1614491 results (page 14 of 64580)
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Contraction-based Neural Control for Cooperative Aerial Payload Transportation with Variable-length Cables
This paper presents a novel neural nonlinear control framework for a multi-drone slung payload system with variable-length cables and a rigid-body payload. The equations of motion are formulated into a decoupled structure, where the payload and cable length dynamics are governed by independent control channels, facilitating modularized controller design on reduced-order subsystems. A neural contro…
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Advancing Threshold-Inception Modeling for Predictive Simulation of Ionic Wind Fan Performance
This study investigates the predictive capability of a threshold inception-based multiphysics modeling approach for ionic wind fans by direct comparison with experimental measurements. A wire-to-cylinder electroaerodynamic (EAD) fan with variable electrode spacing is used as a reference system to assess the model's ability to reproduce airflow characteristics, discharge current, and performance tr…
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QPU-scale randomized benchmarking via Bell-pair injection
Mirror randomized benchmarking (MRB) is an established technique that provides a global error metric at the scale of a whole QPU. To expand upon this we introduce Mirror Quantum Awesomeness (MQA), a hybrid protocol that adds a structured entangling layer to MRB circuits. This enables per-edge correlation dynamics to be tracked via mutual information while preserving the MRB infidelity estimate. Th…
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ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a structural scaffold that coordinates retrieval, evidence organization, and generation. However, existing methods either fix the outline before writing or refine it with local heuristics, leading to scaffold drift under co…
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BARReL: a modern backend for Atelier B in Lean
BARReL is a Lean 4 library bridging Atelier B, an industrial tool for the B method, and the Lean proof assistant by enabling users to conduct their formal B developments -- up to machine refinement and implementation -- interactively inside Lean, while retaining standard B syntax. B partial operators are carefully encoded by generating explicit well-definedness conditions, leveraging Lean's depend…
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Dual-Agent Framework for Cross-Model Verified Translation of Natural-Language Protocols into Robotic Laboratory Platform
Biological experiment protocols are written in natural language, whereas automation systems rely on predefined control commands, creating a semantic gap that limits autonomous execution. Microplate-based automatic experiments are particularly challenging due to the need to simultaneously control well mapping, sample-reagent combinations, replicate placement, and parallel dispensing. This study pro…
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Pose6DAug: Physically Plausible Multi-view Object Swapping for Robot Data Augmentation
Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or geometry deviates from the training distribution. The standard remedy is to collect multi-view teleoperation data for every failure case, but this scales poorly in both cost and time. We introduce Pose6DAug, a failure-driv…
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When Calibration Fails the Vulnerable Hospital: Federated Conformal Risk Control via Risk-Curve Shrinkage
Conformal risk control (CRC) provides distribution-free guarantees on segmentation quality by calibrating a prediction-set threshold on held-out data. In federated deployments, the standard approach pools calibration scores across sites into a single threshold. We provide the first quantification, on real multi-institutional brain tumor data (FeTS-2022, 1,251 subjects, 20 institutions), showing th…
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Community detection in small-sample ordinal regimes: A benchmarking framework for Delphi data
The statistical modeling of consensus in Delphi data faces a critical bottleneck: the high dimensionality of questionnaire items relative to the limited sample size of expert panels. This rank deficiency leads traditional latent variable models, such as Principal Component Analysis, to be structurally unstable and prone to overfitting. Addressing this methodological gap, this study proposes a tran…
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When Does Streaming Tool Use Help? Characterizing Tool-Intent Stabilization in Streaming Retrieval-Augmented Generation
Streaming Retrieval-Augmented Generation (Streaming RAG) reduces user-perceived latency by issuing tool queries in parallel with ongoing user input, before the utterance is complete. Reported gains are aggregate, yet the mechanism's benefit is fundamentally query-intrinsic: speculation can only help when the correct tool query becomes determinable before the user stops speaking or typing. We isola…
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Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generation
Generating high-resolution 3D CT volumes with fine details remains challenging due to substantial computational demands and optimization difficulties inherent to existing generative models. In this paper, we propose the Pixel-Level Residual Diffusion Transformer (PRDiT), a scalable generative framework that synthesizes high-quality 3D medical volumes directly at voxel-level. PRDiT introduces a two…
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Hybrid stars with hyperons: structure based on QCD sum rule coupling constants
We present a comprehensive study of hybrid stars composed of hadrons, leptons, and quarks within a relativistic mean-field framework. Using coupling constants derived from QCD sum rules (QCDSR), we first determine the bulk properties of nuclear matter and evaluate the single-particle potentials of nucleons and hyperons to constrain the hadronic sector. The equation of state (EOS) under beta equili…
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FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model
Synthetic data for autonomous driving is surging, powered by diffusion models that promise scalable scene generation. Yet key obstacles remain, as enforcing multi-view and temporal consistency often relies on backbone fine-tuning or added layers, which erodes pre-trained knowledge and weakens text alignment. Models also stay close to the training distribution, struggling under adverse weather and …
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Regular Black Holes from Anisotropic Source with Hydrodynamic Equation of State
We study regular black hole solutions sourced by an anisotropic energy momentum tensor. It is well known that the geometry of the interior of a spherically symmetric regular black hole approaches the dS metric. Having decomposed the energy momentum tensor into its isotropic and anisotropic components, we assume a hydrodynamic equation of state, $P= P(ρ)$, for the pressure, and look for spherically…
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EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Image quality control is vital for a wide range of downstream applications. Deep learning-based image quality assessment methods typically train classifiers on dataset-specific quality labels, inheriting two limitations: (1) generalization is tied to the labeling criteria of the training set and (2) these methods cannot provide spatial feedback on where the quality is degraded, lacking explainabil…
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Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration. Although theoretically sound, such estimates are hard to compute in practical settings and therefore offer limited insight for designing exploration heuristics. Meanwhile, ensembling has emerged as a practical approach, but remains without theoretical justi…
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Personalized Keyword Spotting for User-Defined Keywords Leveraging Text-Independent Speaker Verification
User-defined keyword spotting (UD-KWS) enables zero-shot wake-word detection from text, but existing systems learn speaker-invariant representations that cannot reject impostors uttering the correct keyword. We address this dual zero-shot setting -- unseen keywords and unseen speakers -- with ZP-KWS, a lightweight framework combining a phoneme-supervised audio encoder with a GE2E-pretrained compac…
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Can DFT-trained neural network potentials reproduce structure, solvation, and water-exchange properties in aqueous magnesium solutions?
Magnesium ions play an essential role in many biological processes but remain challenging to model in biomolecular simulations. Despite considerable scientific effort, classical force fields fail to simultaneously reproduce key structural, thermodynamic and kinetic solution properties, likely due to their inability to explicitly account for quantum many-body effects. Here, we develop and systemati…
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Sensorimotor World Models: Perception for Action via Inverse Dynamics
Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions. At the same time, latent JEPA-style world models advocate learning compact predictive states from high-dimensional observations to facilitate the prediction of future states, but end-to-end training of these models is nontrivial because representations…
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Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration
Accurate LiDAR-camera calibration is essential for robust multi-modal perception. Targetless approaches avoid manual setup but remain limited by the scarcity of discriminative cross-modal features. Recent methods address this by reconstructing the scene within a differentiable model, enabling extrinsic optimization through dense photometric supervision. Among these, 3D Gaussian Splatting (3DGS) ha…
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Artificial Intelligence as Game Changer in Cybersecurity: What We Learned in 2025-2026, and how this is relevant for Africa
In 2025 and 2026, two events settled questions that had until then been speculative. In the first, a large language model executed the great majority of a state-aligned cyber-espionage campaign on its own, with human operators intervening at only a few decision points. In the second, the most capable cyber-relevant model was placed under a controlled-access program limited to a vetted set of Unite…
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Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow
Audio editing aims to modify specific content in an existing audio clip according to a natural language instruction while preserving the remaining acoustic content. Despite the remarkable progress of diffusion models, existing training-based editing methods mainly rely on the local inductive biases and cross-attention interaction in convolutional U-Net backbones, which often hinder long-range sema…
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WeGenBench: A Multidimensional Diagnostic Benchmark towards Text-to-Image Model Optimization
Recent text-to-image generation models have demonstrated remarkable capabilities in synthesizing highly realistic images from text inputs alone. Although existing benchmarks can evaluate the generation capabilities of various models to some extent, they struggle to comprehensively and accurately measure performance across multiple dimensions, often failing to reveal the inherent deficiencies of mo…
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Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility
This paper explores the use of generative models to synthesize high-quality, site-specific multiple-input multiple-output (MIMO) channel data, addressing the high cost of the extensive measurement campaigns required to acquire real-world data for AI-native wireless networks. Two location-conditioned generative paradigms are compared: a conditional denoising diffusion implicit model (cDDIM), and a …
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HydraHead: From Head-Level Functional Heterogeneity to Specialized Attention Hybridization
The quadratic complexity of attention poses a critical bottleneck for long-context processing, spurring interest in hybrid attention designs. Most open-source hybrid models adopt a layer-wise strategy. Yet, prior work has noted the inherent difficulty of integrating Linear Attention (LA) with Full Attention (FA), suggesting that the design space of attention hybridization remains underexplored. To…