Development of a Novel Sparse Labeling Method by Machine Learning‐Guided Engineering of Cre‐
            lox
            Recombination

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Involvement of nucleus in the maturation of dengue-2 virus in C6/36 cells

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100162120 ◽

1990 ◽

Vol 48 (3) ◽

pp. 912-913

Author(s):

Jaang J. Wang ◽

Cheng C. Chen ◽

Men F. Shaio ◽

Chia T. Liu ◽

Chung S. Lee ◽

...

Keyword(s):

Culture Medium ◽

Cytopathic Effect ◽

Fetal Bovine Serum ◽

Virus Particles ◽

Electron Microscopies ◽

Embedding Technique ◽

Fetal Bovine ◽

Flat Embedding ◽

20 Nm ◽

Labeling Method

The involvement of nucleus in the maturation processes of Dengue-2 virus in a mosquito cell line, C6/36 cells, has been identified by the electron microscopy and immunocytochemistry. The C6/36 cells were obtained from ATCC and maintained in MEM culture medium containing 10% fetal bovine serum at 28°C. The cell suspensions or cells grown on teflon-coated coverslips were infected with Dengue-2 virus (107/ml) for various time periods of 2 hours, 3, 6, 8, and 10 days. The cells were then fixed in buffered 1.5% glutaraldehyde, and washed in acetone before immunolabeled with monoclonal antibody. An indirect immunocytochemical labeling method of avidin-biotin complex (ABC) conjugated with peroxidase or gold particles (20 nm in diameter) and a flat embedding technique were used to localize the virus particles.At early stages of infections (before 3 days), there were no virion particles detected. After 6 days and on of infections, cytopathic effect (CPE) was observed and showed positive immuno-peroxidase reactions under the light and electron microscopies.

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