Bojana Milo\v{s}evi\'c
https://arxiv.org/abs/2508.11421

Two-Sample Testing with Missing Data via Energy Distance: Weighting and Imputation Approaches
In this paper, we address the problem of two-sample testing in the presence of missing data under a variety of missingness mechanisms. Our focus is on the well-known energy distance-based two-sample test. In addition to the standard complete-case approach, we propose a modification of the test statistic that incorporates all available data, utilizing appropriate weights. The asymptotic null distribution of the test statistic is derived and two resampling procedures for approximating the corresp…

AMICO galaxy clusters in KiDS-1000: cosmological sample
M. Maturi, M. Radovich, L. Moscardini, G. F. Lesci, G. Castignani, F. Marulli, E. A. Puddu, M. Romanello, M. Sereno, C. Giocoli, L. Ingoglia, S. Bardelli, B. Giblin, H. Hildebrandt, S. Joudaki
https://arxiv.org/abs/2507.14338

AMICO galaxy clusters in KiDS-1000: cosmological sample
Context. Galaxy clusters provide key insights into cosmic structure formation, galaxy formation and are essential for cosmological studies. Aims. We present a catalog of galaxy clusters detected in the Kilo-Degree Survey (KiDS-DR4) optimized for cosmological analyses and investigations of cluster properties. Each detection includes probabilistic membership assignments for the KiDS-DR4 galaxies within the magnitude range 15

Efficient Bayesian analysis of kilonovae and GRB afterglows with fiesta
Hauke Koehn, Thibeau Wouters, Peter T. H. Pang, Mattia Bulla, Henrik Rose, Hannah Wichern, Tim Dietrich
https://arxiv.org/abs/2507.13807

Efficient Bayesian analysis of kilonovae and GRB afterglows with fiesta
Gamma-ray burst (GRB) afterglows and kilonovae (KNe) are electromagnetic transients that can accompany binary neutron star (BNS) mergers. Therefore, studying their emission processes is of general interest for constraining cosmological parameters or the behavior of ultra-dense matter. One common method to analyze electromagnetic data from BNS mergers is to sample a Bayesian posterior over the parameters of a physical model for the transient. However, sampling the posterior is computationally co…

Refining music sample identification with a self-supervised graph neural network
Aditya Bhattacharjee, Ivan Meresman Higgs, Mark Sandler, Emmanouil Benetos
https://arxiv.org/abs/2506.14684

Refining music sample identification with a self-supervised graph neural network
Automatic sample identification (ASID), the detection and identification of portions of audio recordings that have been reused in new musical works, is an essential but challenging task in the field of audio query-based retrieval. While a related task, audio fingerprinting, has made significant progress in accurately retrieving musical content under "real world" (noisy, reverberant) conditions, ASID systems struggle to identify samples that have undergone musical modifications. Thus, a system r…

reLAISS: A Python Package for Flexible Similarity Searches of Supernovae and Their Host Galaxies
E. Reynolds, A. Gagliano, V. A. Villar
https://arxiv.org/abs/2507.13449

reLAISS: A Python Package for Flexible Similarity Searches of Supernovae and Their Host Galaxies
Discovery rates of supernovae are expected to surpass one million events annually with the Vera C. Rubin Observatory. With unprecedented sample sizes of both common and rare transient types, photometric classification alone will be insufficient for finding one-in-a-million events and prioritizing the 1% of events for spectroscopic follow-up observations. Here, we present reLAISS, a modified framework for similarity searches of supernovae using extracted features of ZTF light curves and Pan-STAR…

Spectroscopic Characterization of LOFAR Radio-emitting M dwarfs
E. Koo, G. Stefansson, R. D. Kavanagh, M. Delamer, S. Mahadevan, J. R. Callingham, H. Vedantham, P. Robertson, D. Bruijne, C. F. Bender, C. I. Ca\~nas, S. Diddams, J. I. Espinoza-Retamal, R. B. Fernandes, S. Halverson, S. Kanodia, D. Krolikowski, A. S. J. Lin, B. J. S. Pope, A. Roy, C. Schwab, R. Terrien, J. T. Wright

Spectroscopic Characterization of LOFAR Radio-emitting M dwarfs
Recent observations with the LOw Frequency ARray (LOFAR) have revealed 19 nearby M dwarfs showing bright circularly polarised radio emission. One of the possible sources of such emission is through magnetic star-planet interactions (MSPI) with unseen close-in planets. We present initial results from a spectroscopic survey with the Habitable-zone Planet Finder (HPF) and NEID spectrographs designed to characterize this sample and further investigate the origin of the radio emission. We provide fo…

Longitudinal Sampling of URLs From the Wayback Machine
Kritika Garg, Sawood Alam, Dietrich Ayala, Mark Graham, Michele C. Weigle, Michael L. Nelson
https://arxiv.org/abs/2507.14752

Longitudinal Sampling of URLs From the Wayback Machine
We document strategies and lessons learned from sampling the web by collecting 27.3 million URLs with 3.8 billion archived pages spanning 26 years (1996-2021) from the Internet Archive's (IA) Wayback Machine. Our goal is to revisit fundamental questions regarding the size, nature, and prevalence of the publicly archivable web, in particular, to reconsider the question: "How long does a web page last?" Addressing this question requires obtaining a sample of the web. We proposed several dimension…

Time Series Information Visualization -- A Review of Approaches and Tools
Evandro S. Ortigossa, F\'abio F. Dias, Diego C. Nascimento, Luis Gustavo Nonato
https://arxiv.org/abs/2507.14920

Time Series Information Visualization -- A Review of Approaches and Tools
Time series data are prevalent across various domains and often encompass large datasets containing multiple time-dependent features in each sample. Exploring time-varying data is critical for data science practitioners aiming to understand dynamic behaviors and discover periodic patterns and trends. However, the analysis of such data often requires sophisticated procedures and tools. Information visualization is a communication channel that leverages human perceptual abilities to transform abs…

Elephant random walks with multiple extractions and general reinforcement functions
Moumanti Podder, Archi Roy
https://arxiv.org/abs/2507.14626 https://

Elephant random walks with multiple extractions and general reinforcement functions
We consider a generalized model of elephant random walks wherein the walker, during the $(n+1)$-st time-stamp, draws from the past (i.e. the set $\{1,2,\ldots,n\}$) a sample of $k$ time-stamps, either with replacement or without, where $k$ may either remain fixed as $n$ grows, or $k=k(n)$ may grow with $n$. Letting $\{U_{n,1}, U_{n,2}, \ldots, U_{n,k}\}$ denote the time-stamps sampled, the step taken by the walker during the $(n+1)$-st time-stamp, denoted $X_{n+1}$, is a $\pm 1$-valued random v…

Search for resonances decaying to photon pairs with masses between 4.9 and 19.4 GeV
LHCb collaboration, et al.
https://arxiv.org/abs/2507.14390 https://

Search for resonances decaying to photon pairs with masses between 4.9 and 19.4 GeV
A search is presented for axion-like particles (ALPs) with masses between 4.9 and 19.4 GeV decaying to a pair of photons, using proton-proton collisions collected with the LHCb detector during 2018 at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 2.1 fb$^{-1}$. The same strategy and sample is used to search for the decays of the $B^0_s$, $B^0$ and $η_b$ mesons into photon pairs. No significant excess is found. Upper limits on the photon-pair branching fracti…

The oldest tidally induced bar-like galaxy in the IllustrisTNG cluster
Ewa L. Lokas
https://arxiv.org/abs/2508.14555 https://arxiv.org/pdf/2508.14555

The oldest tidally induced bar-like galaxy in the IllustrisTNG cluster
New JWST observations have revealed the presence of a significant number of high-redshift barred galaxies. The origin of these bars remains unclear, and their properties appear difficult to reconcile with the results of cosmological simulations of galaxy formation. I present an example of a tidally induced bar-like galaxy formed at z = 2.9 in the TNG100 suite of the IllustrisTNG simulations. The galaxy was identified among the sample of bar-like galaxies studied before and has the earliest bar …

Label free sub-diffraction imaging using non-linear photon avalanche backlight
Suresh Karmegam, Marcin Szalkowski, Malgorzata Misiak, Katarzyna Prorok, Damian Szyma\'nski, Artur Bednarkiewicz
https://arxiv.org/abs/2507.14667

Label free sub-diffraction imaging using non-linear photon avalanche backlight
Optical imaging below the limit of light diffraction offers an unprecedented opportunity to study outlook, organization, interactions or in-situ functioning of sub-micrometer, highly transparent objects such as subcellular structures in vitro, thin layers or nano-engineered devices. However, most of current methodologies require to use specially designed luminescent labels, which not only may affect the properties of the sample itself, but often are (photo)toxic, susceptible to photobleaching, …

MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
Yi Dong, Yusuke Muraoka, Scott Shi, Yi Zhang
https://arxiv.org/abs/2508.10429 http…

MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
We present MM-Food-100K, a public 100,000-sample multimodal food intelligence dataset with verifiable provenance. It is a curated approximately 10% open subset of an original 1.2 million, quality-accepted corpus of food images annotated for a wide range of information (such as dish name, region of creation). The corpus was collected over six weeks from over 87,000 contributors using the Codatta contribution model, which combines community sourcing with configurable AI-assisted quality checks; e…

Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
Saida Mancer, Abdelhakim Necir, Djamel Meraghni
https://arxiv.org/abs/2507.15744

Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence
By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering improved efficiency in the presence of outliers. These estimators can be viewed as a robust generalization of both weighted least squares and kernel-based tail index estimators. We establish the consistency and asymptotic normality of the proposed class. A simulation study is conducted to assess their finite-sample p…

Improving Reinforcement Learning Sample-Efficiency using Local Approximation
Mohit Prashant, Arvind Easwaran
https://arxiv.org/abs/2507.12383 https://

Improving Reinforcement Learning Sample-Efficiency using Local Approximation
In this study, we derive Probably Approximately Correct (PAC) bounds on the asymptotic sample-complexity for RL within the infinite-horizon Markov Decision Process (MDP) setting that are sharper than those in existing literature. The premise of our study is twofold: firstly, the further two states are from each other, transition-wise, the less relevant the value of the first state is when learning the $ε$-optimal value of the second; secondly, the amount of 'effort', sample-complexity-wise, ex…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
G\'abor Lugosi, Marcos Matabuena
https://arxiv.org/abs/2507.15741 h…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
This paper introduces a framework for uncertainty quantification in regression models defined in metric spaces. Leveraging a newly defined notion of homoscedasticity, we develop a conformal prediction algorithm that offers finite-sample coverage guarantees and fast convergence rates of the oracle estimator. In heteroscedastic settings, we forgo these non-asymptotic guarantees to gain statistical efficiency, proposing a local $k$--nearest--neighbor method without conformal calibration that is ad…

Structural and Connectivity Patterns in the Maven Central Software Dependency Network
Daniel Ogenrwot, John Businge, Shaikh Arifuzzaman
https://arxiv.org/abs/2508.13819 https://…

Structural and Connectivity Patterns in the Maven Central Software Dependency Network
Understanding the structural characteristics and connectivity patterns of large-scale software ecosystems is critical for enhancing software reuse, improving ecosystem resilience, and mitigating security risks. In this paper, we investigate the Maven Central ecosystem, one of the largest repositories of Java libraries, by applying network science techniques to its dependency graph. Leveraging the Goblin framework, we extracted a sample consisting of the top 5,000 highly connected artifacts base…

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training
Limengnan Zhou, Hanzhou Wu
https://arxiv.org/abs/2506.12675 https://

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training
Quantum neural networks (QNNs) leverage quantum computing to create powerful and efficient artificial intelligence models capable of solving complex problems significantly faster than traditional computers. With the fast development of quantum hardware technology, such as superconducting qubits, trapped ions, and integrated photonics, quantum computers may become reality, accelerating the applications of QNNs. However, preparing quantum circuits and optimizing parameters for QNNs require quantu…

Crosslisted article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity...
Mohamed Hassan Abdalla Idris, Jakub Marek Cebula, Jebraeel Gholinezhad…

A lab-on-a-silicon-chip platform for all-electrical antibiotic susceptibility tests with a sample-to-results time within 20 minutes
Zheqiang Xu, Victoria C Nolan, Yingtao Yu, Petra Muir, Sanna Koskiniemi, Zhen Zhang
https://arxiv.org/abs/2508.12775

A lab-on-a-silicon-chip platform for all-electrical antibiotic susceptibility tests with a sample-to-results time within 20 minutes
Rapid antibiotic susceptibility tests (ASTs) are essential for quick selection of effective drugs to treat bacterial infections at an early stage. However, the most widely used phenotypic ASTs in clinical practice often require 24 - 48 hours of pre-culture enrichment and 8 - 20 hours of testing. They are too slow for patients to wait for therapy, even with the most rapid protocol. Here, we report a lab-on-a-silicon chip (LOSC) system, which integrates arrays of silicon nanowire field-effect tra…

An exact test for the mixed membership stochastic block model
Sourav Majumdar
https://arxiv.org/abs/2507.14464 https://arxiv.org/pdf/…

An exact test for the mixed membership stochastic block model
We present the first finite-sample goodness-of-fit test for the mixed membership stochastic block model (MMSBM). Using algebraic statistics theory, we derive a Markov basis that lets a Metropolis-Hastings sampler explore exactly the set of networks compatible with a fitted MMSBM. The resulting exact $p$-value, based on a partial conjunction statistic, requires no asymptotic approximations. Simulations show nominal size and strong power against misspecified block numbers and connection patterns.

Evolving magnetic lives of Sun-like stars. I. Characterisation of the large-scale magnetic field with Zeeman-Doppler imaging
S. Bellotti, T. Lueftinger, S. Boro Saikia, C. P. Folsom, P. Petit, J. Morin, M. Gudel, J. -F. Donati, E. Alecian
https://arxiv.org/abs/2507.14861

Evolving magnetic lives of Sun-like stars. I. Characterisation of the large-scale magnetic field with Zeeman-Doppler imaging
Planets orbiting young, solar-type stars are embedded in a more energetic environment than that of the solar neighbourhood. They experience harsher conditions due to enhanced stellar magnetic activity and wind shaping the secular evolution of a planetary atmosphere. This study is dedicated to the characterisation of the magnetic activity of eleven Sun-like stars, with ages between 0.2 and 6.1 Gyr and rotation periods between 4.6 and 28.7 d. Based on a sub-sample of six stars, we aim to study th…

Comprehensive Statistical Analysis of Initial Lorentz Factor and Jet Opening Angle of Gamma-Ray Bursts
Jian Zhang, Bao-Cheng Qin, Lu-Lu Zhang, Fu-Wen Zhang
https://arxiv.org/abs/2508.13448

Comprehensive Statistical Analysis of Initial Lorentz Factor and Jet Opening Angle of Gamma-Ray Bursts
The initial Lorentz factor ($Γ_{0}$) and jet half-opening angle ($θ_{\rm jet}$) of gamma-ray bursts (GRBs) are critical physical parameters for understanding the dynamical evolution of relativistic jets and the true energy release of GRBs. We compile a sample of 89 GRBs that exhibit an onset bump feature in their early optical or GeV light curves, 42 of which also display a jet break feature, and derive their $Γ_{0}$ and $θ_{\rm jet}$ values. Using this sample, we re-eaxmine the correlation…

Towards safe control parameter tuning in distributed multi-agent systems
Abdullah Tokmak, Thomas B. Sch\"on, Dominik Baumann
https://arxiv.org/abs/2508.13608 https://

Towards safe control parameter tuning in distributed multi-agent systems
Many safety-critical real-world problems, such as autonomous driving and collaborative robots, are of a distributed multi-agent nature. To optimize the performance of these systems while ensuring safety, we can cast them as distributed optimization problems, where each agent aims to optimize their parameters to maximize a coupled reward function subject to coupled constraints. Prior work either studies a centralized setting, does not consider safety, or struggles with sample efficiency. Since w…

Back to poking at the solder cross section test sample.
Initial findings:
1) I should have sanded them more by hand prior to embedding. There's a LOT of material to burn through. I've probably worn out 5-10 P180 sanding disks so far and am not even at the PTHs on the main region of interest yet.
2) The samples are quite a bit off level. I need to figure out a better way to fixture them in the embedding mold so they don't tip forward.
I'll keep on polis…

Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning
Vaishnavi Shrivastava, Ahmed Awadallah, Vidhisha Balachandran, Shivam Garg, Harkirat Behl, Dimitris Papailiopoulos
https://arxiv.org/abs/2508.09726

Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning
Large language models trained with reinforcement learning with verifiable rewards tend to trade accuracy for length--inflating response lengths to achieve gains in accuracy. While longer answers may be warranted for harder problems, many tokens are merely "filler": repetitive, verbose text that makes no real progress. We introduce GFPO (Group Filtered Policy Optimization), which curbs this length explosion by sampling larger groups per problem during training and filtering responses to train on…

Synthesis and Characterization of Mg doped ZnFe$_2$O$_4$
Kemi Y. Adewale, Itegbeyogene P. Ezekiel
https://arxiv.org/abs/2508.13203 https://arxiv.org/pdf/25…

Synthesis and Characterization of Mg doped ZnFe$_2$O$_4$
Single-phase Mg-doped ZnFe$_2$O$_4$ nanoparticles with x= 0, 0.3, 0.5, 0.7 have been prepared by the glycol-thermal method without any subsequent calcination. The crystallite size, microstructure and magnetic properties of the prepared nanoparticles were studied using X-ray diffraction (XRD), high resolution scanning electron microscope (TEM), Mossbauer spectroscopy and vibrating sample magnetometer at room temperature. The XRD results revealed the production of a sharp single cubic spinel stru…

Examining the Impact of Tutorial Activity Engagement on Undergraduate Students' Collaborative Preferences
Sang Hyun Kim, Tanya Evans
https://arxiv.org/abs/2508.13511 https:/…

Examining the Impact of Tutorial Activity Engagement on Undergraduate Students' Collaborative Preferences
This study examines the impact of tutorial engagement on Collaborative Preferences for Learning Mathematics (CPLM) in a tertiary context. A two-way mixed ANOVA analysed these preferences over a semester in a sample of undergraduate students. As expected, collaborative engagement had a significant main effect, with students who collaborated more reporting stronger preferences for working with their peers (higher CPLM). The absence of an interaction effect between the nature of tutorial engagemen…

BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
Sourav Raxit, Abdullah Al Redwan Newaz, Paulo Padrao, Jose Fuentes, Leonardo Bobadilla
https://arxiv.org/abs/2508.13052

BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO). Unlike traditional methods, which often struggle with kinodynamic constraints such as velocity and acceleration limits, the BOW Planner excels by concentrating on a planning window of reachable velocities and employing CBO to sample control inputs efficiently. This approach enables the planner to manage high-dimension…

What a result!
https://mastodon.social/@CyReVolt/114630312818227294
That is a clear 60% for oreboot (9/15).
Though the sample size is small.
Now I need you 9 folks to tell me:
Why oreboot? What do you see in it?

ALMA FACTS III. High-Resolution CO(2-1)/CO(1-0) Maps of Twelve Nearby Galaxies
Amanda M Lee, Jin Koda, Fumi Egusa, Akihiko Hirota, Shinya Komugi, Fumiya Maeda, Tsuyoshi Sawada
https://arxiv.org/abs/2507.13498

ALMA FACTS III. High-Resolution CO(2-1)/CO(1-0) Maps of Twelve Nearby Galaxies
We present early results from a high-resolution analysis ($\sim$100-200pc) of the CO(2-1)/CO(1-0) line ratio in twelve nearby galaxies. We use new ALMA CO(1-0) observations from the Fundamental CO(1-0) Transition Survey (FACTS), and re-imaged CO(2-1) data from PHANGS. We make empirical classifications based on the optical and molecular gas morphologies, which show clear systematic trends in the variation of $R_{21}$ as a function of galactic structure. The sample includes barred and unbarred, a…

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
Jonas Lau, Annie Tran
https://arxiv.org/abs/2506.15294 h…

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt …

OmViD: Omni-supervised active learning for video action detection
Aayush Rana, Akash Kumar, Vibhav Vineet, Yogesh S Rawat
https://arxiv.org/abs/2508.13983 https://

OmViD: Omni-supervised active learning for video action detection
Video action detection requires dense spatio-temporal annotations, which are both challenging and expensive to obtain. However, real-world videos often vary in difficulty and may not require the same level of annotation. This paper analyzes the appropriate annotation types for each sample and their impact on spatio-temporal video action detection. It focuses on two key aspects: 1) how to obtain varying levels of annotation for videos, and 2) how to learn action detection from different annotati…

Finite sample-optimal adjustment sets in linear Gaussian causal models
Nadja Rutsch, Sara Magliacane, St\'ephanie van der Pas
https://arxiv.org/abs/2506.12869

Finite sample-optimal adjustment sets in linear Gaussian causal models
Traditional covariate selection methods for causal inference focus on achieving unbiasedness and asymptotic efficiency. In many practical scenarios, researchers must estimate causal effects from observational data with limited sample sizes or in cases where covariates are difficult or costly to measure. Their needs might be better met by selecting adjustment sets that are finite sample-optimal in terms of mean squared error. In this paper, we aim to find the adjustment set that minimizes the me…

Sample Variance Denoising in Cylindrical 21-cm Power Spectra
Daniela Breitman, Andrei Mesinger, Steven G. Murray, Anshuman Acharya
https://arxiv.org/abs/2507.12545

Sample Variance Denoising in Cylindrical 21-cm Power Spectra
State-of-the-art simulations of reionisation-era 21-cm signal have limited volumes, generally orders of magnitude smaller than observations. Consequently, the Fourier modes in common between simulation and observation have limited overlap, especially in cylindrical (2D) k-space that is natural for 21-cm interferometry. This makes sample variance (i.e. the deviation of the simulated sample from the population mean due to finite box size) a potential issue when interpreting upcoming 21-cm observa…

Sample-Constrained Black Box Optimization for Audio Personalization
Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
https://arxiv.org/abs/2507.12773

Sample-Constrained Black Box Optimization for Audio Personalization
We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter $h^*$, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter $h_i$, and query the user for their satisfaction scores $f(h_i)$. A family of …

ASKAP J144834-685644: a newly discovered long period radio transient detected from radio to X-rays
Akash Anumarlapudi, David L. Kaplan, Nanda Rea, Nicolas Erasmus, Daniel Kelson, Stella Koch Ocker, Emil Lenc, Dougal Dobie, Natasha Hurley-Walker, Gregory Sivakoff, David A. H. Buckley, Tara Murphy, Joshua Pritchard, Laura Driessen, Kovi Rose, Andrew Zic

ASKAP J144834-685644: a newly discovered long period radio transient detected from radio to X-rays
Long-period radio transients (LPTs) are an emerging group of radio transients that show periodic polarised radio bursts with periods varying from a few minutes to a few hours. Fewer than a dozen LPTs have been detected so far, and their origin (source and emission mechanism) remains unclear. Here, we report the discovery of a 1.5 hr LPT, ASKAP J144834-685644, adding to the current sample of sources. ASKAP J144834-685644 is one of the very few LPTs that has been detected from X-rays to radio. It…

Contrastive CUR: Interpretable Joint Feature and Sample Selection for Case-Control Studies
Eric Zhang, Michael Love, Didong Li
https://arxiv.org/abs/2508.11557 https://

Contrastive CUR: Interpretable Joint Feature and Sample Selection for Case-Control Studies
Dimension reduction is an essential tool for analyzing high dimensional data. Most existing methods, including principal component analysis (PCA), as well as their extensions, provide principal components that are often linear combinations of features, which are often challenging to interpret. CUR decomposition, another matrix decomposition technique, is a more interpretable and efficient alternative, offers simultaneous feature and sample selection. Despite this, many biomedical studies involv…

Ly$\alpha$ visibility from z = 4.5 to 11 in the UDS field: evidence for a high neutral hydrogen fraction and small ionized bubbles at z $\sim$ 7
L. Napolitano, L. Pentericci, M. Dickinson, P. Arrabal Haro, A. J. Taylor, A. Calabr\`o, A. Bhagwat, P. Santini, F. Arevalo-Gonzalez, R. Begley, M. Castellano, B. Ciardi, C. T. Donnan, D. Dottorini, J. S. Dunlop, S. L. Finkelstein, A. Fontana, M. Giavalisco, M. Hirschmann, I. Jung, A. M. Koekemoer, V. Kokorev, M. Llerena, R. A. Lucas, S. Masci…

Ly$α$ visibility from z = 4.5 to 11 in the UDS field: evidence for a high neutral hydrogen fraction and small ionized bubbles at z $\sim$ 7
The resonant scattering nature of Ly$α$ photons interacting with neutral hydrogen makes Ly$α$-emitting galaxies (LAEs) robust tracers of the intergalactic neutral hydrogen fraction, and thus sensitive probes of cosmic reionization. We present an extensive study of the Ly$α$ evolution from galaxies at 4.5 $\leq$ z $\leq$ 11 in the UDS field, observed as part of the CAPERS survey, and complemented with spectra from the DAWN JWST Archive. The combined sample includes 651 spectroscopically confi…

Bayesian Optimization-based Search for Agent Control in Automated Game Testing
Carlos Celemin
https://arxiv.org/abs/2508.13121 https://arxiv.org/pdf/2508.1…

Bayesian Optimization-based Search for Agent Control in Automated Game Testing
This work introduces an automated testing approach that employs agents controlling game characters to detect potential bugs within a game level. Harnessing the power of Bayesian Optimization (BO) to execute sample-efficient search, the method determines the next sampling point by analyzing the data collected so far and calculates the data point that will maximize information acquisition. To support the BO process, we introduce a game testing-specific model built on top of a grid map, that featu…

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Zhicheng Yang, Zhijiang Guo, Yinya Huang, Yongxin Wang, Dongchun Xie, Yiwei Wang, Xiaodan Liang, Jing Tang
https://arxiv.org/abs/2508.13755

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models, yet its full potential is hindered by two under-explored dimensions: Depth-the hardest problem a model can sample; Breadth-the number of instances consumed in a single iteration. We dissect the popular GRPO algorithm and reveal a systematic bias: the cumulative-advantage disproportionately weights samples with medium accuracy, while down-weightin…

AMICO galaxy clusters in KiDS-1000: cosmological constraints and mass calibration from counts and weak lensing
G. F. Lesci, F. Marulli, L. Moscardini, M. Maturi, M. Sereno, M. Radovich, M. Romanello, C. Giocoli, A. H. Wright, S. Bardelli, M. Bilicki, G. Castignani, H. Hildebrandt, A. Kannawadi, L. Ingoglia, S. Joudaki, E. Puddu
https://

AMICO galaxy clusters in KiDS-1000: cosmological constraints and mass calibration from counts and weak lensing
We present the joint modelling of weak-lensing and count measurements of the galaxy clusters detected with the AMICO code, in the fourth data release of the Kilo Degree Survey (KiDS-1000). The analysed sample comprises about 8000 clusters, covering an effective area of 839 deg$^{2}$ and extending up to a redshift of $z = 0.8$. Stacked cluster weak-lensing and count measurements have been derived in bins of redshift and intrinsic richness, $λ^*$. Based on self-organising maps, we reconstructed …

The highest mass Kepler red giants -- II. Spectroscopic parameters, the amplitude-activity relation, and unexpected halo orbits
Courtney L. Crawford, Yaguang Li, Daniel Huber, Jie Yu, Timothy R. Bedding, Sarah L. Martell, Benjamin T. Montet, Dennis Stello, Howard Isaacson, Andrew W. Howard, Benjamin J. Fulton, Jingwen Zhang, Alex S. Polanski, Lauren M. Weiss
ht…

The highest mass Kepler red giants -- II. Spectroscopic parameters, the amplitude-activity relation, and unexpected halo orbits
The high-mass (M$>$2 \Msolar{}) Kepler red giant stars are less well-studied than their lower-mass counterparts. In the previous article, we presented a sample of 48 high-mass Kepler red giants and measured their asteroseismic parameters. This article presents spectroscopic measurements from the same sample, using high-resolution Keck/HIRES spectra to determine \Teff{}, [Fe/H], \logg{}, and $v \sin i$. We refined our previous estimates of the stellar masses and radii based on the new \Teff{}. W…

A Common Origin of Normal Type Ia Supernovae Suggested by the Photometric Diversity
Weiyu Wu, Ji-an Jiang, Dezheng Meng, Zelin Xu, Keiichi Maeda, Mamoru Doi, Ken'ichi Nomoto, Naoki Yasuda, Masaomi Tanaka, Toshikazu Shigeyama, Nozomu Tominaga, \v{Z}eljko Ivezi\'c, Peter Yoachim, Saurabh W. Jha, Tinggui Wang, Nao Suzuki, Hisanori Furusawa, Andrew J. Connolly, Satoshi Miyazaki

A Common Origin of Normal Type Ia Supernovae Suggested by the Photometric Diversity
In recent years, with an increasing number of type Ia supernovae (SNe Ia) discovered soon after their explosions, a non-negligible fraction of SNe Ia with early-excess emissions (EExSNe Ia) have been confirmed. In this letter, we present a total of \textbf{67} early-phase normal SNe Ia from published papers and ongoing transient survey projects to systematically investigate their photometric behaviors from very early time. We found that EExSNe Ia in our sample have longer rise and brighter peak…

Communication Complexity of Exact Sampling under R\'enyi Information
Spencer Hill, Fady Alajaji, Tam\'as Linder
https://arxiv.org/abs/2506.12219 ht…

Communication Complexity of Exact Sampling under Rényi Information
We study the problem of communicating a sample from a probability distribution $P$ given shared access to a sequence distributed according to another probability distribution $Q$. Li and El Gamal used the Poisson functional representation to show that the minimum expected message length to communicate a sample from $P$ can be upper bounded by $D(P||Q) + \log (D(P||Q) + 1) + 4$, where $D(\, \cdot \, || \, \cdot\, )$ is the Kullback-Leibler divergence. We generalize …

One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency
Viet T. Tran, Richard Kueng
https://arxiv.org/abs/2508.11744 https://

One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency
Shadow tomography protocols have recently emerged as powerful tools for efficient quantum state learning, aiming to reconstruct expectation values of observables with fewer resources than traditional quantum state tomography. For the particular case of estimating Pauli observables, entangling two-copy measurement schemes can offer an exponential improvement in sample complexity over any single-copy strategy conceivable [1, Huang, Kueng, Preskill, PRL(2021)]. A recent refinement of these ideas b…

A general search for supersymmetric particles in scenarios with compressed mass spectra using proton-proton collisions at $\sqrt{s}$ = 13 TeV
CMS Collaboration
https://arxiv.org/abs/2508.13900

A general search for supersymmetric particles in scenarios with compressed mass spectra using proton-proton collisions at $\sqrt{s}$ = 13 TeV
A general search is presented for supersymmetric particles (sparticles) in scenarios featuring compressed mass spectra using proton-proton collisions at a center-of-mass energy of 13 TeV, recorded with the CMS detector at the LHC. The analyzed data sample corresponds to an integrated luminosity of 138\fbinv. A wide range of potential sparticle signatures are targeted, including pair production of electroweakinos, sleptons, and top squarks. The search focuses on events with a high transverse mom…

SAMPLE -- Stratospheric Altitude Microbiology Probe for Life Existence -- A Method of Collection of Stratospheric Samples Using Balloon-Borne Payload System
Margarita Safonova, Bharat Chandra P, Binukumar G. Nair, Akshay Datey, Dipshikha Chakravortty, Ajin Prakash, Mahesh Babu, Shubham Ghatul, Shubhangi Jain, Rekhesh Mohan, Jayant Murthy
https://

SAMPLE -- Stratospheric Altitude Microbiology Probe for Life Existence -- A Method of Collection of Stratospheric Samples Using Balloon-Borne Payload System
The Earth possesses many environmental extremes that mimic conditions on extraterrestrial worlds. The stratosphere at 30-40 km altitude closely resembles the surface of Mars in terms of pressure, temperature, and radiation levels (UV, proton, and Galactic cosmic rays). While microbial life in the troposphere is well documented, the true upper limit of Earth's biosphere remains unclear. The stratosphere offers a promising environment to explore microbial survival in such extreme conditions. Desp…

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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
Robin Trombetta, Carole Lartizien
https://arxiv.org/abs/2508.12927

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data. It has many applications in fields, such as industrial inspection or medical imaging, where acquiring labels is costly or when we want to avoid introducing biases in the type of anomalies that can be spotted. In this work, we propose a novel UAD method based on prototype learning and introduce a metric to compare a structured set of embeddings …

Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training
Marc Brinner, Sina Zarrie{\ss}
https://arxiv.org/abs/2508.11393 https://a…

Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training
We propose an end-to-end differentiable training paradigm for stable training of a rationalized transformer classifier. Our approach results in a single model that simultaneously classifies a sample and scores input tokens based on their relevance to the classification. To this end, we build on the widely-used three-player-game for training rationalized models, which typically relies on training a rationale selector, a classifier and a complement classifier. We simplify this approach by making …

For all the marketing advice LinkedIn MBAs come up with, a surprising number of people forget one of the most basic steps: The Middle Schooler Test.
The process is simple:
1) Place a sample of your brochure, website, etc. in front of a recently calibrated 12-year-old boy.
2) If they start giggling uncontrollably, identify and correct the source of their amusement. Return to step 1.
3) If they aren't particularly amused, move on with the campaign.

A Spatial-Physics Informed Model for 3D Spiral Sample Scanned by SQUID Microscopy
J. Senthilnath, Jayasanker Jayabalan, Zhuoyi Lin, Aye Phyu Phyu Aung, Chen Hao, Kaixin Xu, Yeow Kheng Lim, F. C. Wellstood
https://arxiv.org/abs/2507.11853

A Spatial-Physics Informed Model for 3D Spiral Sample Scanned by SQUID Microscopy
The development of advanced packaging is essential in the semiconductor manufacturing industry. However, non-destructive testing (NDT) of advanced packaging becomes increasingly challenging due to the depth and complexity of the layers involved. In such a scenario, Magnetic field imaging (MFI) enables the imaging of magnetic fields generated by currents. For MFI to be effective in NDT, the magnetic fields must be converted into current density. This conversion has typically relied solely on a F…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/2]:
- Alpha Estimation via Sample Splitting: A Two-Sample Framework for Stable-like Distributions
Cornelis J. Potgieter, Jacques van Appel, Sudharshan Samaratunga

Monotone Neural Control Barrier Certificates
Alireza Nadali, Ashutosh Trivedi, Majid Zamani, Saber Jafarpour
https://arxiv.org/abs/2508.12178 https://arxiv…

Monotone Neural Control Barrier Certificates
This work presents a neurosymbolic framework for synthesizing and verifying safety controllers in high-dimensional monotone dynamical systems using only linear sample complexity, without requiring explicit models or conservative Lipschitz bounds. The approach combines the expressiveness of neural networks with the rigor of symbolic reasoning via barrier certificates, functional analogs of inductive invariants that formally guarantee safety. Prior data-driven methods often treat dynamics as blac…

The Demographics of Binary Companions to Stripped-Envelope Supernovae: Confronting Observations with Population Synthesis
E. Zapartas, O. D. Fox, J. Su, D. Souropanis, M. R. Drout, K. A. Rocha, S. D. van Dyk, B. F. Williams, M. Briel, M. Renzo, J. J. Andrews, T. Fragos, S. Gossage, M. U. Kruckow, C. Liotine, S. D. Ryder, P. M. Srivastava, E. Teng
https://

The Demographics of Binary Companions to Stripped-Envelope Supernovae: Confronting Observations with Population Synthesis
Stripped-envelope supernovae (SESNe) mark the deaths of massive stars without hydrogen-rich envelopes. Most SESNe likely originate from binary systems where a companion stripped the progenitor of its envelope. Years of HST imaging of nearby SESNe sites have produced a statistically meaningful sample of constraints on surviving binary companions. We assemble the current sample of six companion detections and six non-detections from the literature, re-analyzing whenever needed. We then conduct th…

Diffusion-based Inverse Observation Model for Artificial Skin
Ante Maric, Julius Jankowski, Giammarco Caroleo, Alessandro Albini, Perla Maiolino, Sylvain Calinon
https://arxiv.org/abs/2506.13986

Diffusion-based Inverse Observation Model for Artificial Skin
Contact-based estimation of object pose is challenging due to discontinuities and ambiguous observations that can correspond to multiple possible system states. This multimodality makes it difficult to efficiently sample valid hypotheses while respecting contact constraints. Diffusion models can learn to generate samples from such multimodal probability distributions through denoising algorithms. We leverage these probabilistic modeling capabilities to learn an inverse observation model conditi…

Time-dependent density estimation using binary classifiers
Agnimitra Dasgupta, Javier Murgoitio-Esandi, Ali Fardisi, Assad A Oberai
https://arxiv.org/abs/2506.15505

Time-dependent density estimation using binary classifiers
We propose a data-driven method to learn the time-dependent probability density of a multivariate stochastic process from sample paths, assuming that the initial probability density is known and can be evaluated. Our method uses a novel time-dependent binary classifier trained using a contrastive estimation-based objective that trains the classifier to discriminate between realizations of the stochastic process at two nearby time instants. Significantly, the proposed method explicitly models th…

Conditions for Large-Sample Majorization of Pairs of Flat States in Terms of $\alpha$-$z$ Relative Entropies
Frits Verhagen, Marco Tomamichel, Erkka Haapasalo
https://arxiv.org/abs/2507.07520

Conditions for Large-Sample Majorization of Pairs of Flat States in Terms of $α$-$z$ Relative Entropies
In this work, we offer the first operational interpretation of the $α$-$z$ relative entropies, which were introduced by Jakšić {\it et al.} \cite{Jaksic2012} and Audenaert and Datta \cite{Audenaert_Datta_2015}, where the $α$ and $z$ parameters are truly independent from each other. Namely, we show that these relative entropies appear in the conditions for large-sample or catalytic relative majorization of pairs of flat states and certain generalizations of them. Additionally, the optimal ra…

Imaging systematics induced by galaxy sub-sample fluctuation: new systematics at second order
Hui Kong, Nora Elisa Chisari, Boris Leistedt, Eric Gawiser, Martin Rodr\'iguez-Monroy, Noah Weaverdyck
https://arxiv.org/abs/2506.09481

Imaging systematics induced by galaxy sub-sample fluctuation: new systematics at second order
Imaging systematics refers to the inhomogeneous distribution of a galaxy sample caused by varying observing conditions and astrophysical foregrounds. Current mitigation methods correct the galaxy density fluctuations, caused by imaging systematics assuming that all galaxies in a sample have the same galaxy density fluctuations. Under this assumption, the corrected sample cannot perfectly recover the true correlation function. We name this effect sub-sample systematics. For a galaxy sample, even…

VITA: Vision-to-Action Flow Matching Policy
Dechen Gao, Boqi Zhao, Andrew Lee, Ian Chuang, Hanchu Zhou, Hang Wang, Zhe Zhao, Junshan Zhang, Iman Soltani
https://arxiv.org/abs/2507.13231

VITA: Vision-to-Action Flow Matching Policy
We present VITA, a Vision-To-Action flow matching policy that evolves latent visual representations into latent actions for visuomotor control. Traditional flow matching and diffusion policies sample from standard source distributions (e.g., Gaussian noise) and require additional conditioning mechanisms like cross-attention to condition action generation on visual information, creating time and space overheads. VITA proposes a novel paradigm that treats latent images as the flow source, learnin…

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SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu, Yinghao Cai, Shuo Wang
https://arxiv.org/abs/2506.14648

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback- and sample- efficiency still remain the problems that hinder the application of PbRL. In this paper, we present a novel efficient query selection and preference-guided exploration method, called SENIOR, which could select the meaningful and easy-to-comparison behavior segment pairs to improve human feedback-efficiency…

This https://arxiv.org/abs/2505.18344 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Sample Complexity of Diffusion Model Training Without Empirical Risk Minimizer Access
Diffusion models have demonstrated state-of-the-art performance across vision, language, and scientific domains. Despite their empirical success, prior theoretical analyses of the sample complexity suffer from poor scaling with input data dimension or rely on unrealistic assumptions such as access to exact empirical risk minimizers. In this work, we provide a principled analysis of score estimation, establishing a sample complexity bound of $\widetilde{\mathcal{O}}(ε^{-6})$. Our approach lever…

An Observation on Lloyd's k-Means Algorithm in High Dimensions
David Silva-S\'anchez, Roy R. Lederman
https://arxiv.org/abs/2506.14952 https://

An Observation on Lloyd's k-Means Algorithm in High Dimensions
Clustering and estimating cluster means are core problems in statistics and machine learning, with k-means and Expectation Maximization (EM) being two widely used algorithms. In this work, we provide a theoretical explanation for the failure of k-means in high-dimensional settings with high noise and limited sample sizes, using a simple Gaussian Mixture Model (GMM). We identify regimes where, with high probability, almost every partition of the data becomes a fixed point of the k-means algorith…

The Cosmic Dance: Observational Detection of Coherent Spin in Galaxy Clusters
Xiao-xiao Tang, Peng Wang, Yu Rong, Weiguang cui
https://arxiv.org/abs/2508.13597 https://

The Cosmic Dance: Observational Detection of Coherent Spin in Galaxy Clusters
The spin of galaxy clusters encodes key information about their formation, dynamics, and the influence of large-scale structure. However, whether clusters possess statistically significant spin and how to measure it observationally remain open questions. Here, we present the first observational statistical detection of coherent spin in galaxy clusters, by using a sample of 2,170 systems with $M > 10^{14}\, M_\odot$ selected from a publicly available group catalog based on the SDSS galaxy. Clust…

A Near-Infrared Spectral Library of Very Young Brown Dwarfs and Planetary-Mass Objects in the Orion Nebula Cluster
Elena Manjavacas, Mario Gennaro, Sarah Betti, Laurent Pueyo, William Balmer, Massimo Robberto
https://arxiv.org/abs/2508.14035

A Near-Infrared Spectral Library of Very Young Brown Dwarfs and Planetary-Mass Objects in the Orion Nebula Cluster
Age-benchmark brown dwarfs' and planetary-mass objects' spectroscopy is key to characterize substellar evolution. In this paper we present the JHK medium resolution (R~3000) spectra of 25 7-75 M_Jup (spectral types L3.0-M6.0) brown dwarfs and planetary-mass objects in the Orion Nebula Cluster obtained with MOSFIRE installed at the W. M. Keck I telescope. We obtained the spectral types of the targets in our sample using template brown dwarf and planetary-mass objects' spectra. We confirmed their…

AIM: Amending Inherent Interpretability via Self-Supervised Masking
Eyad Alshami, Shashank Agnihotri, Bernt Schiele, Margret Keuper
https://arxiv.org/abs/2508.11502 https://

AIM: Amending Inherent Interpretability via Self-Supervised Masking
It has been observed that deep neural networks (DNNs) often use both genuine as well as spurious features. In this work, we propose "Amending Inherent Interpretability via Self-Supervised Masking" (AIM), a simple yet interestingly effective method that promotes the network's utilization of genuine features over spurious alternatives without requiring additional annotations. In particular, AIM uses features at multiple encoding stages to guide a self-supervised, sample-specific feature-masking p…

Consistency assessment and regional sample size calculation for MRCTs under random effects model
Xinru Ren, Jin Xu
https://arxiv.org/abs/2508.09443 https://

Consistency assessment and regional sample size calculation for MRCTs under random effects model
Multi-regional clinical trials (MRCTs) have become common practice for drug development and global registration. Once overall significance is established, demonstrating regional consistency is critical for local health authorities. Methods for evaluating such consistency and calculating regional sample sizes have been proposed based on the fixed effects model using various criteria. To better account for the heterogeneity of treatment effects across regions, the random effects model naturally a…

Quantum chemistry with provable convergence via randomized sample-based quantum diagonalization
Samuele Piccinelli, Alberto Baiardi, Max Rossmannek, Almudena Carrera Vazquez, Francesco Tacchino, Stefano Mensa, Edoardo Altamura, Ali Alavi, Mario Motta, Javier Robledo-Moreno, William Kirby, Kunal Sharma, Antonio Mezzacapo, Ivano Tavernelli
https://

Quantum chemistry with provable convergence via randomized sample-based quantum diagonalization
Sample-based quantum diagonalization (SQD) is a recently proposed algorithm to approximate the ground-state wave function of many-body quantum systems on near-term and early-fault-tolerant quantum devices. In SQD, the quantum computer acts as a sampling engine that generates the subspace in which the Hamiltonian is classically diagonalized. A recently proposed SQD variant, Sample-based Krylov Quantum Diagonalization (SKQD), uses quantum Krylov states as circuits from which samples are collected…

Luminaries in the Sky: The TESS Legacy Sample of Bright Stars. I. Asteroseismic detections in naked-eye main-sequence and sub-giant solar-like oscillators
Mikkel N. Lund, Ashley Chontos, Frank Grundahl, Savita Mathur, Rafael A. Garc\'ia, Daniel Huber, Derek Buzasi, Timothy R. Bedding, Marc Hon, Yaguang Li
https://arxiv.org/abs/2508.086…

Luminaries in the Sky: The TESS Legacy Sample of Bright Stars. I. Asteroseismic detections in naked-eye main-sequence and sub-giant solar-like oscillators
We aim to detect and characterise solar-like oscillations in bright naked-eye (V<6) main-sequence (MS) and subgiant stars observed by TESS. We seek to expand the current benchmark sample of oscillators, provide accurate global asteroseismic parameters for these bright targets, and assess their potential for future detailed investigations -- including missions such as the HWO and PLATO. Our sample of bright stars was selected from the Hipparcos/Tycho catalogues. We analysed TESS 120-s and 20-s c…

X-ray selected broad absorption line quasars in SDSS-V: BALs and non-BALs span the same range of X-ray properties
Pranavi Hiremath, Amy L. Rankine, James Aird, W. N. Brandt, Paola Rodr\'iguez Hidalgo, Scott F. Anderson, Catarina Aydar, Claudio Ricci, Donald P. Schneider, M. Vivek, Zsofi Igo, Sean Morrison, Mara Salvato
https://arxiv.or…

X-ray selected broad absorption line quasars in SDSS-V: BALs and non-BALs span the same range of X-ray properties
Broad absorption line (BAL) quasars are often considered X-ray weak relative to their optical/UV luminosity, whether intrinsically (i.e., the coronal emission is fainter) or due to large column densities of absorbing material. The SDSS-V is providing optical spectroscopy for samples of quasar candidates identified by eROSITA as well as Chandra, XMM or Swift, making the resulting datasets ideal for characterising the BAL quasar population within an X-ray selected sample. We use the Balnicity Ind…

Data Compression with Noise Suppression for Inference under Noisy Covariance
Sunao Sugiyama, Minsu Park
https://arxiv.org/abs/2508.14021 https://arxiv.org/…

Data Compression with Noise Suppression for Inference under Noisy Covariance
In many fields including cosmology, statistical inference often relies on Gaussian likelihoods whose covariance matrices are estimated from a finite number of simulations. This finite-sample estimation introduces noise into the covariance, which propagates to parameter estimates, a phenomenon known as the Dodelson-Schneider (DS) effect, leading to inflated uncertainties. While the Massively Optimized Parameter Estimation and Data compression (MOPED) algorithm offers lossless Fisher information-…

Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
Pi-Yueh Chuang, Ahmed Attia, Emil Constantinescu
https://arxiv.org/abs/2508.09347 https…

Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
We present two analytical formulae for estimating the sensitivity -- namely, the gradient or Jacobian -- at given realizations of an arbitrary-dimensional random vector with respect to its distributional parameters. The first formula interprets this sensitivity as partial derivatives of the inverse mapping associated with the vector of 1-D conditional distributions. The second formula, intended for optimization methods that tolerate inexact gradients, introduces a diagonal approximation that re…

Robust and efficient estimation of global quantum properties under realistic noise
Qingyue Zhang, Dayue Qin, Zhou You, Feng Xu, Jens Eisert, You Zhou
https://arxiv.org/abs/2507.13237

Robust and efficient estimation of global quantum properties under realistic noise
Measuring global quantum properties -- such as the fidelity to complex multipartite states -- is both an essential and experimentally challenging task. Classical shadow estimation offers favorable sample complexity, but typically relies on many-qubit circuits that are difficult to realize on current platforms. We propose the robust phase shadow scheme, a measurement framework based on random circuits with controlled-Z as the unique entangling gate type, tailored to architectures such as trapped…

Kernel Two-Sample Testing via Directional Components Analysis
Rui Cui, Yuhao Li, Xiaojun Song
https://arxiv.org/abs/2508.08564 https://arxiv.org/pdf/2508.0…

Kernel Two-Sample Testing via Directional Components Analysis
We propose a novel kernel-based two-sample test that leverages the spectral decomposition of the maximum mean discrepancy (MMD) statistic to identify and utilize well-estimated directional components in reproducing kernel Hilbert space (RKHS). Our approach is motivated by the observation that the estimation quality of these components varies significantly, with leading eigen-directions being more reliably estimated in finite samples. By focusing on these directions and aggregating information a…

CLASSY XIV: Nitrogen Enrichment Shaped by Gas Density and Feedback
Karla Z. Arellano-C\'ordova, Danielle A. Berg, Matilde Mingozzi, Bethan L. James, Fiorenzo Vincenzo, Noah S. J. Rogers, Evan D. Skillman, Ricardo O. Amor\'in, Fergus Cullen, Sophia R. Flury, Valentina Abril-Melgarejo, John Chisholm, Timothy Heckman, Matthew J. Hayes, Svea Hernandez, Nimisha Kumari, Chiaki Kobayashi, Claus Leitherer, Crystal L. Martin, Zorayda Martinez, Themiya Nanayakkara, Kaelee S. Parker, Pete…

CLASSY XIV: Nitrogen Enrichment Shaped by Gas Density and Feedback
We investigate the chemical evolution of N/O using a sample of 45 local star-forming galaxies (SFGs) from the CLASSY survey. This sample spans a wide range of galaxy properties, with robust determinations of nitrogen and oxygen abundances via the direct-$T_{\rm e}$ method. We explore how N/O relates to density structure, stellar mass, star formation rate (SFR), stellar age, compactness, and gas kinematics. In addition, we compare our results with those of galaxies at $z =2-10$ where N/O ratios …

Do more observations bring more information in rare events?
Danyang Huang, Liyuan Wang, Liping Zhu
https://arxiv.org/abs/2506.13671 https://

Do more observations bring more information in rare events?
It is generally believed that more observations provide more information. However, we observe that in the independence test for rare events, the power of the test is, surprisingly, determined by the number of rare events rather than the total sample size. Moreover, the correlations tend to shrink to zero even as the total sample size increases, as long as the proportion of rare events decreases. We demonstrate this phenomenon in both fixed and high-dimensional settings. To address these issues,…

Rademacher learning rates for iterated random functions
Nikola Sandri\'c
https://arxiv.org/abs/2506.13946 https://arxiv.org/pdf/2…

Rademacher learning rates for iterated random functions
Most existing literature on supervised machine learning assumes that the training dataset is drawn from an i.i.d. sample. However, many real-world problems exhibit temporal dependence and strong correlations between the marginal distributions of the data-generating process, suggesting that the i.i.d. assumption is often unrealistic. In such cases, models naturally include time-series processes with mixing properties, as well as irreducible and aperiodic ergodic Markov chains. Moreover, the lear…

Magnetic White Dwarfs in the SDSS 100 pc Sample: Further Evidence of Two Formation Channels
Adam Moss, Mukremin Kilic, Pierre Bergeron, Warren R. Brown, Gracyn Jewett, Marcel A. Ag\"ueros, Maria Camisassa, Anthony Burrow
https://arxiv.org/abs/2507.06102

Magnetic White Dwarfs in the SDSS 100 pc Sample: Further Evidence of Two Formation Channels
We conduct a model atmosphere analysis on all magnetic white dwarfs in the SDSS 100 pc sample. We have 163 magnetic targets in this sample, 87 of which are new discoveries, making this the largest volume-limited survey of magnetic white dwarfs to date. We discuss the distribution of multiple parameters, including mass, cooling age, and field strength. We find strong evidence of two populations of magnetic white dwarfs that form through separate mechanisms based on a cluster analysis of these pa…

LATIS: A Sample of IGM-selected Protoclusters and Protogroups at $z \sim 2.5$
Andrew B. Newman, Mahdi Qezlou, Gwen C. Rudie, Nima Chartab, Guillermo A. Blanc, Daniel D. Kelson, Simeon Bird, Caitlin Casey, Enrico Congiu, Olga Cucciati, Denise Hung, Brian C. Lemaux, Victoria P\'erez, Jorge Zavala
https://arxiv.org/abs/2507.109…

LATIS: A Sample of IGM-selected Protoclusters and Protogroups at $z \sim 2.5$
The Ly$α$ Tomography IMACS Survey (LATIS) has produced large 3D maps of the intergalactic medium (IGM), providing a new window on the cosmic web at $z\sim2.5$. A key advantage of Ly$α$ tomography is that it enables the discovery of overdense regions without the need to detect their galaxy members in spectroscopic surveys, circumventing possible selection biases. We use these maps to identify 37 IGM-selected overdensities as regions of strong and spatially coherent Ly$α$ absorption. Simulatio…

Study of the Connected Four-Point Correlation Function of Galaxies from DESI Data Release 1 Luminous Red Galaxy Sample
J. Hou, R. N. Cahn, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, P. Doel, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, L. Le Guillou, G. Gutierrez, K. Honscheid, D. Huterer, M. Ishak, R. Joyce, S. Juneau, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, C. Lamman, M. Landriau, A. de la Macorra, M. Manera, A. de Mattia, R. Miquel, E. Mueller, S. Nadathur, G.…

Study of the Connected Four-Point Correlation Function of Galaxies from DESI Data Release 1 Luminous Red Galaxy Sample
We present a measurement of the non-Gaussian four-point correlation function (4PCF) from the DESI DR1 Luminous Red Galaxy (LRG) sample. For the gravitationally induced parity-even 4PCF, we detect a signal with a significance of 14.7$σ$ using our fiducial setup. We assess the robustness of this detection through a series of validation tests, including auto- and cross-correlation analyses, sky partitioning across multiple patch combinations, and variations in radial scale cuts. Due to the low co…

Scalable Variational Inference for Multinomial Probit Models under Large Choice Sets and Sample Sizes
Gyeongjun Kim, Yeseul Kang, Lucas Kock, Prateek Bansal, Keemin Sohn
https://arxiv.org/abs/2507.10945

Scalable Variational Inference for Multinomial Probit Models under Large Choice Sets and Sample Sizes
The multinomial probit (MNP) model is widely used to analyze categorical outcomes due to its ability to capture flexible substitution patterns among alternatives. Conventional likelihood based and Markov chain Monte Carlo (MCMC) estimators become computationally prohibitive in high dimensional choice settings. This study introduces a fast and accurate conditional variational inference (CVI) approach to calibrate MNP model parameters, which is scalable to large samples and large choice sets. A f…

Debiased machine learning for combining probability and non-probability survey data
Shaun Seaman
https://arxiv.org/abs/2508.08948 https://arxiv.org/pdf/250…

Debiased machine learning for combining probability and non-probability survey data
We consider the problem of estimating the finite population mean $\bar{Y}$ of an outcome variable $Y$ using data from a nonprobability sample and auxiliary information from a probability sample. Existing double robust (DR) estimators of this mean $\bar{Y}$ require the estimation of two nuisance functions: the conditional probability of selection into the nonprobability sample given covariates $X$ that are observed in both samples, and the conditional expectation of $Y$ given $X$. These nuisance…

Simultaneous estimation of connectivity and dimensionality in samples of networks
Wenlong Jiang, Chris McKennan, Jes\'us Arroyo, Joshua Cape
https://arxiv.org/abs/2508.12483

Simultaneous estimation of connectivity and dimensionality in samples of networks
An overarching objective in contemporary statistical network analysis is extracting salient information from datasets consisting of multiple networks. To date, considerable attention has been devoted to node and network clustering, while comparatively less attention has been devoted to downstream connectivity estimation and parsimonious embedding dimension selection. Given a sample of potentially heterogeneous networks, this paper proposes a method to simultaneously estimate a latent matrix of …