Time-varying β\beta-model for dynamic directed networks

We extend the well-known $\beta$-model for directed graphs to dynamic network setting, where we observe snapshots of adjacency matrices at different time points. We propose a kernel-smoothed likelihood approach for estimating $2n$ time-varying parameters in a network with $n$ nodes, from $N$ snapshots. We establish consistency and asymptotic normality properties of our kernel-smoothed estimators as either $n$ or $N$ diverges. Our results contrast their counterparts in single-network analyses, where $n\to\infty$ is invariantly required in asymptotic studies. We conduct comprehensive simulation studies that confirm our theory's prediction and illustrate the performance of our method from various angles. We apply our method to an email data set and obtain meaningful results

An efficient approach to separate CO2 using supersonic flows for carbon capture and storage

The mitigation of CO2 emissions is an effective measure to solve the climate change issue. In the present study, we propose an alternative approach for CO2 capture by employing supersonic flows. For this purpose, we first develop a computational fluid dynamics (CFD) model to predict the CO2 condensing flow in a supersonic nozzle. Adding two transport equations to describe the liquid fraction and droplet number, the detailed numerical model can describe the heat and mass transfer characteristics during the CO2 phase change process under the supersonic expansion conditions. A comparative study is performed to evaluate the effect of CO2 condensation using the condensation model and dry gas assumption. The results show that the developed CFD model predicts accurately the distribution of the static temperature contrary to the dry gas assumption. Furthermore, the condensing flow model predicts a CO2 liquid fraction up to 18.6% of the total mass, which leads to the release of the latent heat to the vapour phase. The investigation performed in this study suggests that the CO2 condensation in supersonic flows provides an efficient and eco-friendly way to mitigate the CO2 emissions to the environment

DORE: Document Ordered Relation Extraction based on Generative Framework

In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dependencies. However, previous generative methods using lexical representation do not naturally fit document-level relation extraction (DocRE) where there are multiple entities and relational facts. In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Moreover, we design a parallel row generation method to process overlong target sequences. Besides, we introduce several negative sampling strategies to improve the performance with balanced signals. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models. We have released our code at https://github.com/ayyyq/DORE.Comment: Findings of EMNLP 202

A dynamic simulation model for financing strategy management of infrastructure PPP projects

Strategic management is vital for significant infrastructure public-private partnership (PPP) projects characterised by a heavy and irreversible investment over a long period. In PPP projects, the financing strategy relates to the capital structure of the project and the coordination of the participants’ requirements. In this paper, a system dynamics (SD) model is described to analyse the impacts of two types of financing strategies on the needs of creditors, the government, and private investors, considering the dynamic and complex characteristics of infrastructure PPP projects. The proposed model has been implemented on a PPP highway project. A number of experiments were conducted over a 33-year strategic planning horizon as a means of assessing the long-term effects of different financing strategies. The experimental results reveal that the model is a useful tool that could support decision-makers in identifying the intervals with different management focus of financing risk and comparing different financing strategies to choose the optimal one. It is especially helpful for the government to select a financing strategy for infrastructure PPP projects with capital limitations

TR-2008005: Weakly Random Additive Preconditioning for Matrix Computations

Our weakly random additive preconditioners facilitate the solution of linear systems of equa-tions and other fundamental matrix computations. Compared to the popular SVD-based multiplicative preconditioners, these preconditioners are generated more readily and for a much wider class of input matrices. Furthermore they better preserve matrix structure and sparseness and have a wider range of applications, in particular to linear systems with rectangular coefficient matrices. We study the generation of such preconditioners and their impact on conditioning of the input matrix. Our analysis and experiments show the power of our approach even where w