March 2025 arXiv papers — page 39

Hanwen Xing, Christopher Yau

Continual learning (CL) refers to the ability to continuously learn and accumulate new knowledge while retaining useful information from past experiences. Although numerous CL methods have been proposed in recent years, it is not straightforward to deploy them directly to real-world decision-making problems due to their computational cost and lack of uncerta

Nan Jiang, Hongjie Li, Ziye Yuan, Zimo He

Text-guided motion editing enables high-level semantic control and iterative modifications beyond traditional keyframe animation. Existing methods rely on limited pre-collected training triplets, which severely hinders their versatility in diverse editing scenarios. We introduce MotionCutMix, an online data augmentation technique that dynamically generates t

Tohid Kargar Tasooji, Sakineh Khodadadi

Multi-robot coordination is fundamental to various applications, including autonomous exploration, search and rescue, and cooperative transportation. This paper presents an optimal consensus framework for multi-robot systems (MRSs) that ensures efficient rendezvous while minimizing energy consumption and addressing actuator constraints. A critical challenge

A. Candito, A. Dragan, R. Holbrey, A. Ribeiro

Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognized cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast

Ekta Sharma, Kate Pattle, Di Li, Chang Won Lee

We present 850 $\mu$m polarized continuum observations carried out with the POL-2 polarimeter mounted on the James Clerk Maxwell Telescope (JCMT) towards NGC 7023 located in the Cepheus Flare region. NGC 7023 is a reflection nebula powered by a Herbig Ae Be star HD 200775 and also identified as a hub in the hub-filament cloud, LDN 1172/1174. We detect submil

Federico Francesco Luigi Mariani, Michele Zhu, Maurizio Magarini

The development of the new generation of wireless technologies (6G) has led to an increased interest in semantic communication. Thanks also to recent developments in artificial intelligence and communication technologies, researchers in this field have defined new communication paradigms that go beyond those of syntactic communication to post-Shannon and sem

Shiv Shankar, Tomas Geffner

Flow matching models typically use linear interpolants to define the forward/noise addition process. This, together with the independent coupling between noise and target distributions, yields a vector field which is often non-straight. Such curved fields lead to a slow inference/generation process. In this work, we propose to learn flexible (potentially cur

Gernot Eichmann, Christian S. Fischer, Joshua Hoffer

We summarize recent results for four-quark states with hidden and open flavor obtained from a four-quark Bethe-Salpeter/Faddeev-Yakubowsky equation. The approach dynamically predicts the leading internal two-body clusters such as meson-meson, hadroquarkonium or diquark-antidiquark components. For hidden-flavor states, the meson-meson or hadroquarkonium confi

Lane G. Gunderman

Amongst quantum error-correcting codes the surface code has remained of particular promise as it has local and very low-weight checks, even despite only encoding a single logical qubit no matter the lattice size. In this work we discuss new local and low-weight stabilizer codes which are obtained from the recent progress in $2D$ local classical codes. Of not

Xu Du, Xiaohua Zhou, Shijie Zhu

The Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method is a cutting-edge distributed optimization algorithm known for its superior numerical performance. It relies on each agent transmitting information to a central coordinator for data exchange. However, in practical network optimization and federated learning, unreliable information

Nikita Neveditsin, Pawan Lingras, Vijay Mago

This study examines the performance of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA), with a focus on implicit aspect extraction in a novel domain. Using a synthetic sports feedback dataset, we evaluate open-weight LLMs' ability to extract aspect-polarity pairs and propose a metric to facilitate the evaluation of aspect extraction wi

Xueyin Li, Xinkai Jiang, Philipp Dahlinger, Gerhard Neumann

This work explores how force feedback affects various aspects of robot data collection within the Extended Reality (XR) setting. Force feedback has been proved to enhance the user experience in Extended Reality (XR) by providing contact-rich information. However, its impact on robot data collection has not received much attention in the robotics community. T

Pratyush Kumar Singh, Danial Faghihi

We present a thermodynamically consistent three-phase model for the coupled thermal transport and mechanical deformation of ceramic aerogel porous composite materials, which is formulated via continuum mixture theory. The composite comprises a solid silica skeleton, a gaseous fluid phase, and dispersed solid fibers. The thermal transport model incorporates t

Irene Gaiardoni, Mattia Trama, Alfonso Maiellaro, Claudio Guarcello

We investigate spin-to-charge conversion via the Edelstein effect in a 2D Rashba electron gas using the semiclassical Boltzmann approach. We analyze the magnetization arising from the direct Edelstein effect, taking into account an anisotropic Rashba model. We study how this effect depends on the effective masses and Rashba spin--orbit coupling parameters, e

Giovanni Compiani, Ilya Morozov, Stephan Seiler

We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model. This approach enables demand estimation even when researchers lack data on product attributes

Farshad Nazari, Andrei Potanin, Hadi Mohammadigoushki

In this study, we experimentally investigate the rheological and flow behavior of two-dimensional (2D) monodisperse aqueous foams, sheared between parallel plates using a custom-made rheo-optical apparatus with smooth and roughened walls. The foams were prepared using two commercially available detergents, Foam 1 and Foam2, while maintaining similar bubble s

Farzad Milani

In this study, we explore the dynamics of the universe using a modified gravity model represented by $f(R, G, T)$, where $R$ is the Ricci scalar, $G$ is the Gauss-Bonnet invariant, and $T$ is the trace of the stress-energy tensor. The model incorporates two scalar fields and is analyzed within a flat Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) universe.

Géza Ódor, István Papp, Gustavo Deco

We investigate the distance from equilibrium using the Kuramoto model via the degree of fluctuation-dissipation violation as the consequence of different levels of edge weight anisotropies. This is achieved by solving the synchronization equations on the raw, homeostatic weighted and a random inhibitory edge variant of a real full fly (FF) connectome, contai

Lisha Chen, Quan Xiao, Ellen Hidemi Fukuda, Xinyi Chen

Multi-objective learning under user-specified preference is common in real-world problems such as multi-lingual speech recognition under fairness. In this work, we frame such a problem as a semivectorial bilevel optimization problem, whose goal is to optimize a pre-defined preference function, subject to the constraint that the model parameters are weakly Pa

Gregoire F. M. Tomassi, Daniel Veldhuizen, Bruno Melo, Davide Candoli

While the wave packet of a massive particle grows linearly under free dynamics, it grows exponentially in an inverted harmonic potential, offering a pathway to rapidly increase quantum fluctuations to macroscopic dimensions. In this work, we experimentally demonstrate this principle by expanding the center-of-mass thermal state of a 125nm silica nanoparticle

Johannes Lips, Hendrik Lens

Smart balancing, also called passive balancing, is the intentional introduction of active power schedule deviations by balance responsible parties (BRPs) to receive a remuneration through the imbalance settlement mechanism. From a system perspective, smart balancing is meant to reduce the need for, and costs of, frequency restoration reserves (FRR), but it c

Mohammad R. Hajidavalloo, Kaixiang Zhang, Vaibhav Srivastava, Zhaojian Li

Vehicle rollovers pose a significant safety risk and account for a disproportionately high number of fatalities in road accidents. This paper addresses the challenge of rollover prevention using Data-EnablEd Predictive Control (DeePC), a data-driven control strategy that directly leverages raw input-output data to maintain vehicle stability without requiring

Kai North, Christopher Ormerod

Automated scoring (AS) systems used in large-scale assessment have traditionally used small statistical models that require a large quantity of hand-scored data to make accurate predictions, which can be time-consuming and costly. Generative Large Language Models are trained on many tasks and have shown impressive abilities to generalize to new tasks with li

Mario Carneiro, Emily Riehl

Certain results involving "higher structures" are not currently accessible to computer formalization because the prerequisite $\infty$-category theory has not been formalized. To support future work on formalizing $\infty$-category theory in Lean's mathematics library, we formalize some fundamental constructions involving the 1-category of categories. Specif

Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate

As the complexity of modern control systems increases, it becomes challenging to derive an accurate model of the uncertainty that affects their dynamics. Wasserstein Distributionally Robust Optimization (DRO) provides a powerful framework for decision-making under distributional uncertainty only using noise samples. However, while the resulting policies inhe

John T. Antolik, Eli A. Silver, Jesse L. Belden, Daniel M. Harris

We present the CyberDiver, an untethered robotic impactor capable of actively modulating the fluid physics during high-speed water entry. First, we utilize the CyberDiver to extend our understanding of the water entry of passively flexible systems, designing a high-bandwidth controller that enables the CyberDiver to operate as a cyber-physical system that pe

Claudia Grabs, Werner Wirges

We study large deformations of hyperelastic membranes using a purely two-dimensional formulation derived from basic balance principles within a modern geometric setting, ensuring a framework that is independent of an underlying three-dimensional formulation. To assess the predictive capabilities of membrane theory, we compare numerical solutions to experimen

Zhendong Chu, Jian Xie, Shen Wang, Zichao Wang

Education materials for K-12 students often consist of multiple modalities, such as text and images, posing challenges for models to fully understand nuanced information in these materials. In this paper, we propose a unified language and vision assistant UniEDU designed for various educational applications, including knowledge recommendation, knowledge trac

Örs Legeza, Andor Menczer, Ádám Ganyecz, Miklós Antal Werner

We present an efficient orbital optimization procedure that combines the highly GPU accelerated, spin-adapted density matrix renormalization group (DMRG) method with the complete active space self-consistent field (CAS-SCF) approach for quantum chemistry implemented in the ORCA program package. Leveraging the computational power of the latest generation of N

Jun Nishimura, Hiromasa Watanabe

We propose first-principle calculations of an open system based on the real-time path integral formalism treating the environment as well as the system of our interest together on a computer. The sign problem that occurs in applying Monte Carlo methods can be overcome in general by using the so-called Lefschetz thimble method, which has been developed over t

Saron Samuel, Dan DeGenaro, Jimena Guallar-Blasco, Kate Sanders

Videos inherently contain multiple modalities, including visual events, text overlays, sounds, and speech, all of which are important for retrieval. However, state-of-the-art multimodal language models like VAST and LanguageBind are built on vision-language models (VLMs), and thus overly prioritize visual signals. Retrieval benchmarks further reinforce this

Yankai Chen, Taotao Wang, Yixiang Fang, Yunyu Xiao

Node importance estimation, a classical problem in network analysis, underpins various web applications. Previous methods either exploit intrinsic topological characteristics, e.g., graph centrality, or leverage additional information, e.g., data heterogeneity, for node feature enhancement. However, these methods follow the supervised learning setting, overl

Jessica Santiago, Justin Feng, Sebastian Schuster, Matt Visser

The fact that no Hawking radiation from the final stages of evaporating primordial black holes (PBHs) has yet been observed places stringent bounds on their allowed contribution to dark matter. Concretely, for Schwarzschild PBHs, i.e., uncharged and non-rotating black holes, this rules out black hole masses of less than $10^{-15} M_{\odot}$. In this article,

Andrey Yachmenev, Guang Yang

We present the first quantum mechanical study of hyperfine effects in the rotational cluster states of a symmetric triatomic molecule H$_2$S. Rotational clusters arise from spontaneous symmetry breaking induced by high-angular-momentum rotational motions in certain rigid molecules, resulting in dynamic enantiomorphism driven by kinetic distortion effects. Hy

Hansaka Aluvihare, Sivakumar Sivasankar, Xianqi Li, Arjuna Madanayake

True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed that TTD beamformers can be efficiently realized using the elements of delay Vandermonde matrix (DVM), answering the longstanding beam-squint problem. Thus, building on our work on

Christian Muhmann, Reinhard M. Grassmann, Max Bartholdt, Jessica Burgner-Kahrs

In this paper, we propose a dynamic model and control framework for tendon-driven continuum robots (TDCRs) with multiple segments and an arbitrary number of tendons per segment. Our approach leverages the Clarke transform, the Euler-Lagrange formalism, and the piecewise constant curvature assumption to formulate a dynamic model on a two-dimensional manifold

Thijs Hazenberg, Danial Braig, Michal A. Fedoryk, Johannes Mich

This article presents numerical simulations of an iron dust Bunsen flame. The results are validated against experimental results. The burning velocity is extracted from the 3D simulation results, as in the experiments. The agreement of the burning velocity between the model and experiment is the best to date for iron dust flames. A comparison is performed be

Andreas Belaey, Thomas G. Mertens, Thomas Tappeiner

Double-scaled SYK (DSSYK) is known to have an underlying quantum group theoretical description. We precisely pinpoint the quantum group structure, improving upon earlier work in the literature. This allows us to utilize this framework for bulk gravitational applications. We explain bulk discretization in DSSYK from the underlying irreducibility of the repres

Sifis Lagouvardos, Yannis Bollanos, Michael Debono, Neville Grech

Smart contracts are small programs that run autonomously on the blockchain, using it as their persistent memory. The predominant platform for smart contracts is the Ethereum VM (EVM). In EVM smart contracts, a problem with significant applications is to identify data structures (in blockchain state, a.k.a. "storage"), given only the deployed smart contract c

Francisco Coelho, Bruno Dinis, Dietmar Seipel, Salvador Abreu

Logic programs, more specifically, Answer-set programs, can be annotated with probabilities on facts to express uncertainty. We address the problem of propagating weight annotations on facts (eg probabilities) of an ASP to its standard models, and from there to events (defined as sets of atoms) in a dataset over the program's domain. We propose a novel appro

Georg von Hippel, Konstantin Ottnad

We determine the low-energy constants (LECs) $f_0$, $L_4^r$ and $L_5^r$ of SU(3) Chiral Perturbation Theory ($\chi$PT) from a lattice QCD calculation of the scalar form factors of the pion with fully controlled systematics. Lattice results are computed on a large set of $N_f=2+1$ gauge ensembles covering four lattice spacings $a\in[0.049,0.086]\mathrm{fm}$,

Eloy Anguiano Batanero, Ángela Fernández, Álvaro Barbero

The increasing complexity of power grid management, driven by the emergence of prosumers and the demand for cleaner energy solutions, has needed innovative approaches to ensure stability and efficiency. This paper presents a novel approach within the model-free framework of reinforcement learning, aimed at optimizing power network operations without prior ex

Konstantin Ottnad, Georg von Hippel

We present a lattice QCD calculation of the pion scalar form factor and associated radii with fully controlled systematics. Lattice results are computed on a large set of 17 gauge ensembles with $N_f=2+1$ Wilson Clover-improved sea quarks. These ensembles cover four values of the lattice spacing between $a=0.049\mathrm{fm}$ and $a=0.086\mathrm{fm}$, a pion m

M. H. Stockett, A. Subramani, C. Liu, S. J. P. Marlton

Indene (C$_9$H$_8$) is the only polycyclic pure hydrocarbon identified in the interstellar medium to date, with an observed abundance orders of magnitude higher than predicted by astrochemical models. The dissociation and radiative stabilization of vibrationally-hot indene cations is investigated by measuring the time-dependent neutral particle emission rate

Yuhao Huang, Ao Chang, Haoran Dou, Xing Tao

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation

Kai Nakamura

We initiate the study of torus surgeries on knot traces. Our key technical insight is realizing the annulus twisting construction of Osoinach as a torus surgery on a knot trace. We present several applications of this idea. We find exotic elliptic surfaces that can be realized as surgery on null-homologously embedded traces in a manner similar to that propos

Xingyu Peng, Si Liu, Chen Gao, Yan Bai

The task of LiDAR-based 3D Open-Vocabulary Detection (3D OVD) requires the detector to learn to detect novel objects from point clouds without off-the-shelf training labels. Previous methods focus on the learning of object-level representations and ignore the scene-level information, thus it is hard to distinguish objects with similar classes. In this work,

Shuaikai Shi, Qijun Zong

Distributed acoustic sensing (DAS) technology represents an innovative fiber-optic-based sensing methodology that enables real-time acoustic signal monitoring through the detection of minute perturbations along optical fibers. This sensing approach offers compelling advantages, including extensive measurement ranges, exceptional spatial resolution, and an ex

Han Wang, Yongjie Ye, Bingru Li, Yuxiang Nie

We introduce Vision as LoRA (VoRA), a novel paradigm for transforming an LLM into an MLLM. Unlike prevalent MLLM architectures that rely on external vision modules for vision encoding, VoRA internalizes visual capabilities by integrating vision-specific LoRA layers directly into the LLM. This design allows the added parameters to be seamlessly merged into th

Tomáš Jakl

We review four areas of theoretical computer science which share technical or philosophical ideas with the work of Belnap on his useful four-valued logic. Perhaps surprisingly, the inspiration by Belnap-Dunn logic is acknowledged only in the study of d-frames. The connections of Belnap's work and linear logic, Blame Calculus or the study of LVars are not ope

Gabriel R. Palma, Mariusz Skoczeń, Phil Maguire

We investigated the use of Empirical Mode Decomposition (EMD) combined with Gaussian Mixture Models (GMM), feature engineering and machine learning algorithms to optimize trading decisions. We used five, two, and one year samples of hourly candle data for GameStop, Tesla, and XRP (Ripple) markets respectively. Applying a 15 hour rolling window for each marke

Alessio Cela, Carl Lian

When $a\ge2$, we show that a general pointed curve never interpolates through the expected number of points in the Hirzebruch surface $\mathcal{H}_a$, with one exception. In the exceptional case, the number of such interpolating maps is determined by the geometric Tevelev degrees of $\mathbb{P}^1$, which have been previously computed.

Gongzhu Yin, Hongli Zhang, Yuchen Yang, Yi Luo

N-ary relational facts represent semantic correlations among more than two entities. While recent studies have developed link prediction (LP) methods to infer missing relations for knowledge graphs (KGs) containing n-ary relational facts, they are generally limited to transductive settings. Fully inductive settings, where predictions are made on previously u

Igor G. Vladimirov, Ian R. Petersen, Guodong Shi

This paper considers a network of open quantum harmonic oscillators which interact with their neighbours through direct energy and field-mediated couplings and also with external quantum fields. The position-momentum dynamic variables of the network are governed by linear quantum stochastic differential equations associated with the nodes of a graph whose ed

Benjamin Laufer, Jon Kleinberg, Hoda Heidari

Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches to AI safety. We present a strategic model that explores the interactions between safety regulation, the general-purpose AI creators, and domain specialists--those who adapt the technology for specific

Erenay Karacan, Conor Mc Keever, Michael Foss-Feig, David Hayes

Eigenstate filters underpin near-optimal quantum algorithms for ground state preparation. Their realization on current quantum computers, however, poses a challenge as the filters are typically represented by deep quantum circuits. Additionally, since the filters are created probabilistically, their circuits need to be rerun many times when the associated su

Jim Achterberg, Bram van Dijk, Saif ul Islam, Hafiz Muhammad Waseem

Synthetic data offers a promising solution to privacy concerns in healthcare by generating useful datasets in a privacy-aware manner. However, although synthetic data is typically developed with the intention of sharing said data, ambiguous reidentification risk assessments often prevent synthetic data from seeing the light of day. One of the main causes is

Viktor Schlegel, Anil A Bharath, Zilong Zhao, Kevin Yee

Privacy-preserving synthetic data offers a promising solution to harness segregated data in high-stakes domains where information is compartmentalized for regulatory, privacy, or institutional reasons. This survey provides a comprehensive framework for understanding the landscape of privacy-preserving synthetic data, presenting the theoretical foundations of

Yinan Sun, Xiongkuo Min, Zicheng Zhang, Yixuan Gao

The rapid development of multimodal large language models has resulted in remarkable advancements in visual perception and understanding, consolidating several tasks into a single visual question-answering framework. However, these models are prone to hallucinations, which limit their reliability as artificial intelligence systems. While this issue is extens

Yuyang Peng, Shishi Xiao, Keming Wu, Qisheng Liao

Recently, state-of-the-art text-to-image generation models, such as Flux and Ideogram 2.0, have made significant progress in sentence-level visual text rendering. In this paper, we focus on the more challenging scenarios of article-level visual text rendering and address a novel task of generating high-quality business content, including infographics and sli

Samuel Desrochers

In this paper, we study the list object functor $L : \mathcal{C} \rightarrow \mathcal{C}$ for a general category $\mathcal{C}$ with finite limits and parametrized list objects. We show that $L$ is polynomial as long as $\mathcal{C}$ is extensive.

Takara Abe, Tomohiko G. Sano

Snap-buckling is a rapid shape transition in slender structures, appearing as a fundamental switching mechanism of natural and man-made systems. Boundary conditions of structures are crucial to predict and control their snap-buckling behavior. However, the general framework that relates boundary conditions, geometry, and performance of structures is still ab

José Galindo-Jiménez

We give an alternative proof of the Kulikov-Persson-Pinkham Theorem for a proper K\"ahler degeneration of K-trivial smooth surfaces. After running the Minimal Model Program, the obtained minimal dlt model has mild singularities which we resolve via Brieskorn's simultaneous resolutions and toric resolutions.

Joshua Chan, Christian Matthes, Xuewen Yu

Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To ad

Chenjing Bu

We construct and study Donaldson-Thomas invariants counting orthogonal and symplectic objects in linear categories, which are a generalization of the usual Donaldson-Thomas invariants from the structure groups $\mathrm{GL} (n)$ to the groups $\mathrm{O} (n)$ and $\mathrm{Sp} (2n)$, and a special case of the intrinsic Donaldson-Thomas theory developed by the

Johannes Lips, Boyana Georgieva, Dominik Schlipf, Hendrik Lens

Single imbalance pricing provides an incentive to balance responsible parties (BRPs) to intentionally introduce power schedule deviations in order to reduce the control area imbalance and receive a remuneration through the imbalance settlement mechanism. This is called smart balancing or passive balancing and is actively encouraged in, e.g., the Netherlands

Emilio Minichiello

These notes detail the basics of the theory of Grothendieck toposes from the viewpoint of coverages. Typically one defines a site as a (small) category equipped with a Grothendieck topology. However, it is often desirable to generate a Grothendieck topology from a smaller structure, such as a Grothendieck pretopology, but these require some pullbacks to exis

Mingze Sun, Shiwei Mao, Keyi Chen, Yurun Chen

Recent advancements in large-scale generative models have significantly improved the quality and diversity of 3D shape generation. However, most existing methods focus primarily on generating static 3D models, overlooking the potentially dynamic nature of certain shapes, such as humanoids, animals, and insects. To address this gap, we focus on rigging, a fun

Sadaf Khademi, Mehran Shabanpour, Reza Taleei, Anastasia Oikonomou

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. A crucial challenge for early diagnosis is differentiating uncertain cases with similar visual characteristics and closely annotation scores. In clinical practice, radiologists rely on quantitative, hand-crafted Radiomic features extracted from Computed Tomography (CT) image

Yi Song

HCMU surfaces are compact Riemann surfaces equipped with the Calabi extremal K\"{a}hler metric and a finite number of singularities. By using both the classical football decomposition introduced by Chen-Chen-Wu and the description of the geometric structure of HCMU surfaces by Lu-Xu, we can use weighted plane trees to characterize HCMU spheres with a single

Debidutta Pradhan, Rojalin Swain, Souvagya Kumar Biswal, Jagadish Kumar

We have investigated the electronic, vibrational, optical, thermal and piezoelectric properties of LiNbO$_3$, LiTaO$_3$ and Li$_2$NbTaO$_6$ using the first-principles calculation based on the density functional theory. It also shows structural phase transition below $T_c$ due to ionic displacement that may alter the properties of material. We have checked th

Hozefa Jesawada, Antonio Acernese, Giovanni Russo, Carmen Del Vecchio

Ensuring robustness against epistemic, possibly adversarial, perturbations is essential for reliable real-world decision-making. While the Probabilistic Ensembles with Trajectory Sampling (PETS) algorithm inherently handles uncertainty via ensemble-based probabilistic models, it lacks guarantees against structured adversarial or worst-case uncertainty distri

Cristian J. Vaca-Rubio, Vaishnavi Kasuluru, Engin Zeydan, Luis Blanco

Efficient resource management is critical for Non-Terrestrial Networks (NTNs) to provide consistent, high-quality service in remote and under-served regions. While traditional single-point prediction methods, such as Long-Short Term Memory (LSTM), have been used in terrestrial networks, they often fall short in NTNs due to the complexity of satellite dynamic

Daniel Ivan Ramirez Montaño

We obtain a Szeg\"o limit theorem for a family of Toeplitz operators defined on the weighted Bergman space of the unit ball $\mathbb{B}_{n}$. The symbols of these operators are supported on some isotropic or co-isotropic submanifold $\Gamma \subseteq \mathbb{B}_{n}$ and can be seen, in general, as measures that are singular with respect to the Lebesgue measu

Bin Wang

We study the Dirichlet problem for functions whose graphs are spacelike hypersurfaces with prescribed curvature in the Minkowski space and we obtain some new interior second order estimates for admissible solutions to the corresponding fully nonlinear elliptic partial differential equations.

Claude Duhr, Sara Maggio, Christoph Nega, Benjamin Sauer

We show how a method to construct canonical differential equations for multi-loop Feynman integrals recently introduced by some of the authors can be extended to cases where the associated geometry is of Calabi-Yau type and even beyond. This can be achieved by supplementing the method with information from the mixed Hodge structure of the underlying geometry

Xiangwen Zhang, Qian Zhang, Longfei Han, Qiang Qu

Collecting real-world vehicle accident videos for autonomous driving research is challenging due to their rarity and complexity. While existing driving video generation methods may produce visually realistic videos, they often fail to deliver physically realistic simulations because they lack the capability to generate accurate post-collision trajectories. I

Maximilian Plattner, Arturs Berzins, Johannes Brandstetter

Foundation models for 3D shape generation have recently shown a remarkable capacity to encode rich geometric priors across both global and local dimensions. However, leveraging these priors for downstream tasks can be challenging as real-world data are often scarce or noisy, and traditional fine-tuning can lead to catastrophic forgetting. In this work, we tr

Antoine Schieb, Bilal Hadjadji, Natalia Fernanda Valderrama, Daniel Tshokola Mweze

Histopathology slide digitization introduces scanner-induced domain shift that can significantly impact computational pathology models based on deep learning methods. In the state-of-the-art, this shift is often characterized at a broad scale (slide-level or dataset-level) but not patch-level, which limits our comprehension of the impact of localized tissue

Theo Di Piazza, Carole Lazarus, Olivier Nempont, Loic Boussel

The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, and report generation, to assist radiologists with their growing workload. Multi-label classification of Three-Dimensional (3D) CT scans is a challenging task due to the volumetric

Davey Plugers, Kunihiko Kaneko

In multi-cellular organisms, cells differentiate into multiple types as they divide. States of these cell types, as well as their numbers, are known to be robust to external perturbations; as conceptualized by Waddington's epigenetic landscape where cells embed themselves in valleys corresponding to final cell types. How is such robustness achieved by develo

Eva Santos

The Pierre Auger Observatory, located near the town Malarg\"ue in the province of Mendoza, Argentina, is the largest cosmic-ray detector in existence, covering an area of 3000 km2. The upgraded Observatory, in Phase II of operations, consists of a surface array of 1660 stations combining water Cherenkov, scintillator, and radio detectors. A subset of station

Ferdinand Grueneis

When the rate of shot noise is controlled by on-off states we speak of intermittent shot noise. The on-off states lead to alternately occurring clusters of events and intermissions, respectively. We derive the power spectrum of the intermittent shot noise by applying the Wiener-Khinchin theorem. Besides reduced shot noise, we obtain excess noise, which depen

Yuanpeng Deng, Peilin Kang, Xiang Xu, Hui Li

The emergence upon cooling of an ordered solid phase from a liquid is a remarkable example of self-assembly, which has also major practical relevance. Here, we use a recently developed committor-based enhanced sampling method [Kang et al., Nat. Comput. Sci. 4, 451-460 (2024); Trizio et al., Nat. Comput. Sci. 1-10 (2025)] to explore the crystallization transi

Raj Sanjay Shah, Lei Xu, Qianchu Liu, Jon Burnsky

Behavioral therapy notes are important for both legal compliance and patient care. Unlike progress notes in physical health, quality standards for behavioral therapy notes remain underdeveloped. To address this gap, we collaborated with licensed therapists to design a comprehensive rubric for evaluating therapy notes across key dimensions: completeness, conc

Matilda Häggblom

Inclusion dependencies form one of the most widely used dependency classes. We extend existing results on the axiomatization and computational complexity of their implication problem to two extended variants. We present an alternative completeness proof for standard inclusion dependencies and extend it to inclusion dependencies with repetitions that can expr

István Gyöngy, Nicolai V. Krylov

This paper is a continuation of [26]. Here theorems on conditional uniqueness and regularity for solutions to stochastic Navier-Stokes equations in $\mathbb R^d$ are presented.

Jiepeng Wang, Zhaoqing Wang, Hao Pan, Yuan Liu

A unified diffusion framework for multi-modal generation and understanding has the transformative potential to achieve seamless and controllable image diffusion and other cross-modal tasks. In this paper, we introduce MMGen, a unified framework that integrates multiple generative tasks into a single diffusion model. This includes: (1) multi-modal category-co

Martin Donati, Thierry Gallay

We study the evolution of a concentrated vortex advected by a smooth, divergence-free velocity field in two space dimensions. In the idealized situation where the initial vorticity is a Dirac mass, we compute an approximation of the solution which accurately describes, in the regime of high Reynolds numbers, the motion of the vortex center and the deformatio

Dmytro Humeniuk, Foutse Khomh

Testing autonomous robotic systems, such as self-driving cars and unmanned aerial vehicles, is challenging due to their interaction with highly unpredictable environments. A common practice is to first conduct simulation-based testing, which, despite reducing real-world risks, remains time-consuming and resource-intensive due to the vast space of possible te

Han Wu, Yuxuan Yao, Shuqi Liu, Zehua Liu

The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative thinking. However, this progress often comes at the cost of efficiency, as models tend to overthink, generating redundant reasoning steps without proportional improvements in output qua

H. Monteiro, C. Mendes de Oliveira, P. Amram, L. Stanghellini

We present a detailed 3D photoionization model of the planetary nebula NGC 3132, constrained by the latest observations. Using the MOCASSIN code, the model incorporates integrated and spatially resolved spectroscopy, velocity-resolved line profiles, emission line maps, and photometry, including recent high-quality data from MUSE (VLT) and JWST among others.

Jeffery L Painter, Gregory E Powell, Andrew Bate

Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduce PVLens, an automated system that extracts labeled safety information from FDA Structured Product Labels (SPLs) and maps terms to MedDRA. PVLens integrates automation with expert oversight through a web-based re

Jin Qiao, Qing-Guo Huang, Tao Zhu, Wen Zhao

In this work, we test for gravitational parity violation in the PSR J1141-6545 system by analyzing the orbital plane inclination precession induced by the misalignment between the white dwarf's spin axis and the system's total angular momentum. Using the parity-violating metric of gravity that incorporates terms from both the exterior and boundary of the fie

H. R. Hu, Y. F. Guo, X. F. Yang, Z. Yan

With the recent implementation of a radio-frequency quadrupole (RFQ) cooler-buncher and a multi-step laser resonance ionization technique, our previously developed collinear laser spectroscopy setup has been successfully upgraded into a fully functional collinear resonance ionization spectroscopy system. The new system was fully characterized using a bunched

Kostas D. Housiadas

An exact similarity solution of the lubrication equations for the steady flow of a viscoelastic Oldroyd-B fluid in a contracting and symmetric hyperbolic channel is derived. The solution is valid for small values of the Deborah number, De (the ratio of the polymer's longest relaxation time to a characteristic residence time of the fluid in the channel), all

Valentina Anita Carriero, Mario Scrocca, Ilaria Baroni, Antonia Azzini

Processes, workflows and guidelines are core to ensure the correct functioning of industrial companies: for the successful operations of factory lines, machinery or services, often industry operators rely on their past experience and know-how. The effect is that this Procedural Knowledge (PK) remains tacit and, as such, difficult to exploit efficiently and e

Sashuai Zhou, Hai Huang, Yan Xia

Multi-modal models excel in cross-modal tasks but are computationally expensive due to their billions of parameters. Parameter-efficient fine-tuning (PEFT) offers a solution by adding small trainable components while freezing pre-trained parameters. However, existing methods primarily focus on uni-modal processing, overlooking the critical modal fusion neede

Christian Kuehn

We consider two minimal mathematical models for cancer dynamics and self-adaptation. We aim to capture the interplay between the rapid progression of cancer growth and the possibility to leverage and enhance self-adaptive defense mechanisms of an organism, e.g., motivated by immunotherapy. Yet, our two models are more abstract and generic encapsulating the e

Andy Chu, Rashik Shrestha, Yu Gu, Jason N. Gross

Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and