Tootfinder

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies
Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Ionut Anghel
https://arxiv.org/abs/2506.06325

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies
This paper proposes a decentralized model of energy cooperation between microgrids, in which decisions are made locally, at the level of the microgrid community. Each microgrid is modeled as an autonomous agent that adopts a Hawk or Dove strategy, depending on the level of energy stored in the battery and its role in the energy trading process. The interactions between selling and buying microgrids are modeled through an evolutionary algorithm. An individual in the algorithm population is repre…

"Ideal" Topological Heavy Fermion Model in Two-dimensional Moir\'e Heterostructures with Type-II Band Alignment
Yunzhe Liu, Anoj Aryal, Dumitru Calugaru, Zhenyao Fang, Kaijie Yang, Haoyu Hu, Qimin Yan, B. Andrei Bernevig, Chao-xing Liu
https://arxiv.org/abs/2507.06168

"Ideal" Topological Heavy Fermion Model in Two-dimensional Moiré Heterostructures with Type-II Band Alignment
Topological flat bands play an essential role in inducing exotic interacting physics, ranging from fractional Chern insulators to superconductivity, in moiré materials. When topological flat bands possess concentrated quantum geometry, a topological heavy fermion (THF) model was proposed as the starting point to describe the interacting moiré physics. In this work, we propose a design principle for realizing "ideal" THF model, which can host an exact flat band with "ideal quantum geometry", n…

The role of the initial distribution in population survival within a bounded habitat
Rafael de la Rosa, Elena Medina
https://arxiv.org/abs/2509.06179 https://

The role of the initial distribution in population survival within a bounded habitat
In this paper, we analyze the role of initial conditions in population persistence. Specifically, we consider the reaction-diffusion equation $u_t\,=\,D\,(u^{ν-1}\,u_x)_x\,+\,a\,u^μ$, with $μ,ν>0$, accompanied by hostile boundary conditions and examine two families of one-parametric initial distributions, including homogeneous distributions. The model was previously studied by Colombo and Anteneodo (2018). They determined appropriate habitat sizes $l$ for the survival of a population, whose…

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Xintong Hu, Yixuan Chen, Rui Yang, Wenxiang Guo, Changhao Pan
https://arxiv.org/abs/2507.06116

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Automatic speech quality assessment plays a crucial role in the development of speech synthesis systems, but existing models exhibit significant performance variations across different granularity levels of prediction tasks. This paper proposes an enhanced MOS prediction system based on self-supervised learning speech models, incorporating a Mixture of Experts (MoE) classification head and utilizing synthetic data from multiple commercial generation models for data augmentation. Our method buil…

MMOC: Self-Supervised EEG Emotion Recognition Framework with Multi-Model Online Collaboration
Hanqi Wang, Yang Liu, Peng Ye, Liang Song
https://arxiv.org/abs/2507.03977

MMOC: Self-Supervised EEG Emotion Recognition Framework with Multi-Model Online Collaboration
Electroencephalography (EEG) emotion recognition plays a crucial role in human-computer interaction, particularly in healthcare and neuroscience. While supervised learning has been widely used, its reliance on manual annotations introduces high costs and potential bias. Self-supervised learning (SSL) offers a promising alternative by generating labels through pretext tasks. However, high inter-subject variability in EEG signals leads to significant data drift, limiting self-supervised models' g…

SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Alexander Scarlatos, Nigel Fernandez, Christopher Ormerod, Susan Lottridge, Andrew Lan
https://arxiv.org/abs/2507.05129

SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Item (question) difficulties play a crucial role in educational assessments, enabling accurate and efficient assessment of student abilities and personalization to maximize learning outcomes. Traditionally, estimating item difficulties can be costly, requiring real students to respond to items, followed by fitting an item response theory (IRT) model to get item difficulty estimates. This approach cannot be applied to the cold-start setting for previously unseen items either. In this work, we pr…

Influence of Large Mean Delay on Distributed Delay Differential Equations Dynamics: Application to a Neural Mass Model
Isam Al-Darabsah, Sue Ann Campbell, Bootan Rahman
https://arxiv.org/abs/2509.05466

Influence of Large Mean Delay on Distributed Delay Differential Equations Dynamics: Application to a Neural Mass Model
Delay differential equations (DDEs) with large delays play a pivotal role in understanding stability and bifurcations in systems ranging from neural networks to laser dynamics. While prior work has extensively studied DDEs with discrete delays, the impact of distributed delays has been less explored. This paper investigates the spectrum of linear DDEs with a uniformly distributed delay kernel, with mean delay $τ_m$ and a half-width of $ρ$. When $τ_m\to\infty$, we carry out asymptotic analysi…

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
Franco Galante, Chiara Ravazzi, Luca Vassio, Michele Garetto, Emilio Leonardi
https://arxiv.org/abs/2507.03448

Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A…

Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives
Sarah H. Cen, Salil Goyal, Zaynah Javed, Ananya Karthik, Percy Liang, Daniel E. Ho
https://arxiv.org/abs/2509.05627

Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives
AI audits play a critical role in AI accountability and safety. One branch of the law for which AI audits are particularly salient is anti-discrimination law. Several areas of anti-discrimination law implicate the "less discriminatory alternative" (LDA) requirement, in which a protocol (e.g., model) is defensible if no less discriminatory protocol that achieves comparable performance can be found with a reasonable amount of effort. Notably, the burden of proving an LDA exists typically falls on…

Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
Yushang Zhao, Yike Peng, Li Zhang, Qianyi Sun, Zhihui Zhang, Yingying Zhuang
https://arxiv.org/abs/2509.04751

Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
With the rapid expansion of user bases on short video platforms, personalized recommendation systems are playing an increasingly critical role in enhancing user experience and optimizing content distribution. Traditional interest modeling methods often rely on unimodal data, such as click logs or text labels, which limits their ability to fully capture user preferences in a complex multimodal content environment. To address this challenge, this paper proposes a multimodal foundation model-based…

V-Associated Production & Vector Boson Fusion as an LHC Signature of CP Violation
Rodrigo Capucha, \'Alvaro Lozano-Onrubia, Luca Merlo, Jos\'e Miguel No, Rui Santos
https://arxiv.org/abs/2507.05942

V-Associated Production & Vector Boson Fusion as an LHC Signature of CP Violation
We investigate the role of vector boson fusion (VBF) and associated production with an electroweak boson (V-AP) of beyond-the-Standard-Model Higgses at the LHC in probing CP violation in extended Higgs sectors. Resonant production of a new Higgs boson through V-AP/VBF subsequently decaying into a Z boson and a 125 GeV Higgs boson h would be a robust sign of CP violation, since h has been measured to be (predominantly) a CP-even state. After identifying this and other sets of signatures which re…

“Extensive bidirectional charging functions are also available for the new BMW iX3, meaning that it can be used as an energy supply source for a variety of applications. The Vehicle-to-Load (V2L) function turns the new BMW iX3 into a mobile powerbank, which can supply electric devices with power while on the move. Vehicle-to-Home (V2H) hands it the role of storage unit for solar energy generated by home-fitted photovoltaic systems. [1/2]

The start of a new era. The new BMW iX3.
BMW presents the first series-produced Neue Klasse model. The new-generation BMW iX3 is a fully electric Sports Activity Vehicle (SAV) that gives customers their first on-the-road taste of a whole new era of Sheer Driving Pleasure. An all-new design language gives the car a character-rich appearance that faithfully conveys the technological progress made by the new model. With sixth-generation BMW eDrive technology, a range of up to 805 kilometres (500 miles) and a peak charging rate of 400 kW,…

A Thermodynamic Model for Thermomigration in Metals
Daniel J. Long, Edmund Tarleton, Alan C. F. Cocks, Felix Hofmann
https://arxiv.org/abs/2508.05327 https://

A Thermodynamic Model for Thermomigration in Metals
We investigate the mechanisms involved in the thermomigration of interstitial hydrogen in metals. Using statistical thermodynamics, we develop a comprehensive mechanistic model to capture the controlling effects. Crucially, through validation against published experimental data, our results demonstrate that an electron-wind effect plays a significant role, particularly for materials in which the thermomigration direction matches the heat flux. These findings provide new insights into the factor…

Self-organized first-order transition from foreshock to mainshock in earthquake sequences induced by heat, fluid pressure, and porosity
Takehito Suzuki, Hiroshi Matsukawa
https://arxiv.org/abs/2507.05546

Self-organized first-order transition from foreshock to mainshock in earthquake sequences induced by heat, fluid pressure, and porosity
Earthquake cycles are studied by taking into account the interactions among slip, fluid pressure, temperature, and porosity on the fault planes, which are known to play a crucial role in earthquake dynamics. The spring-block model with a single block is employed. A first-order transition from foreshock to mainshock occurring spontaneously in earthquake sequences is discovered both analytically and numerically. This transition is induced by these interactions. It is shown that the function of th…

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Shahana Yasmin Chowdhury, Bithi Banik, Md Tamjidul Hoque, Shreya Banerjee
https://arxiv.org/abs/2507.07046

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for …

Entanglement switching via mobility edges in a quasiperiodic chain
YouYoung Joung, Junmo Jeon, SungBin Lee
https://arxiv.org/abs/2507.06305 https://…

Entanglement switching via mobility edges in a quasiperiodic chain
We propose quasiperiodic chains with tunable mobility edge physics, as a promising platform for engineering long-range quantum entanglement. Using the generalized Aubry-André model, we show that the mobility edges play a key role in manipulating long-range indirect interactions in these systems. Near the mobility edge, critical states exhibit unexpectedly strong correlations between sites that share similar local structures, regardless of their spatial separation. Remarkably, by tuning the mob…

Nonreciprocal Model B: The role of mobilities and nonreciprocal interfacial forces
Bibhut Sahoo, Rituparno Mandal, Peter Sollich
https://arxiv.org/abs/2508.02814 https://…

Nonreciprocal Model B: The role of mobilities and nonreciprocal interfacial forces
We study a non-reciprocal version of Model B, as the continuum theory for non-reciprocal particle mixtures. In contrast to non-reciprocal Cahn-Hilliard models, it is important in this context to consider the dependence of mobility coefficients on the local concentrations. We show that a homogeneous state that is linearly stable for one form of the mobility can be unstable for a different form of mobility, an effect that would be impossible in equilibrium and implies a crucial role for mobilitie…

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
Yang Liu, Chaoyu Yuan
https://arxiv.org/abs/2509.04315 https://arxiv.org/pdf/2509…

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
Uplift models play a critical role in modern marketing applications to help understand the incremental benefits of interventions and identify optimal targeting strategies. A variety of techniques exist for building uplift models, and it is essential to understand the model differences in the context of intended applications. The uplift curve is a widely adopted tool for assessing uplift model performance on the selection universe when observations are available for the entire population. Howeve…

Role of non-extensivity $q$-parameter in tectonic seismic forecasting
Oscar Sotolongo-Costa, Miguel Eduardo Mora-Ramos
https://arxiv.org/abs/2508.04932 https://

Role of non-extensivity $q$-parameter in tectonic seismic forecasting
By writing total Tsallis entropy as a function of non-extensivity q-parameter withing the fragment-asperity model for earthquakes, a critical range of values is identified: 1.4

Not that America should hold us up as a role model, of course.
England turned 250 in the second half of the 12th century (depending on how you define "England" as a country), when kings and barons were romping around slaughtering each other and the population with gay abandon.
Great Britain turned 250 in 1957, at which point we'd only just finally got past post-WW2 rationing and still possibly clung on to some delusions of being an Empire.
And the UK has another 26 years to go before we get to have a 250th birthday. Maybe we'll be doing good by then (but probably not).

Tribrid Inflation, Type II Leptogenesis, and Observable Gravitational Waves in $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$
Waqas Ahmed, Saleh O. Allehabi, Farishta Israr, Mansoor Ur Rehman
https://arxiv.org/abs/2507.05564

Tribrid Inflation, Type II Leptogenesis, and Observable Gravitational Waves in $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$
We present a concrete realization of tribrid inflation within the framework of the gauge group $SU(3)_c \times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$. In this model, inflation is driven by the neutral components of left-handed Higgs triplets, which also play a central role in generating the observed baryon asymmetry of the universe via non-thermal leptogenesis. Tiny neutrino masses arise naturally through a type-II seesaw mechanism, facilitated by right-handed triplet fields. Supergravity co…

Forgive and Forget? An Industry 5.0 Approach to Trust-Fatigue Co-regulation in Human-Cobot Order Picking
Soumyadeep Dhar
https://arxiv.org/abs/2508.03765 https://

Forgive and Forget? An Industry 5.0 Approach to Trust-Fatigue Co-regulation in Human-Cobot Order Picking
This paper investigates the critical role of trust and fatigue in human-cobot collaborative order picking, framing the challenge within the scope of Logistics 5.0 -- the implementation of human-robot symbiosis in smart logistics. We propose a dynamic, leader-follower Stackelberg game to model this interaction, where utility functions explicitly account for human fatigue and trust. Through agent-based simulations, we demonstrate that while a naive model leads to a "trust death spiral," a refined…

FUTransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation
Akwasi Asare, Mary Sagoe, Justice Williams Asare
https://arxiv.org/abs/2508.03758

FUTransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentation
Automated segmentation of diabetic foot ulcers (DFUs) plays a critical role in clinical diagnosis, therapeutic planning, and longitudinal wound monitoring. However, this task remains challenging due to the heterogeneous appearance, irregular morphology, and complex backgrounds associated with ulcer regions in clinical photographs. Traditional convolutional neural networks (CNNs), such as U-Net, provide strong localization capabilities but struggle to model long-range spatial dependencies due to…

Experimental demonstration of the clock asynchrony model in space-borne gravitational wave detection
Ming-Yang Xua, Yu-Jie Tan, Ning Ma, Ao-Ting Fang, Yi-Jun Xia, Cheng-Gang Shao
https://arxiv.org/abs/2507.02286

Experimental demonstration of the clock asynchrony model in space-borne gravitational wave detection
Space-borne gravitational wave detection will open the observation window in the 0.1 mHz$-$1 Hz bandwidth, playing a crucial role in the development of cosmology and physics. Precise clock synchronization among satellites is essential for the accurate detection of gravitational wave signals. However, the independent clock counting mechanisms of each satellite pose a significant challenge. This work reports the mathematical model of clock asynchrony, which is mainly dominated by the constant ter…

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield, Yuriy Brun
https://arxiv.org/abs/2508.00140

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five sta…

Constraining $\Lambda$CDM, $\omega$CDM, and $\omega_0\omega_a$CDM models with DESI DR2 BAO: Redshift-Resolved Diagnostics and the Role of $r_d$
Seokcheon Lee
https://arxiv.org/abs/2507.01380

Constraining $Λ$CDM, $ω$CDM, and $ω_0ω_a$CDM models with DESI DR2 BAO: Redshift-Resolved Diagnostics and the Role of $r_d$
We present a detailed comparison of $Λ$CDM, $ω$CDM, and $ω_0ω_a$CDM cosmologies using the latest redshift-resolved BAO measurements from DESI DR2. By separating $r_d$-dependent and $r_d$-independent observables and applying both fixed and model-derived sound horizon values, we test the consistency of each model across 24 observables spanning redshift 0.2 to 2.3. Our results show that the $ω$CDM model is statistically favored over $Λ$CDM, with lower $χ^2$, AIC, and BIC values and reduced…

Comparing Cluster-Based Cross-Validation Strategies for Machine Learning Model Evaluation
Afonso Martini Spezia, Mariana Recamonde-Mendoza
https://arxiv.org/abs/2507.22299 https…

Comparing Cluster-Based Cross-Validation Strategies for Machine Learning Model Evaluation
Cross-validation plays a fundamental role in Machine Learning, enabling robust evaluation of model performance and preventing overestimation on training and validation data. However, one of its drawbacks is the potential to create data subsets (folds) that do not adequately represent the diversity of the original dataset, which can lead to biased performance estimates. The objective of this work is to deepen the investigation of cluster-based cross-validation strategies by analyzing the perform…

Analysing contrarian behaviour using nonlinear biased $q$-voter model
Amit Pradhan, Pratik Mullick, Parongama Sen
https://arxiv.org/abs/2509.01982 https://…

Analysing contrarian behaviour using nonlinear biased $q$-voter model
We investigate the role of contrarians in a recently proposed weighted-influence variant of the $q$-voter model. In this framework, non-unanimous influence groups affect the focal agent through weighted contributions governed by a bias parameter $p$. We extend this setting by introducing a fraction $α$ ($α> 0$) of contrarians, defined as agents who systematically oppose the prevailing influence irrespective of whether the group is unanimous or divided. Analytical mean-field calculations and M…

Flavour Physics Beyond the Standard Model
Wolfgang Altmannshofer, Peter Stangl
https://arxiv.org/abs/2508.03950 https://arxiv.org/pdf/2508.03950

Flavour Physics Beyond the Standard Model
Flavour physics plays a central role in the search for physics beyond the Standard Model, posing fundamental questions whose answers may point to new physics scales far above the electroweak scale. The flavour structure of the Standard Model is strikingly hierarchical, hinting at underlying organizing principles yet to be uncovered. At the same time, flavour-changing processes rank among the most sensitive probes of new physics. In this chapter, we review how precision measurements of flavour-v…

Interpolation in model theory
Jouko V\"a\"an\"anen
https://arxiv.org/abs/2507.19097 https://arxiv.org/pdf/2507.19097

Interpolation in model theory
We bring an abstract model theory perspective to interpolation. We ask, what is the role of interpolation in the study of extensions of first order logic, such as infinitary logics, generalized quantifiers and higher order logics? The abstract model theory approach reveals the basic connections between various interpolation properties in isolation, on their own, as well as with respect to other model theoretic properties, such as compactness.

Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model
Joseph Jackson, Georgiy Lapin, Jeremy E. Thompson
https://arxiv.org/abs/2509.03832 https://

Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model
Social media echo chambers play a central role in the spread of misinformation, yet existing models often overlook the influence of individual confirmation bias. An existing model of echo chambers is the "gravity well" model, which creates an analog between echo chambers and spatial gravity wells. We extend this established model by introducing a dynamic confirmation bias variable that adjusts the strength of pull based on a user's susceptibility to belief-reinforcing content. This variable is …

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Muhammed Shuaibi, Daniel S. Levine, Kyle Michel, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Keian Noori, Lafe J. Purvis, Tingling Rao, Brandon M. Wood, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom, Zachary W. Ulissi, Anuroop Sriram

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
Crystal Structure Prediction (CSP) of molecular crystals plays a central role in applications, such as pharmaceuticals and organic electronics. CSP is challenging and computationally expensive due to the need to explore a large search space with sufficient accuracy to capture energy differences of a few kJ/mol between polymorphs. Dispersion-inclusive density functional theory (DFT) provides the required accuracy but its computational cost is impractical for a large number of putative structures…

Towards a classification of topological defects in $K3$ sigma models
Roberta Angius, Stefano Giaccari
https://arxiv.org/abs/2508.03612 https://arxiv.org/pd…

Towards a classification of topological defects in $K3$ sigma models
Given a $K3$ surface, a supersymmetric non-linear K3 sigma model is the internal superconformal field theory (SCFT) in a six dimensional compactification of type IIA superstring on $\mathbb{R}^{1,5} \times K3$. These models have attracted attention due to the discovery of Mathieu moonshine phenomena for the elliptic genera of K3 surfaces, and have played a pivotal role in extending Mukai's theorem on classification of symplectic automorphisms of $K3$ surfaces. We report on recent progress (arXi…

Dam Management in the Era of Climate Change
Cristina Di Girolami, M'hamed Mrad, Ga\"igi, Vathana Ly Vath, Simone Scotti
https://arxiv.org/abs/2508.02636 https://

Dam Management in the Era of Climate Change
Climate change has a dramatic impact, particularly by concentrating rainfall into a few short periods, interspersed by long dry spells. In this context, the role of dams is crucial. We consider the optimal control of a dam, where the water level must not exceed a designated safety threshold, nor fall below a minimum level to ensure functionality and sustainability for for the outgoing river. To model dry spells and intense rainfall events, commonly referred to as water bombs, we introduce a Haw…

Nested importance sampling for Bayesian inference: error bounds and the role of dimension
Fabi\'an Gonz\'alez, V\'ictor Elvira, Joaqu\'in Miguez
https://arxiv.org/abs/2507.04163

Nested importance sampling for Bayesian inference: error bounds and the role of dimension
Many Bayesian inference problems involve high dimensional models for which only a subset of the model variables are actual estimation targets. All other variables are just nuisance variables that one would ideally like to integrate out analytically. Unfortunately, such integration is often impossible. However, there are several computational methods that have been proposed over the past 15 years that replace intractable analytical marginalisation by numerical integration, typically using differ…

Agentic AI in Product Management: A Co-Evolutionary Model
Nishant A. Parikh
https://arxiv.org/abs/2507.01069 https://arxiv.org/pdf/25…

Agentic AI in Product Management: A Co-Evolutionary Model
This study explores agentic AI's transformative role in product management, proposing a conceptual co-evolutionary framework to guide its integration across the product lifecycle. Agentic AI, characterized by autonomy, goal-driven behavior, and multi-agent collaboration, redefines product managers (PMs) as orchestrators of socio-technical ecosystems. Using systems theory, co-evolutionary theory, and human-AI interaction theory, the framework maps agentic AI capabilities in discovery, scoping, b…

Ab Initio Study of $^7$Li with Coupled $^6$Li $n$ and $^6$He $p$ Mass Partitions
Jakub Herko (TRIUMF, Lawrence Livermore National Laboratory), Konstantinos Kravvaris (Lawrence Livermore National Laboratory), Petr Navr\'atil (TRIUMF, University of Victoria), Sofia Quaglioni (Lawrence Livermore National Laboratory), Guillaume Hupin (Universit\'e Paris-Saclay, CNRS/IN2P3, IJCLab), Mark A. Caprio (Department of Physics and Astronomy, University of Notre Dame)

Ab Initio Study of $^7$Li with Coupled $^6$Li + $n$ and $^6$He + $p$ Mass Partitions
Background: Lithium plays an important role in nuclear astrophysics, fusion energy generation, and nuclear technology. From a theoretical point of view, the nucleus $^7$Li presents a remarkable challenge, as its bound states and resonances can be understood as being formed by a $^4$He and $^3$H pair, or simultaneously, a single neutron/proton coupled to a $^6$Li/$^6$He core. In light of this complexity, a consistent description of $^7$Li bound-state and continuum properties in a unified model p…

A semi-analytical solution for the lubrication force between two spheres approaching in viscoelastic fluids described by the Oldroyd-B model under small Deborah numbers
Alan Rosales-Romero, Adolfo V\'azquez-Quesada, Marco Ellero, J. Esteban L\'opez-Aguilar
https://arxiv.org/abs/2507.02266…

A semi-analytical solution for the lubrication force between two spheres approaching in viscoelastic fluids described by the Oldroyd-B model under small Deborah numbers
Viscoelastic fluids play a critical role in various engineering and biological applications, where their lubrication properties are strongly influenced by relaxation times ranging from microseconds to minutes. Although the lubrication mechanism for Newtonian fluids is well-established, its extension to viscoelastic materials - particularly under squeezing flow conditions - remains less explored. This study presents a semi-analytical solution for the lubrication force between two spheres approac…

Planning Persuasive Trajectories Based on a Leader-Follower Game Model
Chaozhe R. He, Yichen Dong, Nan Li
https://arxiv.org/abs/2507.22022 https://arxiv.or…

Planning Persuasive Trajectories Based on a Leader-Follower Game Model
We propose a framework that enables autonomous vehicles (AVs) to proactively shape the intentions and behaviors of interacting human drivers. The framework employs a leader-follower game model with an adaptive role mechanism to predict human interaction intentions and behaviors. It then utilizes a branch model predictive control (MPC) algorithm to plan the AV trajectory, persuading the human to adopt the desired intention. The proposed framework is demonstrated in an intersection scenario. Simu…

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
Ranyang Zhou, Abeer Matar A. Almalky, Gamana Aragonda, Sabbir Ahmed, Filip Roth Tr{\o}nnes-Christensen, Adnan Siraj Rakin, Shaahin Angizi
https://arxiv.org/abs/2507.02206 …

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
True Random Number Generators (TRNGs) play a fundamental role in hardware security, cryptographic systems, and data protection. In the context of Deep NeuralNetworks (DNNs), safeguarding model parameters, particularly weights, is critical to ensure the integrity, privacy, and intel-lectual property of AI systems. While software-based pseudo-random number generators are widely used, they lack the unpredictability and resilience offered by hardware-based TRNGs. In this work, we propose a novel an…

The role of visual experience in haptic spatial perception: evidence from early blind, late blind, and sighted individuals #haptics

The role of visual experience in haptic spatial perception: evidence from early blind, late blind, and sighted individuals - Biology of Sex Differences
There is contradictory evidence on the effect that visual experience has on haptic abilities. Indeed, some studies have documented that a lack of vision (blindness) results in decreased haptic perception, whereas other studies report an enhanced haptic ability in blind individuals. To examine the role of vision in haptic spatial processing, we recruited early blind, late blind, and sighted participants. Each participant completed a haptic task in which they explored a two-piece LEGO model for e…

Role-Aware Language Models for Secure and Contextualized Access Control in Organizations
Saeed Almheiri, Yerulan Kongrat, Adrian Santosh, Ruslan Tasmukhanov, Josemaria Vera, Muhammad Dehan Al Kautsar, Fajri Koto
https://arxiv.org/abs/2507.23465

Role-Aware Language Models for Secure and Contextualized Access Control in Organizations
As large language models (LLMs) are increasingly deployed in enterprise settings, controlling model behavior based on user roles becomes an essential requirement. Existing safety methods typically assume uniform access and focus on preventing harmful or toxic outputs, without addressing role-specific access constraints. In this work, we investigate whether LLMs can be fine-tuned to generate responses that reflect the access privileges associated with different organizational roles. We explore t…

The model fitting of Gaia DR3 Classical Cepheid light and radial velocity curves
R. Molinaro, M. Marconi, G. De Somma, V. Ripepi, S. Leccia, I. Musella, T. Sicignano, E. Trentin, M. Gatto
https://arxiv.org/abs/2506.22635

The model fitting of Gaia DR3 Classical Cepheid light and radial velocity curves
Classical Cepheids are key astrophysical laboratories for studying stellar structure and evolution, and for calibrating the cosmic distance scale. Despite major progress, uncertainties remain regarding their masses, luminosities, distances, and the role of processes like core overshooting, rotation, and mass loss. The high-precision data from ESA Gaia's third data release offer an opportunity to address these issues. This study aims to estimate the structural parameters and distances of a sampl…

The Magnetic Ground State of Atacamite Cu$_2$Cl(OH)$_3$: The Crucial Role of Frustrated Zigzag Chains Revealed by Inelastic Neutron Scattering
J. L. Allen, L. Heinze, R. A. Mole, S. S\"ullow, O. Janson, S. Nishimoto, R. A. Lewis, K. C. Rule
https://arxiv.org/abs/2508.02201

The Magnetic Ground State of Atacamite Cu$_2$Cl(OH)$_3$: The Crucial Role of Frustrated Zigzag Chains Revealed by Inelastic Neutron Scattering
We report inelastic neutron scattering (INS) measurements on the magnetically frustrated $S=\frac12$ sawtooth-chain compound atacamite Cu$_2$Cl(OH)$_3$ featuring inequivalent Cu(1) and Cu(2) sites. Transverse to the sawtooth chains, INS reveals two dispersive spin-wave modes and a gap of at least 0.75 meV. This behavior is rationalized within a zigzag-chain model of Cu(2) spins in an effective magnetic field of Cu(1) spins. The model is compatible with first-principles calculations and accounts…

Pok\'eAI: A Goal-Generating, Battle-Optimizing Multi-agent System for Pokemon Red
Zihao Liu, Xinhang Sui, Yueran Song, Siwen Wang
https://arxiv.org/abs/2506.23689

PokéAI: A Goal-Generating, Battle-Optimizing Multi-agent System for Pokemon Red
We introduce PokéAI, the first text-based, multi-agent large language model (LLM) framework designed to autonomously play and progress through Pokémon Red. Our system consists of three specialized agents-Planning, Execution, and Critique-each with its own memory bank, role, and skill set. The Planning Agent functions as the central brain, generating tasks to progress through the game. These tasks are then delegated to the Execution Agent, which carries them out within the game environment. Up…

When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery
Shirong Xu, Jingnan Zhang, Junhui Wang
https://arxiv.org/abs/2507.01613

When Less Is More: Binary Feedback Can Outperform Ordinal Comparisons in Ranking Recovery
Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We propose a general parametric framework for modeling ordinal paired comparisons without ties. The model adopts a generalized additive structure, featuring a link function that quantifies the preference difference between two items and…

LLM-based Triplet Extraction for Automated Ontology Generation in Software Engineering Standards
Songhui Yue
https://arxiv.org/abs/2509.00140 https://arxiv…

LLM-based Triplet Extraction for Automated Ontology Generation in Software Engineering Standards
Ontologies have supported knowledge representation and whitebox reasoning for decades; thus, the automated ontology generation (AOG) plays a crucial role in scaling their use. Software engineering standards (SES) consist of long, unstructured text (with high noise) and paragraphs with domain-specific terms. In this setting, relation triple extraction (RTE), together with term extraction, constitutes the first stage toward AOG. This work proposes an open-source large language model (LLM)-assiste…

Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Claudio Pomo, Matteo Attimonelli, Danilo Danese, Fedelucio Narducci, Tommaso Di Noia
https://arxiv.org/abs/2508.04571

Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear whether their gains stem from true multimodal understanding or increased model complexity. This work investigates the role of multimodal item embeddings, emphasizing the semantic informativeness of the representations. Initial experiments reveal that embeddings from standard extractors (e.g., ResNet50, Sentence-Bert)…

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.03248 https://

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Recent advancements in semantic communication have primarily focused on image transmission, where neural network (NN)-based joint source-channel coding (JSCC) modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between users and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we prop…

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Sheng-Bin Duan, Fu-Chao Xie, Wen-Kai Wang, Si-Cheng Wang, Ling-Yun Li, Tian Tu, Zeng-Guang Hou
https://arxiv.org/abs/2506.20966…

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveraging strengths of VLM in vision perception and instruction understanding, VLA models exhibit promising generalization across diverse manipulation tasks. However, applications demanding high precision and accuracy reveal performance gaps without further adaptation. Evidence from multiple domains highlights the critical role of post-training to align fou…

Violation of kinetic uncertainty relation in maser heat engines: Role of spontaneous emission
Varinder Singh, Euijoon Kwon, Jae Sung Lee
https://arxiv.org/abs/2508.18619 https:/…

Violation of kinetic uncertainty relation in maser heat engines: Role of spontaneous emission
We investigate the kinetic uncertainty relation (KUR)-a fundamental trade-off between dynamical activity and current fluctuations-in two configurations of a maser heat engine. We find that KUR violations arise only in one model. This asymmetry originates from spontaneous emission, which breaks the structural symmetry between the configurations and modifies their coherence dynamics. While we analyze several contributing factors-including statistical signatures such as the Fano factor and the rat…

The role of zealots in the spread of linguistic traits
Vivian Dornelas, Celia Anteneodo, Renan Nunes, Els Heinsalu, Marco Patriarca
https://arxiv.org/abs/2508.01500 https://

The role of zealots in the spread of linguistic traits
We investigate the diffusion of linguistic innovations on a fully connected network in order to understand the emergence of linguistic diversity. We employ an agent-based dynamics based on the Axelrod model, where interactions between agents are driven by homophily and social influence, with the difference that we assume that all agents share a number of common features that ensure a finite probability of pairwise interaction. We start from a homogeneous population and introduce zealots that ac…

Views: A Hardware-friendly Graph Database Model For Storing Semantic Information
Yanjun Yang, Adrian Wheeldon, Yihan Pan, Alex Serb
https://arxiv.org/abs/2508.18123 https://

Views: A Hardware-friendly Graph Database Model For Storing Semantic Information
The graph database (GDB) is an increasingly common storage model for data involving relationships between entries. Beyond its widespread usage in database industries, the advantages of GDBs indicate a strong potential in constructing symbolic artificial intelligences (AIs) and retrieval-augmented generation (RAG), where knowledge of data inter-relationships takes a critical role in implementation. However, current GDB models are not optimised for hardware acceleration, leading to bottlenecks in…

Understanding neutrino pion production with the GiBUU model
Qiyu Yan, Kaile Wen, Kai Gallmeister, Xianguo Lu, Ulrich Mosel, Yangheng Zheng
https://arxiv.org/abs/2507.20539 https…

Understanding neutrino pion production with the GiBUU model
Pion production is a major source of systematic uncertainty in neutrino oscillation measurements. We report a systematic investigation of neutrino-induced pion production using MINERvA and MicroBooNE data within the GiBUU theoretical framework. The analysis begins by establishing baseline model parameters using inclusive and pionless data from MINERvA, MicroBooNE, and T2K experiments. We then examine the role of in-medium effects, including resonance broadening and nucleon-nucleon final-state i…

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Yiming Lin, Yuchen Niu, Shang Wang, Kaizhu Huang, Qiufeng Wang, Xiao-Bo Jin
https://arxiv.org/abs/2508.21816

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image, the model must first classify the main visual events (verb classification), then identify the participating entities and their semantic roles (semantic role labeling), and finally localize these entities in the image (semantic role localization). Existing methods treat verb cl…

The Impact of Baryonic Effects on the Dynamical Masses Inferred Using Satellite Kinematics
Josephine F. W. Baggen, Frank C. van den Bosch, Kaustav Mitra
https://arxiv.org/abs/2509.01690

The Impact of Baryonic Effects on the Dynamical Masses Inferred Using Satellite Kinematics
Satellite kinematics offers a powerful method to infer dynamical halo masses and has been demonstrated to yield tight constraints on the galaxy-halo connection. However, previous studies have assumed that the halos in which the satellites orbit are composed solely of dark matter, neglecting the role of baryons. Here, we develop an analytical model incorporating stars, gas, and the adiabatic response of dark matter to assess the impact of baryonic effects on the inference from satellite kinemati…

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems
Kun Jiang
https://arxiv.org/abs/2507.22916 https://arxiv…

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems
In our previous work, we proposed a novel neuron model based on symmetric differential equations and demonstrated its potential as an efficient signal propagator. Building upon that foundation, the present study delves deeper into the intrinsic dynamics and functional diversity of this model. By systematically exploring the parameter space and employing a range of mathematical analysis tools, we theoretically reveal the system 's core property of functional duality. Specifically, the model exhi…

Trade Policy and Structural Change
Hayato Kato, Kensuke Suzuki, Motoaki Takahashi
https://arxiv.org/abs/2508.01360 https://arxiv.org/pdf/2508.01360

Trade Policy and Structural Change
We examine how tariffs affect sectoral composition and welfare in an economy with nonhomothetic preferences and sectors being complements -- key drivers of structural change. Beyond their conventional role in trade protection, tariffs influence industrial structure by altering relative prices and income levels. We qualitatively characterize these mechanisms and use a quantitative dynamic model to show that a counterfactual 20-percentage-point increase in U.S. manufacturing tariffs since 2001 wo…

Susceptibility for extremely low external fluctuations and critical behaviour of Greenberg-Hastings neuronal model
Joaquin Almeira, Daniel A. Martin, Dante R. Chialvo, Sergio A. Cannas
https://arxiv.org/abs/2506.22629

Susceptibility for extremely low external fluctuations and critical behaviour of Greenberg-Hastings neuronal model
We consider the scaling behaviour of the fluctuation susceptibility associated with the average activation in the Greenberg-Hastings neural network model and its relation to microscopic spontaneous activation. We found that, as the spontaneous activation probability tends to zero, a clear finite size scaling behaviour in the susceptibility emerges, characterized by critical exponents which follow already known scaling laws. This shows that the spontaneous activation probability plays the role o…

Continuous spin superparticle model
I. L. Buchbinder, S. A. Fedoruk
https://arxiv.org/abs/2506.19709 https://arxiv.org/pdf/2506.19709…

Continuous spin superparticle model
We construct a new model of a particle propagating in $4D$, ${\cal N}=1$ superspace that describes the dynamics of a continuous spin irreducible representation of the Poincaré supergroup. The model is characterized by two-component Weyl spinor additional even variables playing the role of extra coordinates. A canonical formulation, specific local fermionic $κ$-symmetry, and a compete system of bosonic and fermionic constraints are derived. All bosonic constrains are first-class, while fermion…

Model Reduction of Homogeneous Polynomial Dynamical Systems via Tensor Decomposition
Xin Mao, Can Chen
https://arxiv.org/abs/2506.19165 https://

Model Reduction of Homogeneous Polynomial Dynamical Systems via Tensor Decomposition
Model reduction plays a critical role in system control, with established methods such as balanced truncation widely used for linear systems. However, extending these methods to nonlinear settings, particularly polynomial dynamical systems that are often used to model higher-order interactions in physics, biology, and ecology, remains a significant challenge. In this article, we develop a novel model reduction method for homogeneous polynomial dynamical systems (HPDSs) with linear input and out…

Defect-Mediated Aggregation and Motility-Induced Phase Separation in Active XY Model
Shun Inoue, Satoshi Yukawa
https://arxiv.org/abs/2507.17108 https://ar…

Defect-Mediated Aggregation and Motility-Induced Phase Separation in Active XY Model
We propose an ``active XY model'' that incorporates key elements of both the classical XY model and the Vicsek model to study the role of topological defects in active matter systems. This model features self-propelled particles with XY spin degrees of freedom on a lattice and introduces a self-propulsion parameter controlling the directional bias of particle motion. Using numerical simulations, we demonstrate that self-propulsion induces motility-induced phase separation (MIPS), where particle…

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

Specifying Composites in Structural Equation Modeling: Traditional, Recent and New Approaches
J\"org Henseler, Xi Yu, Tamara Schamberger, Gregory R. Hancock, Florian Schuberth
https://arxiv.org/abs/2509.01292

Specifying Composites in Structural Equation Modeling: Traditional, Recent and New Approaches
Composites, or linear combinations of variables, play an important role in multivariate behavioral research. They appear in the form of indices, inventories, formative constructs, parcels, and emergent variables. Although structural equation modeling is widely used to study relations between variables, current approaches to incorporating composites have one or more limitations. These limitations include not modeling composite creation, not employing weights as model parameters, not being able t…

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Yuhao Wang, Lingjuan Miao, Zhiqiang Zhou, Yajun Qiao, Lei Zhang
https://arxiv.org/abs/2509.02424

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Infrared and visible image fusion plays a critical role in enhancing scene perception by combining complementary information from different modalities. Despite recent advances, achieving high-quality image fusion with lightweight models remains a significant challenge. To bridge this gap, we propose a novel collaborative distillation and self-learning framework for image fusion driven by reinforcement learning. Unlike conventional distillation, this approach not only enables the student model t…

Slip-flow theory for thermo-osmosis based on a kinetic model with near-wall potential
Tetsuro Tsuji, Koichiro Takita, Satoshi Taguchi
https://arxiv.org/abs/2506.20229

Slip-flow theory for thermo-osmosis based on a kinetic model with near-wall potential
In this paper, thermal-slip coefficients in slip boundary conditions of the Stokes equation are derived using the generalized slip-flow theory, with special interest in the role of near-wall potential in micro- and nanoscale flows. As the model of fluids and fluid-solid interaction, we employ the model Boltzmann equation for dilute gases and the diffuse-reflection boundaries with near-wall potential, respectively. It is found that, when the mean free path of gas molecules and the effective rang…

Xray2Xray: World Model from Chest X-rays with Volumetric Context
Zefan Yang, Xinrui Song, Xuanang Xu, Yongyi Shi, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
https://arxiv.org/abs/2506.19055

Xray2Xray: World Model from Chest X-rays with Volumetric Context
Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structural superposition, which constrains their effectiveness in precise disease diagnosis and risk prediction. To address the limitations of 2D CXRs, this study introduces Xray2Xray, a novel World Model that learns latent representations encoding 3D structural information from chest X-rays. Xray2Xray captures the latent rep…

Constraining parameters of spinor field dark energy: An alternative to $\Lambda$CDM model under the spherically symmetric FLRW space-time
Bijan Saha, Mahendra Goray
https://arxiv.org/abs/2506.16323

Constraining parameters of spinor field dark energy: An alternative to $Λ$CDM model under the spherically symmetric FLRW space-time
This study constrains the cosmological parameters within the scope of a spherically symmetric FLRW cosmological model, the role of a nonlinear spinor field in the universe's evolution. To test this approach, we incorporate the recent Cosmic chronometers, Supernova, and Sloan Digital Sky Survey data. It is found that if spherical coordinates give the FLRW model, the energy-momentum tensor (EMT) of the spinor field possesses nontrivial non-diagonal components. These non-diagonal components of EMT…

IndusGCC: A Data Benchmark and Evaluation Framework for GUI-Based General Computer Control in Industrial Automation
Xiaoran Yang, Yuyang Du, Kexin Chen, Soung Chang Liew, Jiamin Lu, Ziyu Guo, Xiaoyan Liu, Qun Yang, Shiqi Xu, Xingyu Fan, Yuchen Pan, Taoyong Cui, Hongyu Deng, Boris Dudder, Jianzhang Pan, Qun Fang, Pheng Ann Heng
https://arxi…

IndusGCC: A Data Benchmark and Evaluation Framework for GUI-Based General Computer Control in Industrial Automation
As Industry 4.0 progresses, flexible manufacturing has become a cornerstone of modern industrial systems, with equipment automation playing a pivotal role. However, existing control software for industrial equipment, typically reliant on graphical user interfaces (GUIs) that require human interactions such as mouse clicks or screen touches, poses significant barriers to the adoption of code-based equipment automation. Recently, Large Language Model-based General Computer Control (LLM-GCC) has e…

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling
Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio, Antinisca Di Marco, Davide Di Ruscio
https://arxiv.org/abs/2507.14735

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling
The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for…

Testing Hypotheses regarding Covariance and Correlation matrices with the R package CovCorTest
Paavo Sattler, Svenja Jedhoff
https://arxiv.org/abs/2507.03406

Testing Hypotheses regarding Covariance and Correlation matrices with the R package CovCorTest
In addition to the commonly analyzed measures of location, dispersion measurements such as variance and correlation provide many valuable information. Consequently, they play a crucial role in multivariate statistics, which leads to tests regarding covariance and correlation matrices. Furthermore, also the structure of these matrices leads to important hypotheses of interest, since it contains substantial information about the underlying model. In fact, assumptions regarding the structures of c…

Computationally Tractable Offline Quantum Experimental Design for Nuclear Spin Detection
B. Varona-Uriarte, F. Belliardo, T. H. Taminiau, C. Bonato, E. Garrote, J. Casanova
https://arxiv.org/abs/2508.21450

Computationally Tractable Offline Quantum Experimental Design for Nuclear Spin Detection
The characterization of nuclear spin environments in solid-state devices plays an important role in advancing quantum technologies, yet traditional methods often demand long measurement times. To address this challenge, we extend our recently developed deep-learning-based SALI model (Signal-to-image ArtificiaL Intelligence) by introducing the surrogate information gain (SIG) to optimize the selection of data points in the measurements. This approach significantly reduces time requirements in ex…

Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents
Haichang Li
https://arxiv.org/abs/2506.22815 …

Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents
This position paper aims to rethink the role and design of memory in Large Language Model (LLM)-based agent systems. We observe that while current memory practices have begun to transcend the limitations of single interactions, they remain conceptually grounded in "bound memory" in terms of design concept-where memory is treated as local state attached to specific context or entities, forming "memory silos" that impede cross-entity collaboration. To overcome this architectural bottleneck, this …

Exploring Scaling Laws of CTR Model for Online Performance Improvement
Weijiang Lai, Beihong Jin, Jiongyan Zhang, Yiyuan Zheng, Jian Dong, Jia Cheng, Jun Lei, Xingxing Wang
https://arxiv.org/abs/2508.15326

Exploring Scaling Laws of CTR Model for Online Performance Improvement
CTR models play a vital role in improving user experience and boosting business revenue in many online personalized services. However, current CTR models generally encounter bottlenecks in performance improvement. Inspired by the scaling law phenomenon of LLMs, we propose a new paradigm for improving CTR predictions: first, constructing a CTR model with accuracy scalable to the model grade and data size, and then distilling the knowledge implied in this model into its lightweight model that can…

Can News Predict the Direction of Oil Price Volatility? A Language Model Approach with SHAP Explanations
Romina Hashamia, Felipe Maldonado
https://arxiv.org/abs/2508.20707 https…

Can News Predict the Direction of Oil Price Volatility? A Language Model Approach with SHAP Explanations
Financial markets can be highly sensitive to news, investor sentiment, and economic indicators, leading to important asset price fluctuations. In this study we focus on crude oil, due to its crucial role in commodity markets and the global economy. Specifically, we are interested in understanding the directional changes of oil price volatility, and for this purpose we investigate whether news alone -- without incorporating traditional market data -- can effectively predict the direction of oil …

From Contrast to Commonality: Audio Commonality Captioning for Enhanced Audio-Text Cross-modal Understanding in Multimodal LLMs
Yuhang Jia, Xu Zhang, Yong Qin
https://arxiv.org/abs/2508.01659

From Contrast to Commonality: Audio Commonality Captioning for Enhanced Audio-Text Cross-modal Understanding in Multimodal LLMs
Audio Captioning (AC) plays a pivotal role in enhancing audio-text cross-modal understanding during the pretraining and finetuning of multimodal large language models (MLLMs). To further strengthen this alignment, recent works have proposed Audio Difference Captioning (ADC), which takes multiple audio inputs and encourages the model to describe their differences, thereby promoting fine-grained audio discrimination. However, despite its effectiveness in enabling difference-telling and detailed d…

Data Efficacy for Language Model Training
Yalun Dai, Yangyu Huang, Xin Zhang, Wenshan Wu, Chong Li, Wenhui Lu, Shijie Cao, Li Dong, Scarlett Li
https://arxiv.org/abs/2506.21545

Data Efficacy for Language Model Training
Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or optimal subset of training data. Techniques such as data filtering, sampling, and selection play a crucial role in this area. To complement it, we define Data Efficacy, which focuses on maximizing performance by optimizing the organization of training data and remains relatively underexplored. This work introduces a gene…

Extending the Dynamical Systems Toolkit: Coupled Fields in Multiscalar Dark Energy
Daniele Licciardello, Saba Rahimy, Ivonne Zavala
https://arxiv.org/abs/2509.02539 https://

Extending the Dynamical Systems Toolkit: Coupled Fields in Multiscalar Dark Energy
We study the dynamics of a two-field scalar model consisting of an axion-saxion pair with both kinetic and potential couplings, as motivated by string theory compactifications. We extend the dynamical systems (DS) toolkit by introducing a new set of variables that not only close the system and enable a systematic stability analysis, but also disentangle the role of the kinetic coupling. Within this framework we derive a compact, general expression for the non-geodesicity (turning-rate) paramete…

Gravitational form factors of the nucleon in the Skyrme model based on scale-invariant chiral perturbation theory
Mitsuru Tanaka, Daisuke Fujii, Mamiya Kawaguchi
https://arxiv.org/abs/2507.21220

Gravitational form factors of the nucleon in the Skyrme model based on scale-invariant chiral perturbation theory
We investigate the role of the QCD scale anomaly in the gravitational form factors of the nucleon -- particularly the $D(t)$ form factor -- as well as the associated stress distribution and internal forces, using a Skyrme model based on the scale-invariant chiral perturbation theory. A distinctive feature of this model is the inclusion of both the pion and the scalar meson, which respectively capture the effects of the current quark mass and gluonic quantum contributions to the scale anomaly. B…

Geometric Effects on Tunneling in Driven Quantum Systems
Shintaro Takayoshi, Takashi Oka
https://arxiv.org/abs/2509.03674 https://arxiv.org/pdf/2509.03674

Geometric Effects on Tunneling in Driven Quantum Systems
We review quantum tunneling provoked by external field driving, focusing on the role of geometric effects. The discussion begins with an overview of tunneling phenomena, including the Landau-Zener model and the Schwinger effect, both of which are essential frameworks to describe the generation of elementary excitation of the system. We also refer to the relation between the modern theory of polarization and the geometry of the system, and introduce the shift vector via adiabatic perturbation th…

Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study
Saravanan Venkatachalam
https://arxiv.org/abs/2508.21622

Integrating Large Language Models with Network Optimization for Interactive and Explainable Supply Chain Planning: A Real-World Case Study
This paper presents an integrated framework that combines traditional network optimization models with large language models (LLMs) to deliver interactive, explainable, and role-aware decision support for supply chain planning. The proposed system bridges the gap between complex operations research outputs and business stakeholder understanding by generating natural language summaries, contextual visualizations, and tailored key performance indicators (KPIs). The core optimization model address…

Improving Code LLM Robustness to Prompt Perturbations via Layer-Aware Model Editing
Shuhan Liu, Xing Hu, Kerui Huang, Xiaohu Yang, David Lo, Xin Xia
https://arxiv.org/abs/2507.16407

Improving Code LLM Robustness to Prompt Perturbations via Layer-Aware Model Editing
Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the model. However, prior studies have shown that LLMs are highly sensitive to prompt perturbations. Minor modifications in wording, syntax, or formatting can significantly reduce the functional correctness of generated code. As perturbations frequently occur in real-world scenarios, improving the robustness of LLMs to prom…

The dominant role of jetting in micron- and sub-micron sea spray produced by bubble bursting: a revised model and comparison with measurements
Alfonso M. Ganan-Calvo, Miguel A. Herrada, Jose M. Lopez-Herrera
https://arxiv.org/abs/2506.17836

The dominant role of jetting in micron- and sub-micron sea spray produced by bubble bursting: a revised model and comparison with measurements
The primary physical mechanism governing the production of sub-micron sea spray aerosols (SSA) -- specifically the competition between film and jet droplets from bubble bursting -- has remained a subject of intense debate. This work presents a revised, first-principles model that establishes the overwhelming dominance of jetting for producing micron and sub-micron aerosols. Our approach first rules out the film droplet mechanism as a primary contributor for this size range by demonstrating thro…

Spatiotemporal hierarchy of thermal avalanches
Vladimir Yu. Rudyak, Dor Shohat, Yoav Lahini
https://arxiv.org/abs/2507.02573 https://…

Spatiotemporal hierarchy of thermal avalanches
In amorphous solids, frustrated elastic interactions conspire with thermal noise to trigger anomalously slow sequences of plastic rearrangements, termed "thermal avalanches", which play an important role during creep and glassy relaxations. Here we uncover the complex spatiotemporal structure of thermal avalanches in simulations of a model amorphous solid. We systematically disentangle mechanical and thermal triggering during logarithmic creep, revealing a hierarchy of fast localized cascades l…

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Pengyu Han, Zeyi Liu, Shijin Chen, Dongliang Zou, Xiao He
https://arxiv.org/abs/2506.17740

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these me…

Pyramid Hierarchical Masked Diffusion Model for Imaging Synthesis
Xiaojiao Xiao, Qinmin Vivian Hu, Guanghui Wang
https://arxiv.org/abs/2507.16579 https://

Pyramid Hierarchical Masked Diffusion Model for Imaging Synthesis
Medical image synthesis plays a crucial role in clinical workflows, addressing the common issue of missing imaging modalities due to factors such as extended scan times, scan corruption, artifacts, patient motion, and intolerance to contrast agents. The paper presents a novel image synthesis network, the Pyramid Hierarchical Masked Diffusion Model (PHMDiff), which employs a multi-scale hierarchical approach for more detailed control over synthesizing high-quality images across different resolut…

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Zofia Pizo\'n, Shinji Kimijima, Grzegorz Brus
https://arxiv.org/abs/2506.17224

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Hydrogen's role is growing as an energy carrier, increasing the need for efficient production, with methane steam reforming being the most widely used technique. This process is crucial for applications like fuel cells, where hydrogen is converted into electricity, pushing for reactor miniaturization and optimized process control through numerical simulations. Existing models typically address either kinetic or equilibrium regimes, limiting their applicability. Here we show a surrogate model ca…

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Dongli He, Hu Wang, Mohammad Yaqub
https://arxiv.org/abs/2507.22802 https://ar…

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements heavily depends on the expertise of sonographers, posing a significant challenge in low-income countries due to the scarcity of trained personnel. To address this issue, we leverage FetalCLIP, a vision-language model pretrained on a curated dataset of over 210,000 fetal ultrasound image-caption pairs, to perform auto…

Mott metal-insulator transition in a modified periodic Anderson model: Insights from entanglement entropy and role of short-range spatial correlations
Ankur Majumder, Sudeshna Sen
https://arxiv.org/abs/2506.16533

Mott metal-insulator transition in a modified periodic Anderson model: Insights from entanglement entropy and role of short-range spatial correlations
The Mott-Hubbard metal-insulator transition is a paradigmatic phenomenon where Coulomb interactions between electrons drive a metal-insulator phase transition. It has been extensively studied within the Hubbard model, where a quantum critical transition occurs at a finite temperature second-order critical point. This work investigates the Mott metal-insulator transition in a modified periodic Anderson model that may be viewed as a three-orbital lattice model including an interacting, localized …

Unveiling the Influence of Amplifying Language-Specific Neurons
Inaya Rahmanisa, Lyzander Marciano Andrylie, Krisna Mahardika Ihsani, Alfan Farizki Wicaksono, Haryo Akbarianto Wibowo, Alham Fikri Aji
https://arxiv.org/abs/2507.22581

Unveiling the Influence of Amplifying Language-Specific Neurons
Language-specific neurons in LLMs that strongly correlate with individual languages have been shown to influence model behavior by deactivating them. However, their role in amplification remains underexplored. This work investigates the effect of amplifying language-specific neurons through interventions across 18 languages, including low-resource ones, using three models primarily trained in different languages. We compare amplification factors by their effectiveness in steering to the target …

Landau-de Gennes Modelling of Confinement Effects and Cybotactic Clusters in Bent-Core Nematic Liquid Crystals
Yucen Han, Prabakaran Rajamanickam, Bedour Alturki, Apala Majumdar
https://arxiv.org/abs/2509.00334

Landau-de Gennes Modelling of Confinement Effects and Cybotactic Clusters in Bent-Core Nematic Liquid Crystals
We study bent-core nematic (BCN) systems in two-dimensional (2D) and three-dimensional (3D) settings, focusing on the role of cybotactic clusters, phase transitions, confinement effects and applied external fields. We propose a generalised version of Madhusudhana's two-state model for BCNs in [Madhusudana NV, Physical Review E, 96(2), 022710] with two order parameters: $\mathbf{Q}_g$ to describe the ambient ground-state (GS) molecules and $\mathbf{Q}_c$ to describe the ordering within the cybot…

Role of meson exchange and final-state interaction in $J/\psi$-meson photoproduction
Sang-Ho Kim
https://arxiv.org/abs/2507.22405 https://arxiv.org/pdf/250…

Role of meson exchange and final-state interaction in $J/ψ$-meson photoproduction
We investigate $J/ψ$ photoproduction off the nucleon using a dynamical model based on a Hamiltonian framework. First, Pomeron exchange is regarded as a background contribution that accounts for the gradual rise of the total cross section with increasing beam energy. Then, the meson-exchange and direct $J/ψ$-radiation contributions are included as part of the Born term. More specifically, we examine the contributions of the light-meson [$π^0(135)$, $η(548)$, $η'(958)$, $f_1(1285)$] and char…

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
Li-Hsiang Shen, Jyun-Jhe Huang
https://arxiv.org/abs/2507.16204

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
A space-air-ground integrated network (SAGIN) architecture is proposed, empowered by multi-functional reconfigurable intelligent surfaces (MF-RIS) capable of simultaneously reflecting, amplifying, and harvesting wireless energy. The MF-RIS plays a pivotal role in addressing the energy shortages of low-Earth orbit (LEO) satellites operating in shadowed regions, while explicitly accounting for both communication and computing energy consumption across the SAGIN nodes. To maximize the long-term en…

COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation
Pakizar Shamoi, Nuray Toganas, Muragul Muratbekova, Elnara Kadyrgali, Adilet Yerkin, Ayan Igali, Malika Ziyada, Ayana Adilova, Aron Karatayev, Yerdauit Torekhan
https://arxiv.org/abs/2507.11488

COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation
Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human color perception. This paper introduces the Human Perception-Based Fuzzy Color Model, COLIBRI (Color Linguistic-Based Representation and Interpretation), designed to bridge the gap between computational color representations and human visual perception. The proposed model uses fuzzy sets and logic to create a framewo…

Chirality structure of vector like new physics operators in charged current transitions
Sajawal Zafar, Qazi Maaz Us Salam, Rana Khan, Ishtiaq Ahmed, Rizwan Khalid
https://arxiv.org/abs/2507.23721

Chirality structure of vector like new physics operators in charged current transitions
We investigate the cascade decay $B^{*0}_{s} \rightarrow D_s^-(\rightarrow τ^-\,\barν_τ)\,\ell^{+}\,ν_\ell$ induced by flavor changing charged currents in the context of the Standard Model and in vector-like couplings beyond the Standard Model. We employ the helicity amplitude formalism for analysis and highlight the role of new vector-like couplings in charged current interactions. We find, in particular, that while new left handed chiral-vector like interactions contribute to the branchin…

FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction
Ravi Shankar Prasad, Dinesh Singh
https://arxiv.org/abs/2508.18031 https://

FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction
Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from their remains plays a crucial role when all other identification methods fail. Traditional methods for this task, such as clay-based craniofacial reconstruction, require expert domain knowledge and are a time-consuming process. At the same time, other probabilistic generative models like the statistical shape model or the Basel face model fail to ca…

A Factorized Mass Structure of Fermions and Its Fit
Qingfeng Cao, Ying Zhang
https://arxiv.org/abs/2507.01480 https://arxiv.org/pdf/2…

A Factorized Mass Structure of Fermions and Its Fit
The structure of the mass matrix, a challenging problem in the Standard Model, is closely related to flavor phenomenology and the understanding of the Yukawa interaction. We derive a factorized mass structure based on observed fermion mass hierarchies, investigating the role of $SO(2)^f$ family symmetry in explaining the approximate degeneracy of light quark generations and its connection to flavor mixing. Our calculation includes the slightest modification from the lightest fermions by $\mathc…

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Chengqi Dong, Fenghe Tang, Rongge Mao, Xinpei Gao, S. Kevin Zhou
https://arxiv.org/abs/2508.15476

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Medical image segmentation plays a pivotal role in disease diagnosis and treatment planning, particularly in resource-constrained clinical settings where lightweight and generalizable models are urgently needed. However, existing lightweight models often compromise performance for efficiency and rarely adopt computationally expensive attention mechanisms, severely restricting their global contextual perception capabilities. Additionally, current architectures neglect the channel redundancy issu…

The role of the pion mass on the QCD phase diagram in the $T-eB$ plane
Chowdhury Aminul Islam, Mahammad Sabir Ali, Rishi Sharma
https://arxiv.org/abs/2508.14189 https://

The role of the pion mass on the QCD phase diagram in the $T-eB$ plane
We investigated the role of the pion mass on the QCD phase diagram in the $T-eB$ plane using an effective model treatment. Such treatments are able to capture the main features predicted by first-principles calculations. We also employed the model to estimate the pion mass beyond which the inverse magnetic catalysis (IMC) effect disappears. The value is found to be independent of the strength of the magnetic field.