Does Rational Bubbles Exist in the Taiwan Stock Market? Evidence from a Nonparametric Cointegration Test

In this study, we revisit the issue as to the presence of rational bubbles in the Taiwan stock market during the June 1991 to February 2005 period using the Bierens (1997) nonparametric cointegration tests. The results from the Bierens nonparametric cointegration test attest to the absence of rational bubbles in the Taiwan stock market.Rational Bubbles Taiwan Stock Market Nonparametric Cointegration Test

Shape restricted regression with random Bernstein polynomials

Shape restricted regressions, including isotonic regression and concave regression as special cases, are studied using priors on Bernstein polynomials and Markov chain Monte Carlo methods. These priors have large supports, select only smooth functions, can easily incorporate geometric information into the prior, and can be generated without computational difficulty. Algorithms generating priors and posteriors are proposed, and simulation studies are conducted to illustrate the performance of this approach. Comparisons with the density-regression method of Dette et al. (2006) are included.Comment: Published at http://dx.doi.org/10.1214/074921707000000157 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Mycobacterium tuberculosis and M. bovis infection in Feedlot Deer (Cervus unicolor swinhoei and C. nippon taiouanus) in Taiwan

Background/purposeMycobacterium bovis frequently infects wild and farm deer species with tuberculosis. This study investigated mycobacterial infection in two native deer species Cervus unicolor swinhoei (Formosan Sambar, Sambar) and C. nippon taiouanus (Formasan Sika, Sika).MethodsBased on different sampling sources of 19 intradermal tuberculin test (ITT) Sambar, mycobacterial infection and/or species were detected by acid-fast stain, duplex polymerase chain reaction (PCR) and multiplex nested PCR (mnPCR) methods, traditional mycobacterial culture and gross lesion. Blood samples of 167 Sambar deer and 147 Sika deer were then tested by duplex PCR and mnPCR methods to investigate the prevalence of mycobacterial infection. Sequence variations of these mycobacterial species were analyzed as well.ResultsDuplex PCR and mnPCR assays could differentiate between MTBC (M. bovis and M. tuberculosis) and M. avium, as well as between M. bovis and M. tuberculosis, respectively. These PCR methods showed a higher detection rate than traditional culture and matched the gross lesions examined in 19 ITT-examined Sambar. Therefore, the mycobacterial infection in blood samples of 314 deer samples was detected using these PCR methods. Duplex PCR and mnPCR showed an identical prevalence of 16.1% in Sambar and 8.2% in Sika and a significant difference in prevalence between these two deer species. M. bovis and M. tuberculosis were the species detected in feedlot Sambar and Sika. M. tuberculosis was found only and first in Sambar fed in central Taiwan. Sequence analysis revealed diverse genetic variations in M. bovis and M. tuberculosis associated with deer subspecies.ConclusionMultiplex PCR methods were established, and M. bovis and M. tuberculosis were identified in feedlot deer in Taiwan. Sequence variations indicated diverse sources of both mycobacterial species

Measuring fraud in insurance industry: The case of automobile insurance in Taiwan

By conducting an extensive exploration on claim data, this paper attempts to investigate the fraud problem in Taiwan automobile physical damage insurance. Based on the different claim patterns between data in calendar year and policy year, excess claims are significantly identified in the last month of policy year. Censored regression provides robust estimation concerning the sources of the fraud payment

Leveling Maintenance Mechanism by Using the Fabry-Perot Interferometer with Machine Learning Technology

This study proposes a method for maintaining parallelism of the optical cavity of a laser interferometer using machine learning. The Fabry-Perot interferometer is utilized as an experimental optical structure in this research due to its advantage of having a brief optical structure. The supervised machine learning method is used to train algorithms to accurately classify and predict the tilt angle of the plane mirror using labeled interference images. Based on the predicted results, stepper motors are fixed on a plane mirror that can automatically adjust the pitch and yaw angles. According to the experimental results, the average correction error and standard deviation in 17-grid classification experiment are 32.38 and 11.21 arcseconds, respectively. In 25-grid classification experiment, the average correction error and standard deviation are 19.44 and 7.86 arcseconds, respectively. The results show that this parallelism maintenance technology has essential for the semiconductor industry and precision positioning technology

MADS-Box Gene Classification in Angiosperms by Clustering and Machine Learning Approaches

The MADS-box gene family is an important transcription factor family involved in floral organogenesis. The previously proposed ABCDE model suggests that different floral organ identities are controlled by various combinations of classes of MADS-box genes. The five-class ABCDE model cannot cover all the species of angiosperms, especially the orchid. Thus, we developed a two-stage approach for MADS-box gene classification to advance the study of floral organogenesis of angiosperms. First, eight classes of reference datasets (A, AGL6, B12, B34, BPI, C, D, and E) were curated and clustered by phylogenetic analysis and unsupervised learning, and they were confirmed by the literature. Second, feature selection and multiple prediction models were curated according to sequence similarity and the characteristics of the MADS-box gene domain using support vector machines. Compared with the BindN and COILS features, the local BLAST model yielded the best accuracy. For performance evaluation, the accuracy of Phalaenopsis aphrodite MADS-box gene classification was 93.3%, which is higher than 86.7% of our previous classification prediction tool, iMADS. Phylogenetic tree construction – the most common method for gene classification yields classification errors and is time-consuming for analysis of massive, multi-species, or incomplete sequences. In this regard, our new system can also confirm the classification errors of all the random selection that were incorrectly classified by phylogenetic tree analysis. Our model constitutes a reliable and efficient MADS-box gene classification system for angiosperms

D4AM: A General Denoising Framework for Downstream Acoustic Models

The performance of acoustic models degrades notably in noisy environments. Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. However, existing training objectives of SE methods are not fully effective at integrating speech-text and noisy-clean paired data for training toward unseen ASR systems. In this study, we propose a general denoising framework, D4AM, for various downstream acoustic models. Our framework fine-tunes the SE model with the backward gradient according to a specific acoustic model and the corresponding classification objective. In addition, our method aims to consider the regression objective as an auxiliary loss to make the SE model generalize to other unseen acoustic models. To jointly train an SE unit with regression and classification objectives, D4AM uses an adjustment scheme to directly estimate suitable weighting coefficients rather than undergoing a grid search process with additional training costs. The adjustment scheme consists of two parts: gradient calibration and regression objective weighting. The experimental results show that D4AM can consistently and effectively provide improvements to various unseen acoustic models and outperforms other combination setups. Specifically, when evaluated on the Google ASR API with real noisy data completely unseen during SE training, D4AM achieves a relative WER reduction of 24.65% compared with the direct feeding of noisy input. To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems. Our code is available at https://github.com/ChangLee0903/D4AM

Serum total antioxidant capacity reflects severity of illness in patients with severe sepsis

INTRODUCTION: We conducted the present study to evaluate the changes in serum total antioxidant capacity (TAC) in patients with severe sepsis and to investigate the association between serum TAC and clinical severity. METHOD: This was a prospective observational study involving a sample of patients who met established criteria for severe sepsis and were admitted to the emergency department of a university teaching hospital. Serum TAC was determined using the total radical-trapping antioxidant parameter method. The levels of TAC, uric acid, albumin, and bilirubin in sera were obtained in the emergency department and evaluated to determine whether there were any correlations between the major antioxidant biomarkers and clinical severity of sepsis. The Acute Physiology and Chronic Health Evaluation (APACHE) II score was used for clinical evaluation of the severity of sepsis. RESULTS: A total of 73 patients with sepsis, with a mean (± standard deviation) APACHE II score of 23.2 ± 8.2 and a mortality rate of 26.0%, were included. Seventy-six healthy individuals served as control individuals. Among the patients, serum TAC levels correlated significantly with APACHE II scores. Patients who died also had higher TAC than did those who survived. Serum uric acid levels correlated significantly with serum TAC and APACHE II scores in patients with severe sepsis. CONCLUSION: Elevated serum TAC level may reflect clinical severity of sepsis. In addition, serum uric acid levels appear to contribute importantly to the higher TAC levels observed in patients with severe sepsis