A method and an apparatus for retrieving a target are provided. The method may include: obtaining at least one image and a description text of a designated object; extracting image features of the image and text features of the description text by using a pre-trained cross-media feature extraction network; and matching the image features with the text features to determine an image that contains the designated object.

Provided is an LSTM-based hot-rolling roll-bending force predicting method including the steps of acquiring final rolling data of a stand of a stainless steel rolling mill when performing a hot rolling process, and dividing the data into a training set traindata and a test set testdata; normalizing the traindata; building a matrix P; using a last row of the matrix P as a label of the training set, namely a true value; calculating and updating an output value and the true value of a network; after network training is completed, taking the last m output data of the LSTM network as an input at a next moment, and then obtaining an output of the network at the next moment, wherein the output is a predicted value of the roll-bending force at the next moment; repeating the steps until a sufficient number of prediction data is obtained; and comparing the processed data with the true value in the testdata to check the validity of the network.

The present disclosure provides intent recognition model training and intent recognition methods and apparatuses, and relates to the field of artificial intelligence technologies. The intent recognition model training method includes: acquiring training data including a plurality of training texts and first annotation intents of the plurality of training texts; constructing a neural network model including a feature extraction layer and a first recognition layer; and training the neural network model according to word segmentation results of the plurality of training texts and the first annotation intents of the plurality of training texts to obtain an intent recognition model. The method for intent recognition includes: acquiring a to-be-recognized text; and inputting word segmentation results of the to-be-recognized text to an intent recognition model, and obtaining a first intent result and a second intent result of the to-be-recognized text according to an output result of the intent recognition model.

A method for training a neural network, including: determining a neural network; training the neural network at a first learning rate according to a first optimization mode, where the first learning rate is updated each time the neural network is trained; mapping the first learning rate of the first optimization mode to a second learning rate of a second optimization mode in the same vector space; determining the second learning rate satisfies a preset update condition; and continuing to train the neural network at the second learning rate according to the second optimization mode.

This application discloses a video clip positioning method performed by a computer device. In this application, clip features of video clips in a video are determined according to the unit features of video units within the video clips, so that the acquired clip features integrate the features of the video units and the time sequence correlation between the video units; and then the clip features of the video clips and a text feature of a target text are fused. The features of video clip dimensions and the time sequence correlation between the video clips are fully used in the feature fusion process, so that more accurate attention weights can be acquired based on the fused features. The attention weights are used to represent matching degrees between the video clips and the target text, and then a target video clip matching the target text can be positioned more accurately.

This disclosure provides riboregulators specific for particular viruses or for particular human transcription factors. The viral-specific riboregulators may be used to detect the presence of the particular virus, and this may enable diagnosis of an infection. The transcription factor specific riboregulators may be used to detect the presence and/or measure the level of the particular transcription factor, and this may enable diagnosis or prognosis of a particular condition such as cancer.

The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to predict a type of data that one or more numerical characters and/or one or more natural language word characters of a document correspond to. For instance, a Question Answering systems can be used to predict that a particular number value corresponds to a date, a billing amount, a page number, or the like.

A system and method of combining and improving sets of diverse prescriptors for Evolutionary Surrogate-assisted Prescription (ESP) model is described. The prescriptors are distilled into neural networks and evolved further using ESP. The system and method can handle diverse sets of prescriptors in that it makes no assumptions about the form of the input (i.e., contexts) of the initial prescriptors; it relies only on the prescriptions made in order to distill each prescriptor to a neural network with a fixed form. The resulting set of high performing prescriptors provides a practical way for ESP to incorporate external human and machine knowledge and generate more accurate and fitting set of solutions.

A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.

System and Method for Efficiently Identifying a Subject

A system and a method for efficiently identifying a subject is provided. The invention provides for segmenting micro-voltage digital signals into intervals of a pre-defined time period. Further, the invention provides for transforming the segmented micro-voltage digital signals into a frequency domain for computing on a Mel's scale. The Mel's scale provides a unique signature of the subject in the form of a Melspectrogram image. Lastly, the invention provides for passing the Melspectrogram image through a trained deep learning model. The features associated with the Melspectrogram image are extracted into a feature map for obtaining predicted labels associated with the subject based on labels used during training of the deep learning model for identifying the subject.