Abstract: In an embodiment, a device includes: a sensor die having a first surface and a second surface opposite the first surface, the sensor die having an input/output region and a first sensing region at the first surface; an encapsulant at least laterally encapsulating the sensor die; a conductive via extending through the encapsulant; and a front-side redistribution structure on the first surface of the sensor die, the front-side redistribution structure being connected to the conductive via and the sensor die, the front-side redistribution structure covering the input/output region of the sensor die, the front-side redistribution structure having a first opening exposing the first sensing region of the sensor die.

Abstract: A method for training a dialog generation model includes: acquiring a dialog data set, and encoding a post in the dialog data set by using an encoder in the dialog generation model to obtain an encoded representation of the post; fusing, by using a decoder in the dialog generation model, the encoded representation of the post and knowledge information corresponding to the post that is obtained from a knowledge base question answering model through transfer learning to obtain a predicted response corresponding to the post; determining a value of a loss function of the dialog generation model based on the predicted response and the annotated response that correspond to the post; and updating a model parameter of the dialog generation model based on the value of the loss function. A method, an apparatus, a device, and a computer storage medium for generating a dialog are also provided.

Abstract: A method for training a dialog generation model includes: acquiring a dialog data set, and encoding a post in the dialog data set by using an encoder in the dialog generation model to obtain an encoded representation of the post; fusing, by using a decoder in the dialog generation model, the encoded representation of the post and knowledge information corresponding to the post that is obtained from a knowledge base question answering model through transfer learning to obtain a predicted response corresponding to the post; determining a value of a loss function of the dialog generation model based on the predicted response and the annotated response that correspond to the post; and updating a model parameter of the dialog generation model based on the value of the loss function. A method, an apparatus, a device, and a computer storage medium for generating a dialog are also provided.

Abstract: A mechanism is provided for identifying a performance bottleneck of a transaction in a transaction processing system. At a predefined time point, status information of an interaction between the transaction and a processing component among one or more processing components in the transaction processing system is collected. A duration of the interaction on the basis of the status information is determined. In response to the duration exceeding a predefined threshold, the interaction is identified as the performance bottleneck of the transaction in order to make changes to the transaction processing system thereby improving performance.

Abstract: A mechanism is provided for identifying a performance bottleneck of a transaction in a transaction processing system. At a predefined time point, status information of an interaction between the transaction and a processing component among one or more processing components in the transaction processing system is collected. A duration of the interaction on the basis of the status information is determined. In response to the duration exceeding a predefined threshold, the interaction is identified as the performance bottleneck of the transaction in order to make changes to the transaction processing system thereby improving performance.