Is the List of Incomplete Open Cubes Complete?

Variations of Incomplete Open Cubes is the major project by the twentieth-century conceptual artist Sol LeWitt. In this paper we interpret the enumerative component of the project as embeddings of graphs. This formulation permits use of an algorithm to check the completeness of the list of the structures produced by the artist. Our conclusion is that the artist found the correct number of structures (that is, 122), but that his list contains a mistake in the presentation of a pair of incomplete cubes, a discovery that appears not to have been noted before.     

Transparent niobate glass-ceramics for optical limiting

The construction of nonlinear optical materials featuring asymmetric transmission of light is of great technological importance for various applications, including optical switching and optical power limiting. A significant challenge is the scalable fabrication of material candidates with good photochemical stability, high optical transmittance, and excellent optical limiting performance. Here, we present a nanocrystallization avenue for constructing hybrid optical limiting materials that exhibit ultrafast and robust optical limiting performance. The experimental results show that the controllable relaxation of a niobate glass may lead to the clustering of Nb-O units and contracting of the bandgap. It results in the notable improvement in nonlinear optical properties, including the enhanced saturation irradiance (380 GW/cm²), doubly increased nonlinear coefficient, and decreased limiting threshold (200 GW/cm²). Our results suggest a promising material that exhibits promising applications for protecting eyes and sensitive components from laser-induced damage.     

简易棋盘格图像标定系统应用

棋盘格图像在摄像机标定中有非常广泛的应用,对于复杂环境且测量精度要求非常高的场合,会需要特定的且有一定复杂度的算法,但通常情况下,使用这样的标定系统是没有必要的,且会让初次应用的人觉得困难。这里提供一套应用简单,快速,对棋盘格图像具有旋转不变性,良好的鲁棒性,且对非复杂环境具有高精度的标定系统。所有算法均是在前辈们的算法上稍作改变而来,应用效果非常好。     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Semantic word shifts in a scientific domain

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.