October 2025 arXiv papers — page 44

Sam Pollard, Michael Wray

As we become increasingly dependent on vision language models (VLMs) to answer questions about the world around us, there is a significant amount of research devoted to increasing both the difficulty of video question answering (VQA) datasets, and the context lengths of the models that they evaluate. The reliance on large language models as backbones has lea

Tawsif Tashwar Dipto, Azmol Hossain, Rubayet Sabbir Faruque, Md. Rezuwan Hassan

Conventional research on speech recognition modeling relies on the canonical form for most low-resource languages while automatic speech recognition (ASR) for regional dialects is treated as a fine-tuning task. To investigate the effects of dialectal variations on ASR we develop a 78-hour annotated Bengali Speech-to-Text (STT) corpus named Ben-10. Investigat

Qifan Yang, Xiao Fu, Xuhe Gong, Jingchen Lian

Amorphous solid-state electrolytes (SSEs) offer unique advantages for next-generation batteries, but their rational design is hindered by an unclear structure-property relationship. This study establishes universal design principles through atomistic simulations of 32 amorphous Li-M-X systems (M = B, Al, Si, P; X = F, Cl, Br, I, O, S, Se, N). We identify fou

Gorka Guardiola Múzquiz

We extend the classical results of Stanislaw Golab, on the values of pi in arbitrary normed planes, to asymmetric norms where the unit ball has one axis of symmetry. First, we characterize the values of $\pi_B$ for different families of polygons as unit ball B. Then we prove that $\pi_B \ge 3$, and can take all possible values and is not bounded in such spac

Daniel J. Netherwood, Robert J. Whittaker

We present a theoretical description of the fluid--structure interaction observed within a Starling resistor. The typical setup consists of a pre-stretched finite length thin-walled elastic tube mounted between two rigid tubes. The collapsible section is enclosed within a pressure chamber and a viscous fluid is driven through the system by imposing an axial

Dina Barak-Pelleg, Daniel Berend

We study a labeled variant of the classical Coupon Collector Problem (CCP), recently introduced by Tan et al., where coupons arrive in groups and only the set of labels is revealed. The goal is to determine the expected number of group drawings required to uniquely identify the labeling of all coupons. We focus on the case where groups consist of pairs ($k=2

Diego Pavon

By applying the generalized second law to the apparent horizon of a homogeneous and isotropic universe and imposing that the equation of state is no less than $-1$, it is seen that universes with either flat or closed spatial sections are consistent with the joint consideration of the aforesaid law and the dominant energy condition, but not so universes with

Victor Gorbenko, Aleksandr Zhabin

Quantum groups have a long and fruitful history of applications in integrable systems. Can quantum group symmetries exist in the absence of integrability? We provide an explicit example of a system with quantum group global symmetry which is chaotic. The example is a spin chain with next-to-nearest interaction term. We show the chaotic behavior of the system

Jonas Thoen Faber, Reetika Joshi, Luc Rouppe van der Voort, Sven Wedemeyer

Flare ribbons serve as chromospheric footprints of energy deposition resulting from particle acceleration during magnetic reconnection. Their fine-scale structure provides a valuable tool for probing the dynamics of the flare reconnection process. Our goal is to investigate the fine-scale structure of flare ribbons through multiple observations of flares, ut

Alexandre P Uchoa, Carlo E T Oliveira, Claudia L R Motta, Daniel Schneider

The integration of Large Language Models into Intelligent Tutoring Systems pre-sents significant challenges in aligning with diverse and often conflicting values from students, parents, teachers, and institutions. Existing architectures lack for-mal mechanisms for negotiating these multi-stakeholder tensions, creating risks in accountability and bias. This p

Josh P. Peters, Florin Teleanu, Huijing Zou, Ehtisham Rasool

[1-13C]pyruvate is the most widely used tracer for hyperpolarized metabolic magnetic resonance imaging, with profound applications in tumor and inflammation diagnosis as well as treatment monitoring. The most fundamental hurdle to broader application, however, remains the rapid polarization relaxation and the associated signal loss. Here, we report a method

Sahaya Vijay Jeyaraj, Roy S, Govardhan S, Tony Augustine

We have developed PyTIE (Python Topological Indices Expressions) which is defined as the collections of Python packages such as PyTIE D, PyTIE DS, PyTIE SMS DE, and PyTIE SMS DSE, which are open-source software packages and cross-platform Python package designed to expedite the retrieval of results for mathematics, chemistry and chemical engineering research

Moritz Bensberg, Silvia Alessandrini, Mattia Melosso, Cristina Puzzarini

Quantum chemistry provides accurate and reliable methods to investigate reaction pathways of reactive molecular systems relevant to the interstellar medium. However, the exhaustive exploration of a reactive network is often a daunting task, resulting in unexplored reactive channels that affect kinetic outcomes and branching ratios. Here, an automated workflo

Stefan M. Fischer, Johannes Kiechle, Laura Daza, Lina Felsner

In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the patch size during model training, resulting in an improved class balance for smaller patch sizes and accelerated convergence of the training process. We evaluate our curriculum app

Carmine Zaccagnino, Fabio Quattrini, Vittorio Pippi, Silvia Cascianelli

Generating faithful and readable styled text images (especially for Styled Handwritten Text generation - HTG) is an open problem with several possible applications across graphic design, document understanding, and image editing. A lot of research effort in this task is dedicated to developing strategies that reproduce the stylistic characteristics of a give

José N. V. Gomes, Matheus Hudson, Carlos M. de Sousa

The main objective of this article is to study the mean curvature flow into an ambient compact smooth manifold M with boundary and with a Riemannian metric that evolves by a self-similar solution of the Ricci flow coupled with the harmonic map heat flow of a map from M to a Riemannian manifold N. In this context, we address a functional associated with this

A. Barsode, K. N. Maity, P. Ajith

A small fraction of gravitational-wave (GW) signals detected by ground-based observatories will be strongly lensed by intervening galaxies or clusters. This may produce multiple copies of the signals (i.e., lensed images) arriving at different times at the detector. These, if observed, could offer new probes of astrophysics and cosmology. However, identifica

Ning Ning

Hidden Quantum Markov Models (HQMMs) extend classical Hidden Markov Models to the quantum domain, offering a powerful probabilistic framework for modeling sequential data with quantum coherence. However, existing HQMM learning algorithms are highly sensitive to data corruption and lack mechanisms to ensure robustness under adversarial perturbations. In this

Moinak Ghosh, Stefan Heinze, Souvik Paul

Using first-principles density functional theory (DFT) combined with atomistic spin simulations, we explore the possibility of realizing zero-field isolated skyrmions in three 4$d$/Co atomic bilayers -- Rh/Co, Pd/Co, and Ru/Co -- grown on the Re(0001) surface. Our investigation employs an extended atomistic spin model, which goes beyond the standard model by

Mfano Charles, Sayoki G. Mfinanga, G. A. Lyakurwa, Delfim F. M. Torres

Rabies continues to pose a significant zoonotic threat, particularly in areas with high populations of domestic dogs that serve as viral reservoirs. This study conducts a comparative analysis of Stochastic Continuous-Time Markov Chain (CTMC) and deterministic models to gain insights into rabies persistence within human and canine populations. By employing a

Anton Savostianov, Michael T. Schaub, Benjamin Stamm

Low-pass graph filters are fundamental for signal processing on graphs and other non-Euclidean domains. However, the computation of such filters for parametric graph families can be prohibitively expensive as computation of the corresponding low-frequency subspaces, requires the repeated solution of an eigenvalue problem. We suggest a novel algorithm of low-

Klaus Zauner, Hubert Gattringer, Andreas Mueller

Resourceful operation and design of robots is key for sustainable industrial automation. This will be enabled by lightweight design along with time and energy optimal control of robotic manipulators. Design and control of such systems is intertwined as the control must take into account inherent mechanical compliance while the design must accommodate the dyn

Runqiao Li

This work follows the spirit of Andrews' series of papers on Partition Analysis. In $2011$, Savage and Sills found new sum sides for the little G\"ollnitz identities and provided their partition interpretations. It turns out that similar companions exist for a mod $8$ partition identity due to Andrews. In this work, we use MacMahon's Partition Analysis to st

Wisdom Boinde, Igor Minevich, Dipesh Poudel

The Lights Out Puzzle represents a cellular automaton based on a grid of squares where clicking a square changes its state and the states of surrounding squares. A "quiet pattern" is a way to click such that in the end, no change is effected. We introduce a way to "evolve" quiet patterns in smaller grids into ones in $p$ times larger grids when the number of

Nejc Vovk, Jana Wedel, Paul Steinmann, Jure Ravnik

In this study, we investigate the behaviour of sedimenting solid particles and the influence of microscopic particle dynamics on the collective motion of a sedimenting cloud. Departing from conventional direct numerical simulations (DNS), we introduce a novel machine learning framework, the Interaction-Decomposed Neural Network (IDNN), to model hydrodynamic

Mirco Ciallella

Embedded, or immersed, approaches have the goal of reducing to the minimum the computational costs associated with the generation of body-fitted meshes by only employing fixed, possibly Cartesian, meshes over which complex boundaries can move freely. However, this boundary treatment introduces a geometrical error of the order of the mesh size that, if not tr

Raúl Gutiérrez, Víctor Rampérez, Horacio Paggi, Juan A. Lara

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of compute

F. D. Amaro, R. Antonietti, E. Baracchini, L. Benussi

Dark matter, which is considered to account for approximately the 27% of the Universe's energy-mass content, remains an open issue in modern particle physics along with its composition. The CYGNO Experiment aims to exploit an innovative approach applied to the direct detection search of low energy nuclear recoils possibly induced by cold particle-like dark m

Richard J. Murchie, John Jeffers

Object detection and range finding using a weak light source is vulnerable to jamming and spoofing attacks by an intruder. Quantum illumination with nonsimultaneous, phase-insensitive coincidence measurements can provide jamming resilience compared to identical measurements for classical illumination. We extend an experimentally-feasible object detection and

Klaus Zauner, Josef El Dib, Hubert Gattringer, Andreas Mueller

Motion planning for robotic manipulators relies on precise knowledge of the environment in order to be able to define restricted areas and to take collision objects into account. To capture the workspace, point clouds of the environment are acquired using various sensors. The collision objects are identified by region growing segmentation and VCCS algorithm.

Mohammad Dastranj, Jouni Mattila

A novel modular modeling and control framework based on Lagrangian mechanics is proposed for multibody systems, motivated by the challenges of modular control of systems with closed kinematic chains and by the need for a modeling framework that remains locally updatable under reconfiguration of body-level geometric and inertial properties. In the framework,

Razaib Tariq, Minji Heo, Simon S. Woo, Shahroz Tariq

Deepfake detection remains a pressing challenge, particularly in real-world settings where smartphone-captured media from digital screens often introduces Moir\'e artifacts that can distort detection outcomes. This study systematically evaluates state-of-the-art (SOTA) deepfake detectors on Moir\'e-affected videos, an issue that has received little attention

Hongyi Wang, Zhengjie Zhu, Jiabo Ma, Fang Wang

The rapid digitization of histopathology slides has opened up new possibilities for computational tools in clinical and research workflows. Among these, content-based slide retrieval stands out, enabling pathologists to identify morphologically and semantically similar cases, thereby supporting precise diagnoses, enhancing consistency across observers, and a

Victor N. Mitryakhin, Ivan A. Solovev, Alexander Steinhoff, Jaewon Lee

The interaction of a quantum two-level system with a resonant driving field results in the emergence of Rabi oscillations, which are the hallmark of a controlled manipulation of a quantum state on the Bloch sphere. This all-optical coherent control of solid-state two-level systems is crucial for quantum applications. In this work we study Rabi oscillations e

Hong Wang, Wenkai Yang, Jie Wang, Huanshuo Dong

Recent advances in data-driven approaches, such as neural operators (NOs), have shown substantial efficacy in reducing the solution time for integrated circuit (IC) thermal simulations. However, a limitation of these approaches is requiring a large amount of high-fidelity training data, such as chip parameters and temperature distributions, thereby incurring

V. D. Pipwala, H. N. Lala, B. Lemasle, E. K. Grebel

Several standard candles have been tested and used to measure accurate extragalactic distances over the past decades. There have been discussions regarding the possibility of using Type-II Cepheids (T2Cs) as an alternative tool, but rarely was this ever implemented. The aim of this project is to assert the use of T2Cs as a new avenue for calibrating the extr

Fraser Bradfield

The Fluorescence detector Array of Single-pixel Telescopes aims to deploy an array of simplified, autonomous fluorescence telescopes over an area of $\sim60,000$ km$^{2}$ to observe ultra-high energy cosmic rays. The unprecedented size of such an array will enable measurements of cosmic rays with energies above 10$^{20}$ eV with large statistics, providing n

P. F. L. Maxted, N. J. Miller, T. A. Baycroft, D. Sebastian

BEBOP-3 is detached eclipsing binary star that shows total eclipses of a faint M~dwarf every 13.2 days by a 9$^{\rm th}$-magnitude F9V star. High precision radial velocity measurements have recently shown that this binary star is orbited by a planet with an orbital period $\approx 550$ days. The extensive spectroscopy used to detect this circumbinary planet

Alois Thomas, Maya Varma, Jean-Benoit Delbrouck, Curtis P. Langlotz

Automating radiology report generation with Large Vision-Language Models (LVLMs) holds great potential, yet these models often produce clinically critical hallucinations, posing serious risks. Existing hallucination detection methods frequently lack the necessary sentence-level granularity or robust generalization across different LVLM generators. We introdu

Hong Wang, Jie Wang, Jian Luo, huanshuo dong

Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learning, neural eigenvalue methods have attracted significant attention as a forward pass of inference requires only a tiny fraction of the computation time compared to traditional solvers. However, a key limitation is the

Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn

We introduce a novel, drop-in modification to Monte Carlo Tree Search's (MCTS) decision policy that we call AUPO. Comparisons based on a range of IPPC benchmark problems show that AUPO clearly outperforms MCTS. AUPO is an automatic action abstraction algorithm that solely relies on reward distribution statistics acquired during the MCTS. Thus, unlike other a

David Krieg, Erich Novak, Leszek Plaskota, Mario Ullrich

It is impossible to recover a vector from $\mathbb{R}^m$ with less than $m$ linear measurements, even if the measurements are chosen adaptively. Recently, it has been shown that one can recover vectors from $\mathbb{R}^m$ with arbitrary precision using only $O(\log m)$ continuous (even Lipschitz) adaptive measurements, resulting in an exponential speed-up of

Quan Gu, Han Ye, Junjie Chen, Xiongfeng Ma

Quantum computers have the potential to break classical cryptographic systems by efficiently solving problems such as the elliptic curve discrete logarithm problem using Shor's algorithm. While resource estimates for factoring-based cryptanalysis are well established, comparable evaluations for Shor's elliptic curve algorithm under realistic architectural co

Cinzia Di Giusto, Giorgio Bacci

This volume contains the proceedings of EXPRESS/SOS 2025: the Combined 32nd International Workshop on Expressiveness in Concurrency and the 22nd Workshop on Structural Operational Semantics, which was held in Aarhus, Denmark, as an affiliated workshop of CONFEST 2025. The EXPRESS/SOS workshop series aims at bringing together researchers interested in the for

Patrick Haller, Fabio Barth, Jonas Golde, Georg Rehm

Vision-language models (VLMs) have demonstrated remarkable progress in multimodal reasoning. However, existing benchmarks remain limited in terms of high-quality, human-verified examples. Many current datasets rely on synthetically generated content by large language models (LLMs). Furthermore, most datasets are limited to English, as manual quality assuranc

Abhishek Chaudhary, Andreas Prohl

We present high-order numerical schemes for linear stochastic heat and wave equations with Dirichlet boundary conditions, driven by additive noise. Standard Euler schemes for SPDEs are limited to an order convergence between 1/2 and 1 due to the low temporal regularity of noise. For the stochastic heat equation, a modified Crank-Nicolson scheme with proper n

Shuai Li, Shenglong Zhou

Unconstrained binary integer programming (UBIP) poses significant computational challenges due to its discrete nature. We introduce a novel reformulation approach using a piecewise cubic function that transforms binary constraints into continuous equality constraints. Instead of solving the resulting constrained problem directly, we develop an exact penalty

Amal Abed, Ivan Lukic, Jörg K. H. Franke, Frank Hutter

Large language models (LLMs) have shown impressive promise in code generation, yet their progress remains limited by the shortage of large-scale datasets that are both diverse and well-aligned with human reasoning. Most existing resources pair problems with solutions, but omit the intermediate thought process that guides coding. To close this gap, we present

Hoonhee Cho, Jae-Young Kang, Giwon Lee, Hyemin Yang

End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common real-world challenge due to diverse vehicle configurations, remains an open problem. In this work, we propose VR-Drive, a nove

Giulia Pusceddu, Giulio Antonio Abbo, Francesco Rea, Tony Belpaeme

This study investigates whether the opinions of robotic agents can influence human decision-making when robots display value awareness (i.e., the capability of understanding human preferences and prioritizing them in decision-making). We designed an experiment in which participants interacted with two Furhat robots - one programmed to be Value-Aware and the

Lukas Bierling, Davide Pasero, Fleur Dolmans, Helia Ghasemi

Accurate vertex-level contact prediction between humans and surrounding objects is a prerequisite for high fidelity human object interaction models used in robotics, AR/VR, and behavioral simulation. DECO was the first in the wild estimator for this task but is limited to binary contact maps and struggles with soft surfaces, occlusions, children, and false-p

Guanwang Jiang, Ziye Jia, Can Cui, Lijun He

The low-altitude networks (LANs) integrating unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) have become a promising solution for the rising computation demands. However, the uncertain task sizes and high mobility of UAVs pose great challenges to guarantee the quality of service. To address these issues, we propose an LAN architecture wher

E. Benhamou, JJ. Ohana, B. Guez, E. Setrouk

In response to growing demand for resilient and transparent financial instruments, we introduce a novel framework for replicating private equity (PE) performance using liquid, AI-enhanced strategies. Despite historically delivering robust returns, private equity's inherent illiquidity and lack of transparency raise significant concerns regarding investor tru

Pengcheng Zhang

We discuss several congruences satisfied by the coefficients of meromorphic modular forms, or equivalently, the $p$-adic behaviors of meromorphic modular forms under the $U_p$ operator, that are summarized from numerical experiments. In the generic case, we observe the connection to symmetric powers of elliptic curves, while in the CM case, we furthermore ob

Junpei Komiyama, Kyoungseok Jang, Junya Honda

We consider the best arm identification problem, where the goal is to identify the arm with the highest mean reward from a set of $K$ arms under a limited sampling budget. This problem models many practical scenarios such as A/B testing. We consider a class of algorithms for this problem, which is provably minimax optimal up to a constant factor. This idea i

Etienne Goffinet, Shane Bergsma, Avraham Sheinin, Natalia Vassilieva

Continual pre-training (CPT) for domain adaptation must balance target-domain gains with stability on the base domain. Existing CPT scaling laws typically assume a fixed pre-training budget, which limits their ability to forecast adaptation outcomes for models trained at different tokens-per-parameter (PTPP). We present \emph{PTPP-aware} adaptation scaling l

Sören Christensen, Jan Kallsen, Claudia Strauch, Lukas Trottner

We consider the problem of recovering a latent signal $X$ from its noisy observation $Y$. The unknown law $\mathbb{P}^X$ of $X$, and in particular its support $\mathscr{M}$, are accessible only through a large sample of i.i.d.\ observations. We further assume $\mathscr{M}$ to be a low-dimensional submanifold of a high-dimensional Euclidean space $\mathbb{R}^

Bastien Giraud, Rahul Nellikath, Johanna Vorwerk, Maad Alowaifeer

The AC Optimal Power Flow (AC-OPF) problem is central to power system operation but challenging to solve efficiently due to its nonconvex and nonlinear nature. Neural networks (NNs) offer fast surrogates, yet their black-box behavior raises concerns about constraint violations that can compromise safety. We propose a verification-informed NN framework that i

Samson Seifu Bekele, Maregnesh Mechal Wolde, Claus Führer, Nils-Otto Kitterød

This paper presents a method for mathematical modelling of surfaces conditioned on empirical data. It is based on solving a discrete biharmonic equation over a domain with given inner point and inner curve data. The inner curve data is used to model boundary values while the inner point data is used for modeling a load vector with the goal to generate a smoo

Jamy-Jayme Thézier, Aurélien Barrau, Killian Martineau, Maxime De Sousa

Deforming the algebra of constraint is a well-known approach to effective loop quantum cosmology. More generally, it is a consistent way to modify gravity from the Hamiltonian perspective. In this framework, the Hamiltonian (scalar) constraint is usually the only one to be holonomy corrected. As a heuristic hypothesis, we consider the possibility to also cor

Ludovica Buelli

The aim of this work is to give a description of the locally trivial monodromy group of irreducible symplectic varieties arising from moduli spaces of semistable sheaves on Abelian surfaces with non-primitive Mukai vector. The outcome is that the locally trivial monodromy group of a singular moduli space of this type is isomorphic to the monodromy group of a

Mohammed Charkaoui, Rajae Sammani, El Hassan Saidi, Rachid Ahl Laamara

The minimal Weak Gravity Conjecture (WGC) predicts the emergence of towers of superextremal states in both weak and strong coupling limits. In this work, we study M-theory compactified on a special class of Calabi-Yau threefolds to construct a 5D effective field theory (EFT) that accommodates both weak and strong gauge coupling limits. Building on a classifi

Timo Freiesleben, Sebastian Zezulka

Predictive benchmarking, the evaluation of machine learning models based on predictive performance and competitive ranking, is a central epistemic practice in machine learning research and an increasingly prominent method for scientific inquiry. Yet, benchmark scores alone provide at best measurements of model performance relative to an evaluation dataset an

Pascal Benschop, Cristian Meo, Justin Dauwels, Jelte P. Mense

The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge for public safety and security, as the timely detection of anomalous or criminal events is crucial for effective response and prevention. The ability for an embodied agent to recogn

Ali Fata, Hossein Rahmani, Parinaz Soltanzadeh, Amirhossein Derakhshan

Relation extraction between drugs plays a crucial role in identifying drug drug interactions and predicting side effects. The advancement of machine learning methods in relation extraction, along with the development of large medical text databases, has enabled the low cost extraction of such relations compared to other approaches that typically require expe

Yuki Ota, Yuki Funabora

This paper presents a novel Embroidery Actuator, a fabric-integrated pneumatic actuator that enables diverse and controllable deformations through embroidery pattern design. Unlike conventional fabric actuators that rely on fiber- or thread-shaped actuators, the proposed actuator is fabricated by directly stitching an inflatable tube onto the fabric using a

Mushal Zia, Faisal Suwayyid, Guo-Wei Wei

While persistent homology is widely used for data shape analysis, persistent commutative algebra (PCA) has seen limited adoption in machine learning and data science. Unlike persistent homology, which delivers topological invariants in the form of Betti numbers, PCA provides both algebraic invariants and graded Betti numbers. However, graded Betti numbers ha

Lukas Henneke, Frank Kurth

Radio frequency (RF) signal recognition plays a critical role in modern wireless communication and security applications. Deep learning-based approaches have achieved strong performance but typically rely heavily on extensive training data and often fail to generalize to unseen signals. In this paper, we propose a method to learn discriminative embeddings wi

Alberto Facchini

In this paper we extend to left skew trusses $(T,+,\circ,\sigma)$ previous work on left skew rings. We had presented a left skew ring as a group $(N,+)$ with two binary operations $\circ$ and $\cdot$ with $\circ$ associative, $\cdot$ left distributive over the addition $+$ of the group, and such that the difference of the two operations $\circ$ and $\cdot$ i

Junho Kim, Young Min Kim

Connecting current observations with prior experiences helps robots adapt and plan in new, unseen 3D environments. Recently, 3D scene analogies have been proposed to connect two 3D scenes, which are smooth maps that align scene regions with common spatial relationships. These maps enable detailed transfer of trajectories or waypoints, potentially supporting

E. Benhamou, JJ. Ohana, B. Guez, E. Setrouk

In this work, we introduce PEARL (Private Equity Accessibility Reimagined with Liquidity), an AI-powered framework designed to replicate and decode private equity funds using liquid, cost-effective assets. Relying on previous research methods such as Erik Stafford's single stock selection (Stafford) and Thomson Reuters - Refinitiv's sector approach (TR), our

Shuai Huang, Wenxuan Zhao, Jun Gao

As large language models (LLMs) develop anthropomorphic abilities, they are increasingly being deployed as autonomous agents to interact with humans. However, evaluating their performance in realistic and complex social interactions remains a significant challenge. Most previous research built datasets through simulated agent-to-agent interactions, which fai

Andrij Vasylenko, Federico Ottomano, Christopher M. Collins, Rahul Savani

Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidate crystal structures. We introduce a reliable low-cost proxy for structural novelty as a conditioning property to steer generation towards novel yet physically plausible structures

A. Armeni, B. Stelzer, A. Frasca, C. F. Manara

Magnetic winds are a key mechanism for angular momentum removal in young stars. In this work, we aim at characterizing the multi-component outflow of RU Lup. The unprecedented high resolution of the Echelle SPectrograph for Rocky Exoplanets and Stable Spectroscopic Observations (ESPRESSO) enabled a detailed study of the forbidden emission lines and the blues

Luigi Gisolfi, Dominic Dirkx, Sam Fayolle, Valerio Filice

The TU Delft Astrodynamics Toolbox (Tudat) is a free open-source software suite for research and education in astrodynamics. Initially focused on numerical simulations of orbital dynamics and state estimation, it enables combining optical and radiometric tracking data from multiple sources to estimate the dynamics and parameters of natural and artificial bod

Debanjali Bhattacharya, Neelam Sinha

In this study, we propose the use of persistent homology -- specifically Betti curves for brain age prediction and for distinguishing between healthy and pathological aging. The proposed framework is applied to 100 structural MRI scans from the publicly available ADNI dataset. Our results indicate that Betti curve features, particularly those from dimension-

Sumit Goel, Yiqing Yan, Jeffrey Zeidel

We study the effect of interim feedback policies in a dynamic all-pay auction where two players bid over two stages to win a common-value prize. We show that sequential equilibrium outcomes are characterized by Cheapest Signal Equilibria, wherein stage 1 bids are such that one player bids zero while the other chooses a cheapest bid consistent with some signa

Alexandre Popier, Laurent Denis, Dorian Cacitti-Holland

We develop a Malliavin calculus for nonlinear Hawkes processes in the sense of Carlen and Pardoux. This approach, based on perturbations of the jump times of the process, enables the construction of a local Dirichlet form. As an application, we establish criteria for the absolute continuity of solutions to stochastic differential equations driven by Hawkes p

Arnav Sukhija, Lenart Treven, Jin Cheng, Florian Dörfler

Fixed-frequency control in robotics imposes a trade-off between the efficiency of low-frequency control and the robustness of high-frequency control, a limitation not seen in adaptable biological systems. We address this with a reinforcement learning approach in which policies jointly select control actions and their application durations, enabling robots to

Claire Barraud

This article proposes a complementary theoretical framework in behavioural finance by interpreting financial markets during boom-and-bust episodes as a Le Bonian crowd. While behavioural finance has documented the limits of individual rationality through biases and heuristics, these contributions remain primarily microeconomic. A second, more macroeconomic s

Hemakshi Jani, Mitish Karia, Meet Gohil, Rahul Bhadja

This paper describes the development of Psychlysis, a work-in-progress questionnaire-based machine learning application analyzing the user's current state of mind and suggesting ways to improve their mood using Machine Learning. The application utilizes the OCEAN model to understand the user's personality traits and make customized suggestions to enhance the

Souleymane Sanogo, Marlon Maranan, Andreas H. Fink, Beth J. Woodhams

Two extreme flood-inducing precipitation events in two cities in Mali, on 08 August 2012 in San (127 mm) and on 25 August 2019 in Kenieba (126 mm), are investigated with respect to rainfall structures, dynamical forcings, and the ability of the ICOsahedral Nonhydrostatic (ICON) model to represent their evolution. Two sets of experiments with convective param

Jingjun Bi, Fadi Dornaika

Recently, graph-based semi-supervised learning and pseudo-labeling have gained attention due to their effectiveness in reducing the need for extensive data annotations. Pseudo-labeling uses predictions from unlabeled data to improve model training, while graph-based methods are characterized by processing data represented as graphs. However, the lack of clea

Hau-Wen Huang, Chin-Yen Lee

We employ a skew group ring of $\mathbb Z/2\mathbb Z$ over $U(\mathfrak{sl}_2)$ to construct modules over the universal Bannai--Ito algebra. In addition, we give the conditions under which the defining generators act as Leonard triples on the resulting modules. As a combinatorial realization, we establish an algebra homomorphism from the universal Bannai--It

Mohsen Ahmadvand, Pedro Souto

The Optimism derivation pipeline is engineered for correctness and liveness, not for succinct validity proofs. A straightforward port to a zkVM imposes significant overheads, making validity proofs significantly more costly than necessary. We systematically identify inefficiencies in the current design, analyze their impact on proving costs, and provide a so

Zakaria Boutakka, Nouhaila Innan, Muhammed Shafique, Mohamed Bennai

Quantum computing presents a promising path toward precise quantum chemical simulations, particularly for systems that challenge classical methods. This work investigates the performance of the Variational Quantum Eigensolver (VQE) in estimating the ground-state energy of the silicon atom, a relatively heavy element that poses significant computational compl

Sébastien Petit, Sébastien Marmin, Nicolas Fischer

This article introduces tools to analyze set-valued data statistically. The tools were initially developed to analyze results from an interlaboratory comparison made by the Electromagnetic Compatibility Working Group of Eurolab France, where the goal was to select a consensual set of injection points on an electrical device. Families based on the Hamming-dis

Marah Ghoummaid, Vladimir Tchuiev, Ofek Glick, Michal Moshkovitz

AI-based code generation is increasingly prevalent, with GitHub Copilot estimated to generate 46% of the code on GitHub. Accurately evaluating how well generated code aligns with developer intent remains a critical challenge. Traditional evaluation methods, such as unit tests, are often unscalable and costly. Syntactic similarity metrics (e.g., BLEU, ROUGE)

Luís Daniel Abreu, Michael Speckbacher, Erling A. T. Svela

We obtain large sieve type inequalities for the Rayleigh quotient of the restriction of phase space representations of higher rank operators, via an operator analogue of the short-time Fourier transform (STFT). The resulting bounds are referred to as `quantum large sieve inequalities'. On the shoulders of Donoho and Stark, we demonstrate that these inequalit

Giacomo D'Amico, Michele Doro, Michela De Caria

We present a novel method for both forecasting and recasting upper limits (ULs) on dark matter (DM) annihilation cross sections, $\left< \sigma v \right>^{UL}$, or decay lifetime $\tau^{LL}$ . The forecasting method relies solely on the instrument response functions (IRFs) to predict ULs for a given observational setup, without the need for full analysis pip

Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat

Learning diverse skills without hand-crafted reward functions could accelerate reinforcement learning in downstream tasks. However, existing skill discovery methods focus solely on maximizing the diversity of skills without considering human preferences, which leads to undesirable behaviors and possibly dangerous skills. For instance, a cheetah robot trained

Rongxin Chen, Yunfan Li, Yige Yuan, Bingbing Xu

Multi-personality generation for LLMs, enabling simultaneous embodiment of multiple personalization attributes, is a fundamental challenge. Existing retraining-based approaches are costly and poorly scalable, while decoding-time methods often rely on external models or heuristics, limiting flexibility and robustness. In this paper, we propose a novel Multi-P

Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao

Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardized, objective benchmarks - leading to weak baselines, reporting bias, and inconsistent evaluations across methods. This undermines reproducibility, misguides resource allocation, a

Renli Xu, Chen Wu, Jian Liu, Bin Hong

A relativistic Hartree-Bogoliubov (RHB) model based on quark-meson coupling is developed, with a new parametrization derived from experimental observables. Using this model, we systematically investigate the ground-state properties of even-even nuclei spanning $8\leq Z\leq118$, including binding energies, quadrupole deformations, root-mean-square (rms) charg

Wanke Hu

Under the coset formulation of pure Einstein spacetime, we solve the reduction problem of the nonlinear \(\sigma\)-model for this spacetime and present the process of the inverse scattering technique after simplification. Through the simplified inverse scattering process, taking the pure Rindler metric as the background, we introduce a pair of solitons on th

Hang Lei, Shengyi Zong, Zhaoyan Li, Ziren Zhou

The screenplay serves as the foundation for television production, defining narrative structure, character development, and dialogue. While Large Language Models (LLMs) show great potential in creative writing, direct end-to-end generation approaches often fail to produce well-crafted screenplays. We argue this failure stems from forcing a single model to si

Wilhelmina Maryann Joseph, Beate Stelzer, Salvatore Orlando, Moritz Klawin

Context. Stellar coronae are unresolved in X-rays, so inferences about their structure rely on spectral analysis. The "Sun-as-an-X-ray-star" (SaXS) approach uses the Sun as a spatially resolved template to interpret stellar spectra, but previous SaXS implementations were indirect and computationally heavy. Aims. We present a new SaXS implementation that conv

Zile Yang, Ling Li, Na Di, Jinlong Pang

Supervised Fine-Tuning (SFT) adapts pre-trained Large Language Models (LLMs) to domain-specific instructions by training on a carefully curated subset of high-quality instruction-response pairs, typically drawn from a larger dataset that often contains many low-quality or noisy samples. However, existing quality-first paradigms often overlook valuable signal

Maximilian Buthenhoff, Yusuke Nishida

In flat-band superconductors, the electron pairing is strongly enhanced so that the critical temperature scales linearly with the interaction strength. Identifying the governing pairing mechanism in flat-band superconducting systems is therefore a central task, which may be constrained by experimental probes via low-temperature scaling measurements. A key ob

Philipp Götz, Gloria Dal Santo, Sebastian J. Schlecht, Vesa Välimäki

Reverberation conveys critical acoustic cues about the environment, supporting spatial awareness and immersion. For auditory augmented reality (AAR) systems, generating perceptually plausible reverberation in real time remains a key challenge, especially when explicit acoustic measurements are unavailable. We address this by formulating blind estimation of a