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Phase error of the demodulation clock in the Coriolis vibratory gyroscope system allows the quadrature errors to leak into the sense channel and causes significant bias and temperature drift at the rate output. A phase self-correction method to suppress the temperature drift of the bias in gyroscopes is proposed. Through sweeping the demodulation clock phase and simultaneously monitoring the mechanical quadrature error output in gyroscopes, the optimal demodulation clock phase with minimum relatively phase shift is determined. Thus the bias influenced by the temperature and surroundings can be calibrated on-chip at start-up or when the environment changes drastically without the requirement of the complicated instruments. The proposed approach is validated by a silicon MEMS gyroscope with the natural frequency of 2.8kHz, which shows nearly 22 times improvement in the temperature sensitivity of the system bias, from 550mdeg/s/^∘C down to 24.7mdeg/s/^∘C.

Using convexity properties of reciprocals of zeta functions, especially the reciprocal of the Riemann zeta function, we show that certain weighted Mertens sums are biased in favor of square-free integers with an odd number of prime factors. We study such type of bias for different ranges of the parameters and then consider generalizations to Mertens sums supported on semigroups of integers generated by relatively large subsets of prime numbers. We further obtain a wider range for the parameters both unconditionally and then conditionally on the Riemann Hypothesis. At the same time, we extend to certain semigroups, two classical summation formulas originating from the works of Landau concerning the behavior of derivatives of the reciprocal of the Riemann zeta function at $s=1$.

Titanium (Ti) thin films with (002) or (100) texture are favored for many applications. In this paper, Ti thin films were prepared by high-power pulsed magnetron sputtering, and the texture of Ti thin films was successfully tailored by adjusting the pulse width, substrate bias and magnetic field strength. It is found that the peak power and average power of the Ti target are increased by increasing sputtering pulse width and decreasing magnetic field strength, which raise the Ti plasma flux and ion/atom ratio of Ti ions in front of the substrate simultaneously. Ti thin films with a highly (002) out-of-plane texture can be obtained with higher pulse width and lower magnetic field strength. The Ti thin films with highly (100) out-of-plane texture can be achieved with shorter pulse width, lower magnetic field strength and substrate bias.

In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably class imbalance and covariate shift. In this work, we aim to shed light on this topic in order to increase the overall attention to this issue in the field of machine learning. We propose a scalable novel framework for reducing multiple biases in high-dimensional data sets in order to train more reliable predictors. We apply our methodology to the detection of irregular power usage from real, noisy industrial data. In emerging markets, irregular power usage, and electricity theft in particular, may range up to 40% of the total electricity distributed. Biased data sets are of particular issue in this domain. We show that reducing these biases increases the accuracy of the trained predictors. Our models have the potential to generate significant economic value in a real world application, as they are being deployed in a commercial software for the detection of irregular power usage.

We report on a theoretical study of magnetoresistance (MR) effect in a magnetically modulated semiconductor heterostructure (MMSH) under an applied bias, which can be constructed on surface of ${\mathrm{\text{GaAs/Al}}}_x{\mathrm{\text{Ga}}}_{1-x}\mathrm{\text{As}}$ heterostructure by depositing two asymmetric ferromagnetic (FM) stripes. Bias-dependent transmission and conductance are calculated numerically, on the basis of both improved transfer matrix method (ITMM) and Landauer–Büttiker conductance theory. An obvious MR effect appears because of a significant difference of transmission between parallel and antiparallel (AP) magnetization configurations. Moreover, MR ratio can be tuned by the bias. These interesting features not only provide an alternative way to manipulate MR effect, but also may lead to an electrically-controllable MR device.

This study examines the accuracy and bias associated with the analysts' earnings forecasts of Taiwanese firms. Using the forecast data of individual analysts over 1991–1997 from the I/B/E/S database, we find that analysts' forecasts of earnings are generally more accurate than the predictions of a naïve forecasting model. However, this superiority seems to be largely confined to shorter forecast horizons. We also find that the analysts' earnings forecasts of Taiwanese firms are optimistically biased and that the bias depends on the nature of the earnings news. In addition, analysts' forecasts appear to be more accurate for larger firms and the bias also decreases with firm size. We find some variation in forecast accuracy and bias across industries but the overall results are not driven by any specific time period.

This paper presents some novel entropy estimators of a continuous random variable using simple random sampling (SRS), ranked set sampling (RSS), and double RSS (DRSS) schemes. The theoretical results of the proposed entropy estimators are derived. The proposed entropy estimators are compared in terms of the bias and the root mean squared errors, theoretically and numerically, with the Vasicek O. [A test for normality based on sample entropy, J. R. Stat. Soc. B38:54–59, 1976.] entropy estimators using SRS, RSS, and DRSS schemes. It turns out that the new novel entropy estimators are substantially better than the existing Vasicek’s entropy estimators.

Point estimation can be used in both predictive and descriptive data mining tasks. Three classical point estimation methods — the method of moments, maximum likelihood estimation, and the Expectation-Maximization algorithm — are discussed in this chapter, followed by a review of measurements of estimation performance. This chapter intends to introduce basic concepts and methods of point estimation. These concepts and methods are the basis for more advanced estimation techniques.