October 2025 arXiv papers — page 56

Matthew Lowery, Zhitong Xu, Da Long, Keyan Chen

Learning mappings between functional spaces, also known as function-on-function regression, is a fundamental problem in functional data analysis with broad applications, including spatiotemporal forecasting, curve prediction, and climate modeling. Existing approaches often struggle to capture complex nonlinear relationships and/or provide reliable uncertaint

Bojan Basrak

Based on their earlier studies of the arcsine law, Pitman and Yor in \cite{PY97} constructed a widely adopted PD($\alpha, \theta)$ family of random mass-partitions with parameters $\alpha \in [0,1),\ \theta+\alpha>0$. We propose an alternative model based on generalized perpetuities, which extends the PD family in a continuous manner, incorporating any $\alp

Necdet Serhat Aybat, Jiang Hu, Zhanwang Deng

We study the minimax problem $\min_{x\in M} \max_y f_r(x,y):=f(x,y)-h(y)$, where $M$ is a compact submanifold, $f$ is continuously differentiable in $(x, y)$, $h$ is a closed, weakly-convex (possibly non-smooth) function and we assume that the regularized coupling function $-f_r(x,\cdot)$ is either $\mu$-PL for some $\mu>0$ or concave ($\mu = 0$) for any fix

Eduardo B. Molinero, Bruno Amorim, Misha Ivanov, Graham G. Brown

We develop the semiconductor Wannier equations (SWEs), a real-time, real-space formulation of ultrafast light-matter dynamics in crystals, by deriving the equations of motion for the electronic reduced density matrix in a localized Wannier basis. Working in real space removes the structure-gauge ambiguities that hinder reciprocal-space semiconductor Bloch eq

Anchit Jain, Stephen Bates

Decomposing prediction uncertainty into aleatoric (irreducible) and epistemic (reducible) components is critical for the reliable deployment of machine learning systems. While the mutual information between the response variable and model parameters is a principled measure for epistemic uncertainty, it requires access to the parameter posterior, which is com

Petros Prastakos, Kayhan Behdin, Rahul Mazumder

Sparse variable selection improves interpretability and generalization in high-dimensional learning by selecting a small subset of informative features. Recent advances in Mixed Integer Programming (MIP) have enabled solving large-scale non-private sparse regression - known as Best Subset Selection (BSS) - with millions of variables in minutes. However, exte

Pawan Kumar, Belur Ravindra, Partha Chowdhury, Bidya Binay Karak

The Sun's magnetic field shows the 11-year solar cycle and shorter periodicities, popularly known as the quasi-biennial oscillations (QBOs) and Rieger-type periods, or ``season of the Sun." Although several theories have been proposed to explain the origin of QBOs and Rieger-type periods, no single theory has widespread acceptance. We explore whether the \bl

Ahan Mishra

The pinwheel problem is a real-time scheduling problem that asks, given $n$ tasks with periods $a_i \in \mathbb{N}$, whether it is possible to infinitely schedule the tasks, one per time unit, such that every task $i$ is scheduled in every interval of $a_i$ units. We study a corresponding version of this packing problem in the covering setting, stylized as t

Noah Seekins, Alexander J. Wagner

We developed a method for significantly lowering the viscosity achievable for a hydrodynamic lattice gas method. The key advance is the derivation of a mirror state that allows for a reduction of viscosity by more than an order of magnitude over existing lattice gas methods.

Khatoon Khedri, Reza Rawassizadeh, Qifu Wen, Mehdi Hosseinzadeh

Graph neural networks (GNNs) are known to operate with high accuracy on learning from graph-structured data, but they suffer from high computational and resource costs. Neural network compression methods are used to reduce the model size while maintaining reasonable accuracy. Two of the common neural network compression techniques include pruning and quantiz

James Thiering, Tarun Sethupat Radha Krishna, Dylan Zelkin, Ashis Kumer Biswas

With the rise of online and virtual learning, monitoring and enhancing student engagement have become an important aspect of effective education. Traditional methods of assessing a student's involvement might not be applicable directly to virtual environments. In this study, we focused on this problem and addressed the need to develop an automated system to

Mohammad Ali Etemadi Naeen, Hoda Mohammadzade, Saeed Bagheri Shouraki

Anomaly detection in surveillance videos remains a challenging task due to the diversity of abnormal events, class imbalance, and scene-dependent visual clutter. To address these issues, we propose a robust deep learning framework that integrates human-centric preprocessing with spatio-temporal modeling for multi-class anomaly classification. Our pipeline be

V Venktesh, Deepali Prabhu, Avishek Anand

Fact-checking numerical claims is critical as the presence of numbers provide mirage of veracity despite being fake potentially causing catastrophic impacts on society. The prior works in automatic fact verification do not primarily focus on natural numerical claims. A typical human fact-checker first retrieves relevant evidence addressing the different nume

Yuli Slavutsky, Sebastian Salazar, David M. Blei

This paper studies prediction with multiple candidate models, where the goal is to combine their outputs. This task is especially challenging in heterogeneous settings, where different models may be better suited to different inputs. We propose input adaptive Bayesian Model Averaging (IA-BMA), a Bayesian method that assigns model weights conditional on the i

Paul C. Parsons

As science gateways mature, sustainability has become a central concern for funders, developers, and institutions. Although user experience (UX) is increasingly acknowledged as vital, it is often approached narrowly--limited to interface usability or deferred until late in development. This paper argues that UX should be understood not as a discrete feature

M. G. Kim, S. Boney, L. Burgard, L. Rutowski

We report coherent X-ray imaging of antiferromagnetic (AFM) domains and domain walls in MnBi$_2$Te$_4$, an intrinsic AFM topological insulator. This technique enables direct visualization of domain morphology without reconstruction algorithms, allowing us to resolve antiphase domain walls as distinct dark lines arising from the A-type AFM structure. The wall

Marcus Thomas

Contemporary machine learning optimizes for predictive accuracy, yet systems that achieve state of the art performance remain causally opaque: their internal representations provide no principled handle for intervention. We can retrain such models, but we cannot surgically edit specific mechanisms while holding others fixed, because learned latent variables

Zhimin Chen, Chenyu Zhao, Ka Chun Mo, Yunjiang Jiang

Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential modeling techniques, particularly transformer-like architectures, has led to significant advancements recently (e.g., HSTU, SIM, and TWIN models). While scaling to ultra-long user hi

Simon Bortz, Steven Hofmann, José María Martell, Kaj Nyström

Let $\Omega \subset \mathbb{R}^{n+1}$ be an open set in space-time with boundary $\Sigma = \partial \Omega$. Under minimal and natural background assumptions - namely, that $\Sigma$ is time-symmetrically parabolic Ahlfors--David regular and that $\Omega$ satisfies an interior corkscrew condition - we treat a one-phase parabolic free boundary problem which es

Daniel G. P. Petrini, Braz Izaias da Silva Junior

We present a case study applying the SpecC methodology within a system-level hardware/software co-design flow to a PCM-to-PWM converter, the core of a Class-D audio amplifier. The converter was modeled and explored with SpecC methodology to derive an HW/SW partition. Using system-level estimates and fast functional simulation, we evaluated mappings that meet

Hyeonsu Kang, Emily Bao, Anjan Goswami

Vision-language models (VLMs) are increasingly used to evaluate multimodal content, including presentation slides, yet their slide-specific understanding remains underexplored {despite their growing role as critics in agentic, model-forward pipelines}. We introduce VLM-SlideEval, an evaluation framework that probes VLMs along three axes: (1) element-level ex

Kijung Jeon, Michael Muehlebach, Molei Tao

Sampling from constrained statistical distributions is a fundamental task in various fields including Bayesian statistics, computational chemistry, and statistical physics. This article considers the cases where the constrained distribution is described by an unconstrained density, as well as additional equality and/or inequality constraints, which often mak

Arghyadeep Basu

This thesis investigates the Epoch of Cosmic Reionization (EoR), a key period in the early Universe when the first luminous sources formed and their radiation transformed the intergalactic medium (IGM) from neutral to ionized. Understanding this process reveals how the first stars and galaxies formed, influenced their surroundings, and shaped large-scale str

Benjamin Reichman, Adar Avsian, Larry Heck

This work investigates how large language models (LLMs) internally represent emotion by analyzing the geometry of their hidden-state space. The paper identifies a low-dimensional emotional manifold and shows that emotional representations are directionally encoded, distributed across layers, and aligned with interpretable dimensions. These structures are sta

Bence G. Márkus, Anna Nyáry, Dávid Beke, Sivaviswa Radhakrishnan

The development of multifunctional solid-state materials is key to advancing lithium-ion batteries with enhanced safety and simplified architectures. Here, we report a scalable, highly efficient (near $100\%$), solvent-free mechanochemical synthesis of hexagonal boron nitride (hBN) functionalized with lithium oxalate (Li$_2$C$_2$O$_4$), yielding a novel lame

Shahrzad Haddadan, Sara Ahmadian

The classic Mallows model is a foundational tool for modeling user preferences. However, it has limitations in capturing real-world scenarios, where users often focus only on a limited set of preferred items and are indifferent to the rest. To address this, extensions such as the top-k Mallows model have been proposed, aligning better with practical applicat

Po-Chen Kuo, Han Hou, Will Dabney, Edgar Y. Walker

Learning a compact representation of history is critical for planning and generalization in partially observable environments. While meta-reinforcement learning (RL) agents can attain near Bayes-optimal policies, they often fail to learn the compact, interpretable Bayes-optimal belief states. This representational inefficiency potentially limits the agent's

The Electron-Positron Alliance, :, Anthony Badea, Austin Baty

The strong coupling constant ($\alpha_{S}$) is a fundamental parameter of quantum chromodynamics (QCD), the theory of the strong force. Some of the earliest precise constraints on $\alpha_{S}$ came from measurements of event shape observables, such as thrust ($T$), using hadronic $Z$ boson decays produced in $e^+e^-$ collisions. However, recent work has reve

Shayne Longpre, Sneha Kudugunta, Niklas Muennighoff, I-Hung Hsu

Scaling laws research has focused overwhelmingly on English -- yet the most prominent AI models explicitly serve billions of international users. In this work, we undertake the largest multilingual scaling laws study to date, totaling 774 multilingual training experiments, spanning 10M-8B model parameters, 400+ training languages and 48 evaluation languages.

Pooja Rani, Dominik M. Juraschek

Van der Waals ferroelectrics are conventionally switched by sliding the different layers between stacking orders with opposing electric polarizations. Ultrashort laser pulses have been proposed to launch shear modes and induce switching, with often unfeasible large pulse energies however. Here, we demonstrate switching of ferroelectricity in bilayer hexagona

Patrick Koller, Amil V. Dravid, Guido M. Schuster, Aggelos K. Katsaggelos

Robustness has become one of the most critical problems in machine learning (ML). The science of interpreting ML models to understand their behavior and improve their robustness is referred to as explainable artificial intelligence (XAI). One of the state-of-the-art XAI methods for computer vision problems is to generate saliency maps. A saliency map highlig

Rick Chen, Joseph Ternasky, Aaron Ontoyin Yin, Xianling Mu

Large language models (LLMs) can already identify patterns and reason effectively, yet their variable accuracy hampers adoption in high-stakes decision-making applications. In this paper, we study this issue from a venture capital perspective by predicting idea-stage startup success based on founder traits. (i) To build a reliable prediction model, we introd

Tianxiang Wang, Yingtong Ke, Dhananjay Bhaskar, Smita Krishnaswamy

Single-cell technologies generate high-dimensional point clouds of cells, enabling detailed characterization of complex patient states and treatment responses. Yet each patient is represented by an irregular point cloud rather than a simple vector, making it difficult to directly quantify and compare biological differences between individuals. Nonlinear meth

Ziyang Xu, Olaf Wysocki, Christoph Holst

Reliable quantification of uncertainty in Mobile Laser Scanning (MLS) point clouds is essential for ensuring the accuracy and credibility of downstream applications such as 3D mapping, modeling, and change analysis. Traditional backward uncertainty modeling heavily rely on high-precision reference data, which are often costly or infeasible to obtain at large

Jair Koiller

Rubber rolling (no-slip and no-twist) of a convex body on the plane under the influence of gravity is a SE(2) Chaplygin system, that reduces to the sphere of Poisson vectors. I comment upon an observation by A.V Borisov and I.S. Mamaev (Regular and Chaotic Dynamics, 13(5):443-490, 2008) for the case of surfaces of revolution [also in A. V. Borisov, I. S. Mam

Jincheng Zhou, Mengbo Wang, Anqi He, Yumeng Zhou

Causal discovery from observational data is a fundamental task in artificial intelligence, with far-reaching implications for decision-making, predictions, and interventions. Despite significant advances, existing methods can be broadly categorized as constraint-based or score-based approaches. Constraint-based methods offer rigorous causal discovery but are

Harsha Karunanayaka, Siavash Rezazadeh

Stability of bipedal systems in frontal plane is affected by the hip offset, to the extent that adjusting stride time using feedforward retraction and extension of the legs can lead to stable oscillations without feedback control. This feedforward stabilization can be leveraged to reduce the control effort and energy expenditure and increase the locomotion r

Rezvan Alamian, Sören Müller, Uwe Steinmetz, Christian Henrich

This paper suggests a novel rotor-cooling shaft concept for high-performance electric motors that increases the effectiveness of cooling and is yet simple and cost-effective to manufacture. We investigate the thermal performance of four shaft geometries for rotor cooling in automotive applications. The proposed tooth-guided liquid-cooling shaft design aims t

Yilin Zhang, Wenda Xu, Zhongtao Liu, Tetsuji Nakagawa

Quality Estimation (QE) metrics are vital in machine translation for reference-free evaluation and increasingly serve as selection criteria in data filtering and candidate reranking. However, the prevalence and impact of length bias in QE metrics have been underexplored. Through a systematic study of top-performing learned and LLM-as-a-Judge QE metrics acros

Yassine Chemingui, Aryan Deshwal, Alan Fern, Thanh Nguyen-Tang

We study the problem of Offline Safe Reinforcement Learning (OSRL), where the goal is to learn a reward-maximizing policy from fixed data under a cumulative cost constraint. We propose a novel OSRL approach that frames the problem as a minimax objective and solves it by combining offline RL with online optimization algorithms. We prove the approximate optima

Nikita Karagodin, Shu Ge, Yury Polyanskiy, Philippe Rigollet

We study the effect of normalization schemes on token representations in deep transformers. Modeling their evolution as interacting particles on the sphere, we show that normalization acts as a form of speed regulation. This perspective enables a unified analysis of several schemes -- including Post-LN, Pre-LN, Mix-LN, Peri-LN, nGPT -- revealing how they inf

Alfonso Zack Robles, Alexander I. Nesterov, Claudia Moreno

In the framework of the quasigroup approach to conservation laws in general relativity, we show how the infinite-parametric Newman-Unti group of asymptotic symmetries can be reduced to the Poincare quasigroup. We compute Noether's charges associated with any element of the Poincare quasialgebra. The integral conserved quantities of energy momentum and angula

Yifan Liu, Qianfeng Wen, Jiazhou Liang, Mark Zhao

Natural Language Recommendation (NLRec) generates item suggestions based on the relevance between user-issued NL requests and NL item description passages. Existing NLRec approaches often use Dense Retrieval (DR) to compute item relevance scores from aggregation of inner products between user request embeddings and relevant passage embeddings. However, DR vi

Mohamed Shamseldein

Conventional AC Power Flow (ACPF) solvers like Newton-Raphson (NR) face significant computational and convergence challenges in modern, large-scale power systems. This paper proposes a novel, two-stage hybrid method that integrates a Physics-Informed Graph Neural Network (GNN) with a robust, iterative Linear State Estimation (LSE) refinement step to produce

Mariia Stepanova, Minh Ngo, Mashnoon Alam Sakib, Wills Harris

Phonon polaritons in van der Waals crystals offer mid-infrared light confinement deep below the diffraction limit, making them promising for nanophotonics applications. However, the practical use of phonon polaritons remains limited, in part due to the lack of precise control over the phonon polariton dispersion, as crystal lattice vibrations are often inert

Johannes Feldl, Roland Gillen, Janina Maultzsch, Alexandra Papadogianni

In this study, we performed comprehensive investigations on the Raman active phonon modes in cubic bixbyite In$_2$O$_3$, an important oxide based, wide-bandgap semiconductor. Fundamental insights into the lattice dynamics are revealed, by determining the atomistic contribution to all modes and their frequencies by density functional perturbation theory calcu

Naina Balepur, Xingrui Pei, Hari Sundaram

Many governmental bodies are adopting AI policies for decision-making. In particular, Reinforcement Learning has been used to design policies that citizens would be expected to follow if implemented. Much RL work assumes that citizens follow these policies, and evaluate them with this in mind. However, we know from prior work that without institutional trust

Shilin You, Gael Luna, Juned Shaikh, David Gostin

We present an algorithm for planning trajectories that avoid obstacles and satisfy key-door precedence specifications expressed with a fragment of signal temporal logic. Our method includes a novel exact convex partitioning of the obstacle free space that encodes connectivity among convex free space sets, key sets, and door sets. We then construct an augment

Eugene V. Stepanov, Alexander F. Gutsol

A theoretical approach to describing transport of an entire ensemble of clusters with different sizes as a single species in gas has been developed. The major assumption is an existence of local partial chemical equilibrium between the clusters. It is shown that thermal diffusion emerges in the collective description as a significant factor even if it is neg

Haireguli Aihemaiti, Esmat Dastanpour, Anders Bergman, Levente Vitos

Using ab initio Density Functional Theory (DFT) calculations, we investigate the electronic structure, phase stability, and magnetic properties of equiatomic binary alloys between Al and 3d magnetic transition elements (Cr, Mn, Fe, Co, and Ni). Thermodynamically, all five binary aluminides are more stable in the ordered B2 phase than in the disordered body c

Ana-Maria Boldeanu, Mircea Neagu

In this paper we develop, via the least squares variational method, the Lagrange-Hamilton geometry (in the sense of nonlinear connections, d-torsions and Lagrangian Yang-Mills electromagnetic-like energy) produced by a dynamical system governing the spreading of COVID-19 disease. The Jacobi stability of this dynamical system is also discussed.

Fraisse Sacré Takouchouang, Ho Tuong Vinh

Sign languages play a crucial role in the communication of deaf communities, but they are often marginalized, limiting access to essential services such as healthcare and education. This study proposes an automatic sign language recognition system based on a hybrid CNN-LSTM architecture, using Mediapipe for gesture keypoint extraction. Developed with Python,

Or Ronai, Vladimir Kulikov, Tomer Michaeli

The remarkable success of diffusion and flow-matching models has ignited a surge of works on adapting them at test time for controlled generation tasks. Examples range from image editing to restoration, compression and personalization. However, due to the iterative nature of the sampling process in those models, it is computationally impractical to use gradi

Yangqin Jiang, Chao Huang

With the advancement of multimodal large language models (MLLMs), building GUI agent systems has become an increasingly promising direction--especially for mobile platforms, given their rich app ecosystems and intuitive touch interactions. Yet mobile GUI agents face a critical dilemma: truly on-device models (4B or smaller) lack sufficient performance, while

Md Saiful Islam Sajol, Magesh Rajasekaran, Hayden Gemeinhardt, Adam Bess

Computationally predicting protein-protein interactions (PPIs) is challenging due to the lack of integrated, multimodal protein representations. DPEB is a curated collection of 22,043 human proteins that integrates four embedding types: structural (AlphaFold2), transformer-based sequence (BioEmbeddings), contextual amino acid patterns (ESM-2: Evolutionary Sc

T. Tony Cai, Xiang Li, Qi Long, Weijie J. Su

Text watermarking plays a crucial role in ensuring the traceability and accountability of large language model (LLM) outputs and mitigating misuse. While promising, most existing methods assume perfect pseudorandomness. In practice, repetition in generated text induces collisions that create structured dependence, compromising Type I error control and invali

Dalen Dockery, Marie Jameson

Recent work of Garvan, Sellers, Smoot, and others has made connections between infinite families of congruences for various partition functions. Here, we apply this approach to families of congruences for PED and POD partitions and find that they are naturally linked to congruence families for overpartitions into odd parts and overpartitions.

Alexandre Goncalves, Yee Man Margaret Ng

On July 1, 2025, YouTube retired its decade-long public "Trending" pages, ending platform-curated, non-personalized video discovery. The Trending list had long served as a vital lens into algorithmic influence, cultural diffusion, and crisis communication globally, offering a rare "ground-truth" reference to study global attention and cultural salience. We p

Michael J. Cervia

The low energy effective field theory of interacting neutrinos derived from the Standard Model may be framed as a pointlike interaction and thereby modeled on a lattice of neutrino momenta. We identify a path to take a continuum limit of this lattice problem in the center of momentum frame. In this limit, the weak interaction is found to become trivial betwe

Ruchir Namjoshi, Nagasai Thadishetty, Vignesh Kumar, Hemanth Venkateshwara

In recent years, diffusion models have demonstrated remarkable success in high-fidelity image synthesis. However, fine-tuning these models for specialized domains, such as medical imaging, remains challenging due to limited domain-specific data and the high computational cost of full model adaptation. In this paper, we introduce Lite-Diff (Lightweight Diffus

Yu. M. Poluektov

A method for describing charged relativistic Fermi fields is proposed, in which particles of opposite charges are treated equally and states with negative energy are excluded. The concept of charge quantum number is introduced. Fields of particles and antiparticles with different charge quantum numbers are associated with wave functions for which the Born in

Stefan Julian Kooy, Jean Paul Sebastian Piest, Rob Henk Bemthuis

Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect role

Matthew J. Colbrook, Zlatko Drmač, Andrew Horning

Koopman operators provide a linear framework for data-driven analyses of nonlinear dynamical systems, but their infinite-dimensional nature presents major computational challenges. In this article, we offer an introductory guide to Koopman learning, emphasizing rigorously convergent data-driven methods for forecasting and spectral analysis. We provide a unif

Jacob S. Palmer, Kaitlin N. Smith

A variety of past research on superconducting qubits shows that these devices exhibit considerable variation and thus cannot be accurately depicted by a uniform noise model. To combat this often unrealistic picture of homogeneous noise in quantum processors during runtime, our work aims to define the boundaries of acceptable defectiveness (BADs), or the uppe

Ltaief Ben Ltaief, Keshav Sishodia, Robert Richter, Martí Pi

The microhydration of rock salt (NaCl) molecules was investigated using high-resolution Penning ionization electron spectroscopy (PIES) in helium nanodroplets. Although model calculations predict that NaCl molecules are fully submerged inside the droplets, PIES of NaCl are highly resolved, in stark contrast to other molecular species. Co-doping the droplets

Zhenya Huang, Jiayu Liu, Xin Lin, Zhiyuan Ma

Math word problem (MWP) serves as a fundamental research topic in artificial intelligence (AI) dating back to 1960s. This research aims to advance the reasoning abilities of AI by mirroring the human-like cognitive intelligence. The mainstream technological paradigm has evolved from the early rule-based methods, to deep learning models, and is rapidly advanc

Inwoo Hwang, Yushu Pan, Elias Bareinboim

Understanding the predictions made by deep learning models remains a central challenge, especially in high-stakes applications. A promising approach is to equip models with the ability to answer counterfactual questions -- hypothetical ``what if?'' scenarios that go beyond the observed data and provide insight into a model reasoning. In this work, we introdu

Dain Kim, Tristan Ozuch

We prove that on ALF $n$-manifolds with $n\ge 4$ the Ricci flow preserves the ALF structure, and develop a weighted Fredholm framework adapted to ALF manifolds. Motivated by Perelman's $\lambda$-functional, we define a renormalized functional $\lambda_{\mathrm{ALF}}$ whose gradient flow is the Ricci flow. It is built from a relative mass with respect to a re

Caitlin Callaghan, David J Reinkensmeyer

Recalling previously experienced movements is essential for a range of activities, including sports, music, and rehabilitation, yet little is known about the accuracy and decay of proprioceptive working memory. We examined how introducing a short-term memory component affected movement reproduction accuracy by comparing movement reproduction under two condit

Michał Bortkiewicz, Władysław Pałucki, Mateusz Ostaszewski, Benjamin Eysenbach

Reinforcement learning (RL) promises to solve long-horizon tasks even when training data contains only short fragments of the behaviors. This experience stitching capability is often viewed as the purview of temporal difference (TD) methods. However, outside of small tabular settings, trajectories never intersect, calling into question this conventional wisd

Johannes Hagel

Starting from the nonlinear ODE $z'' + f(t)\,z + g(t)\, z^{m}=0$ with $m>1$, we show that after a suitable normal-form reduction of any Hill equation one may, without loss of generality, fix the linear part as $f(t)\equiv \omega^{2}$ (with $\omega>0$ constant). For the class $z''+\omega^{2}z+g(t)\, z^{m}=0$ with $m>1$, our goal is to compile a catalogue of a

Emanuele Rossi

Graph Neural Networks (GNNs) have become a central tool for learning on graph-structured data, yet their applicability to real-world systems remains limited by key challenges such as scalability, temporality, directionality, data incompleteness, and structural uncertainty. This thesis introduces a series of models addressing these limitations: SIGN for scala

Yupeng Qi, Ran Xu, Xu Chu

Large language models (LLMs) are establishing new paradigms for engineering applications by enabling natural language control of complex computational workflows. This paper introduces FeaGPT, the first framework to achieve complete geometry-mesh-simulation workflows through conversational interfaces. Unlike existing tools that automate individual FEA compone

Miriam Schwarze, Hui Khee Looe, Björn Poppe, Leo Thomas

Cross-section data unavailability for non-water materials in track structure simulation software necessitates nanodosimetric quantity transformation from water to other materials. Cluster dose calculation transformation initially employed mass-density-based scaling - an approach resulting in a physically unrealistic material-independence of the cluster dose

Mateo Clemente, Leo Brunswic, Rui Heng Yang, Xuan Zhao

Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originally developed for vision tasks like image and video generation, many of their inference strategies have been directly transferred to control domains without adaptation. In this work,

Jordan M. Slagowski, Yuhao Yan, Jessica R. Miller, John W. Hayes

Purpose: To characterize image quality, imaging dose, and dose calculation accuracy for an upright CT scanner with a six-degree-of-freedom patient positioning system. Methods: Imaging dose (CTDIvol) was measured at 120 kVp and 200 mAs. Image quality was evaluated using an ACR-464 phantom. Mean CT number accuracy was assessed within inserts of known material

Lucas Adams Cowan, Ronja Eilfort, Kerry Seekamp, Julianna Tymoczko

Web graphs form a family of planar directed graphs with boundary that can be used to model quantum $\mathfrak{sl}_n$-invariant vectors. Standard Young tableaux on an $n \times k$ rectangle naturally index a basis for $\mathfrak{sl}_n$ web graphs. We prove that evacuation of the tableau $T$ corresponds to reflection of the associated web graph $w_T$ up to equ

O. Petruk, R. Bandiera, T. Kuzyo, R. Brose

When a supernova remnant (SNR) interacts with the dense material of an interstellar cloud, its shock wave decelerates rapidly, and the post-shock temperature drops to levels that permit efficient cooling of the shocked plasma. At this stage, the shock enters the post-adiabatic phase of its evolution. During this phase, the internal structure of the SNR under

Wilmer Smilde

Relative algebroids provide a framework that unifies Lie algebroids with partial differential equations. In this set of notes, we explain how relative algebroids arise from geometric problems, and give an introduction to their structural theory. We also discuss their relation to and relevance for partial differential equations with symmetry.

Dogyun Park, Moayed Haji-Ali, Yanyu Li, Willi Menapace

Diffusion Transformers (DiTs) deliver state-of-the-art generative performance but their quadratic training cost with sequence length makes large-scale pretraining prohibitively expensive. Token dropping can reduce training cost, yet na\"ive strategies degrade representations, and existing methods are either parameter-heavy or fail at high drop ratios. We pre

Ricardo Espíndola, Viktor Jahnke, Keun-Young Kim

We study information recovery in black hole evaporation using traversable wormhole protocols in AdS$_2$ Jackiw-Teitelboim gravity with matter fields coupled to an external bath. By introducing a simple non-local interaction between left and right radiation regions, we generate negative-energy shockwaves that render the wormhole traversable. We compute the re

Faria Huq, Elijah L. Claggett, Hirokazu Shirado

Group segregation or cohesion can emerge from micro-level communication, and AI-assisted messaging may shape this process. Here, we report a preregistered online experiment (N = 557 across 60 sessions) in which participants discussed controversial political topics over multiple rounds and could freely change groups. Some participants received real-time messa

Havva Alizadeh Noughabi, Julien Serbanescu, Fattane Zarrinkalam, Ali Dehghantanha

Despite recent advances, Large Language Models remain vulnerable to jailbreak attacks that bypass alignment safeguards and elicit harmful outputs. While prior research has proposed various attack strategies differing in human readability and transferability, little attention has been paid to the linguistic and psychological mechanisms that may influence a mo

Haireguli Aihemaiti, Esmat Dastanpour, Shashank Chaturvedi, Shuo Huang

Using Density Functional Theory (DFT) calculations and Monte-Carlo (MC) simulations, we investigate the recently reported magnetic transition in B2 Al-Cr-Co alloys. The Cr sublattice is alloyed with different amounts of Co in the antiferromagnetic (AFM) B2 AlCr binary alloy and the resulting exchange interactions are analyzed within the Heisenberg Hamiltonia

Jacob Trzaska, Amit Ashok

Spatial mode sorting has come to prominence as an optical processing modality capable of saturating fundamental limits to numerous sensing tasks including wavefront sensing, coronagraphy, and superresolution imaging. But despite their promising theoretical advantages, contemporary mode sorters often feature large crosstalk, high loss, or sort modes that are

Starlika Bauskar, Jade Jiao, Narayanan Kannan, Alexander Kimm

Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions. Most methods operate on a single graph, typically the minimal free energy (MFE) structure, for low-energy ensembles (conformations) representative of structures at thermodynamic equilibrium. We introd

Janet, Lin, Liangwei Zhang

This chapter bridges technical analysis and organizational preparedness by tracing the path from layered failure modes to reliability awareness in generative and agentic AI systems. We first introduce an 11-layer failure stack, a structured framework for identifying vulnerabilities ranging from hardware and power foundations to adaptive learning and agentic

Thomas Michael Keller, Zachary Martin, Alexa Renner, Gabriel Roca

For a finite group $G$, the prime graph $\Gamma(G)$ (also known as Gruenberg-Kegel graph) is defined to be the graph where the vertices are the primes that divide $|G|$ such that two vertices $p$ and $q$ share an edge if and only if there is an element of order $pq$ in $G$. The prime graphs of solvable groups have been classified. The prime graphs of groups

Ji Huang, Mengfei Li, Shuai Shao

Large language models (LLMs) offer a promising way to simulate human survey responses, potentially reducing the cost of large-scale data collection. However, existing zero-shot methods suffer from prompt sensitivity and low accuracy, while conventional fine-tuning approaches mostly fit the training set distributions and struggle to produce results more accur

Yang Xu, Chiwoo Park

We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitations of conventional Jump Gaussian Processes (JGP) in high-dimensional input spaces by integrating region-specific, locally linear projections with JGP modeling. These projections e

Arnaud Salvador, Tyler D. Robinson

A next generation of space-based observatories aims to detect and characterize potentially Earth-like exoplanets around Sun-like stars using reflected light spectroscopy. However, it remains unclear how such direct imaging observations$-$limited in spectral coverage and signal-to-noise ratio (S/N)$-$translate into constraints on atmospheric composition and h

Zhixin Pan, Ziyu Shu, Linh Nguyen, Amberbir Alemayoh

The globalized semiconductor supply chain has made Hardware Trojans (HT) a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ad hoc feature selection and the lack of adaptivity, all of w

Denis Boyer, Satya N. Majumdar

We study the emergence of correlations between $N$ components of the position of a diffusive walker in $N$ dimensions that starts at the origin and resets to previously visited sites with certain probabilities. This is equivalent to $N$ independent one-dimensional diffusive processes starting from the origin and being subject to simultaneous resetting to pos

Josip Tomo Licardo, Nikola Tankovic

Large Language Models (LLMs) offer state-of-the-art performance in natural language understanding and generation tasks. However, the deployment of leading commercial models for specialized tasks, such as e-commerce, is often hindered by high computational costs, latency, and operational expenses. This paper investigates the viability of smaller, open-weight

Weiyu Chen, Arnaud Delorme

Detecting single-trial P300 from EEG is difficult when only a few labeled trials are available. When attempting to boost a small target set with a large source dataset through transfer learning, cross-dataset shift arises. To address this challenge, we study transfer between two public visual-oddball ERP datasets using five shared electrodes (Fz, Pz, P3, P4,

Avinash Sah, Ting-Yong Lim, Clayton Conner, Amarnath Chakraborty

Altermagnetic (AM) compounds have recently emerged as a promising platform for realizing unconventional quantum phases, enabled by their unique spin-split band structure at zero net magnetization. Here, we present a first-principles investigation of magnetically intercalated transition metal dichalcogenides (TMDs) of the form XY$_4$Z$_8$ (X $=$ Mn, Fe, Co, N

Benjamin Lange, Geoff Keeling, Arianna Manzini, Amanda McCroskery

We argue that accountability mechanisms are needed in human-AI agent relationships to ensure alignment with user and societal interests. We propose a framework according to which AI agents' engagement is conditional on appropriate user behaviour. The framework incorporates design-strategies such as distancing, disengaging, and discouraging.

Musengamana Jean de Dieu, Ruiyin Li, Peng Liang, Mojtaba Shahin

Stack Overflow (SO), a leading online community forum, is a rich source of software development knowledge. However, locating architectural knowledge, such as architectural solutions remains challenging due to the overwhelming volume of unstructured content and fragmented discussions. Developers must manually sift through posts to find relevant architectural

Jennifer Shi, Christopher K. Frantz, Christian Kimmich, Saba Siddiki

Designing institutions for social-ecological systems requires models that capture heterogeneity, uncertainty, and strategic interaction. Multiple modeling approaches have emerged to meet this challenge, including empirical game-theoretic analysis (EGTA), which merges ABM's scale and diversity with game-theoretic models' formal equilibrium analysis. The newly

Adam Kanigowski, Maksym Radziwiłł

Let $\Gamma\subset PSL(2,\mathbb{R})$ be such that the space $X=\Gamma\backslash PSL(2,\mathbb{R})$ is not compact. Let $(h_t)$ be the horocycle flow acting on $X$. We show that for every $x\in X$ that is not periodic for $(h_t)$ and for every $\delta\in (0,1)$ the orbit $\{h_{n^{2-\delta}}x\}_{n\in \mathbb{N}}$ is dense in $X$. Assuming additionally the Har