321 papers
This paper introduces two novel rate-and-state-dependent friction models within the Friction with Bristle Dynamics (FrBD) framework based on Generalized Maxwell and Generalized Kelvin-Voigt viscoelastic elements, demonstrating that they satisfy boundedness and passivity for any physically meaningful parameters and illustrating their utility for control design in robotics.
This paper introduces a lightweight, text-only adaptation method for LLM-based ASR systems that frames domain adaptation as a text denoising task, effectively improving performance on new domains while preserving critical speech-text alignment without requiring architectural changes.
This paper demonstrates that self-supervised speech models encode speech using phonologically interpretable and compositional vectors, revealing that linear arithmetic operations on these representations can systematically manipulate and generate phonological features across 96 languages.
This paper evaluates small language models for leader-follower role classification in human-robot interaction, demonstrating that fine-tuned models achieve high accuracy and low latency on edge devices, though performance degrades in one-shot modes due to architectural limitations with increased context.
This paper proposes a scalable and convergent Generalized Power Iteration Precoding (GPIP) framework for massive MIMO systems that reduces computational complexity by reformulating high-dimensional beamforming into a lower-dimensional user-centric optimization, while ensuring robustness under imperfect channel state information and providing theoretical convergence guarantees.
This paper proposes a three-stage framework that filters communication-layer defects from AIS data to reliably detect and classify wide-area GNSS spoofing and jamming events, achieving a 98.6% reduction in false alarms compared to naive clustering methods.
This paper introduces DRAFTO, a novel trajectory optimization algorithm for robotic manipulators that decouples reduced-space Gauss-Newton descent with adaptive feasibility repair to efficiently generate smooth, safe, and constraint-compliant paths, demonstrating superior performance over existing planners in diverse and complex manipulation tasks.
This paper introduces TATIC, a unified framework that combines torque-based force estimation with a task-aware Temporal Convolutional Network to infer both discrete task-level intent and continuous motion parameters from brief physical corrections, enabling robust online adaptation in human-robot collaboration.
This paper introduces V2A-DPO, a novel Direct Preference Optimization framework for flow-based video-to-audio generation that leverages a comprehensive AudioScore system, automated preference pair data generation, and curriculum learning to achieve state-of-the-art performance aligned with human preferences.
This paper investigates the impact of parameter selection on the convergence of a modified ant colony algorithm to optimize the hierarchical structure of an industrial distributed control system, offering tuning recommendations applicable to broader combinatorial optimization problems.
This paper proposes a Transformer-based framework for audio-visual emotion recognition that addresses frame-rate mismatches through Temporally-aligned Rotary Position Embeddings and a Cross-Temporal Matching loss to enhance multimodal feature alignment and fusion.
This paper investigates the use of a text-trained large language model adapted for speech to localize fake words in partially edited audio, revealing that while the model effectively identifies edits by leveraging specific training patterns like word-level polarity substitutions, it struggles to generalize to unseen editing styles.
This paper demonstrates that a lightweight configuration of the pre-trained XLS-R model, utilizing only its first three layers, achieves state-of-the-art cough activity detection for automatic tuberculosis screening, significantly outperforming existing baselines while offering the computational efficiency required for smartphone-based deployment.
This paper proposes a self-speculative decoding framework that leverages a CTC encoder as a draft model to simultaneously accelerate auto-regressive inference and improve word error rates in speech recognition, achieving a 4.4x speedup with minimal accuracy loss on the HuggingFace Open ASR benchmark.
This paper proposes a novel adaptive algorithm for multi-robot multitask coverage that combines a federated control strategy with a multitask Gaussian Process framework to learn unknown sensory demands, achieving sublinear cumulative regret compared to an oracle with prior knowledge.
This paper extends irreversible port-Hamiltonian system formulations from one-dimensional to N-dimensional boundary-controlled distributed parameter systems, providing a unified, thermodynamically consistent framework for modeling conduction-diffusion phenomena that preserves energy balance and entropy production while enabling structure-preserving numerical control.
This paper establishes the necessity of cross-epoch Doppler coupling for bounded phase uncertainty in LEO inter-satellite links, derives a corresponding posterior Cramér-Rao bound, and proposes a hybrid robust filtering framework that significantly outperforms standard extended Kalman filtering by effectively mitigating hardware impairments and measurement outliers.
This paper introduces EquivAnIA, a robust spectral method for rotation-equivariant anisotropic image analysis using cake wavelets and ridge filters, which effectively preserves directional properties under numerical rotations and demonstrates success in angular image registration tasks.
This paper proposes an enhanced Distributed Kalman-Consensus Filter for multi-object tracking in mobile robot networks that combines the MOTLEE framework's frame-alignment methodology with a novel adaptive uncertainty weighting mechanism to dynamically mitigate the impact of heterogeneous localization errors and communication latency, resulting in improved tracking accuracy.
This paper introduces Hybrid eTFCE-GRF, a novel method that combines the union-find algorithm for exact cluster-size retrieval with analytical Gaussian random field theory to enable fast, permutation-free, and statistically rigorous voxel-based morphometry inference.