The Informativeness of Text, the Deep Learning Approach

This paper uses a deep learning natural language processing approach (Google's Bidirectional Encoder Representations from Transformers, hereafter BERT) to comprehensively summarize financial texts and examine their informativeness. First, we compare BERT's effectiveness in sentiment classification in financial texts with that of a finance specific dictionary, the naïve Bayes, and Word2Vec, a shallow machine learning approach. We find that first, BERT outperforms all other approaches, and second, pre-training BERT with financial texts further improves its performance. Using BERT, we show that conference call texts provide information to investors and that other less accurate approaches underestimate the economic significance of textual informativeness by at least 25%. Last, textual sentiments summarized by BERT can predict future earnings and capital expenditure, after controlling for financial statement based determinants commonly used in finance and accounting research

Box-counting measure of metric spaces

In this paper, we introduce a new notion called the \emph{box-counting measure} of a metric space. We show that for a doubling metric space, an Ahlfors regular measure is always a box-counting measure; consequently, if $E$ is a self-similar set satisfying the open set condition, then the Hausdorff measure restricted to $E$ is a box-counting measure. We show two classes of self-affine sets, the generalized Lalley-Gatzouras type self-affine sponges and Bara\'nski carpets, always admit box-counting measures; this also provides a very simple method to calculate the box-dimension of these fractals. Moreover, among others, we show that if two doubling metric spaces admit box-counting measures, then the multi-fractal spectra of the box-counting measures coincide provided the two spaces are Lipschitz equivalent

Integrating SPC and EPC for Multivariate Autocorrelated Process

Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling’s T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process

Internship Evaluation from the Perspective of Technological University Students in Taiwan

Abstract-Recently senior technological university students are asked whether they are going to choose an internship course or not. What is the key determinant considered by them? Do the incentives provided by the enterprise and school work? The aim of the paper is to propose an internship evaluation model by Analytic Hierarchy Process (AHP) method, from the technological university students' view in Taiwan. The results indicate that the incentives provided by the enterprise and school do matter. Such an internship evaluation model could serve as a decision-making mechanism for the schools, students and enterprises

Antimicrobial susceptibility patterns among Escherichia coli urinary isolates from community-onset health care-associated urinary tract infection

Urinary tract infection (UTI) is traditionally classified as community-acquired (CA) and hospital-acquired (HA). Community-onset health care-associated (HCA) infection is a new category that has gained increasing attention. The study aimed to compare the disk susceptibility of nonrepetitive Escherichia coli urinary isolates from HCA-UTI (n = 100) with that of E. coli isolates from CA-UTI (n = 85) and HA-UTI (n = 106). We found that the susceptibility pattern of HCA-UTI E. coli isolates was similar to that of HA-UTI E. coli isolates, but significantly different from that of CA-UTI E. coli isolates. In particular, the proportion of extended-spectrum β-lactamase-producing isolates was significantly higher in HCA-UTI than that in CA-UTI (30.0% vs. 3.5%, p < 0.001). We recommend that when treating HCA-UTI, it is necessary to take urine cultures for susceptibility testing to guide definite antibiotic therapy

Dyonic Reissner-Nordstrom Black Holes and Superradiant Stability

Black holes immersed in magnetic fields are believed to be important systems in astrophysics. One interesting topic on these systems is their superradiant stability property. In the present paper, we analytically obtain the superradiantly stable regime for the asymptotically flat dyonic Reissner-Nordstrom black holes with charged massive scalar perturbation. The effective potential experienced by the scalar perturbation in the dyonic black hole background is obtained and analyzed. It is found that the dyonic black hole is superradiantly stable in the regime $0<r_{-}/r_{+}<2/3$, where $r_\pm$ are the event horizons of the dyonic black hole. Compared with the purely electrically charged Reissner-Nordstrom black hole case, our result indicates that the additional coupling of the charged scalar perturbation with the magnetic field makes the black hole and scalar perturbation system more superradiantly unstable, which provides further evidence on the instability induced by magnetic field in black hole superradiance process