December 2025 arXiv papers — page 25

Anbang Liu, Shaochong Lin, Jingchuan Chen, Peng Wu

Scheduling problems are a fundamental class of combinatorial optimization problems that underpin operational efficiency in manufacturing, logistics, and service systems. While operations research has traditionally developed solver-centric methods emphasizing model structure and optimality, recent advances in machine learning are reshaping scheduling toward a

Lou van den Dries

We characterize intrinsically the truncation structures on valued fields arising from embeddings into Hahn fields with truncation closed image.

Irched Chafaa, Giacomo Bacci, Luca Sanguinetti

Power allocation remains a fundamental challenge in wireless communication networks, particularly under dynamic user loads and large-scale deployments. While Transformerbased models have demonstrated strong performance, their computational cost scales poorly with the number of users. In this work, we propose a novel hybrid Tree-Transformer architecture that

Deniz Akdemir

Modern machine learning embeddings provide powerful compression of high-dimensional data, yet they typically destroy the geometric structure required for classical likelihood-based statistical inference. This paper develops a rigorous theory of likelihood-preserving embeddings: learned representations that can replace raw data in likelihood-based workflows -

Bernat Frangi

This study presents a comparative numerical analysis of Gold (Au) and high-index semiconductor nanoparticles for refractive index sensing in the visible range. While Au nanoparticles demonstrate high sensitivity ($\approx 150$ nm per refractive index unit), their performance is constrained by ohmic losses. In contrast, high-index dielectrics are shown to exh

Sandy H. S. Herho, Siti N. Kaban

This study introduces amangkurat, an open-source Python library designed for the robust numerical simulation of relativistic scalar field dynamics governed by the nonlinear Klein-Gordon equation in $(1+1)$D spacetime. The software implements a hybrid computational strategy that couples Fourier pseudo-spectral spatial discretization with a symplectic St\o rme

Sandy Hardian Susanto Herho, Sito Fossy Biosa, Siti Nurzannah Kaban, Rusmawan Suwarman

We present 1d-qt-ideal-solver, an open-source Python library for simulating one-dimensional quantum tunneling dynamics under idealized coherent conditions. The solver implements the split-operator method with second-order Trotter-Suzuki factorization, utilizing FFT-based spectral differentiation for the kinetic operator and complex absorbing potentials to el

J Krishnamoorthi, Anil Kumar, Sanjib Kumar Agarwalla

The neutral-current (NC) non-standard interactions (NSI) of neutrinos with fermions can modify the flavor oscillations of atmospheric neutrinos as they propagate through the Earth. We present constraints on the NC-NSI parameters $\varepsilon_{\mu\tau}$ and $\varepsilon_{\tau\tau}-\varepsilon_{\mu\mu}$ (one at a time) using a high-purity sample of $\nu_{\mu}$

Hadi Mohammadi, Tamas Kozak, Anastasia Giachanou

Chain-of-thought (CoT) reasoning has emerged as a powerful technique for improving the problem-solving capabilities of large language models (LLMs), particularly for tasks requiring multi-step reasoning. However, recent studies show that CoT explanations often fail to reflect the model's actual reasoning process, as models may produce coherent yet misleading

Ziqi Jin, Bin Wang, Xiang Lin, Lidong Bing

Diffusion models offer appealing properties for language generation, such as parallel decoding and iterative refinement, but the discrete and highly structured nature of text challenges the direct application of diffusion principles. In this paper, we revisit diffusion language modeling from the view of diffusion process and language modeling, and outline fi

Shiyan Liu, Jian Ma, Rui Qu

As Retrieval-Augmented Generation (RAG) systems evolve toward more sophisticated architectures, ensuring their trustworthiness through explainable and robust evaluation becomes critical. Existing scalar metrics suffer from limited interpretability, inadequate uncertainty quantification, and computational inefficiency in multi-system comparisons, hindering re

Fanglin Xu, Wei Zhang, Jian Yang, Guo Chen

The rapid advancement of code large language models (LLMs) has sparked significant research interest in systematically evaluating their code generation capabilities, yet existing benchmarks predominantly assess models at a single structural granularity and focus on limited programming languages, obscuring fine-grained capability variations across different c

Pierre Mackenzie, Maya Shah, Patrick Frenett

Topic segmentation using generative Large Language Models (LLMs) remains relatively unexplored. Previous methods use semantic similarity between sentences, but such models lack the long range dependencies and vast knowledge found in LLMs. In this work, we propose an overlapping and recursive prompting strategy using sentence enumeration. We also support the

Chen Su, Yuanhe Tian, Yan Song

With the advancement of large language models (LLMs), diverse time series analysis tasks are reformulated as time series question answering (TSQA) through a unified natural language interface. However, existing LLM-based approaches largely adopt general natural language processing techniques and are prone to reasoning errors when handling complex numerical s

Yuming Gu, Yizhi Wang, Yining Hong, Yipeng Gao

Embodied visual planning aims to enable manipulation tasks by imagining how a scene evolves toward a desired goal and using the imagined trajectories to guide actions. Video diffusion models, through their image-to-video generation capability, provide a promising foundation for such visual imagination. However, existing approaches are largely forward predict

Paul Schneider, Amalie Schramm

Structured deliberation has been found to improve the performance of human forecasters. This study investigates whether a similar intervention, i.e. allowing LLMs to review each other's forecasts before updating, can improve accuracy in large language models (GPT-5, Claude Sonnet 4.5, Gemini Pro 2.5). Using 202 resolved binary questions from the Metaculus Q2

Bharath Nunepalli

Retrieval-augmented generation (RAG) introduces a practical control problem: retrieval depth and generation behavior must be chosen per query to satisfy service-level objectives (SLOs) such as cost, refusal rate, and hallucination risk. This work models per-query control as a small discrete action: choose a retrieval depth and a generation mode (guarded vs.

Mohamad Alansari, Muzammal Naseer, Hasan Al Marzouqi, Naoufel Werghi

\noindent Memory has become the central mechanism enabling robust visual object tracking in modern segmentation-based frameworks. Recent methods built upon Segment Anything Model 2 (SAM2) have demonstrated strong performance by refining how past observations are stored and reused. However, existing approaches address memory limitations in a method-specific m

Pierre Mackenzie

The Johnson-Lindenstrauss (JL) theorem states that a set of points in high-dimensional space can be embedded into a lower-dimensional space while approximately preserving pairwise distances with high probability Johnson and Lindenstrauss (1984). The standard JL theorem uses dense random matrices with Gaussian entries. However, for some applications, sparse r

Tomas Ortega, Chun-Yin Huang, Xiaoxiao Li, Hamid Jafarkhani

Distributed learning, particularly Federated Learning (FL), faces a significant bottleneck in the communication cost, particularly the uplink transmission of client-to-server updates, which is often constrained by asymmetric bandwidth limits at the edge. Biased compression techniques are effective in practice, but require error feedback mechanisms to provide

Martín Cera, Pedro García-Vázquez, Juan Carlos Valenzuela-Tripodoro

A Roman dominating function for a (non-weighted) graph $G=(V,E)$, is a function $f:V\rightarrow \{0,1,2\}$ such that every vertex $u\in V$ with $f(u)=0$ has at least {one} neighbor $v\in V$ such that $f(v)=2$. The minimum weight $\sum_{v\in V}f(v)$ of a Roman {dominating function} $f$ on $G$ is called the Roman domination number of $G$ and is denoted by $\ga

Pierre Mackenzie

Progress in automatic chord recognition has been slow since the advent of deep learning in the field. To understand why, I conduct experiments on existing methods and test hypotheses enabled by recent developments in generative models. Findings show that chord classifiers perform poorly on rare chords and that pitch augmentation boosts accuracy. Features ext

Nemanja Stefan Perović, Keshav Singh, Chih-Peng Li, Octavia A. Dobre

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Although the gai

Gustavo de Paula Ramos

We are interested in the existence and asymptotic behavior of ground states of the following normalized nonlocal semilinear problem: \[ \begin{cases} - \Delta u + (V - \omega) u + (K_{a, b} \ast u^2) u = 0 &\text{in} ~ \mathbb{R}^3; \\ \|u\|_{\mathscr{L}^2}^2 = \mu, \end{cases} \] where \[ K_{a, b} (x) := \frac{1}{|x|} \left( \frac{4}{3} e^{- b |x|} - \frac{

Sheo Yon Jhin, Noseong Park

Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series forecasting models leverage external sources (e.g., news and social media) to capture human factors, but these approaches incur high data dependency costs in terms of financial, co

Alireza Rezaee

Dynamic nonzero sum games are widely used to model multi agent decision making in control, economics, and related fields. Classical methods for computing Nash equilibria, especially in linear quadratic settings, rely on strong structural assumptions and become impractical for nonlinear dynamics, many players, or long horizons, where multiple local equilibria

Tim Maudlin

Since its inception, quantum theory has been the subject of fierce interpretive controversy, which persists to this day. Disputed topics include the basic ontology and dynamics of the theory, the role (if any) of measurement, the meaning of probability, and the issue of non-locality. But there is yet another problem that has been largely ignored: how the the

P. Marziani, E. Bon, S. Panda, N. Bon

We present observational evidence supporting the presence of a stratified accretion disk wind in active galactic nuclei (AGN), based on multi-wavelength spectroscopic analysis of broad and narrow emission lines. The diversity in emission line profiles, ionization potentials, and kinematic signatures suggests a structured outflow emerging from the accretion d

Mojtaba Eshghie, Melissa Mazura, Alexandre Bartel

We frame Ethereum transactions reverted by invariants-require(<invariant>)/ assert(<invariant>)/if (<invariant>) revert statements in the contract implementation-as a positive signal of active on-chain defenses. Despite their value, the defensive patterns in these transactions remain undiscovered and underutilized in security research. We present Raven, a fr

Jiacheng Ye, Shansan Gong, Jiahui Gao, Junming Fan

While autoregressive Large Vision-Language Models (VLMs) have achieved remarkable success, their sequential generation often limits their efficacy in complex visual planning and dynamic robotic control. In this work, we investigate the potential of constructing Vision-Language Models upon diffusion-based large language models (dLLMs) to overcome these limita

Felipe Rivera-Mesas

In this article, we extend the van Hamel-Lichtenbaum duality theorem to (not necessarily smooth) proper and geometrically integral varieties defined over a $p$-adic field $k$. More precisely, we prove that for such variety $X$ there exists a natural continuous perfect pairing \[ \mathrm{Br}_1(X)\times H_0(X,\mathbb{Z})_\tau^{\wedge} \to \mathbb{Q}/\mathbb{Z}

Zhiqiang Wan, Heng Zhang

We prove $\ell^{1}\!\to\!\ell^{\infty}$ dispersive estimates for the discrete Klein--Gordon equation on $\mathbb Z$ with small real-analytic quasi-periodic potentials, showing that the time-decay rate persists as $(\tfrac13)^{-}$. As applications, we derive the corresponding Strichartz estimates and establish small-data global well-posedness for the associat

Dafeng Zhang, Yongqi Song, Shizhuo Liu

The method used to measure relationships between face embeddings plays a crucial role in determining the performance of face clustering. Existing methods employ the Jaccard similarity coefficient instead of the cosine distance to enhance the measurement accuracy. However, these methods introduce too many irrelevant nodes, producing Jaccard coefficients with

Jingchun Li, Zhaoyang Lu, Jianqiang Xu, Wei Chen

The fast-ion (FI) on turbulent transport is one of the key topics of magnetic confinement fusion. This work focus on the impact of FI pressure gradients on turbulence in a high-\b{eta} plasma scenario using gyrokinetic simulations. Linear analyses reveal that FIs strongly stabilize ion temperature gradient (ITG) modes via the thermal-ion dilution, while thei

Abdullah Aydın, Erdal Bayram, İshak Aydın

This paper introduces statistical order convergence and its pointwise variant for sequences of order bounded operators between Riesz spaces. We establish fundamental properties: uniqueness of the limit, stability under lattice operations, and a characterization via natural density linking it to classical order convergence. Explicit examples show that statist

Tatsuhiro Misumi

We develop a systematic Hamiltonian formulation of minimally doubled lattice fermions in (3+1) dimensions, derive their nodal structures (structures of zeros), and classify their symmetry patterns for both four-component Dirac and two-component Weyl constructions. Motivated by recent single-Weyl proposals based on Bogoliubov-de Gennes (BdG) representation, w

Zhongyang Liu, Haoyu Pei, Xiangyi Xiao, Xiaocong Du

Due to the high value and high failure rates of startups, predicting their success is a critical challenge. Existing approaches typically model startup success from a single decision-maker's perspective, overlooking the collective dynamics that dominate real-world venture capital (VC) decision-making. We propose SimVC-CAS, a collective agent system that simu

Jean Tapie, Philipp del Hougne

Most use cases of reconfigurable antennas require an accurate forward model mapping configuration to radiated field (and reflections at feeds). Emerging dynamic metasurface antennas (DMAs) confront the conventional approach of extracting such a model from a numerical simulation with multiple challenges. First, the cost of accurately simulating an intricate a

Hesam Taghipour, Alireza Rezaee, Farshid Hajati

The global gold market, by its fundamentals, has long been home to many financial institutions, banks, governments, funds, and micro-investors. Due to the inherent complexity and relationship between important economic and political components, accurate forecasting of financial markets has always been challenging. Therefore, providing a model that can accura

Junshu Dai, Yu Wang, Tongya Zheng, Wei Ji

The precise prediction of human mobility has produced significant socioeconomic impacts, such as location recommendations and evacuation suggestions. However, existing methods suffer from limited generalization capability: unimodal approaches are constrained by data sparsity and inherent biases, while multi-modal methods struggle to effectively capture mobil

Zhiqiang Gao, Shihao Gao, Zixing Zhang, Yihao Guo

Understanding sentiment in multimodal conversations is a complex yet crucial challenge toward building emotionally intelligent AI systems. The Multimodal Conversational Aspect-based Sentiment Analysis (MCABSA) Challenge invited participants to tackle two demanding subtasks: (1) extracting a comprehensive sentiment sextuple, including holder, target, aspect,

Bin Wang, Yang Xu, Huan Zhao, Hao Zhang

Speech-driven 3D talking head generation aims to produce lifelike facial animations precisely synchronized with speech. While considerable progress has been made in achieving high lip-synchronization accuracy, existing methods largely overlook the intricate nuances of individual speaking styles, which limits personalization and realism. In this work, we pres

Tao Zhou, Lingyu Shu, Zixing Zhang, Jing Han

Deep learning has shown great promise in physiological signal analysis, yet its progress is hindered by heterogeneous data formats, inconsistent preprocessing strategies, fragmented model pipelines, and non-reproducible experimental setups. To address these limitations, we present Tyee, a unified, modular, and fully-integrated configurable toolkit designed f

Quang-Hung Bui, Anh Son Ta

Training Large Language Models (LLMs) is highly memory-intensive due to optimizer state overhead. The FRUGAL framework mitigates this with gradient splitting, but its static hyperparameters -- the subspace ratio ($\rho$) and update frequency ($T$) -- require costly manual tuning, limiting adaptability. We present AdaFRUGAL, which automates this process by in

Igor Filikhin, Roman Ya. Kezerashvili, Branislav Vlahovic

We investigate bound states of light $\Omega_{3x}$-clusters ($x = s, c$), motivated by the $\Omega_{3s}N$ potential recently developed by the HAL QCD collaboration. To regularize this potential, we remove the deeply attractive core at $r < 0.4~\mathrm{fm}$ and parametrize the long-range component ($r > 0.4~\mathrm{fm}$) using a two-range Gaussian form. This

Milad Asadpour, Alireza Rezaee, Farshid Hajati

According to the advent of cryptocurrencies and Bitcoin, many investments and businesses are now conducted online through cryptocurrencies. Among them, Bitcoin uses blockchain technology to make transactions secure, transparent, traceable, and immutable. It also exhibits significant price fluctuations and performance, which has attracted substantial attentio

Jianquan Ge, Ya Tao

In this paper, we investigate the rigidity problems of complete hypersurfaces with constant mean curvature and constant scalar curvature in Euclidean spaces. Firstly, under some conditions of Gaussian-Kronecker curvature, we provide characterizations for the unsolved cases of N\'u\~nez's theorems in dimensions 4 and 5, as well as several rigidity results und

Shinnosuke Kosaka

Let $R$ be a commutative noetherian local ring. In this paper, we study the self-duality and eventual periodicity of minimal free resolutions of finitely generated $R$-modules in terms of their syzygy modules and Ext modules. As an application, we recover theorems of Dey.

Ewa Duda, Adamina Korwin-Szymanowska

Urban food production is becoming an increasingly significant topic in the context of climate change and food security. Conducting research on this subject is becoming an essential element of urban development, deepening knowledge regarding the benefits, challenges, and potential for the development of urban agriculture as an alternative form of food product

Alfonso M. Ganan-Calvo

Nanoplastics (NPs) are increasingly detected in human blood and organs at concentrations reaching hundreds to thousands of parts per million, yet no quantitative framework has linked short-term experimental uptake kinetics to long-term, organ-specific accumulation. Here we analytically review the most reliable uptake and depuration datasets available in tele

Vladimir Vatutin, Elena Dyakonova

Let $\{Z_{m},m\geq 0\}$ be a critical branching process in random environment and $\{S_{m},m\geq 0\}$ be its associated random walk. Assuming that the increments distribution of the associated random walk belongs without centering to the domain of attraction of an $\alpha $-stable law we prove conditional limit theorems describing, as $n\rightarrow \infty $,

A. Paz Jalca, W. H. Painado Lozano, D. E. Gonzalez-Chavez, L. Saba

In this work, we study the anisotropic magnetoresistance (AMR) behavior of [001] epitaxial Fe65Co35 thin films along different crystallographic directions as a function of temperature. The AMR ratio is found to strongly depend on the current orientation relative to the crystal axes, reaching 0.16 % and 0.10 % at room temperature when the current is applied a

Jewel Rana Palit, Vijayalakshmi K Kumarasamy, Osama A. Osman

Automated Vehicles (AV) hold potential to reduce or eliminate human driving errors, enhance traffic safety, and support sustainable mobility. Recently, crash data has increasingly revealed that AV behavior can deviate from expected safety outcomes, raising concerns about the technology's safety and operational reliability in mixed traffic environments. While

Taeyun Kim

We show that differentiable sorting and ranking operators are structurally incompatible with within-vector rank normalization. We formalize admissibility through monotone invariance (C1), batch independence (C2), and a rank-space stability condition (C3). Gap-sensitive relaxations such as SoftSort violate (C1) by a quantitative margin that depends on the tem

Chuang Xu, Alan Pak Tao Lau

We propose the feed-forward perturbation-based nonlinearity compensation method using the received signal, which outperforms conventional decision-based ones and eliminates the need for decision feedback. Additionally, combining half-half dispersion compensation with pre-/ post-compensation yields further performance gains.

Eran Agmon, Ryan K Spangler

Building multiscale biological models requires the integration of independently developed submodels, which means moving shared variables between them and coordinating when each submodel runs. Existing tools typically address isolated biological mechanisms with specific numerical methods, rarely specify which variables each model reads and writes, how those v

Andrea Giorgini, Jingning He, Hao Wu

We investigate a hydrodynamic system of Navier--Stokes/Cahn--Hilliard type, which describes the motion of a two-phase flow of two incompressible fluids with unmatched densities coupled with a soluble chemical species. Derived from Onsager's variational principle, this thermodynamically consistent diffuse-interface model incorporates both the chemotaxis effec

Philipp J. di Dio, Lars-Luca Langer

For any closed $K\subseteq\mathbb{R}^n$, in [P.\ J.\ di\,Dio, K.\ Schm\"udgen: $K$-Positivity Preserver and their Generators, SIAM J.\ Appl.\ Algebra Geom.\ 9 (2025), 794--824] all $K$-positivity preserver have been characterized, i.e., all linear maps $T:\mathbb{R}[x_1,\dots,x_n]\to\mathbb{R}[x_1,\dots,x_n]$ such that $Tp\geq 0$ on $K$ for all $p\geq 0$ on

Miguel J. Torrejon, S. Blazquez, Jesus Algaba, M. M. Conde

In this work, we determine the dissociation temperature of the hydrogen (H$_2$) hydrate by computer simulation using two different methods. In both cases, the molecules of water and H$_2$ are modeled using the TIP4P/Ice and a modified version of the Silvera and Goldman models respectively, and the Berthelot combining rule for the cross water-H$_2$ interactio

Xinyang Li, Hian Zing Voon, Vlad C. Andrei, Alexander Sessler

We demonstrate a real-time implementation of multi-target detection and tracking using 5G New Radio (NR) physical downlink shared channel (PDSCH) waveform with 400 MHz bandwidth at 28 GHz carrier frequency. The hardware platform is built on a radio frequency system-on-chip (RFSoC) 4x2 board connected with a pair of Sivers EVK02001 mmWave beamformers for tran

Marwan Taher, Ignacio Alzugaray, Kirill Mazur, Xin Kong

Multi-view 3D geometry networks offer a powerful prior but are prohibitively slow for real-time applications. We propose a novel way to adapt them for online use, enabling real-time 6-DoF pose tracking and online reconstruction of objects and scenes from monocular RGB videos. Our method rapidly selects and manages a set of images as keyframes to map a scene

Saksham Sethi, Fan Wei

Given a permutation $\pi$, let $\text{Av}_n(\pi)$ be the number of permutations of length $n$ that avoid $\pi$ as a subpermutation. The celebrated resolution of the Stanley-Wilf conjecture by Marcus and Tardos confirmed that the limit $L(\pi) = \lim_{n \to \infty} |\text{Av}_n(\pi)|^{1/n}$ exists. A central and challenging question concerns the behavior of $

Yong Xiao, Xubo Li, Haoran Zhou, Yingyu Li

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decision-making, dynamic environmental adaptation, and complex missions. It has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-opt

Syed Luqman Shah, Nurul Huda Mahmood

Accurate channel state information (CSI) acquisition under tight pilot and training-energy constraints is essential for next-generation wireless networks. In this work, we model the wireless channel as a proper complex Gaussian process over the transmit and receive antenna arrays, reducing pilot overhead and training energy by estimating the CSI from partial

Matteo Crispino, Niklas Witt, Stefan Enzner, Tommaso Gorni

Kagome metals exhibit rich correlated-electron physics, yet a systematic understanding of the degree of correlation across transition-metal species remains elusive. Using density-functional theory plus multi-orbital slave-spin mean-field theory, we investigate electronic correlations in the Ti-, V-, and Cr-based 135 compounds with Sb and Bi pnictogens. We fi

Xuewei Zhang, Bailing Tian, Kai Zheng, Yulin Hui

Real-time and collision-free motion planning remains challenging for robotic manipulation in unknown environments due to continuous perception updates and the need for frequent online replanning. To address these challenges, we propose a parallel mapping and motion planning framework that tightly integrates Euclidean Distance Transform (EDT)-based environmen

M. Looshorn, B. M. Döhring, P. -M. Hillenbrand, M. Martins

Using the photon-ion merged-beams technique at the PETRA\,III synchrotron light source, we have measured cross sections for double and up to tenfold photoionization of La$^{+}$ ions by a single photon in the energy range 820--1400~eV, where resonances and thresholds occur that are associated with the excitation or ionization of one $M$-shell electron. These

Guancheng Du, Yong Hu, Wenqing Wang, Yaming Yang

Large Language Models (LLMs) have shown significant progress, but understanding long and complex documents remains challenging. Many long-context benchmarks have been proposed, but they face several limitations, including task realism, data scalability, and data quality. To this end, we introduce SagaScale, a realistic, scalable, and high-quality long-contex

Rosie Wood, Ondřej Mašek, Valentina Erastova

We report a molecular dynamics investigation of 2,4-dichlorophenoxyacetic acid (2,4-D) adsorption at the aqueous-biochar interface using experimentally constrained woody biochar models representative of softwood-derived biochars produced at 400, 600 and 800 ${\deg}$C. The models reproduce experimental descriptors (H/C, O/C, aromaticity, true density, and sur

Luca Ion, Adam Smith

Solving interacting multi-particle systems is a central challenge in quantum chemistry and condensed matter physics. In this work, we investigate the computation of ground states and ground-state energies for the He-H+ and H2O molecules using quantum computing techniques. We employ the variational quantum eigensolver (VQE), implemented both on a quantum comp

S. Blazquez, M. M. Conde, C. Vega

In this work we discuss the use of scaled charges when developing force fields for NaCl in water. We shall develop force fields for Na$^+$ and Cl$^-$ using the following values for the scaled charge (in electron units) : 0.75, 0.80, 0.85, 0.92 along with the TIP4P/2005 model of water (for which previous force fields were proposed for q = 0.85 and q = 1). The

Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Arefin Ittesafun Abian, Yan Zhang

Gliomas are among the most aggressive cancers, characterized by high mortality rates and complex diagnostic processes. Existing studies on glioma diagnosis and classification often describe issues such as high variability in imaging data, inadequate optimization of computational resources, and inefficient segmentation and classification of gliomas. To addres

Rajesh P. N. Rao, Vishwas Sathish, Linxing Preston Jiang, Matthew Bryan

The phenomenal advances in large language models (LLMs) and other foundation models over the past few years have been based on optimizing large-scale transformer models on the surprisingly simple objective of minimizing next-token prediction loss, a form of predictive coding that is also the backbone of an increasingly popular model of brain function in neur

Francesco Romor, Federico Pichi, Giovanni Stabile, Gianluigi Rozza

We demonstrate exponential convergence of Reduced Order Model (ROM) approximations for mixed boundary value problems of the stationary, incompressible Navier-Stokes equations in plane, polygonal domains $\Omega$. Admissible boundary conditions comprise mixed BCs, no-slip, slip and open boundary conditions, subject to corner-weighted analytic boundary data an

Raahul Singh, Ashutosh Pandey

Photometric Redshift is critical for analyzing astronomical objects, but existing ML methods often overlook the aleatoric uncertainties inherent in observed data. We introduce Starkindler, a novel training objective that explicitly incorporates observational errors into the model's objective function, thereby directly accounting for aleatoric uncertainty. Un

Gabriele Barbagallo, José Luis V. Cerdeira, Carmen Gómez-Fayrén, Patrick Meessen

Minimal 5-dimensional supergravity compactified on a circle gives the T$^{3}$ model of $\mathcal{N}=2,d=4$ supergravity, whose duality group is SL$(2,\mathbb{R})$. We study exhaustively the relations between all the local and global symmetries of both theories and between the corresponding conserved currents and charges, including the on-shell closed general

Atakan Işık, Selin Vulga Işık, Ahmet Feridun Işık, Mahşuk Taylan

Respiratory sound classification is hindered by the limited size, high noise levels, and severe class imbalance of benchmark datasets like ICBHI 2017. While Transformer-based models offer powerful feature extraction capabilities, they are prone to overfitting and often converge to sharp minima in the loss landscape when trained on such constrained medical da

Mattia Tarabolo, Luca Dall'Asta, Roberto Mulet

Classical approaches to ecological stability rely on fully connected interaction models, yet real ecosystems are sparse and structured--a feature that qualitatively reshapes their collective dynamics. Here, we establish a thermodynamically exact stability phase diagram for generalized Lotka-Volterra dynamics on sparse random graphs, resolving how finite conn

Deep Shankar Pandey, Hyomin Choi, Qi Yu

Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requir

Xuan Luo, Kailai Zhang, Xifeng Yan

When reading books, humans focus primarily on the current page, flipping back to recap prior context only when necessary. Similarly, we demonstrate that Large Language Models (LLMs) can learn to dynamically determine when to attend to global context. We propose All-or-Here Attention (AHA), which utilizes a binary router per attention head to dynamically togg

N. Dinh, M. A. Goberna, D. H. Long, M. Volle

We introduce in this paper the so-called robust generalized S-procedure associated with a given robust optimization problem. We provide a primal characterization for the validity of this procedure as well as a dual characterization under the assumption that the decision space is locally convex. We also analyze an extension of the mentioned robust S-procedure

Jiahua Yang, Zhen Lu, Yue Yang

Quantum computing holds potential for accelerating the simulation of fluid dynamics. However, hardware noise in the noisy intermediate-scale quantum era significantly distorts simulation accuracy. Although error magnitudes are frequently quantified, the specific physical effects of quantum noise on flow simulation results remain largely uncharacterized. We i

Ben Wang, Minghao Mi, Huangqiuchen Wang, Qian Xie

Accurate phase estimation plays a pivotal role in quantum metrology, yet its precision is significantly affected by noise, particularly phase-diffusive noise caused by phase drift. To address this challenge, the joint estimation of phase and phase diffusion has emerged as an effective approach, transforming the problem into a multi-parameter estimation task.

Xiaoda Xu, Jun Xian

We establish sharp non-asymptotic probabilistic bounds for the star discrepancy of double-infinite random matrices -- a canonical model for sequences of random point sets in high dimensions. By integrating the recently proved \textbf{optimal covering numbers for axis-parallel boxes} (Gnewuch, 2024) into the dyadic chaining framework, we achieve \textbf{expli

Johann A. Makowsky

Let $\mathcal{P}$ be a graph property. A $\mathcal{P}$-coloring with at most $k$ colors is a coloring of the vertices of a simple graph $G$ such that each color class induces a graph in $\mathcal{P}$. Harary polynomials are generalizations of the chromatic polynomial for simple graphs based on conditional colorings. We denote by $\chi_{\mathcal{P}}(G; k)$ th

Fynn Jerome Aschmoneit, Bastiaan Cockx

The intersection of two orthogonal cylinders represents a classical problem in computational geometry with direct applications to engineering design, manufacturing, and numerical simulation. While analytical solutions exist for the fully intersecting case, the Steinmetz solid, partial intersections with arbitrary depth ratios require numerical methods or app

Fatihcan M. Atay

We consider consensus of multi-agent systems as a dual problem to Markov processes. Based on an exchange of relevant notions and results between the two fields, we present a uniform framework which admits the introduction and treatment of time delays in a common setting. We study both information propagation and information processing delays, and for each ca

Shermin Sultana Setu, Mst. Amena Akter Pinky, Md. Abdul Awal, Sheekar Banerjee

The rapid evolution of the Internet of Medical Things (IoMT) technology has become a transformative force in modern healthcare, particularly in elderly patient management. The current elderly care system faces significant challenges, including insufficient long-term care resources and poor communication between healthcare providers. To address this limitatio

Chuang-Chieh Lin, Chi-Jen Lu, Po-An Chen, Chih-Chieh Hung

We formulate two-party policy competition as a two-player non-cooperative game, generalizing Lin et al.'s work (2021). Each party selects a real-valued policy vector as its strategy from a compact subset of Euclidean space, and a voter's utility for a policy is given by the inner product with their preference vector. To capture the uncertainty in the competi

Yuta Nakayama

We discuss the asymptotics of the Archimedean part of the Arakelov intersection number. The theorem is motivated by recent conjectures and their proof strategy by Gao and Zhang on the Northcott property of the Beilinson--Bloch height pairing. Our method involves a homological algebra interpretation of the Archimedean height by Hain. This interpretation allow

Jaebin Lee, Hankook Lee

In machine learning, effective modeling requires a holistic consideration of how to encode inputs, make predictions (i.e., decoding), and train the model. However, in time-series forecasting, prior work has predominantly focused on encoder design, often treating prediction and training as separate or secondary concerns. In this paper, we propose TimePerceive

Indra Kumar Banerjee, Nabarun Chakrabarty, Ujjal Kumar Dey

We construct a classically scale invariant version of the Zee-Babu model governed by an $U(1)_{B-L}$ gauge symmetry wherein three right handed neutrinos with identical gauge charges are present. A $\mathbb{Z}_2$ symmetry is additionally imposed such that the lightest right handed neutrino becomes a dark matter candidate. A spontaneous breakdown of the $U(1)_

Bomont Jean-Marc, Bretonnet Jean-Louis, Costa Dino, Pastore Giorgio

Framing the glass formation within standard statistical mechanics is an outstanding problem of condensed matter theory. To provide new insight, we investigate the structural properties of the Lennard-Jones fluid in the very-low temperature regime, by using a replicated version of the refined HMSA theory of the liquid state, combined with an appropriate split

Francisco J. Aragón-Artacho, Heinz H. Bauschke, César López-Pastor

In this note, we provide explicit expressions for the projections onto the graph of a quadratic polynomial. The projections are obtained by examining the critical points of the associated quartic polynomial, that is, the roots of the cubic polynomial defining its derivative. We also focus on the case where the point we project lies on the vertical line defin

Jesen Zhang, Ningyuan Liu, Kaitong Cai, Sidi Liu

Multimodal LLMs often produce fluent yet unreliable reasoning, exhibiting weak step-to-step coherence and insufficient visual grounding, largely because existing alignment approaches supervise only the final answer while ignoring the reliability of the intermediate reasoning process. We introduce SR-MCR, a lightweight and label-free framework that aligns rea

Promit Ghosal, Guilherme L. F. Silva

Stochastic growth models in the Kardar-Parisi-Zhang (KPZ) universality class exhibit remarkable fluctuation phenomena. While a variety of powerful methods have led to a detailed understanding of their typical fluctuations or large deviations, much less is known about behavior on intermediate, or moderate deviation, scales. Addressing this problem requires re

Tsuyoshi Yoneda

We consider the incompressible three-dimensional Euler equations for a vortex ring with Kelvin waves undergoing radially expanding Lagrangian transport. To clarify the fundamental mechanisms underlying nonlinear scale-local deformations of the vortex structure, we develop a geometric Lagrangian framework that avoids singular integral representations of the p

Tingyu Zhao, Balázs Maga, Pierfrancesco Dionigi, Gergely Ódor

The quantum internet is a rapidly developing technological reality, yet, it remains unclear what kind of quantum network structures might emerge. Since indirect quantum communication is already feasible and preserves absolute security of the communication channel, a new node joining the quantum network does not need to connect directly to its desired target.

Tengtao Guo, Yuxuan Zhou, Jiahui Feng, Xinyu Zhao

Contrary to the conventional view that noise is detrimental, we show that mixed noise can protect entanglement in a two-atom-cavity system. Specifically, the leakage of the cavity and the stochastic atom-cavity couplings are modeled as two types of noises. From the analytical derivation of the dynamical equations, the mechanism of the entanglement protection

David Nolland

This paper argues that the traditional opposition between determinism and indeterminism in physics is representational rather than ontological. Deterministic-stochastic dualities are available in principle, and arise in a non-contrived way in many scientifically important models. When dynamical systems admit mathematically equivalent deterministic and stocha