Using Pattern Recognition for Investment Decision Support in Taiwan Stock Market

In Taiwan stock market, it has been accumulated large amounts of time series stock data and successful investment strategies. The stock price, which is impacted by various factors, is the result of buyer-seller investment strategies. Since the stock price reflects numerous factors, its pattern can be described as the strategies of investors. In this paper, pattern recognition concept is adapted to match the current stock price trend with the repeatedly appearing past price data. Accordingly, a new method is introduced in this research that extracting features quickly from stock time series chart to find out the most critical feature points. The matching can be processed via the corresponding information of the feature points. In other words, the goal is to seek for the historical repeatedly appearing patterns, namely the similar trend, offering the investors to make investment strategies

Quantum storage and manipulation of heralded single photons in atomic quantum memories

We demonstrate the storage and manipulation of narrowband heralded single photons from a cavity-enhanced spontaneous parametric downconversion (SPDC) source in the atomic quantum memory based on electromagnetically induced transparency. We show that nonclassical correlations are preserved between the heralding and the retrieved photons after storage process. By varying the intensity of the coupling field during retrieval process, we further demonstrate that the waveform or bandwidth of the single photons can be manipulated and the nonclassical correlation between the photon pairs can be even enhanced. Unlike previous works, our SPDC source is single mode in frequency, which not only reduces the experimental complexity arising from external filtering but also increases the useful photon generation rate. Our results can be scaled up with ease and thus lay the foundation for future realization of large-scale applications in quantum information processing

Fisher information analysis on quantum-enhanced parameter estimation in electromagnetically-induced-transparency spectrum with single photons

Electromagnetically-induced-transparency (EIT) spectroscopy has been used as a sensitive sensor in quantum metrology applications. The sensitivity of a sensor strongly depends on the measurement precision of EIT spectrum. In this work, we present a theoretical study of the spectral lineshape measurement on a three-level $\Lambda$-type EIT media based on Fisher information (FI) analysis. Using two kinds of probing source: the single-photon Fock state and the coherent state, we calculate the FI in an EIT medium and quantify the quantum advantage and limitations of the single-photon probe. The analysis of FI structure also provides a clear picture to classify the spectral lineshape into two different regimes, the EIT and Aulter-Townes splitting (ATS). This work provides a systematic analysis of the single-photon EIT spectrum, which provides essential knowledge of quantum sensing based on EIT and deepens our understanding of spectral characteristics of $\Lambda$-type media.Comment: 15 pages, 15 figure

DDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In Machine-Assisted Skin Disease Detection

Skin tone as a demographic bias and inconsistent human labeling poses challenges in dermatology AI. We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models. To study this, we first propose a robust labeling method to quantify color contrast scores of each image and validate our method by showing small labeling variations. More importantly, applying our method to \textit{the only} diverse-skin tone and pathologically-confirmed skin disease dataset DDI, yields \textbf{DDI-CoCo Dataset}, and we observe a performance gap between the high and low color difference groups. This disparity remains consistent across various state-of-the-art (SoTA) image classification models, which supports our hypothesis. Furthermore, we study the interaction between skin tone and color difference effects and suggest that color difference can be an additional reason behind model performance bias between skin tones. Our work provides a complementary angle to dermatology AI for improving skin disease detection.Comment: 5 pages, 4 figures, 2 tables, Accepted to ICASSP 202

PHYSIOLOGICAL AND ELECTROMYOGRAPHIC RESPONSES AT THREE LEVELS OF BICYCLE SEAT HEIGHT

Recently, bicycle riding has become one of the most popular exercises. As the use time increased, the risk of pedalling injury raised. Holmes (1994) indicated that inappropriate bicycle saddle height could result in lower limbs injuries. The motivation of this study was to find out the best riding position that could effectively use energy from the physiology and electromyography measures. The oxygen consumption (VO2), heart rate (HR), respiratory exchange ratio (RER) and the muscle activity (electromyography, EMG) from rectus femoris (RF) and biceps femoris (BF) of lower limb were collected during a 6 min cycling trail in three different heights of bicycle saddle. The purpose of this study was to compare the effects of three different types of bicycle seat heights and different perspectives of muscle activity and physiology’s parameters