An Oral History Project of Chinese Americans: Life in Between

This is a paper combines my observations and interviewees' testimonies. It is a project about Chinese Americans, who carry two cultures in their bodies and get over difficult transitions over years

Rapid Determination of Suitable Reinforcement Type in Continuous-Fibre-Reinforced Composites For Multiple Load Cases

With respect to their extraordinary weight-specific mechanical properties, continuous-fibre-reinforced plastics (CoFRP) have drawn increasing attention for use in load bearing structures. Significant effort has been carried out with respect to optimising CoFRP-components for maximum structural performance [1, 2]. Besides conventional optimisation techniques, e.g. topology or thickness optimisation, CoFRPs offer further potential for tailoring to lightweight requirements: Apart from choice of fibre and matrix material, the inherently anisotropic behaviour gives additional design freedom for engineering, e.g. fibre orientation and stacking sequence. Although offering high lightweight potential, determining a robust optimum with CoFRPs is a great challenge, especially, when multiple load cases need to be considered. This work proposes numerical analysis of principal stresses in multiple load cases to assess the lightweight potential when applying CoFRPs. Three common layups are considered: quasi-isotropic (QI) as well as bidirectional (BD) and unidirectional (UD) reinforcement. Principal stresses and their directions are obtained in Finite Element simulations. In extension of previous work [3,4], an algorithm is presented which methodologically determines the most suitable layup-type and orientation for each element across all considered load cases: Thereby, regions are identified, in which UD, BD or QI, respectively, are most favourable. Subsequently, each element per region is accordingly updated with new material properties, the simulations are rerun und the evaluation procedure repeated until convergence. The multi-load-case optimisation results are compared against separate optimisation of each individual load case and found to give meaningful results. The methodology is demonstrated using two generic geometries and one real-world load-bearing component. It is found to reliably allocate most beneficial reinforcement types with low computational effort compared to iterative parameter optimisation algorithms and is thus deemed to facilitate a lean part and process design under consideration of multiple load cases

Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework

Recent years have witnessed the great success of Graph Neural Networks (GNNs) in handling graph-related tasks. However, MLPs remain the primary workhorse for practical industrial applications due to their desirable inference efficiency and scalability. To reduce their gaps, one can directly distill knowledge from a well-designed teacher GNN to a student MLP, which is termed as GNN-to-MLP distillation. However, the process of distillation usually entails a loss of information, and ``which knowledge patterns of GNNs are more likely to be left and distilled into MLPs?" becomes an important question. In this paper, we first factorize the knowledge learned by GNNs into low- and high-frequency components in the spectral domain and then derive their correspondence in the spatial domain. Furthermore, we identified a potential information drowning problem for existing GNN-to-MLP distillation, i.e., the high-frequency knowledge of the pre-trained GNNs may be overwhelmed by the low-frequency knowledge during distillation; we have described in detail what it represents, how it arises, what impact it has, and how to deal with it. In this paper, we propose an efficient Full-Frequency GNN-to-MLP (FF-G2M) distillation framework, which extracts both low-frequency and high-frequency knowledge from GNNs and injects it into MLPs. Extensive experiments show that FF-G2M improves over the vanilla MLPs by 12.6% and outperforms its corresponding teacher GNNs by 2.6% averaged over six graph datasets and three common GNN architectures

Genomic Scaffold Filling Revisited

The genomic scaffold filling problem has attracted a lot of attention recently. The problem is on filling an incomplete sequence (scaffold) I into I\u27, with respect to a complete reference genome G, such that the number of adjacencies between G and I\u27 is maximized. The problem is NP-complete and APX-hard, and admits a 1.2-approximation. However, the sequence input I is not quite practical and does not fit most of the real datasets (where a scaffold is more often given as a list of contigs). In this paper, we revisit the genomic scaffold filling problem by considering this important case when, (1) a scaffold S is given, the missing genes X = c(G) - c(S) can only be inserted in between the contigs, and the objective is to maximize the number of adjacencies between G and the filled S\u27 and (2) a scaffold S is given, a subset of the missing genes X\u27 subset X = c(G) - c(S) can only be inserted in between the contigs, and the objective is still to maximize the number of adjacencies between G and the filled S\u27\u27. For problem (1), we present a simple NP-completeness proof, we then present a factor-2 greedy approximation algorithm, and finally we show that the problem is FPT when each gene appears at most d times in G. For problem (2), we prove that the problem is W[1]-hard and then we present a factor-2 FPT-approximation for the case when each gene appears at most d times in G

Discussion on Current Pollution Status andLegislation of Environmental Hormone in China

AbstractEnvironmental hormone mainly cause harm to the reproductive function of human being and animal kingdom, which is a serious threat to human and animal survival or reproduction. This paper blacklisted 70 kinds of environmental hormones, studied pollution situation of some environmental hormone (which are remaining in the atmosphere, water, sediment, soil, seafood and human tissue) in Chinese Environment and population, and outlined the environmental hormone pollution hazards. Therefore, China should strengthen basic research in environmental hormone pollution, strengthen the environmental management and the publicity of environmental law to curb environmental hormone pollution. In addition, some legislative measures should be proposed, laws and regulations of environmental hormones should be formulated, and the law enforcement should be strengthen to control environmental hormones

Unifying Gaussian Dynamic Term Structure Models from a Heath-Jarrow-Morton Perspective

In this paper, we show that most existing Gaussian dynamic term structure models (GDTSMs) can be nested as special cases under a unified Heath-Jarrow-Morton (HJM)-based framework of GDTSM construction. Our study provides not only a systematic way to examine the commonality of many seemingly distinct GDTSMs, but also a novel and convenient approach to constructing GDTSMs that are otherwise unavailable or intractable under the traditional approach. In our empirical study using the Euro area forward rates, we conduct a specification analysis based on this novel approach. The analysis reveals that the traditional models impose restrictive constraints limiting their flexibility in capturing key features of the correlations and volatilities of the forward rates