Tootfinder

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
Chuan He, Zhaosong Lu
https://arxiv.org/abs/2510.11676 https://a…

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex function whose subgradients are estimated under heavy-tailed noise. Existing work often employs gradient clipping or normalization techniques in stochastic first-order methods to address heavy-tailed noise. In this paper, we demonstrate that a vanilla stochastic algorithm -- without additional modifications such as clipping or normalization -- can achieve op…

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Strong normalization through idempotent intersection types: a new syntactical approach
Pablo Barenbaum, Simona Ronchi Della Rocca, Cristian Sottile

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Limitations of Normalization in Attention Mechanism
Timur Mudarisov, Mikhail Burtsev, Tatiana Petrova, Radu State

Principles of Safe AI Companions for Youth: Parent and Expert Perspectives
Yaman Yu, Mohi, Aishi Debroy, Xin Cao, Karen Rudolph, Yang Wang
https://arxiv.org/abs/2510.11185 http…

Principles of Safe AI Companions for Youth: Parent and Expert Perspectives
AI companions are increasingly popular among teenagers, yet current platforms lack safeguards to address developmental risks and harmful normalization. Despite growing concerns, little is known about how parents and developmental psychology experts assess these interactions or what protections they consider necessary. We conducted 26 semi structured interviews with parents and experts, who reviewed real world youth GenAI companion conversation snippets. We found that stakeholders assessed risks…

from my link log —
Synthetic aperture radar autofocus and calibration.
https://hforsten.com/synthetic-aperture-radar-autofocus-and-calibration.html
saved 2025-10-11

Synthetic aperture radar autofocus and calibration
3D trajectory position error estimation autofocus, antenna pattern normalization, and polarimetric calibration for drone mounted SAR radar.

Adaptive Stain Normalization for Cross-Domain Medical Histology
Tianyue Xu, Yanlin Wu, Abhai K. Tripathi, Matthew M. Ippolito, Benjamin D. Haeffele
https://arxiv.org/abs/2510.06592

Adaptive Stain Normalization for Cross-Domain Medical Histology
Deep learning advances have revolutionized automated digital pathology analysis. However, differences in staining protocols and imaging conditions can introduce significant color variability. In deep learning, such color inconsistency often reduces performance when deploying models on data acquired under different conditions from the training data, a challenge known as domain shift. Many existing methods attempt to address this problem via color normalization but suffer from several notable dra…

Geometric Interpretation of the Redshift Evolution of H_0(z)
Seokcheon Lee
https://arxiv.org/abs/2511.07454 https://arxiv.org/pdf/2511.07454 https://arxiv.org/html/2511.07454
arXiv:2511.07454v1 Announce Type: new
Abstract: Recent analyses of the Master Type Ia supernova (SN Ia) sample have revealed a mild redshift dependence in the inferred local Hubble parameter, often expressed as tilde{H}_0(z) = H_0 (1 z)^{-\alpha}, where \alpha quantifies possible departures from the standard cosmological time dilation relation. In this work, we show that such an empirical scaling can be interpreted as a purely geometric effect arising from a small, gauge-dependent normalization of cosmic time within the Robertson-Walker metric. This interpretation naturally unifies the observed redshift evolution of tilde{H}_0(z) and the corresponding deviation in SN Ia light-curve durations under a single geometric time-normalization framework. We demonstrate that this mapping leaves all background distances--linked to the Hubble radius in the general-relativistic frame--unchanged, while the apparent evolution in SN Ia luminosity distances arises from the redshift dependence of the Chandrasekhar mass. The result provides a unified and observationally consistent explanation of the mild Hubble-tension trend as a manifestation of the geometric structure of cosmic time rather than a modification of the expansion dynamics.
toXiv_bot_toot

This guy made a Synthetic Aperture Radar (i.e. a radar that gives you camera-like pictures) mounted on a drone. Scroll to the bottom for actual sample pictures and a movie. Lots of tech detail and open-source designs and software.
https://hforsten.com/synthetic-apertur

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
Yudong Wei, Liang Zhang, Bingcong Li, Niao He
https://arxiv.org/abs/2510.01175 https://

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
While normalization techniques are widely used in deep learning, their theoretical understanding remains relatively limited. In this work, we establish the benefits of (generalized) weight normalization (WN) applied to the overparameterized matrix sensing problem. We prove that WN with Riemannian optimization achieves linear convergence, yielding an exponential speedup over standard methods that do not use WN. Our analysis further demonstrates that both iteration and sample complexity improve p…

Accepting Normalization via Markov Magmoids
Elena Di Lavore, Mario Rom\'an
https://arxiv.org/abs/2510.01131 https://arxiv.org/pdf/2510.01131

Accepting Normalization via Markov Magmoids
Normalization of probability distributions is not a distributive law. We introduce distributive sesquilaws to abstract normalization and, from their axioms, we derive some of its properties. In particular, normalized channels form a unital magmoid with an action of the category of subdistributions; its possibilistic analogue is the action from may-must relations into the category of relations. We argue that the magmoid of normalized channels is the stochastic analog of the category of relations.

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Jan Fiszer, Dominika Ciupek, Maciej Malawski
https://arxiv.org/abs/2510.07126

Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate da…

A New Normalization Form for Limited Distinct Attributes
Niko S. Snell, Rayen C. Lee
https://arxiv.org/abs/2510.02865 https://arxiv.org/pdf/2510.02865

A New Normalization Form for Limited Distinct Attributes
In modern databases, the practice of data normalization continues to be important in improving data integrity, minimizing redundancies, and eliminating anomalies. However, since its inception and consequent improvements, there have been no attempts to document a method which constrains the values of attributes capable of only possessing a limited quantity of values. These non-limited distinct attributes pose a problem throughout many relational databases as they have the potential to cause data…

Exploring Database Normalization Effects on SQL Generation
Ryosuke Kohita
https://arxiv.org/abs/2510.01989 https://arxiv.org/pdf/2510.01989

Exploring Database Normalization Effects on SQL Generation
Schema design, particularly normalization, is a critical yet often overlooked factor in natural language to SQL (NL2SQL) systems. Most prior research evaluates models on fixed schemas, overlooking the influence of design on performance. We present the first systematic study of schema normalization's impact, evaluating eight leading large language models on synthetic and real-world datasets with varied normalization levels. We construct controlled synthetic datasets with formal normalization (1N…

Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
Peiyu Liu, Jianhao Ding, Zhaofei Yu
https://arxiv.org/abs/2509.23253 https://

Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
Spiking neural networks (SNNs) have garnered significant attention as a central paradigm in neuromorphic computing, owing to their energy efficiency and biological plausibility. However, training deep SNNs has critically depended on explicit normalization schemes, such as batch normalization, leading to a trade-off between performance and biological realism. To resolve this conflict, we propose a normalization-free learning framework that incorporates lateral inhibition inspired by cortical cir…

"For Afghan women, the stakes could not be any clearer. Every gesture towards normalization tells them their rights can be traded away for political convenience. Every meeting that sidelines the issue of gender apartheid reinforces the message that their lives are secondary to geopolitical interests. "
UN Report Details Deepening Crisis for Afghan Women and Warns Against Normalising Taliban Rule - Feminist Majority Foundation
https://feminist.org/news/un-report-details-deepening-crisis-for-afghan-women-and-warns-against-normalising-taliban-rule/

The idea that propaganda's main effect is in what it makes normal keeps sticking with me. I see it everywhere now. And it's how so much media that doesn't set off alarm bells as trying to manipulate so effectively works as propaganda. Reject or accept the overt message, the covert normalization works either way.

Revised comment on the paper titled "The Origin of Quantum Mechanical Statistics: Insights from Research on Human Language
Miko{\l}aj Sienicki, Krzysztof Sienicki
https://arxiv.org/abs/2512.07881 https://arxiv.org/pdf/2512.07881 https://arxiv.org/html/2512.07881
arXiv:2512.07881v1 Announce Type: new
Abstract: This short note comments on \citet{Aerts2024Origin}, which proposes that ranked word frequencies in texts should be read through the lens of Bose--Einstein (BE) statistics and even used to illuminate the origin of quantum statistics in physics. The core message here is modest: the paper offers an interesting analogy and an eye-catching fit, but several key steps mix physical claims with definitions and curve-fitting choices. We highlight three such points: (i) a normalization issue that is presented as "bosonic enhancement", (ii) an identification of rank with energy that makes the BE fit only weakly diagnostic of an underlying mechanism, and (iii) a baseline comparison that is too weak to support an ontological conclusion. We also briefly flag a few additional concerns (interpretation drift, parameter semantics, and reproducibility).
toXiv_bot_toot

Guardian US EIC Betsy Reed says the outlet expects to bring in $50M this year, up from $43M last year, and plans to grow newsletters and launch a news podcast (Ivan L. Nagy/Columbia Journalism Review)
https://www.cjr.org/the-interview/betsy-re

Betsy Reed on covering Trump and the ‘normalization of extremism.’
The editor in chief of Guardian US says staying above the fray doesn’t cut it.

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Zihan Wei, Tapabrata Chakraborti
https://arxiv.org/abs/2509.22712 https://

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such as race, age,and gender. Current bias mitigation methods usually either achieve high level of fairness with the degradation of accuracy, or only improve the model fairness on a single attribute. Additionally usually most bias mitigation strategies are either pre hoc through data processing or post hoc through fairnes…

A new application of the Fox-Wright functions: the coherent states formalism
Dusan Popov
https://arxiv.org/abs/2510.04874 https://arxiv.org/pdf/2510.04874

A new application of the Fox-Wright functions: the coherent states formalism
In this paper we extend the applicability of Fox-Wright functions beyond mathematics, specifically in quantum physics. We focused our attention on a new application, on the connection between the Fox-Wright functions and the generalized coherent states formalism. We constructed the generalized coherent states in the Barut-Girardello manner, in which the Fox-Wright functions play the role of normalization functions, and we demonstrated that the Fox-Wright coherent states satisfy all general cond…

Bayesian Framework for the E1 and E2 Astrophysical Factors at 300 keV from Subthreshold and Ground-State Asymptotic Normalization Coefficients
A. M. Mukhamedzhanov
https://arxiv.org/abs/2509.17102

Bayesian Framework for the E1 and E2 Astrophysical Factors at 300 keV from Subthreshold and Ground-State Asymptotic Normalization Coefficients
The $^{12}\mathrm{C}(α,γ)^{16}\mathrm{O}$ reaction governs the carbon-to-oxygen ratio set during helium burning, shaping white-dwarf structure and Type~Ia supernova yields. At the astrophysical energy $E \approx 300~\mathrm{keV}$, the cross section is controlled by the subthreshold $1^{-}$ (7.12~MeV) and $2^{+}$ (6.92~MeV) states, whose contributions depend on their asymptotic normalization coefficients (ANCs) $C_{1}$ and $C_{2}$, respectively. We perform a Bayesian analysis of the $S_{E1}(30…

U-SWIFT: A Unified Surface Wave Inversion Framework with Transformer via Normalization of Dispersion Curves
Tianjian Cheng, Hongrui Xu, Jiayu Feng, Xiongyu Hu, Chaofan Yao
https://arxiv.org/abs/2509.24872

U-SWIFT: A Unified Surface Wave Inversion Framework with Transformer via Normalization of Dispersion Curves
Deep learning is an increasingly popular approach for inverting surface wave dispersion curves to obtain Vs profiles. However, its generalizability is constrained by the depth and velocity scales of training data. We propose a unified deep learning framework that overcomes this limitation via normalization of dispersion curves. By leveraging the scaling properties of dispersion curves, our approach enables a single, pre-trained model to predict Vs profiles across diverse scales, from shallow su…

Normalized Indexing for Ramification Subgroups
Stephen DeBacker, David Schwein, Cheng-Chiang Tsai
https://arxiv.org/abs/2509.14881 https://arxiv.org/pdf/25…

Normalized Indexing for Ramification Subgroups
This expository note introduces a normalization of the indexing of the lower and upper numbering ramification subgroups of local class field theory. We then look at how this normalization interacts with base change for Langlands parameters.

Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation
Vaibhav Srivastav, Steven Zheng, Eric Bezzam, Eustache Le Bihan, Nithin Koluguri, Piotr \.Zelasko, Somshubra Majumdar, Adel Moumen, Sanchit Gandhi
https://arxiv.org/abs/2510.06961

Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation
Despite rapid progress, ASR evaluation remains saturated with short-form English, and efficiency is rarely reported. We present the Open ASR Leaderboard, a fully reproducible benchmark and interactive leaderboard comparing 60+ open-source and proprietary systems across 11 datasets, including dedicated multilingual and long-form tracks. We standardize text normalization and report both word error rate (WER) and inverse real-time factor (RTFx), enabling fair accuracy-efficiency comparisons. For E…

13 Billion Years of MgII Absorber Evolution
Christopher W. Churchill (New Mexico State University), Asif Abbas (New Mexico State University), Glenn G. Kapcrzak (Swinburne University of Technology), Nikole M. Nielsen (Swinburne University of Technology, University of Oklahoma)
https://arxiv.org/abs/2510.01430

13 Billion Years of MgII Absorber Evolution
Applying "apportioned integrals," we use dN/dX measurements to determine the MgII absorber equivalent width distribution function for Wr > 0.03 [angstroms] and 0 < z < 7. Adopting a Schechter distribution, f(z,W)dW = Phi* (W/W*)^alpha e^{-W/W*} dW/W*, we present the normalization, Phi*(z), the characteristic equivalent width, W*(z), and the weak-end slope, alpha(z), as smooth functions of redshift. Measurements of dN/dX are robust for z < 4 but less so at z > 4 for weaker absorbers (Wr < 0.3 [a…

Factorizations of 3d Interval Partition Functions
Boan Zhao, Panos Betzios, Paul Luis Roehl
https://arxiv.org/abs/2509.23902 https://arxiv.org/pdf/2509.239…

Factorizations of 3d Interval Partition Functions
We show that interval partition functions (transition amplitudes) of three- dimensional N = 2 theories admit factorizations into sums of products of hemisphere partition functions with additional normalization factors. We prove the factorization explic- itly for supersymmetric quantum electrodynamics and Chern-Simons-Yang-Mills theories. In the former case, we interpret the factorization geometrically in terms of the factorization of equivariant K-theory classes. In the latter case, we prove th…

Ever needed to duplicate a #keycloak realm.json, change a few values and re-import into the same keycloak ?
this is a Keycloak realm rekeying utility, safe UUID regeneration and alias normalization to avoid duplicate constraints during import https://

GitHub - rmdes/realm-duplicator-keycloak: Tools to rekey Keycloak realm JSON: regenerate IDs, normalize references, and eliminate collisions for seamless multi-realm imports.
Tools to rekey Keycloak realm JSON: regenerate IDs, normalize references, and eliminate collisions for seamless multi-realm imports. - rmdes/realm-duplicator-keycloak

Noncoincidence $f(Q)$-Cosmology with Dark Matter Coupled to Gravity
A. Abebe, P. S. Apostolopoulos, A. Giacomini, G. Leon, F. Moncada, A. Paliathanasis
https://arxiv.org/abs/2510.00535

Noncoincidence $f(Q)$-Cosmology with Dark Matter Coupled to Gravity
We investigate FLRW cosmology in the framework of symmetric teleparallel $f(Q)$ gravity with a nonminimal coupling between dark matter and the gravitational field. In the noncoincidence gauge, the field equations admit an equivalent multi-scalar field representation, which we investigate the phase-space using the Hubble-normalization approach. We classify all stationary points for arbitrary function $f(Q)$ and we discuss the physical properties of the asymptotic solutions. For the power-law the…

Scaling properties of nuclear parton distributions in short-range-correlation motivated two-component parametrization
Petja Paakkinen
https://arxiv.org/abs/2510.00252 https://…

Scaling properties of nuclear parton distributions in short-range-correlation motivated two-component parametrization
We provide some critical remarks on the recently proposed two-component parametrization of nuclear parton distribution functions, which was motivated by the apparent correlation between the nuclear modifications of structure functions and nucleon-nucleon short-range correlation phenomena. This parametrization, we show, is invariant under a rescaling transformation of the involved abundance coefficients, which means that the global normalization of these coefficients cannot be meaningfully deter…

Fast Real-Time Pipeline for Robust Arm Gesture Recognition
Mil\'an Zsolt Bagladi, L\'aszl\'o Guly\'as, Gerg\H{o} Szalay
https://arxiv.org/abs/2509.25042 https://…

Fast Real-Time Pipeline for Robust Arm Gesture Recognition
This paper presents a real-time pipeline for dynamic arm gesture recognition based on OpenPose keypoint estimation, keypoint normalization, and a recurrent neural network classifier. The 1 x 1 normalization scheme and two feature representations (coordinate- and angle-based) are presented for the pipeline. In addition, an efficient method to improve robustness against camera angle variations is also introduced by using artificially rotated training data. Experiments on a custom traffic-control …

On convergence of normal form transformations
Valery G. Romanovski, Sebastian Walcher
https://arxiv.org/abs/2510.00925 https://arxiv.org/pdf/2510.00925

On convergence of normal form transformations
We discuss various aspects concerning transformations of local analytic, or formal, vector fields to Poincare-Dulac normal form, and the convergence of such transformations. We first review A.D. Bruno's approach to formal normalization, as well as convergence results in presence of certain (simplified) versions of Bruno's ``Condition A'', and along the way we also identify a large class of systems that satisfy Bruno's diophantine ``Condition omega''. We retrace the proof steps in Bruno's work, …

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
Shin Kiyohara, Chisa Shibui, Soungmin Bae, Yu Kumagai
https://arxiv.org/abs/2510.00513 h…

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
Recent advances in materials informatics have expanded the number of synthesizable materials. However, screening promising candidates, such as semiconductors, based on defect properties remains challenging. This is primarily due to the lack of a general framework for predicting defect formation energies in multiple charge states from structural data. In this Letter, we present a protocol, namely data normalization, Fermi level alignment, and treatment of perturbed host states, and validate it b…

Distressing news articles that probably won't get attention in the US - The echos of these #racist acts are heard around the globe, with the continuing normalization of atrocious, abhorent behavior.
New Slovenian law treats entire Romany minority ‘as a security threat’

Distressing news articles that probably won't get attention in the US - The echos of these #racist acts are heard around the globe, with the continuing normalization of atrocious, abhorent behavior.
New Slovenian law treats entire Romany minority ‘as a security threat’

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Shane Bergsma, Bin Claire Zhang, Nolan Dey, Shaheer Muhammad, Gurpreet Gosal, Joel Hestness
https://arxiv.org/abs/2509.25087

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Effective LLM training relies on *consistency*, meaning that key quantities -- such as final losses and optimal hyperparameters -- scale predictably across model sizes. Qiu et al. (2025) recently showed that this consistency extends beyond scalars: whole training loss curves can *collapse* onto a universal trajectory after a simple normalization. What remains unclear is whether this phenomenon holds for LLM families trained under *practical scaling recipes*, where width, depth, learning rate, b…

Mechanizing Synthetic Tait Computability in Istari
Runming Li, Yue Yao, Robert Harper
https://arxiv.org/abs/2509.11418 https://arxiv.org/pdf/2509.11418

Mechanizing Synthetic Tait Computability in Istari
Categorical gluing is a powerful technique for proving meta-theorems of type theories such as canonicity and normalization. Synthetic Tait Computability (STC) provides an abstract treatment of the complex gluing models by internalizing the gluing category into a modal dependent type theory with a phase distinction. This work presents a mechanization of STC in the Istari proof assistant. Istari is a Martin-Löf-style extensional type theory with equality reflection. Equality reflection eliminate…

Online Adaptation via Dual-Stage Alignment and Self-Supervision for Fast-Calibration Brain-Computer Interfaces
Sheng-Bin Duan, Jian-Long Hao, Tian-Yu Xiang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Zeng-Guang Hou
https://arxiv.org/abs/2509.19403

Online Adaptation via Dual-Stage Alignment and Self-Supervision for Fast-Calibration Brain-Computer Interfaces
Individual differences in brain activity hinder the online application of electroencephalogram (EEG)-based brain computer interface (BCI) systems. To overcome this limitation, this study proposes an online adaptation algorithm for unseen subjects via dual-stage alignment and self-supervision. The alignment process begins by applying Euclidean alignment in the EEG data space and then updates batch normalization statistics in the representation space. Moreover, a self-supervised loss is designed …

Studying $\textrm{QED}_3$ with radial quantization on the lattice -- I. Free limit
Peter A. Boyle, Richard C. Brower, George T. Fleming, Emanuel Katz, Nobuyuki Matsumoto, Rohan Misra
https://arxiv.org/abs/2510.03085

Studying $\textrm{QED}_3$ with radial quantization on the lattice -- I. Free limit
To investigate the three-dimensional quantum electrodynamics in the radial quantization on the lattice, the lattice action is constructed and the free limit is studied on $S^2 \times \mathbb{R}$. With the overlap fermion, it is numerically verified that the important symmetries of the theory can be realized on the lattice. The analytic correlators are derived and compared to the lattice results, which agree including the overall normalization. The $O(a^2)$-scaling is confirmed toward the analyt…

Local Density-Based Anomaly Score Normalization for Domain Generalization
Kevin Wilkinghoff, Haici Yang, Janek Ebbers, Fran\c{c}ois G. Germain, Gordon Wichern, Jonathan Le Roux
https://arxiv.org/abs/2509.10951

Local Density-Based Anomaly Score Normalization for Domain Generalization
State-of-the-art anomalous sound detection (ASD) systems in domain-shifted conditions rely on projecting audio signals into an embedding space and using distance-based outlier detection to compute anomaly scores. One of the major difficulties to overcome is the so-called domain mismatch between the anomaly score distributions of a source domain and a target domain that differ acoustically and in terms of the amount of training data provided. A decision threshold that is optimal for one domain m…

A multi-object approach for studying exoplanet atmospheres using high-resolution spectrographs
Manjunath Bestha, Athira Unni, T. Sivarani, Dhanush S R, Lokesh Manickavasaham, Parvathy M, Devika K Divakar, Arun Surya
https://arxiv.org/abs/2509.18721

Normalization-equivariant Diffusion Models: Learning Posterior Samplers From Noisy And Partial Measurements
Brett Levac, Jon Tamir, Marcelo Pereyra, Julian Tachella
https://arxiv.org/abs/2510.11964

Normalization-equivariant Diffusion Models: Learning Posterior Samplers From Noisy And Partial Measurements
Diffusion models (DMs) have rapidly emerged as a powerful framework for image generation and restoration. However, existing DMs are primarily trained in a supervised manner by using a large corpus of clean images. This reliance on clean data poses fundamental challenges in many real-world scenarios, where acquiring noise-free data is hard or infeasible, and only noisy and potentially incomplete measurements are available. While some methods can train DMs using noisy data, they are generally eff…

Non-commutative multiple bi-orthogonal polynomials: formal approach and integrability
Adam Doliwa
https://arxiv.org/abs/2510.02207 https://arxiv.org/pdf/25…

Non-commutative multiple bi-orthogonal polynomials: formal approach and integrability
We define the non-commutative multiple bi-orthogonal polynomial systems, which simultaneously generalize the concepts of multiple orthogonality, matrix orthogonal polynomials and of the bi-orthogonality. We present quasideterminantal expressions for such polynomial systems in terms of formal bi-moments. The normalization functions for such monic polynomials satisfy the non-commutative Hirota equations, while the polynomials provide solution of the corresponding linear system. This shows, in par…

Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
Juan B. Cabral, Alvaro Roy Schachner
https://arxiv.org/abs/2509.24996

Addressing Methodological Uncertainty in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological uncertainty through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to…

On analytic functions related to Booth-lemniscate
Md Firoz Ali, Md Nurezzaman, Lokenath Thakur
https://arxiv.org/abs/2509.15534 https://arxiv.org/pdf/2509.…

On analytic functions related to Booth-lemniscate
For $0\le α\le 1 $, let $\mathcal{BS}(α)$ be the class of all analytic functions in the unit disk $\mathbb{D}:=\{~z\in\mathbb{C}:|z|<1\}$ with normalization $f(0)=0$ and $f'(0)=1$ that satisfy the subordinate relation $zf'(z)/f(z)-1\prec z/(1-αz^2)$ and $\mathcal{BK}(α)$ be the class of all functions $f$ for which $zf' \in \mathcal{BS}(α)$. In this article, we obtain a sharp estimate of the initial Taylor coefficients and logarithmic coefficients for functions in the classes $\ma…

Microscopic study of the asymptotic behavior of the reduced width amplitude in $^7$Li and $^7$Be
H. J. Zhu, M. J. Lyu, Q. Zhao, Z. Cheng, J. Q. Tian, M. Kimura, T. Myo, H. Horiuchi, H. Toki, M. Isaka, H. Takemoto, Akinobu Dot\'e, N. Wan
https://arxiv.org/abs/2509.23613

Microscopic study of the asymptotic behavior of the reduced width amplitude in $^7$Li and $^7$Be
We investigate the effects of different basis model spaces on the calculation of reduced width amplitude (RWA) and asymptotic normalization coefficient (ANC) for the $^{7}$Li and $^{7}$Be nuclei. The two-cluster model ($α+t/^3$He) and three-cluster model ($α+d+n/p$) with the generator coordinates method (GCM) are applied to calculate the wave function of $^7$Li and $^7$Be. Specifically, the model space for the three-cluster model is constructed upon a sufficiently broad space of two-cluster c…

G\"odel Mirror: A Formal System For Contradiction-Driven Recursion
Jhet Chan
https://arxiv.org/abs/2509.16239 https://arxiv.org/pdf/2509.16239

Gödel Mirror: A Formal System For Contradiction-Driven Recursion
We introduce the Gödel Mirror, a formal system defined in Lean 4 that treats contradiction as a control signal for recursive structural evolution. Inspired by Gödelian self-reference, our system's operational semantics encode symbolic paradoxes as deterministic transitions. Unlike systems designed to guarantee normalization, the Gödel Mirror is a minimal and verifiable architecture that leverages a controlled, non-terminating loop as a productive feature. Our Lean 4 mechanization proves …

A Scalable Formula for the Moments of a Family of Self-Normalized Statistics
Haolin Zou, Heyuan Yao, Victor de la Pe\~na
https://arxiv.org/abs/2509.14428 https://

A Scalable Formula for the Moments of a Family of Self-Normalized Statistics
Following the student t-statistic, normalization has been a widely used method in statistic and other disciplines including economics, ecology and machine learning. We focus on statistics taking the form of a ratio over (some power of) the sample mean, the probabilistic features of which remain unknown. We develop a unified formula for the moments of these self-normalized statistics with non-negative observations, yielding closed-form expressions for several important cases. Moreover, the compl…

CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Zijie Xu, Xinyu Shi, Yiting Dong, Zihan Huang, Zhaofei Yu
https://arxiv.org/abs/2509.23791

CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision-making on neuromorphic hardware by mimicking the event-driven dynamics of biological neurons. However, due to the discrete and non-differentiable nature of spikes, directly trained SNNs rely heavily on Batch Normalization (BN) to stabilize gradient updates. In online Reinforcement Learning (RL), imprecise BN statistics hinder exploitation, resulting in slower convergence and suboptimal policies. This challenge limits…

Asymptotic behavior of the extremal position in a multi-type branching random walk with heavy-tailed displacements
Krzysztof Kowalski
https://arxiv.org/abs/2509.14808 https://…

Asymptotic behavior of the extremal position in a multi-type branching random walk with heavy-tailed displacements
We consider a multi-type branching random walk with displacements that have either regularly varying or semi-exponential tails. We investigate the asymptotic behavior of the rightmost particle in irreducible and reducible regimes and identify the correct normalization for different settings.

Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications
P K Dutta, Anushri Chowdhury, Anouska Bhattacharyya, Shakya Chakraborty, Sujatra Dey
https://arxiv.org/abs/2510.00035

Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications
Deep learning integration into medical imaging systems has transformed disease detection and diagnosis processes with a focus on pneumonia identification. The study introduces an intricate deep learning system using Convolutional Neural Networks for automated pneumonia detection from chest Xray images which boosts diagnostic precision and speed. The proposed CNN architecture integrates sophisticated methods including separable convolutions along with batch normalization and dropout regularizati…

The AURORA Survey: Ionizing Photon Production Efficiency with Minimal Nebular Dust Attenuation Systematics
Anthony J. Pahl, Alice Shapley, Naveen A. Reddy, Ryan Sanders, Michael W. Topping, Danielle A. Berg, Callum T. Donnan, James S. Dunlop, Richard S. Ellis, N. M. F\"orster Schreiber, K. Glazebrook, Derek J. McLeod, Max Pettini, Daniel Schaerer
https://

The AURORA Survey: Ionizing Photon Production Efficiency with Minimal Nebular Dust Attenuation Systematics
We present ionizing photon production efficiencies ($ξ_{\rm ion}$) for 63 z=1.5-6.9 star-forming galaxies using precise nebular dust attenuation corrections from the JWST/AURORA survey. A subset of objects within AURORA have individually-determined nebular dust attenuation curves, which vary significantly in shape and normalization, resulting in reduced systematic uncertainty when constraining the total attenuation of H$α$ luminosity, and thus the intrinsic ionizing output within our sample. …

The Structure of Extremal Bad Science Matrices
Shridhar Sinha
https://arxiv.org/abs/2509.10580 https://arxiv.org/pdf/2509.10580

The Structure of Extremal Bad Science Matrices
We study the 'bad science matrix problem': among all matrices $A\in\mathbb{R}^{n\times n}$ whose rows have unit $\ell_2$-norm, determine the maximum of $β(A)=\frac{1}{2^n}\sum_{x\in\{\pm1\}^n}\|Ax\|_\infty$. Steinerberger [1] (arXiv:2402.03205) showed that the optimal asymptotic rate is $(1+o(1))\sqrt{2\log n}$, and that this rate is attained with high probability by matrices with i.i.d. $\pm1$ entries after normalization. More recent explicit constructions [2] (arXiv:2408.00933) ach…

Some Structures arising from the Farey Fractal
Shai Haran
https://arxiv.org/abs/2509.10959 https://arxiv.org/pdf/2509.10959

Some Structures arising from the Farey Fractal
This work explores new arithmetic and combinatorial structures arising from the interplay between Farey-type graphs, Fibonacci expansions, and operadic constructions. We introduce Fibonadic numbers, defined as an inverse limit under the Zeckendorf shift, equipped with a metric, order, and commutative rig structure. A normalization lemma provides canonical representatives, while quotients of the non-zero Fibonadic numbers under shifts and it's fundamental domain covering the circle via phi-value…

Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models
Weimin Wu, Xuefeng Song, Yibo Wen, Qinjie Lin, Zhihan Zhou, Jerry Yao-Chieh Hu, Zhong Wang, Han Liu
https://arxiv.org/abs/2509.12266

Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models
We introduce Genome-Factory, an integrated Python library for tuning, deploying, and interpreting genomic models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. It also includes quality control, such as GC content normalization. For model tuning, Genome-Factory…

From Autoencoders to CycleGAN: Robust Unpaired Face Manipulation via Adversarial Learning
Collin Guo
https://arxiv.org/abs/2509.12176 https://arxiv.org/pdf…

From Autoencoders to CycleGAN: Robust Unpaired Face Manipulation via Adversarial Learning
Human face synthesis and manipulation are increasingly important in entertainment and AI, with a growing demand for highly realistic, identity-preserving images even when only unpaired, unaligned datasets are available. We study unpaired face manipulation via adversarial learning, moving from autoencoder baselines to a robust, guided CycleGAN framework. While autoencoders capture coarse identity, they often miss fine details. Our approach integrates spectral normalization for stable training, i…

Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Hailay Kidu Teklehaymanot, Wolfgang Nejdl
https://arxiv.org/abs/2510.12389

Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Tokenization disparities pose a significant barrier to achieving equitable access to artificial intelligence across linguistically diverse populations. This study conducts a large-scale cross-linguistic evaluation of tokenization efficiency in over 200 languages to systematically quantify computational inequities in large language models (LLMs). Using a standardized experimental framework, we applied consistent preprocessing and normalization protocols, followed by uniform tokenization through …

Quantum entropy and cardinality of the rational numbers
Kaushik Ghosh
https://arxiv.org/abs/2509.12972 https://arxiv.org/pdf/2509.12972

Quantum entropy and cardinality of the rational numbers
We compare two methods for evaluating cardinality of the Cartesian product $N \times N$ of the set of natural numbers $N$. The first is used to explain the thermodynamics of black body radiation by using convergent functions on $N \times N$. Cardinality of $N \times N$ enters through the partition function that acts as the normalization constant of a probability distribution over $N \times N$. Here, $N \times N$ is given a greater cardinality than $N$. The expression of the partition function a…