Tootfinder

Thermodynamics and Statistical Equilibrium of Large-Scale Hydroelastic Wave Turbulence
Marlone Vernet, Eric Falcon
https://arxiv.org/abs/2506.03353 https:/…

Thermodynamics and Statistical Equilibrium of Large-Scale Hydroelastic Wave Turbulence
Understanding how statistical equilibrium can occur in out-of-equilibrium systems is of paramount interest, as it would enable the use of statistical mechanics tools to these systems. Here, we report the first experimental evidence of statistical equilibrium of the large scales of hydroelastic turbulent waves driven by small-scale random forcing. The wave field statistics at scales larger than the forcing scale, resolved in space and time, align well with the predictions of Rayleigh-Jeans equil…

Bra och underhållande text kring begreppen statistisk signifikans och konfidensintervall.
How a Cup of Tea Laid the Foundations for Modern Statistical Analysis https://www.wired.com/story/how-a-cup-of-t…

How a Cup of Tea Laid the Foundations for Modern Statistical Analysis
Scientific experiments run today are based on research practices that evolved out of a British tea-tasting experiment in the 1920s.

Large Deviations for Sequential Tests of Statistical Sequence Matching
Lin Zhou, Qianyun Wang, Yun Wei, Jingjing Wang
https://arxiv.org/abs/2506.03976 http…

Large Deviations for Sequential Tests of Statistical Sequence Matching
We revisit the problem of statistical sequence matching initiated by Unnikrishnan (TIT 2015) and derive theoretical performance guarantees for sequential tests that have bounded expected stopping times. Specifically, in this problem, one is given two databases of sequences and the task is to identify all matched pairs of sequences. In each database, each sequence is generated i.i.d. from a distinct distribution and a pair of sequences is said matched if they are generated from the same distribu…

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition
Stefano Bae, Dario Bocchi, Luca Maria Del Bono, Luca Leuzzi
https://arxiv.org/abs/2507.02574

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition
Inverse problems arise in situations where data is available, but the underlying model is not. It can therefore be necessary to infer the parameters of the latter starting from the former. Statistical mechanics offers a toolbox of techniques to address this challenge. In this work, we illustrate three of the main methods: the Maximum Likelihood, Maximum Pseudo-Likelihood, and Mean-Field approaches. We begin with a thorough theoretical introduction to these methods, followed by their application…

A statistical theory for polymer elasticity: from molecular kinematics to continuum behavior
Lin Zhan, Siyu Wang, Rui Xiao, Shaoxing Qu, Paul Steinmann
https://arxiv.org/abs/2507.02361

A statistical theory for polymer elasticity: from molecular kinematics to continuum behavior
Predicting the macroscopic mechanical behavior of polymeric materials from the micro-structural features has remained a challenge for decades. Existing theoretical models often fail to accurately capture the experimental data, due to non-physical assumptions that link the molecule kinematics with the macroscopic deformation. In this work, we construct a novel Hamiltonian for chain segments enabling a unified statistical description of both individual macromolecular chains and continuum polymer …

This https://arxiv.org/abs/2405.17492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

StatWhy: Formal Verification Tool for Statistical Hypothesis Testing Programs
Statistical methods have been widely misused and misinterpreted in various scientific fields, raising significant concerns about the integrity of scientific research. To mitigate this problem, we propose a tool-assisted method for formally specifying and automatically verifying the correctness of statistical programs. In this method, programmers are required to annotate the source code of the statistical programs with the requirements for these methods. Through this annotation, they are reminde…

Statistical shell model for neutrinoless double $\beta$-decay nuclear transition matrix elements: Results for $^{76}$Ge, $^{82}$Se, $^{100}$Mo, $^{124}$Sn, $^{130}$Te and $^{136}$Xe
V. K. B. Kota, R. Sahu
https://arxiv.org/abs/2507.02707

Statistical shell model for neutrinoless double $β$-decay nuclear transition matrix elements: Results for $^{76}$Ge, $^{82}$Se, $^{100}$Mo, $^{124}$Sn, $^{130}$Te and $^{136}$Xe
Statistical shell model (also called spectral distribution method or statistical spectroscopy method) based on random matrix theory and spherical shell model gives a theory for calculating neutrinoless double beta decay nuclear transition matrix elements (NDBD-NTME). This theory is briefly described and then applied to $^{76}$Ge, $^{82}$Se, $^{100}$Mo, $^{124}$Sn,$^{130}$Te and $^{136}$Xe NDBD-NTME. In these calculations, the Bethe's spin-cutoff factor and a bivariate correlation coefficient ar…

This https://arxiv.org/abs/2505.19145 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Do Large Language Models (Really) Need Statistical Foundations?
Large language models (LLMs) represent a new paradigm for processing unstructured data, with applications across an unprecedented range of domains. In this paper, we address, through two arguments, whether the development and application of LLMs would genuinely benefit from foundational contributions from the statistics discipline. First, we argue affirmatively, beginning with the observation that LLMs are inherently statistical models due to their profound data dependency and stochastic genera…

A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
Nirhoshan Sivaroopan, Kaushitha Silva, Chamara Madarasingha, Thilini Dahanayaka, Guillaume Jourjon, Anura Jayasumana, Kanchana Thilakarathna
https://arxiv.org/abs/2507.01976

A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data that preserves real-world characteristics while addressing key challenges such as data scarcity, privacy concerns, and purity constraints associated with real data. In this survey, we provide a comprehensive review of synthetic network traffic generation approaches, covering essential aspects such as data types, gene…

\phi-Sectional curvature of statistical structures on almost contact metric manifolds
Abbas Heydari, Sadegh Mohammadi
https://arxiv.org/abs/2507.00811 http…

ϕ-Sectional curvature of statistical structures on almost contact metric manifolds
In this article, we study and analyze the ϕ-sectional curvature induced by a statistical structure on an almost contact metric manifold. We demonstrate that this sectional curvature is always non-positive. Additionally, we present equivalent statements regarding the vanishing of this type of sectional curvature. Furthermore, we derive a sufficient condition for an almost contact statistical manifold to be classified as a cosymplectic statistical manifold.

Type II radio bursts and space weather phenomena: A statistical study
Pooja Devi, Rositsa Miteva, Ramesh Chandra, Kostadinka Koleva, Bendict Lawrance
https://arxiv.org/abs/2507.01391

Type II radio bursts and space weather phenomena: A statistical study
In this paper, we present for the first time a comprehensive statistical study between type II radio bursts from the metric (m) to the dekameric-hectometric (DH) domain and their associated solar and space weather (SW) phenomena, namely, solar flares (SFs), sunspot (SN) configurations, filament eruptions, coronal mass ejections (CMEs), their interplanetary (IP) counterparts (ICMEs) and shocks, in situ detected particles and geomagnetic storms (GSs). The m-only and m+DH radio signatures are iden…

This https://arxiv.org/abs/2502.10527 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Algorithms and Hardness for Estimating Statistical Similarity
We introduce and study the computational problem of determining statistical similarity between probability distributions. For distributions $P$ and $Q$ over a finite sample space, their statistical similarity is defined as $S_{\mathrm{stat}}(P, Q) := \sum_x \min(P(x), Q(x))$. Despite its fundamental nature as a measure of similarity between distributions, capturing essential concepts such as Bayes error in prediction and hypothesis testing, this computational problem has not been previously exp…

This https://arxiv.org/abs/2506.00486 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

It Takes a Good Model to Train a Good Model: Generalized Gaussian Priors for Optimized LLMs
Despite rapid advancements in the research and deployment of large language models (LLMs), the statistical distribution of model parameters, as well as their influence on initialization, training dynamics, and downstream efficiency, has received surprisingly little attention. A recent work introduced BackSlash, a training-time compression algorithm. It first demonstrated that pre-trained LLM parameters follow generalized Gaussian distributions (GGDs) better. By optimizing GG priors during train…

Statistical mechanics of vector Hopfield network near and above saturation
Flavio Nicoletti, Francesco D'Amico, Matteo Negri
https://arxiv.org/abs/2507.02586

Statistical mechanics of vector Hopfield network near and above saturation
We study analytically and numerically a Hopfield fully-connected network with $d$-vector spins. These networks are models of associative memory that generalize the standard Hopfield with Ising spins. We study the equilibrium and out-of-equilibrium properties of the system, considering the system in its retrieval phase $α<α_c$ and beyond. We derive the Replica Symmetric solution for the equilibrium thermodynamics of the system, together with its phase diagram: we find that the retrieval phase …

This https://arxiv.org/abs/2505.00849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TherMod Communication: Low Power or Hot Air?
The Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme leverages statistical physics to enable secure communication with zero average power flow in a wired channel. While the original KLJN scheme requires significant power for operation, a recent wireless modification, TherMod, proposed by Basar claims a "low power" implementation. This paper critically examines this claim. We explain that the additional components inherent in Basar's wireless adaptation substantially increase power …

Imprints of information scrambling on eigenstates of a quantum chaotic system
Bikram Pain, Ratul Thakur, Sthitadhi Roy
https://arxiv.org/abs/2507.02853 htt…

Imprints of information scrambling on eigenstates of a quantum chaotic system
How are the spatial and temporal patterns of information scrambling in locally interacting quantum many-body systems imprinted on the eigenstates of the system's time-evolution operator? We address this question by identifying statistical correlations among sets of minimally four eigenstates that provide a unified framework for various measures of information scrambling. These include operator mutual information and operator entanglement entropy of the time-evolution operator, as well as more c…

Statistical Interaction Driven Thermoelectricity and Violation of Wiedemann-Franz Law
Sampurna Karmakar, Amulya Ratnakar, Sourin Das
https://arxiv.org/abs/2506.01930

Statistical Interaction Driven Thermoelectricity and Violation of Wiedemann-Franz Law
Quantum transport anomalies in systems obeying Haldane-Wu fractional exclusion statistics, characterized by the statistical interactions parameter $g$ are investigated. We identify particle-hole symmetry breaking of the Haldane-Wu distribution function via its deviations of the maximum entropy ($\mathcal{S}_{g}^{max}$), evaluated at the chemical potential, from the value ${k_B} \ln 2$ (a value that holds only at the free fermion limit, $g=1$). A duality relation, $g\,\mathcal{S}_{g}^{max}=\math…

This https://arxiv.org/abs/2504.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Statistical Physics of the Polarised IKKT Matrix Model
The polarised IKKT matrix model is the worldpoint theory of $N$ D-instantons in a background three-form flux of magnitude $Ω$, and promises to be a highly tractable model of holography. The matrix integral can be viewed as a statistical physics partition function with inverse temperature $Ω^4$. At large $Ω$ the model is dominated by a matrix configuration corresponding to a 'polarised' spherical D1-brane. We show that at a critical value of $Ω^2 N$ the model undergoes a first order phase tr…

This https://arxiv.org/abs/2504.20618 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Statistical Channel Based Low-Complexity Rotation and Position Optimization for 6D Movable Antennas Enabled Wireless Communication
Six-dimensional movable antenna (6DMA) is a promising technology to fully exploit spatial variation in wireless channels by allowing flexible adjustment of three-dimensional (3D) positions and rotations of antennas at the transceiver. In this paper, we investigate the practical low-complexity design of 6DMA-enabled communication systems, including transmission protocol, statistical channel information (SCI) acquisition, and joint position and rotation optimization of 6DMA surfaces based on the …

Relativistic Limits of Decoding: Critical Divergence of Kullback-Leibler Information and Free Energy
Tatsuaki Tsuruyama
https://arxiv.org/abs/2507.02596 ht…

Relativistic Limits of Decoding: Critical Divergence of Kullback-Leibler Information and Free Energy
We present a statistical mechanical framework based on the Kullback-Leibler divergence (KLD) to analyze the relativistic limits of decoding time-encoded information from a moving source. By modeling the symbol durations as entropy-maximizing sequences and treating the decoding process as context-sensitive inference, we identify KLD between the sender and receiver distributions as a key indicator of contextual mismatch. We show that, under Lorentz transformations, this divergence grows with the …

This https://arxiv.org/abs/2505.04947 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prot…

Improving statistical learning methods via features selection without replacement sampling and random projection
Sulaiman khan, Muhammad Ahmad, Fida Ullah, Carlos Aguilar Iba\~nez, Jos\'e Eduardo Valdez Rodriguez
https://arxiv.org/abs/2506.00053

Improving statistical learning methods via features selection without replacement sampling and random projection
Cancer is fundamentally a genetic disease characterized by genetic and epigenetic alterations that disrupt normal gene expression, leading to uncontrolled cell growth and metastasis. High-dimensional microarray datasets pose challenges for classification models due to the "small n, large p" problem, resulting in overfitting. This study makes three different key contributions: 1) we propose a machine learning-based approach integrating the Feature Selection Without Re-placement (FSWOR) technique…

This https://arxiv.org/abs/2409.09019 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Nonequilibrium Phenomenology of Identified Particle Spectra in Heavy-Ion Collisions at LHC Energies
We employ the Zubarev approach of the non-equilibrium statistical operator to investigate the enhancement of the low-$p_T$ region of pion spectra, introducing an effective pion chemical potential to describe the overpopulation of low-energy pion states. We test a corresponding freeze-out approach by analyzing the transverse-momentum spectra of identified particles measured recently with high precision by the ALICE Collaboration in Pb+Pb collisions at CERN LHC. A blast-wave model and a blast-wav…

Strategies and statistical evaluation of Italy's regional model for COVID-19 restrictions
Giuseppe Drago, Giulia Marcon, Alberto Lombardo, Giuseppe Aiello
https://arxiv.org/abs/2507.02504

Strategies and statistical evaluation of Italy's regional model for COVID-19 restrictions
This study presents a comprehensive assessment of the Italian risk model used during the COVID-19 pandemic to guide regional mobility restrictions through a colour-coded classification system. The research focuses on evaluating the variables selected by the Italian Ministry of Health for this purpose and their effectiveness in supporting public health decision-making. The analysis adopts a statistical framework which combines data reduction and regression modelling techniques to enhance interpr…

Generalized Brans-Dicke theory from Verlinde's entropic gravity
Salih Kibaro\u{g}lu, Mustafa Senay
https://arxiv.org/abs/2506.02729 https://

Generalized Brans-Dicke theory from Verlinde's entropic gravity
In this work, we develop a q-deformed scalar-tensor theory of gravitation by combining Verlinde's entropic gravity paradigm with statistical deformation effects. The resulting model modifies the Brans-Dicke framework through a deformation function, treated as a constant rescaling factor for the effective gravitational coupling. We derive the corresponding q-deformed field equations and analyze their theoretical consistency including the recovery of standard gravitational models in specific limi…

A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
Esteban Guti\'errez, Frederic Font, Xavier Serra, Lonce Wyse
https://arxiv.org/abs/2506.04073

A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds characterized by stochastic structure and perceptual stationarity. Drawing inspiration from the statistical and perceptual framework of McDermott and Simoncelli, TexStat identifies similarities between signals belonging to the same texture category without relying on temporal structure. We also propose using TexStat as a validation metric alongside Frechet Audio Dista…

Testing for large-dimensional covariance matrix under differential privacy
Shiwei Sang, Yicheng Zeng, Xuehu Zhu, Shurong Zheng
https://arxiv.org/abs/2506.02410

Testing for large-dimensional covariance matrix under differential privacy
The increasing prevalence of high-dimensional data across various applications has raised significant privacy concerns in statistical inference. In this paper, we propose a differentially private integrated statistic for testing large-dimensional covariance structures, enabling accurate statistical insights while safeguarding privacy. First, we analyze the global sensitivity of sample eigenvalues for sub-Gaussian populations, where our method bypasses the commonly assumed boundedness of data co…

ESWC 2025 has just started with the presentation of the submission numbers to the individual tracks:
research track: 98 papers
resource track: 36 papers
in-use track: 22 papers
Looking forward to great presentations and discussions!
#eswc2025 #semweb

Turbulence in Simulated Local Cluster Analogs: one-to-one comparisons between SLOW and XRISM/Hitomi
Frederick Groth, Milena Valentini, Benjamin A. Seidel, Stephan Vladutescu-Zopp, Veronica Biffi, Klaus Dolag, Jenny G. Sorce
https://arxiv.org/abs/2507.02041

Turbulence in Simulated Local Cluster Analogs: one-to-one comparisons between SLOW and XRISM/Hitomi
The XRISM Resolve X-ray spectrometer allows to gain detailed insight into gas motions of the intra cluster medium (ICM) of galaxy clusters. Current simulation studies focus mainly on statistical comparisons, making the comparison to the currently still small number of clusters difficult due to unknown selection effects. This study aims to bridge this gap, using simulated counterparts of Coma, Virgo, and Perseus from the SLOW constrained simulations. These clusters show excellent agreement in th…

Rigorous Derivation of the Wave Kinetic Equation for $\beta$-FPUT System
Boyang Wu
https://arxiv.org/abs/2506.02948 https://arxiv.org…

Rigorous Derivation of the Wave Kinetic Equation for $β$-FPUT System
Wave kinetic theory has been suggested as a way to understand the longtime statistical behavior of the Fermi-Pasta-Ulam-Tsingou (FPUT) system, with the aim of determining the thermalization time scale. The latter has been a major problem since the model was introduced in the 1950s. In this thesis we establish the wave kinetic equation for a reduced evolution equation obtained from the $β$-FPUT system by removing the non-resonant terms. We work in the kinetic limit $N\to \infty$ and $β\to 0$ u…

This https://arxiv.org/abs/2412.18407 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Statistical Framework for Ranking LLM-Based Chatbots
Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets for ranking models in open-ended conversational tasks. Building upon this foundation, we propose a statistical framework that incorporates key advancements to address specif…

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Andr\'as Gy\"orgy, Tor Lattimore, Nevena Lazi\'c, Csaba Szepesv\'ari
https://arxiv.org/abs/2506.23908

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Sound deductive reasoning -- the ability to derive new knowledge from existing facts and rules -- is an indisputably desirable aspect of general intelligence. Despite the major advances of AI systems in areas such as math and science, especially since the introduction of transformer architectures, it is well-documented that even the most advanced frontier systems regularly and consistently falter on easily-solvable deductive reasoning tasks. Hence, these systems are unfit to fulfill the dream o…

Disintegration theorem for multifunctions, with applications to empirical Wasserstein distances and average-case statistical bounds
James Allen Fill, Lachlan Ewen MacDonald
https://arxiv.org/abs/2507.01236

Disintegration theorem for multifunctions, with applications to empirical Wasserstein distances and average-case statistical bounds
We prove a generalisation of the disintegration theorem to the setting of multifunctions between Polish probability spaces. Whereas the classical disintegration theorem guarantees the disintegration of a probability measure along the partition of the underlying space by the fibres of a measurable function, our theorem gives necessary and sufficient conditions for the measure to disintegrate along a cover of the underlying space defined by the fibres of a measurable multifunction. Building on th…

Recommendation Algorithms on Social Media: Unseen Drivers of Political Opinion
Waseq Billah
https://arxiv.org/abs/2507.01978 https://…

Recommendation Algorithms on Social Media: Unseen Drivers of Political Opinion
Social media broadly refers to digital platforms and applications that simulate social interactions online. This study investigates the impact of social media platforms and their algorithms on political interest among users. As social media usage continues to rise, platforms like Facebook and X (formerly Twitter) play increasingly pivotal roles in shaping political discourse. By employing statistical analyses on data collected from over 3,300 participants, this research identifies significant d…

The 3D Cosmic Shoreline for Nurturing Planetary Atmospheres
Zach K. Berta-Thompson, Patcharapol Wachiraphan, Catriona Murray
https://arxiv.org/abs/2507.02136

The 3D Cosmic Shoreline for Nurturing Planetary Atmospheres
Various ``cosmic shorelines" have been proposed to delineate which planets have atmospheres. The fates of individual planet atmospheres may be set by a complex sea of growth and loss processes, driven by unmeasurable environmental factors or unknown historical events. Yet, defining population-level boundaries helps illuminate which processes matter and identify high-priority targets for future atmospheric searches. Here, we provide a statistical framework for inferring the position, shape, and …

What if we find nothing? Bayesian analysis of the statistical information of null results in future exoplanet habitability and #biosignature surveys: https://arxiv.org/abs/2504.06779 -> interview with the first author: https://www.youtube.com/watch?v=FbnmxiTlBUo

[2025-06-05 Thu (UTC), 1 new article found for q-fin.ST Statistical Finance]
#toXiv_bot_toot

The Bayesian Finite Element Method in Inverse Problems: a Critical Comparison between Probabilistic Models for Discretization Error
Anne Poot, Iuri Rocha, Pierre Kerfriden, Frans van der Meeer
https://arxiv.org/abs/2506.02815

The Bayesian Finite Element Method in Inverse Problems: a Critical Comparison between Probabilistic Models for Discretization Error
When using the finite element method (FEM) in inverse problems, its discretization error can produce parameter estimates that are inaccurate and overconfident. The Bayesian finite element method (BFEM) provides a probabilistic model for the epistemic uncertainty due to discretization error. In this work, we apply BFEM to various inverse problems, and compare its performance to the random mesh finite element method (RM-FEM) and the statistical finite element method (statFEM), which serve as a fr…

Local dark matter density from Gaia DR3 K-dwarfs using Gaussian processes
Laurin S\"oding, Ruben Bartel, Philipp Mertsch
https://arxiv.org/abs/2506.02956

Local dark matter density from Gaia DR3 K-dwarfs using Gaussian processes
Modern astrophysical surveys have produced a wealth of data on the positions and velocities of stars in the Milky Way with varying accuracies. An ever-increasing detail in observational data calls for more complex physical models and in turn more sophisticated statistical methods to extract information. We perform a vertical Jeans analysis, including a local approximation of the tilt term, using a sample of $200\,000$ K-dwarf stars from the Gaia DR3 catalogue. After combination with the Survey-…

Observation of orbitally excited $B_{c}^{ }$ states
LHCb collaboration, et al.
https://arxiv.org/abs/2507.02149 https://arxiv.org/pdf…

Observation of orbitally excited $B_{c}^{+}$ states
The observation of a wide peaking structure in the $B_{c}^{+} γ$ mass spectrum is reported using proton-proton collision data collected by the LHCb detector at center-of-mass energies of $7$, $8$ and $13~\text{TeV}$, corresponding to a total integrated luminosity of $9~\text{fb}^{-1}$. The statistical significance over the background-only hypothesis exceeds seven standard deviations. The width of the observed structure is larger than the expectation from a single-peak hypothesis, and is well d…

Network structural change point detection and reconstruction for balanced neuronal networks
Kai Chen, Mingzhang Wang, Songting Li, Douglas Zhou
https://arxiv.org/abs/2507.02450

Network structural change point detection and reconstruction for balanced neuronal networks
Understanding brain dynamics and functions critically depends on knowledge of the network connectivity among neurons. However, the complexity of brain structural connectivity, coupled with continuous modifications driven by synaptic plasticity, makes its direct experimental measurement particularly challenging. Conventional connectivity inference methods based on neuronal recordings often assumes a static underlying structural connectivity and requires stable statistical features of neural acti…

This https://arxiv.org/abs/2505.18570 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis
Stock price prediction remains a complex and high-stakes task in financial analysis, traditionally addressed using statistical models or, more recently, language models. In this work, we introduce VISTA (Vision-Language Inference for Stock Time-series Analysis), a novel, training-free framework that leverages Vision-Language Models (VLMs) for multi-modal stock forecasting. VISTA prompts a VLM with both textual representations of historical stock prices and their corresponding line charts to pre…

This https://arxiv.org/abs/2410.11635 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Evidence of equilibrium dynamics in human social networks evolving in time
How do networks of relationships evolve over time? We analyse a dataset tracking the social interactions of 900 individuals over four years. Despite continuous shifts in individual relationships, the macroscopic structural properties of the network remain stable, fluctuating within predictable bounds. We connect this stability to the concept of equilibrium in statistical physics. Specifically, we demonstrate that the probabilities governing network dynamics are stationary over time, and key fea…

This https://arxiv.org/abs/2504.09564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The weak-feature-impact effect on the NPMLE in monotone binary regression
The nonparametric maximum likelihood estimator (NPMLE) in monotone binary regression models is studied when the impact of the features on the labels is weak. Here, weakness is colloquially understood as "close to flatness" of the feature-label relationship $x \mapsto \mathbb{P}(Y=1 | X=x)$. Statistical literature provides limit distributions of the NPMLE for the two extremal cases: If the feature-label relation is strictly monotone and sufficiently smooth, then it converges at a nonparamet…

Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
Jiandong Shao, Yao Lu, Jianfei Yang
https://arxiv.org/abs/2506.01734 https://

Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
Large Language Models (LLMs) exhibit impressive performance on complex reasoning tasks, yet they frequently fail on basic numerical problems, producing incorrect outputs. Inspired by Benford's Law -- a statistical pattern where lower digits occur more frequently as leading digits -- we hypothesize that the long-tailed digit distributions in web-collected corpora may be learned by LLMs during pretraining, leading to biased numerical generation. To investigate the hypothesis, we first examine whe…

Replaced article(s) found for math.FA. https://arxiv.org/list/math.FA/new
[1/1]:
- Affine Gateaux Differentials and the von Mises Statistical Calculus
Simone Cerreia-Vioglio, Fabio Maccheroni, Massimo Marinacci, Luigi Montrucchio, Lorenzo Stanca

Anyon superconductivity and plateau transitions in doped fractional quantum anomalous Hall insulators
Pavel A. Nosov, Zhaoyu Han, Eslam Khalaf
https://arxiv.org/abs/2506.02108

Anyon superconductivity and plateau transitions in doped fractional quantum anomalous Hall insulators
Recent experiments reported evidence of superconductivity and re-entrant integer quantum anomalous Hall (RIQAH) insulator upon doping the $ν_e = 2/3$ fractional quantum anomalous Hall states (FQAH) in twisted MoTe${}_2$, separated by narrow resistive regions. Anyons of a FQAH generally have a finite effective mass, and when described by anyon-flux composite fermions (CF), experience statistical magnetic fields with a commensurate filling. Here, we show that most of the experimental observation…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Theory of Casimir Forces: A Unified Approach Using Finite-Temperature Field Theory
P. E. Brandyshev, Yu. A. Budkov

Synergy-Informed Design of Platform Trials for Combination Therapies
Nan Miles Xi, Man Mandy Jin, Lin Wang, Xin Huang
https://arxiv.org/abs/2506.03086 http…

Synergy-Informed Design of Platform Trials for Combination Therapies
Combination drug therapies hold significant promise for enhancing treatment efficacy, particularly in fields such as oncology, immunotherapy, and infectious diseases. However, designing clinical trials for these regimens poses unique statistical challenges due to multiple hypothesis testing, shared control groups, and overlapping treatment components that induce complex correlation structures. In this paper, we develop a novel statistical framework tailored for early-phase translational combina…

Win Probabilities, Hand Sizes, and Game Duration Analysis in the Bhikar-Sawkar Card Game
Mihir Durve
https://arxiv.org/abs/2506.03680 https://

Win Probabilities, Hand Sizes, and Game Duration Analysis in the Bhikar-Sawkar Card Game
We present a Monte Carlo simulation study of the Bhikar-Sawkar card game, a non-deterministic game structurally similar to the classic Beggar-My-Neighbour, which is fully deterministic. Although both games share a common setup, key differences in their rules, particularly the reshuffling of cards after each won hand in Bhikar-Sawkar, introduce stochasticity and significantly increase the space of possible game evolutions. This inherent randomness raises a range of interesting statistical questi…

Towards Trustworthy Sentiment Analysis in Software Engineering: Dataset Characteristics and Tool Selection
Martin Obaidi, Marc Herrmann, Jil Kl\"under, Kurt Schneider
https://arxiv.org/abs/2507.02137

Towards Trustworthy Sentiment Analysis in Software Engineering: Dataset Characteristics and Tool Selection
Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five pla…

This https://arxiv.org/abs/2406.13638 has been replaced.
initial toot: https://mastoxiv.page/@ar…

XENONnT WIMP Search: Signal & Background Modeling and Statistical Inference
The XENONnT experiment searches for weakly-interacting massive particle (WIMP) dark matter scattering off a xenon nucleus. In particular, XENONnT uses a dual-phase time projection chamber with a 5.9-tonne liquid xenon target, detecting both scintillation and ionization signals to reconstruct the energy, position, and type of recoil. A blind search for nuclear recoil WIMPs with an exposure of 1.1 tonne-years (4.18 t fiducial mass) yielded no signal excess over background expectations, from which…

Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
Yiwei Sun
https://arxiv.org/abs/2506.00057 https://arx…

Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
Educators teaching entry-level university engineering modules face the challenge of identifying which topics students find most difficult and how to support diverse student needs effectively. This study demonstrates a rigorous yet interpretable statistical approach -- hierarchical Bayesian modeling -- that leverages detailed student response data to quantify both skill difficulty and individual student abilities. Using a large-scale dataset from an undergraduate Statics course, we identified cl…

Enabling Scalable Distributed Beamforming via Networked LEO Satellites Towards 6G
Yuchen Zhang Seungnyun Kim, Tareq Y. Al-Naffouri
https://arxiv.org/abs/2506.01382

Enabling Scalable Distributed Beamforming via Networked LEO Satellites Towards 6G
In this paper, we propose scalable distributed beamforming schemes over low Earth orbit (LEO) satellite networks that rely solely on statistical channel state information for downlink orthogonal frequency division multiplexing systems. We begin by introducing the system model and presenting a pragmatic yet effective analog beamformer and user-scheduling design. We then derive a closed-form lower bound on the ergodic sum rate, based on the hardening bound, for the digital beamformer design. Next…

Agnostic Reinforcement Learning: Foundations and Algorithms
Gene Li
https://arxiv.org/abs/2506.01884 https://arxiv.org/pdf/2506.01884…

Agnostic Reinforcement Learning: Foundations and Algorithms
Reinforcement Learning (RL) has demonstrated tremendous empirical success across numerous challenging domains. However, we lack a strong theoretical understanding of the statistical complexity of RL in environments with large state spaces, where function approximation is required for sample-efficient learning. This thesis addresses this gap by rigorously examining the statistical complexity of RL with function approximation from a learning theoretic perspective. Departing from a long history of…

Constraints on Barrow and Tsallis Holographic Dark Energy from DESI DR2 BAO data
Giuseppe Gaetano Luciano, Andronikos Paliathanasis, Emmanuel N. Saridakis
https://arxiv.org/abs/2506.03019

Constraints on Barrow and Tsallis Holographic Dark Energy from DESI DR2 BAO data
Barrow and Tsallis Holographic Dark Energy (HDE) are two recently proposed extensions of the standard HDE framework, incorporating generalized corrections to horizon entropy through the use of Barrow and Tsallis entropies. Tsallis entropy arises from non-extensive statistical phenomena which account for long-range correlations and deviations from additivity, while Barrow entropy emerges from quantum-gravitational effects on the horizon geometry, associated with fractal modifications and deforma…

Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning
Nicola Cibin, Bas Mulder, Herman Carstens, Peter Palensky, Alexandru \c{S}tefanov
https://arxiv.org/abs/2507.00522

Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication using Hybrid Statistical-Deep Learning
The digital transformation of power systems is accelerating the adoption of IEC 61850 standard. However, its communication protocols, including Sampled Values (SV), lack built-in security features such as authentication and encryption, making them vulnerable to malicious packet injection. Such cyber attacks can delay fault clearance or trigger unintended circuit breaker operations. While most existing research focuses on detecting cyber attacks in digital substations, intrusion prevention syste…

Revealing a transitional epoch of large-scale cosmic anisotropy in the quasar distribution
Amit Mondal, Biswajit Pandey, Krishna Ghosh
https://arxiv.org/abs/2507.02835

Revealing a transitional epoch of large-scale cosmic anisotropy in the quasar distribution
The Cosmological Principle posits that the Universe is isotropic on the largest scales. While widely supported, this foundational assumption remains testable. We analyse the angular distribution of over one million quasars from the Gaia-unWISE catalogue using Renyi entropy, a multiscale statistical measure sensitive to higher-order clustering. Dividing the sample into three redshift bins, we find that both the low- and high-redshift distributions are statistically consistent with isotropy. Howe…

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
Jigang Fan, Quanlin Wu, Shengjie Luo, Liwei Wang
https://arxiv.org/abs/2506.03237

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges: (1) current datasets and methods are centered on individual protein-ligand complexes and neglect that diverse binding sites may exist across multiple complexes of the same protein, introducing significant statistical bias; (2) ligand binding site detection …

WASP-121b's transmission spectrum observed with JWST/NIRSpec G395H reveals thermal dissociation and SiO in the atmosphere
Cyril Gapp, Thomas M. Evans-Soma, Joanna K. Barstow, Joshua D. Lothringer, David K. Sing, Djemma Ruseva, Eva-Maria Ahrer, Jayesh M. Goyal, Duncan Christie, Laura Kreidberg, Nathan J. Mayne
https://arxiv.o…

WASP-121b's transmission spectrum observed with JWST/NIRSpec G395H reveals thermal dissociation and SiO in the atmosphere
WASP-121b has been established as a benchmark ultrahot Jupiter, serving as a laboratory for the atmospheric chemistry and dynamics of strongly irradiated extrasolar gas giants. Here, we present and analyze WASP-121b's transmission spectrum observed with NIRSpec G395H on board the James Webb Space Telescope and find evidence for the thermal dissociation of H$_2$O and H$_2$ on the planet's permanent dayside. Additionally, we detect SiO at a statistical significance of $5.2σ$ which is compatible …

[2025-07-04 Fri (UTC), 9 new articles found for q-fin.ST Statistical Finance]
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Improved Measurements of $D^ \to \eta e^ \nu_e$ and $D^ \to \eta \mu^ \nu_\mu$
BESIII Collaboration, et al.
https://arxiv.org/abs/2506.02521 https://

Improved Measurements of $D^+ \to ηe^+ν_e$ and $D^+ \to ημ^+ν_μ$
Using 20.3 fb$^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector, we measure the branching fractions of $D^+\to ηe^+ν_e$ and $D^+\to ημ^+ν_μ$ to be $(9.75\pm0.29\pm0.28)\times10^{-4}$ and $(9.08\pm0.35\pm0.23)\times10^{-4}$, where the first and second uncertainties are statistical and systematic, respectively. From a simultaneous fit to their partial decay rates, we determine the product of the hadronic form factor $f^η_+(0)$ an…

This https://arxiv.org/abs/2504.04864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistical parametric simulation studies based on real data
Simulation studies are indispensable for evaluating and comparing statistical methods. The most common simulation approach is parametric simulation, where the data-generating mechanism (DGM) corresponds to a predefined parametric model from which observations are drawn. Many statistical simulation studies aim to provide practical recommendations on a method's suitability for a given application; however, parametric simulations in particular are frequently criticized for being too simplistic and…

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Hang Liu, Anna Scaglione, Sean Peisert
https://arxiv.org/abs/2506.03467

Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
Gaussian Mixture Models (GMMs) are widely used statistical models for representing multi-modal data distributions, with numerous applications in data mining, pattern recognition, data simulation, and machine learning. However, recent research has shown that releasing GMM parameters poses significant privacy risks, potentially exposing sensitive information about the underlying data. In this paper, we address the challenge of releasing GMM parameters while ensuring differential privacy (DP) guar…

Contextual Online Pricing with (Biased) Offline Data
Yixuan Zhang, Ruihao Zhu, Qiaomin Xie
https://arxiv.org/abs/2507.02762 https://a…

Contextual Online Pricing with (Biased) Offline Data
We study contextual online pricing with biased offline data. For the scalar price elasticity case, we identify the instance-dependent quantity $δ^2$ that measures how far the offline data lies from the (unknown) online optimum. We show that the time length $T$, bias bound $V$, size $N$ and dispersion $λ_{\min}(\hatΣ)$ of the offline data, and $δ^2$ jointly determine the statistical complexity. An Optimism-in-the-Face-of-Uncertainty (OFU) policy achieves a minimax-optimal, instance-dependent…

This https://arxiv.org/abs/2502.17701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation dec…

[2025-06-04 Wed (UTC), 5 new articles found for cond-mat.stat-mech Statistical Mechanics]
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Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles
Tom Szwagier, Pierre-Alexandre Mattei, Charles Bouveyron, Xavier Pennec
https://arxiv.org/abs/2507.01542

Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles
Gaussian mixture models (GMMs) are ubiquitous in statistical learning, particularly for unsupervised problems. While full GMMs suffer from the overparameterization of their covariance matrices in high-dimensional spaces, spherical GMMs (with isotropic covariance matrices) certainly lack flexibility to fit certain anisotropic distributions. Connecting these two extremes, we introduce a new family of parsimonious GMMs with piecewise-constant covariance eigenvalue profiles. These extend several lo…

Deterministic Equations for Feedback Control of Open Quantum Systems
Alberto J. B. Rosal, Patrick P. Potts, Gabriel T. Landi
https://arxiv.org/abs/2507.01934

Deterministic Equations for Feedback Control of Open Quantum Systems
Feedback control in open quantum dynamics is crucial for the advancement of various coherent platforms. For a proper theoretical description, however, most feedback schemes rely on stochastic trajectories, which require significant statistical sampling and lack analytical insight. Currently, only a handful of deterministic feedback master equations exist in the literature. In this letter we derive a set of deterministic equations for describing feedback schemes based on generic causal signals. …

Cosmological Dynamics of Accelerating Model in $f(T)$ Gravity with Special Form of Deceleration Parameter
Shivank Pal, Romanshu Garg, Gyan Prakash Singh, Gauree Shanker
https://arxiv.org/abs/2506.03756

Cosmological Dynamics of Accelerating Model in $f(T)$ Gravity with Special Form of Deceleration Parameter
In this paper, the dynamical behavior of the accelerated expansion of the universe is discussed within the framework of $f(T)$ gravity for a particular form of $ f(T)=α(-T)^{n}$. Considering a redshift-dependent parameterization of the deceleration parameter as $q(z)=q_{0}+ q_{1}\left(\frac{ \ln(N+z)}{z+1}-\ln N \right)$. We have derived the Hubble parameter in terms of redshift and discussed its effect on other cosmological parameters. Using Bayesian statistical analysis and $χ^2$-minimizati…

This https://arxiv.org/abs/2211.03243 has been replaced.
link: https://scholar.google.com/scholar?q=a

On the deep-water and shallow-water limits of the intermediate long wave equation from a statistical viewpoint
(Due to the limit on the number of characters for an abstract set by arXiv, the full abstract can not be displayed here. See the abstract in the paper.) We study convergence problems for the intermediate long wave equation (ILW), with the depth parameter $δ> 0$, in the deep-water limit ($δ\to \infty$) and the shallow-water limit ($δ\to 0$) from a statistical point of view. In particular, we establish convergence of invariant Gibbs dynamics for ILW in both the deep-water and shallow-water lim…

Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li, Radha Mastandrea, Vinicius Mikuni, Benjamin Nachman, David Shih, Gup Singh
https://arxiv.org/abs/2506.00119

Generator Based Inference (GBI)
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of the underlying theory. Modern machine learning has supercharged this workflow to enable high-dimensional and unbinned analyses to utilize much more information than ever before. We propose a general framework for describing the integration of machine learning with generators called Generator Based Inference (GBI). A …

[2025-06-04 Wed (UTC), no new articles found for q-fin.ST Statistical Finance]
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Matrix Pencil-Based DoA Estimation for Hybrid Receivers in Snapshot-Limited Scenarios
Mona Mostafa, Ramy H. Gohary, Amr El-Keyi, Yahia A. Eldemerdash Ahmed
https://arxiv.org/abs/2507.02132

Matrix Pencil-Based DoA Estimation for Hybrid Receivers in Snapshot-Limited Scenarios
The goal of this paper is to estimate the directions of arrival (DoAs) for hybrid analog/digital (HAD) receivers when the number of snapshots is too small for statistical averaging to be reliable. This goal is achieved in fully-digital receivers by employing the matrix pencil method (MPM). Unfortunately, the MPM cannot be directly applied in HAD receivers because of the entanglement induced by the underlying analog combiners on the output signals. Furthermore, these analog combiners project the…

Study of $B_{c}(1P)^{ }$ states in the $B_{c}^{ } \gamma$ mass spectrum
LHCb collaboration, et al.
https://arxiv.org/abs/2507.02142 https://

Study of $B_{c}(1P)^{+}$ states in the $B_{c}^{+} γ$ mass spectrum
The study of a wide peaking structure in the $B_{c}^{+} γ$ mass spectrum is reported using a data sample of proton-proton collisions collected by the LHCb detector at center-of-mass energies of $7$, $8$ and $13~\text{TeV}$, corresponding to an integrated luminosity of $9~\text{fb}^{-1}$. The observed structure is consistent with the lowest excited $P$-wave $B_{c}^{+}$ states and exhibits a statistical significance exceeding seven standard deviations relative to the background-only hypothesis. …

This https://arxiv.org/abs/2402.01593 has been replaced.
link: https://scholar.google.com/scholar?q=a

Statistical Accuracy of Approximate Filtering Methods
Estimating the statistics of the state of a dynamical system, from partial and noisy observations, is both mathematically challenging and finds wide application. Furthermore, the applications are of great societal importance, including problems such as probabilistic weather forecasting and prediction of epidemics. Particle filters provide a well-founded approach to the problem, leading to provably accurate approximations of the statistics. However these methods perform poorly in high dimensions…

Last week, we continued our #ISE2025 lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models.
Benefits of NLMs:
- Capturing Long-Range Dependencies
- Computational and Statistical Tractability
- Improved Generalisation
- Higher Accuracy
@…

[2025-06-05 Thu (UTC), 4 new articles found for cond-mat.stat-mech Statistical Mechanics]
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A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Xuchun Duan, Ting Li, Yijun Hou, Yang Wang, Yue Li, Yining Zhang, Zheng Sun, Guiping Zhou
https://arxiv.org/abs/2506.21907

A Statistical Analysis of Magnetic Parameters in Solar Source Regions of Halo-CMEs with and without SEP events
Large SEPs can cause adverse space weather hazard to humans technology and such events are especially associated with halo coronal mass ejections (CMEs). But in turn, a significant portion of halo-CMEs are not associated with large SEPs. The objective of this study is to gain an understanding of the source region distinctions between halo-CMEs in SEP and No-SEP events. Among the 176 halo-CMEs observed from 2010-2024, we screen out 45 large SEP events and 131 No-SEP events from this dataset. It …

A nonparametric statistical method for deconvolving densities in the analysis of proteomic data
Akin Anarat, Jean Krutmann, Holger Schwender
https://arxiv.org/abs/2506.01540

A nonparametric statistical method for deconvolving densities in the analysis of proteomic data
In medical research, often, genomic or proteomic data are collected, with measurements frequently subject to uncertainties or errors, making it crucial to accurately separate the signals of the genes or proteins, respectively, from the noise. Such a signal separation is also of interest in skin aging research in which intrinsic aging driven by genetic factors and extrinsic, i.e.\ environmentally induced, aging are investigated by considering, e.g., the proteome of skin fibroblasts. Since extrin…

Towards Better Attribute Inference Vulnerability Measures
Paul Francis, David Wagner
https://arxiv.org/abs/2507.01710 https://arxiv.o…

Towards Better Attribute Inference Vulnerability Measures
The purpose of anonymizing structured data is to protect the privacy of individuals in the data while retaining the statistical properties of the data. An important class of attack on anonymized data is attribute inference, where an attacker infers the value of an unknown attribute of a target individual given knowledge of one or more known attributes. A major limitation of recent attribute inference measures is that they do not take recall into account, only precision. It is often the case tha…

[2025-07-04 Fri (UTC), 5 new articles found for cond-mat.stat-mech Statistical Mechanics]
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This https://arxiv.org/abs/2505.22221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Tight qubit uncertainty relations studied through weak values in neutron interferometry
In its original formulation, Heisenberg's uncertainty principle describes a trade-off relation between the error of a quantum measurement and the thereby induced disturbance on the measured object. However, this relation is not valid in general. An alternative universally valid relation was derived by Ozawa in 2003, defining error and disturbance in a general concept, experimentally accessible via a tomographic method. Later, it was shown by Hall that these errors correspond to the statistical …

This https://arxiv.org/abs/2411.05791 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Forecasting Company Fundamentals
Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. While statistical and contemporary machine learning methods have been applied to many time series tasks, there is a lack of comparison of these approaches on this particularly challenging data regime. To this end, we try to bridge this gap and thoroughly evaluate the theoretical properties and practical performa…

This https://arxiv.org/abs/2406.19157 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

How to build your latent Markov model -- the role of time and space
Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, the inconsistent use of terminology, and the various inferential approaches and software packages can be overwhelming to practitioners, especially when they are new to this area. With this review-like paper, we thus aim to provide guidance for both statisticians and practitioners working with latent…

Bayesian Analysis of Non-extensive Parameters in Au-Au Collisions
Randy Dobler, Juliana O. Costa, Marcelo D. Alloy, D\'ebora P. Menezes
https://arxiv.org/abs/2507.01646

Bayesian Analysis of Non-extensive Parameters in Au-Au Collisions
In this work, a Bayesian statistical framework is employed to analyze particle yield ratios in Au-Au collisions, utilizing Non-Extensive Statistics (NES). Through Markov Chain Monte Carlo (MCMC) sampling, we systematically estimate key parameters, including the non-extensive factor $q$, temperature $T$, and chemical potential $μ$. Our analysis confirms previous findings highlighting the suitability and robustness of Bayesian methods in describing heavy-ion collision data. A subsequent Bayes fa…

A dynamical coherency gate for state recovery: Statistical requiem for the long arc of cislunar orbital mis-prediction
Binyamin Stivi, Vishnuu Mallik, Lamberto Dell'Elce, Aaron J. Rosengren
https://arxiv.org/abs/2506.22748

A dynamical coherency gate for state recovery: Statistical requiem for the long arc of cislunar orbital mis-prediction
We present a statistically grounded methodology for recovering physically consistent initial conditions from historical two-line element sets (TLEs), enabling accurate long-arc trajectory reconstruction for distant and highly eccentric Earth satellites. The approach combines numerical averaging of osculating orbital elements with Gaussian-mixture-model (GMM) filtering with robust fallback strategies to isolate a dynamically coherent subset of mean elements at a common reference epoch. From this…

Wireless AI Evolution: From Statistical Learners to Electromagnetic-Guided Foundation Models
Jian Xiao, Ji Wang, Kunrui Cao, Xingwang Li, Zhao Chen, Chau Yuen
https://arxiv.org/abs/2507.00366

Wireless AI Evolution: From Statistical Learners to Electromagnetic-Guided Foundation Models
While initial applications of artificial intelligence (AI) in wireless communications over the past decade have demonstrated considerable potential using specialized models for targeted communication tasks, the revolutionary demands of sixth-generation (6G) networks for holographic communications, ubiquitous sensing, and native intelligence are propelling a necessary evolution towards AI-native wireless networks. The arrival of large AI models paves the way for the next phase of Wireless AI, dr…

Tensor Renormalization Group Meets Computer Assistance
Nikolay Ebel, Tom Kennedy, Slava Rychkov
https://arxiv.org/abs/2506.03247 https://

Tensor Renormalization Group Meets Computer Assistance
Tensor renormalization group, originally devised as a numerical technique, is emerging as a rigorous analytical framework for studying lattice models in statistical physics. Here we introduce a new renormalization map - the 2x1 map - which coarse-grains the lattice anisotropically by a factor of two in one direction followed by a 90-degree rotation. We develop a novel graphical language that translates the action of the 2x1 map into a system of inequalities on tensor components, with rigorous e…

A General Approach to the Shape Transition of Run-and-Tumble Particles: The 1D PDMP Framework for Invariant Measure Regularity
Leo Hahn
https://arxiv.org/abs/2506.02274

A General Approach to the Shape Transition of Run-and-Tumble Particles: The 1D PDMP Framework for Invariant Measure Regularity
Run-and-tumble particles (RTPs) have emerged as a paradigmatic example for studying nonequilibrium phenomena in statistical mechanics. The invariant measure of a wide class of RTPs subjected to a potential possesses a density, which is continuous when tumble rates are high and discontinuous when they are low. This key feature is known as shape transition. By comparison with the Boltzmann distribution characteristic of thermodynamic equilibrium, this constitutes a qualitative indicator of the re…

A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits
Joshua Villarreal, Julia Woodward, John Hardin, Janet Conrad
https://arxiv.org/abs/2507.01153

A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits
A critical challenge in particle physics is combining results from diverse experimental setups that measure the same physical quantity to enhance precision and statistical power, a process known as a global fit. Global fits of sterile neutrino searches, hunts for additional neutrino oscillation frequencies and amplitudes, present an intriguing case study. In such a scenario, the key assumptions underlying Wilks' theorem, a cornerstone of most classic frequentist analyses, do not hold. The metho…

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk
Amina El Bernoussi, Mohamed El Arrouchi
https://arxiv.org/abs/2506.23161

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk
This simulation study compares statistical approaches for estimating extreme quantile regression, with a specific application to fire risk forecasting. A simulation-based framework is designed to evaluate the effectiveness of different methods in capturing extreme dependence structures and accurately predicting extreme quantiles. These approaches are applied to fire occurrence data from the Fez-Meknes region, where a positive relationship is observed between increasing maximum temperatures and …

Unveiling the different scaling regimes of the one-dimensional Kardar-Parisi-Zhang--Burgers equation using the functional renormalisation group
Liubov Gosteva, Nicol\'as Wschebor, L\'eonie Canet
https://arxiv.org/abs/2506.03937

Unveiling the different scaling regimes of the one-dimensional Kardar-Parisi-Zhang--Burgers equation using the functional renormalisation group
The Kardar-Parisi-Zhang (KPZ) equation is a celebrated non-linear stochastic equation featuring non-equilibrium scaling. Although in one dimension, its statistical properties are very well understood, a new scaling regime has been reported in recent numerical simulations. This new regime is characterised by a dynamical exponent $z=1$, markedly different from the expected one $z=3/2$ for the KPZ universality class, and it emerges when approaching the inviscid limit. The origin of this scaling ha…

This https://arxiv.org/abs/2407.13958 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Fast and flexible inference for spatial extremes
Statistical modelling of spatial extreme events has gained increasing attention over the last few decades with max-stable processes, and more recently $r$-Pareto processes, becoming the reference tools for the statistical analysis of asymptotically dependent data. Although inference for r-Pareto processes is easier than for max-stable processes, there remain major hurdles for their application to high dimensional datasets within a reasonable timeframe. In addition, both approaches have almost e…

Blind Passive Beamforming for MIMO System
Wenhai Lai, Jiawei Yao, Kaiming Shen
https://arxiv.org/abs/2506.00987 https://arxiv.org/pdf…

Blind Passive Beamforming for MIMO System
Passive beamforming for the intelligent surface (IS)-aided multiple-input multiple-output (MIMO) communication is a difficult nonconvex problem. It becomes even more challenging under the practical discrete constraints on phase shifts. Unlike most of the existing approaches that rely on the channel state information (CSI), this work advocates a blind beamforming strategy without any CSI. Simply put, we propose a statistical method that learns the main feature of the wireless environment from th…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Optimal time estimation and the clock uncertainty relation for stochastic processes
Prech, Landi, Meier, Nurgalieva, Potts, Silva, Mitchison

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression
Marc Ratkovic
https://arxiv.org/abs/2506.23862

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression
We introduce context augmentation, a data-augmentation approach that uses large language models (LLMs) to generate contexts around observed strings as a means of facilitating valid frequentist inference. These generated contexts serve to reintroduce uncertainty, incorporate auxiliary information, and facilitate interpretability. For example, in the two-sample test, we compare the log-probability of strings under contexts from its own versus the other group. We show on synthetic data that the me…

This https://arxiv.org/abs/2412.12405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalized entropy calibration for analyzing voluntary survey data
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized entropy calibration is introduced as a unified tool for calibration weighting to control the selection bias. We first establish the relationship between the generalized calibration weighting and its dual expression for regression estimation. The dual relations…

[2025-06-03 Tue (UTC), 8 new articles found for cond-mat.stat-mech Statistical Mechanics]
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[2025-07-03 Thu (UTC), 6 new articles found for cond-mat.stat-mech Statistical Mechanics]
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