November 2025 arXiv papers — page 69

Gunter Bombaerts, Bram Delisse, Uzay Kaymak

Large language models (LLMs) increasingly mediate human decision-making and behaviour. Ensuring LLM processing of moral meaning therefore has become a critical challenge. Current approaches rely predominantly on bottom-up methods such as fine-tuning and reinforcement learning from human feedback. We propose a fundamentally different approach: embedding moral

Jiajun Zhang, Shijia Luo, Ruikang Zhang, Qi Su

Humor, as both a creative human activity and a social binding mechanism, has long posed a major challenge for AI generation. Although producing humor requires complex cognitive reasoning and social understanding, theories of humor suggest that it follows learnable patterns and structures, making it theoretically possible for generative models to acquire them

Shuo Wang, Yucheng Wang, Guoxin Lian, Yongcai Wang

Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction. However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monoton

Sanjay Mishra

This paper provides a self-contained exploration of subdivisions of simplicial complexes, with emphasis on barycentric subdivision. We present formal definitions of subdivisions, show how the realization of a complex is preserved under subdivision, and illustrate these concepts with explicit examples and detailed diagrams. The paper develops the general meth

Dohyeong Kim, Ingyu Yang

For primes $p$ and $\ell$ such that $\ell$ divides $p-1$, Hirano and Morishita constructed a nonabelian Galois extension of the function field $\mathbb{F}_p(t)$ whose degree is $\ell^3$ and Galois group is of Heisenberg type. Here we analyze how primes of degree one decompose in such extensions. It amounts to investigating the decomposition of the principal

He Huang, Zixuan Hu, Dongxiao Li, Yao Xiao

Video anomaly detection (VAD) plays a vital role in real-world applications such as security surveillance, autonomous driving, and industrial monitoring. Recent advances in large pre-trained models have opened new opportunities for training-free VAD by leveraging rich prior knowledge and general reasoning capabilities. However, existing studies typically rel

Alexander B. Kaganovich

The Two-Measure theory (TMT) has been developing since 1998 and has yielded a number of highly interesting results, including those not realized in traditional field theory models. The most important advantage of TMT as an alternative theory is that, under the conditions under which all classical tests of general relativity are performed, TMT models are able

Di Wu, Liu Liu, Xueyu Yuan, Wenxiao Chen

Articulated objects are ubiquitous in daily environments, and their 3D reconstruction holds great significance across various fields. However, existing articulated object reconstruction methods typically require costly inputs such as multi-stage and multi-view observations. To address the limitations, we propose a category-agnostic articulated object reconst

Lara Hassan, Mohamed ElZeftawy, Abdulrahman Mahmoud

As the Middle East emerges as a strategic hub for artificial intelligence (AI) infrastructure, the feasibility of deploying sustainable datacenters in desert environments has become a topic of growing relevance. This paper presents an empirical study analyzing the energy consumption and carbon footprint of large language model (LLM) inference across four cou

Mustafa Cavus

Traditional boxplots are widely used for summarizing and visualizing the distribution of numerical data, yet they exhibit significant limitations when applied to skewed or heavy-tailed distributions, often leading to misclassification of outliers through swamping -- flagging typical observations as outliers -- or masking -- failing to detect true outliers. T

Manuel Meyer, Selena Barragan, Sergey Krishtopenko, Adriana Wolf

Overcoming the limitations of the von Neumann architecture requires new computational paradigms capable of solving complex problems efficiently. Quantum and neuromorphic computing rely on unconventional materials and device functionalities, yet achieving resilience to imperfections and reliable operation remains a major challenge. This has motivated growing

Ziyang Zhang, Jie Liu, Luca Mottola

The resource demands of deep neural network (DNN) models introduce significant performance challenges, especially when deployed on resource-constrained edge devices. Existing solutions like model compression often sacrifice accuracy, while specialized hardware remains costly and inflexible. Hybrid inference methods, however, typically overlook how operator c

Anubhav Srivastava, Subroto Mukerjee

Transport due to electrons in ultra-clean two dimensional systems can be hydrodynamic in nature with the momentum of the electrons being conserved in the bulk. This hydrodynamic behavior coupled with effects of Berry curvature arising from band structure can give rise to novel vortical transport coefficients relating the stress tensor to gradients in the ele

Alena Vishina, Konstantin Skokov, Hiroki Tsuchiura, Patrik Thunström

In this work, we present the first magnetization measurements of DyCo$_5$ single crystals in magnetic fields up to 14 T, spanning a temperature range up to 600 K. Our investigation reveals several unique features, including a significant magnetization anisotropy and an observed minimum in spontaneous magnetization near the compensation point, phenomena not p

Ken Wang, Zuyi Zhang, Jiuru Zhou

In this paper, we consider the Donaldson gauge functional and the twisted Aubin functionals on almost K\"ahler manifolds. As in K\"ahler geometry, we generalize the inequality between Aubin functionals.

Kushal Agrawal, Frank Xiao, Guido Bergman, Asa Cooper Stickland

The deployment of Large Language Models (LLMs) as tool-using agents causes their alignment training to manifest in new ways. Recent work finds that language models can use tools in ways that contradict the interests or explicit instructions of the user. We study LLM whistleblowing: a subset of this behavior where models disclose suspected misconduct to parti

Sébastien Quistrebert, Jean-Sébastien Lauret, Nikos Fayard

Efficient atom-photon interfaces require the controlled assembly of quantum emitters, where collective effects such as superradiance and subradiance can emerge. Recent experiments with subwavelength arrays of quantum dots have observed superradiance at room temperature, revealing a delicate competition between collective enhancement of coherent emission and

Anna A. Taranenko

The well-known quadrangle criterion states that a latin square is isotopic to the Cayley table of a group if and only if all quadrangles spanned by the same triple of symbols coincide on the fourth symbol. Gowers and Long (2020) reformulated this result in the following way: the Cayley tables of the most associative quasigroups have the maximum number of oct

Jérémie Dentan, Alexi Canesse, Davide Buscaldi, Aymen Shabou

Claim-level Uncertainty Quantification (UQ) is a promising approach to mitigate the lack of reliability in Large Language Models (LLMs). We introduce MUCH, the first claim-level UQ benchmark designed for fair and reproducible evaluation of future methods under realistic conditions. It includes 4,873 samples across four European languages (English, French, Sp

Arnau Mir, Alejandro Mus, Juan Vicente Riera

Discrete fuzzy numbers, and in particular those defined over a finite chain $L_n = \{0, \ldots, n\}$, have been effectively employed to represent linguistic information within the framework of fuzzy systems. Research on total (admissible) orderings of such types of fuzzy subsets, and specifically those belonging to the set $\mathcal{D}_1^{L_n\rightarrow Y_m}

Yijie Zhu, Rui Shao, Ziyang Liu, Jie He

Unified video and action prediction models hold great potential for robotic manipulation, as future observations offer contextual cues for planning, while actions reveal how interactions shape the environment. However, most existing approaches treat observation and action generation in a monolithic and goal-agnostic manner, often leading to semantically misa

Tim Schlippe, Matthias Wölfel, Koena Ronny Mabokela

This study investigates how cultural proximity affects the ability to detect AI-generated fake news by comparing South African participants with those from other nationalities. As large language models increasingly enable the creation of sophisticated fake news, understanding human detection capabilities becomes crucial, particularly across different cultura

Walter Virany, Austin Tripp

Molecular fingerprinting methods use hash functions to create fixed-length vector representations of molecules. However, hash collisions cause distinct substructures to be represented with the same feature, leading to overestimates in molecular similarity calculations. We investigate whether using exact fingerprints improves accuracy compared to standard com

Sarathchandra Khandavilli, Arno Förster, Lucas Visscher

We present a fragment-based framework for analyzing exciton couplings within the $GW$-Bethe-Salpeter Equation formalism using localized molecular orbitals, and assess how excitonic states in molecular dimers can be decomposed into local and charge-transfer (CT) sectors. Our localization procedure preserves orbital orthonormality via a block-diagonal unitary

Peng Chen, Jing Liang, Kang-Jia Qiao, Hui Song

Multi-robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor-intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Paret

Shinta Kasuya, Masahiro Kawasaki, Alexander Kusenko, Shunsuke Neda

We study the primordial black hole (PBH) formation from Q-balls that are non-topological solitons in scalar field theories. We develop a formula for calculating the density perturbations from the Q-ball charge distribution. We also re-examine the condition for the PBH formation in the matter-dominated era and show that the previously derived formula for supe

Tong Wang, Guanyu Yang, Nian Liu, Kai Wang

Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference efficiency and image quality compared to traditional multi-step autoregressive (AR) and diffusion models. However, despite their efficiency, VAR models often suffer from the diversi

Eman Bagheri, Stefan Becker, Philipp Schlatter

The increased friction caused by turbulence is a significant contributor to energy consumption in the fluid-transport and piping industries. Here we describe a passive approach to reduce friction: we show that a local increase in streamwise flow curvature, combined with changing the circular cross-section to an oval, relaminarizes turbulent flow in curved pi

Nanami Nakamura, Yu Nakayama, Ung Nguyen

The fundamental theorem in renormalization group flows in two dimensions is the $c$-theorem, which dictates that the number of degrees of freedom must decrease monotonically along the renormalization group flow. The $k$-theorem claims that the number of charged degrees of freedom also decreases monotonically. Here, $k$ is the current central charge defined b

Jan-Ole Koslik, Fanny Dupont, Marie Auger-Méthé, Marianne Marcoux

1. Hidden Markov models (HMMs) are powerful tools for modelling time-series data with underlying state structure. However, selecting appropriate parametric forms for the state-dependent distributions is often challenging and can lead to model misspecification. To address this, P-spline-based nonparametric estimation of state-dependent densities has been prop

Robert Krahn, Nikson Kanti Paul, Franz Gregor, Do Le Quoc

During the past few years, we have witnessed various efforts to provide confidentiality and integrity for applications running in untrusted environments such as public clouds. In most of these approaches, hardware extensions such as Intel SGX, TDX, AMD SEV, etc., are leveraged to provide encryption and integrity protection on process or VM level. Although al

Yunsung Kim, Mike Hardy, Joseph Tey, Candace Thille

AI-driven automated scoring systems offer scalable and efficient means of evaluating complex student-generated responses. Yet, despite increasing demand for transparency and interpretability, the field has yet to develop a widely accepted solution for interpretable automated scoring to be used in large-scale real-world assessments. This work takes a principl

Junming Liu, Yifei Sun, Weihua Cheng, Yujin Kang

Magnetic Resonance Imaging (MRI) plays a crucial role in brain disease diagnosis, but it is not always feasible for certain patients due to physical or clinical constraints. Recent studies attempt to synthesize MRI from Computed Tomography (CT) scans; however, low-dose protocols often result in highly sparse CT volumes with poor through-plane resolution, mak

Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu

Nowadays, with the development of multi-sensor networks, the distributed cubature Kalman filter is one of the well-known existing schemes for state estimation, for which the influence of the non-Gaussian noise, abnormal data, and communication burden are urgent challenges. In this paper, a distributed cubature Kalman filter based on adaptive minimum error en

Perrine Chassat, Juhyun Park, Nicolas Brunel

Geometric frameworks for analyzing curves are common in applications as they focus on invariant features and provide visually satisfying solutions to standard problems such as computing invariant distances, averaging curves, or registering curves. We show that for any smooth curve in R^d, d>1, the generalized curvatures associated with the Frenet-Serret equa

František Bartoš, Suzanne Hoogeveen, Alexandra Sarafoglou, Samuel Pawel

Empirical claims often rely on one population, design, and analysis. Many-analysts, multiverse, and robustness studies expose how results can vary across plausible analytic choices. Synthesizing these results, however, is nontrivial as all results are computed from the same dataset. We introduce single-dataset meta-analysis, a weighted-likelihood approach th

Samaël Mackowiak

In this paper, the local wellposedness of a general Gross-Pitaevskii equation with rough potential is proven in dimension 2. The class of rough potentials we are considering is large enough to contain the spatial white noise and thus a renormalization procedure may be needed. We first construct the associated Schr\"odinger operator from its quadratic form. T

Keying Zhu, Xingyu Zhou, Jie Yang, Le Liang

Integrated sensing and communication (ISAC) is crucial for low-altitude wireless networks (LAWNs), where the safety-critical demand for high-accuracy sensing creates a trade-off between precision and complexity for conventional methods. To address this, we propose a novel gridless sparse Bayesian learning (SBL) framework for joint super-resolution multi-targ

Felix Stoehr, Andrea Farago, Stefan Curiban, Alisdair Manning

With the exponential growth of astronomical data over time, finding the needles in the haystack is becoming increasingly difficult. The next frontier for science archives is to enable searches not only on observational metadata, but also on the content of the observations themselves. As a step in this direction, we have implemented morphological image simila

Yahan Pu, Lan Wang, Guangquan Zeng, Lizhi Xie

To investigate how the presence of baryons in simulations affects galaxy merger orbits, we compare in detail the merger timescales and orbits of the matched merger pairs in TNG100 hydrodynamical simulations and their corresponding dark-matter-only simulations, for different resolution levels. Compared with the mergers in the TNG100-1-Dark simulation without

Di Wu, Liu Liu, Anran Huang, Yuyan Liu

Articulated objects are pervasive in daily environments, such as drawers and refrigerators. Towards their part-level surface reconstruction and joint parameter estimation, REArtGS introduces a category-agnostic approach using multi-view RGB images at two different states. However, we observe that REArtGS still struggles with screw-joint or multi-part objects

Ziyuan Zheng, Qingqing Wu, Wen Chen, Weiren Zhu

Intelligent surfaces that reshape electromagnetic waves are regarded as disruptive technologies for wireless networks. However, existing designs sit at two costly extremes: dynamic reconfigurable intelligent surfaces (RISs) offer fine beam control but require dense cabling, continuous power consumption, and substantial signaling overhead, whereas low-cost st

João Victor Alves-Laurentino, Fatemeh Pazoki, Luis Felipe Sanz, Juan Antonio González

For glymes of general formula CH3O(CH2CH2O)uCH3, with u = 1, 2, 3, 4, the densities of the (2-propanol + glyme) systems at temperatures ranging from (293.15 to 303.15) K and at pressure 0.1 MPa were determined using a DSA 5000 densimeter (from Anton Paar). The corresponding excess molar volumes were calculated from these density measurements. In addition, ex

Paloma Rabaey, Adrick Tench, Stefan Heytens, Thomas Demeester

Electronic health records (EHRs) form an invaluable resource for training clinical decision support systems. To leverage the potential of such systems in high-risk applications, we need large, structured tabular datasets on which we can build transparent feature-based models. While part of the EHR already contains structured information (e.g. diagnosis codes

Song Jiang, Quan Wang

This paper investigates the stability and bifurcation of the two-dimensional viscous primitive equations with full diffusion under thermal forcing. The system governs perturbations about a motionless basic state with a linear temperature profile in a periodic channel, where the temperature is fixed at $T_0$ and $T_1$ on the bottom and upper boundaries, respe

Bhanu Pratap Paregi, Vaibhav Kumar

Recent point cloud completion models, including transformer-based, denoising-based, and other state-of-the-art approaches, generate globally plausible shapes from partial inputs but often leave local geometric inconsistencies. We propose RL-AD-Net, a reinforcement learning (RL) refinement framework that operates in the latent space of a pretrained point auto

Teng Fu, Mengyang Zhao, Ke Niu, Kaixin Peng

LVLMs have been shown to perform excellently in image-level tasks such as VQA and caption. However, in many instance-level tasks, such as visual grounding and object detection, LVLMs still show performance gaps compared to previous expert models. Meanwhile, although pedestrian tracking is a classical task, there have been a number of new topics in combining

Weiyi Lv, Ning Zhang, Hanyang Sun, Haoran Jiang

Referring Multi-Object Tracking (RMOT) extends conventional multi-object tracking (MOT) by introducing natural language references for multi-modal fusion tracking. RMOT benchmarks only describe the object's appearance, relative positions, and initial motion states. This so-called static regulation fails to capture dynamic changes of the object motion, in

Jingyun Chen, Linghan Cai, Zhikang Wang, Yi Huang

Analyzing whole-slide images (WSIs) requires an iterative, evidence-driven reasoning process that parallels how pathologists dynamically zoom, refocus, and self-correct while collecting the evidence. However, existing computational pipelines often lack this explicit reasoning trajectory, resulting in inherently opaque and unjustifiable predictions. To bridge

Jingwei Guo, Changxing Miao, Weiwei Wang, Guoqing Zhan

P\'{o}lya's conjecture on the eigenvalues of the Laplacian has been one of the core problems in spectral geometry. Building upon the recent breakthrough works on P\'{o}lya's conjecture for balls and annuli by Filonov, Levitin, Polterovich and Sher, we study several aspects of P\'{o}lya's conjecture for balls and cylinders: by refining the purely analytical p

Hanwu Li

In this paper, we study a kind of constrained backward stochastic differential equations (BSDEs) such that the nonlinear expectation of the composition of a loss function and the solution remains above zero. The existence and uniqueness result is established with the help of the Skorokhod problem and the method of contraction mapping. We provide the comparis

Wenzhuo Sun, Mingjian Liang, Wenxuan Song, Xuelian Cheng

In this paper, we propose RoomPlanner, the first fully automatic 3D room generation framework for painlessly creating realistic indoor scenes with only short text as input. Without any manual layout design or panoramic image guidance, our framework can generate explicit layout criteria for rational spatial placement. We begin by introducing a hierarchical st

Jie Ding, Shuai Ma, Xiang Wei, Xiaohua Xu

This article presents the precise asymptotical distribution of two types of critical transmission radii, defined in terms of k-connectivity and the minimum vertex degree, for random geometry graphs distributed over three-dimensional regions.

Linfeng Dong, Yuchen Yang, Hao Wu, Wei Wang

We introduce RacketVision, a novel dataset and benchmark for advancing computer vision in sports analytics, covering table tennis, tennis, and badminton. The dataset is the first to provide large-scale, fine-grained annotations for racket pose alongside traditional ball positions, enabling research into complex human-object interactions. It is designed to ta

Zhan Su, Fengran Mo, Jinghan Zhang, Yuchen Hui

Parametric Retrieval-Augmented Generation (PRAG) is a RAG approach that integrates external knowledge directly into model parameters using a LoRA adapter, aiming at reducing the inference cost compared to traditional RAG. However, current PRAG approaches adopt a \textit{one-to-one} document encoding scheme, using a dedicated LoRA adapter for each individual

Rama Krishna Boya, Mohan Kireeti Magalanadu, Azaruddin Palavalli, Rupa Ganesh Tekuri

Chest radiography remains one of the most widely used imaging modalities for thoracic diagnosis, yet increasing imaging volumes and radiologist workload continue to challenge timely interpretation. In this work, we investigate the use of MedImageInsight, a medical imaging foundational model, for automated binary classification of chest X-rays into Normal and

Raúl Bombín, Alberto S. Muzas, Alfredo Serrano Jiménez, J. Iñaki Juaristi

Two-pulse correlation experiments performed using pulses of different intensities on Pd(111) with different CO coverages showed that the CO photodesorption probability depends on whether the strong or the weak pulse arrives first to the surface, being this difference particularly large for the low-covered surface. Motivated by these experiments, we perform m

Xiangrui Xiong, Yichuan Lu, Zifei Pan, Chang Sun

The growth of Massive Open Online Courses (MOOCs) presents significant challenges for personalized learning, where concept recommendation is crucial. Existing approaches typically rely on heterogeneous information networks or knowledge graphs to capture conceptual relationships, combined with knowledge tracing models to assess learners' cognitive states. How

Wenzhang Du

Training data collected in the wild often contain noisy labels and outliers that substantially degrade the performance and reliability of deep neural networks. While data cleaning is commonly applied as a separate preprocessing stage, such two-stage pipelines neither fully exploit feedback from the downstream model nor adapt to unknown noise patterns. We pro

R. Blasco, S. Valdueza-Felip, D. Montero, M. Sun

Radio-frequency (RF) sputtering is a low-cost technique for the deposition of large-area single-phase AlInN on silicon layers with application in photovoltaic devices. Here, the effect of the Al mole fraction x from 0 to 0.56 on the structural, morphological, electrical, and optical properties of $n$ Al$_x$In$_{1-x}$N layers deposited at 550 $^\circ$C on p-S

M. Sun, R. Gómez, B. Damilano, J. M. Asensi

Here, we report the first experimental demonstration of InN nanowire solar cells deposited by RF sputtering with a bandgap energy of 1.78 eV. By adding an amorphous Si (a-Si) buffer to the n-InN/p-Si structure, we have improved the photovoltaic performance of the resulting devices while maintaining their material quality. We have firstly optimized the deposi

Gyuwon Park

Recent advances in vision-language models (VLMs) have enabled impressive multi-modal reasoning and understanding. Yet, whether these models truly grasp visual persuasion-how visual cues shape human attitudes and decisions-remains unclear. To probe this question, we construct a high-consensus dataset for binary persuasiveness judgment and introduce the taxono

Mehmet Erbay, Birgit Jacob, Timo Reis

Infinite-dimensional differential algebraic equations (short DAEs) with input and output are studied. The concepts of operator nodes and system nodes are extended to systems which additionally may include algebraic constraints. Extrapolation spaces are investigated for differential-algebraic equations, and solutions of the extrapolated DAE are characterized

Albert Piwonski, Mirsad Hadžiefendić

This work addresses the question of how generative artificial intelligence can be used to reduce the time required to set up electromagnetic simulation models. A chatbot based on a large language model is presented, enabling the automated generation of simulation models with various functional enhancements. A chatbot-driven workflow based on the large langua

Zhigang Song, Kai Chang

Originally introduced in optics, the Pancharatnam-Berry phase is a general concept of geometric phase defined for any two interfering polarization states. In electronic systems, however, its counterpart has long been overlooked due to the absence of electron polarization. Here, using large-scale first-principles calculations, we investigate the electronic st

Sufang Wang, Wei Zhang

Let $G$ be a connected graph, and let $b$ and $k$ be two positive integers with $b\equiv1$ (mod 2). A $[1,b]$-odd factor of $G$ is a spanning subgraph $F$ of $G$ with $d_F(v)\equiv1$ (mod 2) and $1\leq d_F(v)\leq b$ for every $v\in V(G)$. A graph $G$ is called $k$-critical with respect to $[1,b]$-odd factor if $G-X$ contains a $[1,b]$-odd factor for every $X

Neda Ahmadi, Ameneh Sheikhan, Corinna Kollath

We investigate scattering phenomena in a one-dimensional attractive Bose-Hubbard model with a time-periodically modulated impurity. We analyze both single-particle and pair (doublon) transmission, exploring a range of interaction strengths and drive amplitudes. Our exact numerical results reveal excellent quantitative agreement with analytical predictions in

T. Akiyama, P. Bydžovský, T. Gogami, K. Itabashi

The electroproduction of $\eta '$ mesons from a $\mathrm{^{1}H}$ target at $W=2.13~\text{GeV}$, $Q^{2} = 0.46~\left( \text{GeV}/c\right)^{2}$ and $\cos \theta^{\text{CM}}_{\gamma^{*}\eta'} \approx -1$ has been experimentally measured. The differential cross section of virtual-photoproduction has been obtained as $4.4 \pm 0.8 ~\left( \text{stat.} \right) \pm

Ishant Kohar, Aswanth Krishnan

Current AI agents excel in familiar settings, but fail sharply when faced with novel tasks with unseen vocabularies -- a core limitation of procedural memory systems. We present the first benchmark that isolates procedural memory retrieval from task execution, evaluating whether agents can recognize functionally equivalent procedures that span different obje

Yijun Yuan

In this article, we study the descent of $(\varphi,\tau)$-modules over perfectoid period rings in characteristic $p$ via Berger and Rozensztajn's theory of super-H\"{o}lder vectors. This is a generalization of their work on $(\varphi,\Gamma)$-modules. As an application, we answer a question of Caruso regarding the connection between $(\varphi,\tau)$-modules

Barbara Catinella, Luca Cortese, Jiayi Sun, Toby Brown

The Multiphase Astrophysics to Unveil the Virgo Environment (MAUVE) project is a multi-facility programme exploring how dense environments transform galaxies. Combining a VLT/MUSE P110 Large Programme and ALMA observations of 40 late-type Virgo Cluster galaxies, MAUVE resolves star formation, kinematics, and chemical enrichment within their molecular gas dis

Zhijie Chen, Xiang Chen, Ziming Li, Jiacheng Xue

Context: Software Vulnerability Assessment (SVA) plays a vital role in evaluating and ranking vulnerabilities in software systems to ensure their security and reliability. Objective: Although Large Language Models (LLMs) have recently shown remarkable potential in SVA, they still face two major limitations. First, most LLMs are trained on general-purpose cor

Rafał Maciuła, Markos Maniatis, Otto Nachtmann, Antoni Szczurek

We discuss charm associated production of the pseudoscalar Higgs boson $h''$ in the MCPM'. In our analysis we assume $m_{h''}$ = 95.4 GeV, which corresponds to an enhancement observed by the CMS collaboration in the $\gamma \gamma$ channel. As discussed recently, the MCPM' is consistent with the CMS enhancement. In this model the $h''$ Higgs boson of this ma

Ingo Siegert, Jan Nehring, Aranxa Márquez Ampudia, Matthias Busch

In recent times, discussions on social media platforms have increasingly come under scrutiny due to the proliferation of science denial and fake news. Traditional solutions, such as regulatory actions, have been implemented to mitigate the spread of misinformation; however, these measures alone are not sufficient. To complement these efforts, educational app

Michael Ruderman, Elia Brescia, Luigi P. Savastio, Paolo R. Massenio

The paper addresses the problem of estimating robustly the external load torque in rotary actuator systems, when only the generated motor drive torque and angular displacement are the available input and output. We compare, theoretically and experimentally, two sufficiently established linear observation techniques (i) reduced-order Luenberger observer and (

Xiaoye Tang

For a small quantaloid $\mathcal{Q}$, we introduce $\mathcal{M}$-(co)complete $\mathcal{Q}$-categories, i.e., (co)complete $\mathcal{Q}$-categories up to Morita equivalence, as Eilenberg--Moore algebras of the presheaf monad on the category of $\mathcal{Q}$-categories and left adjoint $\mathcal{Q}$-distributors, and characterize such $\mathcal{Q}$-categories

Qingmeng Wei, Yaqi Xu

This paper studies infinite horizon linear quadratic (LQ) mean field problems with common noise and regime switching, covering both control and game formulations. To establish a theoretical foundation for the LQ framework, we first analyze fully coupled forward-backward stochastic differential equations (FBSDEs) of conditional McKean-Vlasov type with Markovi

Jithin Krishnan

Building reliable retrieval-augmented generation (RAG) systems requires more than adding powerful components; it requires understanding how they interact. Using ablation studies on 50 queries (15 answerable, 10 edge cases, and 25 adversarial), we show that enhancements such as hybrid retrieval, ensemble verification, and adaptive thresholding provide almost

Haocun Yu, Dorotea Macri, Thomas Morling, Eleonora Polini

Quantum mechanics and general relativity are the foundational pillars of modern physics, yet experimental tests that combine the two frameworks remain rare. Measuring optical phase shifts of massless photons in a gravitational potential provides a unique quantum platform to probe gravity beyond Newtonian descriptions, but laboratory-based interferometers hav

Mohammad Mehdi Sadeghi, Mustafa Sarisaman

We establish an intuitive connection between transformation optics (TO) and the classical invariants of etendue and radiance that hasn't been made before. Through explicit application of the optical metric formulation of TO, we demonstrate that any smooth, passive, impedance-matched transformation performs as a canonical (symplectic) mapping on optical phase

Man Yiu Tsang, Tony Sit, Hoi Ying Wong

Contextual stochastic optimization is an advanced methodology to model uncertainty in the presence of contextual information during decision planning processes. Although classical methodologies focus on minimizing the expectation of a random loss, in many applications, risk-averse decision-makers may be interested in minimizing a specific quantile as a more

Kazuya Kato, Chikara Nakayama, Sampei Usui

Based on the strong analogy between the category of log mixed Hodge structures and the category ${\cal A}_X$ of $\ell$-adic nature, which we have introduced in the previous part and is closely related to the weight-monodromy conjecture, we prove the $\ell$-adic analogues of some theorems in Hodge theory related to the SL(2)-orbit theorem.

David Rohr

ALICE is the dedicated heavy ion experiment at the LHC at CERN and records lead-lead collisions at a rate of up to 50 kHz. The detector with the highest data rate of up to 3.4 TB/s is the TPC. ALICE performs the full online TPC processing corresponding to more than 95\% of the total workload on GPUs, and when there is no beam in the LHC, the online computing

Elisabeth Vogel, Peter Langendörfer

Cyber-physical systems of systems (CPSoS) are highly complex, dynamic environments in which technical, cybernetic and organisational subsystems interact closely with one another. Dynamic, continuously adaptable resilience is required to ensure their functionality under variable conditions. However, existing resilience architectures usually only deal with ada

Cong Zhang, Chunhao Cai

We consider a Cox--Ingersoll--Ross (CIR) type short rate model driven by a mixed fractional Brownian motion. Let $M=B+B^H$ be a one-dimensional mixed fractional Brownian motion with Hurst index $H>1/2$, and let $\mathbf{M}=(M,\mathbb{M}^{\mathrm{It\hat{o}}})$ denote its canonical It\^o rough path lift. We study the rough differential equation \begin{equation

Lingyan Ruan, Bin Chen, Taehyun Rhee

Consistent and natural camera lens blur is important for seamlessly blending 3D virtual objects into photographed real-scenes. Since lens blur typically varies with scene depth, the placement of virtual objects and their corresponding blur levels significantly affect the visual fidelity of mixed reality compositions. Existing pipelines often rely on camera p

Yiwen Ying, Hanjing Ye, Senzi Luo, Luyao Liu

Obstacle avoidance in complex and dynamic environments is a critical challenge for real-time robot navigation. Model-based and learning-based methods often fail in highly dynamic scenarios because traditional methods assume a static environment and cannot adapt to real-time changes, while learning-based methods rely on single-frame observations for motion co

Junjie Hao, Chun Wang, Ying Qiao, Qiuyue Zuo

Large language models and knowledge graphs offer strong potential for advancing research on historical culture by supporting the extraction, analysis, and interpretation of cultural heritage. Using Hunan's modern historical celebrities shaped by Huxiang culture as a case study, pre-trained large models can help researchers efficiently extract key information

Zi-Long Yang, Shi-Wen He, Lin-Cheng Wang, Si-Tong Jin

Bell state analysis (BSA) constitutes a foundational operation for distinguishing Bell states in numerous quantum information processing (QIP) protocols. In this work, we propose a theoretical scheme for realizing a perfect BSA tailored for polarized Bell states, with assistance from orbital angular momentum (OAM) and path entanglement. The linear-optics-bas

Guoquan Gao

We prove the geometric Bombieri-Lang conjecture for projective varieties which have finite maps to abelian varieties over function fields of characteristic 0. This generalizes the recent results of Xie-Yuan, which require either the hyperbolicity assumption or the non-isotriviality assumption. The proof builds upon their strategy for constructing entire curv

Ling Zhou, Yuhong Yang

Consider nonparametric domain adaptation for regression, which assumes the same conditional distribution of the response given the covariates but different marginal distributions of the covariates. An important goal is to understand how the source data may improve the minimax convergence rate of learning the regression function when the likelihood ratio of t

Zexi Tan, Xiaopeng Luo, Yunlin Liu, Yiqun Zhang

Multivariate Time-Series (MTS) clustering discovers intrinsic grouping patterns of temporal data samples. Although time-series provide rich discriminative information, they also contain substantial redundancy, such as steady-state machine operation records and zero-output periods of solar power generation. Such redundancy diminishes the attention given to di

Wangqian Chen, Junting Chen, Shuguang Cui

Open and intelligent radio access networks (RANs) envisioned for 6G require accurate and reusable wireless channel knowledge for intelligent inference and control. However, full-dimensional channel state information (CSI) and accurate location labels are difficult to acquire and maintain across open and multi-vendor deployments. This paper develops a self-lo

Tengxiao Liu, Zifeng Wang, Jin Miao, I-Hung Hsu

Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction. However, we found that simply increasing the tool-call budget fails to improve performance, as agents lack "budget awar

Jinhyeong Park, Shaheryar Muhammad, Seangmin Lee, Jong Taek Lee

Current face de-identification methods that replace identifiable cues in the face region with other sacrifices utilities contributing to realism, such as age and gender. To retrieve the damaged realism, we present FLUID (Face de-identification in the Latent space via Utility-preserving Identity Displacement), a single-input face de-identification framework t

Abu Kaisar Mohammad Masum, Naveed Mahmud, M. Hassan Najafi, Sercan Aygun

Fine-tuning BERT for text classification can be computationally challenging and requires careful hyper-parameter tuning. Recent studies have highlighted the potential of quantum algorithms to outperform conventional methods in machine learning and text classification tasks. In this work, we propose a hybrid approach that integrates an n-qubit quantum circuit

Xi Wang, Xianyao Ling, Kun Li, Gang Yin

The rapid progress in Generative AI and Agent technologies is profoundly transforming enterprise data management and analytics. Traditional database applications and system deployment are fundamentally impacted by AI-driven tools, such as Retrieval-Augmented Generation (RAG) and vector database technologies, which provide new pathways for semantic querying o

Patrick Amadeus Irawan, Ikhlasul Akmal Hanif, Muhammad Dehan Al Kautsar, Genta Indra Winata

Although the cultural dimension has been one of the key aspects in evaluating Vision-Language Models (VLMs), their ability to remain stable across diverse cultural inputs remains largely untested, despite being crucial to support diversity and multicultural societies. Existing evaluations often rely on benchmarks featuring only a singular cultural concept pe

Claus Metzner, Achim Schilling, Thomas Kinfe, Andreas Maier

Reservoir computers, based on large recurrent neural networks with fixed random connections, are known to perform a wide range of information processing tasks. However, the nature of data transformations within the reservoir, the interplay of input matrix, reservoir, and readout layer, as well as the effect of varying design parameters remain poorly understo

Huyuan Chen, Florica C. Cîrstea, Aleksandar Miladinovic

In this paper, we describe the set of all positive distributional $C^1(\mathbb R^N\setminus \{0\})$-solutions of elliptic equations with mixed reaction terms of the form $$ \mathbb L_{\rho,\lambda,\tau}[u]:= \Delta u-(N-2+2\rho) \frac{x\cdot \nabla u}{|x|^2} +\lambda \frac{u^\tau |\nabla u|^{1-\tau}}{|x|^{1+\tau}}=|x|^\theta u^q\quad \mbox{in } \mathbb R^N\s