A note on eigenvalues of random block Toeplitz matrices with slowly growing bandwidth

This paper can be thought of as a remark of \cite{llw}, where the authors studied the eigenvalue distribution $\mu_{X_N}$ of random block Toeplitz band matrices with given block order $m$. In this note we will give explicit density functions of $\lim\limits_{N\to\infty}\mu_{X_N}$ when the bandwidth grows slowly. In fact, these densities are exactly the normalized one-point correlation functions of $m\times m$ Gaussian unitary ensemble (GUE for short). The series $\{\lim\limits_{N\to\infty}\mu_{X_N}|m\in\mathbb{N}\}$ can be seen as a transition from the standard normal distribution to semicircle distribution. We also show a similar relationship between GOE and block Toeplitz band matrices with symmetric blocks.Comment: 6 page

A Novel Multiplex Network-based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

This work was supported by National Natural Science Foundation of China under Grant No. 61473203, and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.Peer reviewedPostprin

Method to determine test profile in accelerated reliability demonstration test under Type-I censoring

Conventional reliability demonstration test (RDT) based on statistical method is widely used in industry as it is simple and convenient to apply. But for products with high reliability and long life, this test method fails to satisfy the demand for short cycle and low cost, and is liable to cause the phenomenon of over-test and short-test. This paper gives a method to determine the accelerated stress profile for RDT under multiple stresses and mechanisms, making it faster to make decision of accept or reject. By raising the levels of sensitive stresses that the product would experience, the test time can be cut down remarkably. We can derive the overall acceleration factor based on the narrow reliability bounds theory. Then we choose the test plan referring to GJB 899A. Furthermore, combined with the reliability qualification test (RQT) profile, the accelerated test profile is acquired. An example is given to illustrate the superior performance of the proposed method over traditional methods

Dashboards to increase data-driven decision-making based on player's behaviour

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe gaming industry has seen a massive increase over the last decade, having also seen an important boom during the COVID-19 Pandemic. The growth of the game industry has led to a huge increase in the number of new players and a rise in the popularity of Free to Play (F2P) games. As a result, there has been a shift in the gaming industry from a flash game to an online game that is attracting a lot of players everyday. This new model of gaming has proven to be a successful way to gain revenue. This report will detail a clear roadmap through my journey as a Junior Data Analyst and how BI reporting tools such as Dashboards are the foundation of data literacy which leads to understanding player's behaviour and to data-driven decision around improvement and game innovation

Drivers of Research Impact: Evidence from the Top Three Finance Journals

We study the characteristics of all published papers in the top three finance journals (JF, JFE, and RFS) and how these paper characteristics affect the number of citations in Google Scholar and the Web of Science database. First, we find the characteristics in the universalist perspective remain constant while the characteristics in the constructivist and presentation perspectives increase over time. Second, some characteristics are significantly different between the high impact and the low impact papers. Third, paper quality, research method, journal placement, and paper age are the most important drivers. Last, different drivers play different roles in different journals

Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled images. In this paper, we reveal that cross-window consistency (CWC) is helpful in comprehensively extracting auxiliary supervision from unlabeled data. Additionally, we propose a novel CWC-driven progressive learning framework to optimize the deep network by mining weak-to-strong constraints from massive unlabeled data. More specifically, this paper presents a biased cross-window consistency (BCC) loss with an importance factor, which helps the deep network explicitly constrain confidence maps from overlapping regions in different windows to maintain semantic consistency with larger contexts. In addition, we propose a dynamic pseudo-label memory bank (DPM) to provide high-consistency and high-reliability pseudo-labels to further optimize the network. Extensive experiments on three representative datasets of urban views, medical scenarios, and satellite scenes demonstrate our framework consistently outperforms the state-of-the-art methods with a large margin. Code will be available publicly