FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition [PDF]
arXivReal-life applications of action recognition often require a fine-grained understanding of subtle movements, e.g., in sports analytics, user interactions in AR/VR, and surgical videos. Although fine-grained actions are more costly to annotate, existing semi-supervised action recognition has mainly focused on coarse-grained action recognition.
arxiv
Enhanced Fog Water Harvesting on Superhydrophobic Steel Meshes
Advanced Engineering Materials, EarlyView.Fog harvesting using mesh designs offers a sustainable solution to water scarcity. This study highlights key considerations for fog harvesting research and develops a methodology for a standardized protocol reflecting fog characteristics and environmental conditions.
Pegah Sartipizadeh +3 more
wiley +1 more source
Deformation Behavior of La2O3‐Doped Copper during Equal Channel Angular Pressing
Advanced Engineering Materials, EarlyView.By additions of strengthening elements and/or structure optimization, the mechanical properties of copper can be increased while keeping favorable electric conductivity. By combining addition of La2O3 and processing by equal channel angular pressing, substructure development is achieved, leading to increase in microhardness to more than double the ...
Lenka Kunčická +2 more
wiley +1 more source
Grain Refinement Mechanism of 5A06 Aluminum Alloy Sheets during Cold Rotary Forging. [PDF]
Materials (Basel), 2023 Han X, Chen W, Hu X, Hua L, Chai F.
europepmc +1 more source
MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning [PDF]
arXivLarge Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples and voting among generated samples. While these improve performance, they often reach a saturation point. Refinement offers an alternative by using LLM-generated feedback to improve solution quality.
arxiv
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Effect of Multiple Reverse Transformation Treatments on Grain Refinement and Mechanical Properties of Biomedical Co-Cr-Mo-N Alloys Fabricated by Electron Beam Melting. [PDF]
Materials (Basel), 2023 Wang H, Miyagi T, Chiba A.
europepmc +1 more source
Advanced Engineering Materials, EarlyView.Acoustic emissions during direct laser interference patterning are shown to strongly correlate with surface texture depth. By implementing a closed‐loop control system utilizing acoustic feedback, consistent microstructures with uniform depth are achieved despite laser power fluctuations.
Tobias Steege +4 more
wiley +1 more source
Pulsed laser-assisted additive manufacturing of Ti-6Al-4V for in-situ grain refinement. [PDF]
Sci Rep, 2022 Yoon H +5 more
europepmc +1 more source