November 2025 arXiv papers — page 149

Dan Liu, Nikita Dvornik, Xue Liu

Deep neural networks (DNNs) are used in many applications, but their large size and high computational cost make them hard to run on devices with limited resources. Two widely used techniques to address this challenge are weight quantization, which lowers the precision of all weights, and structured sparsity, which removes unimportant weights while retaining

John E. Bravo, Jean C. Cortissoz

In this paper we extend Yau's celebrated Liouville theorem to the biharmonic case. Namely, we show that in a complete Riemannian manifold with a pole and nonnegative Ricci curvature, any biharmonic function of subquadratic growth must be harmonic, and hence, any biharmonic function of sublinear growth must be constant. Our proof relies on a new local $L^2$ e

Miguel Tierz

We obtain explicit double-contour representations for the correlation kernels of the discrete orthogonal ($\beta=1$) and symplectic ($\beta=4$) random matrix ensembles with Meixner, Charlier, and Krawtchouk weights. A single Cauchy--difference--quotient composition identity expresses all $\beta=1,4$ blocks in terms of the projection kernel and bounded ration

Gerasimos Damigos, Achilleas Santi Seisa, Nikolaos Stathoulopoulos, Sara Sandberg

This article presents a novel framework for real-time Light Detection and Ranging (LiDAR) data transmission that leverages rate-adaptive technologies and point cloud encoding methods to ensure low-latency, and low-loss data streaming. The proposed framework is intended for, but not limited to, robotic applications that require real-time data transmission ove

Thomas Moorcroft, Alberto Amo, François Copie, Stéphane Randoux

Particles subject to weak contact interactions in a finite-size lattice tend to thermalise. The Hamiltonian evolution ensures energy conservation and the final temperature is fully determined by the initial conditions. In this work we show that equilibration processes are radically different in lattices subject to discrete-step unitary evolution, which have

Arief Anbiya

Numerical methods for a two-dimensional ``bad'' Boussinesq equation: $u_{tt} = u_{xx} + u_{xxxx} + u_{yy} - 3 (u^{2})_{xx}$ are presented with good accuracy. The methods mainly depend on pseudo-spectral Fourier with a trimming of carefully chosen high-frequency Fourier modes. One method also relies on Runge-Kutta fourth order (RK4), and another method relies

Álvaro Castañeda, Verónica Poblete, Gonzalo Robledo

This paper develops a comprehensive theory generalizing exponential decay patterns for evolution processes in Banach spaces. We replace classical exponential bounds with more flexible decay rates governed by an increasing homeomorphism $h$. The core of our approach lies in constructing particular group structures induced by $h$, which allow us to define gene

Srihari R, Ayesha Taranum, Karthik, Mohammed Usman Hussain

As cyber threats continue to evolve in complexity and frequency, robust endpoint protection is essential for organizational security. This paper presents "Endpoint Security Agent: A Comprehensive Approach to Real-time System Monitoring and Threat Detection" a modular, real-time security solution for Windows endpoints. The agent leverages native tools like WM

Bhawna Mukhija, Michel Curé, Ignacio Araya, Catalina Arcos

Context. Rapid rotation in massive stars leads to gravity darkening and oblateness, significantly affecting their radiation-driven winds. These effects can alter wind dynamics and play a role in forming slowly equatorial outflowing winds. Aims. This work investigates the transition region where the fast solution (i.e. high terminal velocities) of radiation-d

Grace Guinan, Michelle A. Smeaton, Brian C. Wyatt, Steven Goldy

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscop

Amin Ebrahimi, Farzan Haddadi

Hybrid Quantum Classical (HQC) algorithms constitute one of the most effective paradigms for exploiting the computational advantages of quantum systems in large-scale numerical tasks. By operating in high-dimensional Hilbert spaces, quantum circuits enable exponential speed-ups and provide access to richer representations of cost landscapes compared to purel

Arpan Phukan, Anupam Pandey, Deepjyoti Bodo, Asif Ekbal

Multi-hop Question Generation (QG) effectively evaluates reasoning but remains confined to text; Video Question Generation (VideoQG) is limited to zero-hop questions over single segments. To address this, we introduce VideoChain, a novel Multi-hop Video Question Generation (MVQG) framework designed to generate questions that require reasoning across multiple

Elizabeth Maggie Penn, John W. Patty

This paper characterizes optimal classification when individuals adjust their behavior in response to the classification rule. We model the interaction between a designer and a population as a Stackelberg game: the designer selects a classification rule anticipating how individuals will comply, cheat, or abstain in order to obtain a favorable classification.

Sreeveni Das, Rhodri Mansell, Aarne Piha, Lukáš Flajšman

In-materio computing exploits the intrinsic physical dynamics of materials to perform complex computations, enabling low-power, real-time data processing by embedding computation directly within physical layers. Here, we demonstrate a voltage-controlled magneto-ionic device that functions as a reservoir computer capable of forecasting chaotic time series. Th

João Vitorino, Daniela Pinto, Eva Maia, Ivone Amorim

To ensure that Machine Learning (ML) models can perform a robust detection and classification of cyberattacks, it is essential to train them with high-quality datasets with relevant features. However, it can be difficult to accurately represent the complex traffic patterns of an attack, especially in Internet-of-Things (IoT) networks. This paper studies the

Chen Liu, Can Han, Weishi Xu, Yaqi Wang

Surface electromyography (sEMG)-based gesture recognition plays a critical role in human-machine interaction (HMI), particularly for rehabilitation and prosthetic control. However, sEMG-based systems often suffer from the scarcity of informative training data, leading to overfitting and poor generalization in deep learning models. Data augmentation offers a

Srihari R, Adarsha B, Mohammed Usman Hussain, Shweta Singh

Users of government employment websites commonly face engagement and accessibility challenges linked to navigational complexity, a dearth of language options, and a lack of personalized support. This paper introduces JobSphere, an AI-powered career assistant that is redefining the employment platform in Punjab called PGRKAM. JobSphere employs Retrieval-Augme

Andrey Savchenko, Oleg Kachan

Accurate forecasting of multivariate time series data remains a formidable challenge, particularly due to the growing complexity of temporal dependencies in real-world scenarios. While neural network-based models have achieved notable success in this domain, complex channel-dependent models often suffer from performance degradation compared to channel-indepe

Ruiyu Qiu, Rui Wang, Guanghui Yang, Xiang Li

Lexicographic multi-objective problems, which consist of multiple conflicting subtasks with explicit priorities, are common in real-world applications. Despite the advantages of Reinforcement Learning (RL) in single tasks, extending conventional RL methods to prioritized multiple objectives remains challenging. In particular, traditional Safe RL and Multi-Ob

Antoine Thibault Vié, Roberto Galeazzi, Dimitrios Papagergiou

The recent advancements in autonomous and remote operation of maritime vessels necessitates the development of robust and reliable communication systems to support high-bandwidth applications such as real-time monitoring, navigation, and control. Existing communication channel models, including Rayleigh and Rician fading, are inadequate to accurately describ

Zeyang Hu, Kilian Fritsch, Oleg Pronin

We demonstrate a single-stage multipass cell (MPC) compressor driven directly by a 500 nJ, 5 W, 120 fs, 10 MHz oscillator that delivers efficient spectral broadening from 800 to 1200 nm with >75% transmission. The compressed pulses approach the Fourier limit (FTL) of 8.1 fs, representing, to the best of our knowledge, the first realization of supercontinuum

Jan Alexander Koziol, Kai Phillip Schmidt

We use superconducting qubit quantum annealing devices to determine the ground state of Ising models with algebraically decaying competing long-range interactions in the thermodynamic limit. This is enabled by a unit-cell-based optimization scheme, in which the finite optimizations on each unit cell are performed using commercial quantum annealing hardware.

Yuqi Wang, Yadong Zeng, Ralf Deiterding, Jinhui Yang

This work presents a high-order finite-difference adaptive mesh refinement (AMR) framework for robust simulation of shock-turbulence interaction problems. A staggered-grid arrangement, in which solution points are stored at cell centers instead of at the vertices, is presented to address the boundary conservation issues encountered in previous studies. The k

Zhiyang Chen, Chen Zhang, Hao Fang, Runmin Cong

Underwater instance segmentation (UIS), integrating pixel-level understanding and instance-level discrimination, is a pivotal technology in marine resource exploration and ecological protection. In recent years, large-scale pretrained visual foundation models, exemplified by DINO, have advanced rapidly and demonstrated remarkable performance on complex downs

Gary Greaves, Huu An Phan

For a fixed integer $e \geqslant 3$ and $n$ large enough, we show that the number of congruence classes modulo $2^e$ of characteristic polynomials of $n \times n$ symmetric $\{\pm 1\}$-matrices with constant diagonal is equal to $2^{\binom{e-2}{2}}$ if $n$ is even or $2^{\binom{e-2}{2}+1}$ if $n$ is odd, thereby solving a conjecture of Greaves and Yatsyna fr

Victor Pacifique Rwandarwacu

Effective visualization of survival data is essential for clinician interpretation and patient communication. While Kaplan-Meier (KM) plots are widely used, Mean Residual Life (MRL) plots may offer a more intuitive display of prognosis over time. However, little is known about clinicians' knowledge and preferences regarding these alternatives. This pre-post

Eunice Chan, Hanghang Tong

With the growing adoption of AI and machine learning systems in real-world applications, ensuring their fairness has become increasingly critical. The majority of the work in algorithmic fairness focus on assessing and improving the fairness of machine learning systems. There is relatively little research on fairness vulnerability, i.e., how an AI system's f

Nicholas Taormina, Emir Bilgili, Jason Gibson, Richard Hennig

A machine learned interatomic potential for AlN was developed using the ultra-fast force field (UF3) methodology. A strong agreement with density functional theory calculations in predicting key structural and mechanical properties, including lattice constants, elastic constants, cohesive energy, and surface energies has been demonstrated. The potential was

Stanislav Selitskiy

We investigate a number of Artificial Neural Network architectures (well-known and more ``exotic'') in application to the long-term financial time-series forecasts of indexes on different global markets. The particular area of interest of this research is to examine the correlation of these indexes' behaviour in terms of Machine Learning algorithms cross-tra

D. -M. Mei, N. Budhathoki, S. A. Panamaldeniya, K. -M. Dong

We present a germanium phonon-to-charge transducer that integrates a slow-phonon phononic-crystal (PnC) region with radio-frequency quantum point-contact (RF-QPC) readout at 4 K, and we evaluate its dark-sector reach. A calibrated signal-collection model, which combines geometric guiding, propagation survival, and multiplicity-assisted primary-phonon detecti

Rakesh Saini, Nora Mohamed, Saif Al-Kuwari, Ahmed Farouk

The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising approach for solving NP hard combinatorial optimization problems on noisy intermediate-scale quantum (NISQ) hardware. However, its performance is critically dependent on the selection of the circuit depth a parameter that must be specified a priori without clear guidance. In thi

Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum

Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can be personalized, especially for high-risk individuals. While recent methods increasingly incorporate longitudinal information from prior mammograms, accurate spatial alignment across time points remains a key chal

T. J. L. C. Bakx, Laura Sommovigo, Yoichi Tamura, Renske Smit

We present an Atacama Large Millimeter/submillimeter Array (ALMA) Band 9 continuum detection ($3.3 \sigma$) of MACS0416_Y1 that confirms the suspected warm dust (91$^{+62}_{-35}$ K) of this Lyman-Break Galaxy (LBG) at $z = 8.3$ with $\log_{10} M_{\ast}/$M$_{\odot} = 9.0 \pm 0.1$. A modified black-body fit to the ALMA Bands 3 through 9 data of MACS0416_Y1 fin

Mohammadreza Bakhshizadeh Mohajer, Daniela Tuninetti, Luca Barletta

This paper derives a Ziv-Zakai Bound (ZZB) on the Mean Squared Error (MSE) for Direction-of-Arrival (DoA) estimation in co-located Multiple-Input Multiple-Output (MIMO) radar systems and provides closed-form expressions that hold for multi-target scenarios. Unlike classical results that address single-input multiple-output systems with complex Gaussian input

Zhiheng Xi, Chenyang Liao, Guanyu Li, Yajie Yang

Despite rapid development, large language models (LLMs) still encounter challenges in multi-turn decision-making tasks (i.e., agent tasks) like web shopping and browser navigation, which require making a sequence of intelligent decisions based on environmental feedback. Previous work for LLM agents typically relies on elaborate prompt engineering or fine-tun

Pier Luigi Silvestrelli, S. Subashchandrabose, Alberto Ambrosetti, Maria Clelia Righi

Transition-metal dichalcogenides (TMDs) are valuable as solid lubricants because of their layered structure, which allows for easy shearing along the basal planes. Using Density Functional Theory (DFT) we conducted a first-principles study of the sliding properties of several TMD bilayers: MoS$_2$, MoTe$_2$, WS$_2$, WSe$_2$, VS$_2$, VSe$_2$, TaS$_2$, TaSe$_2

Abhirup Chatterjee, Sobhan Kumar Sounda

Geometric Phase in Quantum Mechanics is generally formulated entirely in terms of geometric structure of the Complex Hilbert Space. We will exploit this fact in case of mixed states for three level open systems undergoing depolarization using the eight dimensional Poincare sphere in the SU(2) Polarisation picture and non unit vector rays in H3 within the lim

K. Moral Figueroa, E. Gallo, H. Jung, S. Taheri Monfared

Parton densities are obtained from a solution of the extended DGLAP-type evolution equation that includes both QCD and electroweak contributions. The equations are solved using the Parton-Branching (PB) approach, and the evolution is performed at next-to-leading order for QCD partons and leading order for electroweak bosons. The initial QCD parton distributi

Mohsen Amiri

Let $\psi(G) = \sum_{g \in G} o(g)$ denote the sum of element orders of a finite group $G$. It is known that among groups of order $n$, the cyclic group $C_n$ maximizes $\psi$. T\u{a}rn\u{a}uceanu proved that two finite abelian $p$-groups of the same order are isomorphic if and only if they have the same sum of element orders, and conjectured this for arbitr

Soyeong Jeong, Aparna Elangovan, Emine Yilmaz, Oleg Rokhlenko

Large Language Models (LLMs) have demonstrated remarkable success in conversational systems by generating human-like responses. However, they can fall short, especially when required to account for personalization or specific knowledge. In real-life settings, it is impractical to rely on users to detect these errors and request a new response. One way to add

Barnabás Deme, Jean-Baptiste Fouvry

Building upon a thermodynamic formalism, we show that self-gravitating systems in hydrostatic equilibrium with a uniform density are maximal entropy states when submitted to perturbations which are slow on dynamical timescale. We coin this phenomenon "thermodynamic blocking", given its similarity with the more general "kinetic blocking". This result underlin

Shuaimin Li, Liyang Fan, Yufang Lin, Zeyang Li

Existing paper review methods often rely on superficial manuscript features or directly on large language models (LLMs), which are prone to hallucinations, biased scoring, and limited reasoning capabilities. Moreover, these methods often fail to capture the complex argumentative reasoning and negotiation dynamics inherent in reviewer-author interactions. To

Xiaoyu Fan, Lin Guo, Ruizhen Jia, Yang Tian

Artificial Intelligence (AI)-aided drug discovery is an active research field, yet AI models often exhibit poor accuracy in regression tasks for molecular property prediction, and perform catastrophically poorly for out-of-distribution (OOD) molecules. Here, we present MolRuleLoss, a substructure-substitution-rule-informed framework that improves the accurac

Hala Alqubelat, Carlo F. Manara, Justyn Campbell-White, Monika G. Petr-Gotzens

Multiplicity in pre-main-sequence (PMS) systems shapes circumstellar and circumbinary disks, often producing features such as inner cavities, spiral arms, and gas streamers that facilitate mass transfer between the disk and stars. Consequently, accretion in eccentric close binaries is highly variable and synchronized with their orbits, producing bursts near

Qingshan Xu, Jiao Liu, Shangshu Yu, Yuxuan Wang

In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but generalize worse to future prediction. This is because existing methods ignore the intrinsic physical properties of deformable objects, resulting in the limited physical learning i

Dmytro Hospodarchuk

This article explores the design and experimentation of a neural network architecture capable of dynamically adjusting its internal structure based on the input data. The proposed model introduces a routing mechanism that allows each layer to influence how its outputs are propagated through the network, enabling iterative and adaptive computation. This conce

J. S. Martin, J. Kobus, J. Varga, A. Matter

The T-Tauri type young stellar object RY Tau exhibits a dust depleted inner cavity characteristic of a transition disk. We constrain the spatial distribution and mineralogy of dust in the RY Tau protoplanetary disk in the inner few astronomical units using spectrally resolved interferometric observations in the L, M, and N bands obtained with VLTI/MATISSE. E

Krishna Neupane, Igor Griva

Corporate insiders have control of material non-public preferential information (MNPI). Occasionally, the insiders strategically bypass legal and regulatory safeguards to exploit MNPI in their execution of securities trading. Due to a large volume of transactions a detection of unlawful insider trading becomes an arduous task for humans to examine and identi

Ly Tran Ho Khanh, Dongxuan Zhu, Man-Chung Yue, Viet Anh Nguyen

Best-of-$N$ reasoning improves the accuracy of language models in solving complex tasks by sampling multiple candidate solutions and then selecting the best one based on some criteria. A critical bottleneck for this strategy is the output diversity limit, which occurs when the model generates similar outputs despite stochastic sampling, and hence recites the

Cícero Carvalho, Hiram H. López, Rodrigo San-José

The generalized Hamming weights (GHWs) of a linear code C extend the concept of minimum distance, which is the minimum cardinality of the support of all one-dimensional subspaces of C, to the minimum cardinality of the support of all r-dimensional subspaces of the code. In this work, we introduce Cartesian square-free codes, which are linear codes generated

Masahiro Kato

This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. In our setting, we can use not only the standard triple of covariates, treatment indicator, and outcome, but also unlabeled auxiliary covariates. For this problem, we develop efficiency bounds and efficient estimators w

Arshyn Altybay, Michael Ruzhansky

We address the inverse problem of identifying a time-dependent potential coefficient in a one-dimensional diffusion equation subject to Dirichlet boundary conditions and a nonlocal integral overdetermination constraint reflecting spatially averaged measurements. After establishing well-posedness for the forward problem and deriving an a priori estimate that

Valentin Tablan, Scott Taylor, Gabriel Hurtado, Kristoffer Bernhem

The transition from human-centric to agent-centric software development practices is disrupting existing knowledge sharing environments for software developers. Traditional peer-to-peer repositories and developer communities for shared technical knowledge and best practice have witnessed dramatic drops in participation in a short period of time. At the same

Isaac Smith, Catherine Cerny, Keren Sharon, Guillaume Mahler

We present the first strong gravitational lensing model for the cluster PSZ2 G118.46+39.32 (z = 0.3967) using new NIRCam imaging from the Strong LensIng and Cluster Evolution (SLICE) JWST program. We leverage the broad coverage of the SLICE ultrawide JWST filters to identify new lensed galaxies, some of which are not visible in HST, to model the cluster's ma

Luca Bindini, Simone Giovannini, Simone Marinai, Valeria Nardoni

This work investigates the ability of Vision Large Language Models (VLLMs) to understand and interpret the structure of tables in scientific articles. Specifically, we explore whether VLLMs can infer the hierarchical structure of tables without additional processing. As a basis for our experiments we use the PubTables-1M dataset, a large-scale corpus of scie

Chase van de Geijn, Ayush Paliwal, Timo Lüddecke, Alexander S. Ecker

Transformers rely on positional encoding to compensate for the inherent permutation invariance of self-attention. Traditional approaches use absolute sinusoidal embeddings or learned positional vectors, while more recent methods emphasize relative encodings to better capture translation equivariances. In this work, we propose RollPE, a novel positional encod

Takahiro Ishikawa-Aso, Atsushi Yano, Yutaro Kobayashi, Takumi Jin

The Directed Acyclic Graph (DAG) task model for real-time scheduling finds its primary practical target in Robot Operating System 2 (ROS 2). However, ROS 2's publish/subscribe API leaves DAG precedence constraints unenforced: a callback may publish mid-execution, and multi-input callbacks let developers choose topic-matching policies. Thus preserving DAG sem

Loren Kohnfelder, Adam Shostack

Threat modeling has long guided software development work, and we consider how Public Threat Models (PTM) can convey useful security information to others. We list some early adopter precedents, explain the many benefits, address potential objections, and cite regulatory drivers. Internal threat models may not be directly suitable for disclosure so we provid

Laura Bragagnolo, Leonardo Barcellona, Stefano Ghidoni

Accurate 3D human pose estimation is fundamental for applications such as augmented reality and human-robot interaction. State-of-the-art multi-view methods learn to fuse predictions across views by training on large annotated datasets, leading to poor generalization when the test scenario differs. To overcome these limitations, we propose SkelSplat, a novel

Marco Radaelli, Claudia Benedetti, Stefano Olivares

We investigate the use of discrete-time quantum walks to sample from an almost-uniform distribution, in the absence of any external source of randomness. Integers are encoded on the vertices of a cycle graph, and a quantum walker evolves for a fixed number of steps before its position is measured and recorded. The walker is then reset to the measured site, a

Kaiyi Xu, Junchao Gong, Zhiwang Zhou, Zhangrui Li

With the advancement of meteorological instruments, abundant data has become available. Current approaches are typically focus on single-variable, single-region tasks and primarily rely on deterministic modeling. This limits unified synthesis across variables and regions, overlooks cross-variable complementarity and often leads to over-smoothed results. To a

David Towers, Ismael Gutierrez, Luis Fernandez

We introduce and investigate the solvable graph $\Gamma_\mathfrak{S}(L)$ of a finite-dimensional Lie algebra $L$ over a field $F$. The vertices are the elements outside the solvabilizer $\sol(L)$, and two vertices are adjacent whenever they generate a solvable subalgebra. After developing the basic properties of solvabilizers and $S$-Lie algebras, we establi

Vojtěch Novák, Silvie Illésová, Tomáš Bezděk, Ivan Zelinka

The optimization of Variational Quantum Eigensolver is severely challenged by finite-shot sampling noise, which distorts the cost landscape, creates false variational minima, and induces statistical bias called winner's curse. We investigate this phenomenon by benchmarking eight classical optimizers spanning gradient-based, gradient-free, and metaheuristic m

Xiang Chen, Kun Yue, Wenjie Liu, Zhenyu Zhang

Graph Contrastive Learning (GCL) has emerged as a powerful paradigm for training Graph Neural Networks (GNNs) in the absence of task-specific labels. However, its scalability on large-scale graphs is hindered by the intensive message passing mechanism of GNN and the quadratic computational complexity of contrastive loss over positive and negative node pairs.

Hoang-Hung Vo

We investigate Liouville-type results, existence, uniqueness and symmetry to the solution of nonlinear nonlocal elliptic equations of the form \[ Lu = |x|^{\gamma}\,H(u)\,G(\nabla u), \qquad x\in\R^n, \] where $L$ is a symmetric, translation-invariant, uniformly elliptic integro--differential operator of order $2s\in(0,2)$, and $H,G$ satisfy general structur

Gonzalo Camacho, Julio I. de Vicente

The existence of a maximally entangled pure state is a cornerstone result of entanglement theory that has paramount consequences in quantum information theory. A natural generalization of this property is to consider whether a notion of maximal entanglement is possible among all states with the same spectrum (where the aforementioned case of pure states corr

Masih Aminbeidokhti, Subhankar Roy, Eric Granger, Elisa Ricci

Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely underrepresented. While recent work shows that parameter-efficient fine-tuning (PEFT) methods like LoRA and AdaptFormer preserve tail-class performance on foundation models such as CLIP, we find that they do so at the cos

C. Sardón, X. Zhao

In this paper, we investigate analytic divergence-free vector fields and vector fields admitting a Jacobi multiplier on $n$-dimensional Riemannian manifolds. We first introduce a functional acting on the space of divergence-free vector fields that quantifies the fraction of the manifold foliated by ergodic invariant tori, and establish a Kolmogorov--Arnold--

Feng Guo, Yuntao Wen, Shen Gao, Junshuo Zhang

Machine unlearning, which selectively removes harmful knowledge from a pre-trained model without retraining from scratch, is crucial for addressing privacy, regulatory compliance, and ethical concerns in Large Language Models (LLMs). However, existing unlearning methods often struggle to thoroughly remove harmful knowledge, leaving residual harmful knowledge

Vishal Kumar, Shubhra Mishra, Rebecca Hao, Rizwaan Malik

Large Language Models (LLMs) are increasingly being adopted as tools for learning; however, most tools remain text-only, limiting their usefulness for domains where visualizations are essential, such as mathematics. Recent work shows that LLMs are capable of generating code that compiles to educational figures, but a major bottleneck remains: scalable evalua

Eranga Bandara, Safdar H. Bouk, Sachin Shetty, Ravi Mukkamala

Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level

Yi Cai, Thibaud Ardoin, Mayank Gulati, Gerhard Wunder

Feature attribution has gained prominence as a tool for explaining model decisions, yet evaluating explanation quality remains challenging due to the absence of ground-truth explanations. To circumvent this, explanation-guided input manipulation has emerged as an indirect evaluation strategy, measuring explanation effectiveness through the impact of input mo

Rafael Prieto-Curiel

Despite the evident drawbacks, car ownership and usage continue to rise globally, leading to increased pollution and urban sprawl. As alternatives, Active Mobility and Public Transport are promoted for their health, economic, and environmental benefits. However, the efficiency of Public Transport varies widely. Metro systems, in particular, offer a high-capa

Alan Magdaleno, Pietro Bonazzi, Tommaso Polonelli, Michele Magno

Contactless Electrooculography (EOC) using electric charge variation (QVar) sensing has recently emerged as a promising eye-tracking technique for wearable devices. QVar enables low-power and unobtrusive interaction without requiring skin-contact electrodes. Previous work demonstrated that such systems can accurately classify eye movements using onboard Tiny

Chen Zhao, Haobo Jia, Zhuqing Jia

We consider the problem of Robust Dynamic Coded Distributed Storage (RDCDS) with partially storage constrained servers where the goal is to enable robust (resilient to server dropouts) and efficient (as measured by the communication costs) read and update operations, subject to the constraint that the storage at $S$ out of $N$ servers is limited by $1/K_c$ t

Dehan Shen, Changhao Chen

Learning-based inertial odometry has achieved remarkable progress in pedestrian navigation. However, extending these methods to quadruped robots remains challenging due to their distinct and highly dynamic motion patterns. Models that perform well on pedestrian data often experience severe degradation when deployed on legged platforms. To tackle this challen

Oleg Antipin, Jahmall Bersini, Jacob Hafjall, Giulia Muco

We develop a semiclassical framework to determine scaling dimensions of neutral composite operators in scalar conformal field theories. For the critical Ising $\lambda\phi^4$ theory in $d=4-\epsilon$, we obtain the full spectrum of composite operators built out of $n$ fields transforming in the traceless-symmetric Lorentz representations to next-to-leading o

Ishara Hewa Pathiranage, Aneta Neumann

The open-pit mine scheduling problem (OPMSP) is a complex, computationally expensive process in long-term mine planning, constrained by operational and geological dependencies. Traditional deterministic approaches often ignore geological uncertainty, leading to suboptimal and potentially infeasible production schedules. Chance constraints allow modeling of s

Anton Gusarov, Anastasia Volkova, Valentin Khrulkov, Andrey Kuznetsov

While Retrieval-Augmented Generation (RAG) methods commonly draw information from unstructured documents, the emerging paradigm of GraphRAG aims to leverage structured data such as knowledge graphs. Most existing GraphRAG efforts focus on Resource Description Framework (RDF) knowledge graphs, relying on triple representations and SPARQL queries. However, the

Minh Xuan Bui, Nguyen Thien Dat, Van Hong Lam, Tran Le Anh Quan

This paper presents the analytical design of a new wide tuning range and low-noise millimeter-wave voltage control oscillators (VCO) for 5G technology. The small signal model analysis and phase noise of the VCOs will be presented to evaluate the start-up oscillation condition, oscillation frequency, and phase noise affecting factors. Theoretical analysis and

Kunjing Yang, Zhiwei Wang, Minru Bai

Image fusion aims to integrate structural and complementary information from multi-source images. However, existing fusion methods are often either highly task-specific, or general frameworks that apply uniform strategies across diverse tasks, ignoring their distinct fusion mechanisms. To address this issue, we propose a mechanism-aware unsupervised general

Sweta Banerjee, Timo Gosch, Sara Hester, Viktoria Weiss

The annotation of large scale histopathology image datasets remains a major bottleneck in developing robust deep learning models for clinically relevant tasks, such as mitotic figure classification. Folder-based annotation workflows are usually slow, fatiguing, and difficult to scale. To address these challenges, we introduce SWipeable ANnotations (SWAN), an

Daniela Iglesias, Isabel Rebollido, Azib Norazman, Colin Snodgrass

We give a general overview of what the scientific community refers to as "exocomets". The general definition of exocomets, as presented in this work, is discussed and compared with Solar System comets and interstellar objects, addressing their detection around main-sequence stars as well as orbiting white dwarfs. We introduce the different types of exocomet

Nan Bao, Yifan Zhao, Lin Zhu, Jia Li

Semantic segmentation has achieved great success in ideal conditions. However, when facing extreme conditions (e.g., insufficient light, fierce camera motion), most existing methods suffer from significant information loss of RGB, severely damaging segmentation results. Several researches exploit the high-speed and high-dynamic event modality as a complement

Vladimir U. Nazarov, E. K. U. Gross

The Exact Factorization (EF) theory aims at the separation of the nuclear and electronic degrees of freedom in the many-body (MB) quantum mechanical problem. Being formally equivalent to the solution of the MB Schr\"{o}dinger equation, EF sets up a strategy for the construction of efficient approximations in the theory of the correlated electronic-nuclear mo

Quan Fu, Xin Wang, Rui Xiong

Superconducting transmon qubits are a promising platform for quantum computation, yet they face significant fidelity degradation due to connectivity noise, particularly in the intermediate coupling regime where noise levels are substantial. While prior works suggest that high fidelity requires operating in regimes with strongly suppressed noise, maintaining

Euclid Collaboration, J. Bel, S. Gouyou Beauchamps, P. Baratta

We investigate the non-Gaussian features in the distribution of the matter power spectrum multipoles. Using the COVMOS method, we generate 100\,000 mock realisations of dark matter density fields in both real and redshift space across multiple redshifts and cosmological models. We derive an analytical framework linking the non-Gaussianity of the power spectr

Hassan Ataalite, Jiri Houska, Deepika Thakur, Michaela Cervena

We investigate the effects of W incorporation into Cu-Zr thin film metallic glasses using molecular dynamics (MD) simulations combined with magnetron sputtering. All studies are carried out in the whole range of W concentrations (0 to 100 at. %) and the MD studies also in a wide range of incident energies (1 to 500 eV) and deposition angles (0 to 60{\deg}).

Yue Min, Shaobo Wang, Jiaze Li, Tianle Niu

Data condensation techniques aim to synthesize a compact dataset from a larger one to enable efficient model training, yet while successful in unimodal settings, they often fail in multimodal scenarios where preserving intricate inter-modal dependencies is crucial. To address this, we introduce ImageBindDC, a novel data condensation framework operating withi

Ezra Gayawan, Osafu Augustine Egbon, Edson Utazi, Jamila Abubakar Umar

Immunization remains one of the most effective public health interventions, substantially reducing childhood morbidity and mortality worldwide. Yet, gender disparity and women's disempowerment continue to hinder access to vaccination services in low- and middle-income countries. In Nigeria, variations in social norms and cultural values shape gender roles, l

Raphael Schwinger, Ben McEwen, Vincent S. Kather, René Heinrich

Passive acoustic monitoring enables large-scale biodiversity assessment, but reliable classification of bioacoustic sounds requires not only high accuracy but also well-calibrated uncertainty estimates to ground decision-making. In bioacoustics, calibration is challenged by overlapping vocalisations, long-tailed species distributions, and distribution shifts

Fedor Sergeev, Manuel Burger, Polina Leshetkina, Vincent Fortuin

Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous features from different data sources. The grouping of features based on similarity and relevance to the prediction task has been shown to enhance the performance of deep learning arch

Zhenglei Li, Qigang Liang, Xuejun Xu

In this work, we propose an a pointwise a posteriori error estimator for conforming finite element approximations of eigenfunctions corresponding to multiple and clustered eigenvalues of elliptic operators. It is proven that the pointwise a posteriori error estimator is reliable and efficient, up to some logarithmic factors of the mesh size. The constants in

Jae Joong Lee, Bedrich Benes

Generating ground-level images from aerial views is a challenging task due to extreme viewpoint disparity, occlusions, and a limited field of view. We introduce Top2Ground, a novel diffusion-based method that directly generates photorealistic ground-view images from aerial input images without relying on intermediate representations such as depth maps or 3D

Angelo Passariello, Pietro Catalano, Carmine De Lucia, Renato Tognaccini

A scale-resolving simulation methodology that includes stochastic energy backscatter is incorporated into a proprietary block-structured compressible flow solver. Particular attention is devoted to the discretisation of the convective terms in the averaged/filtered governing equations. The objective is to achieve satisfactory dissipation and dispersion prope

Maximilian Krone

Mader proved that every sufficiently large graph with average degree at least $(2+\sqrt{2})k$ has a $(k+1)$-connected subgraph. He also conjectured that an average degree of at least $3k$ is sufficient. The best known sufficient factor was improved by multiple authors but never reached $3$. In the present paper, it is further improved to $3.109$. In addition

Yuting Tang, Yufan He, Yi Zhong, Xijun Wang

We introduce a new analytical framework, developed based on the spatial network calculus, for performance assessment of Low Earth Orbit (LEO) satellite networks. Specifically, we model the satellites' spatial positions as a strong ball-regulated point process on the sphere. Under this model, proximal points in space exhibit a locally repulsive property, refl

Devon Stockall, Matthew Yu

We develop a unified categorical framework for gauging both continuous and finite symmetries in arbitrary spacetime dimensions. Our construction applies to geometric categories i.e. categories internal to stacks. This generalizes the familiar setting of fusion categories, which describe finite group symmetries, to the case of Lie group symmetries. Within thi

F. Yang, L. Q. Chen

Ferroelectric quantum criticality in perovskite oxides offers a fertile ground for emergent collective phenomena. Here we develop a first-principles-inspired quantum-statistics-based theoretical analysis of the ferroelectric order and interfacial superconductivity in lightly-doped quantum paraelectric, niobium (Nb)-doped KTaO$_3$. We demonstrate that local d

Yi Yang, Haowen Li, Tianxiang Li, Boyu Cao

Text-to-music generation technology is progressing rapidly, creating new opportunities for musical composition and editing. However, existing music editing methods often fail to preserve the source music's temporal structure, including melody and rhythm, when altering particular attributes like instrument, genre, and mood. To address this challenge, this pap