Tootfinder

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…

$\text{C}^{2}\text{BNVAE}$: 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…

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

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…

❝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…

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…

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…

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…

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 …

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…

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

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…

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…

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 $K(\mathbb{P}^1)$.

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…

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…

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 …

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 $Σ$.

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…

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…

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…

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…

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…

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…

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…

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…

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 …

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…

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…

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 …

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…

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 …

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…

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…

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…

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…

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…

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…

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…

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

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…

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…