FORECASTING DAILY VOLATILITY USING RANGE-BASED DATA

Users of agricultural markets frequently need to establish accurate representations of expected future volatility. The fact that range-based volatility estimators are highly efficient has been acknowledged in the literature. However, it is not clear whether using range-based data leads to better risk management decisions. This paper compares the performance of GARCH models, range-based GARCH models, and log-range based ARMA models in terms of their forecasting abilities. The realized volatility will be used as the forecasting evaluation criteria. The conclusion helps establish an efficient forecasting framework for volatility models.Marketing,

New Method of High Quality and High Speed Drilling Based on Stratigraphic Naturally Whipstocking Law

High steep dip formation is one of the important keys effecting well deviation and azimuth during drilling. According to space gridding data of formation,calculation method of angle and tendency and analysis model of offset of deviation and azimuth are derived considering formation anisotropy. Combined with the field experiment, calculation orbit is similar to true track, also increasing drilling speed. The result shows that using formation natural deflecting law to optimize well position and design well trajectory, to increase drill pressure in order to increase ROP while not effect reaching the target naturally are feasible. This indicates analysis method is correct and reasonable. It has important theoretical value and practical worth in engineering.Key words: Formation anisotropy; Whipstocking law; Weight on bit; New drilling metho

RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images

With the rapid development of the image generation technologies, the malicious abuses of the GAN-generated fingerprint images poses a significant threat to the public safety in certain circumstances. Although the existing universal deep forgery detection approach can be applied to detect the fake fingerprint images, they are easily attacked and have poor robustness. Meanwhile, there is no specifically designed deep forgery detection method for fingerprint images. In this paper, we propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images, to the best of our knowledge. Specifically, we firstly construct a ridge stream, which exploits the grayscale variations along the ridges to extract unique fingerprint-specific features. Then, we construct a generation artifact stream, in which the FFT-based spectrums of the input fingerprint images are exploited, to extract more robust generation artifact features. At last, the unique ridge features and generation artifact features are fused for binary classification (\textit{i.e.}, real or fake). Comprehensive experiments demonstrate that our proposed approach is effective and robust with low complexities.Comment: 10 pages, 8 figure

Machine Learning for Cancer Drug Combination

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154605/1/cpt1773_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154605/2/cpt1773.pd