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arXiv preprints from January 1, 2026 through September 23, 2026 — 11:43:20 EST

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Posted in stat.AP · 2026-07-28 · Léa Gondian, Thimothée Thiery

Validation of methods to estimate the uncertainty of buildings energy savings in a controlled numerical setting and Bayesian energy signature with autocorrelated errors

In the field of building energy efficiency, the measurement and verification (M&V) of energy savings following energy efficiency measures often relies on the use of a calibrated statistical model. In order to obtain reliable estimates, the estimation of uncertainties associated with this procedure is recognized as a crucial aspect of...

💬 0 commentsarXiv:2607.25382v1PDF
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Posted in stat.ME · 2026-07-28 · Kotaro Sasaki, Hisashi Noma

Penalized likelihood inference for beta-binomial meta-analysis of proportions of rare events

In meta-analyses of proportions, the event of interest is often rare, resulting in sparse event counts and frequent zero-event studies. The beta-binomial model has been used as a flexible random-effects model for pooling overdispersed and rare-event proportions. However, the commonly used maximum likelihood estimator (MLE) may be...

💬 0 commentsarXiv:2607.25320v1PDF
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Posted in math.NA · 2026-07-28 · Guan-Yu Chen, Dong-Yue Xie, Xi Yang, Zun-Hao Zheng

Sequential Preconditioned Conjugate Gradient Method for Linear Statistical Models

We propose a randomized iterative method for the ordinary least-squares estimation problem in large-scale linear statistical models, namely the Sequential Preconditioned Conjugate Gradient Method (SPCG). SPCG constructs a sequence of sketched least-squares subproblems with increasing sketch sizes, applies PCG as the inner solver, and...

💬 0 commentsarXiv:2607.25272v1PDF
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Posted in stat.AP · 2026-07-28 · Juan Francisco, Mandujano Reyes

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks

Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's latent ability from the properties of individual benchmark items. Existing neural IRT approaches, including PSN-IRT, estimate these quantities using point estimates, limiting uncertainty...

💬 0 commentsarXiv:2607.25257v1PDF
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Posted in stat.ME · 2026-07-28 · Deepani Hemachandra, Jagath Senarathne, Mahasen Dehideniya

A Copula-Based Regression Framework for Enhanced Prediction under Heteroscedasticity

Classical regression approaches, including ordinary least squares, rely on strong assumptions such as constant variance and normality of residuals, which are often violated in real-world data. Although log-transformation is commonly used to stabilise variance, it may introduce re-transformation bias and fail to address...

💬 0 commentsarXiv:2607.25250v1PDF
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Posted in stat.ME · 2026-07-28 · Chunlei Ge, W. John Braun

Differential Equation-Constrained Exponential-Type Local Polynomial Regression Under Model Misspecification

The issue of model misspecification is critical, yet it is often regarded as unavoidable in applied statistical modeling. Model misspecification can be mitigated by incorporating informative features and strengthening model formulations, such as through the integration of domain knowledge or structural constraints. In this paper, we...

💬 0 commentsarXiv:2607.25248v1PDF
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Posted in stat.ML · 2026-07-28 · Zeyu Bian, Ying Zhou, Yifan Cui

Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions

Standard offline reinforcement learning (RL) algorithms typically assume that the actions in the dataset are observed without error. However, in many real-world applications, the true actions are unobserved and only noisy proxies are available, causing existing RL methods to yield biased and potentially misleading conclusions. We...

💬 0 commentsarXiv:2607.25241v1PDF
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Posted in cs.LG · 2026-07-28 · Yunwei Ren, Zihao Wang, Jason D. Lee

Algorithmic Separation between Constant-Depth and Logarithmic-Depth Neural Networks

Despite the empirical advantages of deep networks over shallow ones, theoretical depth separations largely concern approximation power, while algorithmic results are mostly limited to comparisons between two- and three-layer networks. In this work, we prove the first algorithmic separation between constant-depth and logarithmic-depth...

💬 0 commentsarXiv:2607.25200v1PDF
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Posted in stat.ML · 2026-07-28 · Michael Pokojovy, J. Marcus Jobe, Simon Lacoste-Julien

Lloyd's $K$-Means Clustering Algorithm Is Frank-Wolfe in Disguise

Lloyd's $K$-means algorithm, also known as naïve $K$-means, is a widely used ad hoc optimization heuristic, designed to minimize the sum of squared errors (SSE) across all $K$-partitions of a dataset via iterative cluster refinement. In this work, we establish a novel connection between Lloyd's algorithm and the Frank-Wolfe (FW)...

💬 0 commentsarXiv:2607.25190v1PDF
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Posted in astro-ph.SR · 2026-07-28 · T. A. Movsessian, T. Yu. Magakian, A. V. Moiseev

Long-Slit Spectroscopy Of Herbig-Haro Outflow System, Associated With IRAS 01166+6635

We present long-slit spectroscopic observations of the outflow associated with an infrared source IRAS 01166+6635, conducted with the 6-m telescope of the Special Astrophysical Observatory using the SCORPIO-2 focal reducer. The structure of the flow as investigated in detail, its position-velocity diagrams are constructed. The...

💬 0 commentsarXiv:2607.26033v1PDF
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Posted in astro-ph.CO · 2026-07-28 · Jakkrit Sangtawee, Antonio De Felice, Khamphee Karwan

Minimally modified gravity with Laplacian auxiliary constraints and an inflationary realization

We construct a minimally modified gravity theory that propagates only two tensorial gravitational degrees of freedom around a spatially flat Friedmann--Lemaître--Robertson--Walker (FLRW) background and admits a predictive cosmological perturbation theory. We first show that, in the original four-constraint construction, the...

💬 0 commentsarXiv:2607.26031v1PDF
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Posted in cond-mat.mtrl-sci · 2026-07-28 · Payel Shee, Ipek Efe, Jingwen Li, Kshitij V. Goyal, Morgan Trassin, Shovon Pal

Soft-mode nonlinearities away from ferroelectric phase transition

The interplay between ionic and electronic subsystems dictates the behavior of structural phase transitions in polar dielectrics, a coupling mediated by soft optical phonon modes. In incipient ferroelectrics such as SrTiO$_3$ (STO), strong local-field effects can drive the lattice into a non-perturbative regime near the phase...

💬 0 commentsarXiv:2607.26030v1PDF
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Posted in quant-ph · 2026-07-28 · Viktoria Noel, Michael Fleischhauer, Igor Lesanovsky

Path integral approach to the truncated Wigner approximation of driven-dissipative spins

Phase-space approaches such as the truncated Wigner approximation (TWA) provide an efficient semiclassical framework for performing approximate simulations of the dynamics of open quantum many-body systems outside the reach of exact numerical methods but beyond the mean-field level. For bosonic systems, TWA is known to be equivalent...

💬 0 commentsarXiv:2607.26029v1PDF
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Posted in cond-mat.mtrl-sci · 2026-07-28 · Arpita Dutta, Pratyay Mukherjee, Ritwik Mondal, Shovon Pal

The interplay of crystal-field transitions and exchange spin dynamics in a ferrimagnet

Rare-earth iron garnets offer an ideal platform for exploring the interplay of low-energy excitations and the complex temperature-dependent magnetization dynamics. In these systems, exchange coupling between rare-earth and iron sublattices generates high-frequency collective spin excitations. In addition, the robust spin-orbit...

💬 0 commentsarXiv:2607.26026v1PDF
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Posted in astro-ph.CO · 2026-07-28 · Mikel Martin Barandiaran, Jessica A. Cowell, David Alonso, Javier Carrón Duque, Juan García-Bellido

Weighted Webs: Morphology-Informed Marked Fields

The morphology of the cosmic web formed by the late-time matter distribution encodes cosmological information beyond that contained in standard two-point statistics. Marked power spectra provide a computationally efficient framework to access this higher-order information, by studying the two-point statistics of the density field...

💬 0 commentsarXiv:2607.26021v1PDF
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Posted in hep-ph · 2026-07-28 · Sukanta Dutta, Purnath Unnikrishnan, Yashasvi

Probing Lepton-Flavor-Violating Four-Lepton Operators at a Muon Collider

We investigate charged lepton-flavour violation (LFV) induced by dimension-six four-lepton operators within the Standard Model Effective Field Theory at a proposed high-energy muon collider. We study the processes $μ^{+}μ^{-}\to e^{\pm}τ^{\mp}$, $μ^{+}μ^{-}\to e^{\pm}μ^{\mp}$, and $μ^{+}μ^{-}\to μ^{\pm}τ^{\mp}$ at $\sqrt{s}=3$, $10$,...

💬 0 commentsarXiv:2607.26020v1PDF
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Posted in cond-mat.mes-hall · 2026-07-28 · Wayne M. Witzel, Jesse J. Lutz, Matthew D. Grace, Natalie D. Foster, Ryan M. Jock, Dwight R. Luhman

Predicting the Slow Drift of Nuclear Spin Noise in Semiconductor Spin Qubits

The dynamics of a nuclear spin bath generates magnetic noise that is a key contributor to the decoherence of electron spin qubits in electrostatically-defined quantum dots. In this paper, we extend the cluster correlation expansion (CCE) technique, which has proven useful for predicting solid-state qubit coherence times across various...

💬 0 commentsarXiv:2607.26019v1PDF
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Posted in cond-mat.mtrl-sci · 2026-07-28 · Jared C. Stimac, Fei Zhou, Kyle Bushick, Bo Lei, Sebastien Hamel, Amit Samanta, Vincenzo Lordi

Extracting Atomic Environments for Machine Learning Interatomic Potentials

In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to hundreds of millions of atoms. However, the force-field models that drive those simulations are generally trained with Density Functional Theory (DFT)...

💬 0 commentsarXiv:2607.26018v1PDF
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Posted in astro-ph.SR · 2026-07-28 · Vincenzo Timmel, André Csillaghy, Christian Monstein

Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data

Solar radio bursts are signatures of energetic events associated with solar flares and coronal mass ejections and can interfere with terrestrial and space-based communication systems. Real-time automatic burst monitoring enables early warnings tens of minutes to hours before associated particles reach Earth and provides the basis for...

💬 0 commentsarXiv:2607.26014v1PDF
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Posted in hep-th · 2026-07-28 · Mohsen Alishahiha, Mohammad Javad Vasli

Krylov-Space Memory Cores

We introduce Krylov-space memory cores as stationary, depth-resolved structures that reveal how anomalous initial-state memory is organized inside the Krylov space of otherwise thermalizing nonintegrable systems. The stationary occupation profile identifies where late-time probability is concentrated along the Krylov chain, while...

💬 0 commentsarXiv:2607.26011v1PDF
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Posted in gr-qc · 2026-07-28 · Xi-Li Zhang, Jing Ren

Causal Green function decomposition for quantum black hole seismology

The growing sensitivity of gravitational-wave detectors enables increasingly precise tests of black hole (BH) ringdown spectroscopy. BH quasinormal modes (QNMs) are, however, spectrally unstable: small near-horizon modifications can produce a drastically different QNM spectrum, while causality requires the prompt ringdown to remain...

💬 0 commentsarXiv:2607.26010v1PDF
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Posted in astro-ph.CO · 2026-07-28 · Qinglin Ma, Cheng Li

Anisotropic Secondary Bias of Dark Matter Haloes in a $Λ$CDM Universe

Secondary bias is the dependence of halo clustering on properties beyond halo mass. Using the $z=0$ TNG300-1-Dark simulation, we study anisotropic secondary bias (ASB): the variation of secondary bias with direction relative to the halo major axis. We first use ordinary, orientation-averaged secondary bias (OSB) as a baseline to...

💬 0 commentsarXiv:2607.26009v1PDF
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Posted in cond-mat.mtrl-sci · 2026-07-28 · I. C. J. Yap, B. Doerschel, S. Q. Jin, T. T. Dang, P. M. Scott, H. C. Hofsaess, D. C. Lupascu, A. Krawczuk, J. H. Schell

Singular geometry and eigenframe topology in local rank-2 tensor observables

Symmetric second-rank tensors are reported through magnitude-ordered principal values and axes. This representation folds tensor space: although the physical tensor remains smooth, the reported parameters develop cusps and exchange labels when one principal value crosses zero or two become degenerate. It conceals a global effect: an...

💬 0 commentsarXiv:2607.26008v1PDF
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Posted in astro-ph.IM · 2026-07-28 · Theo Richardson, Adam Ginsburg, Sergey Koposov

The IMF package: a toolkit implementing mass functions and statistical tools to analyze them

Mass functions are used in all areas of astrophysics. The stellar initial mass function (IMF), in particular, plays a central role in modeling stellar populations in galaxies. However, few dedicated tools for working directly with the IMF and its precursor functions are widely available. We present the $\texttt{imf}$ package, a Python...

💬 0 commentsarXiv:2607.26007v1PDF
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Posted in astro-ph.SR · 2026-07-28 · Haiyu Li, Tobías Felipe, Elena Khomenko, Hui Tian, Paul Rajaguru, Yuhang Gao

Understanding the Travel-time Asymmetry of Acoustic Waves in Sunspots With Time-distance Helioseismology

Mapping the subsurface structure and flow field of sunspots has been a challenging task for helioseismology. In this work, we investigate the propagation of acoustic waves in a sunspot in NOAA active region 11312 using time-distance helioseismology. Travel times of waves traveling into and out of the sunspot are measured as functions...

💬 0 commentsarXiv:2607.26006v1PDF