Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Design, characterisation and application of structural and multifunctional composites to large ship structures

Composite materials have been widely used in the aerospace, automotive and ship (particularly naval military vessels) industries, primarily due to their outstanding specific strength and stiffness. Over the last few decades, a large amount of research has been carried out to improve their performance further by introducing other functionalities such as noise and vibration control, self-repair, thermal insulation and energy harvesting/storage. Although more than half-century has passed since the boatbuilding sectors accepted the use of composite materials, it is still difficult to find a significant application of composite materials in the large cargo shipbuilding sectors. However, recent guidance from international regulations for environment protection strongly demands lightweight and eco-friendly cargo ships to achieve considerable reductions in fuel consumption and toxic gas emissions. Based on the considerable structural, environmental and economical benefits achievable from the adoption of structural and multifunctional composites, the aim of the research presented in this PhD thesis is, therefore, to design, characterise and implement structural and multifunctional composites in cargo ship structures which are currently made of steel. Firstly, a patch-wise layup and damping treatment method has been proposed to improve the vibration performance of composite ship structures. The determined design reduced the vibration response level by over 50% compared to that of a benchmarked quasi-isotropic (QI) design. The patch-wise design approach provided a unique opportunity to conceptualise composite wave-breaker with a considerable reduction in vibration response level, mass savings and cost-effectiveness compared to conventional designs. Multifunctional composites offer the opportunity to take traditional composites beyond the typical structural role by embedding, for instance, energy storage. Thus, a novel design methodology was investigated to achieve optimised multifunctional microstructures. Numerical predictions on the multifunctionality were successfully validated by experiments using 3D printed specimens. As an extension of this study, the optimised multi-scale structures for multifunctional composites have been investigated and have demonstrated very promising multifunctionality (i.e. high stiffness and high ionic conductivity) with considerable savings in computation cost. The contributions of this research would provide great insights into their potential uses in industrial applications and the next steps in academic research.Open Acces

Neural Process based Bayesian Optimization for Semiconductor Design Factor Search under Constraint

Graduate School of Artificial IntelligenceBayesian Optimization (BO) using Gaussian Process (GP) is a conventional choice to solve a black-box optimization problem. However, adopting the Gaussian Process can cause trouble situations when (i). the optimization iteration needs to be large or (ii). the surrogate function should capture complex func-tional form, which is hard for mere joint Gaussian distribution assumption. Since the success of Deep Neural Networks, there have been many kinds of research to overcome such limitations by substitution the Gaussian Process with Neural Networks, called Neural Process families (NPs). This paper compares different experimental aspects when varying the surrogate models in a semiconductor design factor search problem. Comparisons include computational cost, optimization performance, and the change of acquisition value mapping over search space varying the choices of surrogate model of BO. As the result, it is shown that GPs computational cost grows exponentially as the BO iteration becomes larger, while NPs computational cost grows only on a linear scale, outperforming the optimization performance slightly with the proper choice of training hyperparameter.ope

Flexible PI-Based Plant Drought Stress Sensor for Real-Time Monitoring System in Smart Farm

Plant growth and development are negatively affected by a wide range of external stresses, including water deficits. Especially, plants generally reduce the stomatal aperture to decrease transpiration levels upon drought stress. Advanced technologies, such as wireless communications, the Internet of things (IoT), and smart sensors have been applied to practical smart farming and indoor planting systems to monitor plants’ signals effectively. In this study, we develop a flexible polyimide (PI)-based sensor for real-time monitoring of water conditions in tobacco plants. The stoma response, by which a plant adjusts to drought stress to maintain homeostasis, can be confirmed through the examination of evaporated water. Using a flexible PI-based sensor, a plant’s response variation is translated into an electrical signal. The sensors are integrated with a Bluetooth (BLE) module and a processing module and show potential as smart real-time water sensors in smart farms

Blue Light Upregulates Auxin Signaling and Stimulates Root Formation in Irregular Rooting of Rosemary Cuttings

Irregular rooting of rosemary stem cuttings, causing differences in either stem maturation or responses to growth conditions, restricts uniform production. Here, rooting efficiency of apical, middle, and basal cuttings from rosemary stems was evaluated by controlling light conditions to prevent irregular rooting. The types of light applied to the cuttings were natural sunlight (NSL), fluorescent, red, and blue (BL) light. Among these light sources, BL significantly induced root growth of not only basal cuttings, but also apical and middle cuttings, whereas NSL induced poor root formation in apical and middle cuttings. In particular, the roots of apical cuttings exposed to BL grew twice as fast as those exposed to other types of light. The overexpression of BL-induced IAA synthetic genes confirmed the rooting patterns. IAA synthetic genes were significantly upregulated by BL in the apical and middle cuttings. Irradiating with 50 μmol photons m−2 s−1 BL resulted in similar root production levels among the cutting positions with high biomass, guaranteeing the successful production of uniform cuttings. Thus, the application of proper high-intensity BL promoted healthy, similar-quality rosemary cuttings among stem cutting positions