November 2025 arXiv papers — page 60

Victor Ferrari, Marcio Pereira, Lucas Alvarenga, Gustavo Leite

This paper proposes SConvTransform, a Transform dialect extension that provides operations for optimizing 2D convolutions in MLIR. Its main operation, SConvOp, lowers Linalg convolutions into tiled and packed generic operations through a fully declarative transformation pipeline. The process is guided by a Convolution Slicing Analysis that determines tile si

Liangliang Chen, Huiru Xie, Zhihao Qin, Yiming Guo

This research full paper presents an enhancement pipeline for large language models (LLMs) in assessing homework for an undergraduate circuit analysis course, aiming to improve LLMs' capacity to provide personalized support to electrical engineering students. Existing evaluations have demonstrated that GPT-4o possesses promising capabilities in assessing stu

Ben Prystawski, Dilip Arumugam, Noah D. Goodman

Humans' distinctive role in the world can largely be attributed to our capacity for iterated learning, a process by which knowledge is expanded and refined over generations. A range of theories seek to explain why humans are so adept at iterated learning, many positing substantial evolutionary discontinuities in communication or cognition. Is it necessary to

Sergey Gaifullin, Veronika Kikteva

In this paper we obtain a criterion of flexibility for an affine complexity-zero horospherical variety. This result generalizes previously known results on flexibility of normal horospherical varieties, horospherical varieties with an action of a semisimple group, and non-normal toric varieties.

Mukaram Shahid, Kunal Das, Hadia Ushaq, Hongwei Zhang

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling Secondary Users~(SUs) to access underutilized spectrum bands while protecting Primary Users~(PUs). While existing models have made progress, they often degrade in perfor

Dhavit Prem, Fernando Sotomayor, Alvaro Saldívar, Rosario Guzman-Jimenez

Yupana is an Inca device used for arithmetic operations. This article describes a new arithmetical system: Tawa Pukllay (TP), where arithmetic operations do not require mental calculations: no carries, no borrows, no memorization of multiplication tables, nor trial and error procedures for divisions. Instead, user recognizes patterns and makes predefined mov

Yana Teplitskaya

In this work, I collect and discuss a series of open questions in one-dimensional geometric optimization in Euclidean spaces. The focus is on two classes of problems: maximal distance minimizers and Steiner trees. Maximal distance minimizers concern finding a connected set of minimal length whose closed $r$-neighborhood covers a given compact set, whereas St

Zoey Zhou

We survey classical localization problems arising from quantum network models in symmetry class C and their mappings to history-dependent random walks on directed lattices. We describe how localization versus delocalization of trajectories can be analysed using percolation methods and combinatorial enumeration of path intersection patterns. In particular, we

Shangyuan Yuan, Preston Fairchild, Yu Mei, Xinyu Zhou

Accurate shape reconstruction is essential for precise control and reliable operation of soft robots. Compared to sensor-based approaches, vision-based methods offer advantages in cost, simplicity, and ease of deployment. However, existing vision-based methods often rely on complex camera setups, specific backgrounds, or large-scale training datasets, limiti

Matthijs van der Lende, Juan Cardenas-Cartagena

Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural networks have enabled breakthroughs in RL, they often lack calibrated uncertainty estimates. We introduce Deep Gaussian Process Proximal Policy Optimization (GPPO), a scalable, mo

Yifei He, Pranit Chawla, Yaser Souri, Subhojit Som

Computer use agents (CUAs) can operate real-world digital interfaces but remain difficult to train due to the high cost of graphical user interface (GUI) interaction and the scarcity of high-quality trajectory data. Existing datasets rely on human demonstrations, limiting scalability. A natural alternative is to synthesize data from strong CUAs, yet their ro

Kunwoo Lee, Dhivya Sreedhar, Pushkar Saraf, Chaeeun Lee

We explore surface electromyography (sEMG) as a non-invasive input modality for mapping muscle activity to keyboard inputs, targeting immersive typing in next-generation human-computer interaction (HCI). This is especially relevant for spatial computing and virtual reality (VR), where traditional keyboards are impractical. Using attention-based architectures

Walter Rieck, Anton Frisk Kockum, Guangze Chen

Bound states in the continuum (BICs) are spatially localized modes embedded in the spectrum of extended states, typically stabilized by symmetry or interference. While extensively studied in single-particle and linear systems, the many-body regime of BICs remains largely unexplored. Here, we demonstrate that giant atoms, quantum emitters coupled nonlocally t

J. T. Hansen, F. Gargiulo, J. B. Mathiassen, J. H. Müller

The deterministic control of light-matter interactions at the level of single particles and on subwavelength scales is central to quantum optics and hybrid integrated quantum technologies. However, combining cold atom research with nanophotonic devices in a fully controllable platform remains a major experimental challenge. Here, we demonstrate the determini

Nick Plewacki, Benjamin Kale, Manu Kamin, Luis Bravo

Hypersonic flight poses unique propulsion challenges, requiring engines that maintain thrust, efficiency, and stability across a wide range of operating conditions. These engines must transition smoothly between flight regimes and altitudes. Scramjets (supersonic combustion ramjets) play a key role in addressing these challenges. Recent advancements in high-

Alexander Htet Kyaw, Haotian Ma, Sasa Zivkovic, Jenny Sabin

Recent advances in augmented reality (AR) have enabled interactive systems that assist users in physical assembly tasks. In this paper, we present an AR-assisted assembly workflow that leverages object recognition and hand tracking to (1) identify custom components, (2) display step-by-step instructions, (3) detect assembly deviations, and (4) dynamically up

Ahmed Gomaa, Annette Schwarz, Ludwig Singer, Arnd Dörfler

Background: Differentiating radiation necrosis (RN) from tumor progression after stereotactic radiosurgery (SRS) remains a critical challenge in brain metastases. While histopathology represents the gold standard, its invasiveness limits feasibility. Conventional supervised deep learning approaches are constrained by scarce biopsy-confirmed training data. Se

Jiuzhou Fu, Luanzheng Guo, Nathan R. Tallent, Dongfang Zhao

The Hausdorff distance (HD) is a robust measure of set dissimilarity, but computing it exactly on large, high-dimensional datasets is prohibitively expensive. We propose \textbf{ProHD}, a projection-guided approximation algorithm that dramatically accelerates HD computation while maintaining high accuracy. ProHD identifies a small subset of candidate "extrem

Benjamin Bakker, Ananth N. Shankar, Jacob Tsimerman

Let $C/k$ be a smooth curve over a finite field of characteristic $p>0$. We prove that there are finitely many principally polarized abelian schemes of given dimension $g$ over $C$ up to $p$-power isogeny. For curves over $\overline{k}$, we prove that the moduli space of such abelian schemes is finite type up to $p$-power isogeny. Moreover, we generalize thi

Thomas W. Radford, Idris A Ajia, Latif Rozaqi, Priya Deoli

Reconfigurable photonic devices are rapidly emerging as a cornerstone of next generation optical technologies, with wide ranging applications in quantum simulation, neuromorphic computing, and large-scale photonic processors. A central challenge in this field is identifying an optimal platform to enable compact, efficient, and scalable reconfigurability. Opt

Pavan Narahari, Suraj Rajendran, Lorena Bori, Jonas E. Malmsten

The success of in vitro fertilization (IVF) at many clinics relies on the accurate morphological assessment of day 5 blastocysts, a process that is often subjective and inconsistent. While artificial intelligence can help standardize this evaluation, models require large, diverse, and balanced datasets, which are often unavailable due to data scarcity, natur

Hugo A Castillo-Sánchez, Weston Ortiz, Richard Martin, Rukiye Tuna

We propose a modified suspension balance model (SBM) for the flow of red blood cells (RBCs) and other deformable particle suspensions in confined geometries. Specifically, the method includes the hydrodynamic lift force generated by deformable particles interacting with walls leading to a cell-free layer. The lift force is added to the SBM to drive RBCs migr

Rodrigo de Souza Bulhões, Marina Silva Paez, Dani Gamerman

In this paper, we propose a Bayesian matrix-variate spatiotemporal modeling framework for jointly analyzing multiple response variables observed at spatial locations over time. The approach relaxes the standard assumption of spatial isotropy by incorporating a deformation-based mechanism, allowing the covariance structure to capture directional effects and n

Haoming Wang, Qiyao Xue, Wei Gao

Modern vision-language models (VLMs) are expected to have abilities of spatial reasoning with diverse scene complexities, but evaluating such abilities is difficult due to the lack of benchmarks that are not only diverse and scalable but also fully customizable. Existing benchmarks offer limited customizability over the scene complexity and are incapable of

Genesis Hang, Annie Chen, Hope Neveux, Matthew K. Nock

Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To addres

Andrzej Tokajuk, Jarosław A. Chudziak

Forecasting cryptocurrency prices is hindered by extreme volatility and a methodological dilemma between information-scarce univariate models and noise-prone full-multivariate models. This paper investigates a partial-multivariate approach to balance this trade-off, hypothesizing that a strategic subset of features offers superior predictive power. We apply

Wentao Yang, Bao Yan, Muxi Zheng, Quanfeng Lu

The integer factorization problem (IFP) underpins the security of RSA, yet becomes efficiently solvable on a quantum computer through Shor's algorithm. Regev's recent high-dimensional variant reduces the circuit size through lattice-based post-processing, but introduces substantial space overhead and lacks practical implementations. Here, we propose a qubit

Jaeho Kim, Daniel David, Ana Vizitiv

This paper evaluates Tucker decomposition and Singular Value Decomposition (SVD) for compressing neuroimaging data. Tucker decomposition preserves multi-dimensional relationships, achieving superior reconstruction fidelity and perceptual similarity. SVD excels in extreme compression but sacrifices fidelity. The results highlight Tucker decomposition's suitab

Ruoxin Zhang, Zhizhao Wen, Chao Wang, Chenchen Tang

With the rapid evolution of large language models, retrieval enhanced generation technology has been widely used due to its ability to integrate external knowledge to improve output accuracy. However, the performance of the system is highly dependent on the quality of the retrieval module. If the retrieval results have low relevance to user needs or contain

Tyler A. Gordon, Natalie M. Batalha, Natasha E. Batalha, Artyom Aguichine

JWST has already observed near-infrared transmission spectra of over a dozen super-Earths and sub-Neptunes. While some observations have allowed astronomers to characterize sub-Neptunes in unprecedented detail, small feature amplitudes and poorly-understood systematics have led to ambiguous results for others. Using the first seven targets from the COMPASS p

Kyle W. Fruhling, Enrique O. González Delgado, Siddharth Nandanwar, Xiaohan Yao

We present a comprehensive study of the magnetocaloric effect (MCE) in the Nd$T_4$B system where $T$ = Fe, Co, and Ni. These compounds are ferromagnetic kagome materials with tunable ordering temperatures, transition width, and magnetic moments depending on the choice of transition metal. Thus, they are good candidates for investigating the MCE. We character

Faheem Nizar, Elias Lumer, Anmol Gulati, Pradeep Honaganahalli Basavaraju

Recent advances in Large Language Model Multi-Agent Systems enable scalable orchestration and retrieval of specialized, parallelized subagents, each equipped with hundreds or thousands of Model Context Protocol (MCP) servers and tools. However, existing agent, MCP, and retrieval methods typically match queries against a single agent description, obscuring fi

Xiaorong Zou

The trigonometric interpolation has been recently applied to solve a second-order Fredholm integro-differentiable equation (FIDE). It achieves high accuracy with a moderate size of grid points and effectively addresses singularities of kernel functions. In addition, it work well with general boundary conditions and the framework can be generalized to work fo

Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Dheeraj Kulshrestha, Rajiv Ramnath

Document Visual Question Answering (VQA) requires models to not only extract accurate textual answers but also precisely localize them within document images, a capability critical for interpretability in high-stakes applications. However, existing systems achieve strong textual accuracy while producing unreliable spatial grounding, or sacrifice performance

Pranav Subbaraman, Fang Sun, Jinxi Yu, Yue Yao

Time series forecasting drives operational decisions under tight latency budgets, and autoregressive time series foundation models (TSFMs) increasingly deliver the most accurate forecasts. That accuracy is paid for at inference, since a horizon of $H$ steps takes $\lceil H / P\rceil$ sequential forward passes of a large model, so latency grows with exactly t

Harshith Alagandala

Let $M$ be a smooth manifold of dimension $n$ embedded in $\mathbb{C}^n$. If $T_pM \subset T_p\mathbb{C}^n$ is a totally real subspace for $p\in M$, then $M$ is locally polynomially convex at $p$. For a generic embedding $M$, we are interested in assessing polynomial convexity of $M$ at a CR-singularity, i.e., at a point $p\in M$ where $T_pM$ is not totally

Isaac Goldbring, Fabrice Nonez

We use nonstandard methods to prove the direct integral version of the Spectral Theorem for Unbounded Self-adjoint Operators. Our proof avoids the standard reduction to the case of bounded normal operators via the Cayley transform and, as such, works uniformly for both real and complex Hilbert spaces. Our method also yields a new nonstandard proof of the spe

Nikolai Nikolski

The aim of these notes is to discuss the completeness of the dilated systems in a most general framework of an arbitrary sequence lattice $X$, including weighted $\ell^p$ spaces. In particular, general multiplicative and completely multiplicative sequences are treated. After the Fourier--Bohr transformation, we deal with the cyclicity property in function sp

Sristy Sumana Nath, Banani Roy, Munima Jahan

Maintaining traceability links between software release notes and corresponding development artifacts, e.g., pull requests (PRs), commits, and issues, is essential for managing technical debt and ensuring maintainability. However, in open-source environments where contributors work remotely and asynchronously, establishing and maintaining these links is ofte

David J. Fernández C., O. Pavón-Torres

By employing supersymmetric quantum mechanics, we present a general algorithm to construct supersymmetric partner potentials and hence derive exact stationary solutions of the inhomogeneous nonlinear Schr\"odinger equation (INLSE). This is possible due to the connection between the INLSE and the nonlinear Schr\"odinger equation (NLSE), which can be establish

Yutong Wu, Yifan Wang, Qining Zhang, Chuan Zhou

Lung cancer is one of the most commonly diagnosed cancers, and early diagnosis is critical because the survival rate declines sharply once the disease progresses to advanced stages. However, achieving an early diagnosis remains challenging, particularly in distinguishing subtle early signals of malignancy from those of benign conditions. In clinical practice

Dileep Kumar, Wajiha Shireen

Modular Multilevel Converter-based High Voltage Direct Current (MMC-HVDC) system is a promising technology for integration of offshore wind farms (OWFs). However, onshore AC faults on MMC-HVDC reduce the power transfer capability of onshore converter station, leading to surplus power accumulation in HVDC link. This surplus power causes a rapid rise in DC-lin

Yixuan Jia, Qingyuan Li, Jonathan P. How

Autonomous off-road navigation requires robots to estimate terrain traversability from onboard sensors and plan motion accordingly. Conventional approaches typically rely on sampling-based planners such as MPPI to generate short-term control actions that aim to minimize traversal time and risk measures derived from the traversability estimates. These planner

Lee Ackerman

This paper introduces the Creative Intelligence Loop (CIL), a novel socio-technical framework for responsible human-AI co-creation. Rooted in the 'Workflow as Medium' paradigm, the CIL proposes a disciplined structure for dynamic human-AI collaboration, guiding the strategic integration of diverse AI teammates who function as collaborators while the human re

Adam Callaghan, Karl Mason, Patrick Mannion

This paper addresses a critical gap in Multi-Objective Multi-Agent Reinforcement Learning (MOMARL) by introducing the first dedicated inner-loop actor-critic framework for continuous state and action spaces: Multi-Objective Multi-Agent Actor-Critic (MOMA-AC). Building on single-objective, single-agent algorithms, we instantiate this framework with Twin Delay

Jun Wang, Jie Su, Leslie Greengard, Shidong Jiang

We present a family of integral equation-based solvers for the heat equation, reaction-diffusion systems, the unsteady Stokes equation and the incompressible Navier-Stokes equations in two space dimensions. Our emphasis is on the development of methods that can efficiently follow complex solution features in space-time by refinement and coarsening at each ti

Dmitrii Korikov

We establish the degeneration of the Schottky double of a genus 1 Riemann surface with boundary as its DN map tends to the DN map of the unit disk.

Shrenik Zinage, Peter Meckl, Ilias Bilionis

Accurate prediction of engine-out NOx is essential for meeting stringent emissions regulations and optimizing engine performance. Traditional approaches rely on models trained on data from a small number of engines, which can be insufficient in generalizing across an entire population of engines due to sensor biases and variations in input conditions. In rea

Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Dheeraj Kulshrestha, Rajiv Ramnath

Mixture-of-Experts (MoE) architectures are increasingly adopted in large language models (LLMs) for their scalability and efficiency. However, their modular structure introduces a unique vulnerability: adversaries can attempt to compress or repurpose models by pruning experts and cheaply fine-tuning the remainder, effectively bypassing licensing and security

Elias Lumer, Matt Melich, Olivia Zino, Elena Kim

Recent advancements in Retrieval-Augmented Generation (RAG) have enabled Large Language Models to answer financial questions using external knowledge bases of U.S. SEC filings, earnings reports, and regulatory documents. However, existing work lacks systematic comparison of vector-based and non-vector RAG architectures for financial documents, and the empiri

Felipe Lara, Rishabh Pandey, Vinay Singh

This paper studies a multiobjective bilevel optimization problem where each objective is a fractional function. By reformulating the problem into a single-level one, we establish refined necessary and sufficient optimality conditions. These results are derived using ${\partial}_D$-nonsmooth Abadie-type constraint qualifications and generalized convexity conc

Mukai Yu, Mosam Dabhi, Liuyue Xie, Sebastian Scherer

Modern perception increasingly relies on fisheye, panoramic, and other wide field-of-view (FoV) cameras, yet most pipelines still apply planar CNNs designed for pinhole imagery on 2D grids, where pixel-space neighborhoods misrepresent physical adjacency and models are sensitive to global rotations. Traditional spherical CNNs partially address this mismatch b

Enrico Pallotta, Sina Mokhtarzadeh Azar, Lars Doorenbos, Serdar Ozsoy

Egocentric video generation with fine-grained control through body motion is a key requirement towards embodied AI agents that can simulate, predict, and plan actions. In this work, we propose EgoControl, a pose-controllable video diffusion model trained on egocentric data. We train a video prediction model to condition future frame generation on explicit 3D

Juvenal Bassa, Arghya Chattopadhyay, Sudhir Malik, Mario Escabi Rivera

Data Quality Monitoring (DQM) is a crucial component of particle physics experiments and ensures that the recorded data is of the highest quality, and suitable for subsequent physics analysis. Due to the extreme environmental conditions, unprecedented data volumes, and the sheer scale and complexity of the detectors, DQM orchestration has become a very chall

Jasper Nie, Christian Muise, Victoria Armstrong

Business Process Model and Notation (BPMN) is a widely used standard for modelling business processes. While automated planning has been proposed as a method for simulating and reasoning about BPMN workflows, most implementations remain incomplete or limited in scope. This project builds upon prior theoretical work to develop a functional pipeline that trans

Kaier Liang, Licheng Luo, Yixuan Wang, Mingyu Cai

Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning frameworks that leverage conformal prediction (CP): a global planner that integrates Safe Interval Path Planning (SIPP) for uncertainty-aware trajectory generation, and a local planner th

Atiqah Almuzaini, Çağın Ararat, Jin Ma

We revisit the well-studied superhedging problem under proportional transaction costs in continuous time using the recently developed tools of set-valued stochastic analysis. By relying on a simple Black-Scholes-type market model for mid-prices and using continuous trading schemes, we define a dynamic family of superhedging sets in continuous time and expres

Tianqi Qiao, Marie Maros

We propose and analyze a variant of Sparse Polyak for high dimensional M-estimation problems. Sparse Polyak proposes a novel adaptive step-size rule tailored to suitably estimate the problem's curvature in the high-dimensional setting, guaranteeing that the algorithm's performance does not deteriorate when the ambient dimension increases. However, convergenc

Daniel Cicala, Yi Jiang, Jane HyoJin Lee, Kristin Kurianski

Public health outcomes can be heavily influenced by the landscape of public opinion; hence, it is important to understand how that landscape changes over time. For one, opinions on public health issues are responsive to official pronouncements, whether from the governmental or professional medical establishments. Additionally, in today's world of high speed

Israel Puerta-Merino, Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares

The use of Large Language Models (LLMs) for generating Automated Planning (AP) models has been widely explored; however, their application to Hierarchical Planning (HP) is still far from reaching the level of sophistication observed in non-hierarchical architectures. In this work, we try to address this gap. We present two main contributions. First, we propo

Chunming He, Rihan Zhang, Dingming Zhang, Fengyang Xiao

Deep unfolding networks (DUNs) have recently advanced concealed object segmentation (COS) by modeling segmentation as iterative foreground-background separation. However, existing DUN-based methods (RUN) inherently couple background estimation with image restoration, leading to conflicting objectives and requiring pre-defined degradation types, which are unr

Pasquale De Marinis, Uzay Kaymak, Rogier Brussee, Gennaro Vessio

Few-Shot Semantic Segmentation (FSS) models achieve strong performance in segmenting novel classes with minimal labeled examples, yet their decision-making processes remain largely opaque. While explainable AI has advanced significantly in standard computer vision tasks, interpretability in FSS remains virtually unexplored despite its critical importance for

Sheridan Feucht, Byron Wallace, David Bau

In order to predict the next token, LLMs must represent semantic and surface-level information about the current word. Previous work identified two types of attention heads that disentangle this information: (i) Concept induction heads, which copy word meanings, and (ii) Token induction heads, which copy literal token representations (Feucht et al., 2025). W

Jeffrey J. Early, Gerardo Hernández-Dueñas, Leslie M. Smith, Cimarron Wortham

A challenge in physical oceanography is quantifying the energy content of waves and balanced flows and the fluxes that connect these reservoirs with their sources and sinks. Methodological limitations have prevented decompositions for realistic flows with non-hydrostatic motions and variable stratification. We present a framework that separates the flow into

Yinuo Zhang, Walter J. Smith, Giovanni Esteves, Eric A. Stach

Al1-xScxN has attracted significant interest due to its large remnant polarization and low processing temperature when compared to other ferroelectric material systems. However, device dielectric failure before ferroelectric switching remains a critical limitation for AlScN-based memory devices. With the continuing trend toward device miniaturization, expand

Valentina Sánchez, Çiçek Güven, Koen Haak, Theodore Papamarkou

We propose a framework for constructing combinatorial complexes (CCs) from fMRI time series data that captures both pairwise and higher-order neural interactions through information-theoretic measures, bridging topological deep learning and network neuroscience. Current graph-based representations of brain networks systematically miss the higher-order depend

Abdelrahman Abdelmotlb, Abdallah Taman, Sherif Mostafa, Moustafa Youssef

Indoor localization systems commonly rely on fingerprinting, which requires extensive survey efforts to obtain location-tagged signal data, limiting their real-world deployability. Recent approaches that attempt to reduce this overhead either suffer from low representation ability, mode collapse issues, or require the effort of collecting data at all target

Martin Schuck, Alexander von Rohr, Angela P. Schoellig

Three-dimensional rigid-body transforms, i.e. rotations and translations, are central to modern differentiable machine learning pipelines in robotics, vision, and simulation. However, numerically robust and mathematically correct implementations, particularly on SO(3), are error-prone due to issues such as axis conventions, normalizations, composition consis

Edward E. Allen, Kyle Celano, Sarah K. Mason

E\u{g}ecio\u{g}lu and Remmel provide a combinatorial proof (using special rim hook tableaux) that the product of the Kostka matrix $K$ and its inverse $K^{-1}$ equals the identity matrix $I$. They then pose the problem of proving the reverse identity $K^{-1}K =I$ combinatorially. Sagan and Lee prove a special case of this identity using overlapping special r

Sangam Ghimire, Nirjal Bhurtel, Roshan Sahani, Sudan Jha

With the increasing use and adoption of cloud and cloud-native computing, the underlying technologies (i.e., containerization and virtualization) have become foundational. However, strict isolation and maintaining runtime security in these environments has become increasingly challenging. Existing approaches like seccomp and Mandatory Access Control (MAC) fr

Shreyas Kumar, Barat S, Debojit Das, Yug Desai

Delicate snap-fit assemblies, such as inserting a lens into an eye-wear frame or during electronics assembly, demand timely engagement detection and rapid force attenuation to prevent overshoot-induced component damage or assembly failure. We address these challenges with two key contributions. First, we introduce SnapNet, a lightweight neural network that d

Chunming He, Rihan Zhang, Zheng Chen, Bowen Yang

Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited for blind image restoration (BIR). Existing DUNs suffer from: (1) \textbf{Degradation-specific dependency}, as their optimization frameworks are tied to a known degradation model, making them unsuitable for BIR tas

Rajat Bhattacharjya, Sing-Yao Wu, Hyunwoo Oh, Chaewon Nam

Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that onboard CNNs cannot provide. While Vision-Language Models (VLMs) offer this semantic reasoning, their high resource demands make on-device deployment infeasible, and naive cloud offloading fails under the low-bandwidth, unstable networks endemic to disaster zone

Sangam Ghimire, Paribartan Timalsina, Nirjal Bhurtel, Bishal Neupane

As the demand grows for scalable and privacy-aware AI systems, Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training without moving raw data. At the same time, the combination of high-performance computing (HPC) and cloud infrastructure offers vast computing power but introduces new complexities, especially when d

Randy Davila, Beyzanur Ispir

We investigate machine learning approaches to approximating the \emph{domination number} of graphs, the minimum size of a dominating set. Exact computation of this parameter is NP-hard, restricting classical methods to small instances. We compare two neural paradigms: Convolutional Neural Networks (CNNs), which operate on adjacency matrix representations, an

Kingshuk Adhikary, Darren W. Moore, Radim Filip

The linear and phase insensitive absorption of a single quanta via coherent interactions with a saturable system, even a single ground state qubit, is sufficient to deterministically generate quantum non-Gaussian states in an oscillator, even stimulated merely by increasing thermal oscillator energy. However, the resultant states only approach Fock states an

Shixiao Liang, Chengyuan Ma, Pei Li, Haotian Shi

Real-time traffic crash detection is critical in intelligent transportation systems because traditional crash notifications often suffer delays and lack specific, lane-level location information, which can lead to safety risks and economic losses. This paper proposes a real-time, lane-level crash detection approach for freeways that only leverages sparse tel

Aram Manaselyan, Vram Mughnetsyan, Anna Asatryan, Albert Kirakosyan

A new mechanism for charge carrier scattering in transition-metal dichalcogenide monolayers is proposed on the basis of the theory of two-dimensional nonlocal screening developed for the dielectric function of thin-layer insulating materials (P. Cudazzo et al. PRB 84, 085406 (2011)). The expressions for the transport relaxation time and for the electron mobi

Yomal De Mel, Nisansa de Silva

This study introduce GeeSanBhava, a high-quality data set of Sinhala song comments extracted from YouTube manually tagged using Russells Valence-Arousal model by three independent human annotators. The human annotators achieve a substantial inter-annotator agreement (Fleiss kappa = 84.96%). The analysis revealed distinct emotional profiles for different song

H. R. Paz

Engineering programmes in Latin America combine high structural rigidity, intense assessment cultures and persistent socio-economic inequality, producing dropout rates that remain stubbornly high despite increasingly accurate early-warning models. Predictive learning analytics can identify students at risk, but they offer limited guidance on which concrete c

Sovan Boxi, Saikat Das, Nayantara Gupta

The highest-energy cosmic neutrino detected by the ARCA detector of KM3NeT has reignited the quest to pinpoint the sources of ultrahigh-energy cosmic rays (UHECRs; $E\gtrsim 0.1$ EeV). By uncovering the associated multimessenger signals, we investigate the origin of the 220 PeV $\nu_\mu$ event KM3-230213A from an unknown transient that accelerated cosmic ray

D. D. Reshetnikov, A. S. Losev

The paper considers the propagation of Laguerre-Gaussian beams in a turbulent gas medium. As demonstrated by numerical modelling and the decomposition of Laguerre-Gaussian modes according to the basis of orthogonal Zernike polynomials, disparities in the stability of Laguerre-Gaussian modes with differing radial and azimuthal numbers are evident. The demonst

Tamás Tekeli, Andrea Pugliese, Cinzia Soresina

Population heterogeneity is a key factor in epidemic dynamics, influencing both transmission and final epidemic size. While heterogeneity is often modelled through age structure, spatial location, or contact patterns, differences in host susceptibility have recently gained attention, particularly during the COVID-19 pandemic. Building on the framework of Die

Lucas P. Amaral, Luben M. C. Cabezas, Thiago R. Ramos, Gustavo H. G. A. Pereira

Dirichlet regression models are suitable for compositional data, in which the response variable represents proportions that sum to one. However, there are still no well-established methods for constructing valid prediction sets in this context, especially considering the geometry of the compositional space. In this work, we investigate conformal prediction-b

Yilong Wang, Cheng Qian, Ruomeng Fan, Edward Johns

We propose Observer Actor (ObAct), a novel framework for active vision imitation learning in which the observer moves to optimal visual observations for the actor. We study ObAct on a dual-arm robotic system equipped with wrist-mounted cameras. At test time, ObAct dynamically assigns observer and actor roles: the observer arm constructs a 3D Gaussian Splatti

Shuhuan Wang, Yuzhen Xie, Jiayi Li

Astronomical imaging confronts an efficiency-resolution tradeoff that limits large-scale morphological classification and redshift prediction. We introduce WaveletMamba, a theory-driven framework integrating wavelet decomposition with state-space modeling, mathematical regularization, and multi-level bias correction. WaveletMamba achieves 81.72% +/- 0.53% cl

Junrui Zhang, Xinyu Zhao, Jie Peng, Chenjie Wang

Multimodal learning has shown significant superiority on various tasks by integrating multiple modalities. However, the interdependencies among modalities increase the susceptibility of multimodal models to adversarial attacks. Existing methods mainly focus on attacks on specific modalities or indiscriminately attack all modalities. In this paper, we find th

Christoph Goldgruber, Benedikt Pittl, Erich Schikuta

The increasing reliance on dynamic pricing models, such as spot instances, in public cloud environments presents new challenges for workload scheduling and reliability. While these models offer cost advantages, they introduce volatility and uncertainty that are not fully addressed by current allocation algorithms or simulation tools. This work contributes to

Gabriel M. Arantes, Giancarlo Lucca, Eduardo N. Borges, Richard F. Pinto

The growing performance gap between multi-core CPUs and main memory necessitates hardware-aware software design paradigms. This study provides a comprehensive performance analysis of Data Oriented Design (DOD) versus the traditional Object-Oriented Design (OOD), focusing on cache utilization and efficiency in multi-threaded environments. We developed and com

Chunming He, Rihan Zhang, Longxiang Tang, Ziyun Yang

Existing methods for label-deficient concealed object segmentation (LDCOS) either rely on consistency constraints or Segment Anything Model (SAM)-based pseudo-labeling. However, their performance remains limited due to the intrinsic concealment of targets and the scarcity of annotations. This study investigates two key questions: (1) Can consistency constrai

Huaidian Hou, Arpan Kusari, Brian T. W. Lin

Car-Following is a broadly studied state of driving, and many modeling approaches through various heuristics and engineering methods have been proposed. Congestion is a common traffic phenomenon also widely investigated, both from macroscopic and microscopic perspectives. Yet, current literature lack a unified evaluation of Car-Following models with naturali

Claudio Vestini, Idris Kempf

Constrained quadratic programs and Euclidean projections are ubiquitous in engineering, arising in machine learning, estimation, control, and signal processing. Dykstra's algorithm is an iterative scheme for computing the Euclidean projection of an initial point onto the intersection of convex sets by successively projecting onto each set. Its low per-iterat

Xiaofan Li, Yanpeng Sun, Chenming Wu, Fan Duan

We observe that recent advances in multimodal foundation models have propelled instruction-driven image generation and editing into a genuinely cross-modal, cooperative regime. Nevertheless, state-of-the-art editing pipelines remain costly: beyond training large diffusion/flow models, they require curating massive high-quality triplets of \{instruction, sour

Konstantinos Alexoudis, Luke Silvestre, Tom Huiskamp, Jasper Müller

We present $\phi$-OTDR for detecting and localising full spark-gap breakdowns by analysing backscattered light phase and frequency-domain signatures during high-voltage discharges synchronised with oscilloscope-recorded events. Measuring sub-kHz confirms clear discharge signatures and acoustic reconstruction over long links with $\approx$ 10 m spatial resolu

Songlin Lyu, Francesco Amodio, Giovanni De Gregorio, Nunzio Itaco

We present the results of the application of a nuclear potential consisting of two- and three-nucleon contact interactions in nuclear structure investigations. The nuclear Hamiltonian has been derived for a very low-energy regime within the framework of the effective field theory, its low-energy constants have been fitted to a few low-energy nucleon-nucleon

Marzieh Basiri Abarghoei, Mohammad Reza Ahmadi Zand

This paper presents a distributed Lyapunov-based control framework for achieving both complete and phase synchronization in a class of leader-follower multi-agent systems composed of identical chaotic agents. The proposed approach introduces a novel nonlinear coupling mechanism and utilizes Lyapunov stability theory combined with matrix measure analysis to d

Gilles Zumbach

For long term investments, model portfolios are defined at the level of indexes, a setup known as Strategic Asset Allocation (SAA). The possible outcomes at a scale of a few decades can be obtained by Monte Carlo simulations, resulting in a probability density for the possible portfolio values at the investment horizon. Such studies are critical for long ter

Sangam Ghimire, Nigam Niraula, Nirjal Bhurtel, Paribartan Timalsina

Metadata hotspots remain one of the key obstacles to scalable Input/Output (I/O) in both High-Performance Computing (HPC) and cloud-scale storage environments. Situations such as job start-ups, checkpoint storms, or heavily skewed namespace access can trigger thousands of concurrent metadata requests against a small subset of servers. The result is long queu

Dachuan Zhao, Weiyue Li, Zhenda Shen, Yushu Qiu

Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify demographic biases, resulting in biased associations and misaligned predictions in downstream tasks. Such behavior undermines fairness and distorts the intended alignment between vision and language. Recent post-hoc approaches

Kanji Mori, Tomoya Takiwaki, Kazunori Kohri

Recent studies on the dark photon (DP) production in collapsing stars argue that the cooling effect induced by DPs can hinder supernova explosions and lead to a ``failing supernova" constraint on the photon-DP mixing parameter $\epsilon$. In order to verify the idea, we perform two-dimensional neutrino-radiation hydrodynamic simulations coupled with the DP p