Tootfinder

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
Salman Habib, Remi Chou, Taejoon Kim
https://arxiv.org/abs/2508.15523 https://

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
We propose SPOF (Stabilization of Perturbed Loss Function), a differentially private training mechanism intended for multi-user local differential privacy (LDP). SPOF perturbs a stabilized Taylor expanded polynomial approximation of a model's training loss function, where each user's data is privatized by calibrated noise added to the coefficients of the polynomial. Unlike gradient-based mechanisms such as differentially private stochastic gradient descent (DP-SGD), SPOF does not require inject…

Extremum Seeking (ES) is Practically Stable Whenever Model-Based ES is Stable
Patrick McNamee, Zahra Nili Ahmadabadi, Mirslav Krsti\'c
https://arxiv.org/abs/2507.15713

Extremum Seeking (ES) is Practically Stable Whenever Model-Based ES is Stable
Extremum seeking control (ESC) are optimization algorithms in continuous time, with model-based ESCs using true derivative information of the cost function and model-free ESCs utilizing perturbation-based estimates instead. Stability analysis of model-free ESCs often employs the associated average system, whose stability is dependent on the selection of the dither signal. We demonstrate first the challenge of this analysis approach by showing selections of relative dither amplitudes and rates a…

Approximation Techniques for the Reconstruction of the Probability Measure and the Coupling Parameters in a Curie-Weiss Model for Large Populations
Miguel Ballesteros, Ivan Naumkin, Gabor Toth
https://arxiv.org/abs/2507.17073

Approximation Techniques for the Reconstruction of the Probability Measure and the Coupling Parameters in a Curie-Weiss Model for Large Populations
The Curie-Weiss model, originally used to study phase transitions in statistical mechanics, has been adapted to model phenomena in social sciences where many agents interact with each other. Reconstructing the probability measure of a Curie-Weiss model via the maximum likelihood method runs into the problem of computing the partition function which scales exponentially with the population. We study the estimation of the coupling parameters of a multi-group Curie-Weiss model using large populati…

Partition function for several Ising model interface structures
Alessio Squarcini, Piotr Nowakowski, Douglas B. Abraham, Anna Macio{\l}ek
https://arxiv.org/abs/2506.17170

Partition function for several Ising model interface structures
We employ a procedure that enables us to calculate the excess free energies for a finite Ising cylinder with domain walls analytically. This procedure transparently covers all possible configurations of the domain walls under given boundary conditions and allows for a physical interpretation in terms of coarse-grained quantities such as surface and point tensions. The resulting integrals contain all the information about finite-size effects; we extract them by careful asymptotic analysis using …

The Liouville model in the $L^1$ phase: coupling and extreme values
Michael Hofstetter, Ofer Zeitouni
https://arxiv.org/abs/2508.15689 https://arxiv.org/pd…

The Liouville model in the $L^1$ phase: coupling and extreme values
We establish a strong coupling between the Liouville model and the Gaussian free field on the two dimensional torus in the $L^1$ phase $β\in (0, 8π)$, such that the difference of the two fields is a Hölder continuous function. The coupling originates from a Polchinski renormalisation group approach, which was previously used to prove analogous results for other Euclidean field theories in dimension two. Our main observations for the Liouville model are that the Polchinski flow has a definite…

Bayesian Semiparametric Orthogonal Tucker Factorized Mixed Models for Multi-dimensional Longitudinal Functional Data
Arkaprava Roy, Abhra Sarkar
https://arxiv.org/abs/2506.16668

Bayesian Semiparametric Orthogonal Tucker Factorized Mixed Models for Multi-dimensional Longitudinal Functional Data
We introduce a novel longitudinal mixed model for analyzing complex multidimensional functional data, addressing challenges such as high-resolution, structural complexities, and computational demands. Our approach integrates dimension reduction techniques, including basis function representation and Tucker tensor decomposition, to model complex functional (e.g., spatial and temporal) variations, group differences, and individual heterogeneity while drastically reducing model dimensions. The mod…

A new 1D $V_p$ and $V_s$ velocity model of the western Rift of Corinth, Greece, using a fully non-linear tomography algorithm
Mark S. Noble (GEOSCIENCES), Alexandrine Gesret (GEOAZUR 7329), H\'el\`ene Lyon-Caen (GEOAZUR 7329), Anne Deschamps (GEOAZUR 7329)
https://arxiv.org/abs/2506.16222

A new 1D $V_p$ and $V_s$ velocity model of the western Rift of Corinth, Greece, using a fully non-linear tomography algorithm
The objective of this study is to propose a new updated accurate 1-D $V_p$ and $V_s$ velocity model of the western Rift of Corinth for precise absolute earthquake locations. The methodology used to obtain this new model associates two fully non-linear algorithms, a complete exploration of the $V_p$ and $V_s$ model space combined with a grid search method for earthquake locations to minimize the P and S arrival time residuals. We calculated the misfit function for approximately 2.10 6 velocity m…

Bayesian preference elicitation for decision support in multiobjective optimization
Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo
https://arxiv.org/abs/2507.16999

Bayesian preference elicitation for decision support in multiobjective optimization
We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesian model to estimate the decision-maker's utility function based on pairwise comparisons. Aided by this model, a principled elicitation strategy selects queries interactively to balance exploration and exploitation, guiding the discovery of high-utility solutions. The approach is flexible: it can be used interactively…

High to low temperature: $O(N)$ model at large $N$
Justin R. David, Srijan Kumar
https://arxiv.org/abs/2508.14872 https://arxiv.org/pdf/2508.14872

High to low temperature: $O(N)$ model at large $N$
We study the $O(N)$ vector model for scalars with quartic interaction at large $N$ on $S^1\times S^2$ without the singlet constraint. The non-trivial fixed point of the model is described by a thermal mass satisfying the gap equation at large $N$. We obtain the partition function and the energy density for the model as a series at low temperature in units of the radius of the sphere. We show these results agree with the Borel-Padé extrapolations of the high temperature expansions of the partit…

An nth-cousin mating model and the n-anacci numbers
Elisa Heinrich Mora, Noah A. Rosenberg
https://arxiv.org/abs/2506.16577 https://a…

An nth-cousin mating model and the n-anacci numbers
In seeking to understand the size of inbred pedigrees, J. Lachance (J. Theor. Biol. 261, 238-247, 2009) studied a population model in which, for a fixed value of $n$, each mating occurs between $n$th cousins. We explain a connection between the second-cousin case of the model ($n=2$) and the Fibonacci sequence, and more generally, between the $n$th-cousin case and the $n$-anacci sequence $(n \geq 2)$. For a model with $n$th-cousin mating $(n \geq 1)$, we obtain the generating function describin…

Simulating realistic Lyman-$\alpha$ emitting galaxies including the effect of radiative transfer
Hasti Khoraminezhad, Shun Saito, Max Gronke, Chris Byrohl
https://arxiv.org/abs/2507.16707

Simulating realistic Lyman-$α$ emitting galaxies including the effect of radiative transfer
We present an empirical yet physically motivated simulation of realistic Lyman-$α$ emitters (LAEs) at $z\sim2-3$, crucial for ongoing and forthcoming cosmological LAE surveys. We combine an empirical $\mathtt{UniverseMachine}$ galaxy-halo model with a simple spherical expanding shell model for the Lyman-$α$ radiative transfer, calibrating only three free parameters to simultaneously reproduce the observed Lyman-$α$ luminosity function and the angular clustering. Our LAE model is further supp…

Dynamical analysis of bulk viscous cosmological model in $F(T)$ gravity
Partha Sarathi Debnath, Chanchal Mondal, B. C. Paul
https://arxiv.org/abs/2507.15754

Dynamical analysis of bulk viscous cosmological model in $F(T)$ gravity
Bulk viscous cosmological models is presented in the teleparallel ($F(T)$, where $T$ denotes torsion) gravity. In the teleparallel gravity, the Lagrangian of the gravitational action contains a general function $F(T)= T+ f(T)=(1+ γ) T+α(-T)^n$, where $γ$, $n$ and $α$ are dimensional constants. Cosmological solutions in Eckart theory and Truncated Israel Stewart theory are talked about in $F(T)=(1+ γ) T+α(-T)^n$ gravity form which is one of the most generalized gravity form in the torsiona…

Non-Local Correlation Effects in DC and Optical Conductivity of the Hubbard Model
Nagamalleswararao Dasari, Hugo U. R. Strand, Martin Eckstein, Alexander I. Lichtenstein, Evgeny A. Stepanov
https://arxiv.org/abs/2507.16673

Non-Local Correlation Effects in DC and Optical Conductivity of the Hubbard Model
Conductivity is one of the most direct probes of electronic systems, yet its theoretical description remains challenging in the presence of strong non-local correlations. In this Letter, we analyze the conductivity of the half-filled single-band Hubbard model and identify the role of spatial correlations across the Mott transition. We show that in the correlated metallic regime, an accurate description of the conductivity requires not only the correct spectral function but also the inclusion of…

Ultra-Efficient Contracts: Breaking the Substitutes Barrier in Combinatorial Contracts
Michal Feldman, Liat Yashin
https://arxiv.org/abs/2506.18008 https:/…

Ultra-Efficient Contracts: Breaking the Substitutes Barrier in Combinatorial Contracts
We study the optimal contract problem in the framework of combinatorial contracts, introduced by Duetting et al. [FOCS'21], where a principal delegates the execution of a project to an agent, and the agent can choose any subset from a given set of costly actions. At the core of the model is a reward function - a monotone set function that maps each set of actions taken by the agent into an expected reward to the principal. To incentivize the agent, the principal offers a contract specifying the…

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
Chen Chen, Lijin Wang, Yanzhao Cao, Xupeng Cheng
https://arxiv.org/abs/2507.14467

Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network
In this paper we propose a novel neural network model for learning stochastic Hamiltonian systems (SHSs) from observational data, termed the stochastic generating function neural network (SGFNN). SGFNN preserves symplectic structure of the underlying stochastic Hamiltonian system and produces symplectic predictions. Our model utilizes the autoencoder framework to identify the randomness of the latent system by the encoder network, and detects the stochastic generating function of the system thr…

Reference-Free Iterative Learning Model Predictive Control with Neural Certificates
Wataru Hashimoto, Kazumune Hashimoto, Masako Kishida, Shigemasa Takai
https://arxiv.org/abs/2507.14025

Reference-Free Iterative Learning Model Predictive Control with Neural Certificates
In this paper, we propose a novel reference-free iterative learning model predictive control (MPC). In the proposed method, a certificate function based on the concept of Control Lyapunov Barrier Function (CLBF) is learned using data collected from past control executions and used to define the terminal set and cost in the MPC optimization problem at the current iteration. This scheme enables the progressive refinement of the MPC's terminal components over successive iterations. Unlike existing…

Jump Complexity of Deterministic Finite Automata with Translucent Letters
Szil\'ard Zsolt Fazekas, Victor Mitrana, Andrei P\u{a}un, Mihaela P\u{a}un
https://arxiv.org/abs/2506.18393

Jump Complexity of Deterministic Finite Automata with Translucent Letters
We investigate a dynamical complexity measure defined for finite automata with translucent letters (FAwtl). Roughly, this measure counts the minimal number of necessary jumps for such an automaton in order to accept an input. The model considered here is the deterministic finite automaton with translucent letters (DFAwtl). Unlike in the case of the nondeterministic variant, the function describing the jump complexity of any DFAwtl is either bounded by a constant or it is linear. We give a polyn…

Two-Person Cooperative Games with delta-Rationality
Fang-Fang Tang, Yongsheng Xu
https://arxiv.org/abs/2506.16465 https://arxiv.org/p…

Two-Person Cooperative Games with delta-Rationality
A player's payoff is modeled as consisting of two parts: a rational-value part and a distortion-value part. It is argued that the (total) payoff function should be used to explain and predict the behaviors of the players, while the rational value function should be used to conduct welfare analysis of the final outcome. We use the Nash demand game to illustrate our model.

Speed of sound peak in isospin QCD: a natural prediction of the Medium Separation Scheme
Bruno S. Lopes, Dyana C. Duarte, Ricardo L. S. Farias, Rudnei O. Ramos
https://arxiv.org/abs/2507.14343

Speed of sound peak in isospin QCD: a natural prediction of the Medium Separation Scheme
We present predictions for the zero-temperature equation of state at finite isospin density using the Nambu--Jona-Lasinio (NJL) model within the Medium Separation Scheme (MSS) -- a scheme that explicitly disentangles medium effects from the ultraviolet divergent vacuum terms. Recent lattice QCD results reveal a nonmonotonic speed of sound ($c_s^2$) as a function of isospin chemical potential ($μ_I$), exhibiting explicit violation of the conformal bound $c_s^2 = 1/3$. These findings have attrac…

Online Joint Replenishment Problem with Arbitrary Holding and Backlog Costs
Yossi Azar, Shahar Lewkowicz
https://arxiv.org/abs/2507.16096 https://

Online Joint Replenishment Problem with Arbitrary Holding and Backlog Costs
In their seminal paper Moseley, Niaparast, and Ravi introduced the Joint Replenishment Problem (JRP) with holding and backlog costs that models the trade-off between ordering costs, holding costs, and backlog costs in supply chain planning systems. Their model generalized the classical the make-to-order version as well make-to-stock version. For the case where holding costs function of all items are the same and all backlog costs are the same, they provide a constant competitive algorithm, leav…

Loss-Complexity Landscape and Model Structure Functions
Alexander Kolpakov
https://arxiv.org/abs/2507.13543 https://arxiv.org/pdf/250…

Loss-Complexity Landscape and Model Structure Functions
We develop a framework for dualizing the Kolmogorov structure function $h_x(α)$, which then allows using computable complexity proxies. We establish a mathematical analogy between information-theoretic constructs and statistical mechanics, introducing a suitable partition function and free energy functional. We explicitly prove the Legendre-Fenchel duality between the structure function and free energy, showing detailed balance of the Metropolis kernel, and interpret acceptance probabilities a…

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Nicholas Mohammad, Nicola Bezzo
https://arxiv.org/abs/2507.14700

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle avoidance by enabling precise and agile trajectory tracking, however, existing methods lack formal safety assurances. To address this issue, we propose a general Control Lyapunov Function (CLF) and Control Barrier Function (CBF) enabled MPCC framework that enforces safety constraints derived from a free-space corridor …

Suppression of blow-up in 3-D Keller-Segel system with fractional diffusion via Couette flow in whole space
Shijin Deng, Binbin Shi, Weike Wang, Yucheng Wang
https://arxiv.org/abs/2507.16160

Suppression of blow-up in 3-D Keller-Segel system with fractional diffusion via Couette flow in whole space
In this paper, we consider a Keller-Segel model with a fractional diffusion term in $\mathbb{R}^3$ in the background of a Couette flow. We show that when the background Couette flow is large enough, the dissipation enhancement induced could prevent the blow-up of solutions and thus prove the global existence and also obtain time decay rates of the solution in $L^p$ norm. The main tool of the proof is a corresponding Green's function and the key estimate is its $L^1$ estimate without singulariti…

Dark energy era with a resolution of Hubble tension in generalized entropic cosmology
Priyanka Adhikary, Sudipta Das, Sergei D. Odintsov, Tanmoy Paul
https://arxiv.org/abs/2507.15273

Dark energy era with a resolution of Hubble tension in generalized entropic cosmology
We propose a new dark energy (DE) model from four parameter generalized entropy function of apparent horizon in a spatially flat universe. Such kind of generalized entropy is able to generalize all the known entropies proposed so far, for suitable representations of the entropic parameters. It turns out that the scenario can describe the correct thermal history of the universe, with the sequence of matter and dark energy epochs. Comparing with the $Λ$CDM model, the proposed generalized entropi…

Bjork-DEbrowere algebras topologies and comparison of regularities
Anatole Khelif, Dimitris Scarpalezos
https://arxiv.org/abs/2506.17054 https://

Bjork-DEbrowere algebras topologies and comparison of regularities
In this paper we investigate properties of the family of weight functions and especiallyin the "weight function" model. We etablish in theintroduced algebra topological sharp structures analogous tothe ones introduced in Colombeau algebra.

Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization
Aijing Kong, Chengkai Xu, Xian Wu, Xinbo Chen, Peng Hang
https://arxiv.org/abs/2506.16311

Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization
To enhance the ability for vehicle platoons to respond to emergency scenarios, a platoon distribution reorganization decision-making framework is proposed. This framework contains platoon distribution layer, vehicle cooperative decision-making layer and vehicle planning and control layer. Firstly, a reinforcement-learning-based platoon distribution model is presented, where a risk potential field is established to quantitatively assess driving risks, and a reward function tailored to the platoo…

Dissociation of one-dimensional excitons by static electric field
Adriana Garc\'ia, Alexander L\'opez, Jorge Quereda, Francisco Dom\'inguez-Adame
https://arxiv.org/abs/2507.17337

Dissociation of one-dimensional excitons by static electric field
The quantum states of an electron-hole pair in one-dimensional semiconductors under a static electric field are theoretically analyzed using a two-band model with on-site Coulomb interaction. In the absence of static field, the electron and hole are always bound, forming an exciton regardless of the Coulomb interaction strength, in contrast to what occurs in higher-dimensional semiconductors. The static field modifies the wave function of the electron-hole pair, turning bound states into contin…

Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Yixin Chen, Ying Xiong, Shangyu Wu, Yufei Cui, Xue Liu, Nan Guan, Chun Jason Xue
https://arxiv.org/abs/2508.14323

Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Tool-augmented large language models (LLMs) leverage external functions to extend their capabilities, but inaccurate function calls can lead to inefficiencies and increased costs.Existing methods address this challenge by fine-tuning LLMs or using demonstration-based prompting, yet they often suffer from high training overhead and fail to account for inconsistent demonstration samples, which misguide the model's invocation behavior. In this paper, we trained a behavior-aligned retriever (BAR), …

Dissecting Bar-Induced Stellar Kinematics in Disk Galaxies: The Bisymmetric Model and Rotation Curve Modifications
Seungwon Baek, Woong-Tae Kim, Dajeong Jang, Taehyun Kim
https://arxiv.org/abs/2507.15204

Dissecting Bar-Induced Stellar Kinematics in Disk Galaxies: The Bisymmetric Model and Rotation Curve Modifications
We analyze bars formed in $N$-body simulations to investigate two key aspects of stellar kinematic structure of barred galaxies: the angular distributions of the radial and azimuthal components of stellar velocities, and the impact of bars on rotation curves. We find that stars on bar-supporting $x_1$-like orbits exhibit characteristic sawtooth-like radial velocity patterns and arch-like tangential velocity patterns as a function of azimuth. In contrast, stars on box and disk orbits show little…

Quantum Monte Carlo calculations in the nuclear shell model by the complex Langevin method
Yuhma Asano, Yuta Ito, Jun Nishimura, Noritaka Shimizu
https://arxiv.org/abs/2508.15116

Quantum Monte Carlo calculations in the nuclear shell model by the complex Langevin method
The nuclear shell model is known to describe the properties of various nuclei extremely well. However, the auxiliary-field quantum Monte Carlo calculations cannot be applied to it with general interactions due to the sign problem. The model has therefore been investigated primarily by variational methods, where the accuracy of the results depends crucially on the ansatz for the wave function. Here we perform the auxiliary-field quantum Monte Carlo calculations in the case of small systems at fi…

A time-adaptive optimization approach for reconstructing immune response in a mathematical model of acute HIV infection using clinical data
L. Beilina, I. Gainova, G. Bocharov
https://arxiv.org/abs/2508.13123

A time-adaptive optimization approach for reconstructing immune response in a mathematical model of acute HIV infection using clinical data
The paper proposes a time-adaptive optimization approach for determining the time-dependent immune response function in a mathematical model of acute HIV infection, using clinical data from four untreated patients. We formulate the problem as a parameter identification problem for an immune response system of ODE which includes novel component integrated into the third equation of the classical three-equation HIV model. Tikhonov's regularization method, Lagrangian approach, from which we deri…

On localization for the alloy-type Anderson-Bernoulli model with long-range hopping
Shihe Liu, Yunfeng Shi, Zhifei Zhang
https://arxiv.org/abs/2508.12714 https://

On localization for the alloy-type Anderson-Bernoulli model with long-range hopping
In this paper, we prove the Anderson localization near the spectral edge for some alloy-type Anderson-Bernoulli model on $\mathbb{Z}^d$ with exponential long-range hopping. This extends the work of Bourgain [Geometric Aspects of Functional Analysis, LNM 1850: 77--99, 2004], in which he pioneered a novel multi-scale analysis to treat Bernoulli random variables. Our proof is mainly based on Bourgain's method. However, to establish the initial scales Green's function estimates, we adapt the approa…

Functional Laplace Transform of a Multivariate Hawkes Process, Subsequent Characteristics, and Numerical Approximations
Bartholom\'e Vieille (MIA Paris-Saclay), Rachid Senoussi (BioSP), Samuel Soubeyrand
https://arxiv.org/abs/2507.15370

Functional Laplace Transform of a Multivariate Hawkes Process, Subsequent Characteristics, and Numerical Approximations
Numerous studies grounded on Hawkes processes have been carried out in many fields including finance, biology and social network. Hawkes processes form a class of selfexciting simple point processes. In this article, we consider a general class of multivariate Hawkes processes envisioned to model dynamics of spatio-temporal epidemics. For this class, the igniting baseline intensity is time dependent and the exciting matrix function is a general one, making the model non-Markovian in most of the…

Equivalence between the pair correlation functions of primes and of spins in a two-dimensional Ising model with randomly distributed competing interactions
Zhidong Zhang
https://arxiv.org/abs/2508.14938

Equivalence between the pair correlation functions of primes and of spins in a two-dimensional Ising model with randomly distributed competing interactions
In this work, we prove the equivalence between the pair correlation functions of primes, and of spins in a two-dimensional (2D) Ising model with a mixture of ferromagnetic and randomly distributed competing interactions. At first, we prove that the correlation function between a pair of spins in a distance l within the 2D Ising model is larger than zero at whole temperature region. Second, we prove that the pair correlation function of spins in the model is equivalent to the pair correlation fu…

Simulating from marginal structural models for hazards, cause-specific hazards and subdistribution hazards using general copulas
Shaun R Seaman
https://arxiv.org/abs/2508.15145 …

Simulating from marginal structural models for hazards, cause-specific hazards and subdistribution hazards using general copulas
Seaman and Keogh (Biometrical Journal 2024) proposed a method for simulating data compatible with a marginal structural model (MSM) for the hazard of a survival time outcome. In this short report, I propose two extensions of this method. First, Seaman and Keogh favoured the use of a Gaussian copula, because this enables the function of the confounder history through which the hazard of failure depends on confounders to be interpreted as a risk score. Here, I describe how this interpretation can…

On zeros and algorithms for disordered systems: mean-field spin glasses
Ferenc Bencs, Kuikui Liu, Guus Regts
https://arxiv.org/abs/2507.15616 https://

On zeros and algorithms for disordered systems: mean-field spin glasses
Spin glasses are fundamental probability distributions at the core of statistical physics, the theory of average-case computational complexity, and modern high-dimensional statistical inference. In the mean-field setting, we design deterministic quasipolynomial-time algorithms for estimating the partition function to arbitrarily high accuracy for nearly all inverse temperatures in the second moment regime. In particular, for the Sherrington--Kirkpatrick model, our algorithms succeed for almost …

Exact solution of asymmetric gelation between three walks on the square lattice
Aleksander L Owczarek, Andrew Rechnitzer
https://arxiv.org/abs/2507.17111 https://

Exact solution of asymmetric gelation between three walks on the square lattice
We find and analyse the exact solution of a model of three different polymers with asymmetric contact interactions in two dimensions, modelling a scenario where there are different types of polymers involved. In particular, we find the generating function of three directed osculating walks in star configurations on the square lattice with two interaction Boltzmann weights, so that there is one type of contact interaction between the top pair of walks and a different interaction betw…

Range of topological fluctuations in the three-flavor linear sigma model
G. Fejos, A. Patkos
https://arxiv.org/abs/2507.13581 https://

Range of topological fluctuations in the three-flavor linear sigma model
The topological charge density two-point function is computed in a three-flavor effective linear meson model, including contributions from topological sectors with arbitrary charge. A corresponding composite field is introduced via a Hubbard-Stratonovich transformation. In the $η$-$η$' sector, the model yields very accurate spectroscopy with negligible mixing with the composite field. The resulting topological susceptibility allows for the extraction of a surprisingly large absolute scale for…

Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models
Jian He, Sven Karbach, Asma Khedher
https://arxiv.org/abs/2508.14813 https://

Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models
We study the pricing of European-style options written on forward contracts within function-valued infinite-dimensional affine stochastic volatility models. The dynamics of the underlying forward price curves are modeled within the Heath-Jarrow-Morton-Musiela framework as solution to a stochastic partial differential equation modulated by a stochastic volatility process. We analyze two classes of affine stochastic volatility models: (i) a Gaussian model governed by a finite-rank Wishart process…

An effective physics-informed neural operator framework for predicting wavefields
Xiao Ma, Tariq Alkhalifah
https://arxiv.org/abs/2507.16431 https://

An effective physics-informed neural operator framework for predicting wavefields
Solving the wave equation is fundamental for geophysical applications. However, numerical solutions of the Helmholtz equation face significant computational and memory challenges. Therefore, we introduce a physics-informed convolutional neural operator (PICNO) to solve the Helmholtz equation efficiently. The PICNO takes both the background wavefield corresponding to a homogeneous medium and the velocity model as input function space, generating the scattered wavefield as the output function spa…

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…

Boundary behavior of functions in the Schur-Agler class of the polydisc
Jim Agler, Connor Evans, Zinaida Lykova, N. J. Young
https://arxiv.org/abs/2508.13742 https://

Boundary behavior of functions in the Schur-Agler class of the polydisc
We describe a generalization of the notion of a Hilbert space model of a function $φ$ in the Schur-Agler class of the polydisc. This generalization is well adapted to the investigation of boundary behavior of $φ$ at a mild singularity $τ$ on the $d$-torus. We prove the existence of a generalized model with an enhanced continuity property at such a singularity $τ$. We use this result to prove the directional differentiability of a function $φ$ in the Schur-Agler class at a singular point on…

Understanding Data Influence with Differential Approximation
Haoru Tan, Sitong Wu, Xiuzhe Wu, Wang Wang, Bo Zhao, Zeke Xie, Gui-Song Xia, Xiaojuan Qi
https://arxiv.org/abs/2508.14648

Understanding Data Influence with Differential Approximation
Data plays a pivotal role in the groundbreaking advancements in artificial intelligence. The quantitative analysis of data significantly contributes to model training, enhancing both the efficiency and quality of data utilization. However, existing data analysis tools often lag in accuracy. For instance, many of these tools even assume that the loss function of neural networks is convex. These limitations make it challenging to implement current methods effectively. In this paper, we introduce …

Gaussian Noise Model of Nonlinear Distortions from Semiconductor Optical Amplifiers
Hartmut Hafermann
https://arxiv.org/abs/2507.11517 https://

Gaussian Noise Model of Nonlinear Distortions from Semiconductor Optical Amplifiers
A Gaussian Noise Model of the nonlinear noise power spectral density is developed for a semiconductor optical amplifier as described by the Agrawal model. A simple closed-form expression is obtained for the nonlinear noise-to-signal ratio of broadband wavelength-division multiplexed signals as a function of the Agrawal model parameters, the amplifier output power and the transmission bandwidth. The accuracy of the closed-form expression and its region of validity is assessed in numerical simula…

(Exhaustive) Symbolic Regression and model selection by minimum description length
Harry Desmond
https://arxiv.org/abs/2507.13033 https://

(Exhaustive) Symbolic Regression and model selection by minimum description length
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the…

Interaction-induced nematic Dirac semimetal from quadratic band touching: A constrained-path quantum Monte Carlo study
Zi Hong Liu, Hongyu Lu, Zi Yang Meng, Lukas Janssen
https://arxiv.org/abs/2507.15668

Interaction-induced nematic Dirac semimetal from quadratic band touching: A constrained-path quantum Monte Carlo study
Electronic systems with quadratic band touchings, commonly found in two- and three-dimensional materials such as Bernal-stacked bilayer graphene, kagome metals, HgTe, and pyrochlore iridates, have attracted significant interest concerning the role of interactions in shaping their electronic properties. However, even in the simplest model of spinless fermions on a two-dimensional checkerboard lattice, the quantum phase diagram as a function of nearest-neighbor interaction remains under debate. W…

Quantum Capacitance and Electronic Properties of a Hexagonal Boron Nitride based FET Gas Sensor
Saumen Acharjee
https://arxiv.org/abs/2507.15011 https://…

Quantum Capacitance and Electronic Properties of a Hexagonal Boron Nitride based FET Gas Sensor
We present a comprehensive theoretical investigation of gas sensing in monolayer hexagonal boron nitride (h-BN) based field-effect transistors (FET) using the non-equilibrium Green function formalism and Landauer-Büttiker approach. Moving beyond conventional density functional theory analyses, our framework captures the full device level response by incorporating field-dependent quantum transport and temperature effects. We model the impact of NO, H$_2$S, HF and CO$_2$ gases on the band struct…

PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
Pei Liu, Terry Zhuo, Jiawei Deng, Zhenchang Xing, Qinghua Lu, Xiaoning Du, Hongyu Zhan
https://arxiv.org/abs/2508.11179

PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
The rapid emergence of pretrained models (PTMs) has attracted significant attention from both Deep Learning (DL) researchers and downstream application developers. However, selecting appropriate PTMs remains challenging because existing methods typically rely on keyword-based searches in which the keywords are often derived directly from function descriptions. This often fails to fully capture user intent and makes it difficult to identify suitable models when developers also consider factors s…

Landmark Detection for Medical Images using a General-purpose Segmentation Model
Ekaterina Stansfield, Jennifer A. Mitterer, Abdulrahman Altahhan
https://arxiv.org/abs/2507.11551 …

Landmark Detection for Medical Images using a General-purpose Segmentation Model
Radiographic images are a cornerstone of medical diagnostics in orthopaedics, with anatomical landmark detection serving as a crucial intermediate step for information extraction. General-purpose foundational segmentation models, such as SAM (Segment Anything Model), do not support landmark segmentation out of the box and require prompts to function. However, in medical imaging, the prompts for landmarks are highly specific. Since SAM has not been trained to recognize such landmarks, it cannot …

A new parametric observational study of $f(Q,B)$ gravity with modified chaplygin gas
Amit Samaddar, S. Surendra Singh
https://arxiv.org/abs/2507.14251 http…

A new parametric observational study of $f(Q,B)$ gravity with modified chaplygin gas
In this work, we explore the cosmological dynamics of a modified gravity framework based on the function $f(Q,B)=δQ^{2}+βB$, where $Q$ denotes the nonmetricity scalar and $B$ is the boundary term that relates $Q$ to the Ricci scalar. The matter sector is modeled using the Modified Chaplygin Gas (MCG) with the equation of state $p=Aρ-\frac{B}{ρ^α}$, allowing the model to interpolate between early-time matter behavior and late-time cosmic acceleration. By deriving an analytical expression fo…

LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan, Burak Kantarci, Emil Janulewicz
https://arxiv.org/abs/2507.10903

LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Effective management of Service Function Chains (SFCs) and optimal Virtual Network Function (VNF) placement are critical challenges in modern Software-Defined Networking (SDN) and Network Function Virtualization (NFV) environments. Although Deep Reinforcement Learning (DRL) is widely adopted for dynamic network decision-making, its inherent dependency on structured data and fixed action rules often limits adaptability and responsiveness, particularly under unpredictable network conditions. This…

Stress Softening Damage in Strongly Nonlinear Viscoelastic Soft Materials A Physics Informed Data Driven Constitutive Model with Time Temperature Coupling
Alireza Ostadrahimi, Amir Teimouri, Kshitiz Upadhyay, Guoqiang Li
https://arxiv.org/abs/2507.12694

Stress Softening Damage in Strongly Nonlinear Viscoelastic Soft Materials A Physics Informed Data Driven Constitutive Model with Time Temperature Coupling
This study presents a novel physics informed, data-driven modeling framework for capturing the strongly nonlinear thermo-viscoelastic behavior of soft materials exhibiting stress softening, with emphasis on the Mullins effect. Unlike previous approaches limited to quasi-static or isothermal conditions, our model unifies rate dependence, temperature sensitivity, large strain cyclic loading, and evolving damage mechanisms. Thermodynamic admissibility is ensured via a custom loss function that emb…

Twisted Dirac operators and fractional correlations of the massless sine-Gordon model at the free fermion point
Roland Bauerschmidt, Scott Mason, Christian Webb
https://arxiv.org/abs/2508.14806

Twisted Dirac operators and fractional correlations of the massless sine-Gordon model at the free fermion point
For the massless sine-Gordon model at the free fermion point, in infinite volume, we define the fractional (charge or vertex operator) correlation functions from the probabilistic path integral and prove that they are given by renormalized determinants of massive twisted Dirac operators. The fractional correlation functions are the moments of the imaginary multiplicative chaos, a random generalized function that we construct with respect to the infinite-volume massless sine-Gordon measure. The …

Stellar Mass-Dispersion Measure Correlations Constrain Baryonic Feedback in Fast Radio Burst Host Galaxies
Calvin Leung, Sunil Simha, Isabel Medlock, Daisuke Nagai, Kiyoshi W. Masui, Lordrick A. Kahinga, Adam E. Lanman, Shion Andrew, Kevin Bandura, Alice P. Curtin, B. M. Gaensler, Nina Gusinskaia, Ronniy C. Joseph, Mattias Lazda, Lluis Mas-Ribas, Bradley W. Meyers, Kenzie Nimmo, Aaron B. Pearlman, J. Xavier Prochaska, Mawson W. Sammons, Kaitlyn Shin, Kendrick Smith, Haochen Wang

Stellar Mass-Dispersion Measure Correlations Constrain Baryonic Feedback in Fast Radio Burst Host Galaxies
Low redshift fast radio bursts (FRBs) provide robust measurements of the host-galaxy contribution to the dispersion measure (DM), which can constrain the circumgalactic medium (CGM) of the hosts. We curate a sample of 20 nearby FRBs with low scattering timescales and face-on host galaxies with stellar masses ranging from $10^9 < M^* / M_\odot < 10^{11}$. We fit the distribution of the host galaxy DM to a quadratic model as a function of stellar mass with a mass-independent scatter and find that…

Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
Xingyu Liu, Yubin Li, Guozhang Chen
https://arxiv.org/abs/2506.11062

Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, t…

A continuous-time model to interpolate between speed and function value restart in accelerated first order methods
Juan Jos\'e Maul\'en, Huiyuan Guo, Juan Peypouquet
https://arxiv.org/abs/2506.11267

A continuous-time model to interpolate between speed and function value restart in accelerated first order methods
We introduce a new restarting scheme for a continuous inertial dynamics with Hessian driven-damping, and establish a linear convergence rate for the function values along the restarted trajectories. The proposed routine is a generalization of existing speed restart schemes, and interpolates between speed and function value restarts, considerably delaying the restarting time, while preserving the function value decrease. Numerical experiments show an improvement in the convergence rates for both…

Robin Green Function Estimates and a Model of Mammalian Lungs
Guy David, Stefano Decio, Max Engelstein, Marcel Filoche, Svitlana Mayboroda, Marco Michetti
https://arxiv.org/abs/2507.13168

Robin Green Function Estimates and a Model of Mammalian Lungs
The present paper establishes delicate properties of the Green function with Robin boundary conditions, in particular, elucidating the nature of the passage between the Dirichlet-like and Neumann-like behavior. This yields sharp quantifiable bounds on the corresponding harmonic measure and proves the phase transition in the behavior of the total flow earlier conjectured in physics literature in concert with the efficacy of mammalian lungs.

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Bogdan Burlacu
https://arxiv.org/abs/2508.13859 https://arxiv.org/pdf/2508.13859

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Symbolic regression encompasses a family of search algorithms that aim to discover the best fitting function for a set of data without requiring an a priori specification of the model structure. The most successful and commonly used technique for symbolic regression is Genetic Programming (GP), an evolutionary search method that evolves a population of mathematical expressions through the mechanism of natural selection. In this work we analyze the efficiency of the evolutionary search in GP and…

Predicting function of evolutionarily implausible DNA sequences
Shiyu Jiang, Xuyin Liu, Zitong Jerry Wang
https://arxiv.org/abs/2506.10271 https://

Predicting function of evolutionarily implausible DNA sequences
Genomic language models (gLMs) show potential for generating novel, functional DNA sequences for synthetic biology, but doing so requires them to learn not just evolutionary plausibility, but also sequence-to-function relationships. We introduce a set of prediction tasks called Nullsettes, which assesses a model's ability to predict loss-of-function mutations created by translocating key control elements in synthetic expression cassettes. Across 12 state-of-the-art models, we find that mutation…

Learning Iterated Function Systems from Time Series of Partial Observations
Emilia Gibson, Jeroen S. W. Lamb
https://arxiv.org/abs/2508.13794 https://arxiv…

Learning Iterated Function Systems from Time Series of Partial Observations
We develop a methodology to learn finitely generated random iterated function systems from time-series of partial observations using delay embeddings. We obtain a minimal model representation for the observed dynamics, using a hidden variable representation, that is diffeomorphic to the original system.

Quantum tunneling in the time periodic double well potential
Vladimir Dubinko, Oleksandr Mazmanishvili
https://arxiv.org/abs/2507.06045 https://

Quantum tunneling in the time periodic double well potential
We present the mathematical model and numerical calculation results for the tunneling of the wave function in a time-periodic double-well potential. The bi-quadratic potential of a double-well form is used. Based on a mathematical model of the time evolution of the wave function, a numerical algorithm has been developed and a program has been created for solving the Schrödinger equation, which describes the time evolution of the particle's wave function. As a result of numerical modeling, modu…

Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
Peter L. Taylor
https://arxiv.org/abs/2506.09128 https://

Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
I show how to compute the nonlinear power spectrum across the entire $w(z)$ dynamical dark energy model space. Using synthetic $Λ$CDM data, I train a neural ordinary differential equation (ODE) to infer the evolution of the nonlinear matter power spectrum as a function of the background expansion and mean matter density across $\sim$$9 {\rm \ Gyr}$ of cosmic evolution. After training, the model generalises to {\it any} dynamical dark energy model parameterised by $w(z)$. With little optimisati…

Relationship between a $\Phi^4$ matrix model and harmonic oscillator systems
Harald Grosse, Naoyuki Kanomata, Akifumi Sako, Raimar Wulkenhaar
https://arxiv.org/abs/2507.09454

Relationship between a $Φ^4$ matrix model and harmonic oscillator systems
A Hermitian $Φ^4$ matrix model with a Kontsevich-type kinetic term is studied. It was recently discovered that the partition function of this matrix model satisfies the Schrödinger equation of the $N$-body harmonic oscillator, and that eigenstates of the Virasoro operators can be derived from this partition function. We extend these results and obtain an explicit formula for such eigenstates in terms of the free energy. Furthermore, the Schrödinger equation for the $N$-body harmonic oscillat…

With the 5th #AAS246 presser come the paper https://arxiv.org/abs/2506.07262 (ALMA-JELLY I: High Resolution CO(2-1) Observations of Ongoing Ram Pressure Stripping in NGC 4858 Reveal Asymmetrical Gas Tail Formation and Fallback), the press release https://news.arizona.edu/news/belly-beast-massive-clumps-reveal-star-factories-bygone-era-cosmos (In the belly of the beast: massive clumps reveal star factories from a bygone era of the cosmos) and the papers https://iopscience.iop.org/article/10.3847/1538-4357/ada8a6 (UNCOVERing the Faint End of the z ∼ 7 [O iii] Luminosity Function with JWST's F410M Medium Bandpass Filter) with the press release https://science.nasa.gov/missions/webb/nasas-webb-uncovers-galaxy-population-driving-cosmic-renovation/ and https://arxiv.org/abs/2505.06198 (The "Dark-Matter Dominated" Galaxy Segue 1 Modeled with a Black Hole and no Dark Halo) with the press release https://www.utsa.edu/today/2025/06/story/researchers-offer-alternative-to-dark-matter-model.html

Algorithm for computing the partition function of the Potts model for SP-graphs
Sofya Mukhamedzhanova, Bulat Sabirov, Amir Mukhamedzhanov
https://arxiv.org/abs/2507.22579 https:…

Algorithm for computing the partition function of the Potts model for SP-graphs
The q-state Potts model is a fundamental framework in statistical physics and graph theory, with its partition function encoding rich information about spin configurations. The multivariate Tutte polynomial (known as the partition function of the Potts model) can be defined on an arbitrary finite graph $G$ and encodes a lot of important combinatorial information about the graph. As a special case, it contains the familiar Tutte polynomial with two variables and, consequently, its specialization…

Towards Practical Data-Dependent Memory-Hard Functions with Optimal Sustained Space Trade-offs in the Parallel Random Oracle Model
Jeremiah Blocki, Blake Holman
https://arxiv.org/abs/2508.06795

Towards Practical Data-Dependent Memory-Hard Functions with Optimal Sustained Space Trade-offs in the Parallel Random Oracle Model
Memory-Hard Functions (MHF) are a useful cryptographic primitive to build egalitarian proofs-of-work and to help protect low entropy secrets (e.g., user passwords) against brute-forces attacks. Ideally, we would like for a MHF to have the property that (1) an honest party can evaluate the function in sequential time $Ω(N)$, and (2) any parallel party that evaluates the function is forced to lockup $Ω(N)$ memory for $Ω(N)$ sequential steps. Unfortunately, this goal is not quite achievable, so…

Gray matter abnormalities in sight deprivation and sight restoration https://link.springer.com/article/10.1007/s00429-025-02994-6 "white matter may potentially serve as a biomarker of structural plasticity following sight restoration"; brain plasticity

Gray matter abnormalities in sight deprivation and sight restoration - Brain Structure and Function
Blindness provides a unique model for investigating brain plasticity in response to sensory deprivation. While structural changes in both gray and white matter have been widely documented, particularly in cases of early or congenital visual deprivation, gray matter studies have traditionally focused on cortical thickness, often finding cortical thickening in posterior regions. However, other aspects of gray matter integrity, such as cortical myelin content, remain underexplored. In this study, …

Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment
Yitaek Kim, Christoffer Sloth
https://arxiv.org/abs/2506.14249

Robust Adaptive Time-Varying Control Barrier Function with Application to Robotic Surface Treatment
Set invariance techniques such as control barrier functions (CBFs) can be used to enforce time-varying constraints such as keeping a safe distance from dynamic objects. However, existing methods for enforcing time-varying constraints often overlook model uncertainties. To address this issue, this paper proposes a CBFs-based robust adaptive controller design endowing time-varying constraints while considering parametric uncertainty and additive disturbances. To this end, we first leverage Robust…

Probabilistic algorithm for computing all local minimizers of Morse functions on a compact domain
Mohab Safey El Din (PolSys), Georgy Scholten (LJLL), Emmanuel Tr\'elat (LJLL)
https://arxiv.org/abs/2507.13071

Probabilistic algorithm for computing all local minimizers of Morse functions on a compact domain
Let K be the unit-cube in Rn and f\,: K $\rightarrow$ R^n be a Morse function. We assume that the function f is given by an evaluation program $Γ$ in the noisy model, i.e., the evaluation program $Γ$ takes an extra parameter $η$ as input and returns an approximation that is $η$-close to the true value of f . In this article, we design an algorithm able to compute all local minimizers of f on K . Our algorithm takes as input $Γ$, $η$, a numerical accuracy parameter $ε$ as well as some ext…

A multiple occupancy cell fluid model with competing attraction and repulsion interactions
R. V. Romanik, O. A. Dobush, M. P. Kozlovskii, I. V. Pylyuk, M. A. Shpot
https://arxiv.org/abs/2508.12613

A multiple occupancy cell fluid model with competing attraction and repulsion interactions
An analytically solvable cell fluid model with unrestricted cell occupancy, infinite-range Curie-Weiss-type attraction and short-range intra-cell repulsion is studied within the grand-canonical ensemble. Building on an exact single-integral representation of the grand partition function, we apply Laplace's method to obtain asymptotically exact expressions for the pressure, density and equation of state. The model exhibits a hierarchy of first-order transitions, each terminating at a critical po…

Superconductivity and inhomogeneous charge-ordered state in the two-Dimensional Hubbard model -- Off-Diagonal Wave Function Monte Carlo Studies of Hubbard Model IV
Takashi Yanagisawa
https://arxiv.org/abs/2506.12736

Superconductivity and inhomogeneous charge-ordered state in the two-Dimensional Hubbard model -- Off-Diagonal Wave Function Monte Carlo Studies of Hubbard Model IV
We investigate the ground-state properties of the two-dimensional Hubbard model, based on the off-diagonal wave function variational Monte Carlo method. We use an optimized wave function that is improved from an initial one-body wave function by multiplying by multiple correlation operators that are given by the form of exp($-S$)-type where $S$ is a suitable operator. We examine the inhomogeneous ground state at near 1/8 doping in the strongly correlated region where the on-site Coulomb interac…

Statistical inference of heterogeneous treatment effects using semiparametric single-index model
Jichang Yu, Wenjing Chang, Peichao Yu, Lijun Chen, Yuanshan Wu
https://arxiv.org/abs/2507.13594

Statistical inference of heterogeneous treatment effects using semiparametric single-index model
In recent years, with the rapid development of science and technology, heterogeneous treatment effects have emerged as a focal research topic in statistics, econometrics, and sociology. This paper investigates HTE through semiparametric single-index models based on doubly robust estimation. Departing from conventional approaches, we neither impose boundedness constraints on the link function in single-index models nor restrict its support range. By employing the sieve method to approximate the …

A Stable and Principled Loss Function for Direct Language Model Alignment
Yuandong Tan
https://arxiv.org/abs/2508.07137 https://arxiv.org/pdf/2508.07137

A Stable and Principled Loss Function for Direct Language Model Alignment
The alignment of large language models (LLMs) with human preferences is commonly achieved through Reinforcement Learning from Human Feedback (RLHF). Direct Preference Optimization (DPO) simplified this paradigm by establishing a direct mapping between the optimal policy and a reward function, eliminating the need for an explicit reward model. However, we argue that the DPO loss function is theoretically misaligned with its own derivation, as it promotes the indefinite maximization of a logits d…

Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing
Burak Aydin, Emre Parmaksiz, Ronnie Sircar
https://arxiv.org/abs/2506.13088

Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing
We study the dynamic pricing of discrete goods over a finite selling horizon. One way to capture both the elastic and stochastic reaction of purchases to price is through a model where sellers control the intensity of a counting process, representing the number of sales thus far. The intensity describes the probabilistic likelihood of a sale, and is a decreasing function of the price a seller sets. A classical model for ticket pricing, which assumes a single seller and infinite time horizon, is…

Generalized Quasispecies Model with Time Delays and Periodic Fluctuations in Replication
Nolbert Morales, Edward A. Turner
https://arxiv.org/abs/2508.12424 https://

Generalized Quasispecies Model with Time Delays and Periodic Fluctuations in Replication
In this research, we present a generalized quasispecies model in which population growth is governed by an arbitrary nonlinear function incorporating time delays. We begin by demonstrating that, under the constant population constraint, the dynamics of the system with time delays remain confined to the invariant manifold for both forward and backward time evolution. Furthermore, we establish that in this modified quasispecies model, defined on a single-peak fitness landscape, in the presence of…

Hell or High Water: Evaluating Agentic Recovery from External Failures
Andrew Wang, Sophia Hager, Adi Asija, Daniel Khashabi, Nicholas Andrews
https://arxiv.org/abs/2508.11027 h…

Hell or High Water: Evaluating Agentic Recovery from External Failures
As language model agents are applied to real world problems of increasing complexity, they will be expected to formulate plans across large search spaces. If those plans fail for reasons beyond their control, how well do language agents search for alternative ways to achieve their goals? We devise a specialized agentic planning benchmark to study this question. Each planning problem is solved via combinations of function calls. The agent searches for relevant functions from a set of over four t…

Adelic Models of Percolation
Matilde Marcolli
https://arxiv.org/abs/2508.07601 https://arxiv.org/pdf/2508.07601

Adelic Models of Percolation
Models of long range percolations on lattices and on hierarchical lattices are related through the use of three intermediate geometries: a 1-parameter deformation based on the power mean function, relating lattice percolation to a percolation model governed by the toric volume form; the adelic product formula for a function field, relating the hierarchical lattice model to an adelic percolation model; and the adelic product formula for number fields that relates the toric percolation model on t…

ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs
Peng Ding
https://arxiv.org/abs/2507.10593 https://

ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs
Large Language Model (LLM) applications are increasingly relying on external tools to extend their capabilities beyond text generation. However, current tool integration approaches suffer from fragmentation, protocol limitations, and implementation complexity, leading to substantial development overhead. This paper presents Toolregistry, a protocol-agnostic tool management library that simplifies tool registration, representation, execution, and lifecycle management via a unified interface. Our…

Comparison of three random field sampling methods for high-resolution Bayesian inversion with application to a plane stress problem
Pieter Vanmechelen, Geert Lombaert, Giovanni Samaey
https://arxiv.org/abs/2508.12876

Comparison of three random field sampling methods for high-resolution Bayesian inversion with application to a plane stress problem
Bayesian inversion aims to provide uncertainty quantification of posterior estimates conditional on observations. When the inferred parameter is a continuous spatial function, one common strategy is to model it as a random field. Several random field sampling strategies exist. In this article, we investigate three different approaches in terms of numerical efficiency and influence on the posterior distribution. These approaches are based on (i) Karhunen-Loeve and (ii) wavelet expansions, and (i…

High-Dimensional Model Averaging via Cross-Validation
Zhengyan Wan, Fang Fang, Binyan Jiang
https://arxiv.org/abs/2506.08451 https://…

High-Dimensional Model Averaging via Cross-Validation
Model averaging is an important alternative to model selection with attractive prediction accuracy. However, its application to high-dimensional data remains under-explored. We propose a high-dimensional model averaging method via cross-validation under a general framework and systematically establish its theoretical properties. Each candidate model is fitted using a flexible loss function paired with a general regularizer, and the optimal weights are determined by minimizing a cross-validation…

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…

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
Denisa Martonov\'a, Alain Goriely, Ellen Kuhl
https://arxiv.org/abs/2508.12063

Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
The major challenge in determining a hyperelastic model for a given material is the choice of invariants and the selection how the strain energy function depends functionally on these invariants. Here we introduce a new data-driven framework that simultaneously discovers appropriate invariants and constitutive models for isotropic incompressible hyperelastic materials. Our approach identifies both the most suitable invariants in a class of generalized invariants and the corresponding strain ene…

This https://arxiv.org/abs/2505.22772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Calibrated Value-Aware Model Learning with Stochastic Environment Models
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcement learning. The MuZero loss, which penalizes a model's value function prediction compared to the ground-truth value function, has been utilized in several prominent empirical works in the literature. However, theoretical investigation into its strengths and weaknesses is limited. In this paper, we analyze the family of value-aware model learning losses, w…

Value Function Approximation for Nonlinear MPC: Learning a Terminal Cost Function with a Descent Property
T. M. J. T. Baltussen, C. A. Orrico, A. Katriniok, W. P. M. H. Heemels, D. Krishnamoorthy
https://arxiv.org/abs/2508.05804

Value Function Approximation for Nonlinear MPC: Learning a Terminal Cost Function with a Descent Property
We present a novel method to synthesize a terminal cost function for a nonlinear model predictive controller (MPC) through value function approximation using supervised learning. Existing methods enforce a descent property on the terminal cost function by construction, thereby restricting the class of terminal cost functions, which in turn can limit the performance and applicability of the MPC. We present a method to approximate the true cost-to-go with a general function approximator that is c…

Enhancing Wildlife Density Estimation: A New Two-Parameter Detection Function for Line Transect Sampling
Midhat M. Edous, Omar M. Eidous
https://arxiv.org/abs/2507.10572

Enhancing Wildlife Density Estimation: A New Two-Parameter Detection Function for Line Transect Sampling
Accurate estimation of wildlife density is vital for effective ecological monitoring, conservation, and management. Line transect sampling, a central technique in distance sampling, relies on selecting an appropriate detection function to model the probability of detecting individuals as a function of their distance from the transect line. In this study, we propose a novel two-parameter detection function that extends the flexibility of traditional models such as the half-normal and exponential…

CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
Kejun Li, Zachary Olkin, Yisong Yue, Aaron D. Ames
https://arxiv.org/abs/2508.09354 https://

CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly shaped objectives. In this work, we propose a structured reward shaping framework that leverages model-based trajectory generation and control Lyapunov functions (CLFs) to guide policy learning. We explore two model-based planners for generating reference trajectories: a reduced-order linear inverted pendulum (LIP) mode…

Dynamical Phase Diagram of the REM under independent spin-flips
Chokri Manai, Simone Warzel
https://arxiv.org/abs/2506.15353 https://…

Dynamical Phase Diagram of the REM under independent spin-flips
We study the energy landscape of the Random Energy model (REM) integrated along trajectories of the simple random walk on the hypercube. We show that the quenched cumulant generating function of the time integral of the REM energy undergoes phase transitions in the large $N$ limit for trajectories of any time extent, and identify phases distinguished by the activity and value of the time integral. This is achieved by relating the dynamical behavior to the spectral properties of Hamiltonians ass…

Partition function of 2D Coulomb gases with radially symmetric potentials and a hard wall
Matthias Allard, Peter J. Forrester, Sampad Lahiry, Bojian Shen
https://arxiv.org/abs/2506.14738

Partition function of 2D Coulomb gases with radially symmetric potentials and a hard wall
The large $N$ asymptotic expansion of the partition function for the normal matrix model is predicted to have special features inherited from its interpretation as a two-dimensional Coulomb gas. However for the latter, it is most natural to include a hard wall at the boundary of the droplet. We probe how this affects the asymptotic expansion in the solvable case that the potential is radially symmetric and the droplet is a disk or an annulus. We allow too for the hard wall to be strictly inside…

Effects of CPT violation on the neutrino charge radius in the Standard Model Extension
Yuridia Flores Orea, J. Jes\'us Toscano Ch\'avez
https://arxiv.org/abs/2508.09400 …

Effects of CPT violation on the neutrino charge radius in the Standard Model Extension
CPT-odd effects on the neutrino charge radius are studied within the Standard Model Extension. We consider CPT violation from the electroweak Yang-Mills sector, characterized by Lorentz-violating coefficients $(k_1)_μ$ and $(k_2)_μ$, which have positive mass units. The $\barννγ$ vertex arises at tree level via exchange of two $Z$ bosons. Although suppressed by $\frac{1}{m_Z^4}$, this process is notable. The vertex function $Γ_μ$ includes three independent gauge structures satisfying the …

Real-time bubble nucleation and growth for false vacuum decay on the lattice
Daan Maertens, Jutho Haegeman, Karel Van Acoleyen
https://arxiv.org/abs/2508.13645 https://

Real-time bubble nucleation and growth for false vacuum decay on the lattice
We revisit quantum false vacuum decay for the one-dimensional Ising model, focusing on the real-time nucleation and growth of true vacuum bubbles. Via matrix product state simulations, we demonstrate that for a wide range of parameters, the full time-dependent quantum state is well described by a Gaussian ansatz in terms of domain wall operators, with the associated vacuum bubble wave function evolving according to the linearized time-dependent variational principle. The emerging picture shows …

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…

Supercritical phase of the random connection model
Mathew D. Penrose
https://arxiv.org/abs/2508.11562 https://arxiv.org/pdf/2508.11562

Supercritical phase of the random connection model
Given $d \in {\bf N}, λ>0$, the random connection model in a region $A \subseteq {\bf R}^d$ is a graph with vertex set given by a homogeneous Poisson point process of intensity $λ$ in $A$, with an edge placed between each pair $x,y$ of vertices with probability $ϕ(\|x-y\|)$, where $ϕ: {\bf R}_+ \to [0,1]$ is a nonincreasing finite-range connection function. We show that if $d \geq 3$ and $λ$ is strictly supercritical for $A = {\bf R}^d$, then the model remains supercritical if it…

MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
Chen Cai, Ernesto Dickel Saraiva, Ya-jun Pan, Steven Liu
https://arxiv.org/abs/2507.11211

MPC-based Coarse-to-Fine Motion Planning for Robotic Object Transportation in Cluttered Environments
This letter presents a novel coarse-to-fine motion planning framework for robotic manipulation in cluttered, unmodeled environments. The system integrates a dual-camera perception setup with a B-spline-based model predictive control (MPC) scheme. Initially, the planner generates feasible global trajectories from partial and uncertain observations. As new visual data are incrementally fused, both the environment model and motion planning are progressively refined. A vision-based cost function pr…

Hidden diffeos in the Hamiltonian formulation of a background independent field theory
J. Fernando Barbero G., Bogar D\'iaz, Juan Margalef-Bentabol, Eduardo J. S. Villase\~nor
https://arxiv.org/abs/2507.12184

Hidden diffeos in the Hamiltonian formulation of a background independent field theory
We analyze from a geometric perspective the Hamiltonian formulation of a recent modification of the Husain-Kuchař model where, while preserving the connection as a dynamical variable, the other field is restricted to be the exterior covariant derivative of a Lie algebra-valued function. We prove that 3-dimensional diffeomorphisms can be accommodated among the local gauge transformations of the model in addition to the internal gauge symmetries.

ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht, Melanie N. Zeilinger, Johannes A. Stork
https://arxiv.org/abs/2507.13088

ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. This not only affects the control performance, but also increases the difficulty of designing MPC cost functions that reflect the desired long-term objective. This paper proposes ZipMPC, a method that imitates a long-horizon MPC behaviour by learning a compressed and context-dependent cost function for a short-horizon MP…

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...

A Durrett-Remenik particle system in $\mathbb{R}^d$
Rami Atar
https://arxiv.org/abs/2507.13990 https://arxiv.org/pdf/2507.13990

A Durrett-Remenik particle system in $\mathbb{R}^d$
This paper studies a branching-selection model of motionless particles in $\mathbb{R}^d$, with nonlocal branching, introduced by Durrett and Remenik in dimension $1$. The assumptions on the fitness function, $F$, and on the inhomogeneous branching distribution, are mild. The evolution equation for the macroscopic density is given by an integro-differential free boundary problem in $\mathbb{R}^d$, in which the free boundary represents the least $F$-value in the population. The main result is the…

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa
https://arxiv.org/abs/2507.11718

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Consider the univariate nonparametric regression model with additive Gaussian noise and the representation of the unknown regression function in terms of a wavelet basis. We propose a shrinkage rule to estimate the wavelet coefficients obtained by mixing a point mass function at zero with the Epanechnikov distribution as a prior for the coefficients. The proposed rule proved to be suitable for application in scenarios with low signal-to-noise ratio datasets and outperformed standard and Bayesia…

Virtual Trading in Multi-Settlement Electricity Markets
Agostino Capponi, Garud Iyengar, Bo Yang, Daniel Bienstock
https://arxiv.org/abs/2508.11979 https://

Virtual Trading in Multi-Settlement Electricity Markets
In the Day-Ahead (DA) market, suppliers sell and load-serving entities (LSEs) purchase energy commitments, with both sides adjusting for imbalances between contracted and actual deliveries in the Real-Time (RT) market. We develop a supply function equilibrium model to study how virtual trading-speculating on DA-RT price spreads without physical delivery-affects market efficiency. Without virtual trading, LSEs underbid relative to actual demand in the DA market, pushing DA prices below expected …

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Zihan Wang, Nina Mahmoudian
https://arxiv.org/abs/2508.09971 https://

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Vision-driven autonomous river following by Unmanned Aerial Vehicles is critical for applications such as rescue, surveillance, and environmental monitoring, particularly in dense riverine environments where GPS signals are unreliable. We formalize river following as a coverage control problem in which the reward function is submodular, yielding diminishing returns as more unique river segments are visited, thereby framing the task as a Submodular Markov Decision Process. First, we introduce Ma…