A New Method for Fast Computation of Moments Based on 8-neighbor Chain CodeApplied to 2-D Objects Recognition

2D moment invariants have been successfully applied in pattern recognition tasks. The main difficulty of using moment invariants is the computational burden. To improve the algorithm of moments computation through an iterative method, an approach for fast computation of moments based on the 8-neighbor chain code is proposed in this paper. Then artificial neural networks are applied for 2D shape recognition with moment invariants. Compared with the method of polygonal approximation, this approach shows higher accuracy in shape representation and faster recognition speed in experiment

A study on the negative binomial distribution motivated by Chv\'atal's theorem

Let $B(n,p)$ denote a binomial random variable with parameters $n$ and $p$. Chv\'{a}tal's theorem says that for any fixed $n\geq 2$, as $m$ ranges over $\{0,\ldots,n\}$, the probability $q_m:=P(B(n,m/n)\leq m)$ is the smallest when $m$ is closest to $\frac{2n}{3}$. Motivated by this theorem, in this note we consider the infimum value of the probability $P(X\leq E[X])$, where $X$ is a negative binomial random variable. As a consequence, we give an affirmative answer to the conjecture posed in [Statistics and Probability Letters, 200 (2023) 109871].Comment: 10 page

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

Calcium–magnesium–alumina–silicate (CMAS) resistance of LaPO4 thermal barrier coatings

Nanostructured LaPO4 thermal barrier coatings (TBCs) were prepared by air plasma spraying, and their resistance to calcium–magnesium–alumina–silicate (CMAS) attack at 1250 °C, 1300 °C and 1350 °C was investigated. The reaction products were characterized by X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy and transmission electron microscopy. Exposed to CMAS attack for 0.5 h, a continuous dense reaction layer formed, which was mainly composed of P–Si apatite based on Ca2+xLa8-x(PO4)x(SiO4)6-xO2, anorthite and spinel phases. Beneath the reaction layer, little evidence of CMAS trace could be found. With the increase in temperature and heat treatment duration, the reaction layer became thick, while penetration depth of the molten CMAS changed slightly. Due to the formation of a reaction layer suppressing CMAS further infiltration, LaPO4 TBCs are highly resistant to CMAS attack