Tootfinder

Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G
Huixin Xu, Jianhua Zhang, Pan Tang, Hongbo Xing, Haiyang Miao, Nan Zhang, Jian Li, Jianming Wu, Wenfei Yang, Zhening Zhang, Wei Jiang, Zijian He, Afshin Haghighat, Qixing Wang, Guangyi Liu
https://

Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G
Next generation cellular deployments are expected to exploit the 6-24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies render the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models invalid, giving rise to new features such as near-field propagation a…

Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
Jin Li, Kleanthis Malialis, Stelios G. Vrachimis, Marios M. Polycarpou
https://arxiv.org/abs/2508.16336

Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
Water Distribution Networks (WDNs), critical to public well-being and economic stability, face challenges such as pipe blockages and background leakages, exacerbated by operational constraints such as data non-stationarity and limited labeled data. This paper proposes an unsupervised, online learning framework that aims to detect two types of faults in WDNs: pipe blockages, modeled as collective anomalies, and background leakages, modeled as concept drift. Our approach combines a Long Short-Ter…

A novel fast short-time root music method for vibration monitoring of high-speed spindles
Huiguang Zhang, Baoguo Liu, Wei Feng, Zongtang Li
https://arxiv.org/abs/2506.17600

A novel fast short-time root music method for vibration monitoring of high-speed spindles
Ultra-high-speed spindle bearings challenge traditional vibration monitoring due to broadband noise, non-stationarity, and limited time-frequency resolution. We present a fast Short-Time Root-MUSIC (fSTrM) algorithm that exploits FFT-accelerated Lanczos bidiagonalization to reduce computational complexity from $\mathcal{O}(N^3)$ to $SN\log_2N+S^2(N+S)+M^2(N+M)$ while preserving parametric super-resolution. The method constructs Hankel matrices from 16 ms signal frames and extracts fault fre…

Occurrence of Non-Stationarity at Earth's Quasi-Perpendicular Bow Shock
Ajay Lotekar, Yuri V. Khotyaintsev, Daniel B. Graham, Andrew Dimmock, Andreas Johlander, Ahmad Lalti
https://arxiv.org/abs/2507.13817

Occurrence of Non-Stationarity at Earth's Quasi-Perpendicular Bow Shock
Collisionless shocks can exhibit non-stationary behavior even under steady upstream conditions, forming a complex transition region. Ion phase-space holes, linked to shock self-reformation and surface ripples, are a signature of this non-stationarity. We statistically analyze their occurrence using 521 crossings of Earth's quasi-perpendicular bow shock. Phase-space holes appear in 65% of cases, though the actual rate may be higher as the holes may not be resolved during fast shock crossings. Th…

Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models
Guilherme Zucatelli, Ricardo Barioni, Gabriela Dantas
https://arxiv.org/abs/2508.06405

Acoustic Non-Stationarity Objective Assessment with Hard Label Criteria for Supervised Learning Models
Objective non-stationarity measures are resource intensive and impose critical limitations for real-time processing solutions. In this paper, a novel Hard Label Criteria (HLC) algorithm is proposed to generate a global non-stationarity label for acoustic signals, enabling supervised learning strategies to be trained as stationarity estimators. The HLC is first evaluated on state-of-the-art general-purpose acoustic models, demonstrating that these models encode stationarity information. Furtherm…

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises
Minghui Du, Ziren Luo, Peng Xu
https://arxiv.org/abs/2506.10599

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises
The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequency-domain analysis. This work proposes a unified framework based on short-time Fourier transform (STFT) to enhance parameter estimation of Galactic binary and characterization of instrumental noise under non-stationarity. Segmenting data into locally stationary intervals, we derive STFT-based models for signals and …

Replaced article(s) found for astro-ph.HE. https://arxiv.org/list/astro-ph.HE/new
[1/1]:
- Test for LISA foreground Gaussianity and stationarity: galactic white-dwarf binaries
Buscicchio, Klein, Korol, Di Renzo, Moore, Gerosa, Carzaniga

Distance to stationarity and open set recurrence for ergodic processes on the unit interval with a Bernstein or a Siegmund dual
Fernando Cordero, Gr\'egoire V\'echambre
https://arxiv.org/abs/2507.07587

Distance to stationarity and open set recurrence for ergodic processes on the unit interval with a Bernstein or a Siegmund dual
We use duality techniques - specifically Siegmund and Bernstein duality - as tools to analyse ergodic and recurrence properties of $[0,1]$-valued Markov processes. These dualities enable the derivation of sharp bounds on the distance to stationarity and allow for a novel approach to establishing topological recurrence, whereby ergodicity implies recurrent visits to neighbourhoods of points in the stationary distribution's support. We first relate Siegmund and Bernstein duality in cases where bo…

Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
He Chen, Jiajin Li, Anthony Man-cho So
https://arxiv.org/abs/2506.04587 https://…

Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
Solving bilevel optimization (BLO) problems to global optimality is generally intractable. A common alternative is to compute a hyper-stationary point -- a stationary point of the hyper-objective function formed by minimizing/maximizing the upper-level function over the lower-level solution set. However, existing approaches either yield weak notions of stationarity or rely on restrictive assumptions to ensure the smoothness of hyper-objective functions. In this paper, we remove these impractica…

Genuinely multi-dimensional stationarity preserving global flux Finite Volume formulation for nonlinear hyperbolic PDEs
Wasilij Barsukow, Mirco Ciallella, Mario Ricchiuto, Davide Torlo
https://arxiv.org/abs/2506.21700

Genuinely multi-dimensional stationarity preserving global flux Finite Volume formulation for nonlinear hyperbolic PDEs
Classical Finite Volume methods for multi-dimensional problems include stabilization (e.g. via a Riemann solver), that is derived by considering several one-dimensional problems in different directions. Such methods therefore ignore a possibly existing balance of contributions coming from different directions, such as the one characterizing multi-dimensional stationary states. Instead being preserved, they are usually diffused away by such methods. Stationarity preserving methods use a better s…

Ego-centric Learning of Communicative World Models for Autonomous Driving
Hang Wang, Dechen Gao, Junshan Zhang
https://arxiv.org/abs/2506.08149 https://

Ego-centric Learning of Communicative World Models for Autonomous Driving
We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. MARL is known to suffer from the \textit{partial observability} and \textit{non-stationarity} issues. To tackle these challenges, information sharing is often employed, which however faces major hurdles in practice, including overwhelming communication overhead and scalability concerns. By making use of generative AI embodied in world model together with its latent …

Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting
Agnideep Aich, Ashit Baran Aich, Dipak C. Jain
https://arxiv.org/abs/2507.05470

Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting
We propose Temporal Conformal Prediction (TCP), a novel framework for constructing prediction intervals in financial time-series with guaranteed finite-sample validity. TCP integrates quantile regression with a conformal calibration layer that adapts online via a decaying learning rate. This hybrid design bridges statistical and machine learning paradigms, enabling TCP to accommodate non-stationarity, volatility clustering, and regime shifts which are hallmarks of real-world asset returns, with…

Feature-free regression kriging
Peng Luo, Yilong Wu, Yongze Song
https://arxiv.org/abs/2507.07382 https://arxiv.org/pdf/2507.07382

Feature-free regression kriging
Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models -- such as Ordinary Kriging (OK) -- assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. Ho…

Scale-Dependent Multifractality in Bitcoin Realised Volatility: Implications for Rough Volatility Modelling
Milan Pontiggia (MAGEFI - University of Bordeaux, France)
https://arxiv.org/abs/2507.00575

Scale-Dependent Multifractality in Bitcoin Realised Volatility: Implications for Rough Volatility Modelling
We assess the applicability of rough volatility models to Bitcoin realised volatility using the normalised p-variation framework of Cont and Das (2024). Applying this model free estimator to high-frequency Bitcoin data from 2017 to 2024 across multiple sampling resolutions, we find that the normalised statistic remains strictly negative throughout, precluding the estimation of a valid roughness index. Stationarity tests and robustness checks reveal no significant evidence of non-stationarity or…

Regional Price Dynamics and Market Integration in the U.S. Beef Industry: An Econometric Analysis
Leonardo Manr\'iquez-M\'endez
https://arxiv.org/abs/2507.21950 https://…

Regional Price Dynamics and Market Integration in the U.S. Beef Industry: An Econometric Analysis
The United States, a leading global producer and consumer of beef, continues to face substantial challenges in achieving price harmonization across its regional markets. This paper evaluates the validity of the Law of One Price (LOP) in the U.S. beef industry and investigates causal relationships among regional price dynamics. Through a series of econometric tests, we establish that regional price series are integrated of order one, displaying non-stationarity in levels and stationarity in firs…

On the regularity of almost stable relations
Marcos Gir\'on
https://arxiv.org/abs/2508.00511 https://arxiv.org/pdf/2508.00511

On the regularity of almost stable relations
We develop a general theory of local stability up to belonging to an ideal (e.g. having measure zero). From a model-theoretic perspective, we prove a stationarity principle for almost stable formulas in this sense, and build a topological space of partial types whose Cantor-Bendixson rank is finite. The interaction of this space with Keisler measures and definable groups yields, on the one hand, a regularity lemma for infinite graphs where the edge relation is almost stable, and, on the other h…

Risk-based framework to determine climate-informed design storms for road drainage infrastructure
Mohammad Fereshtehpour, Rashid Bashir, Neil F. Tandon
https://arxiv.org/abs/2508.10183

Risk-based framework to determine climate-informed design storms for road drainage infrastructure
Climate change is amplifying extreme precipitation events in many regions and imposes substantial challenges for the resilience of road drainage infrastructure. Conventional design storm methodologies, which rely on historical trends of rainfall data under a stationarity assumption, may not adequately account for future climate variability. This study introduces a risk-based framework for determining climate-informed design storms tailored to road drainage systems. The proposed framework integr…

Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Xiaoxiao Yang, Chan Feng, Jiancheng Chen
https://arxiv.org/abs/2506.23458

Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Portable and wearable consumer-grade electroencephalography (EEG) devices, like Muse headbands, offer unprecedented mobility for daily brain-computer interface (BCI) applications, including cognitive load detection. However, the exacerbated non-stationarity in portable EEG signals constrains data fidelity and decoding accuracy, creating a fundamental trade-off between portability and performance. To mitigate such limitation, we propose MuseCogNet (Muse-based Cognitive Network), a unified joint …

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…

This https://arxiv.org/abs/2408.11315 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Smoothing Variances Across Time: Adaptive Stochastic Volatility
We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We further enhance the mo…

A universal cutoff phenomenon for mean-field exchange models
Pietro Caputo, Matteo Quattropani, Federico Sau
https://arxiv.org/abs/2506.12816 https://

A universal cutoff phenomenon for mean-field exchange models
We study a broad class of high-dimensional mean-field exchange models, encompassing both noisy and singular dynamics, along with their dual processes. This includes a generalized version of the averaging process as well as some non-reversible extensions of classical exchange dynamics, such as the flat Kac model. Within a unified framework, we analyze convergence to stationarity from worst-case initial data in Wasserstein distance. Our main result establishes a universal cutoff phenomenon at an …

Multifactor Quadratic Hobson and Rogers models
Paolo Foschi
https://arxiv.org/abs/2508.08773 https://arxiv.org/pdf/2508.08773

Multifactor Quadratic Hobson and Rogers models
A multi-factor extension of the Hobson and Rogers (HR) model, incorporating a quadratic variance function (QHR model), is proposed and analysed. The QHR model allows for greater flexibility in defining the moving average filter while maintaining the Markovian property of the original HR model. The use of a quadratic variance function permits the characterisation of weak-stationarity conditions for the variance process and allows for explicit expressions for forward variance. Under the assumptio…

This https://arxiv.org/abs/2410.08862 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Test for LISA foreground Gaussianity and stationarity: extreme mass-ratio inspirals
Extreme Mass Ratio Inspirals (EMRIs) are key observational targets for the Laser Interferometer Space Antenna (LISA) mission. Unresolvable EMRI signals contribute to the formation of a gravitational wave background (GWB). Characterizing the statistical features of the GWB from EMRIs is of great importance, as EMRIs will ubiquitously affect large segments of the inference scheme. In this work, we apply a frequentist test for GWB Gaussianity and stationarity, exploring three astrophysically-motiv…

Tensor-Structured Bayesian Channel Prediction for Upper Mid-Band XL-MIMO Systems
Hongwei Hou, Yafei Wang, Xinping Yi, Wenjin Wang, Dirk T. M. Slock, Shi Jin
https://arxiv.org/abs/2508.08491

Tensor-Structured Bayesian Channel Prediction for Upper Mid-Band XL-MIMO Systems
The upper mid-band balances coverage and capacity for the future cellular systems and also embraces XL-MIMO systems, offering enhanced spectral and energy efficiency. However, these benefits are significantly degraded under mobility due to channel aging, and further exacerbated by the unique near-field (NF) and spatial non-stationarity (SnS) propagation in such systems. To address this challenge, we propose a novel channel prediction approach that incorporates dedicated channel modeling, probab…

Over the Stability Space of a Multivariate Time Series
Roberto V\'asquez-Mart\'inez, Graciela Gonz\'alez-Far\'ias, Jos\'e Ulises M\'arquez Urbina, Francisco Corona
https://arxiv.org/abs/2506.22407

Over the Stability Space of a Multivariate Time Series
This paper jointly addresses the challenges of non-stationarity and high dimensionality in analysing multivariate time series. Building on the classical concept of cointegration, we introduce a more flexible notion, called stability space, aimed at capturing stationary components in settings where traditional assumptions may not hold. We examine the parametric Johansen procedure alongside two non-parametric alternatives based on dimensionality reduction techniques: Partial Least Squares and Pri…

Near-Field Spatial non-Stationary Channel Estimation: Visibility-Region-HMM-Aided Polar-Domain Simultaneous OMP
Thibaut Ceulemans, Cel Thys, Robbert Beerten, Zhuangzhuang Cui, Sofie Pollin
https://arxiv.org/abs/2508.04222

Near-Field Spatial non-Stationary Channel Estimation: Visibility-Region-HMM-Aided Polar-Domain Simultaneous OMP
This work focuses on channel estimation in extremely large aperture array (ELAA) systems, where near-field propagation and spatial non-stationarity introduce complexities that hinder the effectiveness of traditional estimation techniques. A physics-based hybrid channel model is developed, incorporating non-binary visibility region (VR) masks to simulate diffraction-induced power variations across the antenna array. To address the estimation challenges posed by these channel conditions, a novel …

Functional Periodic ARMA Processes
Sebastian K\"uhnert, Juhyun Park
https://arxiv.org/abs/2507.18962 https://arxiv.org/pdf/2507.18962

Functional Periodic ARMA Processes
Periodicity is a common feature in functional time series analysis. While autoregressive models in Hilbert spaces have been extended to incorporate it, existing approaches often remain loosely specified or fail to capture inter-seasonal dependencies. This paper introduces periodic autoregressive moving average (fpARMA) processes in general separable Hilbert spaces that fully model inter-seasonal structure. We establish conditions for periodic stationarity, finite moments, and weak dependence, a…

Cutoff for the Swendsen-Wang dynamics on the complete graph
Antonio Blanca, Zhezheng Song
https://arxiv.org/abs/2507.20482 https://arxiv.org/pdf/2507.20482…

Cutoff for the Swendsen-Wang dynamics on the complete graph
We study the speed of convergence of the Swendsen-Wang (SW) dynamics for the $q$-state ferromagnetic Potts model on the $n$-vertex complete graph, known as the mean-field model. The SW dynamics was introduced as an attractive alternative to the local Glauber dynamics, often offering faster convergence rates to stationarity in a variety of settings. A series of works have characterized the asymptotic behavior of the speed of convergence of the mean-field SW dynamics for all $q \ge 2$ and all val…