Hybrid beamforming for single carrier mmWave MIMO systems

Hybrid analog and digital beamforming (HBF) has been recognized as an attractive technique offering a tradeoff between hardware implementation limitation and system performance for future broadband millimeter wave (mmWave) communications. In contrast to most current works focusing on the HBF design for orthogonal frequency division multiplexing based mmWave systems, this paper investigates the HBF design for single carrier (SC) systems due to the advantage of low peak-to-average power ratio in transmissions. By applying the alternating minimization method, we propose an efficient HBF scheme based on the minimum mean square error criterion. Simulation results show that the proposed scheme outperforms the conventional HBF scheme for SC systems.Comment: IEEE GlobalSIP2018, Feb. 201

HAVE RATING AGENCIES BECOME MORE CONSERVATIVE? EVIDENCES

Beginning in 2008, rating agencies have loosen their rating criteria of Chinese corporate bond rating.  The change in rating standard remains statistically significant after considering the macroeconomic factors.  A lack of diversification in ratings and failure to rate through economic cycle are found. As for the factors that have impact on the rating, Bigger size, higher profitability and better solvency help increase the rating for a corporate bound issuer, while higher liquidity, and lower leverage do harm to the credit rating. Such discoveries are consistent with the consitions in US corporate debt market.  Our conclusion is robust after multicollinearity test and adding additional macroeconomic explanatory variables

Heisenberg Uniqueness Pairs and the wave equation

Given a curve $\Gamma$ and a set $\Lambda$ in the plane, the concept of the Heisenberg uniqueness pair $(\Gamma, \Lambda)$ was first introduced by Hedenmalm and Motes-Rodr\'{\i}gez (Ann. of Math. 173(2),1507-1527, 2011, \cite{HM}) as a variant of the uncertainty principle for the Fourier transform. The main results of Hedenmalm and Motes-Rodr\'{\i}gez concern the hyperbola $\Gamma_{\epsilon}=\{(x_1, x_2)\in \mathbb{R}^2,\, x_1x_2=\epsilon\}$ ($0\ne\epsilon\in \mathbb{R}$) and lattice-crosses $\Lambda_{\alpha\beta}=(\alpha\mathbb{Z}\times \{0\})\cup(\{0\}\times \beta\mathbb{Z})$ ($\alpha, \beta>0$), where it's proved that $(\Gamma_{\epsilon}, \Lambda_{\alpha\beta})$ is a Heisenberg uniqueness pair if and only if $\alpha\beta\leq 1/|\epsilon|$. In this paper, we aim to study the endpoint case (i.e., $\epsilon=0$ in $\Gamma_{\epsilon}$) and investigate the following problem: what's the minimal amount of information required on $\Lambda$ (the zero set) to form a Heisenberg uniqueness pair? When $\Lambda$ is contained in the union of two curves in the plane, we give characterizations in terms of some dynamical system conditions. The situation is quite different in higher dimensions and we obtain characterizations in the case that $\Lambda$ is the union of two hyperplanes.Comment: 24 page

An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability

The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.Comment: 19 pages, 9 figures, 6 table

An All-In-One NLP Stock Market Backtester

Despite the popularity, we noticed that it is rather hard to verify a NLP/text-mining like stock prediction model\u27s performance due to the amount of groundwork needed. It is very typical a researcher will have to gather the plain text data, the company info, the stock market data, and categorize them in a way that is communicable with each other and the model; then the researcher will need to build a virtual trading platform that keeps track of all the trading signals generated by the model, log the activities in a certain way, then do some kinds of visualization for evaluations. To implement all these steps from ground up, it is required for a researcher to have certain level of proficiency on skills which are, from a research stand-point, fairly deviated from the nature of the NLP/text-mining model itself (like scraping a website and understanding the fundamental mechanism of trading in stock market). Thus, we like to build a set of lightweight tools that may automate such process to a certain degree.https://commons.case.edu/intersections-fa20/1022/thumbnail.jp

Examination of an Emerging Community of Practice for Instructional Designers: A Descriptive Case Study in a Midwestern University

This study examined the functioning of a group of instructional designers (IDs) in higher education through the lens of Communities of Practice (CoPs). The study particularly focused on whether and how the grouping of experienced and novice IDs operated as an effective CoP from the perspective of novices. The findings indicated that a group of IDs working in a midwestern university was able to cultivate a CoP within a clearly defined domain, a well-established community, and the shared practice with a specific body of knowledge. Particularly from the perspectives of novices, they highlighted the positive impact while participating in the CoP by contributing to their shared domain and defining who they are, developing expertise by interacting with experienced designers, and learning through different trajectories of participation. The rich description of this case study would further inform educators and practitioners in their efforts to improve the professional preparation and development for novice IDs in the higher education contexts