May 2025 arXiv papers — page 30

Roberto Maiolino, Hannah Uebler, Francesco D'Eugenio, Jan Scholtz

The recent discovery of a large number of massive black holes within the first two billion years after the Big Bang, as well as their peculiar properties, have been largely unexpected based on the extrapolation of the properties of luminous quasars. These findings have prompted the development of several theoretical models for the early formation and growth

Yifan Xie, Mingyang Li, Shoujie Li, Xingting Li

Tactile perception is essential for embodied agents to understand physical attributes of objects that cannot be determined through visual inspection alone. While existing approaches have made progress in visual and language modalities for physical understanding, they fail to effectively incorporate tactile information that provides crucial haptic feedback fo

David A. Craven

The Ingleton inequality first appeared in matroid theory, where Ingleton proved in 1971 that every rank function coming from a representable matroid on four subsets satisfies a particular inequality. Because this inequality is not implied by submodularity, Shannon-type axioms alone, it and various analogues play a central role in separately linear and non-li

Jaehyun Choi, Jiwan Hur, Gyojin Han, Jaemyung Yu

Video dataset condensation aims to reduce the immense computational cost of video processing. However, it faces a fundamental challenge regarding the inseparable interdependence between spatial appearance and temporal dynamics. Prior work follows a static/dynamic disentanglement paradigm where videos are decomposed into static content and auxiliary motion si

Yu Lei, Xingyang Ge, Yi Zhang, Yiming Yang

Understanding whether large language models (LLMs) and the human brain converge on similar computational principles remains a fundamental and important question in cognitive neuroscience and AI. Do the brain-like patterns observed in LLMs emerge simply from scaling, or do they reflect deeper alignment with the architecture of human language processing? This

Krishnendu Gongopadhyay, Lokenath Kundu, Aditya Tiwari

We consider the isometries of the complex hyperbolic bidisk, that is, the product space $\mathbb{H}^2_{\mathbb{C}} \times \mathbb{H}^2_{\mathbb{C}} $, where each factor $ \mathbb{H}^2_{\mathbb{C}} $ denotes the complex hyperbolic plane. We investigate the Dirichlet domain formed by the action of a cyclic subgroup $(g_1, g_2)$, where each $g_i$ is loxodromic.

Jan Hubička, Matěj Konečný, Stevo Todorcevic, Andy Zucker

We show that the big Ramsey degrees of every countable universal $u$-uniform $\omega$-edge-labeled hypergraph are infinite for every $u\geq 2$. Together with a recent result of Braunfeld, Chodounsk\'y, de Rancourt, Hubi\v{c}ka, Kawach, and Kone\v{c}n\'y this finishes full characterisation of unrestricted relational structures with finite big Ramsey degrees.

Artem Moskalev, Mangal Prakash, Junjie Xu, Tianyu Cui

Processing global geometric context while preserving equivariance is crucial when modeling biological, chemical, and physical systems. Yet, this is challenging due to the computational demands of equivariance and global context at scale. Standard methods such as equivariant self-attention suffer from quadratic complexity, while local methods such as distance

Jeongheon Choe, Taketo Handa, Chun-Ying Huang, André Koch Liston

Excitation of ordered quantum phases gives rise to collective modes and quasiparticles, as exemplified by spin waves and magnons emerging from magnetic order. Extending this paradigm to ferroelectric materials suggests the existence of polarization waves and their fundamental quanta, ferrons. Here, we report the generation and transport of polarization waves

Anatoly Galikhanov

We introduce a pro-\'etale geometric object $D_\infty$ arising naturally from the tower of Artin-Schreier extensions in characteristic 2, equipped with a canonical endofunctor $O$ whose fixed points correspond to automorphic representations of $\mathrm{GL}_2(\mathbb{A}_{\mathbb{F}_2})$. The main theorem establishes that invariant predicates on $D_\infty$ par

Valery Alexeev, Philip Engel

We propose modifications to the commonly used definitions of lattice-polarized and lattice-quasipolarized smooth K3 surfaces, collecting various versions of the definition, and determining the effects of these choices on the resulting moduli space. We fill a gap in the theory, by replacing Weyl chambers with the new notion of a ``small cone'': the true datum

Jan Søreng

The objective of this work is to establish a mathematical framework for the study of symmetric shift registers over the field GF(2). The present paper gives a new approach where the symmetric shift registers are represented by associated systems of nonlinear difference equations. Arithmetical progressions will play a central part. This approach clarifies the

Brandon G. Barreto-Rosa, Jean-Philippe Burelle, Anton Lukyanenko, Martha Richey

We describe a new continued fraction system in Minkowski space $\mathbb R^{1,1}$, proving convergence, ergodicity with respect to an explicit invariant measure, and Lagrange's theorem. The proof of ergodicity leads us to the question of exactness for products of dynamical systems. Under technical assumptions, namely Renyi's condition, we show that products o

Yanyi Qu, Haoyang Ma, Wenhui Xiong

Human pose estimation based on Channel State Information (CSI) has emerged as a promising approach for non-intrusive and precise human activity monitoring, yet faces challenges including accurate multi-person pose recognition and effective CSI feature learning. This paper presents MultiFormer, a wireless sensing system that accurately estimates human pose th

Agnideep Aich, Md Monzur Murshed, Sameera Hewage, Amanda Mayeaux

Effective feature selection is critical for robust and interpretable predictive modeling in medicine, especially when risk factors matter most in extreme patient strata. Many standard selectors emphasize average associations and can miss predictors whose relevance is concentrated in the distribution tails. We propose a computationally efficient supervised fi

Jan Novák, Oem Trivedi

We formulate an approach to quantum gravity, called the ring paradigm. Gravity is mediated superluminally, and the graviton is described as a phonon on the grid of matter in the Universe. This theory has very interesting applications to cosmology and would ultimately solve the old problem of the cosmological constant. It further gives new impulses to the sca

Hoang Pham, Thanh-Do Nguyen, Khac-Hoai Nam Bui

Integrating knowledge graphs (KGs) to enhance the reasoning capabilities of large language models (LLMs) is an emerging research challenge in claim verification. While KGs provide structured, semantically rich representations well-suited for reasoning, most existing verification methods rely on unstructured text corpora, limiting their ability to effectively

Long Hui, Wai Lok Yeung

Limited DXA access hinders osteoporosis screening. This proof-of-concept study proposes using widely available knee X-rays for opportunistic Bone Mineral Density (BMD) estimation via deep learning, emphasizing robust uncertainty quantification essential for clinical use. An EfficientNet model was trained on the OAI dataset to predict BMD from bilateral knee

Rosario Uceda-Sosa, Nandana Mihindukulasooriya, Atul Kumar, Sahil Bansal

Logical and probabilistic reasoning tasks that require a deeper knowledge of semantics are increasingly relying on general purpose ontologies such as Wikidata and DBpedia. However, tasks such as entity disambiguation and linking may benefit from domain specific knowledge graphs, which make it more efficient to consume the knowledge and easier to extend with

Alex Iacob, Lorenzo Sani, Mher Safaryan, Paris Giampouras

Scaling foundation model training with Distributed Data Parallel (DDP) methods is bandwidth-limited. Existing infrequent communication methods like Local SGD were designed to synchronize only model parameters and cannot be trivially applied to adaptive optimizers due to additional optimizer states. Current approaches extending Local SGD either lack convergen

Changhao Song, Yazhou Zhang, Hui Gao, Kaiyun Huang

Long chain-of-thought (CoT) reasoning has shown great promise in enhancing the emotion understanding performance of large language models (LLMs). However, current fixed-length CoT methods struggle to balance reasoning depth and efficiency. Simple tasks (e.g., sentiment classification) are over-reasoned, while complex tasks (e.g., sarcasm understanding) lack

T2K Collaboration, K. Abe, S. Abe, R. Akutsu

We report the first measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasi-elastic-like interactions at the gadolinium-loaded Super-Kamiokande detector using the T2K neutrino beam, which has a peak energy of about 0.6 GeV. A total of 30 neutral-current quasi-elastic-like event candidates were selected from T2K data corresponding

Wencheng Lu, Michael J. Hutcheon, Mads F. Hansen, Kapildeb Dolui

The low-pressure stabilization of superconducting hydrides with high critical temperatures ($T_c$s) remains a significant challenge, and experimentally verified superconducting hydrides are generally constrained to a limited number of structural prototypes. Ternary transition-metal complex hydrides (hydrido complexes)-typically regarded as hydrogen storage m

Christine L. Bradley, Rami W. Wehbe, Matthew Smith, Sharmila Padmanabhan

The proposed Carbon Investigation (Carbon-I) Imaging Spectrometer is designed to measure variations of greenhouse gases in Earth's atmosphere. The instrument will survey the Earth from its own spacecraft at an altitude of approximately 610 km. It will use a coarse ground sampling distance (GSD) of <400 m in global mode for land and coastal monitoring and fin

Ziheng Jia, Zicheng Zhang, Zeyu Zhang, Yingji Liang

The data scaling law has been shown to significantly enhance the performance of large multi-modal models (LMMs) across various downstream tasks. However, in the domain of perceptual video quality assessment (VQA), the potential of scaling law remains unprecedented due to the scarcity of labeled resources and the insufficient scale of datasets. To address thi

Zhenglun Kong, Zheng Zhan, Shiyue Hou, Yifan Gong

Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities from other specialized LLMs. Popular methods like ensemble and weight merging require substantial memory and struggle to adapt to changing data environments. Recent efforts have tr

Hosein Gholami, Lennart Kurth, Ugo Mire, Michael Buballa

We present a comprehensive study of the two-flavor Quark--Meson--Diquark (QMD) model by comparing a renormalization approach with a renormalization-group (RG) consistent mean-field formulation based on the functional renormalization group (FRG). The renormalized QMD model allows analytical investigations of key quantities such as the zero-temperature diquark

Vinitra Swamy

Deep neural networks form the backbone of artificial intelligence research, with potential to transform the human experience in areas ranging from autonomous driving to personal assistants, healthcare to education. However, their integration into the daily routines of real-world classrooms remains limited. It is not yet common for a teacher to assign student

Andreas Müller, Stefan Mueller, Tobias Brixner, Sebastian von Mammen

femtoPro is an interactive virtual reality (VR) laser laboratory balancing the contrasting challenges of accuracy and computational efficiency in optics simulations. It can simulate linear and nonlinear optical phenomena in real time, a task that pushes the boundaries of current consumer hardware. This paper details the concept, implementation, and evaluatio

Tim Engelbracht, Petar Lukovic, Tjark Behrens, Kai Lascheit

Recent progress in mixed reality (MR) and robotics is enabling increasingly sophisticated forms of human-robot collaboration. Building on these developments, we introduce a novel MR framework that allows multiple quadruped robots to operate in semantically diverse environments via a MR interface. Our system supports collaborative tasks involving drawers, swi

Paul Hofman, Yusuf Sale, Eyke Hüllermeier

We address the problem of uncertainty quantification and propose measures of total, aleatoric, and epistemic uncertainty based on a known decomposition of (strictly) proper scoring rules, a specific type of loss function, into a divergence and an entropy component. This leads to a flexible framework for uncertainty quantification that can be instantiated wit

Maxence Wynen, Pedro M. Gordaliza, Maxime Istasse, Anna Stölting

Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely on semantic segmentation post-processed with connected components (CC), which cannot separate confluent lesions (aggregates of confluent lesion units, CLUs) due to reliance on spati

Neda Boroumand, Adam Thorpe, Graeme Bart, Lu Wang

In quantum sideband high harmonic generation (QSHHG), high harmonic generation is perturbed by a bright quantum field resulting in harmonic sidebands, with the intent to transfer non-classical properties from the quantum perturbation to the harmonic sidebands. So far, non-classical features have not been found in QSHHG yet. The closed form theory of QSHHG in

Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Juergen Gall

Recent deep learning approaches for river discharge forecasting have improved the accuracy and efficiency in flood forecasting, enabling more reliable early warning systems for risk management. Nevertheless, existing deep learning approaches in hydrology remain largely confined to local-scale applications and do not leverage the inherent spatial connections

Aya Ghaleb, Ameek Malhotra, Gianmassimo Tasinato, Ivonne Zavala

The formation of primordial black holes or other dark matter relics from amplified density fluctuations in the early universe may also generate scalar-induced gravitational waves (GW), carrying vital information about the primordial power spectrum and the early expansion history of our universe. We present a Bayesian approach aimed at reconstructing both the

Franco Flandoli, Matteo Palmieri, Milo Viviani

We give a rigorous mathematical result, supported by numerical simulations, of the aggregation of a concentrated vortex blob with an underlying non-constant vorticity field: the blob moves in the direction of the gradient of the field. It is a unique example of a Lagrangian explanation of aggregation of vortex structures of the same sign in 2D inviscid fluid

Pallavi Bhardwaj, Caitlin Jones, Lasse Dierich, Aleksandar Vučković

In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real-world data is scarce or private, as it can be used to augment or replace existing datasets. As enterprise data is predominantly tabular and heterogeneous, often consisting of both categorical and numerical features, t

R. Altmann, B. Dörich, C. Zimmer

This paper deals with the construction and analysis of two integrators for (semi-linear) second-order partial differential-algebraic equations of semi-explicit type. More precisely, we consider an implicit-explicit Crank-Nicolson scheme as well as an exponential integrator of Gautschi type. For this, well-known wave integrators for unconstrained systems are

Andres Molina-Markham, Luis Robaina, Sean Steinle, Akash Trivedi

Open-ended learning (OEL) -- which emphasizes training agents that achieve broad capability over narrow competency -- is emerging as a paradigm to develop artificial intelligence (AI) agents to achieve robustness and generalization. However, despite promising results that demonstrate the benefits of OEL, applying OEL to develop autonomous agents for real-wor

Siqi Ding, Fengchun Lei, Wei Lin, Andrei Vesnin

Let M be a connected orientable 3-manifold, and F a compact connected orientable surface properly embedded in M. If F cuts M into two connected 3-manifolds X and Y, that is, M=X \cup_F Y, we say that M is an amalgamation of X and Y along F; and if F cuts M into a connected 3-manifold X, we say that M is a self-amalgamation of X along F. A characterization of

Niclas Bernhoff

A semi-classical approach to the study of the evolution of anyonic excitations--elementary particles with fractional statistics, complementing bosons and fermions--is through the Boltzmann equation for anyons. This work reviews a discretized version--a system of partial differential equations--of such a quantum equation. Trend to equilibrium is studied for a

Elena Denisova

We compute the $\delta$-invariant for pairs $(\mathbb{P}^2, \lambda C_d)$, where $C_d$ is a plane curve of degree $d \leq 4$. These computations provide new examples of $K$-stable and $K$-semistable log Fano pairs, and contribute to the study of $K$-stability of log Fano varieties via the Abban-Zhuang method, which reduces higher-dimensional problems to the

Agnideep Aich, Ashit Aich

We introduce the \emph{Symplectic Generative Network (SGN)}, a deep generative model that leverages Hamiltonian mechanics to construct an invertible, volume-preserving mapping between a latent space and the data space. By endowing the latent space with a symplectic structure and modeling data generation as the time evolution of a Hamiltonian system, SGN achi

Fei Qin, Zhanxin Hao, Jifan Yu, Zhiyuan Liu

This study examines the impact of an AI instructional agent on students' perceived learner control and academic performance in a medium demanding course with lecturing as the main teaching strategy. Based on a randomized controlled trial, three instructional conditions were compared: a traditional human teacher, a self-paced MOOC with chatbot support, and an

Ethan Chern, Zhulin Hu, Steffi Chern, Siqi Kou

We present Thinking with Generated Images, a novel paradigm that fundamentally transforms how large multimodal models (LMMs) engage with visual reasoning by enabling them to natively think across text and vision modalities through spontaneous generation of intermediate visual thinking steps. Current visual reasoning with LMMs is constrained to either process

Zijing Ou, Chinmay Pani, Yingzhen Li

Discrete diffusion models have become highly effective across various domains. However, real-world applications often require the generative process to adhere to certain constraints. To this end, we propose a Sequential Monte Carlo (SMC) framework that enables scalable inference-time control of discrete diffusion models through principled importance weightin

Jingxuan Zhou, Yuehao Wu, Yibo Zhang, Yeyubei Zhang

Aiming at the problem of difficulty in accurately identifying graphical implicit correlations in multimodal irony detection tasks, this paper proposes a Semantic Irony Recognition Network (SemIRNet). The model contains three main innovations: (1) The ConceptNet knowledge base is introduced for the first time to acquire conceptual knowledge, which enhances th

Junwen Chen, Heyang Jiang, Yanbin Wang, Keming Wu

Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing text outputs from LLMs. However, the development of multi-layer generative models lags behind that of conventional text-to-image models due to the absence of a large, high-quality c

Yuan Zhang, Feng Chen, Yaolei Qi, Guanyu Yang

Pathology image segmentation across multiple centers encounters significant challenges due to diverse sources of heterogeneity including imaging modalities, organs, and scanning equipment, whose variability brings representation bias and impedes the development of generalizable segmentation models. In this paper, we propose PathFL, a novel multi-alignment Fe

Chao Wang, Chuanhao Nie, Yunbo Liu

Fraud detection remains a critical task in high-stakes domains such as finance and e-commerce, where undetected fraudulent transactions can lead to significant economic losses. In this study, we systematically compare the performance of four supervised learning models - Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and a Gat

Nimmi Das Anthuparambil, Michelle Dargasz, Sonja Timmermann, Anita Girelli

Low-density lipoproteins (LDLs) serve as nutrient reservoirs in egg yolk for embryonic development and as promising drug carriers. Both roles critically depend on their mobility in densely crowded biological environments. Under these crowded conditions, diffusion is hindered by transient confinement within dynamic cages formed by neighboring particles, drive

Kristin Courtney, Priyanga Ganesan, Mateusz Wasilewski

Connectivity is a fundamental property of quantum graphs, previously studied in the operator system model for matrix quantum graphs and via graph homomorphisms in the quantum adjacency matrix model. In this paper, we develop an algebraic characterization of connectivity for general quantum graphs within the quantum adjacency matrix framework. Our approach ex

Agnideep Aich

Classical estimators, the cornerstones of statistical inference, face insurmountable challenges when applied to important emerging classes of Archimedean copulas. These models exhibit pathological properties, including numerically unstable densities, a restrictive lower bound on Kendall's tau, and vanishingly small likelihood gradients, making MLE brittle an

Yimeng Gu, Zhao Tong, Ignacio Castro, Shu Wu

Multimodal out-of-context news is a type of misinformation in which the image is used outside of its original context. Many existing works have leveraged multimodal large language models (MLLMs) for detecting out-of-context news. However, observing the limited zero-shot performance of smaller MLLMs, they generally require label-rich fine-tuning and/or expens

Martina Toscani, Luca Broggi, Alberto Sesana, Elena Maria Rossi

In this paper, we investigate the gravitational wave (GW) emission from stars tidally disrupted by black holes (TDEs), using a semi-analytical approach. Contrary to previous works where this signal is modeled as a monochromatic burst, we here take into account all its harmonic components. On top of this, we also extend the analysis to a population of repeate

Haoran Wang, Guanyu Chen, Bohan Li, Hankun Wang

Neural speech codecs excel in reconstructing clean speech signals; however, their efficacy in complex acoustic environments and downstream signal processing tasks remains underexplored. In this study, we introduce a novel benchmark named Environment-Resilient Speech Codec Benchmark (ERSB) to systematically evaluate whether neural speech codecs are environmen

Bradley Phillips, Edith Elkind, Nicholas Teh, Tomasz Wąs

We study proportional representation in the framework of temporal voting with approval ballots. Prior work adapted basic proportional representation concepts -- justified representation (JR), proportional JR (PJR), and extended JR (EJR) -- from the multiwinner setting to the temporal setting. Our work introduces and examines ways of going beyond EJR. Specifi

Saniya Khan, Richard I. Anderson, Sylvia Ekström, Cyril Georgy

The impact of metallicity on the Cepheid Leavitt law (LL) and, in turn, the Hubble constant, has been the subject of much recent debate. Here, we present a comprehensive analysis of metallicity effects on Cepheid LLs based on synthetic Cepheid populations computed using Geneva models and the SYCLIST tool. We computed 296 co-eval populations in the age range

Siyeop Yoon, Yujin Oh, Pengfei Jin, Sifan Song

We present Surf2CT, a novel cascaded flow matching framework that synthesizes full 3D computed tomography (CT) volumes of the human torso from external surface scans and simple demographic data (age, sex, height, weight). This is the first approach capable of generating realistic volumetric internal anatomy images solely based on external body shape and demo

Parbati Senapati, Ajay Kumar, Prakash Parida

Transition metal dichalcogenides (TMDs) have garnered attention for their potential in thermoelectric applications due to their unique electronic properties and tunable bandgaps. In this study, we systematically explore the electronic and thermoelectric properties of $T^{\prime}-RuX_2$ (X = S, Se) using first-principles calculations and semi-classical Boltzm

Zhonglin Xie, Yiman Fong, Haoran Yuan, Zaiwen Wen

Optimization is an important module of modern machine learning applications. Tremendous efforts have been made to accelerate optimization algorithms. A common formulation is achieving a lower loss at a given time. This enables a differentiable framework with respect to the algorithm hyperparameters. In contrast, its dual, minimizing the time to reach a targe

George N. Katsaros, Konstantinos Nikitopoulos

Neuromorphic computing, inspired by biological neural systems, has emerged as a promising approach for ultra-energy-efficient data processing by leveraging analog neuron structures and spike-based computation. However, its application in communication systems remains largely unexplored, with existing efforts mainly focused on mapping isolated communication a

Wenjie Sun, Bingzhe Wu, Zhile Yang, Chengke Wu

Sparse Autoencoders (SAEs) have emerged as a predominant tool in mechanistic interpretability, aiming to identify interpretable monosemantic features. However, how does sparse encoding organize the representations of activation vector from language models? What is the relationship between this organizational paradigm and feature disentanglement as well as re

Alessandro Bosso, Marco Borghesi, Andrea Iannelli, Giuseppe Notarstefano

This article proposes an approach to design output-feedback controllers for unknown continuous-time linear time-invariant systems using only input-output data from a single experiment. To address the lack of state and derivative measurements, we introduce non-minimal realizations whose states can be observed by filtering the available data. We first apply th

Ahmed Hossam Mohammed, Kishansingh Rajput, Simon Taylor, Denis Furletov

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajecto

Yuanfei Wang, Xinju Huang, Fangwei Zhong, Yaodong Yang

While embodied agents have made significant progress in performing complex physical tasks, real-world applications demand more than pure task execution. The agents must collaborate with unfamiliar agents and human users, whose goals are often vague and implicit. In such settings, interpreting ambiguous instructions and uncovering underlying desires is essent

Dominic Lowe, M. S. Kim, Roberto Bondesan

Gaussian Process Regression is a well-known machine learning technique for which several quantum algorithms have been proposed. We show here that in a wide range of scenarios these algorithms show no exponential speedup. We achieve this by rigorously proving that the condition number of a kernel matrix scales at least linearly with the matrix size under gene

Dingchu Zhang, Yida Zhao, Jialong Wu, Baixuan Li

The rapid advancement of large language models (LLMs) has transformed the landscape of agentic information seeking capabilities through the integration of tools such as search engines and web browsers. However, current mainstream approaches for enabling LLM web search proficiency face significant challenges: supervised fine-tuning struggles with data product

Ronald Orozco López

In this paper, we introduce bivariate polynomial sets of deformed $q$-Appell type, and we study the algebraic properties of these sets. We show the relation between deformed bivariate $q$-Appell polynomials and deformed homogeneous polynomials. Next, we give some of their characterizations and algebraic structure. Then, we introduce the deformed $q$-Appell o

Aixuan Li, Mochu Xiang, Bosen Hou, Zhexiong Wan

Adversarial robustness of BEV 3D object detectors is critical for autonomous driving (AD). Existing invasive attacks require altering the target vehicle itself (e.g. attaching patches), making them unrealistic and impractical for real-world evaluation. While non-invasive attacks that place adversarial objects in the environment are more practical, current me

Angelo A. Casulli, Francesco Hrobat, Daniel Kressner

This work considers large-scale Lyapunov matrix equations of the form $AX + XA = \boldsymbol{c}\boldsymbol{c}^T$, where $A$ is a symmetric positive definite matrix and $\boldsymbol{c}$ is a vector. Motivated by the need to solve such equations in a wide range of applications, various numerical methods have been developed to compute low-rank approximations of

Tzvika Geft, Kostas Bekris, Jingjin Yu

Grid-based storage systems with uniformly shaped loads (e.g., containers, pallets, totes) are commonplace in logistics, industrial, and transportation domains. A key performance metric for such systems is the maximization of space utilization, which requires some loads to be placed behind or below others, preventing direct access to them. Consequently, dense

Long Hui

This paper presents a novel approach to catheter and line position detection in chest X-rays, combining multi-task learning with risk-sensitive conformal prediction to address critical clinical requirements. Our model simultaneously performs classification, segmentation, and landmark detection, leveraging the synergistic relationship between these tasks to i

Osama M. Maklad, Muting Hao

Models of the fluid-structure interaction (FSI) model for the air puff test were analysed. Using Abaqus, the air puff test is applied to eyes with varying biomechanical parameters, such as material properties, corneal thickness, and radius. A reduced order model of the air puff (a turbulent impinging jet) has been acquired to decrease simulation time from 48

Michal Kmicikiewicz, Vincent Fortuin, Ewa Szczurek

Designing protein sequences of both high fitness and novelty is a challenging task in data-efficient protein engineering. Exploration beyond wild-type neighborhoods often leads to biologically implausible sequences or relies on surrogate models that lose fidelity in novel regions. Here, we propose ProSpero, an active learning framework in which a frozen pre-

Maria Jolis, Salvador Ortiz-Latorre, Lluís Quer-Sardanyons

We consider the quasi-linear stochastic wave and heat equations in $\mathbb{R}^d$ with $d\in \{1,2,3\}$ and $d\geq 1$, respectively, and perturbed by an additive Gaussian noise which is white in time and has a homogeneous spatial correlation with spectral measure $\mu_n$. We allow the Fourier transform of $\mu_n$ to be a genuine distribution. Let $u^n$ be th

Hongyi Zhou, Josiah P. Hanna, Jin Zhu, Ying Yang

This paper studies off-policy evaluation (OPE) in reinforcement learning with a focus on behavior policy estimation for importance sampling. Prior work has shown empirically that estimating a history-dependent behavior policy can lead to lower mean squared error (MSE) even when the true behavior policy is Markovian. However, the question of why the use of hi

Beatrice F. R. Citterio, Andrea Tangherloni

Differentiable programming has revolutionised optimisation by enabling efficient gradient-based training of complex models, such as Deep Neural Networks (NNs) with billions and trillions of parameters. However, traditional Evolutionary Computation (EC) and Swarm Intelligence (SI) algorithms, widely successful in discrete or complex search spaces, typically d

Moritz Haas, Sebastian Bordt, Ulrike von Luxburg, Leena Chennuru Vankadara

Scaling limits, such as infinite-width limits, serve as promising theoretical tools to study large-scale models. However, it is widely believed that existing infinite-width theory does not faithfully explain the behavior of practical networks, especially those trained in standard parameterization (SP) meaning He initialization with a global learning rate. Fo

Ke Zhang, Tianyu Ding, Jiachen Jiang, Tianyi Chen

Image cropping is crucial for enhancing the visual appeal and narrative impact of photographs, yet existing rule-based and data-driven approaches often lack diversity or require annotated training data. We introduce ProCrop, a retrieval-based method that leverages professional photography to guide cropping decisions. By fusing features from professional phot

Siyeop Yoon, Sifan Song, Pengfei Jin, Matthew Tivnan

We propose a cascaded 3D diffusion model framework to synthesize high-fidelity 3D PET/CT volumes directly from demographic variables, addressing the growing need for realistic digital twins in oncologic imaging, virtual trials, and AI-driven data augmentation. Unlike deterministic phantoms, which rely on predefined anatomical and metabolic templates, our met

Gakuto Kusuno, Takeshi Hayashida, Takayuki Nagai, Hikaru Watanabe

Raman optical activity (ROA), the dependence of Raman intensity on the circular polarization of incident and scattered light, has traditionally been observed in chiral molecules and magnetic materials, where inversion or time-reversal symmetry is broken. Here we demonstrate that ROA can also arise in a centrosymmetric and non-magnetic ferroaxial crystal. Usi

Yen Meng, Sharon Goldwater, Hao Tang

Modern neural speech models benefit from having longer context, and many approaches have been proposed to increase the maximum context a model can use. However, few have attempted to measure how much context these models actually use, i.e., the effective context. Here, we propose two approaches to measuring the effective context, and use them to analyze diff

Mujtaba Hussain Mirza, Maria Rosaria Briglia, Filippo Bartolucci, Senad Beadini

We aim at using Energy-based Model (EBM) framework to better understand adversarial training (AT) in classifiers, and additionally to analyze the intrinsic generative capabilities of robust classifiers. By viewing standard classifiers through an energy lens, we begin by analyzing how the energies of adversarial examples, generated by various attacks, differ

Adam Arras

We establish a spectral correspondence between random Schr\"odinger operators and deterministic convolution operators on wreath products, generalizing previous results that relate Lamplighter groups to Schr\"odinger operators with Bernoulli potentials. Using this correspondence in both directions, we obtain an elementary criterion for the absolute continuity

Eduardo K. Soares, Gentil D. de Moraes Neto, Fabiano M. Andrade

We present a numerical investigation comparing two entanglement generation protocols in finite XX spin chains with varying spin magnitudes ($s = 1/2, 1, 3/2 $). Protocol 1 (P1) relies on staggered couplings to steer correlations toward the ends of the chain. At the same time, Protocol 2 (P2) adopts a dual-port architecture that uses optimized boundary fields

Abhra Chaudhuri, Anjan Dutta, Tu Bui, Serban Georgescu

We aim to develop a fundamental understanding of modality collapse, a recently observed empirical phenomenon wherein models trained for multimodal fusion tend to rely only on a subset of the modalities, ignoring the rest. We show that modality collapse happens when noisy features from one modality are entangled, via a shared set of neurons in the fusion head

Daniël W. S. Cox, Harish Sasikumar, Ivo M. Vellekoop

We present a method for calibrating the response of a phase-only spatial light modulator in nonlinear microscopy. Our method uses the microscope image itself as calibration measurement and requires no additional hardware components. Our method is adapted to the nonlinear signals encountered in multi-photon excitation fluorescence microscopes, and works well

Yiming Xi, Konstantinos Zygalakis, Marcelo Pereyra

This paper proposes a framework for semantic hypothesis testing tailored to imaging inverse problems. Modern imaging methods struggle to support hypothesis testing, a core component of the scientific method that is essential for the rigorous interpretation of experiments and robust interfacing with decision-making processes. There are three main reasons why

Lorenzo Botti, Daniele Antonio Di Pietro, Francesco Carlo Massa

We propose a Hybrid High-Order (HHO) formulation of the incompressible Navier--Stokes equations, that is well suited to be employed for the simulation of turbulent flows. The spatial discretization relies on hybrid velocity and pressure spaces and the temporal discretization is based on Explicit Singly Diagonal Implicit Runge-Kutta (ESDIRK) methods. The form

Chamila Malagoda Gamage

This study explores how pre- and post-assessments shape learning outcomes in an Introductory Real Analysis course. Pre-assessments act as learning roadmaps, highlighting prior knowledge and guiding student focus, while post-assessments measure growth and conceptual mastery. By analyzing student performance and feedback, we assess their impact on engagement,

Bjoern Bringmann, Gigliola Staffilani

We prove the invariance of the Gibbs measure for the defocusing quintic nonlinear Schr\"odinger equation on the real line. This builds on earlier work by Bourgain, who treated the cubic nonlinearity. The key new ingredient is a growth estimate for the infinite-volume $\Phi^{p+1}_1$-measures, which is proven via the stochastic quantization method.

Qi Gao, Wei Xu, Hanxi Pan, Mowei Shen

In the intelligent era, the interaction between humans and intelligent systems fundamentally involves collaboration with autonomous intelligent agents. Human-AI Collaboration (HAC) represents a novel type of human-machine relationship facilitated by autonomous intelligent machines equipped with AI technologies. In this paradigm, AI agents serve not only as a

J. Altmann, U. Amaldi, M. Barone, A. Bassalat

After the end of World War II, the commitment to confine scientific activities in universities and research institutions to peaceful and civilian purposes has entered, in the form of {\it Civil Clauses}, the charters of many research institutions and universities. In the wake of recent world events, the relevance and scope of such Civil Clauses has been ques

Riccardo Poiani, Martino Bernasconi, Andrea Celli

In pure exploration problems, a statistician sequentially collects information to answer a question about some stochastic and unknown environment. The probability of returning a wrong answer should not exceed a maximum risk parameter $\delta$ and good algorithms make as few queries to the environment as possible. The Track-and-Stop algorithm is a pioneering

Amirhossein Sohrabbeig, Omid Ardakanian, Petr Musilek

Long-term forecasting of multivariate urban data poses a significant challenge due to the complex spatiotemporal dependencies inherent in such datasets. This paper presents DST, a novel multivariate time-series forecasting model that integrates graph attention and temporal convolution within a Graph Neural Network (GNN) to effectively capture spatial and tem

Riccardo Poiani, Martino Bernasconi, Andrea Celli

We study pure exploration problems in which the set of correct answers is possibly infinite. For example, such problems arise when regressing a continuous function on the means of the bandit or when learning Nash equilibria by querying noisy values of the payoff matrix. We derive an instance-dependent lower bound for these problems. By analyzing it, we discu

Matteo Acciai, Liliana Arrachea, Janine Splettstoesser

We present an overview of time-dependent transport phenomena in quantum systems, with a particular emphasis on steady-state regimes. We present the ideas after the main theoretical frameworks to study open-quantum systems out of equilibrium, that are useful to study quantum transport under time-dependent driving. We discuss the fundamentals of the key mechan

Stevan Gajović, Sun Woo Park

We prove that for any number field $K$ and any fixed genus $g \geq 2$, there are infinitely many non-isomorphic hyperelliptic curves of genus $g$ over $K$ whose Jacobians have rank over $K$ equal to each of 0, 1, or 2. As an example of our method, over $\mathbb{Q}$, we prove that there exist infinitely many non-isomorphic hyperelliptic curves of genus two, w

Mohamed R. Elshamy, Mehdi Elahi, Ahmad Patooghy, Abdel-Hameed A. Badawy

Efficient thermal and power management in modern multiprocessor systems-on-chip (MPSoCs) demands accurate power consumption estimation. One of the state-of-the-art approaches, Alternative Blind Power Identification (ABPI), theoretically eliminates the dependence on steady-state temperatures, addressing a major shortcoming of previous approaches. However, ABP