November 2025 arXiv papers — page 197

Qi Li, Jianjun Xu, Pingtao Wei, Jiu Li

With the widespread application of Large Language Models (LLMs), their associated security issues have become increasingly prominent, severely constraining their trustworthy deployment in critical domains. This paper proposes a novel safety response framework designed to systematically safeguard LLMs at both the input and output levels. At the input level, t

Shipeng Cen, Ying Tan

As optimization problems grow increasingly complex and diverse, advancements in optimization techniques and paradigm innovations hold significant importance. The challenges posed by optimization problems are primarily manifested in their non-convexity, high-dimensionality, black-box nature, and other unfavorable characteristics. Traditional zero-order or fir

Evan Parshall, Junaid Ali, Michael Zimmerman

Fantasy football leagues involve strategic player trades to optimize team performance. However, identifying optimal trades is complex due to varying player projections, positional needs, and league-specific scoring. Existing approaches focus on team selection or lineup optimization, but automated trade generation remains underexplored. In this paper, an algo

Kexing Ji, Shiyun Fu, Cuiyun Gao, Yujia Chen

Large Code Models (LCMs) show potential in code intelligence, but their effectiveness is greatly influenced by prompt quality. Current prompt design is mostly manual, which is time-consuming and highly dependent on specific LCMs and tasks. While automated prompt generation (APG) exists in NLP, it is underexplored for code intelligence. This creates a gap, as

Eli Berger, Daniel McGinnis

Let $\mathcal{M}$ and $\mathcal{N}$ be two matroids on the same ground set $V$. Let $A_1,\dots,A_{2n-1}$ be sets which are independent in both $\mathcal{M}$ and $\mathcal{N}$, satisfying $|A_i|\geq \textrm{min}(i,n)$ for all $i$. We show that there exists a partial rainbow set of size $n$, which is independent in both $\mathcal{M}$ and $\mathcal{N}$. This is

Juan Manuel Sánchez-Cerritos, Mayte Torres-Hernández

We present a variational approach to obtain periodic solutions of the $N$-body problem, in particular the 'figure-eight' solution for three equal masses. The central idea is to explicitly optimize the \emph{spatial scale} within the Lagrangian action, leading to the functional $\mathcal F = K^{\alpha/(\alpha+2)} V^{2/(\alpha+2)}$. We prove the existence of c

Ziheng Zhang, Wen Chen, Qingqing Wu, Haoran Qin

This paper proposes a novel multiple intelligent reflecting surfaces (IRSs) collaborative hybrid localization system, which involves deploying multiple IRSs near the target area and achieving target localization through joint time delay and angle estimation. Specifically, echo signals from all reflective elements are received by each sensor and jointly proce

Thomas Manzini, Priyankari Perali, Robin R. Murphy

This paper presents the first AI/ML system for automating building damage assessment in uncrewed aerial systems (sUAS) imagery to be deployed operationally during federally declared disasters (Hurricanes Debby and Helene). In response to major disasters, sUAS teams are dispatched to collect imagery of the affected areas to assess damage; however, at recent d

Zeda Xu, Nikolas Martelaro, Christopher McComb

With generative AI-powered design tools, designers and engineers can efficiently generate large numbers of design ideas. However, efficient exploration of these ideas requires designers to select a smaller group of potential solutions for further development. Therefore, the ability to judge and evaluate designs is critical for the successful use of generativ

Yongxin Zhao, Shenglin Zhang, Yujia Wu, Yuxin Sun

As modern software systems continue to grow in complexity, triage has become a fundamental process in system operations and maintenance. Triage aims to efficiently prioritize, assign, and assess issues to ensure the reliability of complex environments. The vast amount of heterogeneous data generated by software systems has made effective triage indispensable

Ved Prakash Dubey, Shovan Bhaumik

This paper addresses an underwater target tracking problem in which a large number of sonobuoy sensors are deployed on a surveillance region. The region is divided into several sub-regions, where a single tracker, capable of generating track is installed. Each sonobuoy can measure the direction of arrival of acoustic signals (known as bearing angles) and com

Harbir Antil, Rainald Löhner, Felipe Pérez

We propose a linear programming (LP) framework for steady-state diffusion and flux optimization on geometric networks. The state variable satisfies a discrete diffusion law on a weighted, oriented graph, where conductances are scaled by edge lengths to preserve geometric fidelity. Boundary potentials act as controls that drive interior fluxes according to a

Najrin Sultana, Md Rafi Ur Rashid, Kang Gu, Shagufta Mehnaz

LLMs can provide substantial zero-shot performance on diverse tasks using a simple task prompt, eliminating the need for training or fine-tuning. However, when applying these models to sensitive tasks, it is crucial to thoroughly assess their robustness against adversarial inputs. In this work, we introduce Static Deceptor (StaDec) and Dynamic Deceptor (DyDe

Josué G. López-Castillo, Manuel Zamora-Avilés, Gilberto C. Gómez, Ivânio Puerari

The hydrodynamic cosmological simulation, TNG50, is employed to conduct an analysis of multi-spin galaxies that exhibit ringed structures composed of gas and stars that orbit nearly perpendicular around a host galaxy, known as polar ring galaxies (PRG). To ensure a robust sample, we select subhalos based on the angle subtended by the angular momentum profile

Hongbo Lan, Zhenlin An, Haoyu Li, Vaibhav Singh

This paper introduces \sysname, a system that accelerates vision-guided physical property reasoning to enable augmented visual cognition. \sysname minimizes the run-time latency of this reasoning pipeline through a combination of both algorithmic and systematic optimizations, including rapid geometric 3D reconstruction, efficient semantic feature fusion, and

Haitao Lin, Boxin Zhao, Mladen Kolar, Chong Liu

We study how to accelerate Bayesian optimization (BO) on a target task by transferring historical knowledge from related source tasks. Existing work on BO with knowledge transfer either lacks theoretical guarantees or achieves the same regret as BO in the non-transfer setting, $\widetilde{O}(\sqrt{T \gamma_f})$, where $T$ is the number of evaluations of the

Caleb Simiyu Khaemba, Hongsong Feng, Dong Chen, Chun-Long Chen

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra mod

Aditi Chakrabarti, Divya Jaganathan, Robert Haussman, L. Mahadevan

We explore the dynamical response of the free surface of an ultra-soft solid driven by a localized moving pressure disturbance. Experiments reveal a steady V-shaped wake analogous to a surface Mach wedge. A simple geometric argument provides a qualitative explanation consistent with observations. A theoretical framework combining elastodynamic, capillary, an

Seunghee Han, Yeonghun Kang, Taeun Bae, Junho Kim

Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generative models offer a promising way for inverse design, most approaches require large datasets and must be retrained for every new target property. Here, we introduce the EGMOF (Efficie

Yuya Miyaoka, Masaki Inoue

This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the CBF safety filter to the predicted token generated from the baseline LLM, to intervene in the generated text. The safety filter includes two signif

Botong. Zhao, Xubin. Wang, Shujing. Lyu, Yue. Lu

Integrated circuit manufacturing is highly complex, comprising hundreds of process steps. Defects can arise at any stage, causing yield loss and ultimately degrading product reliability. Supervised methods require extensive human annotation and struggle with emergent categories and rare, data scarce defects. Clustering-based unsupervised methods often exhibi

Seyed Mohamad Ali Tousi, G. N. DeSouza

Noisy quantum devices demand error-mitigation techniques to be accurate yet simple and efficient in terms of number of shots and processing time. Many established approaches (e.g., extrapolation and quasi-probability cancellation) impose substantial execution or calibration overheads, while existing learning-based methods have difficulty scaling to large and

Yanan Wang, Xi-hu Wu

This paper investigates a reverse space-time higher-order modified self-steepening nonlinear Schr\"odinger equation, which distinguishes its standard local counterparts through the reverse space-time symmetry. The integrability of this nonlocal equation is rigorously verified by presenting its associated Lax pair and infinitely many conservation laws. Utiliz

Yibo Meng, Ruiqi Chen, Zhuoran Lu, Shuai Ma

This study presents a five-year longitudinal mixed-methods study of 17 Chinese digital painters, examining how their attitudes and practices evolved in response to generative AI. Our findings reveal a trajectory from resistance and defensiveness, to pragmatic adoption, and ultimately to reflective reconstruction, shaped by strong peer pressures and shifting

Qi Feng, Guang Lin, Purav Matlia, Denny Serdarevic

In this paper, we propose a novel data-driven framework for discovering probabilistic laws underlying the Feynman-Kac formula. Specifically, we introduce the first stochastic SINDy method formulated under the risk-neutral probability measure to recover the backward stochastic differential equation (BSDE) from a single pair of stock and option trajectories. U

Moinak Ghoshal, Imran Khan, Phuc Dinh, Z. Jonny Kong

Mobility management in cellular networks, especially the handover (HO) process, plays a key role in providing seamless and ubiquitous Internet access. The wide-scale deployment of 5G and the resulting co-existence of 4G/5G in the past six years have significantly changed the landscape of all mobile network operators and made the HO process much more complex

Christopher B. C. Dean, Maria L. Pérez-Lara, Emma Horton, Matthew Southerby

Objective: To assess the accuracy and computational performance of a stochastic differential equation (SDE)--based model for proton beam dose calculation by benchmarking against Geant4 in simplified phantom geometries. Approach: Building on Crossley et al. (2025), we implemented the SDE model using standard approximations to interaction cross sections and me

Qi Zhang, Yifei Wang, Yisen Wang

Recently, self-supervised contrastive learning has achieved great success on various tasks. However, its underlying working mechanism is yet unclear. In this paper, we first provide the tightest bounds based on the widely adopted assumption of conditional independence. Further, we relax the conditional independence assumption to a more practical assumption o

Jiameng Chen, Yida Xiong, Kun Li, Hongzhi Zhang

Computational antibody design holds immense promise for therapeutic discovery, yet existing generative models are fundamentally limited by two core challenges: (i) a lack of dynamical consistency, which yields physically implausible structures, and (ii) poor generalization due to data scarcity and structural bias. We introduce FP-AbDiff, the first antibody g

Junwu Chen, Jeff Guo, Edvin Fako, Philippe Schwaller

Diffusion models promise to accelerate material design by directly generating novel structures with desired properties, but existing approaches typically require expensive and substantial labeled data ($>$10,000) and lack adaptability. Here we present MatInvent, a general and efficient reinforcement learning workflow that optimizes diffusion models for goal-

Justin Swain, Giordano Tierra

In this work we introduce novel numerical schemes for a penalized version of the ternary Cahn-Hilliard system for the purpose of creating accurate and efficient numerical schemes of interfacial dynamics with three components as well as some results extending these ideas to systems with four or more components. The first scheme is linear, decoupled, first ord

Daniel Wang, Evan Markou, Dylan Campbell

While backpropagation--reverse-mode automatic differentiation--has been extraordinarily successful in deep learning, it requires two passes (forward and backward) through the neural network and the storage of intermediate activations. Existing gradient estimation methods that instead use forward-mode automatic differentiation struggle to scale beyond small n

Julio Armando Mojica Zárate

This work analyzes anisotropic Dirac materials, such as graphene and borophene, under inhomogeneous electric and magnetic fields with position-dependent profiles. Exact solutions of the Dirac--Weyl equation are obtained for singular and exponentially decaying interactions, showing how anisotropy and field shape influence the energy spectrum, Landau levels, a

Abraham Khan, Chao Chen, Vishwas Rao, Arvind K. Saibaba

Kernel matrices are ubiquitous in computational mathematics, often arising from applications in machine learning and scientific computing. In two or three spatial or feature dimensions, such problems can be approximated efficiently by a class of matrices known as hierarchical matrices. A hierarchical matrix consists of a hierarchy of small near-field blocks

Azim Ospanov, Farzan Farnia, Roozbeh Yousefzadeh

We perform a thorough analysis of the formal and informal statements in the miniF2F benchmark from the perspective of an AI system that is tasked to participate in a math Olympiad consisting of the problems in miniF2F. In such setting, the model has to read and comprehend the problems in natural language, formalize them in Lean language, then proceed with pr

Md Sakhawat Hossen, Md. Zashid Iqbal Borshon, A. S. M. Badrudduza

The uprising of deep learning methodology and practice in recent years has brought about a severe consequence of increasing carbon footprint due to the insatiable demand for computational resources and power. The field of text analytics also experienced a massive transformation in this trend of monopolizing methodology. In this paper, the original TF-IDF alg

Katherine C. Kellogg, Bingyang Ye, Yifan Hu, Guergana K. Savova

The rapid adoption of large language models (LLMs) in healthcare has been accompanied by scrutiny of their oversight. Existing monitoring approaches, inherited from traditional machine learning (ML), are task-based and founded on assumed performance degradation arising from dataset drift. In contrast, with LLMs, inevitable model degradation due to changes in

Seok Kim, Jehyun Lee, Siyul Lee, Hyunwoo Oh

We study the BPS states of $U(N)_k\times U(1)_{-k}$ vector Chern-Simons theory on a sphere at weak coupling $\lambda=\frac{N}{k}\ll 1$, dual to an AdS$_4$ higher spin gravity. Higher spin currents are well known to be anomalous at $\lambda\neq 0$. We show that these non-BPS higher spin particles form multi-particle `BPS bounds' at low energy, and interpret t

Milad Hasanzadeh, Amin Kargarian

This paper introduces D2-UC, a quantum-ready framework for the unit commitment (UC) problem that prepares UC for near-term hybrid quantum-classical solvers by combining distributed classical decomposition with distributed quantum execution. We reformulate deterministic and stochastic UC into a three-block alternating direction method of multipliers (ADMM): (

Rafael Jose Moura Silva, Maria Gizele Nascimento, Fumio Machida, Ermeson Andrade

Software aging is a phenomenon that affects long-running systems, leading to progressive performance degradation and increasing the risk of failures. To mitigate this problem, this work proposes an adaptive approach based on machine learning for software aging detection in environments subject to dynamic workload conditions. We evaluate and compare a static

Saad Mankarious, Ayah Zirikly

Mental health disorders affect millions worldwide, yet early detection remains a major challenge, particularly for Arabic-speaking populations where resources are limited and mental health discourse is often discouraged due to cultural stigma. While substantial research has focused on English-language mental health detection, Arabic remains significantly und

Shoumin Liu, Zhaohuan Peng, Xumin Wang

In this paper, we analyze the faithful representations of the dihedral groups, and prove that the Coxeter groups can be determined by the proper joint spectrum of their faithful representations.

Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena, Ziqian Zhong

Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety implications of these extended contexts remain unclear. To bridge this gap, we introduce NINJA (short for Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LM

Yiyi Miao, Taoyu Wu, Tong Chen, Sihao Li

In orthodontic treatment, particularly within telemedicine contexts, observing patients' dental occlusion from multiple viewpoints facilitates timely clinical decision-making. Recent advances in 3D Gaussian Splatting (3DGS) have shown strong potential in 3D reconstruction and novel view synthesis. However, conventional 3DGS pipelines typically rely on densel

Miftahur Rahman, Samuel Adebayo, Dorian A. Acevedo-Mejia, David Hester

The Intermeshed Steel Connection (ISC) system, when paired with robotic manipulators, can accelerate steel-frame assembly and improve worker safety by eliminating manual assembly. Dependable perception is one of the initial stages for ISC-aware robots. However, this is hampered by the absence of a dedicated image corpus, as collecting photographs on active c

Yaling Qi

The availability of multidimensional economic datasets has grown significantly in recent years. An example is bilateral trade values across goods among countries, comprising three dimensions -- importing countries, exporting countries, and goods -- forming a third-order tensor time series. This paper introduces a general Bayesian tensor autoregressive framew

Fabián Sepúlveda-Soto, Lucia Soto-Barrios, Carlos Román, Axel Osses

Fingerprint analysis and fingerprint identification have been the most widely used tools for human identification. To this day, various models have been proposed to explain how fingerprints are formed, ranging from the fibroblast model, which focuses on cell-collagen interactions, to the buckling of thin layers model, both yielding significant results. In th

Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel, Samuel Bright-Thonney

Modern artificial intelligence has revolutionized our ability to extract rich and versatile data representations across scientific disciplines. Yet, the statistical properties of these representations remain poorly controlled, causing misspecified anomaly detection (AD) methods to falter. Weak or rare signals can remain hidden within the apparent regularity

Longling Geng, Edward Y. Chang

Large language models enable flexible multi-agent planning but remain fragile in practice: verification is often circular, state changes are not tracked for repair, and small faults trigger costly global recomputation. We present ALAS, a stateful, disruption-aware framework that separates planning from non-circular validation, records a versioned execution l

Yi Gong, Xinyuan Zhang, Jichen Chai, Yichen Ding

Cardiac contraction is a rapid, coordinated process that unfolds across three-dimensional tissue on millisecond timescales. Traditional optical imaging is often inadequate for capturing dynamic cellular structure in the beating heart because of a fundamental trade-off between spatial and temporal resolution. To overcome these limitations, we propose a high-p

Jonathan Li, Nasim Farahini, Evgenii Iuliugin, Magnus Vesterlund

The proliferation of 100B+ parameter Large Language Models (LLMs) with 100k+ context length support have resulted in increasing demands for on-chip memory to support large KV caches. Techniques such as StreamingLLM and SnapKV demonstrate how to control KV cache size while maintaining model accuracy. Yet, these techniques are not commonly used within industri

L Kaili Diamond, Benjamin Gilbert

We develop a hybrid approach to estimate spatial coordination mechanisms in structural dynamic discrete choice models by combining nested fixed-point (NFXP) dynamic programming with method of simulated moments (MSM), achieving computational tractability in spatial settings while preserving structural interpretation. Applying this framework to GPU replacement

Yuan Shi, John D. Moody

Large magnetic fields, either imposed externally or produced spontaneously, are often present in laser-driven high-energy-density systems. In addition to changing plasma conditions, magnetic fields also directly modify laser-plasma interactions (LPI) by changing participating waves and their nonlonear interactions. In this paper, we use two-dimensional parti

Gowtham Premananth, Carol Espy-Wilson

Language disruptions are one of the well-known effects of schizophrenia symptoms. They are often manifested as disorganized speech and impaired discourse coherence. These abnormalities in spontaneous language production reflect underlying cognitive disturbances and have the potential to serve as objective markers for symptom severity and diagnosis of schizop

Liangze Ke

This paper examines a novel proxy for political polarization, initially proposed by Caliskan et al., which estimates intergroup distances using computer vision. Analyzing 1,400+ YouTube videos with advanced object detection, their study quantifies demographic and religious divides in Turkiye, a deeply polarized nation. Our findings reveal strong correlations

Linglingzhi Zhu, Jonghyeok Lee, Yao Xie

Generalized linear models (GLMs) are fundamental tools for statistical modeling, with maximum likelihood estimation (MLE) serving as the classical approach for parameter inference. While MLE performs well for canonical GLMs, it can become computationally challenging in more general settings with non-canonical, non-smooth, or nonlinear link functions, where t

Gowtham Premananth, Philip Resnik, Sonia Bansal, Deanna L. Kelly

Millions of people suffer from mental health conditions, yet many remain undiagnosed or receive delayed care due to limited clinical resources and labor-intensive assessment methods. While most machine-assisted approaches focus on diagnostic classification, estimating symptom severity is essential for prioritizing care, particularly in resource-constrained s

Gowtham Premananth, Carol Espy-Wilson

Advances in artificial intelligence (AI) and deep learning have improved diagnostic capabilities in healthcare, yet limited interpretability continues to hinder clinical adoption. Schizophrenia, a complex disorder with diverse symptoms including disorganized speech and social withdrawal, demands tools that capture symptom severity and provide clinically mean

Amey Bhangale, Mark Braverman, Subhash Khot, Yang P. Liu

Let $\mathcal{G}$ be a $k$-player game with value $<1$, whose query distribution is such that no marginal on $k-1$ players admits a non-trivial Abelian embedding. We show that for every $n\geq N$, the value of the $n$-fold parallel repetition of $\mathcal{G}$ is $$ \text{val}(\mathcal{G}^{\otimes n}) \leq \frac{1}{\underbrace{\log\log\cdots\log}_{C\text{ tim

Owen John Levens

The triangle of sorted binomial coefficients $\left\langle {n \atop k} \right\rangle = \binom{n}{\lfloor \frac{n - k}{2} \rfloor}$ for $0 \leq k \leq n$ has appeared several times in recent combinatorial works but has evaded dedicated study. Here we refer to $\left\langle {n \atop k} \right\rangle$ as the Pascalian numbers and unify the various perspectives

Preston Firestone, Shubham Ugare, Gagandeep Singh, Sasa Misailovic

Subword tokenization segments input text according to a pre-defined vocabulary to feed it into a language model; the language model, in turn, generates a sequence made from this same vocabulary. The members of the vocabulary can be built of code points or bytes. Using code points means that all members of the vocabulary are valid UTF-8 characters. However, i

Pragya Sharma, Amanda Xiang, Abbas Kiani, John Kaippallimalil

Sixth-generation (6G) networks are envisioned to support interconnected local subnetworks that can share specialized, beyond-connectivity services. However, a standardized architecture for discovering and selecting these services across network boundaries has not existed yet. To address this gap, this paper introduces the Central Repository and Selection Fun

Michel Wong, Ali Alshehri, Sophia Kao, Haotian He

Text Normalization (TN) is a key preprocessing step in Text-to-Speech (TTS) systems, converting written forms into their canonical spoken equivalents. Traditional TN systems can exhibit high accuracy, but involve substantial engineering effort, are difficult to scale, and pose challenges to language coverage, particularly in low-resource settings. We propose

Changhong Li, Biswajit Basu, Shreejith Shanker

FPGAs have been shown to be a promising platform for deploying Quantised Neural Networks (QNNs) with high-speed, low-latency, and energy-efficient inference. However, the complexity of modern deep-learning models limits the performance on resource-constrained edge devices. While quantisation and pruning alleviate these challenges, unstructured sparsity remai

Xiao Li, Kotaro Funakoshi, Manabu Okumura

Emotion recognition in multi-speaker conversations faces significant challenges due to speaker ambiguity and severe class imbalance. We propose a novel framework that addresses these issues through three key innovations: (1) a speaker identification module that leverages audio-visual synchronization to accurately identify the active speaker, (2) a knowledge

Arup Datta, Ahmed Aljohani, Hyunsook Do

Large language models (LLMs) are now widely used to draft and refactor code, but code that works is not necessarily secure. We evaluate secure code generation using the Instruct Prime, which eliminated compliance-required prompts and cue contamination, and evaluate five instruction-tuned code LLMs using a zero-shot baseline and a three-round reflexion prompt

Jinyuxuan Guo, Gurnoor Singh Khurana, Alejandro Gonzalo Grande, Juan C. del Alamo

Background: Non-invasive imaging-based assessment of blood flow plays a critical role in evaluating heart function and structure. Computed Tomography (CT) is a widely-used imaging modality that can robustly evaluate cardiovascular anatomy and function, but direct methods to estimate blood flow velocity from movies of contrast evolution have not been develope

Samuel Awelewa, Maxim Dzero

We study the dynamics of the longitudinal collective mode in an unconventional superconductor. For concreteness, we assume that the superconductor is described by a $d$-wave order parameter with $d_{x^2-y^2}$ symmetry. After the superconductor has been suddenly subjected to a perturbation at time $t=0$, the order parameter exhibits a peculiar oscillatory beh

Thomas C. Sykes, Luke F. L. Alventosa, J. Rafael Castrejon-Pita, Radu Cimpeanu

When a fast droplet impacts a pool, the resulting ejecta sheet dynamics determine the final impact outcome. At low Capillary numbers, the ejecta sheet remains separate from a deep static pool, whilst at higher viscosities it develops into a lamella. Here, we show that the common natural scenario of a slowly moving deep pool can change the upstream impact out

David Lamprecht, Shrirang Chokappa, Alissa M. Freilinger, Barbara Maria Mayer

There is a growing interest in identifying the origin of single-photon emission in hexagonal boron nitride (hBN), with proposed candidates including boron and nitrogen vacancies as well as carbon substitutional dopants. Because photon emission intensity often increases with sample thickness, hBN flakes used in these studies commonly exceed 30 atomic layers.

Prashin Jethwa, Simon Hubmer, Ronny Ramlau, Glenn Van de Ven

Full spectrum fitting is the prevailing method for extracting stellar kinematic and population measurements from 1D galaxy spectra. 3D methods refer to analysis of Integral Field Spectroscopy (IFS) data where spatial and spectral dimensions are modelled simultaneously. While several 3D methods exist for modelling gas structures there has been less investigat

Jaydip Sen

The convergence of the Internet of Things (IoT) and quantum computing is redefining the security paradigm of interconnected digital systems. Classical cryptographic algorithms such as RSA, Elliptic Curve Cryptography (ECC), and Advanced Encryption Standard (AES) have long provided the foundation for securing IoT communication. However, the emergence of quant

Raymond Wiedmann, Dag-Björn Hering, Vanessa Sulaiman, Matthias R. Walther

We investigate quantum effects on magnon excitations in a minimal spin-1/2 Heisenberg model for 2D altermagnets on the square lattice. A continuous similarity transformation is applied in momentum space to derive an effective Hamiltonian that conserves the number of magnon excitations. This allows us to quantitatively calculate the one-magnon dispersion, the

Hellen Videa, M. Angeles Fuentes, Antonio J. Martinez-Martinez

Low-valent Group 13 fragments can serve as neutral two-electron L-type metalloligands to transition-metal (TM) centers, enabling heterometallic M-TM platforms with bonding and reactivity patterns distinct from classical CO, phosphine, and carbene ligation. This chapter develops a unifying, descriptor-based view of aluminylene Al(I), gallylene Ga(I), and indy

Kohta Kasai, Akihiro Uematsu, Tatsuki Kawakane, Yu Wang

Magnetic and polar skyrmions exhibit topologically protected quasiparticle behavior, including emergent fields, deformation, and the formation of a densely packed skyrmion lattice, beyond conventional domain configurations described by Kittel&#39;s law. Analogous to atomic crystals, lattice defects, especially dislocations and their associated strain fields,

Yuuki Adachi, Kazuki Ueda, Yuuki Yasui, Yoshiaki Sugimoto

The integration of single-atom bits enables the realization of the highest data-density memory. Reading and writing information to these bits through mechanical interactions opens the possibility of operating the magnetic devices with low heat generation and high density recording. To achieve this visionary goal, we demonstrate the use of magnetic exchange f

Ievgeniia Korniienko, Pablo Nieves, Jakub Sebesta, Roberto Iglesias

Interatomic potentials are essential for molecular dynamics simulations of magnetic materials, yet incorporating magnetic features into potentials for complex antiferromagnets remains challenging. Nickel oxide (NiO), a prototypical cubic antiferromagnet, exemplifies this difficulty. Here we develop a methodology to integrate magnetic properties into interato

Giulio Caldarelli, Massimiliano Ornaghi

The oracle problem refers to the inability of an agent to know if the information coming from an oracle is authentic and unbiased. In ancient times, philosophers and historians debated on how to evaluate, increase, and secure the reliability of oracle predictions, particularly those from Delphi, which pertained to matters of state. Today, we refer to data ca

A. Osin, A. Levchenko, M. Khodas

We find the extrinsic anomalous Hall conductivity (AHC) to be comparable to the intrinsic one in roughly half of the altermagnetic spin Laue groups in the limit of large exchange splitting. In materials with a finite Dzyaloshinskii-Moriya type interaction, the extrinsic contribution is essential even in the clean limit. In other altermagnets it is mostly neg

Marcia Frómeta Fernández, Diego Hernández Rajkov, Giulia Del Pace, Nicola Grani

Fermionic many-body systems provide an unrivaled arena to investigate how interactions drive the emergence of collective quantum behavior, such as macroscopic coherence and superfluidity. Central to these phenomena is the formation of Cooper pairs, correlated states of two fermions that behave as composite bosons and condense below a critical temperature. Ho

Antoine Marie, Pierre-François Loos

The $GW$ approximation has become a method of choice for predicting quasiparticle properties in solids and large molecular systems, owing to its favorable accuracy-cost balance. However, its accuracy is the result of a fortuitous cancellation of vertex corrections in the polarizability and self-energy. Hence, when attempting to go beyond $GW$ through inclusi

Giancarlo D&#39;Ambrosio, Avital Dery, Yuval Grossman, Teppei Kitahara

We point out that using current knowledge of ${\cal B}(K^0_L\toμ^+μ^-)$ and $ {\cal B}(K^0_L\to γγ)$, one can extract short-distance information from the combined measurement of the time-integrated CP asymmetry, $A_{\rm CP}(K^0\toμ^+μ^-)$, and of ${\cal B}(K^0_S\toμ^+μ^-)$. We discuss the interplay between this set of observables, and demonstrate that determ

Masaki Watabe, Joe Sakamoto, Hideaki Yoshimura, Tomomi Nemoto

The transport of intensity equation (TIE) has revolutionized phase retrieval in optical microscopy, yet its application to complex media with absorption/scattering remains challenging. Here, we present a coupled TIE-TPE (transport of phase equation) framework derived directly from the paraxial wave equation with complex optical potential. By decomposing the

Donghyeon Kim

In this paper, we prove that for a threefold of Fano type $X$ and a movable $\mathbb{Q}$-Cartier Weil divisor $D$ on $X$, the number of smooth varieties that arise during the running of a $D$-MMP is bounded by $1 + h^1(X, 2D)$. Additionally, we prove a partial converse to the Kodaira vanishing theorem for a movable divisor on a threefold of Fano type.

Yusuke Koroyasu, Christopher Stone, Yoichi Ochiai, Takayuki Hoshi

Acoustic levitation enables non-contact manipulation using sound waves. While conventional methods entrap particles at pressure nodes (zero-pressure region surrounded by high-pressure), we demonstrate stable acoustic levitation and translation in mid-air within a high-pressure axial core of a single-sided zero-order Bessel beam for the first time. The trap o

Vladimir Mitankin, Justin Uhlemann

We study local-global principles for semi-integral points on orbifold pairs of Markoff type. In particular, we analyse when these orbifold pairs satisfy weak weak approximation, weak approximation and strong approximation off a finite set of places. We show that Markoff orbifold pairs satisfy the semi-integral Hasse principle and we measure how often such or

A. S. Parvan

This study derives the relativistic transformations of thermodynamic quantities from the Lorentz transformations applied to the four-momentum components of a thermodynamic system, which is stationary in the inertial reference frame $K_0$ and moves at constant velocity relative to the laboratory frame $K$. Thermodynamic variables are introduced into the forma

Otis Chodosh, Chao Li, Paul Minter, Douglas Stryker

We show that a complete, two-sided, stable minimal hypersurface in $\mathbf{R}^5$ is flat.

R. Khorrambakht, Joaquim Ortiz-Haro, Joseph Amigo, Omar Mostafa

Robots must understand their environment from raw sensory inputs and reason about the consequences of their actions in it to solve complex tasks. Behavior Cloning (BC) leverages task-specific human demonstrations to learn this knowledge as end-to-end policies. However, these policies are difficult to transfer to new tasks, and generating training data is cha

Jungjun Choi, Ming Yuan

We study factor models that combine latent factors with firm characteristics and propose a new framework for modeling, estimating, and inferring pricing errors. Following Zhang (2024), our approach decomposes mispricing into two distinct components: inside alpha, explained by firm characteristics but orthogonal to factor exposures, and outside alpha, orthogo

Marc Ryhiner, Yangmeihui Song, Babak Saboury, Gerhard Glatting

This article presents the general framework of theranostic digital twins (TDTs) in computational nuclear medicine, designed to support clinical decision-making and improve cancer patient prognosis through personalized radiopharmaceutical therapies (RPTs). It outlines potential clinical applications of TDTs and proposes a roadmap for successful implementation

Fatemeh Ghaffari, Siddarth Sitaraman, Xutong Liu, Xuchuang Wang

Online learning to rank (OLTR) studies how to recommend a short ranked list of items from a large pool and improves future rankings based on user clicks. This setting is commonly modeled as cascading bandits, where the objective is to maximize the likelihood that the user clicks on at least one of the presented items across as many timesteps as possible. How

Elisa Heinrich-Mora, Marcus Feldman

Evolutionary analyses of large populations commonly incorporate stochasticity through temporal variation in selection while treating genetic transmission as fixed. Much less attention has been given to stochasticity in transmission itself. We study a selected locus with alleles $A$ and $a$ under constant selection, linked to a neutral modifier locus whose al

Sasi M. Behara, Amit Seta

The small-scale turbulent dynamo is a key mechanism for amplifying galactic magnetic fields, yet the resulting field morphology remains poorly understood. Using 3D driven turbulence simulations across a range of compressibilities, characterised by Mach number, and Minkowski functionals, we quantitatively investigate the morphology of magnetic fields generate

Aaina Thapa, Jutta Escher, Emanuel Chimanski, Oliver Gorton

The surrogate reaction method is an alternative to direct measurements of compound nuclear reaction cross sections. We introduce theory tools for extracting capture cross sections from experiments that use proton inelastic scattering as a surrogate reaction mechanism. This makes it possible to constrain compound nucleus decay models which are typically the l

Drago Plecko, Patrik Okanovic, Shreyas Havaldar, Torsten Hoefler

Artificial intelligence (AI) systems hold great promise for advancing various scientific disciplines, and are increasingly used in real-world applications. Despite their remarkable progress, further capabilities are expected in order to achieve more general types of intelligence. A critical distinction in this context is between factual knowledge, which can

S. Isayama, S. Matsukiyo, T. Sano, S. H. Chen

We propose a particle acceleration mechanism driven by large-amplitude Alfv\'en waves in a strong magnetic field. The acceleration process proceeds through multiple stages triggered by counterpropagating wave-particle resonant acceleration (CWRA) via decay instability. Initially, parent and daughter Alfv\'en waves resonantly accelerate particles perpendicula

Martin Carrasco, Olga Zaghen, Kavir Sumaraj, Erik Bekkers

A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing approaches ignore features in favor of structure, making it nigh-impossible to assess to what extent two graphs with close features should be considered similar. We address this by dev

Rafael Costero, Juan Echevarría, Yilen Gómez Maqueo Chew, Alex Ruelas-Mayorga

$\theta^1$ Ori E is a very young and relatively massive pre-main sequence (PMS) spectroscopic and eclipsing binary with nearly identical components. We analyze \'Echelle spectra of the system obtained over fifteen years and report 91 radial velocities measured from cross-correlating the observations with a suitable synthetic spectrum. The spectra of individu

R. A. Konoplya, A. Zhidenko

We construct exact black hole solutions free of curvature singularities, sourced by dark matter halos described by galactic density profiles. Regularity of the geometry is ensured by adopting the relation $P_{r}=-\rho$ between radial pressure and density, which is consistent with the phenomenological freedom of halo models. Under the assumptions of regularit