arXiv:2507.11570v1 Announce Type: new Abstract: Objective: To develop and evaluate machine learning (ML) models for predicting length of stay (LOS) in elective spine surgery, with a focus on the benefits of temporal modeling and model interpretability. Materials and Methods: We compared traditional ML models (e.g., linear regression, random forest, support vector machine (SVM), and XGBoost) […]

[Submitted on 14 Apr 2024 (v1), last revised 16 Jul 2025 (this version, v4)] View a PDF of the paper titled StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging, by Xuelong Li and 8 other authors View PDF HTML (experimental) Abstract:In this paper, we introduce StreakNet-Arch, a real-time, end-to-end binary-classification framework based on

[Submitted on 12 Jul 2025 (v1), last revised 16 Jul 2025 (this version, v2)] View a PDF of the paper titled Extension OL-MDISF: Online Learning from Mix-Typed, Drifted, and Incomplete Streaming Features, by Shengda Zhuo and 4 other authors View PDF HTML (experimental) Abstract:Online learning, where feature spaces can change over time, offers a flexible

[Submitted on 23 Mar 2024 (v1), last revised 16 Jul 2025 (this version, v3)] View a PDF of the paper titled Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training, by Shuai Zhao and 3 other authors View PDF HTML (experimental) Abstract:A primary concern regarding training large language models (LLMs) is whether they

[Submitted on 7 Apr 2024 (v1), last revised 16 Jul 2025 (this version, v3)] View a PDF of the paper titled LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation, by Yousef Sadegheih and 4 other authors View PDF HTML (experimental) Abstract:The rise of Transformer architectures has advanced medical image segmentation, leading to hybrid

arXiv:2507.12417v1 Announce Type: cross Abstract: Humans possess a remarkable capacity for spatial cognition, allowing for self-localization even in novel or unfamiliar environments. While hippocampal neurons encoding position and orientation are well documented, the large-scale neural dynamics supporting spatial representation, particularly during naturalistic, passive experience, remain poorly understood. Here, we demonstrate for the first time that

arXiv:2507.12440v1 Announce Type: cross Abstract: Real robot data collection for imitation learning has led to significant advancements in robotic manipulation. However, the requirement for robot hardware in the process fundamentally constrains the scale of the data. In this paper, we explore training Vision-Language-Action (VLA) models using egocentric human videos. The benefit of using human videos

arXiv:2507.12318v1 Announce Type: cross Abstract: We argue that diffusion models’ success in modeling complex distributions is, for the most part, coming from their input conditioning. This paper investigates the representation used to condition diffusion models from the perspective that ideal representations should improve sample fidelity, be easy to generate, and be compositional to allow out-of-training

arXiv:2507.11971v1 Announce Type: cross Abstract: Current 3D representations like meshes, voxels, point clouds, and NeRF-based neural implicit fields exhibit significant limitations: they are often task-specific, lacking universal applicability across reconstruction, generation, editing, and driving. While meshes offer high precision, their dense vertex data complicates editing; NeRFs deliver excellent rendering but suffer from structural ambiguity, hindering

arXiv:2507.11687v1 Announce Type: cross Abstract: Large Language Models, though successful in code generation, struggle with code quality analysis because they are limited by static training data and can’t easily adapt to evolving best practices. We introduce MetaLint, a new instruction-following framework that formulates code quality analysis as the task of detecting and fixing problematic semantic