Transient response analysis of a submerged floating tunnel under seismic and wave excitations

In this study, a numerical procedure is described for the transient response analysis of a submerged\ud floating tunnel with reference of a designed tunnel in Japan. Tension legs seizing the tunnel are simply modeled by a\ud spring elements and the tunnel itself is assumed by two rigid bodies between which a flexible joint is used. A recorded\ud seismic excitation is used while the wave load is calculated under a specific design condition. Hydro-damping and\ud added mass are considered for numerically modeling the underwater condition. A numerical procedure is validated with\ud compared to the previous results of the designed tunnel. Some modifications are proposed through the validating\ud process in terms of modeling and analysis procedure. Eventually, the modified numerical procedure will be used in\ud analyzing the transient response of a newly designed tunnel

Estimating δ15N and δ13C in Barley and Pea Mixtures Using Near-Infrared Spectroscopy with Genetic Algorithm Based Partial Least Squares Regression

Stable isotope measurements have been increasingly used as a method to obtain information on relationships between plants and their environment (Dawson et al., 2002). Stable isotopes are seen as a powerful tool for advancing our knowledge on stock cycling and, nitrogen and carbon isotopic compositions have provided key insights into biogeochemical interactions between plants, soils and the atmosphere (Robinson, 2001). For the stable isotope measurements, the δ13C isotopic signature has been used successfully to disentangle physiological, ecological and biogeochemical processes and, δ15N studies have significantly improved our knowledge on nitrogen cycling pathways and nitrogen acquisition by plants (Vallano and Sparks, 2008). For the stable isotope measurements, traditional laboratory methods using isotope analysis are accurate and reliable, but usually time-consuming and expensive. Near-infrared spectroscopy (NIRS) analysis provides rapid, accurate and less expensive estimation. NIRS have been made to estimate herbage parameters using statistical methods such as multiple linear regression and partial least square regression (PLSR). PLSR uses all available wavebands in multivariate calibration for quantitative analysis of the spectral data. However, previous studies indicated that PLSR with waveband selection might improve their predictive accuracy in multivariate calibration at laboratory (Leardi, 2000) and the selection of appropriate wavelengths can refine the predictive accuracy of the PLS model by optimizing important spectral wavebands both in laboratory NIRS (Jiang et al., 2002). To optimize important spectral wavebands by wavelength selection, genetic algorithms (GA) is widely used, because GA has the ability to simulate the natural evolution of an individual and GA is well suited for solving variable subset selection problems (Ding et al., 1998). Barley and pea mixture is one of the most important forage species for livestock farming in Korea. To investigate nitrogen fixation and transfer in barley and pea mixture, stable isotope measurements was widely used. However, there was no research to estimate stable isotope in barley and pea mixture using NIRS in Korea. The aim of this study was to investigate performance of NIRS with PLSR using genetic algorithms based wavelength selection (GA-PLSR) and compare with PLSR without wavelength selection (FS-PLSR) for the estimation of δ15N and δ13C in barley and pea mixture

State Complexity of Regular Tree Languages for Tree Matching

We study the state complexity of regular tree languages for tree matching problem. Given a tree t and a set of pattern trees L, we can decide whether or not there exists a subtree occurrence of trees in L from the tree t by considering the new language L′ which accepts all trees containing trees in L as subtrees. We consider the case when we are given a set of pattern trees as a regular tree language and investigate the state complexity. Based on the sequential and parallel tree concatenation, we define three types of tree languages for deciding the existence of different types of subtree occurrences. We also study the deterministic top-down state complexity of path-closed languages for the same problem.</jats:p

Grouping-matrix based Graph Pooling with Adaptive Number of Clusters

Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assume that all input graphs share the same number of clusters. In inductive settings where the number of clusters can vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. The main intuition involves a grouping matrix defined as a quadratic form of the pooling operator, which induces use of binary classification probabilities of pairwise combinations of nodes. GMPool obtains the pooling operator by first computing the grouping matrix, then decomposing it. Extensive evaluations on molecular property prediction tasks demonstrate that our method outperforms conventional methods.Comment: 10 pages, 3 figure

Internal evaluation of a physically-based distributed model using data from a Mediterranean mountain catchment

An evaluation of the performance of a physically-based distributed model of a small Mediterennean mountain catchment is presented. This was carried out using hydrological response data, including measurements of runoff, soil moisture, phreactic surface level and actual evapotranspiration. A-priori model parameterisation was based as far as possible on property data measured in the catchment. Limited model calibration was required to identify an appropriate value for terms controlling water loss to a deeper regional aquifer. The model provided good results for an initial calibration period, when judge in terms of catchment discharge. However, model performance for runoff declined substantially when evaluated againts a consecutive, rather drier, period of data. Evaluation against other catchment responses allowed identification of the problems responsible for the observed lack of model robustness in flow simulation. In particular, it was shown that an incorrect parameterisation of the soil water was preventing adequate representation of drainage from soils during hydrogeraph recessions. This excess moisture was then being removed via an overestimation of evapotranspiration. It also appeared that the model underestimated canopy interception. The results presented here suggest that model evaluation against catchment scale variables summarising its water balance can be of great use in identifying problems with model parameterisation, even for distributed models. Evaluation using spatially distributed data yielded less useful information on model performance, owing to the relative sparseness of data points, and problems of mismatch of scale between the measurement and the model grid.This work was carried out as part of project VAHMPIRE (Validating Hydrological Models using Process Studies and Internal Data from Research Basins: tools for assessing the hydrological impacts of environmental change), which was funded by the European Commission Framework IV Environment and Climate Program (Contract No. ENV4- CT95-0134). Simulations were carried out on a UNIX workstation funded jointly by UK Nirex Ltd. and NERC grant GR3/ E0009.Peer Reviewe

Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (GATE). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that GATE outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.Comment: 12+11 pages, 6+1 figures, 0+7 table

A Note on Optimal Portfolio Selection and Diversification Benefits with a Short Sale Restriction on Real Estate Assets

This paper develops an optimal portfolio selection technique when short sales on real estate assets are restricted. Using the well-known mean-variance efficient concept, we are able to derive the optimal weights for portfolios consisting of both financial assets and real estate assets. Our paper provides a simple but powerful tool for portfolio managers to correctly construct mean-variance portfolios under short sale constraints.

DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset

As sharing images in an instant message is a crucial factor, there has been active research on learning an image-text multi-modal dialogue models. However, training a well-generalized multi-modal dialogue model remains challenging due to the low quality and limited diversity of images per dialogue in existing multi-modal dialogue datasets. In this paper, we propose an automated pipeline to construct a multi-modal dialogue dataset, ensuring both dialogue quality and image diversity without requiring minimum human effort. In our pipeline, to guarantee the coherence between images and dialogue, we prompt GPT-4 to infer potential image-sharing moments - specifically, the utterance, speaker, rationale, and image description. Furthermore, we leverage CLIP similarity to maintain consistency between aligned multiple images to the utterance. Through this pipeline, we introduce DialogCC, a high-quality and diverse multi-modal dialogue dataset that surpasses existing datasets in terms of quality and diversity in human evaluation. Our comprehensive experiments highlight that when multi-modal dialogue models are trained using our dataset, their generalization performance on unseen dialogue datasets is significantly enhanced. We make our source code and dataset publicly available.Comment: NAACL 202