Tootfinder

On function-on-function linear quantile regression
Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang
https://arxiv.org/abs/2510.10792 https://arxiv.o…

On function-on-function linear quantile regression
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated …

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
https://arxiv.org/abs/2510.07700

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We show that constraints on Model-Based Diffusion can lead to catastrophic performance degradation, even on simple 2D systems due to sample inefficiency in the Monte Carlo approximation of the score function. We introduce Emerging-Barrier Model-Based Diffusion (EB-MBD) which uses progressively introduced barrier constraints to avoid these problems, significantl…

Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning
Shangzhe Li, Dongruo Zhou, Weitong Zhang
https://arxiv.org/abs/2510.09487 https://

Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning
We study online adversarial imitation learning (AIL), where an agent learns from offline expert demonstrations and interacts with the environment online without access to rewards. Despite strong empirical results, the benefits of online interaction and the impact of stochasticity remain poorly understood. We address these gaps by introducing a model-based AIL algorithm (MB-AIL) and establish its horizon-free, second-order sample-complexity guarantees under general function approximations for bo…

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya
https://arxiv.org/abs/2510.02404

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its su…

BASILISK III. Stress-testing the Conditional Luminosity Function model
Kaustav Mitra, Frank C. van den Bosch
https://arxiv.org/abs/2510.08421 https://arxiv…

BASILISK III. Stress-testing the Conditional Luminosity Function model
The Conditional Luminosity Function (CLF) is an effective and flexible way of characterizing the galaxy-halo connection. However, it is subject to a particular choice for its parametrization, which acts as a prior assumption. Most studies have been restricted to what has become a standard CLF parametrization with little to no variation. The goal of this paper is to investigate whether this model is sufficient to fully characterize the small-scale data extracted from spectroscopic surveys and to…

Dynamics of feedback Ising model
Yi-Ping Ma, Ivan Sudakow, P. L. Krapivsky, Sergey A. Vakulenko
https://arxiv.org/abs/2510.07301 https://arxiv.org/pdf/2510…

Dynamics of feedback Ising model
We study the dynamics of a mean-field Ising model whose coupling depends on the magnetization via a linear feedback function. A key feature of this linear feedback Ising model (FIM) is the possibility of temperature-induced bistability, where a temperature increase can favor bistability between two phases. We show that the linear FIM provides a minimal model for a transcritical bifurcation as the temperature varies. Moreover, there can be two or three critical temperatures when the external mag…

Kinetic modelling of the CO2 capture and utilisation on NiRu-Ca/Al dual function material via parameter estimation
Meshkat Dolat, Andrew David Wright, Soudabeh Bahrami Gharamaleki, Loukia-Pantzechroula Merkouri, Melis S. Duyar, Michael Short
https://arxiv.org/abs/2510.12439

Kinetic modelling of the CO2 capture and utilisation on NiRu-Ca/Al dual function material via parameter estimation
This study presents a detailed, open-source kinetic modelling computational framework for CO2 capture and utilisation using a newly formulated dual-function material (DFM) comprising 15 wt% Ni, 1 wt% Ru, and 10 wt% CaO supported on spherical alumina. A finite difference reactor model was developed to simulate the cyclic adsorption, purge, and hydrogenation stages. The model incorporates experimentally-derived rate expressions, accounts for system delay via a second-order response function, and …

Chinese AI startup Z.ai releases the GLM-4.6V series of vision models, with support for native function calling, available with 106B and 9B parameters (Carl Franzen/VentureBeat)
https://venturebeat.com/ai/z-ai-debuts-open-source-glm-4-6v-a-n…

Multimodal Function Vectors for Spatial Relations
Shuhao Fu, Esther Goldberg, Ying Nian Wu, Hongjing Lu
https://arxiv.org/abs/2510.02528 https://arxiv.org/…

Multimodal Function Vectors for Spatial Relations
Large Multimodal Models (LMMs) demonstrate impressive in-context learning abilities from limited multimodal demonstrations, yet the internal mechanisms supporting such task learning remain opaque. Building on prior work of large language models, we show that a small subset of attention heads in the vision-language model OpenFlamingo-4B is responsible for transmitting representations of spatial relations. The activations of these attention heads, termed function vectors, can be extracted and man…

A Pseudo-Hermitian Hybrid Model at Finite Temperature: The Role of the Exceptional Points
Ignacio Fushimi (IFLP-UNLP, Argentina), Marta Reboiro (IFLP-UNLP, Argentina)
https://arxiv.org/abs/2510.08773

A Pseudo-Hermitian Hybrid Model at Finite Temperature: The Role of the Exceptional Points
We study a hybrid system formed by an ensemble of colour nitrogen-vacancy centres in diamond interacting with a superconducting flux-qubit at finite temperature. The presence of impurities in the system is modelled through pseudo-hermitian Hamiltonian, by introducing an asymmetry parameter in the interaction between the superconducting flux qubit and the ensemble of colour nitrogen-vacancy centres in diamond. We construct the exact grand partition function of the system, and from it we derive t…

Manifolds and Modules: How Function Develops in a Neural Foundation Model
Johannes Bertram, Luciano Dyballa, T. Anderson Keller, Savik Kinger, Steven W. Zucker
https://arxiv.org/abs/2512.07869 https://arxiv.org/pdf/2512.07869 https://arxiv.org/html/2512.07869
arXiv:2512.07869v1 Announce Type: new
Abstract: Foundation models have shown remarkable success in fitting biological visual systems; however, their black-box nature inherently limits their utility for under- standing brain function. Here, we peek inside a SOTA foundation model of neural activity (Wang et al., 2025) as a physiologist might, characterizing each 'neuron' based on its temporal response properties to parametric stimuli. We analyze how different stimuli are represented in neural activity space by building decoding man- ifolds, and we analyze how different neurons are represented in stimulus-response space by building neural encoding manifolds. We find that the different processing stages of the model (i.e., the feedforward encoder, recurrent, and readout modules) each exhibit qualitatively different representational structures in these manifolds. The recurrent module shows a jump in capabilities over the encoder module by 'pushing apart' the representations of different temporal stimulus patterns; while the readout module achieves biological fidelity by using numerous specialized feature maps rather than biologically plausible mechanisms. Overall, we present this work as a study of the inner workings of a prominent neural foundation model, gaining insights into the biological relevance of its internals through the novel analysis of its neurons' joint temporal response patterns.
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MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis
Yihao Zhi, Chenghong Li, Hongjie Liao, Xihe Yang, Zhengwentai Sun, Jiahao Chang, Xiaodong Cun, Wensen Feng, Xiaoguang Han
https://arxiv.org/abs/2510.07190

MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis
Recent breakthroughs in video generation, powered by large-scale datasets and diffusion techniques, have shown that video diffusion models can function as implicit 4D novel view synthesizers. Nevertheless, current methods primarily concentrate on redirecting camera trajectory within the front view while struggling to generate 360-degree viewpoint changes. In this paper, we focus on human-centric subdomain and present MV-Performer, an innovative framework for creating synchronized novel view vid…

Forecasting Inflation Based on Hybrid Integration of the Riemann Zeta Function and the FPAS Model (FPAS $\zeta$): Cyclical Flexibility, Socio-Economic Challenges and Shocks, and Comparative Analysis of Models
Davit Gondauri
https://arxiv.org/abs/2510.02966

Forecasting Inflation Based on Hybrid Integration of the Riemann Zeta Function and the FPAS Model (FPAS + $ζ$): Cyclical Flexibility, Socio-Economic Challenges and Shocks, and Comparative Analysis of Models
Inflation forecasting is a core socio-economic challenge in modern macroeconomic modeling, especially when cyclical, structural, and shock factors act simultaneously. Traditional systems such as FPAS and ARIMA often struggle with cyclical asymmetry and unexpected fluctuations. This study proposes a hybrid framework (FPAS + $ζ$) that integrates a structural macro model (FPAS) with cyclical components derived from the Riemann zeta function $ζ(1/2 + i t)$. Using Georgia's macro data (2005-2024),…

On Logit Weibull Manifold
Prosper Rosaire Mama Assandje, Joseph Dongho, Thomas Bouetou Bouetou
https://arxiv.org/abs/2510.03299 https://arxiv.org/pdf/2510.…

On Logit Weibull Manifold
In this work, it is shown that there is no potential function on the Weilbull statistical manifold. However, from the two-parameter Weibull model we can extract a model with a potential function called the logit model. On this logit model, there is a completely integrable Hamiltonian gradient system.

The 3-state Potts model on planar triangulations: explicit algebraic solution
Mireille Bousquet-M\'elou, Hadrien Notarantonio
https://arxiv.org/abs/2510.08414 https://

The 3-state Potts model on planar triangulations: explicit algebraic solution
We consider the $3$-state Potts generating function $T(ν,w)$ of planar triangulations; that is, the bivariate series that counts planar triangulations with vertices coloured in $3$ colours, weighted by their size (number of vertices, recorded by the variable $w$) and by the number of monochromatic edges (variable $ν$). This series was proved to be algebraic 15 years ago by Bernardi and the first author: this follows from its link with the solution of a discrete differential equation (DDE), …

A quantum N-dimer model
Daniel C. Douglas, Richard Kenyon, Nicholas Ovenhouse, Samuel Panitch, Sri Tata
https://arxiv.org/abs/2510.07543 https://arxiv.org/…

A quantum N-dimer model
We study a quantum version of the $n$-dimer model from statistical mechanics, based on the formalism from quantum topology developed by Reshetikhin and Turaev (the latter which, in particular, can be used to construct the Jones polynomial of a knot in $\mathbb{R}^3$). We apply this machinery to construct an isotopy invariant polynomial for knotted bipartite ribbon graphs in $\mathbb{R}^3$, giving, in the planar setting, a quantum $n$-dimer partition function. As one application, we compute the …

Bayesian Neural Networks for Functional ANOVA model
Seokhun Park, Choeun Kim, Jihu Lee, Yunseop Shin, Insung Kong, Yongdai Kim
https://arxiv.org/abs/2510.00545 https://

Bayesian Neural Networks for Functional ANOVA model
With the increasing demand for interpretability in machine learning, functional ANOVA decomposition has gained renewed attention as a principled tool for breaking down high-dimensional function into low-dimensional components that reveal the contributions of different variable groups. Recently, Tensor Product Neural Network (TPNN) has been developed and applied as basis functions in the functional ANOVA model, referred to as ANOVA-TPNN. A disadvantage of ANOVA-TPNN, however, is that the compone…

Enhanced Angle-Range Cluster Parameter Estimation in Full-Duplex ISAC Systems
Muhammad Talha, Besma Smida, David Gonz\'alez G
https://arxiv.org/abs/2510.12711 https://

Enhanced Angle-Range Cluster Parameter Estimation in Full-Duplex ISAC Systems
This work studies an integrated sensing and communication (ISAC) framework for targets that are spread both in the angle and range domains. We model each target using a cluster of rays parameterized by a specific density function, and propose a truncated Multiple Signal Classification (MUSIC) spread (TMS) algorithm to accurately estimate the parameters of the density function. Unlike the conventional MUSIC spread (CMS), TMS restricts the signal subspace rank based on the eigen decomposition of …

Retardance of lab grown diamond substrates as a function of thickness: momentum-drift random walk model
Thanh Tran, Phuong Vo, Thomas Sheppard, Timothy Grotjohn, Paul Quayle
https://arxiv.org/abs/2510.05932

Retardance of lab grown diamond substrates as a function of thickness: momentum-drift random walk model
This work studies the correlation between mean retardance and thickness of diamond substrates grown homoepitaxially via microwave plasma-enhanced chemical vapor deposition (MPCVD). We measure the retardance of a diamond substrate in two orientations: perpendicular and parallel to the growth direction. Our experimental results demonstrate that the correlation between mean retardance and thickness differs for these orientations. When measured perpendicular to the growth direction, the mean retard…

Non-exotic wormholes in $f(R,L_m)$ gravity
Sara Rastgoo, Foad Parsaei
https://arxiv.org/abs/2510.11487 https://arxiv.org/pdf/2510.11487

Non-exotic wormholes in $f(R,L_m)$ gravity
In the present analysis, we examine the potential existence of generalized wormhole models within the framework of newly developed extended $f(R,L_m)$ gravity. We investigate both a linear model, $f(R,L_m)=αR+βL_m$, and a non-linear model, $f(R,L_m)=\frac{R}{2}+ L^α_m$, to analyze traversable wormholes. By employing the variational approach, we derive modified versions of the field equations under the influence of an anisotropic matter source. A power-law shape function is applied, resulting…

Development and Validation of a Novel Fresnel Integral Based Method to Model MSF Errors in Optical Imaging
Luuk Zonneveld, Paul Urbach, Aur\`ele Adam
https://arxiv.org/abs/2510.03172

Development and Validation of a Novel Fresnel Integral Based Method to Model MSF Errors in Optical Imaging
Mid-spatial frequency errors of optical components cause degradation of images which are rather difficult to quantify. In this work, we present a model for calculating the point-spread function in the presence of mid-spatial frequency errors which is based on diffraction integrals.The results of the model are compared with experiments.

The hat polykite as an Iterated Function System
Corey de Wit
https://arxiv.org/abs/2510.00409 https://arxiv.org/pdf/2510.00409

The hat polykite as an Iterated Function System
This paper describes the celebrated aperiodic hat tiling by Smith et al. [Comb. Theory 8 (2024), 6] as generated by an overlapping iterated function system. We briefly introduce and study infinite sequences of iterated function systems that converge uniformly in each component, and use this theory to model the hat tiling's associated imperfect substitution system.

Locally Linear Convergence for Nonsmooth Convex Optimization via Coupled Smoothing and Momentum
Reza Rahimi Baghbadorani, Sergio Grammatico, Peyman Mohajerin Esfahani
https://arxiv.org/abs/2511.10239 https://arxiv.org/pdf/2511.10239 https://arxiv.org/html/2511.10239
arXiv:2511.10239v1 Announce Type: new
Abstract: We propose an adaptive accelerated smoothing technique for a nonsmooth convex optimization problem where the smoothing update rule is coupled with the momentum parameter. We also extend the setting to the case where the objective function is the sum of two nonsmooth functions. With regard to convergence rate, we provide the global (optimal) sublinear convergence guarantees of O(1/k), which is known to be provably optimal for the studied class of functions, along with a local linear rate if the nonsmooth term fulfills a so-call locally strong convexity condition. We validate the performance of our algorithm on several problem classes, including regression with the l1-norm (the Lasso problem), sparse semidefinite programming (the MaxCut problem), Nuclear norm minimization with application in model free fault diagnosis, and l_1-regularized model predictive control to showcase the benefits of the coupling. An interesting observation is that although our global convergence result guarantees O(1/k) convergence, we consistently observe a practical transient convergence rate of O(1/k^2), followed by asymptotic linear convergence as anticipated by the theoretical result. This two-phase behavior can also be explained in view of the proposed smoothing rule.
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Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Puneet Velidi, Zhengxiao Wei, Shreena Nisha Kalaria, Yimeng Liu, C\'eline M. Laumont, Brad H. Nelson, Farouk S. Nathoo
https://arxiv.org/abs/2510.10358

Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Concerns about the misuse and misinterpretation of p-values and statistical significance have motivated alternatives for quantifying evidence. We define a generalized form of Jeffreys's approximate objective Bayes factor (eJAB), a one-line calculation that is a function of the p-value, sample size, and parameter dimension. We establish conditions under which eJAB is model-selection consistent and verify them for ten statistical tests. We assess finite-sample accuracy by comparing eJAB with Mark…

Robust Sensor Placement for Poisson Arrivals with False Alarm Aware Spatiotemporal Sensing
Mingyu Kim, Pronoy Sarker, Seungmo Kim, Daniel J. Stilwell, Jorge Jimenez
https://arxiv.org/abs/2510.05343

Robust Sensor Placement for Poisson Arrivals with False Alarm Aware Spatiotemporal Sensing
This paper studies sensor placement when detection performance varies stochastically due to environmental factors over space and time and false alarms are present, but a filter is used to attenuate the effect. We introduce a unified model that couples detection and false alarms through an availability function, which captures how false alarms reduce effective sensing and filtering responses to the disturbance. Building on this model, we give a sufficient condition under which filtering improves…

BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
Sangyun Lee, Brandon Amos, Giulia Fanti
https://arxiv.org/abs/2510.09596 https://

BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the generation. They work under the assumptions that the supervised data provides knowledge to pre-train the model, and the reward function provides dense information about how to further improve the generation quality and correctness. However, in the hardest instances of important problems, two problems arise: (1) the base generative model attains a near-zero re…

Role of universal function of the nuclear proximity potential: A systematic study on the alpha-decay of heavy/super-heavy nuclei and {\alpha}-induced reactions
S. Mohammadi, R. Gharaei, S. A. Alavi
https://arxiv.org/abs/2510.02764

Role of universal function of the nuclear proximity potential: A systematic study on the alpha-decay of heavy/super-heavy nuclei and α-induced reactions
The idea of the universal function is fundamental advantage of proximity potential model. We present a systematic study of the role of universal function of the proximity potential model on alpha decay process for 250 ground state to ground state transitions using WKB approximation. In order to realize this goal, five universal functions proposed in the proximity models gp 77, Guo 2013, Ngo 80, Zhang 2013 and Prox. 2010 have been incorporated into the formalism of Prox. 77. The obtained results…

A physically-informed sea spray generation model for splashing waves
Kaitao Tang, Thomas A. A. Adcock, Wouter Mostert
https://arxiv.org/abs/2510.02486 https://

A physically-informed sea spray generation model for splashing waves
Large sea spray drops - of up to 2mm in diameter - constitute one of the most uncertain factors controlling the intensification of hurricanes and severe storms because their generation mechanisms are not understood. Wave splashing produces among the largest spray drops, but observational data regarding these drops is difficult to obtain and hence cannot inform current modelling efforts. In this study, we instead propose a sea spray generation function (SSGF) for ocean wave splashing by assembli…

Strong-coupling functional renormalization group: Nagaoka ferromagnetism and non-Fermi liquid physics in the Hubbard model at $ U = \infty $
Jonas Arnold, Peter Kopietz, Andreas R\"uckriegel
https://arxiv.org/abs/2510.01909

Strong-coupling functional renormalization group: Nagaoka ferromagnetism and non-Fermi liquid physics in the Hubbard model at $ U = \infty $
We develop an extension of the fermionic functional renormalization group for systems where strong correlations give rise to projected Hilbert spaces. We use our method to calculate the phase diagram and the electronic spectral function of the Hubbard model at infinite on-site repulsion where many-body states involving doubly occupied lattice sites are eliminated from the physical Hilbert space. For a square lattice with nearest-neighbor hopping we find that the ground state evolves from a para…

Bound-Preserving WENO Schemes for Temple-class systems
Wei Chen, Shumo Cui, Kailiang Wu, Tao Xiong, Baoyue Yu
https://arxiv.org/abs/2510.04123 https://arxi…

Bound-Preserving WENO Schemes for Temple-class systems
This paper explores numerical schemes for Temple-class systems, which are integral to various applications including one-dimensional two-phase flow, elasticity, traffic flow, and sedimentation. Temple-class systems are characterized by conservative equations, with different pressure function expressions leading to specific models such as the Aw-Rascle-Zhang (ARZ) traffic model and the sedimentation model. Our work extends existing studies by introducing a moving mesh approach to address the cha…

PentestMCP: A Toolkit for Agentic Penetration Testing
Zachary Ezetta, Wu-chang Feng
https://arxiv.org/abs/2510.03610 https://arxiv.org/pdf/2510.03610

PentestMCP: A Toolkit for Agentic Penetration Testing
Agentic AI is transforming security by automating many tasks being performed manually. While initial agentic approaches employed a monolithic architecture, the Model-Context-Protocol has now enabled a remote-procedure call (RPC) paradigm to agentic applications, allowing for the flexible construction and composition of multi-function agents. This paper describes PentestMCP, a library of MCP server implementations that support agentic penetration testing. By supporting common penetration testing…

The Theory of Strategic Evolution: Games with Endogenous Players and Strategic Replicators
Kevin Vallier
https://arxiv.org/abs/2512.07901 https://arxiv.org/pdf/2512.07901 https://arxiv.org/html/2512.07901
arXiv:2512.07901v1 Announce Type: new
Abstract: This paper develops the Theory of Strategic Evolution, a general model for systems in which the population of players, strategies, and institutional rules evolve together. The theory extends replicator dynamics to settings with endogenous players, multi level selection, innovation, constitutional change, and meta governance. The central mathematical object is a Poiesis stack: a hierarchy of strategic layers linked by cross level gain matrices. Under small gain conditions, the system admits a global Lyapunov function and satisfies selection, tracking, and stochastic stability results at every finite depth. We prove that the class is closed under block extension, innovation events, heterogeneous utilities, continuous strategy spaces, and constitutional evolution. The closure theorem shows that no new dynamics arise at higher levels and that unrestricted self modification cannot preserve Lyapunov structure. The theory unifies results from evolutionary game theory, institutional design, innovation dynamics, and constitutional political economy, providing a general mathematical model of long run strategic adaptation.
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The Diameter of (Threshold) Geometric Inhomogeneous Random Graphs
Zylan Benjert, Kostas Lakis, Johannes Lengler, Raghu Raman Ravi
https://arxiv.org/abs/2510.12543 https://

The Diameter of (Threshold) Geometric Inhomogeneous Random Graphs
We prove that the diameter of threshold (zero temperature) Geometric Inhomogeneous Random Graphs (GIRG) is $Θ(\log n)$. This has strong implications for the runtime of many distributed protocols on those graphs, which often have runtimes bounded as a function of the diameter. The GIRG model exhibits many properties empirically found in real-world networks, and the runtime of various practical algorithms has empirically been found to scale in the same way for GIRG and for real-world networks,…

Robustness of Covariance Estimators with Application in Activity Detection
Hendrik Bernd Zarucha, Peter Jung, Giuseppe Caire
https://arxiv.org/abs/2510.07044 https://

Robustness of Covariance Estimators with Application in Activity Detection
The first part of this work considers a general class of covariance estimators. Each estimator of that class is generated by a real-valued function $g$ and a set of model covariance matrices $H$. If $\bf{W}$ is a potentially perturbed observation of a searched covariance matrix, then the estimator is the minimizer of the sum of $g$ applied to each eigenvalue of $\bf{W}^\frac{1}{2}\bf{Z}^{-1}\bf{W}^\frac{1}{2}$ under the constraint that $\bf{Z}$ is from $H$. It is shown that under mild condition…

IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Wei Wang, Rong Cao, Yi Guo, Zhengyang Chen, Kuan Chen, Yuanyuan Huo
https://arxiv.org/abs/2510.07979 …

IntMeanFlow: Few-step Speech Generation with Integral Velocity Distillation
Flow-based generative models have greatly improved text-to-speech (TTS) synthesis quality, but inference speed remains limited by the iterative sampling process and multiple function evaluations (NFE). The recent MeanFlow model accelerates generation by modeling average velocity instead of instantaneous velocity. However, its direct application to TTS encounters challenges, including GPU memory overhead from Jacobian-vector products (JVP) and training instability due to self-bootstrap processes…

Winding quotients for virtual period maps of rank 1
Kyoji Saito
https://arxiv.org/abs/2510.04558 https://arxiv.org/pdf/2510.04558

Winding quotients for virtual period maps of rank 1
We illustrate a rank 1 model of virtual period maps and their associated winding quotient, where the winding quotient is a new phenomenon appeared in a recent study of virtual period maps and it requires a reformulation of the classical inversion problem for the period maps. We answer to the new inversion problem by introducing the q-multiplicatively periodic function, whose pull-back to the winding covering space is the Weierstrass p-function up to a correction by Eisenstein series E2, and whi…

Universality and kernel-adaptive training for classically trained, quantum-deployed generative models
Andrii Kurkin, Kevin Shen, Susanne Pielawa, Hao Wang, Vedran Dunjko
https://arxiv.org/abs/2510.08476

Universality and kernel-adaptive training for classically trained, quantum-deployed generative models
The instantaneous quantum polynomial (IQP) quantum circuit Born machine (QCBM) has been proposed as a promising quantum generative model over bitstrings. Recent works have shown that the training of IQP-QCBM is classically tractable w.r.t. the so-called Gaussian kernel maximum mean discrepancy (MMD) loss function, while maintaining the potential of a quantum advantage for sampling itself. Nonetheless, the model has a number of aspects where improvements would be important for more general utili…

Correlation function metrology for warm dense matter: Recent developments and practical guidelines
Maximilian Peter B\"ohme, Willow Martin, Hannah Bellenbaum, Magaret Berrens, Jan Vorberger, Sebastian Schwalbe, Zhandos Moldabekov, Thomas Gawne, Sebastien Hamel, Brianna Aguilar-Solis, Abhiraj Sharma, Frank Graziani, Tilo D\"oppner, Siegfried Glenzer, Tobias Dornheim, David Bishel

Correlation function metrology for warm dense matter: Recent developments and practical guidelines
X-ray Thomson scattering (XRTS) has emerged as a valuable diagnostic for matter under extreme conditions, as it captures the intricate many-body physics of the probed sample. Recent advances, such as the model-free temperature diagnostic of Dornheim et al. [Nat.Commun. 13, 7911 (2022)], have demonstrated how much information can be extracted directly within the imaginary-time formalism. However, since the imaginary-time formalism is a concept often difficult to grasp, we provide here a systemat…

A comprehensive comparison of neural operators for 3D industry-scale engineering designs
Weiheng Zhong, Qibang Liu, Diab Abueidda, Seid Koric, Hadi Meidani
https://arxiv.org/abs/2510.05995

A comprehensive comparison of neural operators for 3D industry-scale engineering designs
Neural operators have emerged as powerful tools for learning nonlinear mappings between function spaces, enabling real-time prediction of complex dynamics in diverse scientific and engineering applications. With their growing adoption in engineering design evaluation, a wide range of neural operator architectures have been proposed for various problem settings. However, model selection remains challenging due to the absence of fair and comprehensive comparisons. To address this, we propose and …

On the threshold behaviour of heavy top production
Torbj\"orn Sj\"ostrand
https://arxiv.org/abs/2510.04590 https://arxiv.org/pdf/2510.04590

On the threshold behaviour of heavy top production
The observation of an excess of ttbar production in the threshold region, by CMS and ATLAS, has been interpreted as a toponium contribution, i.e. from below-threshold ttbar virtual states. The news here is the nontrivial experimental extraction of such a signal, not its existence as such. Indeed, already 35+ years ago an NRQCD Green's function approach was used to model the above- and below-threshold production of ttbar pairs in pp/ppbar collisions. The relevant cross section equations from tha…

Deconvolution of Arbitrary Distribution Functions and Densities
Henrik Kaiser
https://arxiv.org/abs/2510.04742 https://arxiv.org/pdf/2510.04742

Deconvolution of Arbitrary Distribution Functions and Densities
In this article we recover the distribution function (and possible density) of an arbitrary random variable that is subject to an additive measurement error. This problem is also known as deconvolution and has a long tradition in mathematics. We show that the model under consideration always can be transformed to a model with a symmetric error variable, whose characteristic function has its values in the unit interval. As a consequence, the characteristic function of the target variable turns o…

GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning
Silvia Sapora, Devon Hjelm, Alexander Toshev, Omar Attia, Bogdan Mazoure
https://arxiv.org/abs/2510.02180

GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning
Inverse Reinforcement Learning aims to recover reward models from expert demonstrations, but traditional methods yield "black-box" models that are difficult to interpret and debug. In this work, we introduce GRACE (Generating Rewards As CodE), a method for using Large Language Models within an evolutionary search to reverse-engineer an interpretable, code-based reward function directly from expert trajectories. The resulting reward function is executable code that can be inspected and verified.…

Line shapes of the Na/K resonance line profiles perturbed by H2 at extreme density
N. F. Allard, J. F. Kielkopf
https://arxiv.org/abs/2510.05763 https://ar…

Line shapes of the Na/K resonance line profiles perturbed by H2 at extreme density
Collision broadening by molecular hydrogen of sodium and potassium is one of the major broadening mechanisms in the atmospheres of brown dwarf stars and exoplanets at an effective temperature of about 1000K. The accurate computation of line profiles from collision broadening at high density requires use of a Fourier transform of the autocorrelation function inside the model atmosphere code. We strongly warn that use of Lorentzian profiles at a high perturber density neglects radiation during cl…

In-flight performance of the IXPE telescopes
Riccardo Ferrazzoli, Enrico Costa, Sergio Fabiani, Philip Kaaret, Stephen L. O'Dell, Brian D. Ramsey, Paolo Soffitta, Luca Baldini, Ronaldo Bellazzini, Alessandro Di Marco, Fabio La Monaca, Luca Latronico, Alberto Manfreda, Fabio Muleri, John Rankin, Carmelo Sgr\`o, Stefano Silvestri, Martin C. Weisskopf
https://…

In-flight performance of the IXPE telescopes
We present a comprehensive characterization of the on-orbit imaging performance of the three telescopes on board the Imaging X-ray Polarimetry Explorer (IXPE). Each telescope comprises a Wolter-I mirror module assembly and a Gas Pixel Detector focal-plane detector unit (DU). We analyze data from point-like X-ray sources and fit a composite point spread function (PSF) model that we compare with ground calibrations. We study the dependence of the PSF parameters and of the angular resolution, in t…

CEPC Technical Design Report -- Reference Detector
The CEPC Study Group
https://arxiv.org/abs/2510.05260 https://arxiv.org/pdf/2510.05260

CEPC Technical Design Report -- Reference Detector
The Circular Electron Positron Collider (CEPC) is a large international scientific project initiated by China's particle physicists to study the Higgs boson and perform critical tests of the Standard Model. Housed in a 100-km circumference tunnel in China, the CEPC will primarily operate as a Higgs factory, producing electron-positron collisions at a center-of-mass energy of 240 GeV. It will also function as a Z factory at 91.2 GeV and operate at the WW production threshold (around 160 GeV). In…

Modeling Emission-Line Surface Brightness in a Multiphase Galactic Wind: An O VI Case Study
Zirui Chen, Zixuan Peng, Kate H. R. Rubin, Timothy M. Heckman, Matthew J. Hayes, Yakov Faerman, Crystal L. Martin, S. Peng Oh, Drummond B. Fielding
https://arxiv.org/abs/2510.02443

Modeling Emission-Line Surface Brightness in a Multiphase Galactic Wind: An O VI Case Study
We present a fast and robust analytic framework for predicting surface brightness (SB) of emission lines in galactic winds as a function of radius up to $\sim 100$ kpc out in the circum-galactic medium. We model multi-phase structure in galactic winds by capturing emission from both the volume-filling hot phase (T $\sim 10^{6-7}$ K) and turbulent radiative mixing layers that host intermediate temperature gas at the boundaries of cold clouds (T $\sim 10^4$ K). Our multi-phase framework makes sig…

Reducing Discomfort in Driving Simulators: Motion Cueing for Motion Sickness Mitigation
Varun Kotian, Vishrut Jain, Andrea Michelle Rios Lazcano, Daan Marinus Pool, Riender Happee, Barys Shyrokau
https://arxiv.org/abs/2510.01986

Reducing Discomfort in Driving Simulators: Motion Cueing for Motion Sickness Mitigation
Driving simulators are increasingly used in research and development. However, simulators often cause motion sickness due to downscaled motion and unscaled veridical visuals. In this paper, a motion cueing algorithm is proposed that reduces motion sickness as predicted by the subjective vertical conflict (SVC) model using model predictive control (MPC). Both sensory conflict and specific force errors are penalised in the cost function, allowing the algorithm to jointly optimise fidelity and com…

Model-Guided Microstimulation Steers Primate Visual Behavior
Johannes Mehrer, Ben Lonnqvist, Anna Mitola, Abdulkadir Gokce, Paolo Papale, Martin Schrimpf
https://arxiv.org/abs/2510.03684

Model-Guided Microstimulation Steers Primate Visual Behavior
Brain stimulation is a powerful tool for understanding cortical function and holds promise for therapeutic interventions in neuropsychiatric disorders. Initial visual prosthetics apply electric microstimulation to early visual cortex which can evoke percepts of simple symbols such as letters. However, these approaches are fundamentally limited by hardware constraints and the low-level representational properties of this cortical region. In contrast, higher-level visual areas encode more complex…

New Classes of Non-monotone Variational Inequality Problems Solvable via Proximal Gradient on Smooth Gap Functions
Lei Zhao, Daoli Zhu, Shuzhong Zhang
https://arxiv.org/abs/2510.12105

New Classes of Non-monotone Variational Inequality Problems Solvable via Proximal Gradient on Smooth Gap Functions
In this paper, we study the local linear convergence behavior of proximal-gradient (PG) descent algorithm on a parameterized gap-function reformulation of a smooth but non-monotone variational inequality problem (VIP). The aim is to solve the non-monotone VI problem without assuming the existence of a Minty-type solution. We first introduce and study various error bound conditions for the gap functions in relation to the VI model. In particular, we show that uniform type error bounds imply leve…

Cosmology in the Hoyle Narlikar gravity
J. K. Singh, Sonal Aggarwal, Shaily, Hamid Shabani
https://arxiv.org/abs/2510.11762 https://arxiv.org/pdf/2510.117…

Cosmology in the Hoyle Narlikar gravity
In this paper, we study the late time cosmic acceleration of the Quintessence model within the framework of Hoyle Narlikar Gravity, consisting of a creation field. Using the Hubble tension as a function of the density parameter for matter, the density parameter for radiation, and the density parameter for dark energy in the covariant formulation, we find the gravitational field equations in the spatially flat, homogeneous, and isotropic spacetime to examine the dynamical mechanism that leads to…

Young functions on varifolds. Part I. Functional analytic foundations
Hsin-Chuang Chou
https://arxiv.org/abs/2510.05639 https://arxiv.org/pdf/2510.05639

Young functions on varifolds. Part I. Functional analytic foundations
In this paper, we study Young functions, a measure-theoretic model for multiple-valued functions, and the convergence of pairs of measures and Young functions via their associated graph measures. This setting allows us to study the convergence of pairs of surfaces and functions thereon, and a compactness theorem is immediate. To develop notions of differentiability of Young functions in the upcoming papers, we also introduce and investigate several test function spaces.

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
Stefano Gagliani, Feliciano Giuseppe Pacifico, Lorenzo Chicchi, Duccio Fanelli, Diego Febbe, Lorenzo Buffoni, Raffaele Marino
https://arxiv.org/abs/2510.12286

Train Stochastic Non Linear Coupled ODEs to Classify and Generate
A general class of dynamical systems which can be trained to operate in classification and generation modes are introduced. A procedure is proposed to plant asymptotic stationary attractors of the deterministic model. Optimizing the dynamical system amounts to shaping the architecture of inter-nodes connection to steer the evolution towards the assigned equilibrium, as a function of the class to which the item - supplied as an initial condition - belongs to. Under the stochastic perspective, po…

Modeling information acquisition via f-divergence and duality
Alex Bloedel, Tommaso Denti, Luciano Pomatto
https://arxiv.org/abs/2510.03482 https://arxiv.o…

Modeling information acquisition via f-divergence and duality
We introduce a new cost function over experiments, f-information, based on the theory of multivariate statistical divergences, that generalizes Sims's classic model of rational inattention as well as the class of posterior-separable cost functions. We characterize its behavioral predictions by deriving optimality conditions that extend those of Matejka and McKay (2015) and Caplin, Dean, and Leahy (2019) beyond mutual information. Using these tools, we study the implications of f-information in …

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Abdullahi Mohammad, Bdah Eya, Bassant Selim
https://arxiv.org/abs/2510.12179 https://

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noise parameters. The proposed model leverages a unified weighted-loss function to enable simultaneous learning of multiple parameters within …

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Pengcheng Li, Manzi Nan, Haojie Zhang, Junhuai Xu, Xilong Xiang, Yijie Wang, Yongjia Wang, Gaochan Yong, Tadaaki Isobe, Zhigang Xiao, Qingfeng Li
https://arxiv.org/abs/2510.12226

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic model followed by a correlation afterburner program, we investigate the effects of the initial neutron density distribution on the charged-pion yield ratio $π^{-}/π^{+}$, the two-pion momentum correlation function, and the emission source dimension. It is found that the $π^{-}/π^{+}$ ratio is sensitive to the initia…

Minimal-Dissipation Learning for Energy-Based Models
Jeff Hnybida, Simon Verret
https://arxiv.org/abs/2510.03137 https://arxiv.org/pdf/2510.03137

Minimal-Dissipation Learning for Energy-Based Models
We show that the bias of the approximate maximum-likelihood estimation (MLE) objective of a persistent chain energy-based model (EBM) is precisely equal to the thermodynamic excess work of an overdamped Langevin dynamical system. We then answer the question of whether such a model can be trained with minimal excess work, that is, energy dissipation, in a finite amount of time. We find that a Gaussian energy function with constant variance can be trained with minimal excess work by controlling o…

Explicit formulae and topological descriptions of action-minimizing sets for 2-locally potentials of the XY model
Yuika Kajihara, Shoya Motonaga, Mao Shinoda
https://arxiv.org/abs/2510.02678

Explicit formulae and topological descriptions of action-minimizing sets for 2-locally potentials of the XY model
We consider ergodic optimization of a symbolic dynamics with uncountable alphabet $[0,1]$ for 2-locally constant potentials with the twist condition, and give explicit formulae of the associated Mather set and the Aubry set. Moreover, we investigate the total disconnectedness of the (quotient) Aubry set, in which case the differentiability of the potential function makes a remarkable difference. Although these results imply that the (quotient) Aubry set is small enough, we give a complete chara…

Angular BAO Measurements with the DESI DR1 BGS Sample
Paula S. Ferreira, Ulisses Ribeiro, Pedro da Silveira Ferreira, Cl\'ecio R. Bom, Armando Bernui
https://arxiv.org/abs/2510.02144

Angular BAO Measurements with the DESI DR1 BGS Sample
We employ a model-independent approach in both the correlation function estimation and the angular BAO feature estimation by computing the angular two-point correlation function. First, we conducted a series of tests to the available DESI tracers to check their representativeness to angular clustering; the result was that, considering the completeness of the first data release across the footprint, we could only make use of the BGS sample for the effective redshifts 0.21 (BGS1) and 0.25 (BGS2).…

A Data-Adaptive Factor Model Using Composite Quantile Approach
Seeun Park, Hee-Seok Oh
https://arxiv.org/abs/2510.00558 https://arxiv.org/pdf/2510.00558

A Data-Adaptive Factor Model Using Composite Quantile Approach
This paper proposes a data-adaptive factor model (DAFM), a novel framework for extracting common factors that explain the structures of high-dimensional data. DAFM adopts a composite quantile strategy to adaptively capture the full distributional structure of the data, thereby enhancing estimation accuracy and revealing latent patterns that are invisible to conventional factor models. In this paper, we develop a practical algorithm for estimating DAFM by minimizing an objective function based o…

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression
Jimin Kim
https://arxiv.org/abs/2510.02316 https://arxiv.org/pdf/251…

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression
Accurate prediction of typhoon trajectories is essential for mitigating the impact of these extreme weather events. This study proposes a functional data analysis (FDA) framework for modeling and forecasting typhoon paths using historical trajectory data. Latitude and longitude sequences are represented as smooth functional observations, and a function-on-function regression model is employed to capture the temporal dynamics of typhoon movement. While the baseline model demonstrates reasonable …

Green's Function-Based Thin Plate Splines via Karhunen-Lo\`eve Expansion for Bayesian Spatial Modeling
Joaquin Cavieres, Sebastian Krumscheid
https://arxiv.org/abs/2510.04256

Green's Function-Based Thin Plate Splines via Karhunen-Loève Expansion for Bayesian Spatial Modeling
Gaussian random field is an ubiquitous model for spatial phenomena in diverse scientific disciplines. Its approximation is often crucial for computational feasibility in simulation, inference, and uncertainty quantification. The Karhunen-Loève Expansion provides a theoretically optimal basis for representing a Gaussian random field as a sum of deterministic orthonormal functions weighted by uncorrelated random variables. While this is a well-established method for dimension reduction and appro…

MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
Alexander Herold, Daniel Sobotka, Lucian Beer, Nina Bastati, Sarah Poetter-Lang, Michael Weber, Thomas Reiberger, Mattias Mandorfer, Georg Semmler, Benedikt Simbrunner, Barbara D. Wichtmann, Sami A. Ba-Ssalamah, Michael Trauner, Ahmed Ba-Ssalamah, Georg Langs

MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. Methods: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhan…

A microscopic approach to nonlinear theory of spin-charge separation
Oleksandr Tsyplyatyev, Yiqing Jin, Mar\'ia Moreno, Wooi Kiat Tan, Christopher J. B. Ford
https://arxiv.org/abs/2510.09515

A microscopic approach to nonlinear theory of spin-charge separation
The fate of spin-charge separation beyond the low energy remains elusive up to now. Here we develop a microscopic theory of the correlation functions using the strong coupling expansion of the Hubbard model and demonstrate its validity down to the experimentally relevant $r_{\rm s}>1$. Evaluating the spectral function, we show the general stability of the nonlinear spin-charge modes in whole energy band and investigate all the nonlinear features systematically. We confirm the general prediction…

Hamilton-Jacobi Reachability for Viability Analysis of Constrained Waste-to-Energy Systems under Adversarial Uncertainty
Achraf Bouhmady, Othman Cherkaoui Dekkaki
https://arxiv.org/abs/2510.11396

Hamilton-Jacobi Reachability for Viability Analysis of Constrained Waste-to-Energy Systems under Adversarial Uncertainty
This paper investigates the problem of maintaining the safe operation of Waste-to-Energy (WtE) systems under operational constraints and uncertain waste inflows. We model this as a robust viability problem, formulated as a zero-sum differential game between a control policy and an adversarial disturbance. Within a Hamilton-Jacobi framework, the viability kernel is characterized as the zero sublevel set of a value function satisfying a constrained Hamilton-Jacobi-Bellman (HJB) equation in the vi…

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
Junsei Ito, Yasuaki Wasa
https://arxiv.org/abs/2510.04591 https://arxiv.org/pdf/…

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed control design method utilizes gradients of the PID gain optimization, achieved through the automatic differentiation of PINNs, to apply model predictive control using a cost function based on tracking error and control inputs. By optimizing PINNs-based PID gains, the method ac…

Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
Xinyue Yu, Hayden Schaeffer
https://arxiv.org/abs/2512.17884 https://arxiv.org/pdf/2512.17884 https://arxiv.org/html/2512.17884
arXiv:2512.17884v1 Announce Type: new
Abstract: Operator learning is a data-driven approximation of mappings between infinite-dimensional function spaces, such as the solution operators of partial differential equations. Kernel-based operator learning can offer accurate, theoretically justified approximations that require less training than standard methods. However, they can become computationally prohibitive for large training sets and can be sensitive to noise. We propose a regularized random Fourier feature (RRFF) approach, coupled with a finite element reconstruction map (RRFF-FEM), for learning operators from noisy data. The method uses random features drawn from multivariate Student's $t$ distributions, together with frequency-weighted Tikhonov regularization that suppresses high-frequency noise. We establish high-probability bounds on the extreme singular values of the associated random feature matrix and show that when the number of features $N$ scales like $m \log m$ with the number of training samples $m$, the system is well-conditioned, which yields estimation and generalization guarantees. Detailed numerical experiments on benchmark PDE problems, including advection, Burgers', Darcy flow, Helmholtz, Navier-Stokes, and structural mechanics, demonstrate that RRFF and RRFF-FEM are robust to noise and achieve improved performance with reduced training time compared to the unregularized random feature model, while maintaining competitive accuracy relative to kernel and neural operator tests.
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The odometer in subcritical activated random walk
Tobias Johnson, Jacob Richey
https://arxiv.org/abs/2510.05514 https://arxiv.org/pdf/2510.05514

The odometer in subcritical activated random walk
We consider the activated random walk particle system, a model of self-organized criticality, on $\mathbb{Z}$ with i.i.d.-Bernoulli initial configuration. We show that at subcritical density, the system's odometer function, which counts the number of actions taken at each site, has a stretched exponential tail. It follows that the expected odometer at each site is finite.

Universal behaviors of the multi-time correlation functions of random processes with renewal: the step noise case (the random velocity of a L\'evy walk)
Marco Bianucci, Mauro Bologna, Daniele Lagomarsino-Oneto, Riccardo Mannella
https://arxiv.org/abs/2510.11747

Universal behaviors of the multi-time correlation functions of random processes with renewal: the step noise case (the random velocity of a Lévy walk)
Stochastic processes with renewal properties are powerful tools for modeling systems where memory effects and long-time correlations play a significant role. In this work, we study a broad class of renewal processes where a variable's value changes according to a prescribed Probability Density Function (PDF), $p(ξ)$, after random waiting times $θ$. This model is relevant across many fields, including classical chaos, nonlinear hydrodynamics, quantum dots, cold atom dynamics, biological motion…

Bulk plasmons in elemental metals
Dario A. Leon, Claudia Cardoso, Kristian Berland
https://arxiv.org/abs/2510.07261 https://arxiv.org/pdf/2510.07261…

Bulk plasmons in elemental metals
The spectral properties, momentum dispersion, and broadening of bulk plasmonic excitations of 25 elemental metals are studied from first principles calculations in the random-phase approximation. Spectral band structures are constructed from the resulting momentum- and frequency-dependent inverse dielectric function. We develop an effective analytical representation of the main collective excitations in the dielectric response, by extending our earlier model based on multipole-Padé approximant…

Maximum softly penalised likelihood in factor analysis
Philipp Sterzinger, Ioannis Kosmids, Irini Moustaki
https://arxiv.org/abs/2510.06465 https://arxiv.o…

Maximum softly penalised likelihood in factor analysis
Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, known as Heywood cases, are characterised by non-positive variance estimates and can cause issues in numerical optimisation procedures or convergence failures, which, in turn, can lead to misleading inferences, particularly regarding factor scores and model selection. We derive sufficient conditions on the model and a penalty to the log-likelihood function that i) guarantee…

The role of the overlap function in describing angular distributions of single-nucleon transfer reactions
M. R. Xie, J. G. Li, N. Keeley, N. Michel, W. Zuo
https://arxiv.org/abs/2510.12103

The role of the overlap function in describing angular distributions of single-nucleon transfer reactions
Single-nucleon transfer reactions offer a valuable way to probe nuclear structure. We explore the effect of directly introducing overlap functions computed using the Gamow shell model (GSM) into reaction calculations, taking the $\left< ^7\mathrm{Li} \mid \protect{^6\mathrm{He}} + p \right>$ single proton overlap as a case study. By incorporating both inter-nucleon correlations and continuum coupling, the GSM provides accurate overlap functions in both interior and asymptotic regions, together …

Polyharmonic Cascade
Yuriy N. Bakhvalov
https://arxiv.org/abs/2512.17671 https://arxiv.org/pdf/2512.17671 https://arxiv.org/html/2512.17671
arXiv:2512.17671v1 Announce Type: new
Abstract: This paper presents a deep machine learning architecture, the "polyharmonic cascade" -- a sequence of packages of polyharmonic splines, where each layer is rigorously derived from the theory of random functions and the principles of indifference. This makes it possible to approximate nonlinear functions of arbitrary complexity while preserving global smoothness and a probabilistic interpretation. For the polyharmonic cascade, a training method alternative to gradient descent is proposed: instead of directly optimizing the coefficients, one solves a single global linear system on each batch with respect to the function values at fixed "constellations" of nodes. This yields synchronized updates of all layers, preserves the probabilistic interpretation of individual layers and theoretical consistency with the original model, and scales well: all computations reduce to 2D matrix operations efficiently executed on a GPU. Fast learning without overfitting on MNIST is demonstrated.
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Observational constraints on f(Q,T) gravity from the mass-radius relation and stability of compact stars
S. K. Maurya, Abdul Aziz, Ksh. Newton Singh, G. Mustafa, Y. Sekhmani, Saibal Ray
https://arxiv.org/abs/2510.06003

Observational constraints on f(Q,T) gravity from the mass-radius relation and stability of compact stars
In this investigation we examine the astrophysical consequences of the influence of pressure anisotropy on the physical properties of observed pulsars within the background of $f(Q,T)$ gravity by choosing a specific form $f(Q, T)=ψ_1\, Q + ψ_2 T$, where $ψ_1$ and $ψ_2$ are the model parameters. Initially, we solve the modified field equations for anisotropic stellar configurations by assuming the physically valid metric potential along with anisotropic function for the distribution of the i…

Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
Haomiao Chen, Keith W Jamison, Mert R. Sabuncu, Amy Kuceyeski
https://arxiv.org/abs/2510.07342

Beyond Grid-Locked Voxels: Neural Response Functions for Continuous Brain Encoding
Neural encoding models aim to predict fMRI-measured brain responses to natural images. fMRI data is acquired as a 3D volume of voxels, where each voxel has a defined spatial location in the brain. However, conventional encoding models often flatten this volume into a 1D vector and treat voxel responses as independent outputs. This removes spatial context, discards anatomical information, and ties each model to a subject-specific voxel grid. We introduce the Neural Response Function (NRF), a fra…

Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
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Flux confinement-deconfinement transition of dimer-loop models on three-dimensional bipartite lattices
Souvik Kundu, Kedar Damle
https://arxiv.org/abs/2510.11607 https://…

Flux confinement-deconfinement transition of dimer-loop models on three-dimensional bipartite lattices
Motivated by recent work that mapped the low-temperature properties of a class of frustrated spin $S=1$ kagome antiferromagnets with competing exchange and single-ion anisotropies to the fully-packed limit (with each vertex touched by exactly one dimer or nontrivial loop) of a system of dimers and nontrivial (length $s > 2$) loops on the honeycomb lattice, we study this fully-packed dimer-loop model on the three-dimensional bipartite cubic and diamond lattices as a function of $w$, the relative…

DeepEN: Personalized Enteral Nutrition for Critically Ill Patients using Deep Reinforcement Learning
Daniel Jason Tan, Jiayang Chen, Dilruk Perera, Kay Choong See, Mengling Feng
https://arxiv.org/abs/2510.08350

DeepEN: Personalized Enteral Nutrition for Critically Ill Patients using Deep Reinforcement Learning
We introduce DeepEN, a deep reinforcement learning (RL) framework for personalized enteral nutrition (EN) in critically ill patients. Trained offline on over 11,000 ICU patients from the MIMIC-IV database, DeepEN generates 4-hourly recommendations for caloric, protein, and fluid intake tailored to each patient's evolving physiology. The model integrates a curated, clinically informed state space with a custom reward function that balances short-term physiological and nutrition-related goals wit…

Scalar-Tensor Symmetric Teleparallel Gravity: Reconstruct the Cosmological History with a Steep Potential
Ghulam Murtaza, Avik De, Andronikos Paliathanasis
https://arxiv.org/abs/2510.03042

Scalar-Tensor Symmetric Teleparallel Gravity: Reconstruct the Cosmological History with a Steep Potential
Within the framework of scalar-non-metricity gravity, we introduce a steep potential together with a power-law coupling function and investigate whether the acceleration phases of the universe can be consistently described by this model. In the symmetric teleparallel formulation, and under a Friedmann--Lemaître--Robertson--Walker background, three distinct branches of the connection arise, leading to three different cosmological scenarios. We perform a detailed dynamical analysis of these mode…

ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui, Veronika Thost, Zhang-Wei Hong, Johannes D\"urholt, Jie Chen, Wojciech Matusik, Mina Konakovi\'c Lukovi\'c
https://arxiv.org/abs/2510.03051

ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its performance hinges on surrogate and acquisition function hyper-parameters that are often hand-tuned and fail to generalize across problem landscapes. We present ZeroShotOpt, a general-purpose, pretrained model for continuous black-box optimization tasks ranging from 2D to 20D. Our approach leverages offline reinforcement …

Interplay of order and disorder in two-dimensional critical systems with mixed boundary conditions
E. Eisenriegler
https://arxiv.org/abs/2510.04238 https://

Interplay of order and disorder in two-dimensional critical systems with mixed boundary conditions
In spin systems such as the Ising model, the local order and disorder can be characterized by the order-parameter and energy density profiles $\langle σ({\bf r}_1) \rangle$ and $\langle ε({\bf r}_2) \rangle$, respectively. Does increasing the order at ${\bf r}_1$ always decrease the disorder at ${\bf r}_2$? Does increasing the disorder at ${\bf r}_2$ always decrease the order at ${\bf r}_1$? The answer to these questions is contained in the cumulant response function $\langleσ({\bf r}_1) \, …

The Non-Attainment Phenomenon in Robust SOCPs
Vinh Nguyen
https://arxiv.org/abs/2510.00318 https://arxiv.org/pdf/2510.00318

The Non-Attainment Phenomenon in Robust SOCPs
A fundamental theorem of linear programming states that a feasible linear program is solvable if and only if its objective function is copositive with respect to the recession cone of its feasible set. This paper demonstrates that this crucial guarantee does not extend to Second-Order Cone Programs (SOCPs), a workhorse model in robust and convex optimization. We construct and analyze a rigorous counterexample derived from a robust linear optimization problem with ellipsoidal uncertainty. The re…

The sharpness of the quark-hadron transition and the properties of hybrid stars
M. B. Albino, R. Fariello, G. Lugones, F. S. Navarra
https://arxiv.org/abs/2510.02053 https://

The sharpness of the quark-hadron transition and the properties of hybrid stars
We investigate the effects of the sharpness of the phase transition between hadronic matter and quark matter on various properties of neutron stars. We construct hybrid equations of state by combining a hadronic model with a quark model using a Gaussian function. This approach introduces a smooth transition characterized by two parameters: one representing the overpressure relative to the first-order phase transition point, and the other related to the range over which the hybrid region extends…