December 2025 arXiv papers — page 69

Vikas Srivastava, Debasish Roy, Sihem Mesnager, Nibedita Kundu

Voting is a primary democratic activity through which voters select representatives or approve policies. Conventional paper ballot elections have several drawbacks that might compromise the fairness, effectiveness, and accessibility of the voting process. Therefore, there is an increasing need to design safer, effective, and easily accessible alternatives. E

Alireza Samadifardheris, Dirk H. J. Poot, Florian Wiesinger, Stefan Klein

Object: To present and evaluate Self-supervised Weighted Image Guided quantitative MRI Super-Resolution (SWIG qMRI SR), a physics-informed framework recovering high-resolution (HR) qMRI from a rapid low-resolution (LR) acquisition guided by routine weighted images (wMRI), without HR training targets. Materials and Methods: A CNN matches acquired wMRI to imag

Calanchi M., Tarsi C

nonlinearities and spatial weights of H\'enon type. Motivated by the symmetry-breaking phenomena observed in semilinear second-order problems -- such as those governed by the H\'enon equation -- we consider weighted functionals of the form \begin{equation*} F_m(u) = \int_B |x|^\alpha \left( e^{\sigma |u|^2} - \sum_{k=0}^m \frac{\sigma^k}{k!} |u|^{2k} \right)

Denis Mikhailapov, Vladimir Berikov

Convolutional neural networks (CNN) for multi-class segmentation of medical images are widely used today. Especially models with multiple outputs that can separately predict segmentation classes (regions) without relying on a probabilistic formulation of the segmentation of regions. These models allow for more precise segmentation by tailoring the network's

Mohammadmahdi Rahimiasl, Ynte Vanderhoydonc, Siegfried Mercelis

Accurately imputing traffic flow at unsensed locations is difficult: loop detectors provide precise but sparse measurements, speed from probe vehicles is widely available yet only weakly correlated with flow, and nearby links often exhibit strong heterophily in the scale of traffic flow (e.g., ramps vs. mainline), which breaks standard GNN assumptions. We pr

Alexey V. Bolsinov, Andrey Yu. Konyaev, Vladimir S. Matveev

We construct integrable Hamiltonian systems such that functionally independent Poisson commuting integrals are quadratic in the momenta. Unlike the classical St\"ackel setting, we allow the associated self-adjoint $(1,1)$-tensors $K_\alpha$ to be non-diagonalisable and have Jordan blocks and points where the Segre characteristic changes. Our construction is

Irina Seregina, Philippe Lalanda, German Vega

Human Activity Recognition is a foundational task in pervasive computing. While recent advances in self-supervised learning and transformer-based architectures have significantly improved HAR performance, adapting large pretrained models to new domains remains a practical challenge due to limited computational resources on target devices. This papers investi

Zhaoqian Gao, Min Yanga

Physics-informed neural networks (PINNs) have recently emerged as a prominent paradigm for solving partial differential equations (PDEs), yet their training strategies remain underexplored. While hard prioritization methods inspired by finite element methods are widely adopted, recent research suggests that easy prioritization can also be effective. Neverthe

Jan Rataj, Ludek Zajicek

The main result of the article is a complete characterization of the local structure of two-dimensional sets with positive reach in $R^d$. We also present a more elementary proof of a recent result of A. Lytchak which describes for $k\leq d$ the local structure of $k$-dimensional sets with positive reach $A$ in $R^d$ at points where the tangent cone of $A$ i

Karen Frilya Celine, Warut Suksompong, Sheung Man Yuen

Picking sequences are well-established methods for allocating indivisible goods. Among the various picking sequences, recursively balanced picking sequences -- whereby each agent picks one good in every round -- are notable for guaranteeing allocations that satisfy envy-freeness up to one good. In this paper, we compare the fairness of different recursively

Namhun Koo, Soonhak Kwon, Minwoo Ko, Byunguk Kim

Recently, several studies have shown that when $q\equiv3\pmod{4}$, for certain choices of $r$, the function $F_r(x)=x^r+x^{r+\frac{q-1}{2}}$ defined over $\Fq$ is locally-APN and has boomerang uniformity at most~$2$. In this paper, we extend these results by showing that if there is at most one $x\in \Fq$ with $\chi(x)=\chi(x+1)=1$ satisfying $(x+1)^r - x^r

Davide Mancino, Davide Rezzoli

How users adapt after being sandwiched remains unclear; this paper provides an empirical quantification. Using transaction level data from November 2024 to February 2025, enriched with mempool visibility and ZeroMEV labels, we track user outcomes after their n-th public sandwich: (i) reactivation, i.e., the resumption of on-chain activity within a 60-day win

Zhaolin Cai, Fan Li, Ziwei Zheng, Haixia Bi

Video Anomaly Detection (VAD) aims to locate events that deviate from normal patterns in videos. Traditional approaches often rely on extensive labeled data and incur high computational costs. Recent tuning-free methods based on Multimodal Large Language Models (MLLMs) offer a promising alternative by leveraging their rich world knowledge. However, these met

Steve Barrett, Anna Bruvere, Sean P. Fillingham, Catherine Rhodes

A major concern amongst AI safety practitioners is the possibility of loss of control, whereby humans lose the ability to exert control over increasingly advanced AI systems. The range of concerns is wide, spanning current day risks to future existential risks, and a range of loss of control pathways from rapid AI self-exfiltration scenarios to more gradual

Daan Di Scala, Sophie Lathouwers, Michael van Bekkum

Trustworthy Artificial Intelligence (TAI) is gaining traction due to regulations and functional benefits. While Functional TAI (FTAI) focuses on how to implement trustworthy systems, Normative TAI (NTAI) focuses on regulations that need to be enforced. However, gaps between FTAI and NTAI remain, making it difficult to assess trustworthiness of AI systems. We

Guner Dilsad Er, Sebastian Trimpe, Michael Muehlebach

Algorithms increasingly operate within complex physical, social, and engineering systems where they are exposed to disturbances, noise, and interconnections with other dynamical systems. This article extends known convergence guarantees of an algorithm operating in isolation (i.e., without disturbances) and systematically derives stability bounds and converg

Tosan Omabegho

ATPases cyclically convert chemical energy in the form of ATP gradients into directed motion inside cells. To function, ATPases rely on allosteric communication between at least two binding sites, an internal signaling mechanism that is not well understood. Here, we model an ATPase-like machine by using a system of mechanical linkages to recreate negative al

M. G. Sousa, O. Ávalos-Ovando, E. Vernek, S. E. Ulloa

We investigate the stability of topological phases in doped Kitaev-Heisenberg ladders by studying the competition with itinerant electrons and the associated charge fluctuations in a Hubbard model on a honeycomb ribbon geometry. We analyze the evolution of string order parameters, spin correlations, and charge fluctuations as functions of hopping amplitude a

Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman

Out of distribution (OOD) detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to adequately model this intra class variation, resulting in degrad

Christophe Prieur, Mircea Lazar, Bogdan Robu

In this paper we consider the limiting case of neural networks (NNs) architectures when the number of neurons in each hidden layer and the number of hidden layers tend to infinity thus forming a continuum, and we derive approximation errors as a function of the number of neurons and/or hidden layers. Firstly, we consider the case of neural networks with a si

Arthur Guijt, Dirk Thierens, Ellen Kerkhof, Jan Wiersma

Deep learning has been shown to be very capable at performing many real-world tasks. However, this performance is often dependent on the presence of large and varied datasets. In some settings, like in the medical domain, data is often fragmented across parties, and cannot be readily shared. While federated learning addresses this situation, it is a solution

Michał Czakon, Rene Poncelet

In recent years, the complete set of cross sections for Large Hadron Collider (LHC) processes ending with three resolved final states consisting of either photons or jets has been evaluated at next-to-next-to-leading order in QCD and leading order in QED. Results for three photons or three jets have only been obtained using the leading-color approximation of

Malak Sadek, Loraine Clarke, Stefania Forlini, Uta Hinrichs

COVID-related closures of public and academic libraries have underlined the importance of online platforms that provide access to digitized print-based collections. However, they also have highlighted the value of in-person handling of print artefacts for sensing and making sense of them. How do existing dominant digital platforms invite and/or discourage em

Yunhao Deng, Fanchen Kong, Xiaoling Yi, Ryan Antonio

The growing disparity between computational power and on-chip communication bandwidth is a critical bottleneck in modern Systems-on-Chip (SoCs), especially for data-parallel workloads like AI. Efficient point-to-multipoint (P2MP) data movement, such as multicast, is essential for high performance. However, native multicast support is lacking in standard inte

M. E. Egwe

Let $\h_1$ be the one-dimensional Heisenberg group. In this paper, we consider some aspects of discrete dynamical systems on $\h_1$ and give a condition for the solution of a cohomological equations on the group.

Francesco Martinelli, Anouk Droux, Claude Ederer

We establish a quantitative relation between the altermagnetic spin-splitting and different higher order multipoles of the charge and magnetization density around the magnetic atoms. Magnetic multipoles such as octupoles or triakontadipoles have been suggested as potential ferroic order parameters for d- and g-wave altermagnetism, respectively, based mainly

Mahesh Keswani, Raunak Bhattacharyya

Safe reinforcement learning (SafeRL) is a prominent paradigm for autonomous driving, where agents are required to optimize performance under strict safety requirements. This dual objective creates a fundamental tension, as overly conservative policies limit driving efficiency while aggressive exploration risks safety violations. The Safety Representations fo

N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain

This work introduces SkinGenBench, a systematic biomedical imaging benchmark that investigates how preprocessing complexity interacts with generative model choice for synthetic dermoscopic image augmentation and downstream melanoma diagnosis. Using a curated dataset of $14,116$ dermoscopic images from HAM10000 and MILK10K across five lesion classes, we evalu

Christian Cella, Sole Ester Sonnino, Marco Faroni, Andrea Zanchettin

The growing integration of mobile robots in shared workspaces requires efficient path planning and coordination between the agents, accounting for safety and productivity. In this work, we propose a digital model-based optimization framework for mobile manipulators in human-robot collaborative environments, in order to determine the sequence of robot base po

Vignesh Sundaramurthy, Abhi Suthar, Pascal Puphal, Congcong Le

Ruddlesden-Popper (RP) nickelates have attracted intense interest following the discovery of superconductivity in several members of the series, including bilayer (BL) La$_3$Ni$_2$O$_7$, trilayer (TL) La$_4$Ni$_3$O$_{10}$, and structural polymorphs composed of monolayer-bilayer or monolayer-trilayer (ML-TL) units. However, an inherent propensity of the RP se

James Hancock, Matthew Craven, Craig McNeile

This study investigates quantum computing approaches for solving the windfarm layout optimization (WFLO) problems formulated as a quadratic unconstrained binary optimization (QUBO) problem. We investigate two encoding methods that require fewer than one qubit per grid point: the previously developed Pauli correlation encoding (PCE) and a novel single-qubit o

Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda, A. Emre Kavur

Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. However, it remains unclear whether these benchmarks provide data that are sufficiently representative, accessible, and reusable to support clinically meaningful AI. In this work, we

Mei Zhao, Lijia Jiang, Tao Yang, Jun-Hui Zheng

We present a comprehensive theoretical study of linear wave scattering from magnetic domain walls with varied twist angles $\Theta$ in spin-$1/2$ Bose-Einstein condensates (BECs). Using a gauge transformation, we show that scattering observables depend solely on the total twist $\Theta$, independent of chirality. Within the Bogoliubov-de Gennes (BdG) framewo

Marco Faroni, Alessio Spanò, Andrea M. Zanchettin, Paolo Rocco

In Human-Robot Collaboration, safety mechanisms such as Speed and Separation Monitoring and Power and Force Limitation dynamically adjust the robot's speed based on human proximity. While essential for risk reduction, these mechanisms introduce slowdowns that makes cycle time estimation a hard task and impact job scheduling efficiency. Existing methods for e

Ge Wang, Xing Liu, Xin Yuan

Video snapshot compressive imaging (SCI) captures dynamic scene sequences through a two-dimensional (2D) snapshot, fundamentally relying on optical modulation for hardware compression and the corresponding software reconstruction. While mainstream video SCI using random binary modulation has demonstrated success, it inevitably results in temporal aliasing du

Nikolaos Nakis

The primary objective of this thesis is to develop novel algorithmic approaches for Graph Representation Learning of static and single-event dynamic networks. In such a direction, we focus on the family of Latent Space Models, and more specifically on the Latent Distance Model which naturally conveys important network characteristics such as homophily, trans

Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng

Multimodal large language models (MLLMs) extend LLMs with visual understanding through a three-stage pipeline: multimodal preprocessing, vision encoding, and LLM inference. While these stages enhance capability, they introduce significant system bottlenecks. First, multimodal preprocessing-especially video decoding-often dominates Time-to-First-Token (TTFT).

Qilong Wang, Xiaofan Ming, Zhenyi Lin, Jinwen Li

Virtual furniture synthesis, which seamlessly integrates reference objects into indoor scenes while maintaining geometric coherence and visual realism, holds substantial promise for home design and e-commerce applications. However, this field remains underexplored due to the scarcity of reproducible benchmarks and the limitations of existing image compositio

Zhongbin Guo, Guangchen Wang

This paper investigates a conditional mean-field type linear quadratic (LQ) optimal control problem with partial observation and regime switching, where the conditional expectations of the state and control given the history of Markov chain enter into the dynamics and cost. The exact regime of Markov chain is accessible, whereas the system state can only be

A. Puchalska, M. N. Cartier van Dissel, P. Gora, M. Iskrzyński

We present a subjective selection of methods for complex systems analysis ranging from statistical tools through numerical methods based on AI to both linear and non-linear ODEs and PDEs. All the notions apply the network structure and are presented in the context of applied problems to visualise the strengths and drawbacks of the approach. The major aim of

Mohammed Altoumaimi, V. V. Loboda

The present study provides the consideration of a mode III interface crack in one-dimentional (1D) piezoelectric quasicrystal under antiplane phonon and phason loading and inplane electric field. Due to complex function approach all required electromechanical parameters are presented through vector-functions analytic in the whole complex plane except the cra

Yishu Yin, Xuehai Qian

SSD-offloaded training offers a practical and promising approach to making LLM training cost-effective. Building on gradient accumulation with micro-batches, this paper introduces GreedySnake, a new SSD-offloaded training system that employs vertical scheduling, which executes all microbatches of a layer before proceeding to the next. Compared to existing sy

Xietao Wang Lin, Juan Ungredda, Max Butler, James Town

Bayesian optimisation has proven to be a powerful tool for expensive global black-box optimisation problems. In this paper, we propose new Bayesian optimisation variants of the popular Knowledge Gradient acquisition functions for problems with \emph{decoupled} black-box constraints, in which subsets of the objective and constraint functions may be evaluated

Kangchen Lv, Mingrui Yu, Yongyi Jia, Chenyu Zhang

Whole-body control of robotic manipulators with awareness of full-arm kinematics is crucial for many manipulation scenarios involving body collision avoidance or body-object interactions, which makes it insufficient to consider only the end-effector poses in policy learning. The typical approach for whole-arm manipulation is to learn actions in the robot's j

A. A. Araújo Filho, Wentao Liu

We investigate quantum information and thermodynamic properties of a new bumblebee black hole arising from spontaneous Lorentz symmetry breaking by analyzing near-horizon physics through complementary quantum probes. We study the degradation of quantum entanglement for field modes shared by inertial and accelerated observers in spacelike and lightlike Lorent

Mathilde Gajda Faanes, David Bouget, Asgeir S. Jakola, Timothy R. Smith

T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) scans are important for diagnosis, treatment planning and monitoring of brain tumors. Depending on the brain tumor type, the FLAIR hyperintensity volume is an important measure to asses the tumor volume or surrounding edema, and an automatic segmentation of this would be

Jasmin Jarecki, Lisa Mehner, Maximilian Mattern, Andrius Jurgilaitis

When the dimensions of structures shrink to the order of the inelastic mean free path of the energy-carrying quasi-particles, the character of energy transport changes from diffusive to ballistic. However, the point of transition remains a matter of debate. Here, we determine the dominant channel of energy transport through a nanoscale Cu layer as a function

SeaQuest Collaboration, C. H. Leung, J. Dove, K. Nagai

The Fermilab E906/SeaQuest collaboration performed measurements of the Drell-Yan process using 120 GeV proton beams bombarding liquid hydrogen and liquid deuterium targets. A combined analysis of all collected data was performed to obtain the final results for the $\sigma_{pd}/2\sigma_{pp}$ Drell-Yan cross section ratio covering the kinematic region of $0.13

Camille Poitras, Marie-Lou Gendron-Marsolais, Valeria Olivares, Yuan Li

We present a comprehensive kinematic and ionization analysis of the warm ionized filaments ($10^4$ K) in M87, the central galaxy of the Virgo cluster, using new integral field spectroscopy from MEGARA (GTC) and SITELLE (CFHT). MEGARA targets the southeastern (SE) filaments (3 kpc from the nucleus), coincident with the only known molecular gas clump, and the

Sujal Chondhekar, Vasanth Murukuri, Rushabh Vasani, Sanika Goyal

Speech enhancement methods are commonly believed to improve the performance of automatic speech recognition (ASR) in noisy environments. However, the effectiveness of these techniques cannot be taken for granted in the case of modern large-scale ASR models trained on diverse, noisy data. We present a systematic evaluation of MetricGAN-plus-voicebank denoisin

Akash Kumar Singh, Ashish Kumar Patra, Anurag K. S. V., Sai Shankar P.

We introduce a quantum algorithm to perform the Laplace transform on quantum computers. Already, the quantum Fourier transform (QFT) is the cornerstone of many quantum algorithms, but the Laplace transform or its discrete version has not seen any efficient implementation on quantum computers due to its dissipative nature and hence non-unitary dynamics. Howev

Alexander K. Chen

Practical utilization of large-scale machine learning requires a powerful compute setup, a necessity which poses a significant barrier to engagement with such artificial intelligence in more restricted system environments. While cloud computing offers a solution to weaker local environments, certain situations like training involving private or sensitive dat

M. Faroni, A. Spano, A. M. Zanchettin, P. Rocco

Ensuring human safety in collaborative robotics can compromise efficiency because traditional safety measures increase robot cycle time when human interaction is frequent. This paper proposes a safety-aware approach to mitigate efficiency losses without assuming prior knowledge of safety logic. Using a deep-learning model, the robot learns the relationship b

Maliha Tabassum, M Shamim Kaiser

Healthcare systems around the world are grappling with issues like inefficient diagnostics, rising costs, and limited access to specialists. These problems often lead to delays in treatment and poor health outcomes. Most current AI and deep learning diagnostic systems are not very interactive or transparent, making them less effective in real-world, patient-

Mustafa Bakr, Smain Amari

We solve the stationary Schr\"odinger equation for a particle confined to a 3D spherical wedge -- the region $\{(r,\theta,\phi): 0 \leq r \leq R,\, 0 \leq \theta \leq \pi,\, 0 \leq \phi \leq \Phi\}$ with Dirichlet BCs on all surfaces. This exactly solvable constrained-domain model exhibits spectral reorganisation under symmetry-breaking BCs and provides an o

CMS Collaboration

Measurements of the spin correlation coefficients in the beam basis are presented for top quark-antiquark ($\mathrm{t\bar{t}}$) systems produced in proton-proton collisions at $\sqrt{s}$ = 13 TeV collected by the CMS experiment in 2016$-$2018, and corresponding to an integrated luminosity of 138 fb$^{-1}$. The $\mathrm{t\bar{t}}$ system is reconstructed from

L. Lafforgue, N. P. Mehta, J. J. A. Houwman, F. Claude

The collisional properties of lanthanides exhibit remarkable complexity due to their many valence electrons, leading to an extraordinarily dense Feshbach spectrum showing signs of quantum chaos. Here we explore the situation of bosonic spin mixtures of erbium, adding the additional spin degree of freedom to the problem. We detect several inter- and intra-spi

Matthieu Mastio, Paul Saves, Benoit Gaudou, Nicolas Verstaevel

Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding

Pingcheng Dong, Yonghao Tan, Xuejiao Liu, Peng Luo

This work presents a 28nm 13.93mm2 CNN-Transformer accelerator for semantic segmentation, achieving 3.86-to-10.91x energy reduction over previous designs. It features a hybrid attention unit, layer-fusion scheduler, and cascaded feature-map pruner, with peak energy efficiency of 52.90TOPS/W (INT8).

Guglielmo Del Col, Väinö Karjalainen, Teemu Hakala, Yibo Zhang

Autonomous aerial navigation in dense natural environments remains challenging due to limited visibility, thin and irregular obstacles, GNSS-denied operation, and frequent perceptual degradation. This work presents an improved deep learning-based navigation framework that integrates semantically enhanced depth encoding with neural motion-primitive evaluation

K. Andrzejewski, K. Bolonek-Lasoń, P. Kosiński

Motivated by the recent rapid development of complexity theory applied to quantum mechanical processes we present the complete derivation of Nielsen's complexity of unitaries belonging to the representations of oscillator group. Our approach is based on the observation that the whole problem refers to the structure of the underlying group. The questions conc

Jacob von Holly-Ponientzietz, Alexander Hoen, Mark Turner, Ambros Gleixner

Probing is an important presolving technique in mixed-integer programming solvers. It selects binary variables, tentatively fixes them to 0 and 1, and performs propagation to deduce additional variable fixings, bound tightenings, substitutions, and implications. In this work, we propose clique probing instead of probing on individual variables, we select cli

Albert Huber, Paul Schreivogl

This work investigates the dynamics of closed quantum systems in the Bloch vector representation using methods from rigid body dynamics and the theory of integrable systems. To this end, equations of motion for Bloch components are derived from the von Neumann equation that are mathematically equivalent to equations of motion for a distribution of point mass

Chris Kapulkin, Yufeng Li

We propose a new cubical type theory, termed (self-deprecatingly) the naive cubical type theory, and study its semantics using the universe category framework, which is similar to Uemura's categories with representable morphisms. In particular, we show that this new type theory admits an interpretation in a wide variety of settings, including simplicial sets

Avishek Sarbajna, Ganesh Ghimire, Ilia Breev, Xavier Zambrana-Puyalto

Controlling light emission at the nanoscale has important applications in solid-state lighting, displays, and quantum light sources. Achieving this control requires both enhanced local electromagnetic fields to boost emission intensity and engineered radiation patterns to direct photons efficiently. Mie voids, consisting of an air cavity surrounded by a high

Yunqi Gao, Leyuan Liu, Yuhan Li, Changxin Gao

With 3D data rapidly emerging as an important form of multimedia information, 3D human mesh recovery technology has also advanced accordingly. However, current methods mainly focus on handling humans wearing tight clothing and perform poorly when estimating body shapes and poses under diverse clothing, especially loose garments. To this end, we make two key

Elizaveta Iarovikova, Fedor Noskov, Georgy Sokolov, Nikolai Terekhov

In this paper, we study the famous Erd\H{o}s--S\'os forbidden intersection problem for words over an alphabet of size $m$: what is the maximal size of a subfamily $\mathcal{F}$ of $[m]^n$ that does not contain two vectors $x, y$ coinciding on exactly $t - 1$ coordinates? We answer this question provided $m \ge \operatorname{poly}(t)$ and $n \ge \operatorname

Davide Giovagnoli, Enzo Maria Merlino, Diego Moreira

We prove a quantitative inhomogeneous Hopf-Oleinik lemma for viscosity solutions of $$|\nabla u|^{\alpha}F(D^{2}u)=f $$ and, more generally, for viscosity supersolutions of $|\nabla u|^{\alpha}\,{M}^-_{\lambda,\Lambda}(D^{2}u)\le f$. The result yields linear boundary growth with universal constants depending only on the structural data. We also exhibit a cou

Andrea Di Giusto, Elisa Gorla, Alberto Ravagnani

We propose a unified theory of generalized weights for linear codes endowed with an arbitrary distance. Instead of relying on supports or anticodes, the weights of a code are defined via the intersections of the code with a chosen family of spaces, which we call a test family. The choice of test family determines the properties of the corresponding generaliz

Qijian Tian, Xin Tan, Jiayu Ying, Xuhong Wang

We present FLEG, a feed-forward network that reconstructs language-embedded 3D Gaussians from arbitrary views. Previous feed-forward language-embedded Gaussian reconstruction methods are restricted to a fixed number of input views and typically attach a language-aligned semantic embedding to each Gaussian, resulting in impractical input settings and semantic

Shuntaro Suzuki, Chia-Chun Dan Hsu, Yu Tsao, Komei Sugiura

Decoding linguistically meaningful representations from non-invasive neural recordings remains a central challenge in neural speech decoding. Among available neuroimaging modalities, magnetoencephalography (MEG) provides a safe and repeatable means of mapping speech-related cortical dynamics, yet its low signal-to-noise ratio and high temporal dimensionality

Kai Wang, Bingcheng Mao, Shuai Jia, Yujie Ding

Automating code review with Large Language Models (LLMs) shows immense promise, yet practical adoption is hampered by their lack of reliability, context-awareness, and control. To address this, we propose Specification-Grounded Code Review (SGCR), a framework that grounds LLMs in human-authored specifications to produce trustworthy and relevant feedback. SGC

A. Madathil-Pottayil, D. J. Walton, Jiachen Jiang, T. Dauser

We present a spectroscopic analysis of XMM-Newton and NuSTAR observations of the 'complex' NLS1 PG 1535+547 at redshift $z=0.038$. These observations span three epochs: 2002 and 2006 with XMM-Newton alone, covering the $0.3-10$ keV energy range, and a coordinated XMM-Newton and NuSTAR observation in 2016, covering the $0.3-60$ keV energy range. The X-ray spe

Zibin Lin, Shengli Zhang, Guofu Liao, Dacheng Tao

Autonomous AI agents lack traceable accountability mechanisms, creating a fundamental dilemma where systems must either operate as ``downgraded tools'' or risk real-world abuse. This vulnerability stems from the limitations of traditional key-based authentication, which guarantees neither the operator's physical identity nor the agent's code integrity. To br

Hamid R. Hamedi, Viačeslav Kudriašov, Mažena Mackoit-Sinkevičienė, Julius Ruseckas

We investigate a phase-controllable mechanism for generating optical torque in a five-level double-tripod (DT) atom-light coupling scheme interacting with four strong coherent control fields as well as two weak optical vortex probe beams carrying orbital angular momentum (OAM). The spatial phase gradients of the OAM-carrying probes induce a quantized torque

Atef Azaiez, David Alireza Anisi

Safety and reliability play a crucial role when designing Robotic Autonomous Systems (RAS). Early consideration of hazards, risks and mitigation actions -- already in the concept study phase -- are important steps in building a solid foundations for the subsequent steps in the system engineering life cycle. The complex nature of RAS, as well as the uncertain

Yoonkwon On, Dae Hee Kim, Sujin Kim, Yong Jin Kim

Optical spectrometers are widely used in scientific and industrial applications, and precise frequency calibration is essential for ensuring their reliable performance. Traditionally, spectrometers have been calibrated using reference gas cells or reference lamps. However, such conventional methods are not enough to meet the demands for high accuracy and sta

Zhen Yang, He Cheng, Si-Han Li

We investigate quantum entanglement and coherence for four classes of Bell-like fermionic states in the vicinity of the event horizon of a Garfinkle-Horowitz-Strominger (GHS) dilaton black hole. Contrary to the common expectation that maximally entangled states always provide superior quantum resources, our results show that their entanglement can be lower t

Christina Goldschmidt, Liam Hill

We introduce a new, relatively simple, line-breaking construction of the $\alpha$-stable tree which realises its random finite-dimensional distributions. This is a direct analogue of Aldous' line-breaking construction of the Brownian continuum random tree, which is based on an inhomogeneous Poisson process. Here, we replace the deterministic rate function fr

Jiaqi Tang, Jianmin Chen, Wei Wei, Xiaogang Xu

Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust MLLMs predominantly rely on implicit training/adaptation that focuses solely on visual encoder generalization, suffering from limited interpretability and isolated optimization. To o

Dominik Köster, Florian Porkert, Klaus Volbert

The increasing use of electric vehicles (EVs) requires efficient route planning solutions that take into account the limited range of EVs and the associated charging times, as well as the different types of charging stations. In this work, we model and solve an electric vehicle routing problem (EVRP) designed for a cross-platform navigation system for indivi

Salar Beigzad

The Forward-Forward algorithm eliminates backpropagation's memory constraints and biological implausibility through dual forward passes with positive and negative data. However, conventional implementations suffer from critical inter-layer isolation, where layers optimize goodness functions independently without leveraging collective learning dynamics. This

Guanwei Cheng

This paper investigates optimal control problems for delayed systems governed by Infinitely Anticipated Backward Stochastic Differential Equations (IABSDEs). Unlike existing frameworks limited to bounded delays, we introduce a generalized formulation utilizing $\sigma$-finite measures that accommodates both long-term memory effects and forward-looking antici

Chunyang Fu, Xiangrui Liu, Shiqi Wang, Zhu Li

Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length coding. Specifically, we employ a differen

Muhammad Haris Khan

Foundation models for protein design raise concrete biosecurity risks, yet the community lacks a simple, reproducible baseline for sequence-level hazard screening that is explicitly evaluated under homology control and runs on commodity CPUs. We introduce SafeBench-Seq, a metadata-only, reproducible benchmark and baseline classifier built entirely from publi

Miguel Valério, Fabio Tamburini, Michele Corazza

We investigate a type of lunar calendar known as lists of the 'nights of the moon', found throughout East Polynesia, including Rapa Nui (Easter Island). Using computational methods, we analyzed the lexical and structural divergence of 49 calendric lists from all major archipelagos, each containing about 30 night names. Our results, presented as a rooted phyl

Louis Gass, Giovanni Peccati

We study the rescaled nodal volume field $\xi_R$ associated with a smooth, stationary Gaussian field on $[0,R]^d$, whose covariance satisfies adequate integrability conditions. Our main theorem shows that, as $R \to \infty$, the process $\xi_R$ converges in distribution, in an appropriate space of c\`adl\`ag mappings, to a standard Brownian sheet. The proof

A. V. Nesterova, N. V. Denisova

This study compares two statistical approaches to image reconstruction in single-photon emission computed tomography (SPECT). We evaluated the widely used Ordered Subset Expectation Maximization (OSEM) algorithm and the newer Maximum a Posteriori approach with Entropy prior (MAP-Ent) approach in the context of quantifying radiopharmaceutical uptake in pathol

T. Estrada, C. Hidalgo

Since the first H-mode transitions were observed in TJ-II plasmas in 2008, an extensive experimental effort has been done aiming a better physics understanding of confinement transitions. In this paper, an overview of the main findings related to the L-H transition in TJ-II is presented including how the radial electric field is driven, which are the possibl

Nanna Berre, Kent-Andre Mardal, André Massing, Ivan Yotov

We propose a novel cut finite element method for the numerical solution of the Biot system of poroelasticity. The Biot system couples elastic deformation of a porous solid with viscous fluid flow and commonly arises on domains with complex geometries that make high-quality volumetric meshing challenging. To address this issue, we employ the cut finite elemen

Peter Lundqvist, Deeepika Venkattu, Miguel Pérez Torres, Javier Moldón

We present LOw Frequency ARray (LOFAR) studies of supernovae SN 1979C, SN 1986J, and SN 2006X, focusing on new observations from the LOFAR Two-metre Sky Survey (LoTSS) and the International LOFAR Telescope (ILT). For Type Ia SN 2006X, we derive a 3$\sigma$ upper limit of 0.7 mJy at 0.146 GHz, and using radio emission models based on the CS15DD2 explosion mod

Muhammad Haris Khan

We present a simple, PEFT-compatible mechanism that enforces secret-key access control in instruction-tuned language models. K-OTG trains on a dual-path corpus: authorized examples (prefixed with a role key) learn the task output, while unauthorized examples learn a visible block token. At inference, a pre-lm_head hook applies an orthonormal transform to the

Barış Kavas, Efe C. Balta, Lars Witte, Michael R. Tucker

This study investigates the stabilization of interlayer temperature in the laser powder bed fusion process through a novel switched layer-to-layer closed-loop feedback controller. The controller architecture aims to measure the interlayer temperature by a laterally positioned thermal camera and maintain a preset reference temperature by switching between the

Siemen Brussee, Pieter A. Valkema, Jurre A. J. Weijer, Thom Doeleman

We introduce PathBench-MIL, an open-source AutoML and benchmarking framework for multiple instance learning (MIL) in histopathology. The system automates end-to-end MIL pipeline construction, including preprocessing, feature extraction, and MIL-aggregation, and provides reproducible benchmarking of dozens of MIL models and feature extractors. PathBench-MIL i

Minsoo Kim, Matthew Brun, Andy Sun, Jip Kim

Optimal transmission switching (OTS) improves optimal power flow (OPF) by selectively opening transmission lines, but its mixed-integer formulation increases computational complexity, especially on large grids. To address this, we propose a dispatch-aware deep neural network (DA-DNN) that accelerates DC-OTS without relying on pre-solved labels, eliminating c

Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki

Medical image classification is a critical task in healthcare, enabling accurate and timely diagnosis. However, deploying deep learning models on resource-constrained edge devices presents significant challenges due to computational and memory limitations. This research investigates a resource-efficient approach to medical image classification by employing m

Sairam VCR, Rishabh Lalla, Aveen Dayal, Tejal Kulkarni

Current state-of-the-art approaches in Source-Free Object Detection (SFOD) typically rely on Mean-Teacher self-labeling. However, domain shift often reduces the detector's ability to maintain strong object-focused representations, causing high-confidence activations over background clutter. This weak object focus results in unreliable pseudo-labels from the

Youjun Deng, Hongyu Liu, Wanjing Tang, Guang-Hui Zheng

This paper investigates surface polariton resonance (SPR) in three-dimensional elastic metamaterials with nanorod geometry. The primary motivation is to surpass the physical limitations imposed by the quasi-static approximation for SPRs through anisotropic geometric design. The analysis boils down to analyzing the spectral properties of the matrix-valued ela

Vipin Kumar, Roberto Rossini, Jonas Paulsen, Anthony Mathelier

Chromatin conformation capture technologies such as Hi-C have revealed that the genome is organized in a hierarchy of structures spanning multiple scales observed at different resolutions. Current algorithms often focus on specific interaction patterns found at a specific Hi-C resolution. We present BHi-Cect 2.0, a method that leverages Hi-C data at multiple

Francois Dufour, Tomas Prieto-Rumeau

In this paper we investigate several geometric properties of the set of occupancy measures. In particular, we analyse the structure of the faces generated by a given occupancy measure, together with their relative algebraic interior. We also determine the affine hulls of these faces and describe the associated parallel linear subspaces. It is shown that thes