Tootfinder

The challenge of HEPA filters in the classrooms.
h/t @…
source: https://xcancel.com/kadamssl/status/1938267359233429683#m

Staining normalization in histopathology: Method benchmarking using multicenter dataset
Umair Khan, Jouni H\"ark\"onen, Marjukka Friman, Leena Latonen, Teijo Kuopio, Pekka Ruusuvuori
https://arxiv.org/abs/2506.19106

Staining normalization in histopathology: Method benchmarking using multicenter dataset
Hematoxylin and Eosin (H&E) has been the gold standard in tissue analysis for decades, however, tissue specimens stained in different laboratories vary, often significantly, in appearance. This variation poses a challenge for both pathologists' and AI-based downstream analysis. Minimizing stain variation computationally is an active area of research. To further investigate this problem, we collected a unique multi-center tissue image dataset, wherein tissue samples from colon, kidney, and skin …

Augmentation Technologies and AI - An Ethical Design Futures Framework
Ann Hill Duin, Isabel Pedersen
https://arxiv.org/abs/2508.16615 https://arxiv.org/pd…

Augmentation Technologies and AI - An Ethical Design Futures Framework
Augmentation technologies, fueled by Artificial Intelligence (AI), are undergoing a process of adaptation and normalization geared to everyday users in various roles as practitioners, educators, and students. While new innovations, applications, and algorithms are developed as augmentation technology, Chapter 1 focuses on human subjects, contexts, and rhetorical strategies proposed for them by external actors. The chapter discusses core functions of technical and professional communication and …

Unified theory of testing relevant hypothesis in functional time series
Leheng Cai, Qirui Hu
https://arxiv.org/abs/2508.18624 https://arxiv.org/pdf/2508.18…

Unified theory of testing relevant hypothesis in functional time series
In this paper, we present a general framework for testing relevant hypotheses in functional time series. Our unified approach covers one-sample, two-sample, and change point problems under contaminated observations with arbitrary sampling schemes. By employing B-spline estimators and the self-normalization technique, we propose nuisance-parameter-free testing procedures, obviating the need for additional procedures such as estimating long-run covariance or measurement-error variance functions. …

Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
Pengfei Fan, Yuli Zhang, Xinheng Wang, Ruiyuan Jiang, Hankang Gu, Dongyao Jia, Shangbo Wang
https://arxiv.org/abs/2506.21269

Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou
This study presents and publicly releases the Suzhou Urban Road Acoustic Dataset (SZUR-Acoustic Dataset), which is accompanied by comprehensive data-acquisition protocols and annotation guidelines to ensure transparency and reproducibility of the experimental workflow. To model the coupling between vehicular noise and driving speed, we propose a bimodal-feature-fusion deep convolutional neural network (BMCNN). During preprocessing, an adaptive denoising and normalization strategy is applied to …

Determination of Asymptotic Normalization Coefficients Based on the Dispersive Optical Model
O. V. Bespalova, L. D. Blokhintsev, A. A. Klimochkina, D. A. Savin
https://arxiv.org/abs/2506.19664

Determination of Asymptotic Normalization Coefficients Based on the Dispersive Optical Model
A method for determining asymptotic normalization coefficients for removing nucleons from nuclei is proposed. It is based on the use of the dispersive optical model potential. Within the method, the strength parameter of the Hartree- Fock type potential at the Fermi energy is the only one fitting parameter. It was found that adjusting this parameter allows us to achieve a coincidence of the calculated binding energies with the experimental ones accurately enough. Specific calculations were carr…

Domain Adaptation via Feature Refinement
Savvas Karatsiolis, Andreas Kamilaris
https://arxiv.org/abs/2508.16124 https://arxiv.org/pdf/2508.16124

Domain Adaptation via Feature Refinement
We propose Domain Adaptation via Feature Refinement (DAFR2), a simple yet effective framework for unsupervised domain adaptation under distribution shift. The proposed method synergistically combines three key components: adaptation of Batch Normalization statistics using unlabeled target data, feature distillation from a source-trained model and hypothesis transfer. By aligning feature distributions at the statistical and representational levels, DAFR2 produces robust and domain-invariant feat…

Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data
Qibing Bai, Sho Inoue, Shuai Wang, Zhongjie Jiang, Yannan Wang, Haizhou Li
https://arxiv.org/abs/2507.17735

Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data
Accent normalization converts foreign-accented speech into native-like speech while preserving speaker identity. We propose a novel pipeline using self-supervised discrete tokens and non-parallel training data. The system extracts tokens from source speech, converts them through a dedicated model, and synthesizes the output using flow matching. Our method demonstrates superior performance over a frame-to-frame baseline in naturalness, accentedness reduction, and timbre preservation across multi…

NeRF-based CBCT Reconstruction needs Normalization and Initialization
Zhuowei Xu, Han Li, Dai Sun, Zhicheng Li, Yujia Li, Qingpeng Kong, Zhiwei Cheng, Nassir Navab, S. Kevin Zhou
https://arxiv.org/abs/2506.19742

NeRF-based CBCT Reconstruction needs Normalization and Initialization
Cone Beam Computed Tomography (CBCT) is widely used in medical imaging. However, the limited number and intensity of X-ray projections make reconstruction an ill-posed problem with severe artifacts. NeRF-based methods have achieved great success in this task. However, they suffer from a local-global training mismatch between their two key components: the hash encoder and the neural network. Specifically, in each training step, only a subset of the hash encoder's parameters is used (local sparse…

Coupled Entropy: A Goldilocks Generalization?
Kenric P. Nelson
https://arxiv.org/abs/2506.17229 https://arxiv.org/pdf/2506.17229

Coupled Entropy: A Goldilocks Generalization?
Nonextensive Statistical Mechanics (NSM) has developed into a powerful toolset for modeling and analyzing complex systems. Despite its many successes, a puzzle arose early in its development. The constraints on the Tsallis entropy are in the form of an escort distribution with elements proportional to $p_i^q$, but this same factor within the Tsallis entropy function is not normalized. This led to consideration of the Normalized Tsallis Entropy (NTE); however, the normalization proved to make th…

A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance
Jie Su, Weiwei Wang, Zhaotian Gu, Dahui Wang, Tianyi Qian
https://arxiv.org/abs/2508.12702

A Unified Cortical Circuit Model with Divisive Normalization and Self-Excitation for Robust Representation and Memory Maintenance
Robust information representation and its persistent maintenance are fundamental for higher cognitive functions. Existing models employ distinct neural mechanisms to separately address noise-resistant processing or information maintenance, yet a unified framework integrating both operations remains elusive -- a critical gap in understanding cortical computation. Here, we introduce a recurrent neural circuit that combines divisive normalization with self-excitation to achieve both robust encodin…

Full normalization for $\kappa^ $-supercompactness
Farmer Schlutzenberg
https://arxiv.org/abs/2506.08287 https://arxiv.org/pdf/2506.0…

Full normalization for $κ^+$-supercompactness
We extend the normalization results of the author's paper "Full normalization for transfinite stacks" [5] to mice at the level of $κ^+$-supercompactness: given a normal iteration strategy $Σ$ for such a mouse $M$, with both $M$ and $Σ$ satisfying certain condensation properties, we extend $Σ$ to a strategy $Σ^*$ for stacks of normal trees, such that every iterate via $Σ^*$ is in fact a normal iterate via $Σ$.

Training a Foundation Model for Materials on a Budget
Teddy Koker, Tess Smidt
https://arxiv.org/abs/2508.16067 https://arxiv.org/pdf/2508.16067

Training a Foundation Model for Materials on a Budget
Foundation models for materials modeling are advancing quickly, but their training remains expensive, often placing state-of-the-art methods out of reach for many research groups. We introduce Nequix, a compact E(3)-equivariant potential that pairs a simplified NequIP design with modern training practices, including equivariant root-mean-square layer normalization and the Muon optimizer, to retain accuracy while substantially reducing compute requirements. Built in JAX, Nequix has 700K paramete…

Finding a Maximal Determinant Principal Submatrix via Hadamard's Inequality and Conic Relaxations
Hao Hu, Stefan Sremac, Hugo J. Woerdeman, Henry Wolkowicz
https://arxiv.org/abs/2508.15608

Finding a Maximal Determinant Principal Submatrix via Hadamard's Inequality and Conic Relaxations
An important yet challenging problem in numerical linear algebra is finding a principal submatrix with the maximum determinant. In this paper, we examine several exact and approximate approaches to this problem. We first propose an upper bound based on Hadamard's inequality, along with a projection scheme based on the Gram-Schmidt process without normalization. This scheme yields a highly effective exact algorithm for solving small- to medium-scale instances. We then study a linear programming …

❝We humans are stability-seeking creatures. Getting accustomed to what used to seem unthinkable can feel like an accomplishment. And when the unthinkable recedes at least a bit…it’s easy to mistake it for proof that the dark times are ending.
But these comparatively small victories don’t alter the direction of our transformation — they don’t even slow it down measurably — even while they appeal to our deep need to normalize.…And so just when we most need to act — while there is indeed room for action and some momentum to the resistance — we tend to be lulled into complacency by the sense of relief on the one hand and boredom on the other.❞
https://www.nytimes.com/2025/05/28/opinion/trump-danger-normalization-shock.html

This https://arxiv.org/abs/2503.09831 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Strong normalization through idempotent intersection types: a new syntactical approach
It is well-known that intersection type assignment systems can be used to characterize strong normalization (SN). Typical proofs that typable lambda-terms are SN in these systems rely on semantical techniques. In this work, we study $Λ_\cap^e$, a variant of Coppo and Dezani's (Curry-style) intersection type system, and we propose a syntactical proof of strong normalization for it. We first design $Λ_\cap^i$, a Church-style version, in which terms closely correspond to typing derivations. Then…

Nonlinear-linear duality for multipath quantum interference
Yi Zheng, Jin-Shi Xu, Chuan-Feng Li, Guang-Can Guo
https://arxiv.org/abs/2508.13855 https://arx…

Nonlinear-linear duality for multipath quantum interference
In quantum optics, the postselection amplitude of a nondegenerate parametric down-conversion (PDC) process is linked to a beamsplitter (BS) via partial time reversal, up to a normalization coefficient which is related to the parametric gain [Proc. Natl. Acad. Sci. USA 117, 33107 (2020)]. A special example where the gain is low is reminiscent of Klyshko's advanced-wave picture in quantum imaging. Here, we propose and prove a generalized duality for multiple spatial paths connecting a quantum non…

Thermodynamics of the Reissner-Nordstr\"om black hole with quintessence matter on the EGUP framework
Hao Chen, Bekir Can L\"utf\"uo\u{g}lu, Hassan Hassanabadi, Zheng-Wen Long
https://arxiv.org/abs/2508.15467

Thermodynamics of the Reissner-Nordström black hole with quintessence matter on the EGUP framework
In this paper, we investigate the quantum correction on thermodynamics of the Reissner-Nordström black hole in the presence of the quintessence matter associated with dark energy. To this end, the modified Hawking temperature, heat capacity, and entropy functions of the black hole are derived. { Investigation reveals that the modified uncertainty principles and normalization factor $α$ restrict the lower bound value of horizon radius to affect the Hawking temperature and the parameter $η_{0}…

RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
Rohit Krishnan, Jon Evans
https://arxiv.org/abs/2508.12165 https://arxiv.org/pdf/2508.12…

RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
This paper introduces RLNVR (Reinforcement Learning from Non-Verified Rewards), a framework for training language models using noisy, real-world feedback signals without requiring explicit human verification. Traditional RLHF requires expensive, verified reward signals that are impractical in many real-world domains. RLNVR addresses this challenge through baseline normalization and semantic similarity-based reward transfer. We demonstrate RLNVR through Walter, a prototype system that optimizes …

On the normalization of trigonometric and hyperbolic B-splines
Hendrik Speleers
https://arxiv.org/abs/2508.01817 https://arxiv.org/pdf/2508.01817

On the normalization of trigonometric and hyperbolic B-splines
Trigonometric and hyperbolic B-splines can be computed via recurrence relations analogous to the classical polynomial B-splines. However, in their original formulation, these two types of B-splines do not form a partition of unity and consequently do not admit the notion of control polygons with the convex hull property for design purposes. In this paper, we look into explicit expressions for their normalization and provide a recursive algorithm to compute the corresponding normalization weight…

Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
Hiroshi Kera, Achim Kehrein
https://arxiv.org/abs/2506.09529

Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
This paper studies the concept and the computation of approximately vanishing ideals of a finite set of data points. By data points, we mean that the points contain some uncertainty, which is a key motivation for the approximate treatment. A careful review of the existing border basis concept for an exact treatment motivates a new adaptation of the border basis concept for an approximate treatment. In the study of approximately vanishing polynomials, the normalization of polynomials plays a vit…

Fibering of double twist knots via the adjoint hyperbolic torsion polynomial
Anh T. Tran
https://arxiv.org/abs/2508.14731 https://arxiv.org/pdf/2508.14731

Fibering of double twist knots via the adjoint hyperbolic torsion polynomial
For a hyperbolic knot $K$ in $S^3$, the adjoint hyperbolic torsion polynomial $\mathcal T^{\mathrm{Ad}}_K(t) \in \mathbb C[t^{\pm 1}]$ is defined as a normalization of the twisted Alexander polynomial of $K$ associated with the $\mathrm{SL}_3(\mathbb C)$-representation obtained by composing the holonomy representation of $K$ with the adjoint action of $\mathrm{SL}_2(\mathbb C)$ on its Lie algebra $\mathfrak{sl}_2(\mathbb C)$. In this paper we consider the adjoint hyperbolic torsion polynomial f…

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
U\u{g}urcan Aky\"uz, Deniz Katircioglu-\"Ozt\"urk, Emre K. S\"usl\"u, Burhan Kele\c{s}, Mete C. Kaya, Gamze Durhan, Meltem G. Akp{\i}nar, Figen B. Demirkaz{\i}k, G\"ozde B. Akar
https://arxiv.org/abs/2508.15452

DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
Numerous deep learning-based solutions have been developed for the automatic recognition of breast cancer using mammography images. However, their performance often declines when applied to data from different domains, primarily due to domain shift - the variation in data distributions between source and target domains. This performance drop limits the safe and equitable deployment of AI in real-world clinical settings. In this study, we present DoSReMC (Domain Shift Resilient Mammography Class…

Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Sofia Ivolgina, P. Thomas Fletcher, Baba C. Vemuri
https://arxiv.org/abs/2507.08261

Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Batch normalization (BN) is a ubiquitous operation in deep neural networks used primarily to achieve stability and regularization during network training. BN involves feature map centering and scaling using sample means and variances, respectively. Since these statistics are being estimated across the feature maps within a batch, this problem is ideally suited for the application of Stein's shrinkage estimation, which leads to a better, in the mean-squared-error sense, estimate of the mean and …

NAM: A Normalization Attention Model for Personalized Product Search In Fliggy
Shui Liu, Mingyuan Tao, Maofei Que, Pan Li, Dong Li, Shenghua Ni, Zhuoran Zhuang
https://arxiv.org/abs/2506.08382

NAM: A Normalization Attention Model for Personalized Product Search In Fliggy
Personalized product search provides significant benefits to e-commerce platforms by extracting more accurate user preferences from historical behaviors. Previous studies largely focused on the user factors when personalizing the search query, while ignoring the item perspective, which leads to the following two challenges that we summarize in this paper: First, previous approaches relying only on co-occurrence frequency tend to overestimate the conversion rates for popular items and underestim…

Robust Symbolic Reasoning for Visual Narratives via Hierarchical and Semantically Normalized Knowledge Graphs
Yi-Chun Chen
https://arxiv.org/abs/2508.14941 https://

Robust Symbolic Reasoning for Visual Narratives via Hierarchical and Semantically Normalized Knowledge Graphs
Understanding visual narratives such as comics requires structured representations that capture events, characters, and their relations across multiple levels of story organization. However, symbolic narrative graphs often suffer from inconsistency and redundancy, where similar actions or events are labeled differently across annotations or contexts. Such variance limits the effectiveness of reasoning and generalization. This paper introduces a semantic normalization framework for hierarchica…

Effective normalization of sub-Riemannian connections
Erlend Grong, Jan Slovak
https://arxiv.org/abs/2508.00108 https://arxiv.org/pdf/2508.00108

Effective normalization of sub-Riemannian connections
We give a new normalization condition for connections on sub-Riemannian manifolds with constant symbols. The condition is formulated in terms of Cartan connections and depends only on the first degree of homogeneity of the curvature. The essential part of our result is to show how a Cartan connection can be uniquely determined by a partial connection on the horizontal bundle. Viewed from the manifold, this observation is equivalent to the following claim: a compatible partial affine connection …

The Ahlfors-Weill reflection on convex domains and Nehari quasidisks
Martin Chuaqui
https://arxiv.org/abs/2507.14291 https://arxiv.or…

The Ahlfors-Weill reflection on convex domains and Nehari quasidisks
The estimate $$\RR\{a_2f\}>-\frac12$$ derived for convex mappings in \cite{FMR}, is interpreted here in terms of the Ahlfors-Weill reflection to show that for such domains $\Om$, the mediatrix of the segment $[w, \mR_w]$ joining a point $w\in\Om$ and its reflection $\mR_w$ lies always outside $\Om$. In particular, the midpoint of the segment is also outside $\Om$. We determine the extremal cases when such a midpoint can lie of the boundary $\partial\Om$. The normalization $\fd=\frac{f}{1+a_2f}$…

Mathematical proof concerning the additivity problem of nonlinear normalized citation counts
Xing Wang, Zhihui Zhang
https://arxiv.org/abs/2508.14595 https://

Mathematical proof concerning the additivity problem of nonlinear normalized citation counts
The issue of whether nonlinear normalized citation counts can be added is critically important in scientometrics because it touches upon the theoretical foundation of underlying computation in the field. In this paper, we provide rigorous mathematical proofs for the key theorems underlying this fundamental issue. Based on these proofs, we ultimately arrive at the following conclusion: a nonlinear normalization method for citation counts must be a non-equidistant transformation; consequently, th…

The absolute insanity that is a journalism hell-bent on ultimate normalization... #nytimes

$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Yifan Zeng
https://arxiv.org/abs/2506.05844

$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Network Intrusion Detection Systems (NIDS) face challenges due to class imbalance, affecting their ability to detect novel and rare attacks. This paper proposes a Dual-Conditional Batch Normalization Variational Autoencoder ($\text{C}^{2}\text{BNVAE}$) for generating balanced and labeled network traffic data. $\text{C}^{2}\text{BNVAE}$ improves the model's adaptability to different data categories and generates realistic category-specific data by incorporating Conditional Batch Normalization (C…

T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
Alva West, Luodan Zhang, Liuliu Zhang, Minjun Zhu, Yixuan Weng, Yue Zhang
https://arxiv.org/abs/2507.23577

T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
The proliferation of sophisticated text generation models necessitates the development of robust detection methods capable of identifying machine-generated content, particularly text designed to evade detection through adversarial perturbations. Existing zero-shot detectors often rely on statistical measures that implicitly assume Gaussian distributions, a premise that falters when confronted with the heavy-tailed statistical artifacts characteristic of adversarial or non-native English texts. …

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
Martin Sondermann, Pinar Bisgin, Niklas Tschorn, Anja Burmann, Christoph M. Friedrich
https://arxiv.org/abs/2507.07058

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
The automated classification of phonocardiogram (PCG) recordings represents a substantial advancement in cardiovascular diagnostics. This paper presents a systematic comparison of four distinct models for heart murmur detection: two specialized convolutional neural networks (CNNs) and two zero-shot universal audio transformers (BEATs), evaluated using fixed-length and heart cycle normalization approaches. Utilizing the PhysioNet2022 dataset, a custom heart cycle normalization method tailored to…

Deformed Schur Indices of BCD-type for N=4 Super Yang-Mills and Symmetric Functions
Gao-fu Ren, Min-xin Huang
https://arxiv.org/abs/2507.11315 https://

Deformed Schur Indices of BCD-type for N=4 Super Yang-Mills and Symmetric Functions
We investigate the deformed Schur index in four dimensional N=4 super Yang-Mills theories with $SO$ and $Sp$ gauge groups, generalizing Hatsuda's recent calculations. We express the deformed Schur index as integrals of Koornwinder polynomials and Macdonald polynomials, then perform the integrals in terms of the normalization constants of Macdonald polynomials. We provide explicit results for some low rank gauge groups and for expansion in a $u$ parameter. We discuss various special limits and t…

Analytic Uniqueness of Ball Volume Interpolation: Categorical Invariance and Universal Characterization
Andreu Ballus Santacana
https://arxiv.org/abs/2506.06885

Analytic Uniqueness of Ball Volume Interpolation: Categorical Invariance and Universal Characterization
We show that the classical volume formula for the unit $x$-ball, \[ V_x = \frac{π^{x/2}}{Γ(x/2+1)}, \] can be characterized as the \emph{unique} analytic continuation of Haar measure normalization and unit ball volumes for $\mathrm{O}(n)$, under principles of categorical invariance and normalization at integer dimensions. We generalize this result to the unitary and symplectic cases, formalize invariance using categorical language, and construct explicit categorical examples with functorial d…

Comparing Normalization Methods for Portfolio Optimization with Reinforcement Learning
Caio de Souza Barbosa Costa, Anna Helena Reali Costa
https://arxiv.org/abs/2508.03910 http…

Comparing Normalization Methods for Portfolio Optimization with Reinforcement Learning
Recently, reinforcement learning has achieved remarkable results in various domains, including robotics, games, natural language processing, and finance. In the financial domain, this approach has been applied to tasks such as portfolio optimization, where an agent continuously adjusts the allocation of assets within a financial portfolio to maximize profit. Numerous studies have introduced new simulation environments, neural network architectures, and training algorithms for this purpose. Amon…

Double Hamiltonian Hopf Bifurcation: normalization and normal form non-integrability
L. M. Lerman, R. Mazrooei-Sebdani, N. E. Kulagin
https://arxiv.org/abs/2505.23991

Double Hamiltonian Hopf Bifurcation: normalization and normal form non-integrability
The double Hamiltonian Hopf bifurcation is studied, i.e. a generic two-parametric unfolding of a smooth Hamiltonian system with four degrees of freedom which has at the critical value of parameters the equilibrium with two pairs of double non semi-simple pure imaginary eigenvalues $\pm iω_1,$ $\pm iω_2,$ $ω_1\ne ω_2$ under an assumption of absence of strong resonances between $ω_1,ω_2$. We derive the normal form of the unfolding, when the ratio $ω_1/ω_2$ is irrational and study the trun…

Intellectual Up-streams of Percentage Scale ($ps$) and Percentage Coefficient ($b_p$) -- Effect Size Analysis (Theory Paper 2)
Xinshu Zhao, Qinru Ruby Ju, Piper Liping Liu, Dianshi Moses Li, Luxi Zhang, Jizhou Francis Ye, Song Harris Ao, Ming Milano Li
https://arxiv.org/abs/2507.13695

Intellectual Up-streams of Percentage Scale ($ps$) and Percentage Coefficient ($b_p$) -- Effect Size Analysis (Theory Paper 2)
Percentage thinking, i.e., assessing quantities as parts per hundred, has traveled widely from Roman tax ledgers to modern algorithms. Building on early decimalization by Simon Stevin in La Thiende (1585) and the 19th-century metrication movement that institutionalized base-10 measurement worldwide (Cajori, 1925), this article traces the intellectual trails through which base-10 normalization, especially 0~1 percentage scale. We discuss commonalities between those Wisconsin-Carolina experiments…

Hierarchical formula classes with respect to semi-classical prenex normalization
Makoto Fujiwara, Taishi Kurahashi
https://arxiv.org/abs/2506.22348 https:/…

Hierarchical formula classes with respect to semi-classical prenex normalization
In [10], the authors formalized the standard transformation procedure for prenex normalization of first-order formulas and showed that the classes $\mathrm{E}_k$ and $\mathrm{U}_k$ introduced in Akama et al. [1] are exactly the classes induced by $Σ_k$ and $Π_k$ respectively via the transformation procedure. In that sense, the classes $\mathrm{E}_k$ and $\mathrm{U}_k$ correspond to $Σ_k$ and $Π_k$ based on classical logic respectively. On the other hand, some transformations of the prenex n…

This https://arxiv.org/abs/2303.17475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning distributed representations with efficient SoftMax normalization
Learning distributed representations, or embeddings, that encode the relational similarity patterns among objects is a relevant task in machine learning. A popular method to learn the embedding matrices $X, Y$ is optimizing a loss function of the term ${\rm SoftMax}(XY^T)$. The complexity required to calculate this term, however, runs quadratically with the problem size, making it a computationally heavy solution. In this article, we propose a linear-time heuristic approximation to compute the …

SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images
Suman Mahapatra, Pradipta Maji
https://arxiv.org/abs/2506.07028 ht…

SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images
Segmentation of nuclei regions from histological images is an important task for automated computer-aided analysis of histological images, particularly in the presence of impermissible color variation in the color appearance of stained tissue images. While color normalization enables better nuclei segmentation, accurate segmentation of nuclei structures makes color normalization rather trivial. In this respect, the paper proposes a novel deep generative model for simultaneously segmenting nucle…

From Local Updates to Global Balance: A Framework for Distributed Matrix Scaling
Giacomo Aletti, Giovanni Naldi
https://arxiv.org/abs/2506.08035 https://…

From Local Updates to Global Balance: A Framework for Distributed Matrix Scaling
This paper investigates matrix scaling processes in the context of local normalization algorithms and their convergence behavior. Starting from the classical Sinkhorn algorithm, the authors introduce a generalization where only a single row or column is normalized at each step, without restrictions on the update order. They extend Birkhoff's theorem to characterize the convergence properties of these algorithms, especially when the normalization sequence is arbitrary. A novel application is exp…

Inverse scattering problem for the third-order equation on the line
Tuncay Aktosun, Ivan Toledo, Mehmet Unlu
https://arxiv.org/abs/2506.09346 https://

Inverse scattering problem for the third-order equation on the line
We consider the third-order linear differential equation $$\displaystyle\frac{d^3ψ}{dx^3}+Q(x)\,\displaystyle\frac{dψ}{dx}+P(x)\,ψ=k^3\,ψ,\qquad x\in\mathbb R,$$ where the complex-valued potentials $Q$ and $P$ are assumed to belong to the Schwartz class. We describe the basic solutions, the scattering coefficients, and the bound-state information, and we introduce the dependency constants and the normalization constants at the bound states. When the secondary reflection coefficients are zer…

Stochastic Conformal Flows in Even Dimensions
Jack Piazza
https://arxiv.org/abs/2506.01217 https://arxiv.org/pdf/2506.01217

Stochastic Conformal Flows in Even Dimensions
We define two stochastic analogs of a geometric flow on even-dimensional manifolds called $Q$-curvature flow, and use the theory of Dirichlet forms to construct weak solutions to both. The first of these flows, which we call the normalized $Q$ flow (NQF), preserves the intrinsic volume normalization from the deterministic setting. The second, which we call the Liouville $Q$ flow (LQF), has a different normalization motivated by a similar flow studied in arXiv:1904.10909. The volume dynamics of …

Kernel Density Balancing
John Park, Ning Hao, Yue Selena Niu, Ming Hu
https://arxiv.org/abs/2506.12626 https://arxiv.org/pdf/2506.126…

Kernel Density Balancing
High-throughput chromatin conformation capture (Hi-C) data provide insights into the 3D structure of chromosomes, with normalization being a crucial pre-processing step. A common technique for normalization is matrix balancing, which rescales rows and columns of a Hi-C matrix to equalize their sums. Despite its popularity and convenience, matrix balancing lacks statistical justification. In this paper, we introduce a statistical model to analyze matrix balancing methods and propose a kernel-bas…

Selberg's Central Limit Theorem weighted by Linear Statistics of Zeta Zeros
Alessandro Fazzari, Maxim Gerspach, Paolo Minelli
https://arxiv.org/abs/2507.04150

Selberg's Central Limit Theorem weighted by Linear Statistics of Zeta Zeros
We consider the value distribution of the logarithm of the Riemann zeta function on the critical line, weighted by the local statistics of zeta zeros. We show that, with appropriate normalization, it satisfies a complex Central Limit Theorem, provided that the Fourier support of the test function in the linear statistics is sufficiently small. For the imaginary part, we extend this support condition up to its natural barrier under the Riemann Hypothesis. Finally, we prove that the correlation b…

MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
Antoine Labatie, Michael Vaccaro, Nina Lardiere, Anatol Garioud, Nicolas Gonthier
https://arxiv.org/abs/2508.10894

MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
Self-supervised learning holds great promise for remote sensing, but standard self-supervised methods must be adapted to the unique characteristics of Earth observation data. We take a step in this direction by conducting a comprehensive benchmark of fusion strategies and reconstruction target normalization schemes for multimodal, multitemporal, and multispectral Earth observation data. Based on our findings, we propose MAESTRO, a novel adaptation of the Masked Autoencoder, featuring optimized …

A scalable quantum-neural hybrid variational algorithm for ground state estimation
Minwoo Kim, Kyoung Keun Park, Uihwan Jeong, Sanghyeon Lee, Taehyun Kim
https://arxiv.org/abs/2507.11002

A scalable quantum-neural hybrid variational algorithm for ground state estimation
We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss function during training, leading to exponential scaling of measurement overhead with qubit number. U-VQNHE resolves these issues, significantly reduces required measurements, and retains improved accuracy and stability over standard variational qu…

Probing the energy-dependent temporal nature of MAXI J1803-298 with AstroSat and NICER
Arbind Pradhan, Akash Garg, Ranjeev Misra, Biplob Sarkar
https://arxiv.org/abs/2508.07843 …

Probing the energy-dependent temporal nature of MAXI J1803-298 with AstroSat and NICER
We performed the spectral and temporal analysis of MAXI J1803-298 using AstroSat/LAXPC and NICER observations taken in May 2021 during the initial phase of the outburst. We found that the source traverses through the hard, intermediate, and soft spectral states during the outburst. The spectrum in all states can be described using soft emissions from the thermal disk and hard emissions from the coronal regions. The variation in the inner disk temperature and normalization of the disk indicates …

Hubness Reduction with Dual Bank Sinkhorn Normalization for Cross-Modal Retrieval
Zhengxin Pan, Haishuai Wang, Fangyu Wu, Peng Zhang, Jiajun Bu
https://arxiv.org/abs/2508.02538 …

Hubness Reduction with Dual Bank Sinkhorn Normalization for Cross-Modal Retrieval
The past decade has witnessed rapid advancements in cross-modal retrieval, with significant progress made in accurately measuring the similarity between cross-modal pairs. However, the persistent hubness problem, a phenomenon where a small number of targets frequently appear as nearest neighbors to numerous queries, continues to hinder the precision of similarity measurements. Despite several proposed methods to reduce hubness, their underlying mechanisms remain poorly understood. To bridge thi…

EffiFusion-GAN: Efficient Fusion Generative Adversarial Network for Speech Enhancement
Bin Wen, Tien-Ping Tan
https://arxiv.org/abs/2508.14525 https://arxi…

EffiFusion-GAN: Efficient Fusion Generative Adversarial Network for Speech Enhancement
We introduce EffiFusion-GAN (Efficient Fusion Generative Adversarial Network), a lightweight yet powerful model for speech enhancement. The model integrates depthwise separable convolutions within a multi-scale block to capture diverse acoustic features efficiently. An enhanced attention mechanism with dual normalization and residual refinement further improves training stability and convergence. Additionally, dynamic pruning is applied to reduce model size while maintaining performance, making…

Anisotropic area measures of convex bodies
Rolf Schneider
https://arxiv.org/abs/2506.08803 https://arxiv.org/pdf/2506.08803

Anisotropic area measures of convex bodies
Motivated by the relative differential geometry, where the Euclidean normal vector of hypersurfaces is generalized by a relative normalization, we introduce anisotropic area measures of convex bodies, constructed with respect to a gauge body. Together with the anisotropic curvature measures, they are special cases of the newly introduced anisotropic support measures. We show that a convex body in ${\mathbb R}^n$, for which the anisotropic area measure of some order $k\in\{0,\dots,n-2\}$ is prop…

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Francesco D'Amico, Dario Bocchi, Luca Maria Del Bono, Saverio Rossi, Matteo Negri
https://arxiv.org/abs/2507.05147

Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Energy-based probabilistic models learned by maximizing the likelihood of the data are limited by the intractability of the partition function. A widely used workaround is to maximize the pseudo-likelihood, which replaces the global normalization with tractable local normalizations. Here we show that, in the zero-temperature limit, a network trained to maximize pseudo-likelihood naturally implements an associative memory: if the training set is small, patterns become fixed-point attractors whos…

Minimal regular normal crossings models of superelliptic curves
Andrew Obus, Padmavathi Srinivasan
https://arxiv.org/abs/2506.24091 https://

Minimal regular normal crossings models of superelliptic curves
Let $K$ be a complete discretely valued field with perfect residue field $k$. If $X \to \mathbb{P}^1_K$ is a $\mathbb{Z}/d$-cover with $\text{char } k \nmid d$, we compute the minimal regular normal crossings model $\mathcal{X}$ of $X$ as the normalization of an explicit normal model $\mathcal{Y}$ of $\mathbb{P}^1_K$ in $K(X)$. The model $\mathcal{Y}$ is given using Mac Lane's description of discrete valuations on the rational function field $K(\mathbb{P}^1)$.

Computing the Bogoliubov-de Gennes excitations of two-component Bose-Einstein condensates
Manting Xie, Yong Zhang
https://arxiv.org/abs/2506.12688 https://…

Computing the Bogoliubov-de Gennes excitations of two-component Bose-Einstein condensates
In this paper, we present an efficient and spectrally accurate numerical method to compute elementary/collective excitations in two-component Bose-Einstein condensates (BEC), around their mean-field ground state, by solving the associated Bogoliubov-de Gennes (BdG) equation. The BdG equation is essentially an eigenvalue problem for a non-Hermitian differential operator with an eigenfunction normalization constraint. Firstly, we investigate its analytical properties, including the exact eigenpai…

Skin modes tunability and self-healing effect in photonic Floquet lattices
Hua-Yu Bai, Yang Chen, Tian-Yang Zhang, Guang-Can Guo, Ming Gong, Xi-Feng Ren
https://arxiv.org/abs/2507.04085

Skin modes tunability and self-healing effect in photonic Floquet lattices
Non-Hermitian systems can exhibit a number of intriguing physics not presented by the Hermitian models, including skin effect, non-Bloch bands, generalized bulk-edge correspondence and self-healing effect (SHE). In this manuscript, we demonstrate that when eigenstates are fully localized at one boundary, the biorthogonal normalization of left and right eigenstates enables the tunability of skin modes through local potential modulation at the opposite boundary,a phenomenon termed as skin mode tu…

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
Francisco Daunas, I\~naki Esnaola, Samir M. Perlaza
https://arxiv.org/abs/2508.03314 htt…

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
The dual formulation of empirical risk minimization with f-divergence regularization (ERM-fDR) is introduced. The solution of the dual optimization problem to the ERM-fDR is connected to the notion of normalization function introduced as an implicit function. This dual approach leverages the Legendre-Fenchel transform and the implicit function theorem to provide a nonlinear ODE expression to the normalization function. Furthermore, the nonlinear ODE expression and its properties provide a compu…

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new/
[1/1]:
Selecting ChIP-seq Normalization Methods from the Perspective of their Technical Conditions

Distributed Average Consensus in Wireless Multi-Agent Systems with Over-the-Air Aggregation
Themistoklis Charalambous, Zheng Chen, Christoforos N. Hadjicostis
https://arxiv.org/abs/2507.22648

Distributed Average Consensus in Wireless Multi-Agent Systems with Over-the-Air Aggregation
In this paper, we address the average consensus problem of multi-agent systems over wireless networks. We propose a distributed average consensus algorithm by invoking the concept of over-the-air aggregation, which exploits the signal superposition property of wireless multiple-access channels. The proposed algorithm deploys a modified version of the well-known Ratio Consensus algorithm with an additional normalization step for compensating for the arbitrary channel coefficients. We show that, …

DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems
Yao Li, Yicheng Liu, Shirou Wang
https://arxiv.org/abs/2508.09529 https://

DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems
This paper introduces a novel deep learning method, called DeepWKB, for estimating the invariant distribution of randomly perturbed systems via its Wentzel-Kramers-Brillouin (WKB) approximation $u_ε(x) = Q(ε)^{-1} Z_ε(x) \exp\{-V(x)/ε\}$, where $V$ is known as the quasi-potential, $ε$ denotes the noise strength, and $Q(ε)$ is the normalization factor. By utilizing both Monte Carlo data and the partial differential equations satisfied by $V$ and $Z_ε$, the DeepWKB method computes $V$ and …

Indirect Measurement of the $^{23}\textbf{Na}(p,\gamma)^{24}$Mg Direct Capture Reaction Rate via ($^3$He,d) Spectroscopy
Kaixin Song, Richard Longland, Caleb Marshall, Kiana Setoodehnia, Federico Portillo Chaves, Axel Fraud
https://arxiv.org/abs/2507.02095

Indirect Measurement of the $^{23}\textbf{Na}(p,γ)^{24}$Mg Direct Capture Reaction Rate via ($^3$He,d) Spectroscopy
The cross section of the $^{23}\text{Na}(p,γ)^{24}\text{Mg}$ reaction is dominated by direct capture at low energies relevant for stellar burning. Such cross sections can be constrained using spectroscopic factors($C^2S$) or asymptotic normalization coefficients(ANCs) from transfer reactions. In this work, the $^{23}\text{Na}(^3\text{He},d)^{24}\text{Mg}$ reaction was measured at $E_{lab}=21$ MeV to extract spectroscopic factors for $^{24}\text{Mg}$ states with excitation energies in $E_x=7\si…

Building a Media Ecosystem Observatory from Scratch: Infrastructure, Methodology, and Insights
Zeynep Pehlivan, Saewon Park, Alexei Sisulu Abrahams, Mika Desblancs-Patel, Benjamin David Steel, Aengus Bridgman
https://arxiv.org/abs/2506.10942

Building a Media Ecosystem Observatory from Scratch: Infrastructure, Methodology, and Insights
Understanding the flow of information across today's fragmented digital media landscape requires scalable, cross-platform infrastructure. In this paper, we present the Canadian Media Ecosystem Observatory, a national-scale infrastructure designed to monitor political and media discourse across platforms in near real time. Media Ecosystem Observatory (MEO) data infrastructure features custom crawlers for major platforms, a unified indexing pipeline, and a normalization layer that harmonizes he…

Explaining the Weak Evolution of the High-Redshift Mass-Metallicity Relation with Galaxy Burst Cycles
Andrew Marszewski, Claude-Andr\'e Faucher-Gigu\`ere, Robert Feldmann, Guochao Sun
https://arxiv.org/abs/2505.22712

Explaining the Weak Evolution of the High-Redshift Mass-Metallicity Relation with Galaxy Burst Cycles
Recent observations suggest a nearly constant gas-phase mass-metallicity relation (MZR) at $z \gtrsim 5$, in agreement with many theoretical predictions. This lack of evolution contrasts with observations at $z \lesssim 3$, which find an increasing normalization of the MZR with decreasing redshift. We analyze a high-redshift suite of FIRE-2 cosmological zoom-in simulations to identify the physical drivers of the MZR. Previous studies have explained the weak evolution of the high-redshift MZR in…

LHAASO Protons versus LHAASO Diffuse Gamma Rays: A Consistency Check
Luis Enrique Espinosa Castro, Francesco Lorenzo Villante, Vittoria Vecchiotti, Carmelo Evoli, Giulia Pagliaroli
https://arxiv.org/abs/2506.06593

LHAASO Protons versus LHAASO Diffuse Gamma Rays: A Consistency Check
We perform the first direct consistency check between the recently measured proton spectrum at the knee by LHAASO and the collaboration's own high-precision mapping of Galactic diffuse gamma-ray emission. By modeling the hadronic gamma-ray production using the updated cosmic-ray spectra, gas templates and cross-section models, we show that the predicted gamma-ray flux robustly overshoots the LHAASO data in both inner and lateral Galactic regions. This persistent mismatch in both normalization a…

Discrete Spectrum and Spectral Rigidity of a Second-Order Geometric Deformation Operator
Anton Alexa
https://arxiv.org/abs/2507.01440 https://

Discrete Spectrum and Spectral Rigidity of a Second-Order Geometric Deformation Operator
We analyze the spectral properties of a self-adjoint second-order differential operator $\hat{C}$, defined on the Hilbert space $L^2([-v_c, v_c])$ with Dirichlet boundary conditions. We derive the discrete spectrum $\{C_n\}$, prove the completeness of the associated eigenfunctions, and establish orthogonality and normalization relations. The analysis follows the classical Sturm--Liouville framework and confirms that the deformation modes $C_n$ form a spectral basis on the compact interval. We f…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Yihao Liu, Savannah P. Hays, Dzung L. Pham, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Jerry L. Prince
https://arxiv.org/a…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation, with deep learning (DL) methods achieving state-of-the-art performance. However, these DL-based methods have yet to simultaneously optimize in-domain accuracy and out-of-domain generalization when trained on a single source with limited data, or their performance has been unsatisfactory. To fill this gap, we propose …

Computation by infinite descent made explicit
Sebastian Enqvist
https://arxiv.org/abs/2506.22206 https://arxiv.org/pdf/2506.22206

Computation by infinite descent made explicit
We introduce a non-wellfounded proof system for intuitionistic logic extended with ordinary inductive and co-inductive definitions, based on a syntax in which fixpoint formulas are annotated with explicit variables for ordinals. We explore the computational content of this system, in particular we introduce a notion of computability and show that every valid proof is computable. As a consequence, we obtain a normalization result for proofs of what we call finitary formulas. A special case of th…

Improving Stereo 3D Sound Event Localization and Detection: Perceptual Features, Stereo-specific Data Augmentation, and Distance Normalization
Jun-Wei Yeow, Ee-Leng Tan, Santi Peksi, Woon-Seng Gan
https://arxiv.org/abs/2507.00874

Improving Stereo 3D Sound Event Localization and Detection: Perceptual Features, Stereo-specific Data Augmentation, and Distance Normalization
This technical report presents our submission to Task 3 of the DCASE 2025 Challenge: Stereo Sound Event Localization and Detection (SELD) in Regular Video Content. We address the audio-only task in this report and introduce several key contributions. First, we design perceptually-motivated input features that improve event detection, sound source localization, and distance estimation. Second, we adapt augmentation strategies specifically for the intricacies of stereo audio, including channel sw…

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
Chaohui Xu, Qi Cui, Chip-Hong Chang
https://arxiv.org/abs/2505.24536

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
The pervasion of large-scale Deep Neural Networks (DNNs) and their enormous training costs make their intellectual property (IP) protection of paramount importance. Recently introduced passport-based methods attempt to steer DNN watermarking towards strengthening ownership verification against ambiguity attacks by modulating the affine parameters of normalization layers. Unfortunately, neither watermarking nor passport-based methods provide a holistic protection with robust ownership proof, hig…

LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation
Jianxiong Gao, Zhaoxi Chen, Xian Liu, Jianfeng Feng, Chenyang Si, Yanwei Fu, Yu Qiao, Ziwei Liu
https://arxiv.org/abs/2508.03694

LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation
Controllable ultra-long video generation is a fundamental yet challenging task. Although existing methods are effective for short clips, they struggle to scale due to issues such as temporal inconsistency and visual degradation. In this paper, we initially investigate and identify three key factors: separate noise initialization, independent control signal normalization, and the limitations of single-modality guidance. To address these issues, we propose LongVie, an end-to-end autoregressive fr…

Lyapunov exponents and phase transition of charged Ads black hole in quintessence
Xiaobo Guo, Rui Yang, Yizhi Liang, Jun Tao
https://arxiv.org/abs/2508.03519 https://

Lyapunov exponents and phase transition of charged Ads black hole in quintessence
This study investigates the phase transitions of RN-AdS black holes immersed in a quintessence field, employing Lyapunov exponents as a dynamical probe to characterize the thermodynamics of black hole. Incorporating quintessence dark energy into the RN-AdS framework, we find that the Lyapunov exponents for null and timelike geodesics display diminished chaotic behavior with increasing normalization factor of the quintessence field. This feature introduces a finite cutoff to the Lyapunov exponen…

A new hierarchical distribution on arbitrary sparse precision matrices
Gianluca Mastrantonio, Pierfrancesco Alaimo Di Loro, Marco Mingione
https://arxiv.org/abs/2506.09607

A new hierarchical distribution on arbitrary sparse precision matrices
We introduce a general strategy for defining distributions over the space of sparse symmetric positive definite matrices. Our method utilizes the Cholesky factorization of the precision matrix, imposing sparsity through constraints on its elements while preserving their independence and avoiding the numerical evaluation of normalization constants. In particular, we develop the S-Bartlett as a modified Bartlett decomposition, recovering the standard Wishart as a particular case. By incorporating…

This https://arxiv.org/abs/2504.19203 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction
Knee osteoarthritis (KOA) is a common joint disease that causes pain and mobility issues. While MRI-based deep learning models have demonstrated superior performance in predicting total knee replacement (TKR) and disease progression, their generalizability remains challenging, particularly when applied to imaging data from different sources. In this study, we have shown that replacing batch normalization with instance normalization, using data augmentation, and applying contrastive loss improve…

Boundary contacts for reflected random walks in the quarter plane
Viet Hung Hoang, Kilian Raschel
https://arxiv.org/abs/2507.04745 https://

Boundary contacts for reflected random walks in the quarter plane
We investigate reflected random walks in the quarter plane, with particular emphasis on the time spent along the reflection boundary axes. Assuming the drift of the random walk lies within the cone, the local time converges -- without the need for normalization -- to a limiting random variable as the walk length tends to infinity. This paper focuses on the properties of these discrete limiting variables. The problem is rooted in probability theory but also has natural connections to statistical…

This https://arxiv.org/abs/2411.19091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hep…

Updated determination of light-cone distribution amplitudes of octet baryons in lattice QCD
We present updated results on the wave function normalization constants and the first moments of the light cone distribution amplitudes for the lowest-lying baryon octet. The analysis is carried out on a large number of $n_f=2+1$ lattice gauge ensembles, including ensembles at physical pion (and kaon) masses. These are spread across five different lattice spacings, enabling a controlled continuum limit. The main differences with respect to our earlier work are the use of two-loop conversion fac…

Metric formalism for precision assessment in non-Hermitian systems
Javid Naikoo, Ravindra W. Chhajlany, Jan Ko{\l}ody\'nski, Adam Miranowicz
https://arxiv.org/abs/2506.22571

Metric formalism for precision assessment in non-Hermitian systems
Enhancing measurement precision in quantum systems is vital for advancing technologies like quantum sensing and communication. Traditionally, quantum Fisher information (QFI) has been the key tool in determining precision limits within Hermitian quantum systems. However, recent research has expanded to non-Hermitian systems exhibiting real spectra. This study investigates various interpretations of non-Hermitian quantum dynamics such as the normalization and metric approaches. We demonstrate th…

Bayesian Learning for Domain-Invariant Speaker Verification and Anti-Spoofing
Jin Li, Man-Wai Mak, Johan Rohdin, Kong Aik Lee, Hynek Hermansky
https://arxiv.org/abs/2506.07536

Bayesian Learning for Domain-Invariant Speaker Verification and Anti-Spoofing
The performance of automatic speaker verification (ASV) and anti-spoofing drops seriously under real-world domain mismatch conditions. The relaxed instance frequency-wise normalization (RFN), which normalizes the frequency components based on the feature statistics along the time and channel axes, is a promising approach to reducing the domain dependence in the feature maps of a speaker embedding network. We advocate that the different frequencies should receive different weights and that the w…

A Structure-Preserving Rational Integrator for the Replicator Dynamics on the Probability Simplex
Mario Pezzella
https://arxiv.org/abs/2507.22609 https://a…

A Structure-Preserving Rational Integrator for the Replicator Dynamics on the Probability Simplex
In this work, we introduce a quadratically convergent and dynamically consistent integrator specifically designed for the replicator dynamics. The proposed scheme combines a two-stage rational approximation with a normalization step to ensure confinement to the probability simplex and unconditional preservation of non-negativity, invariant sets and equilibria. A rigorous convergence analysis is provided to establish the scheme's second-order accuracy, and an embedded auxiliary method is devised…

The tight length spectrum of large-genus random hyperbolic surfaces with many cusps
Timothy Budd, Tanguy Lions
https://arxiv.org/abs/2506.02611 https://

The tight length spectrum of large-genus random hyperbolic surfaces with many cusps
Since the work of Mirzakhani \& Petri \cite{Mirzakhani_petri_2019} on random hyperbolic surfaces of large genus, length statistics of closed geodesics have been studied extensively. We focus on the case of random hyperbolic surfaces with cusps, the number $n_g$ of which grows with the genus $g$. We prove that if $n_g$ grows fast enough and we restrict attention to special geodesics that are \emph{tight}, we recover upon proper normalization the same Poisson point process in the large-$g$ limit …

Solving Boundary Handling Analytically in Two Dimensions for Smoothed Particle Hydrodynamics
Rene Winchenbach, Andreas Kolb
https://arxiv.org/abs/2507.21686 https://

Solving Boundary Handling Analytically in Two Dimensions for Smoothed Particle Hydrodynamics
We present a fully analytic approach for evaluating boundary integrals in two dimensions for Smoothed Particle Hydrodynamics (SPH). Conventional methods often rely on boundary particles or wall re-normalization approaches derived from applying the divergence theorem, whereas our method directly evaluates the area integrals for SPH kernels and gradients over triangular boundaries. This direct integration strategy inherently accommodates higher-order boundary conditions, such as piecewise cubic f…