December 2025 arXiv papers — page 64

W. Andre Perkins, Anna Kwa, Jeremy McGibbon, Troy Arcomano

Kilometer-scale simulations of the atmosphere are an important tool for assessing local weather extremes and climate impacts, but computational expense limits their use to small regions, short periods, and limited ensembles. Machine learning offers a pathway to efficiently emulate these high-resolution simulations. Here we introduce HiRO-ACE, a two-stage AI

J. Mark Keil, Debajyoti Mondal

A geometric intersection graph is constructed over a set of geometric objects, where each vertex represents a distinct object and an edge connects two vertices if and only if the corresponding objects intersect. We examine the problem of finding a maximum clique in the intersection graphs of segments and disks under grounded and stabbed constraints. In the g

Evangelos Vlachos

This letter investigates the coupled control problem in UAV networks utilizing high-frequency hybrid beamsteering. While phased arrays enable rapid electronic scanning, their finite Field of View (FoV) imposes a fundamental constraint that necessitates active mechanical steering of the airframe to maintain connectivity. We propose a decentralized Model Predi

Shiguang Ma, Jie Qing

In this paper we extend the research on potential theory and its geometric applications from Euclidean spaces to homogeneous Carnot groups. We introduce a new approach to use the geometric completeness to estimate the Hausdorff dimension of polar sets of potentials of nonnegative Radon measures for sub-Laplacians in homogeneous Carnot groups. Our approach re

Handhika Satrio Ramadhan, M Naufal Athaullah, Ilham Prasetyo

We revisit the question of whether Dirac-Born-Infeld (DBI) cosmic strings can admit Bogomol'nyi-Prasad-Sommerfield (BPS) configurations. Earlier work by Babichev et al. arXiv:0809.2013 concluded that DBI strings with the standard Mexican-hat potential possess no BPS limit, implying an unavoidable nonzero binding energy. In contrast, using the BPS Lagrangian

Mohammad Zolfaghari, Hedieh Sajedi

Anomaly detection or outlier is one of the challenging subjects in unsupervised learning . This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics . For this purpose , we first pre-train the ResNet-18 network on the ImageNet and then fine-tune it on the MVTech-AD d

Robert J. Elliott, Zhe Yang

In this paper, we extend the results of Elliott and Yang \cite{elliott3} and discuss the control of a stochastic process for which the driving noise is provided by a martingale associated with a semi-Markov Chain. An existence and a comparison theorem are obtained. In our discrete time setting, adjoint processes are provided by backward stochastic difference

Yan Zhao, Yuhang Hou, Xiangbin Cai, Shihao Ru

Collective quantum phenomena, such as the excitation of composite fermions1, spin waves2, and exciton condensation3,4, can emerge in strongly correlated systems like the fractional quantum Hall states5, spin liquids6, or excitonic insulators7. Two-dimensional (2D) moir\'e superlattices have emerged as a powerful platform for exploring such correlated phases

Rohan Shirur, Bryce Morsky

Phage therapy is an alternative treatment method for bacterial infections. It has shown particular promise in reducing bacterial load while preventing antibiotic resistance. Here, we develop a mathematical model of a bacterial infection within a host to study phage therapy. It incorporates interactions between phages, bacteria, the immune system, and antibio

Rui Liu, Dian Yu, Lei Ke, Haolin Liu

Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalent group-based algorithms such as GRPO require multi-rollout sampling for each prompt. While more efficient single-rollout variants have recently been explored in text-only settings

Tsuyoshi Miezaki, Shunya Tamura

In this paper, we present a constructive bijection between a conditioned spanning forest of the wheel graph $W_{n+1}$ and a spanning tree of the fan graph $F_n$. In addition, by applying the effective resistance formula obtained by Bapat and Gupta \cite{bapat-gupta}, we derive an explicit formula for the number of two-component spanning forests of $W_{n+1}$

Wu-Te Yang, Masayoshi Tomizuka

Modeling soft pneumatic actuators with high precision remains a fundamental challenge due to their highly nonlinear and compliant characteristics. This paper proposes an innovative modeling framework based on fractional-order differential equations (FODEs) to accurately capture the dynamic behavior of soft materials. The unknown parameters within the fractio

Yang Liu, Yixian Gao

This paper is concerned with the stability of the inverse boundary value problem for the perturbed fourth-order Schr\"{o}dinger equation in a bounded domain with Cauchy data. We establish stability results for the perturbed potential relying on boundary measurements. The estimates depend on various a priori information regarding the regularity and the suppor

Yudong Liu, Spencer Hallyburton, Jiwoo Kim, Yueqian Lin

Trajectory planning is a fundamental yet challenging component of autonomous driving. End-to-end planners frequently falter under adverse weather, unpredictable human behavior, or complex road layouts, primarily because they lack strong generalization or few-shot capabilities beyond their training data. We propose LLaViDA, a Large Language Vision Driving Ass

Wen Huang, Yuchen Mao, Yanmin Qian

Achieving robust generalization in speech deepfake detection (SDD) remains a primary challenge, as models often fail to detect unseen forgery methods. While research has focused on model-centric and algorithm-centric solutions, the impact of data composition is often underexplored. This paper proposes a data-centric approach, analyzing the SDD data landscape

Vishnu Mohan

Tennis strategy optimization is a challenging sequential decision-making problem involving hierarchical scoring, stochastic outcomes, long-horizon credit assignment, physical fatigue, and adaptation to opponent skill. I present a reinforcement learning framework that integrates a custom tennis simulation environment with a Dueling Double Deep Q-Network(DDQN)

Yizhou Zhang

Empirical power--law scaling has been widely observed across modern deep learning systems, yet its theoretical origins and scope of validity remain incompletely understood. The Generalized Resolution--Shell Dynamics (GRSD) framework models learning as spectral energy transport across logarithmic resolution shells, providing a coarse--grained dynamical descri

Baoling Xie, Jun Lai

We develop a comprehensive analytical and numerical framework for boundary integral equations (BIEs) of the 2D Lam\'e system on cornered domains. By applying local Mellin analysis on a wedge, we obtain a factorizable characteristic equation for the singular exponents of the boundary densities, and clarify their dependence on boundary conditions. The Fredholm

Kanishka Roy, Tahsin Fuad Hasan, Chenfeng Wu, Eshwar Vangala

Current data-driven Wi-Fi-based indoor localization systems face three critical challenges: protecting user privacy, achieving accurate predictions in dynamic multipath environments, and generalizing across different deployments. Traditional Wi-Fi localization systems often compromise user privacy, particularly when facing compromised access points (APs) or

Trevor Stepp, Parthan Olikkal, Ramana Vinjamuri, Rajasekhar Anguluri

Identifying motor synergies -- coordinated hand joint patterns activated at task-dependent time shifts -- from kinematic data is central to motor control and robotics. Existing two-stage methods first extract candidate waveforms (via SVD) and then select shifted templates using sparse optimization, requiring at least two datasets and complicating data collec

Amna Ejaz, Sarah Dodson-Robinson, Charlotte Haley

Astronomers who search for periodic signals using Lomb-Scargle periodograms rely on false alarm level (FAL) estimates to identify statistically significant peaks. Although FALs are often calculated from white noise models, many astronomical time series suffer from red noise. Prewhitening is a statistical technique in which a continuum model is subtracted fro

Haoda Li, Jiahui Chen, Yu Sun, Shaoxu Song

Inconsistent values are commonly encountered in real-world applications, which can negatively impact data analysis and decision-making. While existing research primarily focuses on identifying the smallest removal set to resolve inconsistencies, recent studies have shown that multiple minimum removal sets may exist, making it difficult to make further decisi

Zhi Yin, Xiaoran Li, Zhupeng Han, Shengyu Zhang

Quantum computers promise scalable treatments of electronic structure, yet applying variational quantum eigensolvers (VQE) on realistic drug-like molecules remains constrained by the performance limitations of near-term quantum hardwares. A key strategy for addressing this challenge which effectively leverages current Noisy Intermediate-Scale Quantum (NISQ)

Mingyang Sun, Feng Hong, Weinan Zhang

The development of LLMs has elevated AI agents from task-specific tools to long-lived, decision-making entities. Yet, most architectures remain static and reactive, tethered to manually defined, narrow scenarios. These systems excel at perception (System 1) and deliberation (System 2) but lack a persistent meta-layer to maintain identity, verify reasoning, a

Jose Guerrero, Mirko Gamba

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has emerged as a versatile and reliable diagnostic tool for measuring temperature, pressure, gas composition, and velocity in power generation and propulsion systems. This paper provides a comprehensive review of TDLAS principles and practical considerations for sensor design and implementation. The discuss

Hyeonjin Lee, Jun-Hyuk Kim, Jong-Seok Lee

In recent years, the demand of image compression models for machine vision has increased dramatically. However, the training frameworks of image compression still focus on the vision of human, maintaining the excessive perceptual details, thus have limitations in optimally reducing the bits per pixel in the case of performing machine vision tasks. In this pa

Devang Dhanuka, Nidhi Rastogi

Modern intrusion detection systems (IDS) leverage graph neural networks (GNNs) to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations Centers (SOCs) by making graph-based detection transparent. W

Wei-Ren Syong, Allie Miller, Emma Davis, John R. Pitten

Cryogenic microwave measurement of superconducting quantum devices is complicated by the packaging required to connect devices to control and readout circuitry. In this work, we outline the design and experimental demonstration of a wirebond-free, PCB-free, drop-in microwave package for on-chip superconducting quantum devices. The package is composed of a su

Yilei Wu, Yichi Zhang, Zijian Dong, Fang Ji

Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel disease and potential indicators of neurodegenerative conditions. Despite their clinical significance, automatic enlarged PVS (EPVS) segmentation remains challenging due to their smal

Jundong Xu, Hao Fei, Huichi Zhou, Xin Quan

Although LLMs exhibit strong reasoning capabilities, existing training methods largely depend on outcome-based feedback, which can produce correct answers with flawed reasoning. Prior work introduces supervision on intermediate steps but still lacks guarantees of logical soundness, which is crucial in high-stakes scenarios where logical consistency is paramo

Ting Cui, Zihan Xu, Qinghua Zhang, Xiaodong Zhang

Antiperovskite nitrides (X3AN) are the structural analogues to perovskite oxides, while their epitaxial growth and electronic properties remain largely unexplored. We report the successful synthesis of Ni3InN thin films on substrates with different lattice constants. First-principles phonon calculations confirm the dynamical stability of cubic phase Ni3InN,

Dongha Yoon, Younghoon Min, Hoshik Kim, Sam H. Noh

Disaggregated LLM serving improves resource efficiency by separating the compute-intensive prefill phase from the latency-critical decode phase. However, this architecture introduces a fundamental bottleneck: key/value (KV) tensors generated during prefill must be transferred to decode workers, and existing systems rely on RDMA-based network paths for this e

Dongke Rong, Qinghua Zhang, Ting Cui, Qianying Wang

Twistronics, which exploits moire modulation of lattice and electronic structures in twisted bilayers, has emerged as a powerful approach to engineer novel quantum states. Recent efforts have expanded beyond two dimensional van der Waals (vdWs) crystals to more complex, strongly correlated materials, where interfacial moire effects can dominate physical prop

Alex Foo, Wynne Hsu, Mong Li Lee

Discovering object-centric representations from images can significantly enhance the robustness, sample efficiency and generalizability of vision models. Works on images with multi-part objects typically follow an implicit object representation approach, which fail to recognize these learned objects in occluded or out-of-distribution contexts. This is due to

Anjali Varshney, Aishwery J. Verma, Ritesh Dubey, Sushil Kumar

High-entropy materials have emerged as a promising class of catalysts, driven by their high configurational entropy originating from structural disorder in single-phase multicomponent systems. Despite their potential, the catalytic performance of high-entropy oxides (HEOs) remains relatively underexplored. In this study, we present a simple solution-based co

Jian Yan

This paper proposes the External Hippocampus framework, which models language model reasoning from a cognitive dynamics perspective as the flow of information energy in semantic space. Unlike traditional weight-space optimization methods, this framework constructs topological cognitive maps through dimensionality reduction projection, enabling precise naviga

Muhammad Atif Zaheer

We obtain closed form of some infinite series involving derivatives of an analogue of the Riemann xi function for Dedekind zeta function and nontrivial zeros of Dedekind zeta function assuming the Extended Riemann Hypothesis. Conversely, we prove that if this closed form holds, then all of the zeros of Dedekind zeta function beyond a certain height lie on th

Zihao Deng, Yijia Li, Renrui Zhang, Peijun Ye

Cognitive computing models offer a formal and interpretable way to characterize human's deliberation and decision-making, yet their development remains labor-intensive. In this paper, we propose NL2CA, a novel method for auto-formalizing cognitive decision-making rules from natural language descriptions of human experience. Different from most related work t

José Gaitan, José Madrid

We find the optimal constant $C$ such that \begin{equation*} \|f_1*f_2*\dots*f_{k}\|_{\infty}\geq C\prod_{i=1}^{k}\|f_i\|_1 \end{equation*} for functions $f_i:\{0,1\}^d\to\mathbb{R}$. As applications, we derive bounds for Sidon sets on hypercubes, and, we also obtain bounds for the continuous analogue problem.

Janghyun Baek, Mincheol Chang, Seokha Moon, Seung Joon Lee

Recent query-based 3D object detection methods using camera and LiDAR inputs have shown strong performance, but existing query initialization strategies,such as random sampling or BEV heatmap-based sampling, often result in inefficient query usage and reduced accuracy, particularly for occluded or crowded objects. To address this limitation, we propose ALIGN

Patricia Cahn, Rima Chatterjee, Vladimir Chernov

We construct infinite families of non-simple isotopy classes of links in overtwisted contact structures on $S^1$-bundles over surfaces. These examples include: (1) a pair of Legendrian links that are not Legendrian isotopic, but which are isotopic as framed links, homotopic as Legendrian immersed multi-curves, and have Legendrian-isotopic components and (2)

Junho Lee, Kwanseok Kim, Joonseok Lee

Flow matching has emerged as a powerful generative modeling approach with flexible choices of source distribution. While Gaussian distributions are commonly used, the potential for better alternatives in high-dimensional data generation remains largely unexplored. In this paper, we propose a novel 2D simulation that captures high-dimensional geometric proper

Haoran Wang

The goal of a recently launched project is to extend the Euclidean models in \cite{Wang24,WZZ25-AHP,WZZ25-JDE} to a more general setting of conically singular spaces. In this paper, the main results include a weighted dispersive inequality for the Schr\"odinger equation and a dispersive estimate for the wave equation both with one Aharonov-Bohm solenoid in a

Lantian Li, Yue Pan, Dan Wang, Jingwen Wu

The correctness of the Solidity compiler is crucial for ensuring the security of smart contracts. However, the implementation complexity of its type system often introduces elusive defects. This paper presents the first systematic empirical study on typing-related bugs in the Solidity compiler. To systematically analyze these bugs, we collected 146 officiall

Kaixing Yang, Jiashu Zhu, Xulong Tang, Ziqiao Peng

With the rise of online dance-video platforms and rapid advances in AI-generated content (AIGC), music-driven dance generation has emerged as a compelling research direction. Despite substantial progress in related domains such as music-driven 3D dance generation, pose-driven image animation, and audio-driven talking-head synthesis, existing methods cannot b

Rebecca Salganik, Yibin Wang, Guillaume Salha-Galvan, Jian Kang

Individual fairness, which requires that similar individuals should be treated similarly by algorithmic systems, has become a central principle in fair machine learning. Individual fairness has garnered traction in graph representation learning due to its practical importance in high-stakes Web areas such as user modeling, recommender systems, and search. Ho

Ben Bakary Junior Siriki, Adama Coulibaly

This paper provides a qualitative analysis of a non-uniform Euler-Bernoulli beam with degenerate flexural rigidity, subjected to axial force and boundary control with time delay $\tau > 0$. By reformulating the system as an abstract evolution problem in an augmented Hilbert space incorporating weighted Sobolev spaces, we employ semigroup theory to ensure wel

Qijia Zhai

We present a domain decomposition-based deep learning method for solving elliptic and parabolic interface problems with discontinuous coefficients in two to ten dimensions. Our Multi-Activation Function (MAF) approach employs two independent neural networks, one for each subdomain, coupled through interface conditions in the loss function. The key innovation

Midhat Urooj, Ayan Banerjee, Sandeep Gupta

Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-stakes clinical decision-making. Most existing vision models rely on purely data-driven learning and produce black-box predictions with limited interpretability and poor cross-domain

Pu-Zhao Kow, Mikko Salo, Henrik Shahgholian

We study the scattering behavior of an anisotropic inhomogeneous Lipschitz medium at a fixed wave number, continuing our previous work [SIAM J. Math. Anal., 56(4):4834-4853, 2024] and using free boundary techniques from [arXiv:2506.22328]. Our main results can be categorized into two distinct cases. In the first case, we show that in two dimensions, piecewis

Lena Libon, Meghana Bhange, Rushabh Solanki, Elliot Creager

The current era of AI development places a heavy emphasis on training large models on increasingly scaled-up datasets. This paradigm has catalyzed entirely new product categories, such as LLM chatbots, while also raising concerns about data privacy and consumer choice. In this paper, we consider questions of data portability and user autonomy in the context

T. Van Hoomissen, J. Alhuthali, A. M. Ortiz, D. A. Mariscal

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density ($n_e$) and electron temperature ($T_e$). Deep neural networks can provide accurate estimates of $n_e$ and $T_e$ when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or

Jayani P. Gamage, Dianne Cook, Paul Harrison, Michael Lydeamore

Simulated high-dimensional data is useful for testing, validating, and improving algorithms used in dimension reduction, supervised and unsupervised learning. High-dimensional data is characterized by multiple variables that are dependent or associated in some way, such as linear, nonlinear, clustering or anomalies. Here we provide new methods for generating

A. R. Castelli, K. M. Beck, L. D. H. Alegria, L. A. Martinez

Most quantum error correction (QEC) protocols for superconducting qubits assume spatially and temporally uncorrelated decoherence events; however, recent evidence suggests that cosmic radiation induces spatially correlated errors. We present a platform that sandwiches a superconducting transmon qubit between two microwave kinetic inductance detector (MKID) a

Ahmed Dughayshim

We study well-posedness of the $s$-Schrödinger map equation in dimension $n \geq 3$ in the subcritical regime, more precisely we establish a local well-posedness result when the initial data is $u_{0} \in B^σ_{2,1}$ with $ σ\geq \frac{n+1}{2}$ and $ \Vert u_{0} \Vert_{B^σ_{2,1}} \ll 1.$

Kenichi Takemura

This paper focuses on an integer-valued function $f_A(\sigma) := \operatorname{tr}(A P_\sigma)$ defined uniformly from a specific square matrix $A$ of order $n$ and a permutation $\sigma$ on the symmetric group $S_n$. The main objective of this study is to investigate in detail the algebraic behavior of the Alternating Power Difference (APD), denoted as $APD

Antara Titikhsha, Divyanshu Tak

Intracranial aneurysm rupture causes subarachnoid hemorrhage with mortality near 50%, making early detection critical. Although CTA enables rapid screening, detecting small aneurysms within the complex three-dimensional branching of the Circle of Willis remains expertise-dependent. Existing automated systems are constrained by class imbalance, skull-base art

Jian Ma

This is the monograph on the theory and applications of copula entropy (CE). This book first introduces the theory of CE, including its background, definition, theorems, properties, and estimation methods. The theoretical applications of CE to structure learning, association discovery, variable selection, causal discovery, system identification, time lag est

Jayani P. Gamage, Dianne Cook, Paul Harrison, Michael Lydeamore

Nonlinear dimension reduction methods provide a low-dimensional representation of high-dimensional data by applying a Nonlinear transformation. However, the complexity of the transformations and data structures can create wildly different representations depending on the method and hyper-parameter choices. It is difficult to determine whether any of these re

Hamza Abdelhedi, Yorguin-Jose Mantilla-Ramos, Sina Esmaeili, Annalisa Pascarella

We present Coord2Region, an open-source Python package that streamlines coordinate-based neuroimaging workflows by automatically mapping 3D brain coordinates (e.g., MNI or Talairach) to anatomical regions across multiple atlases. The package links mapped coordinates to meta-analytic resources via the Neuroimaging Meta-Analysis Research Environment (NiMARE) ,

Felipe Martin Rodriguez Fuentes, Bernard Parent

Communication blackout due to the plasma layer creates a critical telemetry gap for re-entry vehicles. To mitigate this, we present the first fully-coupled simulation of high-voltage pulsed discharges interacting with a Mach 24 flowfield using an advanced numerical framework. The results demonstrate that the applied electric field generates a large, non-neut

Steven Hu, Sophia H. Kim, Helena H. Kim, Hugo Mackay

This study analyzes how vibrato changes with finger position along the cello string. Examining 94 excerpts, we found moving the finger toward the bridge strongly increases acoustic vibrato depth ($\rho=0.6902$, $p=1.408\cdot 10^{-14}$). However, the performer's physical finger amplitude simultaneously decreases ($\rho=-0.6391$, $p=4.172\cdot 10^{-12}$). This

Taewon Yang, Jason Hu, Jeffrey A. Fessler, Liyue Shen

Diffusion models learn strong image priors that can be leveraged to solve inverse problems like medical image reconstruction. However, for real-world applications such as 3D Computed Tomography (CT) imaging, directly training diffusion models on 3D data presents significant challenges due to the high computational demands of extensive GPU resources and large

Alex Wilf, Pranjal Aggarwal, Bryan Parno, Daniel Fried

Training models through self-play alone (without any human data) has been a longstanding goal in AI, but its effectiveness for training large language models remains unclear, particularly in code generation where rewards based on unit tests are brittle and prone to error propagation. We study self-play in the verified code generation setting, where formal ve

Hao Li, Daiwei Lu, Jiacheng Wang, Robert J. Webster

This work presents EndoStreamDepth, a monocular depth estimation framework for endoscopic video streams. It provides accurate depth maps with sharp anatomical boundaries for each frame, temporally consistent predictions across frames, and real-time throughput. Unlike prior work that uses batched inputs, EndoStreamDepth processes individual frames with a temp

Wisnu Uriawan, Muhammad Farhan Tarigan, Herdin Kristianjani Zebua, Muhamad Nopid Andriansyah

The development of the digital economy has established e-commerce platforms as the primary space for commercial transactions for the Muslim community. However, innovations in features and business models on these platforms have gave rise to Sharia issues that cannot be fully explained through conventional Fiqh Muamalah contract frameworks. This research aims

Tsung-Han Lu, Zheyu Li, Minxuan Zhou, Tajana Rosing

All-pairs shortest paths (APSP) is a fundamental algorithm used for routing, logistics, and network analysis, but the cubic time complexity and heavy data movement of the canonical Floyd-Warshall (FW) algorithm severely limits its scalability on conventional CPUs or GPUs. In this paper, we propose PIM-FW, a novel co-designed hardware architecture and dataflo

Massimo Della Valle, Maria Teresa Botticella, Enrico Cappellaro, Roberto Ragazzoni

The study of transient phenomena in a multimessenger context is expected to remain a major pillar of astrophysical discovery in the decades ahead. Supernovae, Kilonovae, Black-Hole formation, Novae, GRBs, and tidal disruption events are prime examples, as their earliest phases link electromagnetic radiation to gravitational waves, neutrinos, and high-energy

P. G. Pritchard, James M. Rondinelli

We present a lattice-renormalized formalism for configurational tunneling two-level systems (TLS) that overcomes limitations of minimum-energy-path and light-particle models. Derived from the nuclear Hamiltonian, our formulation introduces composite phonon coordinates to capture lattice distortions between degenerate potential wells. This approach resolves d

Aresh Dadlani, Muthukrishnan Senthil Kumar, Omid Ardakanian, Ioanis Nikolaidis

Timely updates are critical for real-time monitoring and control applications powered by the Internet of Things (IoT). As these systems scale, they become increasingly vulnerable to adversarial attacks, where malicious agents interfere with legitimate transmissions to reduce data rates, thereby inflating the age of information (AoI). Existing adversarial AoI

Xiao-Hu Mo, Jin-Tao Chen, You-Kai Wang

The parametrization forms of charmonium quasi two body decays are discussed in detail in this paper. The symmetry analysis and magnetic transition description are utilized to provide the multi-aspect comprehension of the decay dynamics, including the meson mixing angle, form factor, and $SU(3)$ symmetry breading effect. As the prerequisite, the electromagnet

Rui Xie, Yunhua Fang, Asad Ul Haq, Linsen Ma

LLM inference is increasingly memory bound, and HBM cost per GB dominates system cost. Current HBM stacks include short on-die ECC that tightens binning, raises price, and fixes reliability policy inside the device. This paper asks whether a system can tolerate a much higher raw HBM bit error rate and still keep end-to-end correctness and throughput, without

Cesar Fierro Cota

F-theory compactifications on elliptically fibered Calabi--Yau threefolds yield consistent six-dimensional $\mathcal{N}=(1,0)$ supergravity theories, for which the cancellation of gravitational, gauge and mixed anomalies imposes non-trivial algebraic relations between classical intersection data and enumerative geometry invariants of curves in the fiber. In

Robert Boltje, John Revere McHugh

For a $p$-permutation equivalence between two block algebras of finite groups, we introduce new square diagrams that link the $p$-permutation equivalence via the Brauer construction to local equivalences between stabilizers of corresponding Brauer pairs. These diagrams can be viewed as lifts of the square diagrams in the definition of isotypies. The proof of

Kento Okuyama, Tim Fabian Schaffland, Pascal Kilian, Holger Brandt

Psychological change processes, such as university student dropout in math, often exhibit discrete latent state transitions and can be studied using regime-switching models with intensive longitudinal data (ILD). Recently, regime-switching state-space (RSSS) models have been extended to allow for latent variables and their autoregressive effects. Despite thi

Mohammed Alghadeer, Simon Pettersson Fors, Shuxiang Cao, Simone D. Fasciati

Crosstalk between qubits fundamentally limits the scalability of quantum processors, necessitating physics-based models that can handle the complexity of large qubit arrays. Here, we develop a comprehensive theoretical and experimental framework that captures residual interactions between both adjacent and non-adjacent qubits in fixed-frequency transmon latt

Maximilian Schebek, Nikolas M. Froböse, Bettina G. Keller, Jutta Rogal

Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space distributions of a solute in the gas phase and in solution. Here, we introduce a computational framework based on normalizing flows that

Stergios E. Bachoumas, Panagiotis Artemiadis

Identifying the leader within a robotic swarm is crucial, especially in adversarial contexts where leader concealment is necessary for mission success. This work introduces the interactive Swarm Leader Identification (iSLI) problem, a novel approach where an adversarial probing agent identifies a swarm's leader by physically interacting with its members. We

Lucas Weitzel, Andreas Buchleitner, Dominik Lentrodt

Modern experiments in resonators are moving to ever more extreme quantum regimes, posing major challenges to established theoretical approaches, such as so-called few-mode models. While these models have driven major insights for traditional regimes, they are now hitting their limitations for highly open cavities and extended systems, as encountered in cavit

Jaladhar Mahato, Siyang Wang, Laura J. Kaufman

The origin of the dramatic slowdown of dynamics near the glass transition temperature (Tg) remains a long-standing fundamental and unresolved issue in soft condensed matter. While single-molecule (SM) experiments using fluorescent probes have provided critical insight for molecular and polymeric glass formers through rotational measurements, translational dy

Line Jelver, Joel D. Cox

Plasmons in atomically thin materials offer a compelling route to trigger nonlinear light-matter interactions through extreme optical confinement in the two-dimensional (2D) limit. However, optical nonlocality in plasmons is typically associated with losses in the linear response regime. Here, we show that nonlocal effects mediate strong plasmon-assisted opt

Sayan Jana, Bertin Many Manda, Vassos Achilleos, Dimitrios J. Frantzeskakis

The time evolution of initially localized wavepackets in the discrete Hatano-Nelson lattice displays a rich dynamical structure shaped by the interplay between dispersion and nonreciprocity. Our analysis reveals a characteristic evolution of the wave-packet center of mass, which undergoes an initial acceleration, subsequently slows down, and ultimately enter

Ranajay Datta, Mária Lukáčová-Medviďová, Andreas Schömer, Peter Virnau

We model the flow behaviour of dense melts of flexible and semiflexible ring polymers in the presence of walls using a hybrid multiscale approach. Specifically, we perform molecular dynamics simulations and apply the Irving-Kirkwood formula to determine an averaged stress tensor for a macroscopic model. For the latter, we choose a Cahn-Hilliard-Navier-Stokes

Shogo Yamashita, Esita Pandey, Gerhard H. Fecher, Claudia Felser

First-principles studies were performed on two Mn-based ferrimagnetic Heusler compounds with L21 and B2 structures, that is, Mn2VZ (Z = Al or Ga). The aim was to investigate their magnetic properties, electronic structures, and spin-resolved longitudinal conductivity at finite temperatures. Density functional theory (DFT) and functional integral theory were

Sylvain L. Walsh, Yalu Zhu, Feng Liu, William A. Sirignano

Combustion within a two-dimensional turbine stator passage is numerically investigated in the context of the turbine-burner concept using a Reynolds-Averaged Navier-Stokes framework coupled with a novel flamelet model. The formulation links resolved-scale turbulence quantities with subgrid flamelet dynamics through the local turbulent kinetic energy dissipat

Deepit Sapru

The relationship between crime and the media has long been a focal point of academic research, with traditional media playing a significant role in shaping public perceptions of safety and community well-being. However, the advent of social media has introduced a new dimension to this discourse, offering unique platforms for user-driven discussions. Despite

Alexei V. Tkachenko, Chuntian Cao, Amy C. Marschilok, Deyu Lu

A minimal model of ionic aggregation in concentrated ZnCl$_2$ is developed, guided by molecular dynamics simulations with a machine-learned potential. It explicitly incorporates solvent-site depletion, correlated chloride binding, and allows for loops within Zn-Cl clusters. Dehydration is shown to drive ion binding through two sharp transitions set by the Zn

Thomas W. Hsiao, Lance A. Waller

Traditional geostatistical methods assume independence between observation locations and the spatial process of interest. Violations of this independence assumption are referred to as preferential sampling (PS). Standard methods to address PS rely on estimating complex shared latent variable models and can be difficult to apply in practice. We study the use

Antonio Farina, Alfonso Veropalumbo, Enzo Branchini, Massimo Guidi

Ongoing and future spectroscopic galaxy surveys will cover unprecedented volumes with a number of objects large enough to effectively probe clustering anisotropies through higher-order statistics. In this work, we present a novel and efficient implementation of both a model for the multipole moments of the anisotropic 3-point correlation function (3PCF) and

D. L. Flannery, A. E. Zalesski

We show that finite quasisimple groups of Lie type in characteristic $p$ with an irreducible representation of prime degree $r$ over a finite field of characteristic $p$ have orders bounded above by a function of $r$, independent of $p$. We also bound the number of such groups in terms of $r$. Apart from being notable in their own right, these results have a

Hulin Yang, Gang Li, Haichen Jia, Artem Abanov

In this article, we explain the reason of the apparent contradiction between recent experiments [1] and [2] and earlier theoretical predictions [3] of strongly asymmetric condensate resulting in attractive interaction between the condensate magnons. We show that the relaxation time for equilibrium between two condensates at two minima of energy exceeds the t

Konstantin Larin, Daniel R. Kowal

Statistical models often require inputs that are not completely known. This can occur when inputs are measured with error, indirectly, or when they are predicted using another model. In environmental epidemiology, air pollution exposure is a key determinant of health, yet typically must be estimated for each observational unit by a complex model. Bayesian tw

Ying Xiao, Shangwen Wang, Sicen Liu, Dingyuan Xue

Traditional software fairness research typically emphasizes ethical and social imperatives, neglecting that fairness fundamentally represents a core software quality issue arising directly from performance disparities across sensitive user groups. Recognizing fairness explicitly as a software quality dimension yields practical benefits beyond ethical conside

Robert Streit, Vijay K. Garg

In a capacitated directed graph, it is known that the set of all min-cuts forms a distributive lattice [1], [2]. Here, we describe this lattice as a regular predicate whose forbidden elements can be advanced in constant parallel time after precomputing a max-flow, so as to obtain parallel algorithms for min-cut problems with additional constraints encoded by

Benjamin Costantino, Teresa Picconatto, Mark Taczak, Carl Picconatto

Linear surprisal analysis is applied to state-to-state experimental results for the H-atom abstraction reaction, H + HI -> H2 + I. Contrary to previously reported results that indicated that the products from this reaction were not well fit by a linear surprisal, the reaction can be accurately described by linear surprisal parameters when kinematic energy co

Benjamin Teo, Cécile Ané

Dynamic programming approaches have long been applied to fit models of univariate and multivariate trait evolution on phylogenetic trees for discrete and continuous traits, and more recently adapted to phylogenetic networks with reticulation. We previously showed that various trait evolution models on a network can be readily cast as probabilistic graphical

Yihao Zhao, Yang He, Zhonghan Hu

The Coulomb potential at an interior ion in a finite crystal of size $p$ is given by a linear superposition of contributions from displacement vectors ${\mathbf r}=(x,y,z)$ to its neighbors. This additive structure underlies universal relationships among Madelung constants and applies to both standard periodic boundary conditions and alternative Clifford sup

Ivin Babu, Ronan J. Conlon, Alix Deruelle

We show that any toric asymptotically conical shrinking gradient K\"ahler-Ricci soliton on an anti-canonically polarised resolution of a K\"ahler cone satisfies a complex Monge-Amp\`ere equation. We then set up an Aubin continuity path to solve the resulting equation and show that it has a solution at the initial value of the path parameter in the toric case

Srishti Singh, Hema Srinivasan

Consider a numerical semigroup minimally generated by a subset of the interval $[e,2e-1]$ with multiplicity $e$ and width $e-1$. Such numerical semigroups are called Sally type semigroups. We show that the defining ideals of these semigroup rings, when the embedding dimension is $e-2$, generically have the structure of the sum of two determinantal ideals. Mo

Cristiano da Costa Cunha, Wei Liu, Tim French, Ajmal Mian

Integrating causal inference (CI) with reinforcement learning (RL) has emerged as a powerful paradigm to address critical limitations in classical RL, including low explainability, lack of robustness and generalization failures. Traditional RL techniques, which typically rely on correlation-driven decision-making, struggle when faced with distribution shifts