Abstract: Implementations of this specification provide prediction methods and apparatuses for adjusting computing power. One method comprises receiving a prediction request, wherein the prediction request comprises a sample to be tested, determining a computing power coefficient allocated to the prediction request, wherein the computing power coefficient indicates a proportion of hardware computing power resources allocated to the prediction request to total hardware computing power resources needed for a neural network model to run on a computing platform, determining k sub-networks inn sub-networks of the neural network model to be used for a present time based on the computing power coefficient, where n>2, and inputting the sample to be tested to the k sub-networks to obtain a prediction result.

Abstract: A target sample including a sample feature, a first label, and a second label is obtained, where a user corresponds to the target sample, the first label indicates whether a target object is clicked on by the user, and the second label indicates whether the user implements a target behavior related to the target object. Model processing is performed on the sample feature using a prediction model with a first branch and first probability and a second branch and second probability. A first loss is determined based on a first label value and the first probability. When a predetermined condition is satisfied, the second loss is determined based on a second label value of the second label and the second probability, and a predicted loss of the target sample is determined based on the first loss and the second loss. Training the prediction model based on the predicted loss.

Abstract: The present invention discloses a bi-facial photovoltaic power generation module, comprising a transparent box, and a cell string and a mounting base which are installed inside the transparent box, wherein the transparent box is provided with a positive terminal and a negative terminal, the cell string is formed by connecting several bi-facial cells in series or in parallel with both ends of the cell string respectively provided with a positive wire and a negative wire welded on the positive terminal and the negative terminal, and the mounting base is provided with strip-shaped slots in which the bi-facial cells can be plugged. According to the present invention, the module can simultaneously generate power on front and back sides thereof and improve its power generation efficiency per unit area.

Abstract: The present invention discloses a bi-facial photovoltaic power generation module, comprising a transparent box, and a cell string and a mounting base which are installed inside the transparent box, wherein the transparent box is provided with a positive terminal and a negative terminal, the cell string is formed by connecting several bi-facial cells in series or in parallel with both ends of the cell string respectively provided with a positive wire and a negative wire welded on the positive terminal and the negative terminal, and the mounting base is provided with strip-shaped slots in which the bi-facial cells can be plugged. According to the present invention, the module can simultaneously generate power on front and back sides thereof and improve its power generation efficiency per unit area.

Abstract: Methods and systems for establishing a click-through rate estimation model. A computing device may extract basic characteristics corresponding to a current language channel associated with a server provider. The computing device may combine the basic characteristics to obtain a combination characteristic. The computing device may further obtain an effective high-order characteristic based on the basic characteristics and the combination characteristic and calculate a weight of the effective high-order characteristic. The computing device may generate the CTR estimation model by applying a CTR equation to the weight corresponding to effective high-order characteristic. The implementations may not be limited by human factors, therefore achieving high efficiency in establishing CTR estimation models and high accuracy of the CTR estimation model.