Tootfinder

Parton Distributions on a Quantum Computer
Jiunn-Wei Chen, Yu-Ting Chen, Ghanashyam Meher
https://arxiv.org/abs/2506.16829 https://ar…

Parton Distributions on a Quantum Computer
We perform the first quantum computation of parton distribution function (PDF) with a real quantum device by calculating the PDF of the lightest positronium in the Schwinger model with IBM quantum computers. The calculation uses 10 qubits for staggered fermions at five spatial sites and one ancillary qubit. The most critical and challenging step is to reduce the number of two-qubit gate depths to around 500 so that sensible results start to emerge. The resulting lightcone correlators have excel…

Role of topotactic hydrogen in Superconductivity of Infinite-layer Nickelate NdNiO$_{2}$: A first-principles and variational Monte Carlo study
Manoj Gupta, Arun Kumar Maurya, Amal Medhi, Tanusri Saha Dasgupta
https://arxiv.org/abs/2506.13399

Role of topotactic hydrogen in Superconductivity of Infinite-layer Nickelate NdNiO$_{2}$: A first-principles and variational Monte Carlo study
Employing combination of first-principles calculations, low-energy model construction, and variational Monte Carlo solution of the ab-initio derived Hubbard model, we study the effect of hydrogenation in the electronic structure and superconducting properties of infinite-layer nickelate, NdNiO$_2$. We find that the introduction of hydrogen at the apical oxygen vacancy position strongly influences the Wannier function corresponding to the effective interstitial orbital at the Ni site bound to th…

Density jump as a function of the field for parallel relativistic collisionless shocks
Antoine Bret, Ramesh Narayan
https://arxiv.org/abs/2506.07973 https:…

Density jump as a function of the field for parallel relativistic collisionless shocks
Collisionless shocks are frequently analyzed using the magnetohydrodynamic formalism (MHD), even though the required collisionality hypothesis is not fulfilled. In a previous work \citep{BretJPP2018}, we presented a model of collisionless shock displaying an important departure from the expected MHD behavior, in the case of a strong flow aligned magnetic field. This model was non-relativistic. Here, it is extended to the relativistic regime, considering zero upstream pressure and upstream Loren…

Bayesian Quantification of Observability and Equation of State of Twin Stars
Xavier Grundler, Bao-An Li
https://arxiv.org/abs/2506.13677 https://

Bayesian Quantification of Observability and Equation of State of Twin Stars
The possibility of discovering twin stars, two neutron stars (NSs) with the same mass but different radii, is usually studied in forward modelings by using a restricted number of NS matter equation of state (EOS) encapsulating a first-order phase transition from hadronic to quark matter (QM). Informing our likelihood function with the NS radius data from GW170817 and using a meta-model with 9-parameters capable of mimicking most NS EOSs available in the literature, we conduct a Bayesian quantif…

This https://arxiv.org/abs/2505.21199 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Multi-Event Triggers for Serverless Computing
Function-as-a-Service (FaaS) is an event-driven serverless cloud computing model in which small, stateless functions are invoked in response to events, such as HTTP requests, new database entries, or messages. Current FaaS platform assume that each function invocation corresponds to a single event. However, from an application perspective, it is desirable to invoke functions in response to a collection of events of different types or only with every n\textsuperscript{th} event. To implement thi…

GLASD: A Loss-Function-Agnostic Global Optimizer for Robust Correlation Estimation under Data Contamination and Heavy Tails
Priyam Das
https://arxiv.org/abs/2506.14801

GLASD: A Loss-Function-Agnostic Global Optimizer for Robust Correlation Estimation under Data Contamination and Heavy Tails
Robust correlation estimation is essential in high-dimensional settings, particularly when data are contaminated by outliers or exhibit heavy-tailed behavior. Many robust loss functions of practical interest-such as those involving truncation or redescending M-estimators-lead to objective functions that are inherently non-convex and non-differentiable. Traditional methods typically focus on a single loss function tailored to a specific contamination model and develop custom algorithms tightly c…

This https://arxiv.org/abs/2412.13327 has been replaced.
initial toot: https://mastoxiv.page/@…

The SU(N) Fermi-Hubbard Model on two sites: Bethe Ansatz solution and Quantum Phase Transition of the Lipkin-Meshkov-Glick Model in the large-N limit
We show that the SU(N) Fermi-Hubbard model (FHM) on two sites, where N is the number of flavors of each fermion, corresponds to an exactly solvable two-level many-boson model that Richardson [J. Math. Phys. 9, 1327 (1968)] analytically solved long ago. We express the Bethe ansatz solutions as a function of the physical parameters of the SU(N) FHM, and recast its eigenvalues and eigenstates in terms of the Richardson pair energies and creation operators. In this context, the connection with the …

Rethinking Growth: An Extension of the Solow-Swan Model
Timothy F. Power, Roman G. Smirnov
https://arxiv.org/abs/2506.10864 https://a…

Rethinking Growth: An Extension of the Solow-Swan Model
The aggregate Cobb-Douglas production function stands as a central element in the renowned Solow-Swan model in economics, providing a crucial theoretical framework for comprehending the determinants of economic growth. This model not only guides policymakers and economists but also influences their decisions, fostering sustainable and inclusive development. In this study, we utilize a one-input version of a new generalization of the Cobb-Douglas production function proposed recently, thereby ex…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/8]:
Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Ana...

Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
Ivan A. Kazakov, Iana V. Kulichenko, Egor E. Kovalev, Angelina A. Treskova, Daria D. Barma, Kirill M. Malakhov, Arkady V. Shipulin
https://arxiv.org/abs/2506.13575

Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regre…

Heavy mesons in an effective quark model with a nonlocal interaction
A. Friesen, Yu. Kalinovsky, A. Khmelev
https://arxiv.org/abs/2505.24542 https://

Heavy mesons in an effective quark model with a nonlocal interaction
We discuss the properties of light and heavy mesons in the framework of a model with nonlocal interaction. We start from the Bethe-Salpeter equation, choosing the interaction kernel in a nonlocal form with the Gaussian meson vertex function. We fix the model parameters using electromagnetic and leptonic decay constants. Within this model, we consider the neutral pion transition form factor ${F}_{πγ}$and apply the model to the transition form factors of heavy pseudoscalar mesons $η_c$ and $η…

This https://arxiv.org/abs/2505.09869 has been replaced.
initial toot: https://mastoxiv.page/@a…

How does the limited resolution of space plasma analyzers affect the accuracy of space plasma measurements?
We investigate the systematic errors in measured plasma velocity distribution functions and their corresponding velocity moments, arising from the limited energy and angular resolution of top-hat electrostatic analyzers. For this purpose, we develop a forward model of a concept analyzer that simulates observations of typical solar wind proton plasma particles with their velocities following a Maxwell distribution function. We then review the standard conversion of the observations to physical p…

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
Taozhi Guo, Gumaro Rendon, Rutuja Kshirsagar
https://arxiv.org/abs/2506.09318

Canonical Partition Function on a Quantum Computer through Trotter Interpolation
In this work, we present a Gibbs state observable estimation algorithm based on Trotter interpolation, which reaches a state-of-the-art quantum computational cost of $ \tilde{O}(β\log{1/ε})$. Our approach saves $\log(Γ)$ ancilla qubits compared with the qubitization-based methods for Hamiltonian with $Γ$ stages. To provide a robust assessment of our approach, we benchmark our results against state-of-the-art methodology using the SYK model as a testbed. Our method provides an efficient alte…

Solitary wave solutions, periodic and superposition solutions to the system of first-order (2 1)-dimensional Boussinesq's equations derived from the Euler equations for an ideal fluid model
Piotr Rozmej, Anna Karczewska
https://arxiv.org/abs/2506.08941

Solitary wave solutions, periodic and superposition solutions to the system of first-order (2+1)-dimensional Boussinesq's equations derived from the Euler equations for an ideal fluid model
This article concludes the study of (2+1)-dimensional nonlinear wave equations that can be derived in a model of an ideal fluid with irrotational motion. In the considered case of identical scaling of the $x,y$ variables, obtaining a (2+1)-dimensional wave equation analogous to the KdV equation is impossible. Instead, from a system of two first-order Boussinesq equations, a non-linear wave equation for the auxiliary function $f(x,y,z)$ defining the velocity potential can be obtained, and only f…

This https://arxiv.org/abs/2504.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Statistical Physics of the Polarised IKKT Matrix Model
The polarised IKKT matrix model is the worldpoint theory of $N$ D-instantons in a background three-form flux of magnitude $Ω$, and promises to be a highly tractable model of holography. The matrix integral can be viewed as a statistical physics partition function with inverse temperature $Ω^4$. At large $Ω$ the model is dominated by a matrix configuration corresponding to a 'polarised' spherical D1-brane. We show that at a critical value of $Ω^2 N$ the model undergoes a first order phase tr…

Updated line list for the principal isotopologue of carbon monoxide
Vladimir G. Ushakov, Emile S. Medvedev
https://arxiv.org/abs/2506.12216 https://…

Updated line list for the principal isotopologue of carbon monoxide
The line list for the principal isotopologue of CO calculated earlier by the present authors [1, 2] with the irregular dipole-moment function (DMF) is updated with use of the recent high-precision measurements in the 3-0 [3, 4] (Bielska et al. 2022, Hodges et al. 2025) and 7-0 [5] (Balashov et al. 2023) bands. The new data came in contradiction with the experimental data on the 1-0 band [6, 7]. Therefore, we performed fitting several model DMFs to the modified original data set of Meshkov et al…

Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms
Shiting Huang, Sanjay Pant, Sean McGinty, Richard Good, Ben Shelley, Ankush Aggarwal
https://arxiv.org/abs/2505.01264

Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms
Functional limitation after lung resection surgery has been consistently documented in clinical studies, and right ventricle (RV) dysfunction has been hypothesized as a contributing reason. However, the mechanisms of RV dysfunction after lung resection remain unclear, particularly whether change in afterload or contractility is the main cause. This study is the first to employ a lumped parameter model to simulate the effects of lung resection. The implementation of a computational model allowed…

$L^2$-Hodge theory of Hybrid Landau-Ginzburg models of Calabi-Yau complete intersections
Jeehoon Park, Jaewon Yoo
https://arxiv.org/abs/2505.24218 https://…

$L^2$-Hodge theory of Hybrid Landau-Ginzburg models of Calabi-Yau complete intersections
Given a Calabi-Yau smooth projective complete intersection variety $V$ over $\C$, a hybrid Landau-Ginzburg (LG) model may be associated using the Cayley trick. This hybrid LG model comprises a non-compact Calabi-Yau manifold $X_{CY}$, and a holomorphic function $W$, defined on $X_{CY}$, such that the critical locus of $W$ is isomorphic to $V$. We construct a complete Kähler metric $\mathfrak{g}$ and a bounded Calabi-Yau volume form $Ω$ on $X_{CY}$ such that $(X_{CY},\mathfrak{g}, Ω)$ is a bo…

This https://arxiv.org/abs/2506.01056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MCP-Zero: Proactive Toolchain Construction for LLM Agents from Scratch
Function-calling has enabled large language models (LLMs) to act as tool-using agents, but injecting thousands of tool schemas into the prompt is costly and error-prone. We introduce MCP-Zero, a proactive agent framework that lets the LLM itself decide when and which external tools to retrieve, thereby assembling a task-specific toolchain from scratch. The framework is built upon three components: (1) Proactive Tool Request, where the model emits a structured $\left<\operatorname{tool\_assistan…

Decay of dark energy into dark matter in a metric $f(R)$ gravity: effective running Hubble constant
Giovanni Montani, Mariaveronica De Angelis, Maria Giovanna Dainotti
https://arxiv.org/abs/2506.13288

Decay of dark energy into dark matter in a metric $f(R)$ gravity: effective running Hubble constant
We examine a modified late-Universe dynamics where dark energy decays into dark matter, within the framework of metric $f(R)$-gravity in the Jordan frame. After a detailed analysis of the modified $Λ\text{CDM}$ model, we introduce a theoretical diagnostic tool to capture the emergence of an effective running Hubble constant as a function of redshift. We then compare this theoretical model with the 40-bin analysis of the Supernova Pantheon sample. This comparison allows us to determine the valu…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
Konstantin Avrachenkov, Diego Goldsztajn
https://arxiv.org/abs/2506.09223 htt…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
We consider graphs with two communities and analyze an algorithm for learning the community labels when the edges of the graph and only a small fraction of the labels are known in advance. The algorithm is based on the Glauber dynamics for an Ising model where the energy function includes a quadratic penalty on the magnetization. The analysis focuses on graphs sampled from a Stochastic Block Model (SBM) with slowly growing mean degree. We derive a mean-field limit for the magnetization of each …

BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
Taesoo Park, Mungwi Jeong, Mingyu Park, Narae Kim, Junyoung Kim, Mujung Kim, Jisang Yoo, Hoyun Lee, Sanghoon Kim, Soonchul Kwon
https://arxiv.org/abs/2506.09487

BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation. Built upon the original BemaGAN architecture, BemaGANv2 incorporates major architectural innovations by replacing traditional ResBlocks in the generator with the Anti-aliased Multi-Periodicity composition (AMP) module, which internally applies the Snake activation function to better model periodic structures. In the discrimin…

Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction
Ambrose Emmett-Iwaniw, Nathan Kirk
https://arxiv.org/abs/2506.05391 https:…

Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction
Autoregressive models are often employed to learn distributions of image data by decomposing the $D$-dimensional density function into a product of one-dimensional conditional distributions. Each conditional depends on preceding variables (pixels, in the case of image data), making the order in which variables are processed fundamental to the model performance. In this paper, we study the problem of observing a small subset of image pixels (referred to as a pixel patch) to predict the unobserve…

Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Haoyu Zhai, Shuo Wang, Pirouz Naghavi, Qingying Hao, Gang Wang
https://arxiv.org/abs/2506.12344

Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Gaussian blur is widely used to blur human faces in sensitive photos before the photos are posted on the Internet. However, it is unclear to what extent the blurred faces can be restored and used to re-identify the person, especially under a high-blurring setting. In this paper, we explore this question by developing a deblurring method called Revelio. The key intuition is to leverage a generative model's memorization effect and approximate the inverse function of Gaussian blur for face restora…

This https://arxiv.org/abs/2502.15148 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Antifragility and response to damage in the synchronization of oscillators on networks
In this paper, we introduce a mathematical framework to assess the impact of damage, defined as the reduction of weight in a specific link, on identical oscillator systems governed by the Kuramoto model and coupled through weighted networks. We analyze how weight modifications in a single link affect the system when its global function is to achieve the synchronization of coupled oscillators starting from random initial phases. We introduce different measures that allow the identification of ca…

Cosmographical analysis of sign-switching dark energy
Mariam Bouhmadi-L\'opez, Be\~nat Ibarra-Uriondo
https://arxiv.org/abs/2506.12139 https://

Cosmographical analysis of sign-switching dark energy
Inspired by the well-studied $Λ_{\rm s}$CDM model, we propose and investigate a class of dynamical dark energy models that evolve from a negative cosmological constant, transitioning to a positive value at low redshifts. Specifically, we introduce a dark energy framework based on a generalised ladder-step function for the cosmological constant. Furthermore, we present two additional models in which the cosmological constant undergoes a smooth sign change at a specified redshift. We provide a d…

Geometry Reduced Order Modeling (GROM) with application to modeling of glymphatic function
Andreas Solheim, Geir Ringstand, Per Kristian Eide, Kent-Andre Mardal
https://arxiv.org/abs/2506.09442

Geometry Reduced Order Modeling (GROM) with application to modeling of glymphatic function
Computational modeling of the brain has become a key part of understanding how the brain clears metabolic waste, but patient-specific modeling on a significant scale is still out of reach with current methods. We introduce a novel approach for leveraging model order reduction techniques in computational models of brain geometries to alleviate computational costs involved in numerical simulations. Using image registration methods based on magnetic resonance imaging, we compute inter-brain mappin…

RL-Guided MPC for Autonomous Greenhouse Control
Salim Msaad, Murray Harraway, Robert D. McAllister
https://arxiv.org/abs/2506.13278 https://

RL-Guided MPC for Autonomous Greenhouse Control
The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Control (MPC) to optimize economic benefits in autonomous greenhouses. Previous research has explored the use of RL and MPC for greenhouse control individually, or by using MPC as the function approximator for the RL agent. This study introduces the RL-Guided MPC framework, where a …

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
Shunxin Yao
https://arxiv.org/abs/2506.02790 https://

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
OC-DeepIV is a neural network model designed for estimating causal effects. It characterizes heterogeneity by adding interaction features and reduces redundancy through orthogonal constraints. The model includes two feature extractors, one for the instrumental variable Z and the other for the covariate X*. The training process is divided into two stages: the first stage uses the mean squared error (MSE) loss function, and the second stage incorporates orthogonal regularization. Experimental res…

A Cubic Regularization Method for Multiobjective Optimization
Douglas S. Gon\c{c}alves, Max L. N. Gon\c{c}alves, Jefferson G. Melo
https://arxiv.org/abs/2506.08181

A Cubic Regularization Method for Multiobjective Optimization
This work introduces a new cubic regularization method for nonconvex unconstrained multiobjective optimization problems. At each iteration of the method, a model associated with the cubic regularization of each component of the objective function is minimized. This model allows approximations for the first- and second-order derivatives, which must satisfy suitable error conditions. One interesting feature of the proposed algorithm is that the regularization parameter of the model and the accura…

Anisotropic Josephson coupling of d-vectors arising from interplay with frustrated spin textures
Grayson R. Frazier, Junyi Zhang, Yi Li
https://arxiv.org/abs/2506.15661

Anisotropic Josephson coupling of d-vectors arising from interplay with frustrated spin textures
We propose a Cooper pair analogue of Dzyaloshinskii-Moriya's anisotropic coupling for $d$-vectors in an inhomogeneous unconventional superconductor, which can arise when itinerant electrons couple to a local exchange field of frustrated spin moments. Using perturbative Green's function methods, we examine an $s$-$d$ model on a geometrically frustrated lattice with noncollinear spin texture that breaks time-reversal and parity symmetries, leading to spin-triplet pairing correlations. The effecti…

Higher loops in AdS: applications to boundary CFT
Simone Giombi, Zimo Sun
https://arxiv.org/abs/2506.14699 https://arxiv.org/pdf/2506…

Higher loops in AdS: applications to boundary CFT
The Euclidean Anti-de Sitter (AdS) space provides a natural framework for studying boundary conformal field theory (BCFT). We analyze the conformal boundary conditions of the critical O$(N)$ model in $d=4-ε$ dimensions using the $ε$-expansion, and extract some BCFT observables through higher-loop calculations in AdS. Specifically, in the so-called "ordinary" universality class, we determine the free energy to four-loop order and the one-point function of the lightest O$(N)$ singlet operator t…

Active Gaussian Network Model: a non-equilibrium description of protein fluctuations and allosteric behavior
Giulio Costantini, Lorenzo Caprini, Umberto Marini Bettolo Marconi, Fabio Cecconi
https://arxiv.org/abs/2505.24855

Active Gaussian Network Model: a non-equilibrium description of protein fluctuations and allosteric behavior
Understanding the link between structure and function in proteins is fundamental in molecular biology and proteomics. A central question in this context is whether allostery - where the binding of a molecule at one site affects the activity of a distant site - emerges as a further manifestation of the intricate interplay between structure, function, and intrinsic dynamics. This study explores how allosteric regulation is modified when intrinsic protein dynamics operate under out-of-equilibrium …

The $\alpha$-Attractor E-Model in Warm Inflation: Observational Viability from Planck 2018
Bhargabi Saha, Malay K. Nandy
https://arxiv.org/abs/2506.06717 h…

The $α$-Attractor E-Model in Warm Inflation: Observational Viability from Planck 2018
We explore the inflationary evolution and observational viability of the $α$-attractor E-model in the framework of warm inflation, focusing on both weak and strong dissipative regimes, with a dissipation coefficient linear in temperature. In the strong regime, we account for the growth of inflaton fluctuations due to coupling with the radiation bath via two different forms for the dissipation enhancement function: one associated with plateau-like potentials, and another motivated by the warm l…

Convolutional method for data assimilation An improved method on neuronal electrophysiological data
Dawei Li, Henry D. I. Abarbanel
https://arxiv.org/abs/2506.11365

Convolutional method for data assimilation An improved method on neuronal electrophysiological data
We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timin…

This https://arxiv.org/abs/2411.13983 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and maximize their reward. This approach applies to competitive and cooperative multi-agent motion planning problems which we formulate as const…

Trinity VII. Predictions for the Observable Correlation Functions of Accreting Black Holes
Oddisey Knox, Haowen Zhang, Peter Behroozi
https://arxiv.org/abs/2506.05612

Trinity VII. Predictions for the Observable Correlation Functions of Accreting Black Holes
The quasar correlation function assesses the occurrence of quasar pairs as a function of separation, which is strongly influenced by quasar host halo masses. The empirical Trinity model recently inferred the redshift-dependent relationship between supermassive black hole (SMBH) mass, galaxy mass, and halo mass, using constraints other than correlation functions (e.g., quasar luminosity functions, active galactic nuclei occupation fractions, and SMBH mass-bulge mass relations). Hence, comparing …

$IF-GUIDE$: Influence Function-Guided Detoxification of LLMs
Zachary Coalson, Juhan Bae, Nicholas Carlini, Sanghyun Hong
https://arxiv.org/abs/2506.01790 h…

$IF-GUIDE$: Influence Function-Guided Detoxification of LLMs
We study how training data contributes to the emergence of toxic behaviors in large-language models. Most prior work on reducing model toxicity adopts $reactive$ approaches, such as fine-tuning pre-trained (and potentially toxic) models to align them with human values. In contrast, we propose a $proactive$ approach$-$IF-Guide$-$which leverages influence functions to identify harmful tokens within any training data and suppress their impact during training. To this end, we first show that standa…

This https://arxiv.org/abs/2402.15311 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Scaling Limit of the Kuramoto Model on Random Geometric Graphs
We consider the Kuramoto model on a graph with nodes given by $n$ i.i.d. points uniformly distributed on the $d$ dimensional torus. Two nodes are declared neighbors if they are at distance less than $ε$. We prove a scaling limit for this model in compact time intervals as $n\to\infty$ and $ε\to 0$ such that $ε^{d+2}n/\log n \to \infty$. The limiting object is given by the heat equation. On the one hand this shows that the nonlinearity given by the sine function disappears under this scaling …

Akaike information criterion for segmented regression models
Kazuki Nakajima, Yoshiyuki Ninomiya
https://arxiv.org/abs/2506.08760 https://

Akaike information criterion for segmented regression models
In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the analysis package developed by \cite{KimFFM00} has become a standard tool for analyzing trends in longitudinal data in the field of epidemiology. In addition, it is sometimes natural to expect the regression function to be discontinuous at the change-points, and in the field of epidemiology, this model is used in \cite{J…

A potential-theoretic approach to optimal stopping in a spectrally L\'evy Model
Masahiko Egami, Tomohiro Koike
https://arxiv.org/abs/2506.10538 https:/…

A potential-theoretic approach to optimal stopping in a spectrally Lévy Model
We present a systematic solution method for optimal stopping problem of one-dimensional spectrally negative Lévy processes. Our main tools are based on potential theory, particularly the Riesz decomposition and the maximum principle. This novel approach allows us to handle a broad class of reward functions. That is, we solve the problem in a general setup without relying on specific form of the reward function or on the fluctuation identities. We provide a step-by-step solution procedure, whic…

SuSAM-v2: improved superscaling description of electron and CC neutrino quasielastic scattering using effective mass dynamics
V. L. Martinez-Consentino, P. R. Casale, J. E. Amaro
https://arxiv.org/abs/2506.03934

SuSAM-v2: improved superscaling description of electron and CC neutrino quasielastic scattering using effective mass dynamics
We present an improved version of the Superscaling Analysis with Relativistic Effective Mass, denoted as SuSAM-v2. In the original SuSAM model, a universal scaling function was fitted to a selected set of quasielastic electron scattering (e,e') cross section data, using a phenomenological ansatz inspired by the Relativistic Mean Field model of nuclear matter. In this work, we refine the procedure by first fitting a longitudinal scaling function directly to experimental longitudinal response dat…

Kinetic magnetism in the crossover between the square and triangular lattice Fermi-Hubbard models
Darren Pereira, Erich J. Mueller
https://arxiv.org/abs/2506.15669

Kinetic magnetism in the crossover between the square and triangular lattice Fermi-Hubbard models
We calculate the spin correlations that result from the motion of a single dopant in the hard-core Fermi-Hubbard model, as the geometry evolves from a square to a triangular lattice. In particular, we consider the square lattice with an additional hopping along one diagonal, whose strength is continuously varied. We use a high-temperature expansion which expresses the partition function as a sum over closed paths taken by the dopant. We sample thousands of diagrams in the space of closed paths …

Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
Yu Hao, Hiroyuki Kasahara
https://arxiv.org/abs/2506.09666

Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
This paper develops statistical methods for determining the number of components in panel data finite mixture regression models with regression errors independently distributed as normal or more flexible normal mixtures. We analyze the asymptotic properties of the likelihood ratio test (LRT) and information criteria (AIC and BIC) for model selection in both conditionally independent and dynamic panel settings. Unlike cross-sectional normal mixture models, we show that panel data structures elim…

This https://arxiv.org/abs/2506.01790 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

$IF-GUIDE$: Influence Function-Guided Detoxification of LLMs
We study how training data contributes to the emergence of toxic behaviors in large-language models. Most prior work on reducing model toxicity adopts $reactive$ approaches, such as fine-tuning pre-trained (and potentially toxic) models to align them with human values. In contrast, we propose a $proactive$ approach$-$IF-Guide$-$which leverages influence functions to identify harmful tokens within any training data and suppress their impact during training. To this end, we first show that standa…

Discrete time shadow price revisited
Tomasz Rogala, {\L}ukasz Stettner
https://arxiv.org/abs/2506.14708 https://arxiv.org/pdf/2506.14…

Discrete time shadow price revisited
In the paper discrete time shadow price is constructed for the market with several assets with given bid and ask prices. Shadow price is the price such that the problem of optimal utility from terminal wealth on the market without transaction costs gives the same value function as in the case of bid and ask prices. In the paper we solve first static problem for two assets and then we construct the shadow price for dynamic model. Finally we present construction of the shadow price for several as…

This https://arxiv.org/abs/2309.07443 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Learning Tube-Certified Control using Robust Contraction Metrics
Control design for general nonlinear robotic systems with guaranteed stability and/or safety in the presence of model uncertainties is a challenging problem. Recent efforts attempt to learn a controller and a certificate (e.g., a Lyapunov function or a contraction metric) jointly using neural networks (NNs), in which model uncertainties are generally ignored during the learning process. In this paper, for nonlinear systems subject to bounded disturbances, we present a framework for jointly lear…

Late time behavior in $f(R,\mathcal{L}_{m})$ gravity through Gaussian reconstruction and dynamical stability
Y. Kalpana Devi, S. A. Narawade, B. Mishra
https://arxiv.org/abs/2506.09568

Late time behavior in $f(R,\mathcal{L}_{m})$ gravity through Gaussian reconstruction and dynamical stability
In this paper, we explore modified gravity in the framework of $f(R, \mathcal{L}_m)$ theories by reconstructing the function $f(\mathcal{L}_m)$, where $\mathcal{L}_m = ρ$ is the matter Lagrangian, under the assumption of a pressureless, matter-dominated Universe. Using a non-parametric Gaussian process reconstruction technique applied to Hubble data, we obtain two viable models of $f(\mathcal{L}_m)$ : (i) a power-law model $f_1(\mathcal{L}_m) = α\mathcal{L}_m^{b_1}$ with $b_1 \in [0.018, 0.02…

Non-Separable Halo Bias from High-Redshift Galaxy Clustering
Emy Mons, Vipul Prasad Maranchery, M. S. Suryan Sivadas, Charles Jose
https://arxiv.org/abs/2506.07662

Non-Separable Halo Bias from High-Redshift Galaxy Clustering
The halo model provides a powerful framework for interpreting galaxy clustering by linking the spatial distribution of dark matter haloes to the underlying matter distribution. A key assumption within the linear bias approximation of the halo model is that the halo bias between two halo populations is a separable function of the mass of each population. In this work, we test the validity of the separable bias approximation on quasi-linear scales using both simulations and observational data. Un…

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
Sebasti\'an Rojas-Innocenti, Enrique Baeyens, Alejandro Mart\'in-Crespo, Sergio Saludes-Rodil, Fernando Frechoso Escudero
https://arxiv.org/abs/2506.10824

A Robust Optimization Framework for Flexible Industrial Energy Scheduling: Application to a Cement Plant with Market Participation
This paper presents a scenario based robust optimization framework for short term energy scheduling in electricity intensive industrial plants, explicitly addressing uncertainty in planning decisions. The model is formulated as a two-stage Mixed Integer Linear Program (MILP) and integrates a hybrid scenario generation method capable of representing uncertain inputs such as electricity prices, renewable generation, and internal demand. A convex objective function combining expected and worst cas…

Photon-induced production of the exotic charged charmonium-like state $Z_c(3900)$ off nuclear targets and its internal structure
E. Ya. Paryev
https://arxiv.org/abs/2506.00944

Photon-induced production of the exotic charged charmonium-like state $Z_c(3900)$ off nuclear targets and its internal structure
In this paper, we investigate the possibility to study the famous charged charmonium-like state $Z_c(3900)$ production off nuclear targets and its properties in inclusive photon-induced reactions near the kinematic threshold within the collision model based on the nuclear spectral function. The model accounts for its charged components $Z_c(3900)^{\pm}$ production in direct photon--nucleon interactions as well as three different popular scenarios for their internal structures: compact tetraquar…

This https://arxiv.org/abs/2503.07012 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Large $θ$ angle in two-dimensional large $N$ $\mathbb{CP}^{N-1}$ model
In confining large $N$ theories with a $θ$ angle such as four-dimensional $\mathrm{SU}(N)$ pure Yang-Mills theory, there are multiple metastable vacua and it makes sense to consider the parameter region of ``large $θ$ of order $N$'' despite the fact that $θ$ is a $2π$-periodic parameter. We investigate this parameter region in the two-dimensional $\mathbb{CP}^{N-1}$ model by computing the partition function on $T^2$. When $θ/N$ is of order $ \mathcal{O}(0.1) $ or less, we get perfectly sen…

This https://arxiv.org/abs/2505.21119 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Universal Value-Function Uncertainties
Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, and offline RL. While deep ensembles provide a robust method for quantifying value uncertainty, they come with significant computational overhead. Single-model methods, while computationally favorable, often rely on heuristics and typically require additional propagation mechanisms for myopic uncertainty estimates. In …

Saving or Destroying the Universe with Axion-Like Particles
Anne Mareike Galda, Matthias Neubert
https://arxiv.org/abs/2506.06426 https://

Saving or Destroying the Universe with Axion-Like Particles
Light pseudoscalar resonances that couple to the Standard Model via non-renormalizable operators, such as axions and axion-like particles (ALPs), generate contributions to the renormalization group evolution equations of couplings of dimension-4 and higher-dimensional operators. In particular, they modify the $β$-function of the Higgs quartic coupling and of SM and SMEFT parameters entering this equation, thus having an impact on the instability scale of the electroweak vacuum. We employ this …

This https://arxiv.org/abs/2503.14328 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Risk-Sensitive Model Predictive Control for Interaction-Aware Planning -- A Sequential Convexification Algorithm
This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigatin…

Predictive reinforcement learning based adaptive PID controller
Chaoqun Ma, Zhiyong Zhang
https://arxiv.org/abs/2506.08509 https://ar…

Predictive reinforcement learning based adaptive PID controller
Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support trai…

Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
Penghe Zhang, Naihua Xiu, Houduo Qi
https://arxiv.org/abs/2506.08374

Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
Indicator functions of taking values of zero or one are essential to numerous applications in machine learning and statistics. The corresponding primal optimization model has been researched in several recent works. However, its dual problem is a more challenging topic that has not been well addressed. One possible reason is that the Fenchel conjugate of any indicator function is finite only at the origin. This work aims to explore the dual optimization for the sum of a strongly convex function…