November 2025 arXiv papers — page 49

Dewei Zhou, Mingwei Li, Zongxin Yang, Yu Lu

Conditional image generation enhances text-to-image synthesis with structural, spatial, or stylistic priors, but current methods face challenges in handling conflicts between sources. These include 1) input-level conflicts, where the conditioning image contradicts the text prompt, and 2) model-bias conflicts, where generative biases disrupt alignment even wh

Nitya Tiwari, Parv Maheshwari, Vidisha Agarwal

While recent work has extended CoT to multimodal settings, achieving state-of-the-art results on science question answering benchmarks like ScienceQA, the generalizability of these approaches across diverse domains remains underexplored. This work presents a comprehensive analysis of Multimodal Chain-of-Thought (Multimodal-CoT) reasoning, evaluating its effe

Manish Singh, Arpita Dayama

This work evaluates the effectiveness of spatiotemporal Graph Neural Networks (GNNs) for multi-store retail sales forecasting and compares their performance against ARIMA, LSTM, and XGBoost baselines. Using weekly sales data from 45 Walmart stores, we construct a relational forecasting framework that models inter-store dependencies through a learned adaptive

Bianka Kowalska, Halina Kwaśnicka

The black box nature of deep neural networks poses a significant challenge for the deployment of transparent and trustworthy artificial intelligence (AI) systems. With the growing presence of AI in society, it becomes increasingly important to develop methods that can explain and interpret the decisions made by these systems. To address this, mechanistic int

Lucas Li, Jean-Baptiste Puel, Florence Carton, Dounya Barrit

Perovskite solar cells are promising candidates for next-generation photovoltaics. However, their performance as multi-scale devices is determined by complex interactions between their constituent layers. This creates a vast combinatorial space of possible materials and device architectures, making the conventional experimental-based screening process slow a

Przemyslaw Chojecki

We develop a moduli-theoretic view of psychometric test batteries for AI agents and connect it explicitly to the AAI score developed previously. First, we make precise the notion of an AAI functional on a battery and set out axioms that any reasonable autonomy/general intelligence score should satisfy. Second, we show that the composite index ('AAI-Index') d

Shuai Wang, Daoan Zhang, Tianyi Bai, Shitong Shao

Humans can perceive and understand 3D space and long videos from sequential visual observations. But do vision-language models (VLMs) can? Recent work demonstrates that even state-of-the-art VLMs still struggle to understand 3D space and long videos, although they are powerful in typical vision-language tasks. Current methods often rely on specialized archit

Kyle Verrier, Achille Nazaret, Joseph Futoma, Andrew C. Miller

Whether wearable photoplethysmography (PPG) contains dietary information remains unknown. We trained a language model on 1.1M meals to predict meal descriptions from PPG, aligning PPG to text. PPG nontrivially predicts meal content; predictability decreases for PPGs farther from meals. This transfers to dietary tasks: PPG increases AUC by 11% for intake and

Nancy Lopes Garcia, Denis Araujo Luiz, Daniel Miranda Machado

We investigate rumor spreading in a generalized Maki-Thompson model with spontaneous stifling, evolving on quasi-transitive networks. Individuals are either ignorants, spreaders, or stiflers; spreaders stop by contact with other spreaders or stiflers or after an independent random waiting time sampled from a given distribution, modeling a spontaneous loss of

Antonis Psistakis

In complex systems with many compute nodes containing multiple CPUs that are coherent within each node, a key challenge is maintaining efficient and correct coherence between nodes. The Unimem system addresses this by proposing a virtualized global address space that enables such coherence, relying on the I/O Memory Management Unit (IOMMU) in each node. The

Yingjia Shang, Yi Liu, Huimin Wang, Furong Li

With the rapid advancement of retrieval-augmented vision-language models, multimodal medical retrieval-augmented generation (MMed-RAG) systems are increasingly adopted in clinical decision support. These systems enhance medical applications by performing cross-modal retrieval to integrate relevant visual and textual evidence for tasks, e.g., report generatio

Hang Ding, Xue Wang, Tian Zhou, Tao Yao

Diffusion models have recently shown promise in time series forecasting, particularly for probabilistic predictions. However, they often fail to achieve state-of-the-art point estimation performance compared to regression-based methods. This limitation stems from difficulties in providing sufficient contextual bias to track distribution shifts and in balanci

Rupam Samanta, Wojciech Broniowski

The lattice QCD results for the temperature-dependent magnetic susceptibility of the medium below the cross-over temperature are not possible to reconcile with the widely used Hadron Resonance Gas model, also amended with the physical magnetic moments of hadrons or the pion--vector-meson loops. As noticed earlier, one observes a substantially too strong diam

Mohamed Rissal Hedna, Sesugh Samuel Nder

Computer vision systems are increasingly adopted in modern logistics operations, including the estimation of trailer occupancy for planning, routing, and billing. Although effective, such systems may be vulnerable to physical adversarial attacks, particularly adversarial patches that can be printed and placed on interior surfaces. In this work, we study the

Boyuan Wu

Cooperative Multi-Agent Reinforcement Learning (MARL) faces two major design bottlenecks: crafting dense reward functions and constructing curricula that avoid local optima in high-dimensional, non-stationary environments. Existing approaches rely on fixed heuristics or use Large Language Models (LLMs) directly in the control loop, which is costly and unsuit

Angela Monti, Fasma Diele

In this work, we introduce a Z-control strategy for multi-agent systems of arbitrary order, aimed at driving the agents toward consensus in the highest-order observable state. The proposed framework supports both direct and indirect control schemes, making it applicable in scenarios where high-order derivatives such as acceleration cannot be directly manipul

Riccardo W. Maffucci

For every positive integer $n$, we find a complete classification for planar graphs according to the collection of numbers of common neighbours for every $n$-tuple of distinct vertices. Our results expand the literature on planar graphical degree sequences, that have recently been the object of renewed attention. Here we completely settle the version with no

Bocheng Feng, Ethan O. Nadler, S. Peng Oh, Suoqing Ji

Cosmological N-body simulations have long suggested that the pseudo phase-space density (PPSD), $\rho/\sigma^3$, of cold dark matter haloes follows the universal relation $\rho/\sigma^3 \propto r^{\chi}$, with $\chi \approx -1.875$, as predicted by spherical secondary-infall similarity solutions. This power law appears to hold despite the fact that neither t

Yuan Li, Zicheng Ye, Huazi Zhang, Jun Wang

In this paper, we introduce stitched polar codes, a novel generalization of Ar{\i}kan's regular polar codes. Our core methodology reconfigures the fundamental polarization process by stitching additional structures to enhance the reliability of less reliable information bits in the original code. This approach preserves the polar transformation structure and

Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Amin Ahsan Ali, AKM Mahbubur Rahman

Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, existing federated learning work largely overlooks the security risks that arise when local adaptation occurs at test time. Heterogeneous domain arrivals, diverse adaptation algorithms

Aljaž Zalar, Igor Zobovič

Let $L$ be a linear operator on univariate polynomials of bounded degree taking values in real symmetric matrices, whose moment matrix is positive semidefinite. Assume that $L$ admits a positive matrix-valued representing measure $\mu$. Any finitely atomic representing measure with the smallest sum of the ranks of the matricial masses is called minimal. In t

Dhruva Kashyap, Chaitanya Murti, Pranav K Nayak, Tanay Narshana

Open weight models, which are ubiquitous, rarely provide access to their training data or loss function. This makes modifying such models for tasks such as pruning or unlearning, which are constrained by this unavailability, an active area of research. Existing techniques typically require gradients or ground-truth labels, rendering them infeasible in settin

Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Shinjae Yoo

In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in

Parsa Hatami, Ahmadreza Majlesara, Ali Majlesi, Babak Hossein Khalaj

With the rapid growth of data volume in modern telecommunication networks and the continuous expansion of their scale, maintaining high reliability has become a critical requirement. These networks support a wide range of applications and services, including highly sensitive and mission-critical ones, which demand rapid and accurate detection and resolution

Claude Duhr, Sara Maggio

We obtain and solve the canonical differential equations for the three-loop banana integrals in dimensional regularisation when three of the four masses are equal. The K3 surface associated with the maximal cuts factorises into a product of two elliptic curves. This allows us to express the differential forms in the canonical differential equations in terms

José P. Carvalho, Anders Bodholt Nielsen, David L. Goodwin, Nino Wili

Taking inspiration from NMR spectroscopy, periodic irradiation schemes have recently shown remarkable performance when implemented into pulsed dynamic nuclear polarization (DNP) sequences. This has prompted considerable interest in development of broadband pulsed DNP sequences utilizing such schemes. On this background, most efforts have focused on solid-sta

Mike D. Schneider, Nick Huggett, Niels Linnemann

Once again, dispute has arisen over the interpretation of proposed quantum information theory experiments to probe the quantum nature of gravity by testing for gravitationally induced entanglement (GIE) between two spatially separated massive particles ([2] vs. [16, 17]; further contributions in [11, 13]). The confusion appears to reside in interpreting appl

David Stenger, Armin Lindicke, Alexander von Rohr, Sebastian Trimpe

Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial design using local optimization methods such as gradient descent. We propose local entropy search (LES), a Bayesian optimization paradigm that explicitly targets the solutions reacha

Jacob Miske, Ahyan Maya, Ahnaf Inkiad, Jeffrey Ian Lipton

Robotic surfaces traditionally use materials with a positive Poisson's ratio to push and pull on a manipulation interface. Auxetic materials with a negative Poisson's ratio may expand in multiple directions when stretched and enable conformable interfaces. Here we demonstrate reconfigurable auxetic lattices for robotic surface manipulation. Our approach enab

Minxin Chen

Many real-world bandit problems involve non-stationary reward distributions, where the optimal decision may shift due to evolving environments. However, the performance of some typical Multi-Armed Bandit (MAB) models such as Upper Confidence Bound (UCB) algorithms degrades significantly in non-stationary environments where reward distributions change over ti

Sandip Bera, Sajeev John

We introduce a topologically distinct skyrmion, termed a spin-flux skyrmion, which shares the same real-space magnetization profile as a conventional skyrmion but differs fundamentally in its underlying topological structure. This distinction originates from the path traced by its rotation matrices within the doubly connected SO(3) group manifold, leading to

Ariel Caticha

Entropic dynamics (ED) is a framework that allows one to derive quantum theory as a Hamilton-Killing flow on the cotangent bundle of a statistical manifold. These flows are such that they preserve the symplectic and the (information) metric geometries; they explain the linearity of quantum mechanics and the appearance of complex numbers. In this paper the ED

Yu-Han Chang, Nadia Daniela Rivera Torres, Santiago Figueroa Manrique, Raul A. Robles Robles

In order to transport information with topological protection, we explore experimentally the fast transport time using edge states in one-dimensional Su-Schrieffer-Heeger (SSH) chains. The transport time is investigated in both one- and two-dimensional models with topological non-trivial band structures. The fast transport is inherited with the wavefunction

Jorge Fandinno, Vladimir Lifschitz

In answer set programming, two groups of rules are considered strongly equivalent if they have the same meaning in any context. Strong equivalence of two programs can be sometimes established by deriving rules of each program from rules of the other in an appropriate deductive system. This paper shows how to extend this method of proving strong equivalence t

Yuxuan Wang, Haobin Jiang, Shiqing Yao, Ziluo Ding

Existing humanoid control systems often rely on teleoperation or modular generation pipelines that separate language understanding from physical execution. However, the former is entirely human-driven, and the latter lacks tight alignment between language commands and physical behaviors. In this paper, we present SENTINEL, a fully end-to-end language-action

Carl Lindström, Mahan Rafidashti, Maryam Fatemi, Lars Hammarstrand

Reconstructing dynamic driving scenes is essential for developing autonomous systems through sensor-realistic simulation. Although recent methods achieve high-fidelity reconstructions, they either rely on costly human annotations for object trajectories or use time-varying representations without explicit object-level decomposition, leading to intertwined st

Claudia Collarin, Matteo Fasiolo, Yannig Goude, Simon N. Wood

Transformations of covariates are widely used in applied statistics to improve interpretability and to satisfy assumptions required for valid inference. More broadly, feature engineering encompasses a wider set of practices aimed at enhancing predictive performance, and is typically performed as part of a data pre-processing step. In contrast, this paper int

Nada Bouknana, Mohsen Ahadi, Florian Kaltenberger, Robert Schmidt

Localization is increasingly becoming an integral component of wireless cellular networks. The advent of artificial intelligence (AI) and machine learning (ML) based localization algorithms presents potential for enhancing localization accuracy. Nevertheless, current standardization efforts in the third generation partnership project (3GPP) and the O-RAN All

Ayona Biswas, Arindam Mandal, Aditya Bandopadhyay, Sourav Mitra

Solar energy has enormous potential because there is a worldwide need to meet energy demands. Depleting non-renewable energy resources, increasing carbon emissions, and other environmental effects concern the scientific community to develop an alternative approach to electricity production. In this article, we present the study of a solar-powered Organic Ran

Christos-Nikolaos Zacharopoulos, Revekka Kyriakoglou

How and where does a transformer notice that a sentence has gone semantically off the rails? To explore this question, we evaluated the causal language model (phi-2) using a carefully curated corpus, with sentences that concluded plausibly or implausibly. Our analysis focused on the hidden states sampled at each model layer. To investigate how violations are

Amy K. Strong, Ali Kashani, Claus Danielson, Leila J. Bridgeman

This paper addresses the critical challenge of developing data-driven certificates for the stability and safety of unmodeled dynamical systems by leveraging a tree data structure and an upper bound of the system's Lipschitz constant. Previously, an invariant set was synthesized by iteratively expanding an initial invariant set. In contrast, this work iterati

J. R. Berlioz, D. Bracey, B. Fellenz, M. A. Ibrahim

The Muon to Electron Experiment (Mu2e) requires a uniform beam profile from the Muon Delivery Ring to meet their experimental needs. A specialized Spill Regulation System (SRS) has been developed to help achieve consistent spill uniformity. The system is based on a custom-designed carrier board featuring an Arria 10 SoC, capable of executing real-time feedba

Selena Song, Ziming Xu, Zijun Zhang, Kun Zhou

Diffusion Transformer(DiT) based video generation models have recently achieved impressive visual quality and temporal coherence, but they still frequently violate basic physical laws and commonsense dynamics, revealing a lack of explicit world knowledge. In this work, we explore how to equip them with a plug-and-play memory that injects useful world knowled

Murray Skolnick, Salvatore Torquato

Layered "mosaic" metal-halide perovskite materials display a wide-variety of microstructures that span the order-disorder spectrum and can be tuned via the composition of their constituent B-site octahedral species. Such materials are typically modeled using computationally expensive ab initio methods, but these approaches are greatly limited to small sample

Kalevi Mursula

Sunspots depict large variability during the last 100 years, a period called the Modern Maximum (MM). However, other variables are needed to study the long-term evolution, e.g., of weaker fields and different radiative emissions. Recently, the relation between sunspots and the F10.7 and F30 radio fluxes and the MgII index (proxies of EUV irradiance) was foun

CMS Collaboration

A search for pairs of light neutral pseudoscalar bosons (A) resulting from the decay of a Higgs boson is performed. The search is conducted using LHC proton-proton collision data at $\sqrt{s}$ = 13 TeV, collected with the CMS detector in 2016$-$2018 and corresponding to an integrated luminosity of 138 fb$^{-1}$. The A boson decays into a highly collimated el

Abraham Itzhak Weinberg

Emergent communication in multi-agent systems typically occurs through independent learning, resulting in slow convergence and potentially suboptimal protocols. We introduce TSLEC (Trust-Based Social Learning with Emergent Communication), a framework where agents explicitly teach successful strategies to peers, with knowledge transfer modulated by learned tr

Teresa Guallart-Naval, Robert Asiimwe, Patricia Tusiime, Mary A. Nassejje

Purpose: To demonstrate in-vivo imaging with a low-cost, low-field MRI scanner built and operated in Africa, and to show how systematic hardware and software improvements can mitigate the main operational limitations encountered in low-resource environments. Methods: A 46 mT Halbach scanner located at the Mbarara University of Science and Technology (Uganda)

Jordana Blazek, Frederick C. Harris

We consider a bipartite network of buyers and sellers, where the sellers run locally independent Progressive Second-Price (PSP) auctions, and buyers may participate in multiple auctions, forming a multi-auction market with perfect substitute. The paper develops a projection-based influence framework for decentralized PSP auctions. We formalize primary and ex

Erik P. van den Ban

We give a complete proof of the Maass-Selberg relations for Whittaker integrals on a real reductive group. These relations were asserted In unpublished work of Harish-Chandra, and proven in the basic setting of maximal parabolic parabolic subgroups. Our proof is a reduction to the mentioned basic setting. It makes use of the action of standard intertwining o

Federico Campanini, Francesca Fedele, Emine Yıldırım

Since their introduction, torsion theories have played a key role in the study of abelian and pointed categories. In representation theory, torsion theories and lattices of torsion classes of mod$ A$, for $A$ a finite-dimensional algebra, have been widely studied. The more recent definition of pretorsion theories, that can be given for any category, has expa

Alberto Pacini, Seiji Kajita, Gabriele Losi, Maria Clelia Righi

Atomistic simulations are powerful tools for investigating tribological phenomena at a fundamental level; however, simulating a tribological system remains challenging due to the multiscale nature of frictional processes. Recently, we introduced a hybrid method, QM-GF, that enables an accurate description of both interfacial chemistry and phononic dissipatio

Zhengliang Shi, Yiqun Chen, Haitao Li, Weiwei Sun

Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable outputs, which are beyond single-shot prompting or standard retrieval-augmented generation. Recently, numerous studies have explored Deep Research (DR), which aims to combine the rea

Jianhua Han, Meng Tian, Jiangtong Zhu, Fan He

Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex interactions. However, current vision-language models are weak at spatial grounding and understanding, and VLA systems built on them therefore show limited perception and localizatio

Zecheng Pan, Zhikang Chen, Ding Li, Min Zhang

Merging models fine-tuned for different tasks into a single unified model has become an increasingly important direction for building versatile, efficient multi-task systems. Existing approaches predominantly rely on parameter interpolation in weight space, which we show introduces significant distribution shift in the feature space and undermines task-speci

Federico Felizzi, Olivia Riccomi, Michele Ferramola, Francesco Andrea Causio

Large vision language models (VLMs) have achieved impressive performance on medical visual question answering benchmarks, yet their reliance on visual information remains unclear. We investigate whether frontier VLMs demonstrate genuine visual grounding when answering Italian medical questions by testing four state-of-the-art models: Claude Sonnet 4.5, GPT-4

Xurui Li, Kaisong Song, Rui Zhu, Pin-Yu Chen

Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks. Existing works tend to focus on either isolated jailbreak attacks or static defenses, neglecting the dynamic interplay between evolving threats and safeguards in real-world web contexts. To mitigate these challenges, we

Wanjiang Weng, Xiaofeng Tan, Junbo Wang, Guo-Sen Xie

Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads t

I. Bailleul, M. Hoshino, R. Takano

We prove that the laws of the BPHZ random models satisfy some transportation cost inequalities in the full subcritical regime if there is no 'variance blowup' and the law of the noise is translation invariant and satisfies some transportation cost inequality. We emphasize two consequences of this result or its proof: The automatic integrability properties of

Andrii O. Prykhodko, Ivan O. Starodub, Yaroslav Zolotaryuk

Stationary compact fluxon profiles are shown to be exact solutions of the inductively coupled and dc-biased Josephson junction ladder. Such states do not exist in the parallel Josephson junction array which is described by the standard discrete sine-Gordon equation. It is shown that there are compact fluxon and multi-fluxon states which either satisfy the to

Narinder Kumar Wadhawan

Scientific calculations involving multiplication, division, exponents, inverse exponents of real numbers, geometric mean, reciprocal, Euler number, logarithm, and antilogarithm are generally carried out using battery operated electronic calculators. In this paper, geometric methods employing properties of similar right angled triangles have been devised for

Siddhant Ray, Taveesh Sharma, Jonatas Marques, Paul Schmitt

Present day speed test tools measure peak throughput, but often fail to capture the user-perceived responsiveness of a network connection under load. Recently, platforms such as NDT, Ookla Speedtest and Cloudflare Speed Test have introduced metrics such as ``latency under load'' or ``working latency'' to fill this gap. Yet, the sensitivity of these metrics t

Michał Pilipczuk, Sylvain Schmitz, Henry Sinclair-Banks

We investigate the parameterised complexity of the classic coverability problem for vector addition systems (VAS): given a finite set of vectors $V \subseteq\mathbb{Z}^d$, an initial configuration $s\in\mathbb{N}^d$, and a target configuration $t\in\mathbb{N}^d$, decide whether starting from $s$, one can iteratively add vectors from $V$ to ultimately arrive

Abhishek Karna, Hansen S. Wu, Shailesh Chandrasekharan, Ribhu K. Kaul

Quantum Monte Carlo methods are powerful tools for studying quantum many-body systems but face difficulties in accessing excited states and in treating sign problems. We present a continuous-time path-integral Monte Carlo method for computing the low-lying spectrum of generic quantum Hamiltonians within a projection subspace. The method projects the thermal

Megan H. Martinez, Michael S. Petersen, Carrie Filion, Rashid Yaaqib

We present a data-driven method for determining the inclination and position angle (PA) of disc galaxies using a Fourier-Laguerre basis decomposition of imaging data. We define a dimensionless metric, $\eta$, that characterises the ratio of the quadrupole and monopole coefficients in the Fourier-Laguerre basis function expansion. This metric serves as a robu

Pedro L. Brito de Sá, Haroldo C. D. Lima, Carlos A. R. Herdeiro, Luís C. B. Crispino

We investigate boson stars within the framework of the self-interacting Einstein-Friedberg-Lee-Sirlin (E-FLS) model, constituted by a complex scalar field with a quartic self-interaction and a real scalar field. Our analysis explores the family of static solutions across a broad range of parameters, including the self-interaction of the complex scalar field.

Hikaru Kawai, Nobuyoshi Ohta

We study the quantum aspects of the conformal gravity in four dimensions, specifically addressing a known discrepancy in beta functions between general quadratic curvature theories and conformal gravity, which corresponds to two scalar degrees of freedom. We demonstrate that this mismatch is resolved by carefully introducing gauge-fixing and ghost terms via

Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert, Justin Carpentier

We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences. Our method discovers diverse motion patterns, ranging from trotting to galloping, robust standing policies, jumping, and handstand balancing, purely through the optimization of hig

Sergey Bolotin, Dmitry Treschev

Let $(M,g)$ be a Riemannian manifold, $\Omega\subset M$ a domain with boundary $\Gamma$, and $\phi$ a smooth function such that $\phi|_\Omega > 0$, $\ph|_\Gamma = 0$, and $\nabla\phi|_\Gamma\ne 0$. We study the geodesic flow of the metric $G=g/\phi$. The $G$-distance from any point of $\Omega$ to $\Gamma$ is finite, hence the geodesic flow is incomplete. Reg

Brent Zoomers, Florian Hahlbohm, Joni Vanherck, Lode Jorissen

3D Gaussian Splatting can exploit frustum culling and level-of-detail strategies to accelerate rendering of scenes containing a large number of primitives. However, the semi-transparent nature of Gaussians prevents the application of another highly effective technique: occlusion culling. We address this limitation by proposing a novel method to learn the vie

Ann-Sophia Müller, Moonkwang Jeong, Jiyuan Tian, Meng Zhang

Untethered magnetic manipulation of biomedical millirobots has a high potential for minimally invasive surgical applications. However, it is still challenging to exert high actuation forces on the small robots over a large distance. Permanent magnets offer stronger magnetic torques and forces than electromagnetic coils, however, feedback control is more diff

K. Aldaleh, W. Burstein, G. Garza, G. Hart

We study the Fourier ratio of a signal $f:\mathbb Z_N\to\mathbb C$, \[ \mathrm{FR}(f)\ :=\ \sqrt{N}\,\frac{\|\widehat f\|_{L^1(\mu)}}{\|\widehat f\|_{L^2(\mu)}} \ =\ \frac{\|\widehat f\|_1}{\|\widehat f\|_2}, \] as a simple scalar parameter governing Fourier-side complexity, structure, and learnability. Using the Bourgain--Talagrand theory of random subsets

Itay Cohen, Ethan Fetaya, Amir Rosenfeld

Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One subtle ability is to judge whether an image looks like a given object without being an instance of that object. We study whether vision-language models such as CLIP capture this dist

Teodora Popordanoska, Jiameng Li, Matthew B. Blaschko

Contradictory multimodal inputs are common in real-world settings, yet existing benchmarks typically assume input consistency and fail to evaluate cross-modal contradiction detection - a fundamental capability for preventing hallucinations and ensuring reliability. We introduce CLASH, a novel benchmark for multimodal contradiction detection, featuring COCO i

Ann-Sophia Müller, Moonkwang Jeong, Meng Zhang, Jiyuan Tian

Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bottlenecks. Obtaining these data from real patients and during surgery is very demanding, if even possible, due to legal, ethical, and technical challenges. It is especially difficul

Christoph Schmid, Christopher Buckreus, David Haas, Max Pröpper

By focused $30\,\mathrm{keV}$ He ion beam irradiation, epitaxially grown YBa$_2$Cu$_3$O$_7$ (YBCO) thin films can be driven from the superconducting to the insulating state with increasing irradiation dose. A properly chosen dose suppresses superconductivity down to $4\,\mathrm{K}$, while crystallinity is still preserved. With this approach we create areas o

Arnaldo de Carvalho Junior, Diego Oliveira da Cruz, Bruno da Silva Alves, Fernando da Silva Paulo Junior

This paper introduces Paraconsistent-Lib, an open-source, easy-to-use Python library for building PAL2v algorithms in reasoning and decision-making systems. Paraconsistent-Lib is designed as a general-purpose library of PAL2v standard calculations, presenting three types of results: paraconsistent analysis in one of the 12 classical lattice PAL2v regions, pa

Luiz Paulo de Oliveira, Alexandre Pinho dos Santos Souza, Carlos Gabriel da Silva Santos, Iberê Souza Ribeiro Júnior

Neutron transport inside the reflector tank of the new Brazilian nuclear reactor, called the RMB (Brazilian Multipurpose Reactor), is investigated using the stochastic Monte Carlo method. One of the main characteristics of research nuclear reactors is the core power density, which is proportional to the neutron flux generated in the fission reaction. The RMB

C. D. Leonard, S. Alam, R. Mandelbaum, M. M. Rau

$E_G$ is a summary statistic that combines cosmological observables to achieve a test of gravity that is relatively model-independent. Here, we consider the power of a measurement of $E_G$ using galaxy-galaxy lensing and galaxy clustering with sources from the Rubin Observatory's Legacy Survey of Space and Time (LSST), and lenses from the Dark Energy Spectro

E. V. Gorbar, B. E. Grinyuk, V. P. Gusynin

The nodal structure of bound-state wave functions for one-dimensional quantum systems with quartic energy-momentum dispersion and polynomial potentials is analysed by using the semiclassical approximation and variational approach. For energies of bound states, we derive the quantization condition, obtained by using the complex Wentzel method, where we take i

Mengtian Li, Shengxiang Yao, Yichen Pan, Haiyao Xiao

The efficient reconstruction of high-quality and intuitively editable human avatars presents a pressing challenge in the field of computer vision. Recent advancements, such as 3DGS, have demonstrated impressive reconstruction efficiency and rapid rendering speeds. However, intuitive local editing of these representations remains a significant challenge. In t

Jonathan Laubmann, Manuel Friedrich, Daniel Tenbrinck

We investigate nonlinear eigenproblems for a broad class of proper, closed, convex functionals in reflexive Banach spaces. We develop a dual formulation of the nonlinear eigenproblem using the Fenchel conjugate and establish an equivalence to the primal problem. Further, we introduce a duality gap and a geometric characterization of eigenvectors that apply i

Nithira Jayarathne, Naveen Basnayake, Keshawa Jayasundara, Pasindu Dodampegama

Detecting deepfake images is crucial in combating misinformation. We present a lightweight, generalizable binary classification model based on EfficientNet-B6, fine-tuned with transformation techniques to address severe class imbalances. By leveraging robust preprocessing, oversampling, and optimization strategies, our model achieves high accuracy, stability

Katia Colaneri, Federico D'Amario, Daniele Mancinelli

Given the increasing importance of environmental, social and governance (ESG) factors, particularly carbon emissions, we investigate optimal proportional portfolio insurance (PPI) strategies accounting for carbon footprint reduction. PPI strategies enable investors to mitigate downside risk while retaining the potential for upside gains. This paper aims to d

Ziye Yu, Yuqi Cai

P-wave first-motion polarity plays an important role in resolving focal mechanisms of small to moderate earthquakes (M <= 4.5). High-quality focal mechanism solutions for abundant small events can greatly improve our understanding of regional tectonics, fault geometries, and stress-field characteristics. In this study, we develop an automated focal mechanism

Lakshaditya Singh, Adwait Shelke, Divyansh Agrawal

Designing new protein structures is fundamental to computational biology, enabling advances in therapeutic molecule discovery and enzyme engineering. Existing diffusion-based generative models typically operate in Cartesian coordinate space, where adding noise disrupts strict geometric constraints such as fixed bond lengths and angles, often producing physic

Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl, Till J. Bungert

Semantic segmentation is crucial for various biomedical applications, yet its reliance on large annotated datasets presents a bottleneck due to the high cost and specialized expertise required for manual labeling. Active Learning (AL) aims to mitigate this challenge by querying only the most informative samples, thereby reducing annotation effort. However, i

Hao Wu, Liping Wang

In this paper, we study the decentralized optimization problem of minimizing a finite sum of continuously differentiable and possibly nonconvex functions over a fixed-connected undirected network. We propose a unified decentralized nonconvex algorithmic framework that includes many existing state-of-the-art gradient tracking and quasi-Newton algorithms. A ge

Yung-Hua Chen

This paper develops a Mellin-Laplace analytic framework for the fixed-shift prime correlation r_h(n) = Lambda(n) Lambda(n+h) for h not equal to 0. This sequence has no multiplicative structure, no Euler product, and no singularity at s = 1. For every compactly supported Mellin-Laplace admissible kernel W, the smoothed shifted sum S_{W,h}(N) admits an absolut

Armin MAghami Asl, Yaseen Almleaky

From al-Sufi's tenth-century observation of the Andromeda Galaxy as a "little cloud" to contemporary space missions, Islamic astronomy represents a millennium-spanning tradition of innovation and knowledge. This study traces its trajectory through three phases: the Golden Age (8th to 15th centuries), when scholars such as al-Biruni, al-Battani, and Ibn Sina

Zhaohang Wang, Junhao Hu, Chenggui Yuan

This paper investigates neutral-type McKean-Vlasov stochastic differential equations in which the drift and diffusion coefficients depend on both the segment process and its distribution. Under a one-sided Lipschitz condition on the drift coefficient, we establish a Freidlin-Wentzell-type large deviation principle for the solution process by using the extend

Marzio Galdi, Davide Cannatà, Flavia Celentano, Luigia Rizzo

Objectives. The aim of the present study was to develop a fully deep learning model to reduce the intra- and inter-operator reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction. Methods. Three orthodontists (Os) and three general dental practitioners (GDPs) classified the position of unerupted max

Mansur Ozaman

One of the most important tasks in computer vision is identifying the device using which the image was taken, useful for facilitating further comprehensive analysis of the image. This paper presents comparative analysis of three techniques used in source camera identification (SCI): Photo Response Non-Uniformity (PRNU), JPEG compression artifact analysis, an

Oliver H. E. Philcox, Kunhao Zhong, Salvatore Samuele Sirletti

To efficiently probe primordial non-Gaussianity using Cosmic Microwave Background (CMB) data, we require theoretical predictions that are factorizable, \textit{i.e.}\ those whose kinematic dependence can be separated. This property does not hold for many models, hindering their application to data. In this work, we introduce a general framework for construct

Sergey Bolotin

We prove the existence of chaotic trajectories for the two body problem on a sphere. The trajectories we construct encounter near-collisions and are similar to the second species solutions of Poincar\'e of the classical 3 body problem. The construction uses a general result on Lagrangian systems with Newtonian singularities of the potential which is based on

Alcino Cunha, Nuno Macedo

This paper proposes a technique to help choose the best formal specification candidate among a set of alternatives. Given a set of specifications, our technique generates a suite of test cases that, once classified by the user as desirable or not, narrows down the set of candidates to at most one specification. Two alternative solver-based algorithms are pro

Jeeho Shin, Kyungho Kim, Kijung Shin

Recipe recommendation has become an essential task in web-based food platforms. A central challenge is effectively leveraging rich multimodal features beyond user-recipe interactions. Our analysis shows that even simple uses of multimodal signals yield competitive performance, suggesting that systematic enhancement of these signals is highly promising. We pr

Hatim Chergui, Farhad Rezazadeh, Mehdi Bennis, Merouane Debbah

A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive tendency for large language model (LLM)-powered agents to make high-stakes decisions based on simple averages while ignoring the tail risk of extreme events. This paper proposes an unbiased, risk-aware framework for agen

Jan Pikman, Přemysl Šůcha, Jerguš Suja, Pavel Kulmon

Passive surveillance systems (PSS) detect and track objects that emit electromagnetic signals from hundreds of kilometers away. These systems have a limited number of receivers and can only observe a fraction of the frequencies of interest simultaneously. To improve its behavior, we propose the ResourceTune algorithm, which iteratively constructs optimized s

Jordy de Vries, Lukáš Gráf, Vaisakh Plakkot, Dominik Starý

We revisit scalar emissions in double beta decays of nuclei, often discussed in the context of Majoron models, in light of the latest developments on the study of neutrinoless double beta decay amplitudes from an effective field theory approach. The sensitivity of double beta decay experiments to this process is assessed through an analysis of spectral shape