March 2026 arXiv papers — page 73

Ilya Levin

Generative AI systems produce meaning with a quality indistinguishable from - and occasionally surpassing - human performance, yet the epistemic mechanism through which this occurs remains poorly understood. This paper argues that generative AI instantiates a fundamentally new mode of knowledge production: geometric navigation through high-dimensional manifo

Rodica Dinu, Francesco Navarra

In this paper, we provide a combinatorial characterization of those collections of cells whose inner $2$-minor ideals are complete intersections. More precisely, given a collection of cells $\mathcal C$ and its associated inner $2$-minor ideal $I_{\mathcal C}$, we prove that $I_{\mathcal C}$ is a complete intersection if and only if $\mathcal C$ is a chessbo

Jessica Sanson, Rahul C. Shah, Yazhou Zhu, Rafael Rosales

Wi-Fi sensing has emerged as a promising technique for gesture recognition, yet its practical deployment is hindered by environmental sensitivity and device placement challenges. To overcome these limitations we propose Wi-Fi Range and Doppler (WiRD)-Gest, a novel system that performs gesture recognition using a single, unmodified Wi-Fi transceiver on a comm

Junzhao Zhang, Hsiu-Yuan Huang, Chenming Tang, Yutong Yang

Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limited cultural coverage, which hinder research into fine-grained semantic understanding. To address this, we construct CFMS, the first fine-grained multimodal sarcasm dataset tailored for Chinese social media. It

Jeet Sampat, Maximilian Tornes

We consider the algebra of square matrices of bounded non-commutative (NC) functions over NC operator unit balls (unit balls corresponding to finite-dimensional operator spaces) and characterize cyclic matrix free polynomials with respect to the canonical weak-* topology. More precisely, we show that a matrix free polynomial generates a weak-* dense left/rig

Shreeram Murali, Cristian R. Rojas, Dominik Baumann

While both classical and neural network classifiers can achieve high accuracy, they fall short on offering uncertainty bounds on their predictions, making them unfit for safety-critical applications. Existing kernel-based classifiers that provide such bounds scale with $\mathcal O (n^{\sim3})$ in time, making them computationally intractable for large datase

Afsoon Alidadi Shamsabadi, Cosmas Mwaba, Thomas Nugent, Jie Gao

Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combi

Azuki Kim

Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high-dimensional parameter spaces is intractable. We present ROBOGATE, a deployment risk management framework that combines physics-based simulation with a two-stage adaptive sampling strategy to efficiently discover

Xin Cai, Zhiyuan You, Zhoutong Zhang, Tianfan Xue

Reducing token count is crucial for efficient training and inference of latent diffusion models, especially at high resolution. A common strategy is to build high-compression image tokenizers with more channels per token. However, when trained only for reconstruction, high-dimensional latent spaces often lose meaningful structure, making diffusion training h

Bin Chen, Dechuang Huang

Distributed systems in which concurrent proposals are mutually exclusive face a fundamental stability constraint under network delay. In open systems where global state progression is event-driven rather than round-driven, propagation delay creates a conflict window within which overlapping proposals may generate competing branches. This paper derives a dens

Adam Earnst

We prove asymptotics for mollified first and second moments of subfamilies of Dirichlet $L$-functions given by shrinking angular restrictions on the root number. Using these moments, we prove that for even primitive characters with prime conductor $q$, a positive proportion of the central values $L(1/2,\chi)$ do not vanish as $q\to\infty$.

Jan Boysen, Hristina Uzunova, Heinz Handels, Jan Ehrhardt

A precise spatial delivery of the radiation dose is crucial for the treatment success in radiotherapy. In the lung and upper abdominal region, respiratory motion introduces significant treatment uncertainties, requiring special motion management techniques. To address this, respiratory motion models are commonly used to infer the patient-specific respiratory

Andrei Gaidash, George Miroshnichenko, Anton Kozubov

In this work we present a comprehensive analysis of a post-selective attack on quantum key distribution protocols employing phase-encoded linearly independent coherent states (or similar alternatives). The attack relies on multimode projection onto a Fock subspace and enables probabilistic extraction of information by an eavesdropper. We derive analytical ex

Jiawei Chen, Zhe Chen, Chaoqun Du, Maokui He

Emerging applications such as embodied intelligence, AI hardware, autonomous driving, and intelligent cockpits rely on a real-time perception-decision-action closed loop, posing stringent challenges for streaming video understanding. However, current agents mostly suffer from fragmented capabilities, such as supporting only offline video understanding, lacki

Ekta U. Samani, Christopher G. Atkeson

We use fused deposition modeling (FDM) 3D printing as a case study of how manufacturing robots can use imperfect AI to acquire process expertise. In FDM, print configuration strongly affects output quality. Yet, novice users typically rely on default configurations, trial-and-error, or recommendations from generic AI models (e.g., ChatGPT). These strategies

Kexin Huang, Haoming Meng, Junkang Wu, Jinda Lu

Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-induced changes are sparse, they primarily focus on the \textbf{magnitude} of these updates, largely overlooking their \textbf{direction}. In this work, we argue that the direction of

Ananditha Raghunath, William Thies, Mohit Jain

When medical chatbots provide advice that conflicts with users' lived care experiences, users are left to interpret, negotiate, and evaluate the legitimacy of that guidance. In India, the widespread overuse of antibiotics, antidiarrheals, and injections has shifted patient expectations away from the guideline-aligned advice that chatbots are trained to provi

Laura Davila-Pena, Maria Paola Scaparra, Dick den Hertog

Access to healthcare facilities is a critical issue in developing countries, where limited resources and significant challenges hinder progress toward universal health coverage, one of the targets pursued by the United Nations. As a result, the Operations Research (OR) community has become increasingly active in addressing this issue, employing various techn

Kalyan Barman, Palaniappan Vellaisamy

In this article, we obtain the exact distribution of a linear combination of bilateral gamma (BG) random variables (r.v.s). Next, we discuss the distributional properties of the linear combination of BG r.v.s, including probability density function, cumulant generating function and characteristic function. A Stein characterization is developed, which leads u

Shakthidhar Vilvanathan, Jerin Saji, Kristiana Frei, Jakub Tworzydlo

Graphene quantum point contacts (QPCs) in the quantum Hall regime host competing transport mechanisms including chiral edge propagation, valley degeneracy, and gate-induced mode mixing. Their interplay is not visible in conductance alone. Shot noise directly probes the statistics of transmission eigenvalues, revealing microscopic mode partitioning that condu

Sharon Lapiner, Nir Mandelker, Paz Beniamini, S. Peng Oh

We study the formation of multiphase gas in the post-accretion-shock regions of cosmic sheets, filaments, and the circumgalactic medium (CGM) of haloes, i.e., cosmic web objects (CWOs). Local instabilities in the hot medium result in fragmentation and cooling, eventually forming small-scale overdensities with temperatures of $\sim 10^{4}{\,\rm K}$ in pressur

Margaret-Anne Storey

Generative AI is accelerating software development, but may quietly shift where the most significant risks lie. As AI generates code faster than teams can understand it, two under appreciated forms of debt accumulate: cognitive debt, the erosion of shared understanding across a team, and intent debt, the absence of externalized rationale that developers and

Kaipeng Xu, Zhuo Zhi, Ruixuan Zhao, Keyue Jiang

Building Demand Response (DR) benefits from Model Predictive Control (MPC) because thermal flexibility can be optimized over a future horizon while enforcing operational constraints, but the repeated online optimization required by MPC motivates Differentiable Predictive Control (DPC) as a computationally efficient explicit-policy alternative. Conventional D

Max Upravitelev, Veronika Solopova, Jing Yang, Charlott Jakob

Predicting narrative similarity can be understood as an inherently interpretive task: different, equally valid readings of the same text can produce divergent interpretations and thus different similarity judgments, posing a fundamental challenge for semantic evaluation benchmarks that encode a single ground truth. Rather than treating this multiperspectivit

Hang Dai, Hongwei Fan, Han Zhang, Duojin Wu

The increasing demand for augmented reality and robotics is driving the need for articulated object reconstruction with high scalability. However, existing settings for reconstructing from discrete articulation states or casual monocular videos require non-trivial axis alignment or suffer from insufficient coverage, limiting their applicability. In this pape

X. -L. Wang, Z. Yan, F. -G. Xie, J. -F. Wang

Based on abundant data from Insight-HXMT, we conducted a detailed analysis of type-C quasi-periodic oscillations (QPOs) in the black hole X-ray binary MAXI J1348-630. Type-C QPOs were intensively detected over a broad energy band, with frequencies ranging from 0.24 to 10.3 Hz, and several new evolutionary features were identified. First, although type-C QPOs

Luca Brugnoli, Mathieu Salanne, A. Marco Saitta, Alessandra Serva

Machine-learned interatomic potentials hold the promise to enable the modeling of highly concentrated liquids over meaningful timescales, far from reach for current ab initio electronic structure methods. Here we evaluate the performances of various MACE potentials in modeling a $21 m$ water-in-salt electrolyte based on lithium bis(trifluoromethanesulfonyl)i

Tim Gerlach, Benjamin Hennies, Linda Kleist

While rectangular and box-shaped objects dominate the classic discourse of theoretic investigations, a fascinating frontier lies in packing more complex shapes. Given recent insights that convex polygons do not allow for constant competitive online algorithms for diverse variants under translation, we study orthogonal polygons, in particular of small complex

Syed Usama Imtiaz, Mitra Nasr Azadani, Nasrin Alamdari

Foundation models are now increasingly being developed for Earth observation (EO), yet they often rely on stochastic masking that do not explicitly enforce physics constraints; a critical trustworthiness limitation, in particular for predictive models that guide public health decisions. In this work, we propose SpecTM (Spectral Targeted Masking), a physics-i

Xiao Han, Yuzheng Fan, Sendong Zhao, Haochun Wang

Clinical decision-making agents can benefit from reusing prior decision experience. However, many memory-augmented methods store experiences as independent records without explicit relational structure, which may introduce noisy retrieval, unreliable reuse, and in some cases even hurt performance compared to direct LLM inference. We propose GSEM (Graph-based

Kaipeng Xu, Zhuo Zhi, Keyue Jiang

Learning-based Model Predictive Control (MPC) has emerged as a powerful strategy for building demand response (DR). However, its practical deployment is often hindered by the non-convex optimization problems induced by standard neural network models. These problems lead to long solver times and a lack of global optimality guarantees, making long-horizon real

Xingyu Zhu, Beier Zhu, Shuo Wang, Junfeng Fang

As vision-language models (VLMs) are increasingly deployed in open-world scenarios, they can be easily induced by visual jailbreak attacks to generate harmful content, posing serious risks to model safety and trustworthy usage. Recent activation steering methods inject directional vectors into model activations during inference to induce refusal behaviors an

Artur Boronat

Bounded model finding is a key technique for validating software designs, usually obtained by translating high-level specifications into SAT/SMT problems. Although effective, such translations introduce a semantic gap and a dependency on external tools. We present the \emph{Maude Model Finder} (MMF), a native approach that brings bounded model finding to the

Amy Turnbull, George Hutchinson, Louise Dewick, Ruizhe Li

Objective(s): T$_2$* and susceptibility ($\chi$) MRI mapping provide complimentary measures of the haemodynamic environment in the placenta. The aims of this work were to use these simultaneously obtained measures to investigate the role of oxygen distribution on the well-established reduction of T$_2$* with gestational age found in healthy pregnancies and e

Rui Zhao, Mike Zheng Shou

Recent advancements in video generation models have significantly improved their ability to follow text prompts. However, the customization of dynamic visual effects, defined as temporally evolving and appearance-driven visual phenomena like object crushing or explosion, remains underexplored. Prior works on motion customization or control mainly focus on lo

Tomoya Yanagi, Shunnosuke Ikeda, Ken Kobayashi, Yuichi Takano

Recommender systems play an essential role in online services by providing personalized item lists to support users' decision-making processes. While collaborative filtering methods can achieve high accuracy, it is crucial to consider not only accuracy but also the diversity of recommended items to improve user satisfaction. Although financial portfolio theo

Hoseung Jang, Ginestra Bianconi, Byungjoon Min

Traditional percolation theory assumes static microscopic rules, limiting its ability to describe real-world complex systems where macroscopic order actively regulates local interactions. Here, we introduce feedback percolation, an unified framework that dynamically couples the microscopic activation probability to the macroscopic size of the giant component

Alix Deruelle, Man-Chun Lee, Felix Schulze, Miles Simon

Hamilton's pinching conjecture, that three-dimensional complete non-compact manifolds with pinched Ricci curvature are flat, has recently been resolved using Ricci flow. In this paper we prove a direct analogue of that result in all dimensions. In order to do so we develop a lifting technique that allows us to handle manifolds that are collapsed at infinity.

Chloe Lawlor, Richelle F. van Capelleveen, Guillaume Bourdarot, Christian Ginski

WISPIT 2 is a nearby young star with a multi-ringed disk which was recently confirmed to host a ~4.9 MJup gas giant planet embedded in a large (60 au) gap at a radial separation of 57 au from the host star. We confirm and characterise a second, close-in planet in the WISPIT 2 system using a combination of new VLT/SPHERE H-band dual-polarisation imaging and V

Steve Shkoller

We introduce a low-cost every-$K$-step correction for one-dimensional Euler computations. The correction uses differentiated Riemann variables (DRVs) -- characteristic derivatives that isolate the left acoustic wave, the contact, and the right acoustic wave -- to locate the current wave packet, sample the surrounding constant states, perform a short Newton u

Xi Yang, Aurelie Lozano, Naoki Abe, Bhavya

Despite rapid progress in AI agents for enterprise automation and decision-making, their real-world deployment and further performance gains remain constrained by limited data quality and quantity, complex real-world reasoning demands, difficulties with self-play, and the lack of reliable feedback signals. To address these challenges, we propose a lightweigh

Sylwia Antoniuk, Nina Kamčev, Christian Reiher, Tadej Petar Tukara

A randomly perturbed graph $G^p = G_\alpha \cup G_{n,p}$ is obtained by taking a deterministic $n$-vertex graph $G_\alpha = (V, E)$ with minimum degree $\delta(G)\geq \alpha n$ and adding the edges of the binomial random graph $G_{n,p}$ defined on the same vertex set $V$. For which value $p$ (depending on $\alpha$) does the graph $G^p$ contain a $K_r$-factor

K. Shalaby, T. Hunt, S. Moir, P. Trottier

We present a standalone frequency-offset locking system for controlling narrow-linewidth lasers using off-the-shelf electronic components. We lock two frequency-doubled 1560 nm lasers to a stable primary laser operating at 780 nm via their optical beat note. This radio-frequency beat note is fed through a broadband variable divider, a frequency-to-voltage co

Fabian Merz, Rico Zacher

We show that the nonlocal Fisher information - defined as the entropy dissipation of the Boltzmann entropy for nonlocal heat equations - admits a natural lifting in the sense of Guillen and Silvestre (2025). Important examples include the discrete Fisher information arising in Markov chains and the fractional Fisher information $i_s$ associated with the frac

Zakkarija Micallef, Keerthiga Rajenthiram, Ilias Gerostathopoulos

Machine Learning Operations (MLOps) has become increasingly critical as more organisations move ML models into production. However, the growing landscape of MLOps solutions has introduced complexity for practitioners trying to select appropriate tools. To investigate how and why these tools are adopted in practice, this paper conducts a systematic review of

Tae Gab Ha

We study a coupled system formed by a conservative wave equation and a dissipative Moore-Gibson-Thompson (MGT) equation on a bounded domain. The wave component is driven by the logarithmic source $f(u)=|u|^{\gamma-2}u\ln|u|$, $2<\gamma<\frac{2(n-1)}{n-2}$, and carries no direct damping. Rather than employing cross-multiplier arguments, we introduce the coupl

Caio Vicentino

We present a controlled empirical comparison between autoregressive (AR) and masked diffusion (MDLM) language models. Both models are trained on identical data (50M tokens from TinyStories), identical compute budget (20,000 steps, batch size 32, sequence length 512), and identical hardware (NVIDIA H100 80GB), isolating the generation paradigm as the sole var

Aurora Esteban, Amelia Zafra, Sebastián Ventura

Due to the prevalence of temporal data and its inherent dependencies in many real-world problems, time series classification is of paramount importance in various domains. However, existing models often struggle with series of variable length or high dimensionality. This paper introduces the MIHT (Multi-instance Hoeffding Tree) algorithm, an efficient model

Wei Zhou, Wuyang Li, Junkai Ji, Xueliang Li

Multi-objective re-ranking has become a critical component of modern multi-stage recommender systems, as it tasked to balance multiple conflicting objectives such as accuracy, diversity, and fairness. Existing multi-objective re-ranking methods typically optimize aggregate objectives at the item level using static or handcrafted preference weights. This desi

Duarte Fontes, Dennis Horstmann, Kirill Melnikov, Davide Maria Tagliabue

Understanding the infrared sensitivity of perturbative predictions in QCD is important for assessing the magnitude of possible non-perturbative power corrections to processes with large momentum transfer. In renormalon models, this sensitivity can be related to computable dependences of perturbative quantities on a small gluon mass. However, this procedure c

Di Su, Yining Chen, Tengyao Wang

While change point detection in time series data has been extensively studied, little attention has been given to its generalisation to data observed on spheres or other manifolds, where changes may occur within spatially complex regions with irregular boundaries, posing significant challenges. We propose a new class of estimators, namely, Change Region Iden

Xingyu Zhu, Liang Yi, Shuo Wang, Wenbo Zhu

Multimodal 3D vision-language models show strong generalization across diverse 3D tasks, but their performance still degrades notably under domain shifts. This has motivated recent studies on test-time adaptation (TTA), which enables models to adapt online using test-time data. Among existing TTA methods, cache-based mechanisms are widely adopted for leverag

Avas Banerjee

In this article, we investigate the centered isoperimetric inequality on Cartan-Hadamard manifolds endowed with a warped product structure, namely, among all bounded measurable sets of finite perimeter and prescribed volume, the geodesic ball centered at the pole minimizes the perimeter. Exploiting the interplay between this inequality and the underlying war

Michele N. Notarnicola, Marcin Jarzyna, Radim Filip

Large optical coherent-state superpositions are essential to advance quantum sensing, quantum repeaters and error-correction codes. We propose a deterministic feedforward protocol employing qubit-mode dispersive coupling, currently available in cavity quantum electrodynamics (QED). We show this single-mode protocol to outperform the advanced three-mode Gauss

Ariel Barton, Svitlana Mayboroda, Alberto Pacati

We establish well posedness of the Poisson problem in weak local John domains, for linear second order elliptic equations with real coefficients, and with data in weighted Lebesgue spaces with a very broad range of acceptable parameters.

Boyang Cai, Qiwei Liang, Jiawei Li, Shihang Weng

Does multi-view demonstration truly improve robot manipulation, or merely enhance cross-view robustness? We present a systematic study quantifying the performance gains, scaling behavior, and underlying mechanisms of multi-view data for robot manipulation. Controlled experiments show that, under both fixed and randomized backgrounds, multi-view demonstration

Tarcísio N. Teles, Renato Pakter, Yan Levin

We comment on the recent work by Yamaguchi and Barr\'e [Phys. Rev. E 107, 054203 (2023)], which uses linear stability analysis of the Vlasov equation to characterize phase transitions in a generalized Hamiltonian Mean Field (gHMF) model. By performing extensive molecular dynamics simulations with $N=10^8$ particles, we demonstrate that the bifurcation analys

Pierrick Bousseau

We prove that all geometric helices in the derived category of coherent sheaves on a del Pezzo surface are related by a sequence of elementary operations: rotation, shifting, orthogonal reordering, tensoring by a line bundle, and tilting. As a consequence, any two non-commutative crepant resolutions of the affine cone over a del Pezzo surface are related by

Shouzhen Gu, Lily Wang, Aleksander Kubica

The decoding problem is a ubiquitous algorithmic task in fault-tolerant quantum computing, and solving it efficiently is essential for scalable quantum computing. Here, we prove that minimum-weight decoding is NP-hard in three quintessential settings: (i) the color code with Pauli $Z$ errors, (ii) the surface code with Pauli $X$, $Y$ and $Z$ errors, and (iii

Florian Chudigiewitsch, Till Tantau, Felix Winkler

A DAG compression of a (typically dense) graph is a simple data structure that stores how vertex clusters are connected, where the clusters are described indirectly as sets of reachable sinks in a directed acyclic graph (DAG). They generalize tree compressions, where the clusters form a tree-like hierarchy, and we give the first proof that DAG compressions c

Alessandro Mura, Rosaly M. C. Lopes, Federico Tosi, Peter J. Mouginis-Mark

Recent observations by the JIRAM instrument onboard NASA's Juno mission have confirmed that many of Io's volcanic hot spots are active lava lakes, characterized by a colder central crust surrounded by a hotter peripheral ring. In this study, we investigate the thermal properties of thirty such lava lakes, providing new constraints on their structure and ener

Valentin Petrov

Inasmuch as the removal of refusal behavior from instruction-tuned language models by directional abliteration requires the extraction of refusal-mediating directions from the residual stream activation space, and inasmuch as the construction of the contrast baseline against which harmful prompt activations are compared has been treated in the existing liter

Wen Yang, Wei Wang

Accreting neutron stars exhibit pulsed X-rays and complex temporal variability across multi-wavelengths and different timescales. This variability could be driven by various physical processes including instability or inhomogeneous motions within the accretion flow, thermonuclear bursts on the neutron star surface. In this review, we present a concise overvi

Masashi Sekine

This thesis develops equilibrium asset pricing models in incomplete markets with a large number of heterogeneous agents using mean field game theory. The market equilibrium is characterized by a novel form of mean field backward stochastic differential equations (BSDEs). First, we propose a theoretical model that endogenously derives the equilibrium risk pre

Byungwoo Jeon, Dongyoung Kim, Huiwon Jang, Insoo Kim

Despite the remarkable success of large-scale pre-trained image representation models (i.e., vision encoders) across various vision tasks, they are predominantly trained on 2D image data and therefore often fail to capture 3D spatial relationships between objects and backgrounds in the real world, constraining their effectiveness in many downstream applicati

M. M. Chaichian, M. Gogberashvili, M. N. Mnatsakanova, T. Tsiskaridze

We propose a novel model in which the Universe is created as a pair of coordinate-reversed counterparts, forming a globally CPT-symmetric system that permits local CPT violations within each sector. This framework naturally introduces a mirror universe with opposite chiralities and reversed microscopic time coordinates, providing a geometric interpretation o

Wuyang Luo, Chengkai Tan, Chang Ge, Binye Hong

Artistic font generation aims to synthesize stylized glyphs based on a reference style. However, existing approaches suffer from limited style diversity and coarse control. In this work, we explore the potential of element-driven artistic font generation. Elements are the fundamental visual units of a font, serving as reference images for the desired style.

Risa Shinoda, Kaede Shiohara, Nakamasa Inoue, Hiroaki Santo

Animal vocalizations provide crucial insights for wildlife assessment, particularly in complex environments such as forests, aiding species identification and ecological monitoring. Recent advances in deep learning have enabled automatic species classification from their vocalizations. However, classifying species unseen during training remains challenging.

Lu Chen, Jiali Lan

The theory of sharp geometric inequality in $\mathbb{R}^n$ and inside convex cone has been well-developed, much less known for sharp capillary geometric inequality outside convex domain. Recently, Fusco-Julin-Morini-Pratelli \cite{FJMP} obtained sharp capillary isoperimetric inequality and make it possible to obtain the sharp capillary geometric inequality o

K. O. Nikolaev, D. Raskhodchikov, J. Bensmann, I. V. Borisenko

We study experimentally the nonlinear propagation of short pulses of forward volume spin waves in nanometer-thick YIG films. We show that nonlinearity of the spin system can efficiently counteract dispersion broadening of the pulses, leading to the formation of envelope solitons. We demonstrate that in microscopic YIG systems, microwave powers of the order o

Andreas Thom

Let G be a finitely generated group, let $\pi \colon G \to {\rm GL}(E)$ be a uniformly bounded $c_0$-representation on a superreflexive Banach space $E$, and let $b \colon G \to E$ be a $1$-cocycle for $\pi$. Then $b$ has sublinear growth with respect to the word length. As a corollary we obtain the corresponding Hilbert space statement for strongly mixing u

Yang Xia, Siyuan Liu, Zhanqiang Liu, Weishan Zhang

The evolution of pulsed discharge behavior inside a preformed air bubble in water from the first to subsequent pulses was experimentally investigated using a synchronized needle to bubble system. A positive nanosecond high-voltage pulsed power supply, together with a pulse valve and ICCD imaging, was employed to generate reproducible preformed bubbles and to

Gergely Endrodi, Guy D. Moore, Adam Pieczynski, Alessandro Sciarra

We study QCD with three degenerate flavors of dynamical quarks using first-principles lattice simulations. For a specific choice of imaginary isospin chemical potential, this theory possesses an exact center symmetry, just like pure gauge theory. This exact symmetry is expected to be intact at low temperatures and spontaneously broken in the high-temperature

Soudeep Ghoshal, Himanshu Buckchash

Deep ConvNets suffer from gradient signal degradation as network depth increases, limiting effective feature learning in complex architectures. ResNet addressed this through residual connections, but these fixed short circuits cannot adapt to varying input complexity or selectively emphasize task-relevant features across network hierarchies. This study intro

Florian Chudigiewitsch, Marlene Gründel, Christian Komusiewicz, Nils Morawietz

A relation modification problem gets a logical structure and a natural number k as input and asks whether k modifications of the structure suffice to make it satisfy a predefined property. We provide a complete classification of the classical and parameterized complexity of relation modification problems - the latter w. r. t. the modification budget k - base

Binhong Tan, Zhaoxin Wang, Handing Wang

Text-to-Image (T2I) diffusion models have demonstrated strong generation ability, but their potential to generate unsafe content raises significant safety concerns. Existing inference-time defense methods typically perform category-agnostic token-level intervention in the text embedding space, which fails to capture malicious semantics distributed across the

Dong Heon Cho, Boyuan Chen

Differentiable simulators enable gradient-based optimization of soft robots over material parameters, control, and morphology, but accurately modeling real systems remains challenging due to the sim-to-real gap. This issue becomes more pronounced when geometry is itself a design variable. System identification reduces discrepancies by fitting global material

Trey Li

Landau theory usually treats free-energy coefficients as intrinsic parameters fixed by thermodynamic variables. We show that externally written microscale fields can survive coarse graining and enter the free-energy functional as spatially prescribed coefficient fields. This defines a nonintrinsic sector of Landau theory. The key condition is a hierarchy of

Gian Luca Buono, Stefanie Hollborn, Roland Potthast, Jörg Schäfer

Satellite observations play a critical role in numerical weather prediction where they are assimilated through an observation operator that maps model states to radiances. In the traditional Ensemble Kalman Filter, these observations are used to update the state by weighting their associated errors against model uncertainties to produce an optimal estimate.

Youwen Yuan, Xi Zhao

Reconstructing translucent objects from multi-view images is a difficult problem. Previously, researchers have used differentiable path tracing and the neural implicit field, which require relatively large computational costs. Recently, many works have achieved good reconstruction results for opaque objects based on a 3DGS pipeline with much higher efficienc

Caio Azevedo, Stefano Sabatini, Sascha Hornauer, Fabien Moutarde

To safely operate, an autonomous vehicle must know the future behavior of a potentially high number of interacting agents around it, a task often posed as multi-agent trajectory prediction. Many previous attempts to model social interactions and solve the joint prediction task either add extensive computational requirements or rely on heuristics to label mul

Wei Gong, Zhen-Ze Li, Chang Yu, Zhen Wang

Nanoscale control of optical dispersion is essential for applications ranging from miniaturized spectrometers to color printing, all of which demand broadband spectral tunability. However, the Kramers-Kronig relations impose a fundamental trade-off between dispersion and loss, strictly limiting the design ability of single-material devices across the deep ul

Albert Schimpf, Stefan Wehr, Annette Bieniusa

Erlang's dynamic typing discipline can lead to runtime errors that persist even after process restarts. Some of these runtime errors could be prevented through static type checking. While Erlang provides a type specification language, the compiler does not enforce these types, thereby limiting their role to documentation purposes. Type checking Erlang code i

Kento Kawaharazuka, Keita Yoneda, Shintaro Inoue, Temma Suzuki

Various quadruped robots have been developed to date, and thanks to reinforcement learning, they are now capable of traversing diverse types of rough terrain. In parallel, there is a growing trend of releasing these robot designs as open-source, enabling researchers to freely build and modify robots themselves. However, most existing open-source quadruped ro

Julius Kobialka, Emanuel Sommer, Chris Kolb, Juntae Kwon

Bayesian neural network (BNN) posteriors are often considered impractical for inference, as symmetries fragment them, non-identifiabilities inflate dimensionality, and weight-space priors are seen as meaningless. In this work, we study how overparametrization and priors together reshape BNN posteriors and derive implications allowing us to better understand

Hisham Sati, Urs Schreiber

The Drinfeld center fusion category $\mathcal{Z}(\mathrm{Vec}_G)$ famously models anyons in certain lattice models. Here we demonstrate how its fusion rules may also describe topological order in fractional topological insulator materials, in the vicinity of point defects in the Brillouin zone. Concretely, we prove that $\mathcal{Z}(\mathrm{Vec}_G)$ reflects

Xiangrui Luo

We introduce FA-matrices for computing ranks of vector bundles of coinvariants and conformal blocks associated with modules over vertex operator algebras on the moduli space of stable pointed curves, unifying the notions of fusion and averaging matrices and generalizing Ueno's work. To illustrate, we compute ranks of vector bundles determined by pointed VOAs

Purui Bai, Junxian Duan, Pin Wang, Jinhua Hao

Although diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models and fully exploiting their potential remain significant challenges. To address this issue, we propose ResFlow-Tuner, an image restoration framework based on the state-of-the-art flow

Leopold Mareis, Mathias Drton

Causal effect estimation often succeeds cost-constrained sequential data collection. This work considers multivariate linear front-door models with arbitrary unobserved confounding on treatment and response. We optimize the experimental design by balancing the statistical efficiency and measurement costs through partial data. The full-data efficient influenc

Da-yeon Huh

Kirchhoff index, Kf(G), introduced by Klein and Randic in 1993, represents the total effective resistances between all pairs of vertices in a graph G, where each edge is regarded as a resistor. In this paper, the Kirchhoff indices of a particular sequence of nested geometric graphs with weighted multiple edges, denoted by Gn, are investigated. A recurrence r

Dylan Possamaï, Mateo Rodriguez Polo

This paper addresses the challenge of time-inconsistent stochastic control within a continuous-time framework. Its primary focus lies in uncovering a probabilistic representation, specifically in the shape of a system of backward stochastic differential equations (BSDEs). These equations encapsulate the equilibrium value function essential for resolving case

S. Rathi, I. A. Valuev, Z. Sun, M. Heines

We present a state-of-the-art theoretical approach for computing bound-state energies in muonic atoms, incorporating improved quantum electrodynamics effects and nuclear polarization corrections with a systematic assessment of theoretical uncertainties. Our approach is based on a combination of the $Z\alpha$-expansion and the all-order formalism (Furry pictu

Tomasz Rybotycki, Tomasz Białecki, Josep Batle, Bartłomiej Zglinicki

We demonstrate violation of objective realism in quantum world using unconstrained weak measurements. Instead of limited Leggett-Garg approach with artificial bounds on the observed values, we assume two identical and indepenent weak detectors and final conditioning. The experimental verification has been performed on public quantum computers, IBM and IonQ.

Gang Long, Bo Wang, Philipp Podsiadlowski, Dongdong Liu

Massive He stars are potential candidates of type Ib/c supernova (SN) progenitors. Understanding their final fates remains a key issue in astrophysics. In this work, we investigate the evolution of He stars with initial masses from 5 $M_\odot$ to 65 $M_\odot$, focusing on the presupernova (pre-SN) core structures to assess their explodability. Our simulation

Tianxiang Xu, Xiaoyan Zhu, Xin Lai, Sizhe Dang

Ensuring consistency between research papers and their corresponding software code implementations is a fundamental prerequisite for guaranteeing the reproducibility of scientific findings and the reliability of software systems. However, this issue has received limited attention to date, particularly in the field of bioinformatics, where inconsistencies bet

Xinyan Wang, Xiaogeng Liu, Ming Pei, Chaowei Xiao

Large Reasoning Models (LRMs) often reach a correct solution before their long Chain-of-Thought trace ends, yet continue with redundant verification, repeated attempts, or unnecessary exploration that wastes computation and can even overturn the correct answer. We frame this behavior as a latent productive-to-redundant transition and show it is directly refl

Max Upravitelev, Veronika Solopova, Charlott Jakob, Premtim Sahitaj

Detecting climate disinformation narratives typically relies on fixed taxonomies, which do not accommodate emerging narratives. Thus, we re-frame narrative detection as a retrieval task: given a narrative's core message as a query, rank texts from a corpus by alignment with that narrative. This formulation requires no predefined label set and can accommodate

Xiang-Pan Duan, Lin Chen, Guo-Liang Ma, Carlos A. Salgado

Motivated by color coherence and decoherence effects in the QCD medium, we propose a theoretical framework that combines vacuum-like emissions and medium-induced radiation to study jet quenching and its dependence on jet cone sizes and substructure. In our approach, a jet produced at a hard scale $Q$ first undergoes vacuum-like evolution, as described by the

Jakob Wetzel, Javier Taboada-Gutiérrez, Matthias Roeper, Felix G. Kaps

The control and steering of light at nanometre length scales is crucial for the development of both fundamental science and nanophotonic technologies. Recent advancements have been achieved by exploiting various crystalline anisotropies, allowing for subdiffractional and diffraction-less canalisation of energy. These studies in particular benefit from stacki

Suresh Guttikonda, Maximilian Neidhardt, Vidas Raudonis, Alexander Schlaefer

Optical coherence tomography (OCT) is a non-invasive volumetric imaging modality with high spatial and temporal resolution. For imaging larger tissue structures, OCT probes need to be moved to scan the respective area. For handheld scanning, stitching of the acquired OCT volumes requires overlap to register the images. For robotic scanning and stitching, a t