Journey of water in pine cones

Pine cones fold their scales when it rains to prevent seeds from short-distance dispersal. Given that the scales of pine cones consist of nothing but dead cells, this folding motion is evidently related to structural changes. In this study, the structural characteristics of pine cones are studied on micro-/macro-scale using various imaging instruments. Raindrops fall along the outer scales to the three layers (bract scales, fibers and innermost lignified structure) of inner pine cones. However, not all the layers but only the bract scales get wet and then, most raindrops move to the inner scales. These systems reduce the amount of water used and minimize the time spent on structural changes. The result shows that the pine cones have structural advantages that could influence the efficient motion of pine cones. This study provides new insights to understand the motion of pine cones and would be used to design a novel water transport system.119Ysciescopu

RPLKG: Robust Prompt Learning with Knowledge Graph

Large-scale pre-trained models have been known that they are transferable, and they generalize well on the unseen dataset. Recently, multimodal pre-trained models such as CLIP show significant performance improvement in diverse experiments. However, when the labeled dataset is limited, the generalization of a new dataset or domain is still challenging. To improve the generalization performance on few-shot learning, there have been diverse efforts, such as prompt learning and adapter. However, the current few-shot adaptation methods are not interpretable, and they require a high computation cost for adaptation. In this study, we propose a new method, robust prompt learning with knowledge graph (RPLKG). Based on the knowledge graph, we automatically design diverse interpretable and meaningful prompt sets. Our model obtains cached embeddings of prompt sets after one forwarding from a large pre-trained model. After that, model optimizes the prompt selection processes with GumbelSoftmax. In this way, our model is trained using relatively little memory and learning time. Also, RPLKG selects the optimal interpretable prompt automatically, depending on the dataset. In summary, RPLKG is i) interpretable, ii) requires small computation resources, and iii) easy to incorporate prior human knowledge. To validate the RPLKG, we provide comprehensive experimental results on few-shot learning, domain generalization and new class generalization setting. RPLKG shows a significant performance improvement compared to zero-shot learning and competitive performance against several prompt learning methods using much lower resources

System-reliability-based Disaster Resilience Evaluation of Cable-stayed Bridge under Fire Hazard Using Reliability-Redundancy Analysis

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.The concept of disaster resilience recently emerged in efforts to gain holistic understanding of civil infrastructure systems exposed to various natural or human-made hazards. To effectively evaluate the resilience of complex infrastructure systems generally consisting of many interdependent structural components, Lim et al. (2022) proposed a system-reliability-based framework for disaster resilience. In the proposed framework, the disaster resilience of a civil infrastructure system is characterized by three criteria: reliability, redundancy, and recoverability. For comprehensive resilience analyses at the scale of individual structures, the reliability (β) and redundancy (π) indices were newly defined in the context of component- and system-level reliability analysis, respectively. Reliability-redundancy diagram, i.e., the scatter plot of the reliability and redundancy indices computed for each initial disruption scenario, was also proposed to help a decision-maker check whether the corresponding risk is acceptable for the society. In this paper, we demonstrate the framework through its application to a cable-stayed bridge in South Korea, the Seohae Grand Bridge under fire hazards. First, a probabilistic model is developed to describe the hazard of fire scenarios that may occur on the deck of the cable-stayed bridge. Next, finite element simulations are performed to compute the reliability and redundancy indices through component and system reliability analyses for the fire accident scenarios. An adaptive simulation method, AK-MCS (Echard et al. 2011), is employed to overcome the computational cost issue. The example successfully demonstrates that the reliability-redundancy analysis and diagram facilitate a comprehensive assessment of the disaster resilience of a complex civil infrastructure such as a cable-stayed bridge by using sophisticated computational simulations and advanced reliability methods

Nondestructive discrimination of Bell states between distant parties

Identifying Bell state without destroying it is frequently dealt with in nowadays quantum technologies such as quantum communication and quantum computing. In practice, quantum entangled states are often distributed among distant parties, and it might be required to determine them separately at each location, without inline communication between parties. We present a scheme for discriminating an arbitrary Bell state distributed to two distant parties without destroying it. The scheme requires two entangled states that are pre-shared between the parties, and we show that without these ancillary resources, the probability of non-destructively discriminating the Bell state is bounded by 1/4, which is the same as random guessing. Furthermore, we demonstrate a proof-of-principle experiment through an IonQ quantum computer that our scheme can surpass classical bounds when applied to practical quantum processor.Comment: 9 pages including Appendix, 7 figures and 2 table

Journey of water in pine cones

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