November 2025 arXiv papers — page 186

Ahmed Barbar

We study the recent proposal of arXiv:2405.20366 which poses a precise holographic duality between a 3d TQFT summed over all topologies and a unitary ensemble of boundary 2d CFTs. In that proposal, the sum over topologies is obtained via genus reduction from topologies with a large genus boundary Riemann surface, while the boundary ensemble is given by all C

Daniel Jirovec, Stefano Reale, Pablo Cova-Fariña, Christian Ventura-Meinersen

Quantum simulators enable studies of many-body phenomena which are intractable with classical hardware. Spins in devices based on semiconductor quantum dots promise precise electrical control and scalability advantages, but accessing many-body phenomena has so far been restricted by challenges in nanofabrication and simultaneous control of multiple interacti

Christoph Grüne, Femke Pfaue

The website reductions.network serves as a comprehensive database for exploring problems and reductions between them. It presents several complexity classes in the form of an interconnected graph where problems are represented as vertices, while edges represent reductions between them. This graphical perspective allows for identifying problem clusters and si

Jian Mu, Chaoyun Zhang, Chiming Ni, Lu Wang

We introduce GUI-360$^\circ$, a large-scale, comprehensive dataset and benchmark suite designed to advance computer-using agents (CUAs). CUAs present unique challenges and is constrained by three persistent gaps: a scarcity of real-world CUA tasks, the lack of automated collection-and-annotation pipelines for multi-modal trajectories, and the absence of a un

Sébastien Roux, Tilly Guyot, Abraao Cefas Torres-Dias, Delphine Lagarde

Monolayers of group-VI transition-metal dichalcogenides (TMDs) are two-dimensional semiconductors that exhibit exceptionally strong light-matter coupling yet typically suffer from low emission quantum yields. In this letter, we investigate the heavily n-doped regime of a WSe$_2$ monolayer and show that multi-particle excitonic complexes produce photoluminesc

Gregoris Spanoudes, Marios Costa, Kyproulla Mitsidi, Haralambos Panagopoulos

In this paper we study a complete set of scalar and pseudoscalar four-quark operators, with a particular emphasis on their renormalization within a Gauge-Invariant Renormalization Scheme (GIRS). We focus on operators that do not mix with lower-dimensional operators by virtue of their transformation properties under the flavor-symmetry group. This class inclu

Robin Spanier, Thorsten Hoeser, Claudia Kuenzer

The recent and ongoing expansion of marine infrastructure, including offshore wind farms, oil and gas platforms, artificial islands, and aquaculture facilities, highlights the need for effective monitoring systems. The development of robust models for offshore infrastructure detection relies on comprehensive, balanced datasets, but falls short when samples a

Martin Redmann, Justus Werner

This paper deals with non-Lipschitz nonlinear systems. Such systems can be approximated by a linear map of so-called signatures, which play a crucial role in the theory of rough paths and can be interpreted as collections of iterated integrals involving the control process. As a consequence, we identify a universal bilinear system, solved by the signature, t

Nicolas Angelini, Ursula Molter

We establish a unified Frostman-type framework connecting the classical Hausdorff dimension with the family of intermediate dimensions $\dim_\theta$ recently introduced by Falconer, Fraser and Kempton. We define a new geometric quantity $\mathcal{D}(E)$ and prove that, under mild assumptions, there exists a family of measures $\{\mu_\delta\}$ supported on $E

Frederik Möbius Rygaard, Søren Hauberg, Steen Markvorsen

A central part of geometric statistics is to compute the Fr\'echet mean. This is a well-known intrinsic mean on a Riemannian manifold that minimizes the sum of squared Riemannian distances from the mean point to all other data points. The Fr\'echet mean is simple to define and generalizes the Euclidean mean, but for most manifolds even minimizing the Riemann

Ramy Aboushelbaya, Annika Moslein, Hadi Azar, Hamid Tanhaei

We present a novel self-correcting, high-speed optoelectronic probabilistic computer architecture that leverages source-device independent (SDI) quantum photonic p-bits integrated with robust electronic control. Our approach combines the intrinsic randomness and high bandwidth of quantum photonics with the programmability and scal- ability of classical elect

Elliot Beck, Franziska Eckert, Linus Kühne, Helge Liebert

We develop a resource-efficient methodology for measuring economic outlook in news text that combines document embeddings with synthetic training data generated by large language models. Applied to 27 million news articles, the resulting indicator significantly improves GDP growth forecast accuracy and captures sentiment shifts weeks before official releases

Cécile Hardouin, Xavier Guyon

This paper presents different recursive formulas for computing the marginals and the normalizing constant of a Gibbs distribution $\pi$: The common thread is the use of the underlying Markov properties of such processes. The procedures are illustrated with several examples, particularly the Ising model.

Yehonatan Levin, Uri Israeli, Rivka Bekenstein

We investigate the implementation of photonic cluster state generation protocols using quantum metasurfaces comprising sub-wavelength atomic arrays which enables quantum-controlled reflectivity. These cluster states are generated using fundamental quantum logic gates and enable wide-ranging applications in quantum computation and communication. In the past f

James Taylor

Let $G$ be a group acting on a field $L$, and suppose that $L /L^G$ is a finite extension. We show that the category of semilinear representations of $G$ over $L$ can be described in terms of the category of linear representations of $H$, the kernel of the map $G \rightarrow \mathrm{Aut}(L)$. When $G$ is finite and $L$ has characteristic 0 this provides a ch

Masood Aryapoor

We introduce the notion of Amitsur--Small extensions to generalize a key lemma underlying the Amitsur--Small Theorem to the skew setting. Building on this framework, we establish a skew version of the Amitsur--Small Theorem.

Alexey Vladimirov, Guillermo Portela, Simone Rodini

Quarks and gluon, as quantum particles, are subjects to various effects that go beyond the naive parton picture and are not captured by ordinary parton densities. In this work, we investigate the twist-three parton distribution functions, which encode quantum interference between quark-gluon-quark states, and for the first time, determine them directly from

Jovana Kovačević, Felix Langner, Erfan Tajalli-Ardekani, Marvin Dorn

Integrating flexible loads and storage systems into the residential sector contributes to the alignment of volatile renewable generation with demand. Besides batteries serving as a short-term storage solution, residential buildings can benefit from a Hydrogen (H2) storage system, allowing seasonal shifting of renewable energy. However, as the initial costs o

Arne Van Den Kerchove, Hakim Si-Mohammed, François Cabestaing, Marc M. Van Hulle

Brain-computer interfaces (BCIs) allow direct communication between the brain and external devices, frequently using electroencephalography (EEG) to record neural activity. Dimensionality reduction and structured regularization are essential for effectively classifying task-related brain signals, including event-related potentials (ERPs) and motor imagery (M

Giovanni Barbarino, Nicolas Gillis, Subhayan Saha

Minimum-volume nonnegative matrix factorization (min-vol NMF) has been used successfully in many applications, such as hyperspectral imaging, chemical kinetics, spectroscopy, topic modeling, and audio source separation. However, its robustness to noise has been a long-standing open problem. In this paper, we prove that min-vol NMF identifies the groundtruth

Amogh Sirnoorkar, Winter Allen, Syed Furqan Abbas Hashmi, N. Sanjay Rebello

Comparing abstract concepts (such as electric circuits) with familiar ideas (plumbing systems) through analogies is central to practice and communication of physics. Contemporary research highlights self-generated analogies to better facilitate students' learning than the taught ones. "Spontaneous" and "self-generated" analogies represent the two ways throug

Antonio Maglio, Fabricio Valencia

We define the notion of basic section of an LA-groupoid whose core-anchor map is injective. Such a notion turns out to be Morita invariant, so that it provides a simpler model for the sections of the stacky Lie algebroids presented by such LA-groupoids, yet equivalent to the well-known model provided by their multiplicative sections.

Leire Benito-Del-Valle, Artzai Picón, Daniel Mugica, Manuel Ramos

Herbicide field trials require accurate identification of plant species and assessment of herbicide-induced damage across diverse environments. While general-purpose vision foundation models have shown promising results in complex visual domains, their performance can be limited in agriculture, where fine-grained distinctions between species and damage types

Elvise Berchio, Filomena Feo, Antonio Giuseppe Grimaldi

We study optimization problems for partially hinged rectangular plates, modeling bridge roadways, in the presence of real and artificial obstacles. Real obstacles represent structural constraints to avoid, while artificial ones are introduced to enhance stability. For the former, aiming to prevent collisions, we set up a worst-case optimization problem in wh

Mohamadreza Akbari Pour, Mohamad Sadeq Karimi, Amir Hossein Mazloumi

Health prediction is crucial for ensuring reliability, minimizing downtime, and optimizing maintenance in industrial systems. Remaining Useful Life (RUL) prediction is a key component of this process; however, many existing models struggle to capture fine-grained temporal dependencies while dynamically prioritizing critical features across time for robust pr

Matteo Cercola, Valeria Capretti, Simone Formentin

Learning from human preferences is a cornerstone of aligning machine learning models with subjective human judgments. Yet, collecting such preference data is often costly and time-consuming, motivating the need for more efficient learning paradigms. Two established approaches offer complementary advantages: RLHF scales effectively to high-dimensional tasks s

Zeng Zhiyuan, Jiashuo Liu, Zhangyue Yin, Ge Zhang

While Reinforcement Learning for Verifiable Rewards (RLVR) is powerful for training large reasoning models, its training dynamics harbor a critical challenge: RL overfitting, where models gain training rewards but lose generalization. Our analysis reveals this is driven by policy over-specialization and catastrophic forgetting of diverse solutions generated

Qianyang Li, Xingjun Zhang, Peng Tao, Shaoxun Wang

Forecasting long-term time series in IoT environments remains a significant challenge due to the non-stationary and multi-scale characteristics of sensor signals. Furthermore, error accumulation causes a decrease in forecast quality when predicting further into the future. Traditional methods are restricted to operate in time-domain, while the global frequen

Kosta Dakic, Kanchana Thilakarathna, Rodrigo N. Calheiros, Teng Joon Lim

Multi-drone surveillance systems offer enhanced coverage and robustness for pedestrian tracking, yet existing approaches struggle with dynamic camera positions and complex occlusions. This paper introduces MATRIX (Multi-Aerial TRacking In compleX environments), a comprehensive dataset featuring synchronized footage from eight drones with continuously changin

Maria M. Popova, Sergei N. Yudin, Alexei N. Grum-Grzhimailo, Elena V. Gryzlova

Parity mixing in photoionization, i.e. when emitted electrons have different parities but the same energy, causes interference observable only in angle-resolved measurements. The interference typically manifests as a symmetry violation in the photoelectron angular distributions. The traditional, based on HHG, RABBITT scheme with high-order harmonics separate

Shiwei Ren, Tianci Wen, Yongchun Fang, Biao Lu

The dominant 3D Gaussian splatting (3DGS) acceleration methods fail to properly regulate the number of Gaussians during training, causing redundant computational time overhead. In this paper, we propose FastGS, a novel, simple, and general acceleration framework that fully considers the importance of each Gaussian based on multi-view consistency, efficiently

L. Ducci, S. Mereghetti, F. Pintore, S. Allak

Thanks to a recent observation with XMM-Newton, we discovered periodic pulsations at P= 9.6652 +/- 0.0002 s in a new ultraluminous X-ray source (ULX) in the galaxy NGC 4631. This source, dubbed as X-8, shows one of the largest spin-up rates ever observed, dP/dt = (-9.6 +/- 0.5)*1E-8 s/s. These findings indicate that the compact object is a neutron star, and

Yujie Yang, Shuang Li, Jun Ye, Neng Dong

Video-based Visible-Infrared person re-identification (VVI-ReID) aims to retrieve the same pedestrian across visible and infrared modalities from video sequences. Existing methods tend to exploit modality-invariant visual features but largely overlook gait features, which are not only modality-invariant but also rich in temporal dynamics, thus limiting their

Dan Qiu, Chao Liu, Jennifer A. Johnson, Jiadong Li

We investigated a volume-limited sample of LAMOST main-sequence stars with masses from 0.25 to 1 $M_{\odot}$ and distances of 150-350 pc to explore how the stellar initial mass function (IMF) varies with metallicity. We corrected the spectroscopic selection function by comparing the stellar number densities with the photometric ones at the same colour and ma

Yu Ling, Florent Pawula, Ramzy Daou, Benoît Fauqué

We present a study of low-temperature electric and thermal transport in RuO$_2$, a metallic oxide which has attracted much recent attention. Careful scrutiny of electric resistivity reveals a quadratic temperature dependence below $\sim$ 20 K undetected in previous studies of electronic transport in this material. The prefactor of this T$^2$ resistivity, giv

Bernat Frangi

Accurate thermal analysis is crucial for modern spacecraft, driving demand for reliable modeling tools. This research advances space thermal modeling by improving the simulation accuracy and efficiency of radiative heat transfer, the dominant mode of heat exchange in space. To this end, we incorporate diffuse reflectivity using the Gebhart method, which comp

Piyumi Chathurangika, Tharushika Peiris, Lakmini S. Premadasa, S. S. N. Perera

Dengue continues to pose a major global threat, infecting nearly 390 million people annually. Recognizing the pivotal role of vector competence (vc), recent research focuses on mosquito parameters to inform transmission modeling and vector control strategies.This study models interactions between Aedes vectors and dengue pathogens, highlighting vc as a key d

Jan Sperling, Laura Ares, Elizabeth Agudelo

We discuss the (re-)construction of quasiprobability representations from generic measurements, including noisy ones. Based on the measurement under study, quasiprobabilities and the associated concept of nonclassicality are introduced. A practical concern that we address is the treatment of informationally incomplete and overcomplete measurement scenarios,

Vincent Starck

Although approaches to Independent Component Analysis (ICA) based on characteristic function seem theoretically elegant, they may suffer from implementational challenges because of numerical integration steps or selection of tuning parameters. Extending previously considered objective functions and leveraging results from the continuum Generalized Method of

Gaurang Agrawal, Saptarshi Roy

Preserving quantum correlations such as Bell nonlocality in noisy environments remains a fundamental challenge for quantum technologies. We introduce the Random Access Bell Game (RABG), a task where an entangled particle propagates through a sequence of identical noisy blocks, and the ability to violate a Bell inequality is tested at a randomly chosen point

Sergio Bengoechea, Paul Over, Thomas Rung

This article presents the first complete application of a quantum time-marching algorithm for simulating multidimensional linear transport phenomena with arbitrary boundaries, whereby the success probabilities are problem intrinsic. The method adapts the linear combination of unitaries algorithm to block encode the diffusive dynamics, while arbitrary boundar

Younes Otarid, Mathieu Benoit, Eric Buschmann, Hucheng Chen

Caribou is a versatile data acquisition (DAQ) system developed within several collaborative frameworks (CERN EP R&D, DRD3, AIDAinnova, and Tangerine) to support laboratory and test-beam characterization of novel silicon pixel detectors. It combines a custom Control and Readout (CaR) board with a Xilinx Zynq System-on-Chip (SoC) running project-wide shared fi

E. Trentin, G. Catanzaro, V. Ripepi, E. Luongo

Classical Cepheids are among the most important distance calibrators thanks to the correlation between their period and luminosity, and play a crucial role in the calibration as the first rung of the extragalactic distance ladder. Given their typical age, they also constitute an optimal tracer of the young population in the Galactic disc. We performed a comp

Iker Martín-Álvarez, José I. Aliaga, Maribel Castillo

Dynamic resource management is an increasingly important capability of High Performance Computing systems, as it enables jobs to adjust their resource allocation at runtime. This capability can reduce workload makespan, substantially decreasing job waiting times and optimizing resource allocation. In this context, malleability refers to the ability of applic

Jiahui Wu, Chengjie Lu, Aitor Arrieta, Shaukat Ali

In recent years, autonomous driving systems have made significant progress, yet ensuring their safety remains a key challenge. To this end, scenario-based testing offers a practical solution, and simulation-based methods have gained traction due to the high cost and risk of real-world testing. However, evaluating the realism of simulated scenarios remains di

Qing-Cang Shui, Shu Zhang, Shuang-Nan Zhang, Hua Feng

Dynamic processes in the accretion flow near black holes produce X-ray flux variability, sometimes quasi-periodic. Determining its physical origin is key to mapping accretion geometry but remains unresolved. We perform a novel phase-resolved analysis on a newly discovered quasi-periodic oscillation (QPO) in the active galactic nucleus 1ES 1927+654. For the f

Malte C. Tichy

A sum-wise formulation is proposed for the Kaplan-Meier product limit estimator of partially right-censored survival data. The population estimator is expressed as a sum over the individual units' empirical and semi-empirical contributions, for observed and censored failures, respectively. This intuitive decomposition is applied to visualize the differen

Eric Mörth, Morgan L. Turner, Cydney Nielsen, Xianhao Carton Liu

Advances in spatial omics and high-resolution imaging enable the creation of three-dimensional (3D) tissue maps that capture cellular organization and interactions in situ. While these data provide critical insights into tissue function and disease, their exploration is often constrained by tools limited to 2D displays or stereoscopic rendering without analy

Ming Liu

The widespread deployment of high-resolution visual sensing systems, coupled with the rise of foundation models, has amplified privacy risks in video-based applications. Differentially private pixelization offers mathematically guaranteed protection for visual data through grid-based noise addition, but challenges remain in preserving task-relevant fidelity,

Poulami Bag, Tanwi Debnath, Shubhadip Nayak, Pulak K. Ghosh

We propose a highly efficient mechanism to rectify the motion of active particles by exploiting particle-wall alignment interactions. Through numerical simulations of active particles' dynamics in a narrow channel, we demonstrate that a slight difference in alignment strength between the top and bottom walls or a small gravitational drag suffices to break up

György Kálvin, Péter Vancsó, Márton Szendrő, Konrád Kandrai

Hydrocarbon contamination is an ever-present factor to consider in surface science measurements. In the case of van der Waals material surfaces, the structure of this contamination has become known in recent years as a self-assembled layer of normal-alkanes, resulting from a few days' exposure to ambient air. Knowledge of its composition and structure enable

Kun Yang, Zikang chen, Yanmeng Wang, Zhigen Li

As a key component of large language model (LLM) post-training, Reinforcement Learning from Verifiable Rewards (RLVR) has substantially improved reasoning performance. However, existing RLVR algorithms exhibit distinct stability issues: GRPO (Group Relative Policy Optimization) often suffers from unstable policy updates, while GSPO (Group Sequence Policy Opt

Marawan Elbatel, Anbang Wang, Keyuan Liu, Kaouther Mouheb

This paper does not introduce a novel architecture; instead, it revisits a fundamental yet overlooked baseline: adapting human-centric foundation models for anatomical landmark detection in medical imaging. While landmark detection has traditionally relied on domain-specific models, the emergence of large-scale pre-trained vision models presents new opportun

Diego Olguín, Axel Osses, Héctor Ramírez

In this article, we propose a new filtering algorithm based in the Koopman operator, showing that a nonlinear filtering problem can be seen as an equivalent problem where the dynamics is infinite dimensional, but linear. Using Extended Dynamic Mode Decomposition (EDMD), we create a finite dimensional approximation of the filtering problem of dimension $N$, i

Jinfeng Liang, Haocheng Guo, Ximin Lyu

The tailsitter vertical takeoff and landing (VTOL) UAV is widely used due to its lower dead weight, which eliminates the actuators and mechanisms for tilting. However, the tailsitter UAV is susceptible to wind disturbances in multi-rotor mode, as it exposes a large frontal fuselage area. To address this issue, our tailsitter UAV features a reconfigurable win

Salma Mekaoui, Hiba Sofyan, Imane Amaaz, Imane Benchrif

Extracting topics from text has become an essential task, especially with the rapid growth of unstructured textual data. Most existing works rely on highly computational methods to address this challenge. In this paper, we argue that probabilistic and statistical approaches, such as topic modeling (TM), can offer effective alternatives that require fewer com

Allie Tran, Luca Rossetto

Multimodal co-embedding models, especially CLIP, have advanced the state of the art in zero-shot classification and multimedia information retrieval in recent years by aligning images and text in a shared representation space. However, such modals trained on a contrastive alignment can lack stability towards small input perturbations. Especially when dealing

Sander De Witte, Tom Lefebvre, Thomas Neve, Andras Retzler

This paper investigates the dynamic properties of planar slider-pusher systems as a motion primitive in manipulation tasks. To that end, we construct a differential kinematic model deriving from the limit surface approach under the quasi-static assumption and with negligible contact friction. The quasi-static model applies to generic slider shapes and circul

G. R. Boroun

We present a calculation of the ratio $\frac{\sigma_{r}}{F_{2}}(x, Q^2)$ in momentum-space approach using the Block-Durand-Ha (BDH) parameterization of the proton structure function $F_{2}(x,Q^2)$. The results are compared with H1 data and extended to high inelasticity. We also examine the ratio $\frac{\sigma_{r}}{F_{2}}(\frac{Q^2}{s}, Q^2)$ obtained at a fi

Irene Ferfoglia, Simone Silvetti, Gaia Saveri, Laura Nenzi

Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box deep learning methods, making it hard for humans to understand the rationale behind their output. To take on this challenge, we propose a novel approach, STELLE (Signal Temporal logic

Ha Young Kim, Jun Li, Ana Beatriz Solana, Carolin M. Pirkl

Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists frequently consult case reports and literature when confronted with unfamiliar findings. Following this line of reasoning, we introduce RADAR, Retrieval Augmented Diagnostic Reasoning Ag

Sabbir A. Kahn, Lukas Stampfer, Sara Marti-Sanchez, Dags Olsteins

This work presents multiterminal Josephson junctions in hybrid semiconductor-superconductor InAsSb-Al nanocrosses. Hybrid nanocrosses are grown using molecular beam epitaxy and are formed through As-assisted merging of oppositely directed InAsSb nanowires. We explain this complex ternary merging mechanism using a temperature-dependent phase diagram and inves

Yehor Hudenko, Michal Kolář, Radim Filip, Artem Ryabov

Local quantum coherence in a two-level system (TLS) is typically generated via time-dependent driving. However, it can also emerge autonomously from symmetry-breaking interactions between the TLS and its surrounding environment at a low temperature. Although such environments often consist of interacting atoms or spins, the role of interactions within the en

Maksym Chaudkhari, Christian Gorski, Eduardo Silva

We prove a Central Limit Theorem for the drift of a non-elementary random walk with a finite exponential moment on a wreath product $A\wr H=\bigoplus_{H} A\rtimes H$ with $A$ a non-trivial finite group and $H$ a finitely generated acylindrically hyperbolic group. We also provide the upper bounds on the central moments of the drift. Furthermore, our results e

Péter P. Varjú, Max Wenqiang Xu

Assume that the Riemann hypothesis holds for Dedekind zeta functions. Under this assumption, we prove that a degree $d$ polynomial with random multiplicative $\pm1$ coefficients is irreducible in $\mathbb{Z}[x]$ with probability $1-O(d^{-1/2+\varepsilon})$.

Rasmus Møller-Larsen, Adam Izdebski, Jan Olszewski, Pankhil Gawade

Recent advances in computational methods for designing biological sequences have sparked the development of metrics to evaluate these methods performance in terms of the fidelity of the designed sequences to a target distribution and their attainment of desired properties. However, a single software library implementing these metrics was lacking. In this wor

Raju Kumar Gupta, Sourav Sarkar, Samir Shukla

For a metric space $X$ and $r \geq 0$, the Vietoris-Rips complex $\mathcal{VR}(X;r)$ is a simplicial complex whose simplices are finite subsets of $X$ with diameter at most $r$. Vietoris-Rips complexes have applications in various places, including data analysis, geometric group theory, sensor networks, etc. Consider the integer lattice $\mathbb{Z}^n$ as a m

Zefeng Li, Ning Yang

Although the collaborative filtering (CF) algorithm has achieved remarkable performance in recommendation systems, it suffers from suboptimal recommendation performance due to noise in the user-item interaction matrix. Numerous noise-removal studies have improved recommendation models, but most existing approaches conduct denoising on a single graph. This ma

Shi-Hao Zhang, Zi-Yuan Li, Jing-Fei Zhang, Xin Zhang

The deep connection between black hole thermodynamics and spacetime geometry remains a central focus of general relativity. While recent studies have revealed a precise correspondence for null orbits, given by $K = -\lambda^2$ between the Gaussian curvature $K$ and the Lyapunov exponent $\lambda$, its validity for timelike orbits had remained unknown. Our wo

Zhengru Fang, Yu Guo, Yuang Zhang, Haonan An

Constructing a consistent shared spatial memory is a critical challenge in multi-agent systems, where partial observability and limited bandwidth often lead to catastrophic failures in coordination. We introduce a multi-agent predictive coding framework that formulates coordination as the minimization of mutual uncertainty among agents. Through an informatio

Alex Fang, Thomas Voice, Ruoming Pang, Ludwig Schmidt

Large language models learn from their vast pre-training corpora, gaining the ability to solve an ever increasing variety of tasks; yet although researchers work to improve these datasets, there is little effort to understand how efficient the pre-training apparatus is at extracting ideas and knowledge from the data. In this work, we use retrieval augmented

Yaara Jahn, Orit E. Raz

Let $f\in \mathbb{R}[x_1,\ldots, x_k]$, for $k\ge 2$. For any finite sets $A_1,\ldots, A_k\subset \mathbb{R}$, consider the set $$ f(A_1,\ldots, A_k):=\{f(a_1,\ldots, a_k)\mid (a_1,\cdots,a_k)\in A_1\times\cdots \times A_k\}, $$ that is, the image of $A_1\times \cdots\times A_k$ under $f$. Extending a theorem of Elekes and R\'onyai, which deals with the case

Jindi Zhong, Ziyuan Guo, Hongxia Wang, Huanshui Zhang

This paper addresses the visual localization problem in Global Positioning System (GPS)-denied environments, where computational resources are often limited. To achieve efficient and robust performance under these constraints, we propose a novel algorithm. The algorithm stems from the optimal control principle (OCP). It incorporates diagonal information esti

Pooja Devi, Cristina H. Mandrini, Ramesh Chandra, Germán D. Cristiani

Solar filaments are cool and dense plasma structures suspended in the solar corona against gravity. We present observations of a quiescent filament eruption that occurs on 13 July 2015. The eruption is associated with a two-ribbon GOES B8.9 class flare. Photospheric magnetic flux cancellation is present below the filament during days. This builds up a flux r

Christian Fiedler, Alessandro Scagliotti

The control of ensembles of dynamical systems is an intriguing and challenging problem, arising for example in quantum control. We initiate the investigation of optimal control of ensembles of discrete-time systems, focusing on minimising the average finite horizon cost over the ensemble. For very general nonlinear control systems and stage and terminal cost

Oleg K. Sheinman

Riemann vanishing theorem is a main ingredient of the conventional technique related to the Jacobi inversion problem. In the case of curves with a holomorphic involution, it has been presented quite fully in wellknown Fay's Lectures on theta functions. The case of real algebraic curves with involution is presented with less completeness in the literature. We

Liran Cohen, Yaniv Nemcovesky, Avi Mendelson

Machine unlearning aims to remove the influence of specific training data from a model without requiring full retraining. This capability is crucial for ensuring privacy, safety, and regulatory compliance. Therefore, verifying whether a model has truly forgotten target data is essential for maintaining reliability and trustworthiness. However, existing evalu

Johann Toudert, Rosalía Serna

Achieving state-of-the-art optical data storage requires raising device capacity well above commercial standards. This requires media structured at a much smaller scale and enabling readout at a shorter wavelength. Current CDs, DVDs and Blu-rays are read with visible light, and are based on metallic reflection gratings and phase-change recording layers struc

Marie Du Roy de Chaumaray, Michael Levine, Matthieu Marbac

Theoretical guarantees are established for a standard estimator in a semi-parametric finite mixture model, where each component density is modeled as a product of univariate densities under a conditional independence assumption. The focus is on the estimator that maximizes a smoothed log-likelihood function, which can be efficiently computed using a majoriza

Xuan Zhang, XIao-le Li, Jingjing Niu, Tongxing Yan

While geometric quantum gates are often theorized to possess intrinsic resilience to control errors by exploiting the global properties of evolution paths, this promise has not consistently translated into practical robustness. We present a streamlined framework for nonadiabatic geometric quantum gates (NGQGs) that incorporates additional auxiliary constrain

Yurij V Kalyuzhnyi

Computation of the properties of associative fluids with the particles highly anisotropic in shape, using multi-density perturbation theory of Wertheim, has long been a challenge. We propose a simple and efficient scheme that allow us to perform such computations. The scheme is based on a combination of thermodynamic perturbation theory and the interaction s

Junhui Cao, Denis Novokreschenov, Alexey Kavokin

Arrays of bosonic condensates of exciton-polaritons have emerged as a promising platform for simulating classical XY models, capable of rapidly reaching phase-locked states that may be mapped to arrays of two-dimensional classical spins. However, it remains unclear whether these states genuinely minimize the corresponding XY Hamiltonian and how the convergen

Xinxin Li, Weishuo Xu, Zengguang Zhou, Jingming Shi

Hydrides have long been considered promising candidates for achieving room-temperature superconductivity; however, the extremely high pressures typically required for high critical temperatures remain a major challenge in experiment. Here, we propose a class of high-Tc ambient-pressure superconductors with MH4 stoichiometry. These hydrogen-based compounds ad

Mohammadhossein Homaei, Mehran Tarif, Pablo Garcia Rodriguez, Andres Caro

Digital Twins (DTs) for Water Distribution Networks (WDNs) require accurate state estimation with limited sensors. Uniform sampling often wastes resources across nodes with different uncertainty. We propose an adaptive framework combining LSTM forecasting and Conformal Prediction (CP) to estimate node-wise uncertainty and focus sensing on the most uncertain

Carl Kugblenu, Petri Vuorimaa

Production vector search systems often fan out each query across parallel lanes (threads, replicas, or shards) to meet latency service-level objectives (SLOs). In practice, these lanes rediscover the same candidates, so extra compute does not increase coverage. We present a coordination-free lane partitioner that turns duplication into complementary work at

Alan Seroul, Théo Fagnoni, Inès Adnani, Dana O. Mohamed

This paper introduces the Opus Workflow Evaluation Framework, a probabilistic-normative formulation for quantifying Workflow quality and efficiency. It integrates notions of correctness, reliability, and cost into a coherent mathematical model that enables direct comparison, scoring, and optimization of Workflows. The framework combines the Opus Workflow Rew

Mingzhi Lin, Teng Huang, Han Ding, Cui Zhao

Human Activity Recognition (HAR) using mmWave radar provides a non-invasive alternative to traditional sensor-based methods but suffers from domain shift, where model performance declines in new users, positions, or environments. To address this, we propose mmADA, an Active Domain Adaptation (ADA) framework that efficiently adapts mmWave-based HAR models wit

Hikari Otsuka, Daiki Chijiwa, Yasuyuki Okoshi, Daichi Fujiki

The strong lottery ticket hypothesis (SLTH) conjectures that high-performing subnetworks, called strong lottery tickets (SLTs), are hidden in randomly initialized neural networks. Although recent theoretical studies have established the SLTH across various neural architectures, the SLTH for transformer architectures still lacks theoretical understanding. In

Wen-Yuan Ai, Björn Garbrecht, Carlos Tamarit

The conservation of $CP$ in QCD has been shown to follow from a careful treatment of the path integral and canonical quantization in arXiv:2001.07152 and arXiv:2403.00747. Here, we refute the critique of these results put forth in arXiv:2510.18951. First, using the quantum rotor as an analogue of QCD, it is argued in arXiv:2510.18951 that the topological sus

Hongwei Yao, Yun Xia, Shuo Shao, Haoran Shi

Large language models (LLMs) increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful responses, these guardrails introduce a new class of vulnerabilities by exposing observable decision patterns. In this work, we present the first study of black-box LLM guardrail r

Yuantian Shao, Peisong Wang, Yuanteng Chen, Chang Xu

Large language models (LLMs) have achieved remarkable success, but their rapidly growing scale imposes prohibitive costs in memory, computation, and energy. Post-training quantization (PTQ) is a promising solution for efficient deployment, yet achieving accurate W4A4 quantization remains an open challenge. While most existing methods are designed for INT4 fo

Ziying Li, Xuequan Lu, Xinkui Zhao, Guanjie Cheng

Recent advancements in optimization-based text-to-3D generation heavily rely on distilling knowledge from pre-trained text-to-image diffusion models using techniques like Score Distillation Sampling (SDS), which often introduce artifacts such as over-saturation and over-smoothing into the generated 3D assets. In this paper, we address this essential problem

Markus Herklotz, Niklas Ippisch, Anna-Carolina Haensch

One of the central challenges for instructors is offering meaningful individual feedback, especially in large courses. Faced with limited time and resources, educators are often forced to rely on generalized feedback, even when more personalized support would be pedagogically valuable. To overcome this limitation, one potential technical solution is to utili

Michael Thies

As is well known, the chiral Gross-Neveu model at finite density can be solved semi-classically with the help of the chiral spiral mean field. The fermion spectrum has a single gap right at the Fermi energy, a reflection of the Peierls instability. Here, we divide the N fermion flavors up into two subsets to which we attribute two different densities. The Ha

Peter Stockinger

The article examines the theoretical, methodological, and technical foundations of research on audiovisual corpora within the field of digital humanities. It outlines the main transversal issues underlying the processes of constructing, exploiting, and interpreting such corpora, which are conceived as specific forms of textual data in the broad sense - that

Monica Conte, René van Roij, Marjolein Dijkstra

Inspired by the brain, we present a physical alternative to traditional digital neural networks -- a microfluidic network in which nodes are connected by conical, electrolyte-filled channels acting as memristive iontronic synapses. Their electrical conductance responds not only to electrical signals, but also to chemical, mechanical, and geometric changes. L

Thomas Biscaldi, Romain l'Huillier, Laurent Milot, W Apoutou N'Djin

Current interstitial techniques of tumor ablation face challenges that ultrasound technologies could meet. The ablation radius and directionality of the ultrasound beam could improve the efficiency and precision. Here, a 9gauge MR-compatible dual-mode ultrasound catheter prototype was experimentally evaluated for Ultrasound Imageguided High Intensity Focused

Salima El Kolei, Matthieu Marbac

We present the first method for assessing the relevance of a model-based clustering result in a general framework. Standard validation criteria, like the adjusted Rand index, rely on external labels to assess partition accuracy; consequently, they are inapplicable to real-world clustering problems where labels are missing. In contrast, our method offers an i

Michał Karp, Anna Kubaszewska, Magdalena Król, Robert Król

This study provides an empirical assessment of whether current large language models (LLMs) can pass the official qualifying examination for membership in Poland's National Appeal Chamber (Krajowa Izba Odwo{\l}awcza). The authors examine two related ideas: using LLM as actual exam candidates and applying the 'LLM-as-a-judge' approach, in which model-generate

Yuri B. Ivanov

Predictions of the proton directed flow ($v_1$) in semicentral Au+Au collisions in the energy range between 4.5 and 7.7 GeV are done. The calculations are performed within the model of three-fluid dynamics with crossover equation of state, which well reproduces the proton $v_1$ both below 4.5 GeV and above 7.7 GeV, as well as bulk observables in the energy r