arXiv:2605.02917v1 Announce Type: new
Abstract: Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes of physiologically info…
arXiv:2507.05385v5 Announce Type: replace
Abstract: We introduce EduCoder, a domain-specialized tool designed to support utterance-level annotation of educational dialogue. While general-purpose text annotation tools for NLP and qualitative research a…
arXiv:2508.14994v3 Announce Type: replace-cross
Abstract: In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and ver…
arXiv:2603.07866v2 Announce Type: replace
Abstract: Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions that lead to partial observations, unreliable depth estimates, and the nee…
arXiv:2605.02918v1 Announce Type: new
Abstract: Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize t…
arXiv:2604.10647v3 Announce Type: replace
Abstract: UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physic…
arXiv:2605.03302v1 Announce Type: new
Abstract: This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of underactuated, nonlinea…
arXiv:2601.06445v2 Announce Type: replace
Abstract: Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can generate long stories but overly focus on causal coheren…
arXiv:2605.03578v1 Announce Type: cross
Abstract: Boltzmann Machines trained on evolutionary sequence data have emerged as a powerful paradigm for the data-driven design of artificial proteins. However, the relationship between model architecture, spe…
arXiv:2602.03548v3 Announce Type: replace
Abstract: Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in service dialogues, as they rely on noisy, low-qual…