Fracture and deformation of soft materials and structures

Soft materials and structures are very common in our daily life and industry products, but still shows great potential on many innovative applications. To make better use of them, we need a good understanding of the fracture and deformation of the materials. Starting from soft elastic foams, this paper proposes a scaling law for the fracture energy of soft elastic foam by using the analogy between the cellular structure and the network structure of rubbery polymers. To verify the scaling law, a phase-field model for the fracture processes in soft elastic structures is developed. The numerical simulations in two-dimensional foam structures of various unit-cell geometries have all achieved good agreement with the scaling law. Inspired by the toughening mechanism of double-network (DN) hydrogels, a highly stretchable soft composite and a magnetic double-network composite capable of large recoverable deformation were fabricated. Just as the DN gels, the coexistence of the partially damaged and intact regions resulted in a stable necking in the composite when subjected to uniaxial tension. The propagation of the necking zone corresponded to a plateau on the stress–stretch curve. The experimental observations serve as a good evidence of the fracture process of DN gel. Finally, a phase field model for the fracture of DN gel is proposed. The numerical results are compared with transformation toughening results since the mechanical behavior of both material shows great similarity. The process zone shows similar pattern with what we observed in DN composite experiments

Efficiently Answering Quality Constrained Shortest Distance Queries in Large Graphs

The shortest-path distance is a fundamental concept in graph data analytics and has been extensively studied in literature. In many real-world applications, quality constraints are naturally associated with edges in the graph, and finding the shortest distance between vertices along only valid edges (i.e., edges that satisfy a given quality constraint) is also critical. In this work, we investigate this novel and important problem of quality constraint shortest distance queries. We propose an efficient index structure based on 2-hop labeling approaches. Supported by a path dominance relationship incorporating both quality and length information, we demonstrate the minimal property of the new index. An efficient query processing algorithm is also developed. Extensive experimental studies over real-life datasets demonstrates efficiency and effectiveness of our techniques

Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to pairwise label large-scale bitemporal HSR remote sensing images. In this paper, we propose single-temporal supervised learning (STAR) for change detection from a new perspective of exploiting object changes in unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using \textbf{unpaired} labeled images and generalize to real-world bitemporal images. To evaluate the effectiveness of STAR, we design a simple yet effective change detector called ChangeStar, which can reuse any deep semantic segmentation architecture by the ChangeMixin module. The comprehensive experimental results show that ChangeStar outperforms the baseline with a large margin under single-temporal supervision and achieves superior performance under bitemporal supervision. Code is available at https://github.com/Z-Zheng/ChangeStarComment: ICCV 202